Later Life and Interventions

Part V - Later Life and Interventions

Published online by Cambridge University Press: 28 May 2020

Edited by

Ayanna K. Thomas and

Angela Gutchess

Show author details

Ayanna K. Thomas: Affiliation:
Tufts University, Massachusetts
Angela Gutchess: Affiliation:
Brandeis University, Massachusetts

Book contents

Get access

Type: Chapter
Information: The Cambridge Handbook of Cognitive Aging
A Life Course Perspective
, pp. 591 - 742

DOI: https://doi.org/10.1017/9781108552684 [Opens in a new window]

Publisher: Cambridge University Press

Print publication year: 2020

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Ahearn, E. P., Jamison, K. R., Steffens, D. C., et al. (2001). MRI correlates of suicide attempt history in unipolar depression. Biological Psychiatry, 50(4), 266–270. https://doi.org/10.1016/S0006-3223(01)01098-8 Google Scholar

Ajilore, O., Lamar, M., Leow, A., et al. (2014). Graph theory analysis of cortical-subcortical networks in late-life depression. American Journal of Geriatric Psychiatry, 22(2), 195–206. https://doi.org/10.1016/j.jagp.2013.03.005 CrossRef Google Scholar PubMed

Alexopoulos, G. S., & Areán, P. (2014). A model for streamlining psychotherapy in the RDoC era: The example of “Engage.” Molecular Psychiatry, 19(1), 14–19. https://doi.org/10.1038/mp.2013.150 Google Scholar

Alexopoulos, G. S., Hoptman, M. J., Kanellopoulos, D., et al. (2012). Functional connectivity in the cognitive control network and the default mode network in late-life depression. Journal of Affective Disorders, 139(1), 56–65. https://doi.org/10.1016/j.jad.2011.12.002 Google Scholar

Alexopoulos, G. S., Kiosses, D. N., Klimstra, S., Kalayam, B., & Bruce, M. L. (2002). Clinical presentation of the “depression-executive dysfunction syndrome” of late life. American Journal of Geriatric Psychiatry, 10(1), 98–106. https://doi.org/10.1097/00019442-200201000-00012 Google Scholar

Alexopoulos, G. S., Meyers, B. S., Young, R. C., et al. (1997). Clinically defined vascular depression. American Journal of Psychiatry, 154, 562–565. https://doi.org/10.1176/ajp.154.4.562 Google Scholar PubMed

Alexopoulos, G. S., & Morimoto, S. S. (2011). The inflammation hypothesis in geriatric depression. International Journal of Geriatric Psychiatry, 26(11), 1109–1118. https://doi.org/10.1002/gps.2672 Google Scholar

Alexopoulos, G. S., Raue, P., & Areán, P. (2003). Problem-solving therapy versus supportive therapy in geriatric major depression with executive dysfunction. American Journal of Geriatric Psychiatry, 11(1), 46–52. https://doi.org/10.1097/00019442-200301000-00007 CrossRef Google Scholar PubMed

Alexopoulos, G. S., Raue, P. J., Gunning, F., et al. (2016). “Engage” therapy: Behavioral activation and improvement of late-life major depression. American Journal of Geriatric Psychiatry, 24(4), 320–326. https://doi.org/10.1016/j.jagp.2015.11.006 CrossRef Google Scholar PubMed

Alexopoulos, G. S., Raue, P. J., Kiosses, D. N., et al. (2015). Comparing Engage with PST in late-life major depression: A preliminary report. American Journal of Geriatric Psychiatry, 23(5), 506–513. https://doi.org/10.1016/j.jagp.2014.06.008 Google Scholar

Alexopoulos, G. S., Wilkins, V., Marino, P., et al. (2013). Ecosystem focused therapy in post stroke depression: A preliminary study. International Journal of Geriatric Psychiatry, 27(10), 1053–1060. https://doi.org/10.1002/gps.2822 CrossRef Google Scholar

Allan, C. L., Sexton, C. E., Kalu, U. G., et al. (2012). Does the Framingham Stroke Risk Profile predict white-matter changes in late-life depression? International Psychogeriatrics, 24(4), 524–531. https://doi.org/10.1017/S1041610211002183 Google Scholar

Ancelin, M.-L., Farré, A., Carrière, I., et al. (2015). C-reactive protein gene variants: Independent association with late-life depression and circulating protein levels. Translational Psychiatry, 5(1), e499. https://doi.org/10.1038/tp.2014.145 Google Scholar

Anguera, J. A., Boccanfuso, J., Rintoul, J. L., et al. (2013). Video game training enhances cognitive control in older adults. Nature, 501(7465), 97–101. https://doi.org/10.1038/nature12486 CrossRef Google Scholar PubMed

Anguera, J. A., Gunning, F. M., & Areán, P. A. (2017). Improving late life depression and cognitive control through the use of therapeutic video game technology: A proof-of-concept randomized trial. Depression and Anxiety, 34(6), 508–517. https://doi.org/10.1002/da.22588 CrossRef Google Scholar PubMed

Areán, P., Hegel, M., Vannoy, S., Fan, M.-Y., & Unuzter, J. (2008). Effectiveness of problem-solving therapy for older, primary care patients with depression: results from the IMPACT project. Gerontologist, 48(3), 311–323. http://dx.doi.org/10.1093/geront/48.3.311 Google Scholar

Areán, P. A., Raue, P., Mackin, R. S., et al. (2010). Problem-solving therapy and supportive therapy in older adults with major depression and executive dysfunction. American Journal of Psychiatry, 167(11), 1391–1398. https://doi.org/10.1176/appi.ajp.2010.09091327 CrossRef Google Scholar PubMed

Bakker, N., Shahab, S., Giacobbe, P., et al. (2015). rTMS of the dorsomedial prefrontal cortex for major depression: Safety, tolerability, effectiveness, and outcome predictors for 10 Hz versus intermittent theta-burst stimulation. Brain Stimulation, 8(2), 208–215. https://doi.org/10.1016/j.brs.2014.11.002 CrossRef Google Scholar PubMed

Belvederi Murri, M., Amore, M., Respino, M., & Alexopoulos, G. S. (2018a). The symptom network structure of depressive symptoms in late-life: Results from a European population study. Molecular Psychiatry. https://doi.org/10.1038/s41380-018-0232-0 Google Scholar

Belvederi Murri, M., Ekkekakis, P., Menchetti, M., et al. (2018b). Physical exercise for late-life depression: Effects on symptom dimensions and time course. Journal of Affective Disorders, 230, 65–70. https://doi.org/10.1016/j.jad.2018.01.004 Google Scholar

Beutel, M. E., Brähler, E., Wiltink, J., et al. (2019). New onset of depression in aging women and men: Contributions of social, psychological, behavioral, and somatic predictors in the community. Psychological Medicine, 49(7), 1148–1155. https://doi.org/10.1017/S0033291718001848 Google Scholar

Beyer, J. L., & Johnson, K. G. (2018). Advances in pharmacotherapy of late-life depression. Current Psychiatry Reports, 20(5), 34. https://doi.org/10.1007/s11920-018-0899-6 Google Scholar

Bhalla, R. K., Butters, M. A., Mulsant, B. H., et al. (2006). Persistence of neuropsychologic deficits in the remitted state of late-life depression. American Journal of Geriatric Psychiatry, 14(5), 419–427. https://doi.org/10.1097/01.JGP.0000203130.45421.69 Google Scholar

Bohr, I. J., Kenny, E., Blamire, A., et al. (2013). Resting-state functional connectivity in late-life depression: Higher global connectivity and more long distance connections. Frontiers in Psychiatry, 3, 1–14. https://doi.org/10.3389/fpsyt.2012.00116 CrossRef Google Scholar PubMed

Bremmer, M. A., Beekman, A. T. F., Deeg, D. J. H., et al. (2008). Inflammatory markers in late-life depression: Results from a population-based study. Journal of Affective Disorders, 106(3), 249–255. https://doi.org/10.1016/j.jad.2007.07.002 Google Scholar

Castro, V. M., Gallagher, P. J., Clements, C. C., et al. (2012). Incident user cohort study of risk for gastrointestinal bleed and stroke in individuals with major depressive disorder treated with antidepressants. BMJ Open, 2(2), 1–8. https://doi.org/10.1136/bmjopen-2011-000544 Google Scholar

Catalan-Matamoros, D., Gomez-Conesa, A., Stubbs, B., & Vancampfort, D. (2018). Exercise improves depressive symptoms in older adults: An umbrella review of systematic reviews and meta-analyses. Psychiatry Research, 244(2016), 202–209. https://doi.org/10.1016/j.psychres.2016.07.028 Google Scholar

Chang, K. J., Hong, C. H., Roh, H. W., et al. (2018). A 12-week multi-domain lifestyle modification to reduce depressive symptoms in older adults: A preliminary report. Psychiatry Investigation, 15(3), 279–284. https://doi.org/10.30773/pi.2017.08.10 Google Scholar

Charlton, R. A., Lamar, M., Ajilore, O., & Kumar, A. (2013). Preliminary analysis of age of illness onset effects on symptom profiles in major depressive disorder. International Journal of Geriatric Psychiatry, 28(11), 1166–1174. https://doi.org/10.1002/gps.3939 Google Scholar

Charlton, R. A., Lamar, M., Ajilore, O., & Kumar, A. (2014a). Associations between vascular risk and mood in euthymic older adults: Preliminary findings. American Journal of Geriatric Psychiatry, 22(9), 936–945. https://doi.org/10.1016/j.jagp.2013.01.074 Google Scholar

Charlton, R. A., Lamar, M., Zhang, A., et al. (2018). Associations between pro-inflammatory cytokines, learning, and memory in late-life depression and healthy aging. International Journal of Geriatric Psychiatry, 33(1), 104–112. https://doi.org/10.1002/gps.4686 CrossRef Google Scholar PubMed

Charlton, R. A., Lamar, M., Zhang, A., et al. (2014b). White-matter tract integrity in late-life depression: Associations with severity and cognition. Psychological Medicine, 44(7), 1427–1437. https://doi.org/10.1017/S0033291713001980 Google Scholar

Charlton, R. A., Leow, A., Gadelkarim, J., et al. (2015). Brain connectivity in late-life depression and aging revealed by network analysis. American Journal of Geriatric Psychiatry, 23(6), 642–650. https://doi.org/10.1016/j.jagp.2014.07.008 Google Scholar

Chen, P. S., McQuoid, D. R., Payne, M. E., & Steffens, D. C. (2006). White matter and subcortical gray matter lesion volume changes and late-life depression outcome: A 4-year magnetic resonance imaging study. International Psychogeriatrics, 18(3), 445–456. https://doi.org/10.1017/S1041610205002796 Google Scholar

Coupland, C., Dhiman, P., Morriss, R., et al. (2011). Antidepressant use and risk of adverse outcomes in older people: Population based cohort study. BMJ, 343(7819), 1–15. https://doi.org/10.1136/bmj.d4551 CrossRef Google Scholar PubMed

Diniz, B. S., Sibille, E., Ding, Y., et al. (2015). Plasma biosignature and brain pathology related to persistent cognitive impairment in late-life depression. Molecular Psychiatry, 20(5), 594–601. https://doi.org/10.1038/mp.2014.76 CrossRef Google Scholar PubMed

Dombrovski, A. Y., Siegle, G. J., Szanto, K., et al. (2012). The temptation of suicide: Striatal gray matter, discounting of delayed rewards, and suicide attempts in late-life depression. Psychological Medicine, 42(6), 1203–1215. https://doi.org/10.1017/S0033291711002133 Google Scholar

Elderkin-Thompson, V., Mintz, J., Haroon, E., Lavretsky, H., & Kumar, A. (2007). Executive dysfunction and memory in older patients with major and minor depression. Archives of Clinical Neuropsychology, 22(2), 261–270. https://doi.org/10.1016/j.acn.2007.01.021 Google Scholar

Fabre, I., Galinowski, A., Oppenheim, C., et al. (2004). Antidepressant efficacy and cognitive effects of repetitive transcranial magnetic stimulation in vascular depression: An open trial. International Journal of Geriatric Psychiatry, 19(9), 833–842. https://doi.org/10.1002/gps.1172 CrossRef Google Scholar PubMed

Farioli-Vecchioli, S., Sacchetti, S., di Robilant, N. V., & Cutuli, D. (2018). The role of physical exercise and omega-3 fatty acids in depressive illness in the elderly. Current Neuropharmacology, 16(3), 308–326. https://doi.org/10.2174/1570159X15666170912113852 Google Scholar

Gellis, Z. D., & Bruce, M. L. (2010). Problem-solving therapy for subthreshold depression in home healthcare patients with cardiovascular disease. American Journal of Geriatric Psychiatry, 18(6), 464–474. https://doi.org/10.1097/JGP.0b013e3181b21442 Google Scholar

Gellis, Z. D., McGinty, J., Horowitz, A., Bruce, M. L., & Misener, E. (2007). Problem-solving therapy for late-life depression in home care: A randomized field trial. American Journal of Geriatric Psychiatry, 15(11), 968–978. https://doi.org/10.1097/JGP.0b013e3180cc2bd7 CrossRef Google Scholar PubMed

Gunning-Dixon, F. M., Hoptman, M. J., Lim, K. O., et al. (2008). Macromolecular white matter abnormalities in geriatric depression: A magnetization transfer imaging study. American Journal of Geriatric Psychiatry, 16(4), 255–262. https://doi.org/10.1097/JGP.0000300628.33669.03 Google Scholar

Gunning-Dixon, F. M., Walton, M., Cheng, J., et al. (2010). MRI signal hyperintensities and treatment remission of geriatric depression. Journal of Affective Disorders, 126(3), 395–401. https://doi.org/10.1016/j.jad.2010.04.004 Google Scholar

Hegeman, A. J. M., Kok, R. M., Van der Mast, R. C., & Giltay, E. J. (2012). Phenomenology of depression in older compared with younger adults: Meta-analysis. British Journal of Psychiatry, 200(4), 275–281. https://doi.org/10.1192/bjp.bp.111.095950 Google Scholar

Hybels, C. F., Pieper, C. F., Payne, M. E., & Steffens, D. C. (2016). Late-life depression modifies the association between cerebral white matter hyperintensities and functional decline among older adults. American Journal of Geriatric Psychiatry, 24(1), 42–49. https://doi.org/10.1016/j.jagp.2015.03.001 Google Scholar

Ilieva, I. P., Alexopoulos, G. S., Dubin, M. J., et al. (2018). Age-related repetitive transcranial magnetic stimulation effects on executive function in depression: A systematic review. American Journal of Geriatric Psychiatry, 26(3), 334–346. https://doi.org/10.1016/j.jagp.2017.09.002 Google Scholar

Jeuring, H. W., Stek, M. L., Huisman, M., et al. (2018). A six-year prospective study of the prognosis and predictors in patients with late-life depression. American Journal of Geriatric Psychiatry, 26(9), 985–997. https://doi.org/10.1016/j.jagp.2018.05.005 Google Scholar

Jorge, R. E., Moser, D. J., Acion, L., & Robinson, R. G. (2008). Treatment of vascular depression using repetitive transcranial magnetic stimulation. Archives of General Psychiatry, 65(3), 268–276. https://doi.org/10.1001/archgenpsychiatry.2007.45 CrossRef Google Scholar PubMed

Kastner, M., Cardoso, R., Lai, Y., et al. (2018). Effectiveness of interventions for managing multiple high-burden chronic diseases in older adults: A systematic review and meta-analysis. Canadian Medical Association Journal, 190(34), 1004–1012. https://doi.org/10.1503/cmaj.171391 Google Scholar

Kaster, T. S., Daskalakis, Z. J., Noda, Y., et al. (2018). Efficacy, tolerability, and cognitive effects of deep transcranial magnetic stimulation for late-life depression: A prospective randomized controlled trial. Neuropsychopharmacology, 43, 2231–2238. https://doi.org/10.1038/s41386-018-0121-x Google Scholar

Kiosses, D. N., Areán, P. A., Teri, L., & Alexopoulos, G. S. (2010). Home-delivered problem adaptation therapy (PATH) for depressed, cognitively impaired, disabled elders: A preliminary study. American Journal of Geriatric Psychiatry, 18(11), 988–998. https://doi.org/10.1097/JGP.0b013e3181d6947d Google Scholar

Kiosses, D. N., Ravdin, L. D., Gross, J. J., et al. (2015). Problem adaptation therapy for older adults with major depression and cognitive impairment: A randomized clinical trial. JAMA Psychiatry, 72(1), 22–30. https://doi.org/10.1001/jamapsychiatry.2014.1305 Google Scholar

Kohler, S., Thomas, A. J., Lloyd, A., et al. (2010). White matter hyperintensities, cortisol levels, brain atrophy and continuing cognitive deficits in late-life depression. British Journal of Psychiatry, 196(2), 143–149. https://doi.org/10.1192/bjp.bp.109.071399 Google Scholar

Krishnan, K. R. R., Goli, V., Ellinwood, E. H., et al. (1988). Leukoencephalopathy in patients diagnosed as major depressive. Biological Psychiatry, 23, 519–522. https://doi.org/10.1016/0006-3223(88)90025-X Google Scholar

Krishnan, K. R., Hays, J. C., & Blazer, D. G. (1997). MRI-defined vascular depression. American Journal of Psychiatry, 154(4), 497–501. https://doi.org/10.1176/ajp.154.4.497 Google Scholar

Krishnan, K. R. R., Taylor, W. D., McQuoid, D. R., et al. (2004). Clinical characteristics of magnetic resonance imaging-defined subcortical ischemic depression. Biological Psychiatry, 55(4), 390–397. https://doi.org/10.1016/j.biopsych.2003.08.014 Google Scholar

Kuceyeski, A., Navi, B. B., Kamel, H., et al. (2015). Exploring the brain’s structural connectome: A quantitative stroke lesion-dysfunction mapping study. Human Brain Mapping, 36, 2147–2160. https://doi.org/10.1002/hbm.22761 Google Scholar

Lantrip, C., Gunning, F. M., Flashman, L., Roth, R. M., & Holtzheimer, P. E. (2017). Effects of transcranial magnetic stimulation on the cognitive control of emotion: Potential antidepressant mechanisms. Journal of ECT, 33, 73–80. https://doi.org/10.1097/YCT.0000000000000386 Google Scholar

Lassalle-Lagadec, S., Sibon, I., Dilharreguy, B., et al. (2012). Subacute default mode network dysfunction in the prediction of post-stroke depression severity. Radiology, 264(1), 218–224. https://doi.org/10.1148/radiol.12111718 Google Scholar

Li, C. T., Chen, M. H., Juan, C. H., et al. (2014). Efficacy of prefrontal theta-burst stimulation in refractory depression: A randomized sham-controlled study. Brain, 137(7), 2088–2098. https://doi.org/10.1093/brain/awu109 Google Scholar

Lim, H. K., Jung, W. S., & Aizenstein, H. J. (2013). Aberrant topographical organization in gray matter structural network in late life depression: A graph theoretical analysis. International Psychogeriatrics/IPA, 25(12), 1929–1940. https://doi.org/10.1017/S104161021300149X CrossRef Google Scholar PubMed

Liston, C., Chen, A. C., Zebley, B. D., et al. (2014). Default mode network mechanisms of transcranial magnetic stimulation in depression. Biological Psychiatry, 76(7), 517–526. https://doi.org/10.1016/j.biopsych.2014.01.023 Google Scholar

Manes, F., Jorge, R., Morcuende, M., et al. (2001). A controlled study of repetitive transcranial magnetic stimulation as a treatment of depression in the elderly. International Psychogeriatrics/IPA, 13(2), 225–231. https://doi.org/10.1017/S1041610201007608 Google Scholar

Manning, K. J., Alexopoulos, G. S., Banerjee, S., et al. (2015). Executive functioning complaints and escitalopram treatment response in late-life depression. American Journal of Geriatric Psychiatry, 23(5), 440–445. https://doi.org/10.1016/j.jagp.2013.11.005 CrossRef Google Scholar PubMed

Mazure, C. M., Maciejewski, P. K., Jacobs, S. C., & Bruce, M. L. (2002). Stressful life events interacting with cognitive/personality styles to predict late-onset major depression. American Journal of Geriatric Psychiatry, 10(3), 297–304. https://doi.org/10.1097/00019442-200205000-00009 Google Scholar

Morimoto, S. S., Gunning, F. M., Murphy, C. F., et al. (2011). Executive function and short-term remission of geriatric depression: The role of semantic strategy. American Journal of Geriatric Psychiatry, 19(2), 115–122. https://doi.org/10.1097/JGP.0b013e3181e751c4 Google Scholar

Morimoto, S. S., Gunning, F. M., Wexler, B. E., et al. (2016). Executive dysfunction predicts treatment response to neuroplasticity-based computerized cognitive remediation (nCCR-GD) in elderly patients with major depression. American Journal of Geriatric Psychiatry, 24(10), 816–820. https://doi.org/10.1016/j.jagp.2016.06.010 Google Scholar

Morimoto, S. S., Wexler, B. E., Liu, J., et al. (2014). Neuroplasticity-based computerized cognitive remediation for treatment-resistant geriatric depression. Nature Communications, 5, 1–7. https://doi.org/10.1038/ncomms5579 Google Scholar

Moser, D. J., Jorge, R. E., Manes, F., et al. (2002). Improved executive functioning following repetitive transcranial magnetic stimulation. Neurology, 58(8), 1288–1290. https://doi.org/10.1212/WNL.58.8.1288 Google Scholar

Mosimann, U. P., Schmitt, W., Greenberg, B. D., et al. (2004). Repetitive transcranial magnetic stimulation: A putative add-on treatment for major depression in elderly patients. Psychiatry Research, 126(2), 123–133. https://doi.org/10.1016/j.psychres.2003.10.006 Google Scholar

Murphy, C. F., Gunning-Dixon, F. M., Hoptman, M. J., et al. (2007). White matter integrity predicts Stroop performance in patients with geriatric depression. Biological Psychiatry, 61(8), 1007–1010. https://doi.org/10.1016/j.biopsych.2006.07.028 Google Scholar

Naarding, P., Tiemeier, H., Breteler, M. M. B., et al. (2007). Clinically defined vascular depression in the general population. Psychological Medicine, 37(3), 383–392. https://doi.org/10.1017/S0033291706009196 Google Scholar

Neviani, F., Murri, M. B., Mussi, C., et al. (2017). Physical exercise for late life depression: Effects on cognition. International Psychogeriatrics, 29(7), 1105–1112. https://doi.org/10.1017/S1041610217000576 CrossRef Google Scholar PubMed

Paranthaman, R., Burns, A. S., Cruickshank, J. K., et al. (2012). Age at onset and vascular pathology in late-life depression. American Journal of Geriatric Psychiatry, 20(6), 524–532. https://doi.org/10.1097/JGP.0b013e318227f85c Google Scholar

Park, J. E., Lee, J. Y., Kim, B. S., et al. (2015). Above-moderate physical activity reduces both incident and persistent late-life depression in rural Koreans. International Journal of Geriatric Psychiatry, 30(7), 766–775. https://doi.org/10.1002/gps.4244 Google Scholar

Pimontel, M. A., Reinlieb, M. E., Johnert, L. C., et al. (2013). The external validity of MRI‐defined vascular depression. International Journal of Geriatric Psychiatry, 28(11), 1189–1196. https://doi.org/10.1002/gps.3943 Google Scholar

Pimontel, M. A., Rindskopf, D., Rutherford, B. R., et al. (2016). A meta-analysis of executive dysfunction and antidepressant treatment response in late-life depression. American Journal of Geriatric Psychiatry, 24(1), 31–41. https://doi.org/10.1016/j.jagp.2015.05.010 CrossRef Google Scholar PubMed

Puglisi, V., Bramanti, A., Lanza, G., et al. (2018). Impaired cerebral haemodynamics in vascular depression: Insights from transcranial doppler ultrasonography. Frontiers in Psychiatry, 9(316), 1–9. https://doi.org/10.3389/fpsyt.2018.00316 CrossRef Google Scholar PubMed

Qiu, W. Q., Himali, J. J., Wolf, P. A., et al. (2017). Effects of white matter integrity and brain volumes on late life depression in the Framingham Heart Study. International Journal of Geriatric Psychiatry, 32, 214–221. https://doi.org/10.1002/gps.4469 Google Scholar

Reinlieb, M. E., Persaud, A., Singh, D., et al. (2014). Vascular depression: Overrepresented among African Americans? International Journal of Geriatric Psychiatry, 29, 470–477. https://doi.org/10.1002/gps.4029 Google Scholar

Sayar, G. H., Ozten, E., Tan, O., & Tarhan, N. (2013). Transcranial magnetic stimulation for treating depression in elderly patients. Neuropsychiatric Disease and Treatment, 9, 501–504. https://doi.org/10.2147/NDT.S44241 Google Scholar

Scott, R., & Paulson, D. (2018). Cerebrovascular burden and depressive symptomatology interrelate over 18 years: Support for the vascular depression hypothesis. International Journal of Geriatric Psychiatry, 33(1), 66–74. https://doi.org/10.1002/gps.4674 Google Scholar

Sexton, C. E., Mcdermott, L., Kalu, U. G., et al. (2012). Exploring the pattern and neural correlates of neuropsychological impairment in late-life depression. Psychological Medicine, 42, 1195–1202. https://doi.org/10.1017/S0033291711002352 Google Scholar

Sheline, Y. I., Price, J. L., Vaishnavi, S. N., et al. (2008). Regional white matter hyperintensity burden in automated segmentation distinguishes late-life depressed subjects from comparison subjects matched for vascular risk factors. American Journal of Psychiatry, 165(4), 524–532. https://doi.org/10.1176/appi.ajp.2007.07010175 Google Scholar

Shi, Y., Zeng, Y., Wu, L., et al. (2017). A study of the brain abnormalities of post-stroke depression in frontal lobe lesion. Scientific Reports, 7(1), 1–10. https://doi.org/10.1038/s41598-017-13681-w Google Scholar

Taragano, F. E., Bagnatti, P., & Allegri, R. F. (2005). A double-blind, randomized clinical trial to assess the augmentation with nimodipine of antidepressant therapy in the treatment of “vascular depression.” International Psychogeriatrics, 17(3), 487–498. https://doi.org/10.1017/S1041610205001493 CrossRef Google Scholar PubMed

Taylor, W. D., MacFall, J. R., Steffens, D. C., et al. (2003a). Localization of age-associated white matter hyperintensities in late-life depression. Progress in Neuro-Psychopharmacology and Biological Psychiatry, 27(3), 539–544. https://doi.org/10.1016/S0278-5846(02)00358-5 Google Scholar

Taylor, W. D., Steffens, D. C., MacFall, J., et al. (2003b). White matter hyperintensity progression and late-life depression outcomes. Archives of General Psychiatry, 60, 1090–1096. https://doi.org/10.1001/archpsyc.60.11.1090 Google Scholar

Thomas, A. J., O’Brien, J. T., Davis, S., et al. (2002). Ischemic basis for deep white matter hyperintensities in major depression: A neuropathological study. Archives of General Psychiatry, 59(9), 785–792. https://doi.org/10.1001/archpsyc.59.9.785 Google Scholar

Underwood, M., Lamb, S. E., Eldridge, S., et al. (2013). Exercise for depression in elderly residents of care homes: A cluster-randomised controlled trial. Lancet, 382, 41–49. https://doi.org/10.1016/S0140-6736(13)60649-2 Google Scholar

Van den Kommer, T. N., Comijs, H. C., Aartsen, M. J., et al. (2013). Depression and cognition: How do they interrelate in old age? American Journal of Geriatric Psychiatry, 21(4), 398–410. https://doi.org/10.1016/j.jagp.2012.12.015 Google Scholar

Vicentini, J. E., Weiler, M., Almeida, S. R. M., et al. (2017). Depression and anxiety symptoms are associated to disruption of default mode network in subacute ischemic stroke. Brain Imaging and Behavior, 11(6), 1571–1580. https://doi.org/10.1007/s11682-016-9605-7 Google Scholar

Victoria, L. W., Gunning, F. M., Bress, J. N., Jackson, D., & Alexopoulos, G. S. (2018). Reward learning impairment and avoidance and rumination responses at the end of Engage therapy of late-life depression. International Journal of Geriatric Psychiatry, 33(7), 948–955. https://doi.org/10.1002/gps.4877 Google Scholar

Wang, L., Leonards, C. O., Sterzer, P., & Ebinger, M. (2014). White matter lesions and depression: A systematic review and meta-analysis. Journal of Psychiatric Research, 56(1), 56–64. https://doi.org/10.1016/j.jpsychires.2014.05.005 Google Scholar

Wolf, P. A., D’Agostino, R. B., Belanger, A. J., & Kannel, W. B. (1991). Probability of stroke: A risk profile from the Framingham Study. Stroke, 22(3), 312–318. https://doi.org/10.1161/01.STR.22.3.312 Google Scholar

Zhang, A., Ajilore, O., Zhan, L., et al. (2013). White matter tract integrity of anterior limb of internal capsule in major depression and type 2 diabetes. Neuropsychopharmacology, 38(8), 1451–1459. https://doi.org/10.1038/npp.2013.41 Google Scholar

References

Agnew-Blais, J. C., Wassertheil-Smoller, S., Kang, J. H., et al. (2015). Folate, vitamin B-6, and vitamin B-12 intake and mild cognitive impairment and probable dementia in the Women’s Health Initiative Memory Study. Journal of the Academy of Nutrition and Dietetics, 115(2), 231–241. doi: 10.1016/j.jand.2014.07.006Google Scholar

Allès, B., Samieri, C., Feart, C., et al. (2012). Dietary patterns: A novel approach to examine the link between nutrition and cognitive function in older individuals. Nutrition Research Reviews, 25(2), 207–222. doi: 10.1017/S0954422412000133CrossRef Google Scholar PubMed

Amadieu, C., Lefèvre-Arbogast, S., Delcourt, C., et al. (2017). Nutrient biomarker patterns and long-term risk of dementia in older adults. Alzheimer’s and Dementia, 13(10), 1125–1132. doi: 10.1016/j.jalz.2017.01.025Google Scholar

Andrieu, S., Guyonnet, S., Coley, N., et al. (2017). Effect of long-term omega 3 polyunsaturated fatty acid supplementation with or without multidomain intervention on cognitive function in elderly adults with memory complaints (MAPT): A randomised, placebo-controlled trial. Lancet Neurology, 16(5), 377–389. doi: 10.1016/S1474-4422(17)30040-6Google Scholar

Annweiler, C., Montero-Odasso, M., Llewellyn, D. J., et al. (2013). Meta-analysis of memory and executive dysfunctions in relation to vitamin D. Journal of Alzheimer’s Disease, 37(1), 147–171. doi: 10.3233/JAD-130452Google Scholar

Atallah, N., Adjibade, M., Lelong, H., et al. (2018). How healthy lifestyle factors at midlife relate to healthy aging. Nutrients, 10(7), 854. doi: 10.3390/nu10070854Google Scholar

Berendsen, A. A., Kang, J. H., van de Rest, O., et al. (2017). The Dietary Approaches to Stop Hypertension diet, cognitive function, and cognitive decline in American older women. Journal of the American Medical Directors Association, 18(5), 427–432. doi: 10.1016/j.jamda.2016.11.026Google Scholar

Boespflug, E. L., McNamara, R. K., Eliassen, J. C., Schidler, M. D., & Krikorian, R. (2016). Fish oil supplementation increases event-related posterior cingulate activation in older adults with subjective memory impairment. Journal of Nutrition, Health and Aging, 20(2), 161–169. doi: 10.1007/s12603-015-0609-6Google Scholar

Brangier, A., Ferland, G., Rolland, Y., et al. (2018). Vitamin K antagonists and cognitive decline in older adults: A 24-month follow-up. Nutrients, 10(6), 666. doi: 10.3390/nu10060666Google Scholar

Bredesen, D. E. (2014). Reversal of cognitive decline: A novel therapeutic program. Aging (Albany NY), 6(9), 707–717. doi: 10.18632/aging.100690CrossRef Google Scholar PubMed

Bredesen, D. E. (2017). The end of Alzheimer’s: The first program to prevent and reverse cognitive decline. New York: Penguin Random House.Google Scholar

Bredesen, D. E., Amos, E. C., Canick, J., et al. (2016). Reversal of cognitive decline in Alzheimer’s disease. Aging (Albany NY), 8(6), 1250–1258. doi: 10.18632/aging.100981Google Scholar

Burckhardt, M., Herke, M., Wustmann, T., et al. (2016). Omega‐3 fatty acids for the treatment of dementia. Cochrane Database of Systematic Reviews, 4, CD009002. doi: 10.1002/14651858.CD009002.pub3Google Scholar

Butler, M., Nelson, V. A., Davila, H., et al. (2018). Over-the-counter supplement interventions to prevent cognitive decline, mild cognitive impairment, and clinical Alzheimer-type dementia: A systematic review. Annals of Internal Medicine, 168(1), 52–62. doi: 10.7326/M17-1530Google Scholar

Cao, L., Tan, L., Wang, H. F., et al. (2016). Dietary patterns and risk of dementia: A systematic review and meta-analysis of cohort studies. Molecular Neurobiology, 53(9), 6144–6154. doi: 10.1007/s12035-015-9516-4Google Scholar

Cardoso, B. R., Cominetti, C., & Cozzolino, S. M. F. (2013). Importance and management of micronutrient deficiencies in patients with Alzheimer’s disease. Clinical Interventions in Aging, 8, 531–542. doi: 10.2147/CIA.S27983Google Scholar

Chhetri, J. K., de Souto Barreto, P., Cantet, C., et al. (2018). Effects of a 3-year multi-domain intervention with or without Omega-3 supplementation on cognitive function in older subjects with increased CAIDE dementia scores. Journal of Alzheimer’s Disease, 64(1), 71–78. doi: 10.3233/JAD-180209Google Scholar

Chiu, C. C., Su, K. P., Cheng, T. C., et al. (2008). The effects of omega-3 fatty acids monotherapy in Alzheimer’s disease and mild cognitive impairment: A preliminary randomized double-blind placebo-controlled study. Progress in Neuro-Psychopharmacology and Biological Psychiatry, 32(6), 1538–1544. doi: 10.1016/j.pnpbp.2008.05.015Google Scholar

Chouet, J., Ferland, G., Féart, C., et al. (2015). Dietary vitamin K intake is associated with cognition and behaviour among geriatric patients: The CLIP study. Nutrients, 7(8), 6739–6750. doi: 10.3390/nu7085306Google Scholar

Commenges, D., Scotet, V., Renaud, S., et al. (2000). Intake of flavonoids and risk of dementia. European Journal of Epidemiology, 16(4), 357–363. doi: 10.1023/a:1007614613771Google Scholar

da Silva, S. L., Vellas, B., Elemans, S., et al. (2014). Plasma nutrient status of patients with Alzheimer’s disease: Systematic review and meta-analysis. Alzheimer’s and Dementia, 10(4), 485–502. doi: 10.1016/j.jalz.2013.05.1771Google Scholar

Devore, E. E., Grodstein, F., van Rooij, F. J., et al. (2010). Dietary antioxidants and long-term risk of dementia. Archives of Neurology, 67(7), 819–825. doi: 10.1001/archneurol.2010.144CrossRef Google Scholar PubMed

Douaud, G., Refsum, H., de Jager, C. A., et al. (2013). Preventing Alzheimer’s disease-related gray matter atrophy by B-vitamin treatment. Proceedings of the National Academy of Sciences USA, 110(23), 9523–9528. doi: 10.1073/pnas.1301816110Google Scholar

Engelborghs, S., Gilles, C., Ivanoiu, A., & Vandewoude, M. (2014). Rationale and clinical data supporting nutritional intervention in Alzheimer’s disease. Acta Clinica Belgica, 69(1), 17–24.CrossRef Google Scholar PubMed

Engelhart, M. J., Geerlings, M. I., Ruitenberg, A., et al. (2002). Dietary intake of antioxidants and risk of Alzheimer disease. JAMA, 287(24), 3223–3229. doi: 10.1179/0001551213Z.0000000006Google Scholar

Farina, N., Llewellyn, D., Isaac, M. G., & Tabet, N. (2017). Vitamin E for Alzheimer’s dementia and mild cognitive impairment. Cochrane Database of Systematic Reviews, 1, CD002854. doi: 10.1002/14651858.CD002854.pub4Google Scholar

Feart, C., Helmer, C., Merle, B., et al. (2017). Associations of lower vitamin D concentrations with cognitive decline and long-term risk of dementia and Alzheimer’s disease in older adults. Alzheimer’s and Dementia,13(11),1207–1216. doi: 10.1016/j.jalz.2017.03.003Google Scholar

Feart, C., Letenneur, L., Helmer, C., et al. (2016). Plasma carotenoids are inversely associated with dementia risk in an elderly French cohort. Journals of Gerontology, Series A: Biomedical Sciences and Medical Sciences, 71(5), 683–688. doi: 10.1093/gerona/glv135Google Scholar

Feart, C., Samieri, C., & Barberger-Gateau, P. (2015). Mediterranean diet and cognitive health: An update of available knowledge. Current Opinion in Clinical Nutrition and Metabolic Care, 18(1), 51–62. doi: 10.1097/MCO.0000000000000131Google Scholar

Feart, C., Samieri, C., Rondeau, V., et al. (2009). Adherence to a Mediterranean diet, cognitive decline, and risk of dementia. JAMA, 302(6), 638–648. doi: 10.1001/jama.2009.1146Google Scholar

Fenech, M. F. (2010). Nutriomes and nutrient arrays – the key to personalised nutrition for DNA damage prevention and cancer growth control. Genome Integrity, 1(1), 11. doi: 10.1186/2041-9414-1-11CrossRef Google Scholar PubMed

Fenech, M. (2017). Vitamins associated with brain aging, mild cognitive impairment, and Alzheimer disease: Biomarkers, epidemiological and experimental evidence, plausible mechanisms, and knowledge gaps. Advances in Nutrition, 8(6), 958–970. doi: 10.3945/an.117.015610Google Scholar

Ferrand, C., Féart, C., Martinent, G., et al. (2017). Dietary patterns in French home-living older adults: Results from the PRAUSE study. Archives of Gerontology and Geriatrics, 74, 88–93. doi: 10.1016/j.archger.2017.01.015Google Scholar

Freund-Levi, Y., Eriksdotter-Jönhagen, M., Cederholm, T., et al. (2006). ω-3 fatty acid treatment in 174 patients with mild to moderate Alzheimer disease: OmegAD study: A randomized double-blind trial. Archives of Neurology, 63(10), 1402–1408. doi: 10.1001/archneur.63.10.1402Google Scholar

Goodwill, A. M., & Szoeke, C. (2017). A systematic review and meta‐analysis of the effect of low vitamin D on cognition. Journal of the American Geriatrics Society, 65(10), 2161–2168. doi: 10.1111/jgs.15012Google Scholar

Hu, F. B. (2002). Dietary pattern analysis: A new direction in nutritional epidemiology. Current Opinion in Lipidology, 13(1), 3–9. doi: 10.1097/00041433-200202000-00002Google Scholar

Hughes, C. F., Ward, M., Tracey, F., et al. (2017). B-vitamin intake and biomarker status in relation to cognitive decline in healthy older adults in a 4-year follow-up study. Nutrients, 9(1), 53. doi: 10.3390/nu9010053Google Scholar

Jiang, X., Huang, J., Song, D., et al. (2017). Increased consumption of fruit and vegetables is related to a reduced risk of cognitive impairment and dementia: Meta-analysis. Frontiers in Aging Neuroscience, 9, 18. doi: 10.3389/fnagi.2017.00018CrossRef Google Scholar

Kado, D. M., Karlamangla, A. S., Huang, M. H., et al. (2005). Homocysteine versus the vitamins folate, B6, and B12 as predictors of cognitive function and decline in older high-functioning adults: MacArthur Studies of Successful Aging. American Journal of Medicine, 118(2), 161–167. doi: 10.1016/j.amjmed.2004.08.01Google Scholar

Kim, H., Kim, G., Jang, W., Kim, S. Y., & Chang, N. (2014). Association between intake of B vitamins and cognitive function in elderly Koreans with cognitive impairment. Nutrition Journal, 13(1), 118. doi: 10.1186/1475-2891-13-118Google Scholar

Lamport, D. J., Saunders, C., Butler, L. T., & Spencer, J. P. (2014). Fruits, vegetables, 100% juices, and cognitive function. Nutrition Reviews, 72(12), 774–789. doi: 10.1111/nure.12149Google Scholar

Lee, A. T., Richards, M., Chan, W. C., et al. (2017). Lower risk of incident dementia among Chinese older adults having three servings of vegetables and two servings of fruits a day. Age and Ageing, 46(5), 773–779. doi: 10.1093/ageing/afx018Google Scholar

Lee, L. K., Shahar, S., Chin, A. V., & Yusoff, N. A. M. (2013). Docosahexaenoic acid-concentrated fish oil supplementation in subjects with mild cognitive impairment (MCI): A 12-month randomised, double-blind, placebo-controlled trial. Psychopharmacology, 225(3), 605–612. doi: 10.1007/s00213-012-2848-0Google Scholar

Lefèvre-Arbogast, S., Féart, C., Dartigues, J. F., et al. (2016). Dietary B vitamins and a 10-year risk of dementia in older persons. Nutrients, 8(12), 761. doi: 10.3390/nu8120761Google Scholar

Lourida, I., Soni, M., Thompson-Coon, J., et al. (2013). Mediterranean diet, cognitive function, and dementia: A systematic review. Epidemiology, 24(4), 479–489. doi: 10.1097/EDE.0b013e3182944410Google Scholar

Lu, Y., An, Y., Guo, J., et al. (2016). Dietary intake of nutrients and lifestyle affect the risk of mild cognitive impairment in the Chinese elderly population: A cross-sectional study. Frontiers in Behavioral Neuroscience, 10, 229. doi: 10.3389/fnbeh.2016.00229Google Scholar

Masana, M. F., Koyanagi, A., Haro, J. M., & Tyrovolas, S. (2017). n-3 Fatty acids, Mediterranean diet and cognitive function in normal aging: A systematic review. Experimental Gerontology, 91, 39–50. doi: 10.1016/j.exger.2017.02.008Google Scholar

Mattson, M. P., & Shea, T. B. (2003). Folate and homocysteine metabolism in neural plasticity and neurodegenerative disorders. Trends in Neurosciences, 26(3), 137–146. doi: 10.1016/S0166-2236(03)00032-8Google Scholar

McCleery, J., Abraham, R. P., Denton, D. A., et al. (2018). Vitamin and mineral supplementation for preventing dementia or delaying cognitive decline in people with mild cognitive impairment. Cochrane Database of Systematic Reviews, 11, CD011905. doi.org/10.1002/14651858.CD011905.pub2 Google Scholar

Merrill, D. A., Siddarth, P., Raji, C. A., et al. (2016). Modifiable risk factors and brain positron emission tomography measures of amyloid and tau in nondemented adults with memory complaints. American Journal of Geriatric Psychiatry, 24(9), 729–737. doi: 10.1016/j.jagp.2016.05.007Google Scholar

Merrill, D. A., & Small, G. W. (2011). Prevention in psychiatry: Effects of healthy lifestyle on cognition. Psychiatric Clinics, 34(1), 249–261. doi: 10.1016/j.psc.2010.11.009Google Scholar

Miller, J. W., Harvey, D. J., Beckett, L. A., et al. (2015). Vitamin D status and rates of cognitive decline in a multiethnic cohort of older adults. JAMA Neurology, 72(11), 1295–1303. doi: 10.1001/jamaneurol.2015.2115Google Scholar

Miller, M. G., Thangthaeng, N., Poulose, S. M., & Shukitt-Hale, B. (2017). Role of fruits, nuts, and vegetables in maintaining cognitive health. Experimental Gerontology, 94, 24–28. doi: 10.1016/j.exger.2016.12.014Google Scholar

Mokry, L. E., Ross, S., Morris, J. A., et al. (2016). Genetically decreased vitamin D and risk of Alzheimer disease. Neurology, 87(24), 2567–2574. doi: 10.1212/WNL.0000000000003430CrossRef Google Scholar PubMed

Moore, E. M., Ames, D., Mander, A. G., et al. (2014). Among vitamin B12 deficient older people, high folate levels are associated with worse cognitive function: Combined data from three cohorts. Journal of Alzheimer’s Disease, 39(3), 661–668. doi: 10.3233/JAD-131265Google Scholar

Morris, M. C., Evans, D. A., Tangney, C. C., et al. (2005). Relation of the tocopherol forms to incident Alzheimer disease and to cognitive change. American Journal of Clinical Nutrition, 81(2), 508–514. doi: 10.1093/ajcn.81.2.508Google Scholar

Morris, M. C., Tangney, C. C., Wang, Y., et al. (2015a). MIND diet slows cognitive decline with aging. Alzheimer’s and Dementia, 11(9), 1015–1022. doi: 10.1016/j.jalz.2015.04.011Google Scholar

Morris, M. C., Tangney, C. C., Wang, Y., et al. (2015b). MIND diet associated with reduced incidence of Alzheimer’s disease. Alzheimer’s and Dementia, 11(9), 1007–1014. doi: 10.1016/j.jalz.2014.11.009Google Scholar

Morris, M. S., Jacques, P. F., Rosenberg, I. H., & Selhub, J. (2007). Folate and vitamin B-12 status in relation to anemia, macrocytosis, and cognitive impairment in older Americans in the age of folic acid fortification. American Journal of Clinical Nutrition, 85(1), 193–200. doi: 10.1093/ajcn/85.1.193CrossRef Google Scholar PubMed

Ngandu, T., Lehtisalo, J., Solomon, A., et al. (2015). A 2 year multidomain intervention of diet, exercise, cognitive training, and vascular risk monitoring versus control to prevent cognitive decline in at-risk elderly people (FINGER): A randomised controlled trial. Lancet, 385(9984), 2255–2263. doi: 10.1016/S0140-6736(15)60461-5Google Scholar

Nooyens, A. C., Milder, I. E., Van Gelder, B. M., et al. (2015). Diet and cognitive decline at middle age: The role of antioxidants. British Journal of Nutrition, 113(9), 1410–1417. doi: 10.1017/S0007114515000720Google Scholar

Norton, S., Matthews, F. E., Barnes, D. E., Yaffe, K., & Brayne, C. (2014). Potential for primary prevention of Alzheimer’s disease: An analysis of population-based data. Lancet Neurology, 13(8), 788–794. doi: 10.1016/S1474-4422(14)70136-XGoogle Scholar

Otaegui-Arrazola, A., Amiano, P., Elbusto, A., Urdaneta, E., & Martínez-Lage, P. (2014). Diet, cognition, and Alzheimer’s disease: Food for thought. European Journal of Nutrition, 53(1), 1–23. doi: 10.1007/s00394-013-0561-3Google Scholar

Pelletier, A., Barul, C., Féart, C., et al. (2015). Mediterranean diet and preserved brain structural connectivity in older subjects. Alzheimer’s and Dementia, 11(9), 1023–1031. doi: 10.1016/j.jalz.2015.06.1888Google Scholar

Péneau, S., Galan, P., Jeandel, C., et al. (2011). Fruit and vegetable intake and cognitive function in the SU. VI. MAX 2 prospective study. American Journal of Clinical Nutrition, 94(5), 1295–1303. doi: 10.3945/ajcn.111.014712CrossRef Google Scholar PubMed

Petersson, S. D., & Philippou, E. (2016). Mediterranean diet, cognitive function, and dementia: A systematic review of the evidence. Advances in Nutrition, 7(5), 889–904. doi: 10.3945/an.116.012138CrossRef Google Scholar PubMed

Presse, N., Belleville, S., Gaudreau, P., et al. (2013). Vitamin K status and cognitive function in healthy older adults. Neurobiology of Aging, 34(12), 2777–2783. doi: 10.1016/j.neurobiolaging.2013.05.031Google Scholar

Presse, N., Shatenstein, B., Kergoat, M. J., & Ferland, G. (2008). Low vitamin K intakes in community-dwelling elders at an early stage of Alzheimer’s disease. Journal of the American Dietetic Association, 108(12), 2095–2099. doi: 10.1016/j.jada.2008.09.013Google Scholar

Raji, C. A., Erickson, K. I., Lopez, O. L., et al. (2014). Regular fish consumption and age-related brain gray matter loss. American Journal of Preventive Medicine, 47(4), 444–451. doi: 10.1016/j.amepre.2014.05.037Google Scholar

Ramos, M. I., Allen, L. H., Mungas, D. M., et al. (2005). Low folate status is associated with impaired cognitive function and dementia in the Sacramento Area Latino Study on Aging. American Journal of Clinical Nutrition, 82(6), 1346–1352. doi: 10.1093/ajcn/82.6.1346Google Scholar

Samieri, C., Maillard, P., Crivello, F., et al. (2012). Plasma long-chain omega-3 fatty acids and atrophy of the medial temporal lobe. Neurology, 79(7), 642–650. doi: 10.1212/WNL.0b013e318264e394Google Scholar

Scarmeas, N., Luchsinger, J. A., Schupf, N., et al. (2009). Physical activity, diet, and risk of Alzheimer disease. JAMA, 302(6), 627–637. doi: 10.1001/jama.2009.1144Google Scholar

Scarmeas, N., Stern, Y., Tang, M. X., Mayeux, R., & Luchsinger, J. A. (2006). Mediterranean diet and risk for Alzheimer’s disease. Annals of Neurology: Official Journal of the American Neurological Association and the Child Neurology Society, 59(6), 912–921. doi: 10.1002/ana.20854Google Scholar

Shakersain, B., Santoni, G., Larsson, S. C., et al. (2016). Prudent diet may attenuate the adverse effects of Western diet on cognitive decline. Alzheimer’s and Dementia, 12(2), 100–109. doi: 10.1016/j.jalz.2015.08.002Google Scholar

Shatenstein, B., Kergoat, M. J., & Reid, I. (2007). Poor nutrient intakes during 1-year follow-up with community-dwelling older adults with early-stage Alzheimer dementia compared to cognitively intact matched controls. Journal of the American Dietetic Association, 107(12), 2091–2099. doi: 10.1016/j.jada.2007.09.008Google Scholar

Siervo, M., Arnold, R., Wells, J. C. K., et al. (2011). Intentional weight loss in overweight and obese individuals and cognitive function: A systematic review and meta‐analysis. Obesity Reviews, 12(11), 968–983. doi: 10.1111/j.1467-789X.2011.00903.xGoogle Scholar

Smith, P. J., & Blumenthal, J. A. (2016). Dietary factors and cognitive decline. Journal of Prevention of Alzheimer’s Disease, 3(1), 53–64. doi: 10.14283/jpad.2015.71Google Scholar

Sofi, F., Abbate, R., Gensini, G. F., & Casini, A. (2010). Accruing evidence on benefits of adherence to the Mediterranean diet on health: An updated systematic review and meta-analysis. American Journal of Clinical Nutrition, 92(5), 1189–1196. doi: 10.3945/ajcn.2010.29673Google Scholar

Solfrizzi, V., Custodero, C., Lozupone, M., et al. (2017). Relationships of dietary patterns, foods, and micro-and macronutrients with Alzheimer’s disease and late-life cognitive disorders: A systematic review. Journal of Alzheimer’s Disease, 59(3), 815–849. doi: 10.3233/JAD-170248Google Scholar

Soutif-Veillon, A., Ferland, G., Rolland, Y., et al. (2016). Increased dietary vitamin K intake is associated with less severe subjective memory complaint among older adults. Maturitas, 93, 131–136. doi: 10.1016/j.maturitas.2016.02.004Google Scholar

Tangney, C. C., Li, H., Wang, Y., et al. (2014). Relation of DASH- and Mediterranean-like dietary patterns to cognitive decline in older persons. Neurology, 83(16), 1410–1416. doi: 10.1212/WNL.0000000000000884Google Scholar

Tussing-Humphreys, L., Lamar, M., Blumenthal, J. A., et al. (2017). Building research in diet and cognition: The BRIDGE randomized controlled trial. Contemporary Clinical Trials, 59, 87–97. doi: 10.1016/j.cct.2017.06.003Google Scholar

Van Dyk, K., & Sano, M. (2007). The impact of nutrition on cognition in the elderly. Neurochemical Research, 32(4–5), 893–904. doi: 10.1007/s11064-006-9241-5CrossRef Google Scholar PubMed

Vogiatzoglou, A., Refsum, H., Johnston, C., et al. (2008). Vitamin B12 status and rate of brain volume loss in community-dwelling elderly. Neurology, 71(11), 826–832. doi: 10.1212/01.wnl.0000325581CrossRef Google Scholar PubMed

Willett, W. C., Sacks, F., Trichopoulou, A., et al. (1995). Mediterranean diet pyramid: A cultural model for healthy eating. American Journal of Clinical Nutrition, 61(6), 1402S–1406S. doi: 10.1093/ajcn/61.6.1402SCrossRef Google Scholar PubMed

Witte, A. V., Fobker, M., Gellner, R., Knecht, S., & Flöel, A. (2009). Caloric restriction improves memory in elderly humans. Proceedings of the National Academy of Sciences USA, 106(4), 1255–1260. doi: 10.1073/pnas.0808587106Google Scholar

References

Aloia, M. S., Ilniczky, N., Di Dio, P., et al. (2003). Neuropsychological changes and treatment compliance in older adults with sleep apnea. Journal of Psychosomatic Research, 54(1), 71–76. doi: 10.1016/s0022-3999(02)00548-2Google Scholar

Alzheimer’s Association (2018). 2018 Alzheimer’s disease facts and figures. Alzheimer’s and Dementia, 14(3), 367–429. doi: 10.1016/j.jalz.2015.02.003Google Scholar

Ancoli-Israel, S. (2000). Insomnia in the elderly: A review for the primary care practitioner. Sleep, 23(Suppl. 1), 23–30.Google Scholar

Ancoli-Israel, S., Kripke, D. F., Klauber, M. R., et al. (1993). Natural history of sleep disordered breathing in community dwelling elderly. Sleep, 16(Suppl. 8), 25–29. doi: 10.1093/sleep/16.suppl_8.s25Google Scholar

Aserinsky, E., & Kleitman, N. (1953). Regularly occurring periods of eye motility and concomitant phenomena during sleep. Science, 18, 274–284. doi: 10.1126/science.118.3062.273Google Scholar

Bauckneht, M., Chincarini, A., De Carli, F., et al. (2018). Presynaptic dopaminergic neuroimaging in REM sleep behavior disorder: A systematic review and meta-analysis. Sleep Medicine Reviews, 41, 266–274. doi: 10.1016/j.smrv.2018.04.001Google Scholar

Benedict, C., Byberg, L., Cedernaes, J., et al. (2015). Self-reported sleep disturbance is associated with Alzheimer’s disease risk in men. Alzheimer’s and Dementia, 11(9), 1090–1097. doi: 10.1016/j.jalz.2014.08.104Google Scholar

Bliwise, D. L., Foley, D. J., Vitiello, M. V., et al. (2009). Nocturia and disturbed sleep in the elderly. Sleep Medicine, 10(5), 540–548. doi: 10.1016/j.sleep.2008.04.002Google Scholar

Borbély, A. A., Daan, S., Wirz‐Justice, A., & Deboer, T. (2016). The two‐process model of sleep regulation: A reappraisal. Journal of Sleep Research, 25(2), 131–143. doi: 10.1111/jsr.12371Google Scholar

Bubu, O. M., Brannick, M., Mortimer, J., et al. (2017). Sleep, cognitive impairment, and Alzheimer’s disease: A systematic review and meta-analysis. Sleep, 40(1), zsw032. doi: 10.1093/sleep/zsw032Google Scholar

Cardinali, D. P., Brusco, L. I., Liberczuk, C., & Furio, A. M. (2002). The use of melatonin in Alzheimer’s disease. Neuro Endocrinology Letters, 23(Suppl. 1), 20–23.Google Scholar

Cassidy-Eagle, E., Siebern, A., Unti, L., Glassman, J., & O’Hara, R. (2018). Neuropsychological functioning in older adults with mild cognitive impairment and insomnia randomized to CBT-I or control group. Clinical Gerontologist, 41(2), 136–144. doi: 10.1080/07317115.2017.1384777Google Scholar

Chen, D.-W., Wang, J., Zhang, L.-L., Wang, Y.-J., & Gao, C.-Y. (2018). Cerebrospinal fluid amyloid-β levels are increased in patients with insomnia. Journal of Alzheimer’s Disease, 61(2), 645–651. doi: 10.3233/JAD-170032Google Scholar

Chiu, H. L., Chan, P. T., Chu, H., et al. (2017). Effectiveness of light therapy in cognitively impaired persons: A metaanalysis of randomized controlled trials. Journal of the American Geriatrics Society, 65(10), 2227–2234. doi: 10.1111/jgs.14990Google Scholar

Crowley, K. (2011). Sleep and sleep disorders in older adults. Neuropsychology Review, 21(1), 41–53. doi: 10.1007/s11065-010-9154-6Google Scholar

Cuellar, N. G., Strumpf, N. E., & Ratcliffe, S. J. (2007). Symptoms of restless legs syndrome in older adults: Outcomes on sleep quality, sleepiness, fatigue, depression, and quality of life. Journal of the American Geriatrics Society, 55(9), 1387–1392. 10.1111/j.1532-5415.2007.01294.xGoogle Scholar

Dang-Vu, T. T., Schabus, M., Desseilles, M., et al. (2010). Functional neuroimaging insights into the physiology of human sleep. Sleep, 33(12), 1589–1603. doi: 10.1093/sleep/33.12.1589Google Scholar

Dement, W. C. (1998). The study of human sleep: A historical perspective. Thorax, 53(Suppl. 3), 2–7. doi: 10.1136/thx.53.2008.S2Google Scholar

Doody, R. S., Thomas, R. G., Farlow, M., et al. (2014). Phase 3 trials of solanezumab for mild-to-moderate Alzheimer’s disease. New England Journal of Medicine, 370(4), 311–321. doi: 10.1056/NEJMoa1312889Google Scholar

Dubé, J., Lafortune, M., Bedetti, C., et al. (2015). Cortical thinning explains changes in sleep slow waves during adulthood. Journal of Neuroscience, 35(20), 7795–7807. doi: 10.1523/JNEUROSCI.3956-14.2015Google Scholar

Duffy, J. F., Willson, H. J., Wang, W., & Czeisler, C. A. (2009). Healthy older adults better tolerate sleep deprivation than young adults: Increased tolerance of sleep deprivation with age. Journal of the American Geriatrics Society, 57(7), 1245–1251. doi: 10.1111/j.1532-5415.2009.02303.xGoogle Scholar

Emamian, F., Khazaie, H., Tahmasian, M., et al. (2016). The association between obstructive sleep apnea and Alzheimer’s disease: A meta-analysis perspective. Frontiers in Aging Neuroscience, 8, 78. doi: 10.3389/fnagi.2016.00078Google Scholar

Ferman, T. J., Boeve, B. F., Smith, G. E., et al. (1999). REM sleep behavior disorder and dementia: Cognitive differences when compared with AD. Neurology, 52(5), 951–951. doi: 10.1212/WNL.52.5.951Google Scholar

Fogel, S. M., Albouy, G., Vien, C., et al. (2014). fMRI and sleep correlates of the age-related impairment in motor memory consolidation: Age-related sleep-dependent impaired memory. Human Brain Mapping, 35(8), 3625–3645. doi: 10.1002/hbm.22426Google Scholar

Furio, A. M., Brusco, L. I., & Cardinali, D. P. (2007). Possible therapeutic value of melatonin in mild cognitive impairment: A retrospective study. Journal of Pineal Research, 43(4), 404–409. doi: 10.1111/j.1600-079X.2007.00491.xGoogle Scholar

Gerrard, J. L., Burke, S. N., McNaughton, B. L., & Barnes, C. A. (2008). Sequence reactivation in the hippocampus is impaired in aged rats. Journal of Neuroscience, 28(31), 7883–7890. doi: 10.1523/JNEUROSCI.1265-08.2008Google Scholar

Gilbert, S. S., Burgess, H. J., Kennaway, D. J., & Dawson, D. (2000). Attenuation of sleep propensity, core hypothermia, and peripheral heat loss after temazepam tolerance. American Journal of Physiology – Regulatory, Integrative and Comparative Physiology, 279(6), R1980–1987. doi: 10.1152/ajpregu.2000.279.6.R1980Google Scholar

Goldstein-Piekarski, A. N., O’Hora, K., Buchanan, A., et al. (2018). The effects of CBT-I on cognitive functioning in individuals with insomnia and mild cognitive impairment. Sleep, 41(Suppl. 1), A154–A155. doi: 10.1093/sleep/zsy061.405Google Scholar

Hebert, L. E., Weuve, J., Scherr, P. A., & Evans, D. A. (2013). Alzheimer disease in the United States (2010–2050) estimated using the 2010 census. Neurology, 80(19), 1778–1783. doi: 10.1212/WNL.0b013e31828726f5Google Scholar

Herings, R. M., Stricker, B. H., de Boer, A., Bakker, A., & Sturmans, F. (1995). Benzodiazepines and the risk of falling leading to femur fractures: Dosage more important than elimination half-life. Archives of Internal Medicine, 155(16), 1801–1807. doi: 10.1001/archinte.1995.00430160149015Google Scholar

Jessen, N. A., Munk, A. S. F., Lundgaard, I., & Nedergaard, M. (2015). The glymphatic system: A beginner’s guide. Neurochemical Research, 40(12), 2583–2599. doi: 10.1007/s11064-015-1581-6Google Scholar

Jouvet, M. (1965). Paradoxical sleep – A study of its nature and mechanisms. Progress in Brain Research, 18, 20–62. doi: 10.1016/S0079-6123(08)63582-7Google Scholar

Ju, Y. E. S., Ooms, S. J., Sutphen, C., et al. (2017). Slow wave sleep disruption increases cerebrospinal fluid amyloid-β levels. Brain, 140(8), 2104–2111. doi: 10.1093/brain/awx148Google Scholar

Kyle, S. D., Sexton, C. E., Feige, B., et al. (2017). Sleep and cognitive performance: Cross-sectional associations in the UK Biobank. Sleep Medicine, 38, 85–91. doi: 10.1016/j.sleep.2017.07.001Google Scholar

Lee, H. B., Ramsey, C. M., Spira, A. P., et al. (2014). Comparison of cognitive functioning among individuals with treated restless legs syndrome (RLS), untreated RLS, and no RLS. Journal of Neuropsychiatry and Clinical Neurosciences, 26(1), 87–91. doi: 10.1176/appi.neuropsych.12120394Google Scholar

Liu, Y. R., Fan, D. Q., Gui, W. J., et al. (2018). Sleep-related brain atrophy and disrupted functional connectivity in older adults. Behavioural Brain Research, 347, 292–299. doi: 10.1016/j.bbr.2018.03.032Google Scholar

Mander, B. A., Marks, S. M., Vogel, J. W., et al. (2015). β-amyloid disrupts human NREM slow waves and related hippocampus-dependent memory consolidation. Nature Neuroscience, 18(7), 1051–1057. doi: 10.1038/nn.3324Google Scholar

McCall, W. V. (2004). Sleep in the elderly: Burden, diagnosis, and treatment. Primary Care Companion to the Journal of Clinical Psychiatry, 6(1), 9–20. doi: 10.4088/pcc.v06n0104Google Scholar

McCurry, S. M., Pike, K. C., Vitiello, M. V., et al. (2011). Increasing walking and bright light exposure to improve sleep in community-dwelling persons with Alzheimer’s disease: Results of a randomized, controlled trial. Journal of the American Geriatrics Society, 59(8), 1393–1402. doi: 10.1111/j.1532-5415.2011.03519.xGoogle Scholar

Milner, C. E., & Cote, K. A. (2008). A dose-response investigation of the benefits of napping in healthy young, middle-aged and older adults. Sleep and Biological Rhythms, 6(1), 2–15. doi: 10.1111/j.1479-8425.2007.00328.xGoogle Scholar

Montplaisir, J., Boucher, S., Poirier, G., et al. (1997). Clinical, polysomnographic, and genetic characteristics of restless legs syndrome: A study of 133 patients diagnosed with new standard criteria. Movement Disorders, 12(1), 61–65. doi: 10.1002/mds.870120111CrossRef Google Scholar PubMed

Murphy, M. P., & LeVine, H. (2010). Alzheimer’s disease and the amyloid-β peptide. Journal of Alzheimer’s Disease, 19(1), 311–323. doi: 10.3233/JAD-2010-1221Google Scholar

National Sleep Foundation (2003). Summary findings of the 2003 Sleep in America Poll. http://sleepfoundation.org/sites/default/files/2003SleepPollExecSumm.pdf Google Scholar

Ohayon, M. M., Carskadon, M. A., Guilleminault, C., & Vitiello, M. V. (2004). Meta-analysis of quantitative sleep parameters from childhood to old age in healthy individuals: Developing normative sleep values across the human lifespan. Sleep, 27(7), 1255–1273. doi: 10.1093/sleep/27.7.1255Google Scholar

Pasula, E. Y., Brown, G. G., McKenna, B. S., et al. (2018). Effects of sleep deprivation on component processes of working memory in younger and older adults. Sleep, 41(3), zsx213. doi: 10.1093/sleep/zsx213Google Scholar

Pearson, V., Allen, R., Dean, T., et al. (2006). Cognitive deficits associated with restless legs syndrome (RLS). Sleep Medicine, 7(1), 25–30. doi: 10.1016/j.sleep.2005.05.006Google Scholar

Peigneux, P., Laureys, S., Fuchs, S., et al. (2004). Are spatial memories strengthened in the human hippocampus during slow wave sleep? Neuron, 44(3), 535–545. doi: 10.1016/j.neuron.2004.10.007Google Scholar

Postuma, R. B., Gagnon, J.-F., Bertrand, J.-A., Genier Marchand, D., & Montplaisir, J. Y. (2015). Parkinson risk in idiopathic REM sleep behavior disorder: Preparing for neuroprotective trials. Neurology, 84(11), 1104–1113. doi: 10.1212/WNL.0000000000001364Google Scholar

Rasch, B., & Born, J. (2013). About sleep’s role in memory. Physiological Reviews, 93(2), 681–766. doi: 10.1152/physrev.00032.2012Google Scholar

Rasch, B., Buchel, C., Gais, S., & Born, J. (2007). Odor cues during slow-wave sleep prompt declarative memory consolidation. Science, 315(5817), 1426–1429. doi: 10.1126/science.1138581Google Scholar

Rechtschaffen, A., Bergmann, B. M., Everson, C. A., Kushida, C. A., & Gilliland, M. A. (1989). Sleep deprivation in the rat: X. Integration and discussion of the findings. Sleep, 12(1), 68–87. doi: 10.1093/sleep/12.1.68Google Scholar

Rhalimi, M., Helou, R., & Jaecker, P. (2009). Medication use and increased risk of falls in hospitalized elderly patients: A retrospective, case-control study. Drugs and Aging, 26(10), 847–852. doi: 10.2165/11317610-000000000-00000Google Scholar

Salthouse, T. A. (1996). The processing-speed theory of adult age differences in cognition. Psychological Review, 103(3), 403–428. doi: 10.1037/0033-295x.103.3.403Google Scholar

Schwarz, J. F. A., Åkerstedt, T., Lindberg, E., et al. (2017). Age affects sleep microstructure more than sleep macrostructure. Journal of Sleep Research, 26(3), 277–287. doi: 10.1111/jsr.12478Google Scholar

Scullin, M. K. (2013). Sleep, memory, and aging: The link between slow-wave sleep and episodic memory changes from younger to older adults. Psychology and Aging, 28(1), 105–114. doi: 10.1037/a0028830Google Scholar

Scullin, M. K. (2017). Do older adults need sleep? A review of neuroimaging, sleep, and aging studies. Current Sleep Medicine Reports, 3(3), 204–214. doi: 10.1007/s40675-017-0086-zGoogle Scholar

Scullin, M. K. (2019). The eight hour sleep challenge during final exams week. Teaching of Psychology, 46(1), 55–63. doi: 10.1177/0098628318816142Google Scholar

Scullin, M. K., & Bliwise, D. L. (2015). Sleep, cognition, and normal aging: Integrating a half century of multidisciplinary research. Perspectives on Psychological Science, 10(1), 97–137. doi: 10.1177/1745691614556680Google Scholar

Scullin, M. K., Fairley, J., Decker, M. J., & Bliwise, D. L. (2017). The effects of an afternoon nap on episodic memory in young and older adults. Sleep, 40(5), zsx035. doi: 10.1093/sleep/zsx035Google Scholar

Scullin, M. K., Fairley, J., Trotti, L., et al. (2015). Sleep correlates of trait executive function and memory in Parkinson’s disease. Journal of Parkinson’s Disease, 5(1), 49–54. doi: 10.3233/JPD-140475Google Scholar

Scullin, M. K., Le, D. T., & Shelton, J. T. (2017). Healthy heart, healthy brain: Hypertension affects cognitive functioning in older age. Translational Issues in Psychological Science, 3(4), 328–337. doi: 10.1037/tps0000131Google Scholar

Scullin, M. K., Trotti, L. M., Wilson, A. G., Greer, S. A., & Bliwise, D. L. (2012). Nocturnal sleep enhances working memory training in Parkinson’s disease but not Lewy body dementia. Brain, 135(9), 2789–2797. doi: 10.1093/brain/aws192Google Scholar

Sharma, R. A., Varga, A. W., Bubu, O. M., et al. (2018). Obstructive sleep apnea severity affects amyloid burden in cognitively normal elderly. A longitudinal study. American Journal of Respiratory and Critical Care Medicine, 197(7), 933–943. doi: 10.1164/rccm.201704-0704OCGoogle Scholar

Shokri-Kojori, E., Wang, G.-J., Wiers, C. E., et al. (2018). β-Amyloid accumulation in the human brain after one night of sleep deprivation. Proceedings of the National Academy of Sciences USA, 115(17), 4483–4488. doi: 10.1073/pnas.1721694115Google Scholar

Smith, M. T., Perlis, M. L., Park, A., et al. (2002). Comparative meta-analysis of pharmacotherapy and behavior therapy for persistent insomnia. American Journal of Psychiatry, 159(1), 5–11. doi: 10.1176/appi.ajp.159.1.5Google Scholar

Spira, A. P., Gonzalez, C. E., Venkatraman, V. K., et al. (2016). Sleep duration and subsequent cortical thinning in cognitively normal older adults. Sleep, 39(5), 1121–1128. doi: 10.5665/sleep.5768Google Scholar

Staresina, B. P., Bergmann, T. O., Bonnefond, M., et al. (2015). Hierarchical nesting of slow oscillations, spindles and ripples in the human hippocampus during sleep. Nature Neuroscience, 18, 1679–1686. doi: 10.1038/nn.4119Google Scholar

Thomas, M., Sing, H., Belenky, G., et al. (2000). Neural basis of alertness and cognitive performance impairments during sleepiness. I. Effects of 24 h of sleep deprivation on waking human regional brain activity. Journal of Sleep Research, 9(4), 335–352. doi: 10.1046/j.1365-2869.2000.00225.xGoogle Scholar

Troussière, A. C., Monaca Charley, C., Salleron, J., et al. (2014). Treatment of sleep apnoea syndrome decreases cognitive decline in patients with Alzheimer’s disease. Journal of Neurology, Neurosurgery and Psychiatry, 85(12), 1405–1408. doi: 10.1136/jnnp-2013-307544Google Scholar

Wallace, A., & Bucks, R. S. (2013). Memory and obstructive sleep apnea: A meta-analysis. Sleep, 36(2), 203–220. doi: 10.5665/sleep.2374Google Scholar

Wilson, M. A., & McNaughton, B. L. (1994). Reactivation of hippocampal ensemble memories during sleep. Science, 265(5172), 676–679. doi: 10.1126/science.8036517Google Scholar

Wu, J. C., Gillin, J. C., Buchsbaum, M. S., et al. (2006). Frontal lobe metabolic decreases with sleep deprivation not totally reversed by recovery sleep. Neuropsychopharmacology, 31(12), 2783–2792. doi: 10.1038/sj.npp.1301166Google Scholar

Xie, L., Kang, H., Xu, Q., et al. (2013). Sleep drives metabolite clearance from the adult brain. Science, 342(6156), 373–377. doi: 10.1126/science.1241224Google Scholar

References

Barnes, D. E., Blackwell, T., Stone, K. L., et al. (2008). Cognition in older women: The importance of daytime movement. Journal of the American Geriatrics Society, 56(9), 1658–1664. http://dx.doi.org/10.1111/j.1532-5415.2008.01841.x Google Scholar

Bennett, D. A., Schneider, J. A., Buchman, A. S., et al. (2005). The Rush Memory and Aging Project: Study design and baseline characteristics of the study cohort. Neuroepidemiology, 25(4), 163–175. http://dx.doi.org/10.1159/000087446 Google Scholar

Boucard, G. K., Albinet, C. T., Bugaiska, A., et al. (2012). Impact of physical activity on executive functions in aging: A selective effect on inhibition among old adults. Journal of Sport and Exercise Psychology, 34(6), 808–827. http://dx.doi.org/10.1123/jsep.34.6.808 Google Scholar

Brookmeyer, R., Johnson, E., Ziegler-Graham, K., & Arrighi, H. M. (2007). Forecasting the global burden of Alzheimer’s disease. Alzheimers and Dementia, 3(3), 186–191. http://dx.doi.org/10.1016/j.jalz.2007.04.381 Google Scholar

Brown, B. M., Peiffer, J., Sohrabi, H. R., et al. (2012). Intense physical activity is associated with cognitive performance in the elderly. Translational Psychiatry, 2(11), e191. http://dx.doi.org/10.1038/tp.2012.118 Google Scholar

Buchman, A., Boyle, P., Yu, L., et al. (2012). Total daily physical activity and the risk of AD and cognitive decline in older adults. Neurology, 78(17), 1323–1329. http://dx.doi.org/10.1212/WNL.0b013e3182535d35 Google Scholar

Caspersen, C. J., Powell, K. E., & Christenson, G. M. (1985). Physical activity, exercise, and physical fitness: Definitions and distinctions for health-related research. Public Health Reports, 100(2), 126–131.Google Scholar

Cavalcante, B. R., Germano-Soares, A. H., Gerage, A. M., et al. (2018). Association between physical activity and walking capacity with cognitive function in peripheral artery disease patients. European Journal of Vascular and Endovascular Surgery, 55(5), 672–678. http://dx.doi.org/10.1016/j.ejvs.2018.02.010 Google Scholar

Colcombe, S., & Kramer, A. F. (2003). Fitness effects on the cognitive function of older adults: A meta-analytic study. Psychological Science, 14(2), 125–130. http://dx.doi.org/10.1111/1467-9280.t01-1-01430 CrossRef Google Scholar PubMed

Doody, R. S., Raman, R., Farlow, M., et al. (2013). A phase 3 trial of semagacestat for treatment of Alzheimer’s disease. New England Journal of Medicine, 369(4), 341–350. http://dx.doi.org/10.1056/NEJMoa1210951 Google Scholar

Erickson, K., & Kramer, A. F. (2009). Aerobic exercise effects on cognitive and neural plasticity in older adults. British Journal of Sports Medicine, 43(1), 22–24. http://dx.doi.org/10.1136/bjsm.2008.052498 Google Scholar

Freedson, P., Bowles, H. R., Troiano, R., & Haskell, W. (2012). Assessment of physical activity using wearable monitors: Recommendations for monitor calibration and use in the field. Medicine and Science in Sports and Exercise, 44(Suppl. 1), 1–4. http://dx.doi.org/10.1249/MSS.0b013e3182399b7e Google Scholar

Freedson, P., Melanson, E., & Sirard, J. (1998). Calibration of the Computer Science and Applications, Inc. accelerometer. Medicine and Science in Sports and Exercise, 30(5), 777–781. http://dx.doi.org/10.1097/00005768-199805000-00021 Google Scholar

Freedson, P., Pober, D., & Janz, K. F. (2005). Calibration of accelerometer output for children. Medicine and Science in Sports and Exercise, 37(Suppl. 11), 523–530. http://dx.doi.org/10.1249/01.mss.0000185658.28284.ba Google Scholar

Goh, J. O., An, Y., & Resnick, S. M. (2012). Differential trajectories of age-related changes in components of executive and memory processes. Psychology of Aging, 27(3), 707–719. http://dx.doi.org/10.1037/a0026715 Google Scholar

Gregory, S. M., Parker, B., & Thompson, P. D. (2012). Physical activity, cognitive function, and brain health: What is the role of exercise training in the prevention of dementia? Brain Sciences, 2(4), 684–708. http://dx.doi.org/10.3390/brainsci2040684 Google Scholar

Halloway, S., Wilbur, J., Schoeny, M. E., & Barnes, L. L. (2017). The relation between physical activity and cognitive change in older Latinos. Biological Research for Nursing, 19(5), 538–548. http://dx.doi.org/10.1177/1099800417715115 Google Scholar

Hayes, S. M., Alosco, M. L., Hayes, J. P., et al. (2015). Physical activity is positively associated with episodic memory in aging. Journal of the International Neuropsychological Society, 21(10), 780–790. http://dx.doi.org/10.1017/S1355617715000910 Google Scholar

Hayes, S. M., Forman, D. E., & Verfaellie, M. (2016). Cardiorespiratory fitness is associated with cognitive performance in older but not younger adults. Journals of Gerontology, Series B: Psychological Sciences and Social Sciences, 71(3), 474–482. http://dx.doi.org/10.1093/geronb/gbu167 Google Scholar

Hayes, S. M., Hayes, J. P., Williams, V. J., Liu, H., & Verfaellie, M. (2017). FMRI activity during associative encoding is correlated with cardiorespiratory fitness and source memory performance in older adults. Cortex, 91, 208–220. http://dx.doi.org/10.1016/j.cortex.2017.01.002 Google Scholar

Hayes, S. M., Salat, D. H., Forman, D. E., Sperling, R. A., & Verfaellie, M. (2015). Cardiorespiratory fitness is associated with white matter integrity in aging. Annals of Clinical Translational Neurology, 2(6), 688–698. http://dx.doi.org/10.1002/acn3.204 Google Scholar

Hillman, C. H., Erickson, K. I., & Kramer, A. F. (2008). Be smart, exercise your heart: Exercise effects on brain and cognition. Nature Reviews Neuroscience, 9(1), 58–65. http://dx.doi.org/10.1038/nrn2298 Google Scholar

John, D., & Freedson, P. (2012). ActiGraph and Actical physical activity monitors: A peek under the hood. Medicine and Science in Sports and Exercise, 44(1 Suppl. 1), 86–89. http://dx.doi.org/10.1249/MSS.0b013e3182399f5e Google Scholar

John, D., Liu, S., Sasaki, J. E., et al. (2011). Calibrating a novel multi-sensor physical activity measurement system. Physiological Measurement, 32(9), 1473–1489. http://dx.doi.org/10.1088/0967-3334/32/9/009 Google Scholar

Johnson, L. G., Butson, M. L., Polman, R. C., et al. (2016). Light physical activity is positively associated with cognitive performance in older community dwelling adults. Journal of Science and Medicine in Sport, 19(11), 877–882. http://dx.doi.org/10.1016/j.jsams.2016.02.002 Google Scholar

Kerr, J., Marshall, S. J., Patterson, R. E., et al. (2013). Objectively measured physical activity is related to cognitive function in older adults. Journal of the American Geriatrics Society, 61(11), 1927–1931. http://dx.doi.org/10.1111/jgs.12524 Google Scholar

Kramer, A. F., & Colcombe, S. (2018). Fitness effects on the cognitive function of older adults: A meta-analytic study – revisited. Perspectives on Psychological Science, 13(2), 213–217. http://dx.doi.org/10.1177/1745691617707316 Google Scholar

Lamb, S. E., Sheehan, B., Atherton, N., et al. (2018). Dementia and physical activity (DAPA) trial of moderate to high intensity exercise training for people with dementia: Randomised controlled trial. BMJ, 361, k1675. http://dx.doi.org/10.1136/bmj.k1675 Google Scholar

Makizako, H., Liu-Ambrose, T., Shimada, H., et al. (2015). Moderate-intensity physical activity, hippocampal volume, and memory in older adults with mild cognitive impairment. Journals of Gerontology, Series A: Biomedical Sciences and Medical Sciences, 70(4), 480–486. http://dx.doi.org/10.1093/gerona/glu136 Google Scholar

Nasreddine, Z. S., Phillips, N. A., Bédirian, V., et al. (2005). The Montreal Cognitive Assessment, MoCA: A brief screening tool for mild cognitive impairment. Journal of the American Geriatrics Society, 53(4), 695–699. http://dx.doi.org/10.1111/j.1532-5415.2005.53221.x Google Scholar

Naveh-Benjamin, M. (2000). Adult age differences in memory performance: Tests of an associative deficit hypothesis. Journal of Experimental Psychology: Learning, Memory, and Cognition, 26(5), 1170–1187. http://dx.doi.org/10.1037/0278-7393.26.5.1170 Google Scholar

Norton, S., Matthews, F. E., & Brayne, C. (2013). A commentary on studies presenting projections of the future prevalence of dementia. BMC Public Health, 13, 1. http://dx.doi.org/10.1186/1471-2458-13-1 Google Scholar

Prince, S. A., Adamo, K. B., Hamel, M. E., et al. (2008). A comparison of direct versus self-report measures for assessing physical activity in adults: A systematic review. International Journal of Behavioral Nutrition and Physical Activity, 5, 56. http://dx.doi.org/10.1186/1479-5868-5-56 Google Scholar

Rzewnicki, R., Vanden Auweele, Y., & De Bourdeaudhuij, I. (2003). Addressing overreporting on the International Physical Activity Questionnaire (IPAQ) telephone survey with a population sample. Public Health Nutrition, 6(3), 299–305. http://dx.doi.org/10.1079/PHN2002427 Google Scholar

Salloway, S., Sperling, R., Fox, N. C., et al. (2014). Two phase 3 trials of bapineuzumab in mild-to-moderate Alzheimer’s disease. New England Journal of Medicine, 370(4), 322–333. http://dx.doi.org/10.1056/NEJMoa1304839 Google Scholar

Shepard, R. J. (2003). Limits to the measurement of habitual physical activity by questionnaires. British Journal of Sports Medicine, 37, 197–206. https://doi.org/10.1136/bjsm.37.3.197 Google Scholar

Smith, P. J., Blumenthal, J. A., Hoffman, B. M., et al. (2010). Aerobic exercise and neurocognitive performance: A meta-analytic review of randomized controlled trials. Psychosomatic Medicine, 72(3), 239–252. http://dx.doi.org/10.1097/PSY.0b013e3181d14633 Google Scholar

Stubbs, B., Chen, L. J., Chang, C. Y., Sun, W. J., & Ku, P. W. (2017). Accelerometer-assessed light physical activity is protective of future cognitive ability: A longitudinal study among community dwelling older adults. Experimental Gerontology, 91, 104–109. http://dx.doi.org/10.1016/j.exger.2017.03.003 Google Scholar

Trost, S. G., Pate, R. R., Freedson, P. S., Sallis, J. F., & Taylor, W. C. (2000). Using objective physical activity measures with youth: How many days of monitoring are needed? Medicine and Science in Sports and Exercise, 32(2), 426–431. http://dx.doi.org/10.1097/00005768-200002000-00025 Google Scholar

Umegaki, H., Makino, T., Uemura, K., et al. (2018). Objectively measured physical activity and cognitive function in urban‐dwelling older adults. Geriatrics and Gerontology International, 18(6), 922–928. http://dx.doi.org/10.1111/ggi.13284 Google Scholar

Vance, D. E., Wadley, V. G., Ball, K. K., Roenker, D. L., & Rizzo, M. (2005). The effects of physical activity and sedentary behavior on cognitive health in older adults. Journal of Aging and Physical Activity, 13(3), 294–313. http://dx.doi.org/10.1123/japa.13.3.294 Google Scholar

Vásquez, E., Strizich, G., Isasi, C. R., et al. (2017). Is there a relationship between accelerometer-assessed physical activity and sedentary behavior and cognitive function in US Hispanic/Latino adults? The Hispanic Community Health Study/Study of Latinos (HCHS/SOL). Preventive Medicine, 103, 43–48. http://dx.doi.org/10.1016/j.ypmed.2017.07.024 Google Scholar

Voss, M. W., Nagamatsu, L. S., Liu-Ambrose, T., & Kramer, A. F. (2011). Exercise, brain, and cognition across the life span. Journal of Applied Physiology, 111(5), 1505–1513. http://dx.doi.org/10.1152/japplphysiol.00210.2011 Google Scholar

Wang, R., Blackburn, G., Desai, M., et al. (2017). Accuracy of wrist-worn heart rate monitors. JAMA Cardiology, 2(1), 104–106. http://dx.doi.org/10.1001/jamacardio.2016.3340 Google Scholar

Wilbur, J., Marquez, D. X., Fogg, L., et al. (2012). The relationship between physical activity and cognition in older Latinos. Journals of Gerontology, Series B: Psychological Sciences and Social Sciences, 67(5), 525–534. http://dx.doi.org/10.1093/geronb/gbr137 Google Scholar

Wilckens, K. A., Erickson, K. I., & Wheeler, M. E. (2018). Physical activity and cognition: A mediating role of efficient sleep. Behavioral Sleep Medicine, 16(6), 569–586. http://dx.doi.org/10.1080/15402002.2016.1253013 Google Scholar

Williams, V. J., Hayes, J. P., Forman, D. E., et al. (2017). Cardiorespiratory fitness is differentially associated with cortical thickness in young and older adults. NeuroImage, 146, 1084–1092. http://dx.doi.org/10.1016/j.neuroimage.2016.10.033 Google Scholar

Zhu, W., Howard, V. J., Wadley, V. G., et al. (2015). Association between objectively measured physical activity and cognitive function in older adults – the reasons for geographic and racial differences in stroke study. Journal of the American Geriatrics Society, 63(12), 2447–2454. http://dx.doi.org/10.1111/jgs.13829 Google Scholar

Zhu, W., Wadley, V. G., Howard, V. J., et al. (2017). Objectively measured physical activity and cognitive function in older adults. Medicine and Science in Sports and Exercise, 49(1), 47–53. http://dx.doi.org/10.1249/MSS.0000000000001079 Google Scholar

References

Agarwal, S., Driscoll, J. C., Gabaix, X., & Laibson, D. (2009). The age of reason: Financial decisions over the lifecycle with implications for regulation. Brookings Papers on Economic Activity, 2, 51–117.Google Scholar

Amer, T., Anderson, J. A. E., Campbell, K. L., Hasher, L., & Grady, C. L. (2016). Age differences in the neural correlates of distraction regulation: A network interaction approach. NeuroImage, 139, 231–239. doi: 10.1016/j.neuroimage.2016.06.036Google Scholar

Anderson, S., White-Schwoch, T., Choi, H. J., & Kraus, N. (2014). Partial maintenance of auditory-based cognitive training benefits in older adults. Neuropsychologia, 62, 286–296. doi: 10.1016/j.neuropsychologia.2014.07.034Google Scholar

Anderson, S., White-Schwoch, T., Parbery-Clark, A., & Kraus, N. (2013). Reversal of age-related neural timing delays with training. Proceedings of the National Academy of Sciences USA, 110(11), 4357–4362. doi: 10.1073/pnas.1213555110Google Scholar

Andrews-Hanna, J. R., Snyder, A. Z., Vincent, J. L., et al. (2007). Disruption of large-scale brain systems in advanced aging. Neuron, 56(5), 924–935. doi: 10.1016/j.neuron.2007.10.038Google Scholar

Anguera, J. A., Boccanfuso, J., Rintoul, J. L., et al. (2013). Video game training enhances cognitive control in older adults. Nature, 501(7465), 97–101. doi: 10.1038/nature12486Google Scholar

Au, J., Sheehan, E., Tsai, N., et al. (2015). Improving fluid intelligence with training on working memory: A meta-analysis. Psychonomic Bulletin and Review, 22(2), 366–377. doi: 10.3758/s13423-014-0699-xGoogle Scholar

Ball, K., Berch, D. B., Helmers, K. F., et al. (2002). Effects of cognitive training interventions with older adults: A randomized controlled trial. JAMA, 288(18), 2271–2281. doi: 10.1001/jama.288.18.2271Google Scholar

Berry, A. S., Zanto, T. P., Clapp, W. C., et al. (2010). The influence of perceptual training on working memory in older adults. PLoS One, 5(7), e11537. doi: 10.1371/journal.pone.0011537Google Scholar

Berry, A. S., Zanto, T. P., Rutman, A. M., Clapp, W. C., & Gazzaley, A. (2009). Practice-related improvement in working memory is modulated by changes in processing external interference. Journal of Neurophysiology, 102, 1779–1789. doi: 10.1152/jn.00179.2009Google Scholar

Blacker, K. J., Negoita, S., Ewen, J. B., & Courtney, S. M. (2017). N-back versus complex span working memory training. Journal of Cognitive Enhancement, 1(4), 434–454. doi: 10.1007/s41465-017-0044-1Google Scholar

Boldrini, M., Fulmore, C. A., Tartt, A. N., et al. (2018). Human hippocampal neurogenesis persists throughout aging. Cell Stem Cell, 22(4), 589–599 e5. doi: 10.1016/j.stem.2018.03.015Google Scholar

Bower, J. D., & Andersen, G. J. (2012). Aging, perceptual learning, and changes in efficiency of motion processing. Vision Research, 61, 144–156. doi: 10.1016/j.visres.2011.07.016Google Scholar

Bower, J. D., Watanabe, T., & Andersen, G. J. (2013). Perceptual learning and aging: Improved performance for low-contrast motion discrimination. Frontiers in Psychology, 4, 66. doi: 10.3389/fpsyg.2013.00066Google Scholar

Boyke, J., Driemeyer, J., Gaser, C., Buchel, C., & May, A. (2008). Training-induced brain structure changes in the elderly. Journal of Neuroscience, 28(28), 7031–7035. doi: 10.1523/JNEUROSCI.0742-08.2008Google Scholar

Burgess, G. C., Gray, J. R., Conway, A. R., & Braver, T. S. (2011). Neural mechanisms of interference control underlie the relationship between fluid intelligence and working memory span. Journal of Experimental Psychology: General, 140(4), 674–692. doi: 10.1037/a0024695Google Scholar

Cashdollar, N., Fukuda, K., Bocklage, A., et al. (2013). Prolonged disengagement from attentional capture in normal aging. Psychology of Aging, 28(1), 77–86. doi: 10.1037/a0029899Google Scholar

Chan, M. Y., Park, D. C., Savalia, N. K., Petersen, S. E., & Wig, G. S. (2014). Decreased segregation of brain systems across the healthy adult lifespan. Proceedings of the National Academy of Sciences USA, 111(46), E4997–E5006. doi: 10.1073/pnas.1415122111Google Scholar

Chein, J. M., & Schneider, W. (2005). Neuroimaging studies of practice-related change: fMRI and meta-analytic evidence of a domain-general control network for learning. Brain Research: Cognitive Brain Research, 25(3), 607–623. doi: 10.1016/j.cogbrainres.2005.08.013Google Scholar

Chelazzi, L., Miller, E. K., Duncan, J., & Desimone, R. (2001). Responses of neurons in macaque area V4 during memory-guided visual search. Cerebral Cortex, 11(8), 761–772. doi: 10.1093/cercor/11.8.761Google Scholar

Clapp, W. C., Rubens, M. T., & Gazzaley, A. (2010). Mechanisms of working memory disruption by external interference. Cerebral Cortex, 20(4), 859–872. doi: 10.1093/cercor/bhp150Google Scholar

Cole, M. W., Yarkoni, T., Repovs, G., Anticevic, A., & Braver, T. S. (2012). Global connectivity of prefrontal cortex predicts cognitive control and intelligence. Journal of Neuroscience, 32(26), 8988–8999. doi: 10.1523/JNEUROSCI.0536-12.2012Google Scholar

Coley, N., Ngandu, T., Lehtisalo, J., et al. (2019). Adherence to multidomain interventions for dementia prevention: Data from the FINGER and MAPT trials. Alzheimer’s and Dementia, 15(6), 729–741. doi: 10.1016/j.jalz.2019.03.005Google Scholar

Daffner, K. R., Zhuravleva, T. Y., Sun, X., et al. (2012). Does modulation of selective attention to features reflect enhancement or suppression of neural activity? Biological Psychology, 89(2), 398–407. doi: 10.1016/j.biopsycho.2011.12.002Google Scholar

Dahlin, E., Neely, A. S., Larsson, A., Bäckman, L., & Nyberg, L. (2008). Transfer of learning after updating training mediated by the striatum. Science, 320(5882), 1510–1512. doi: 10.1126/science.1155466Google Scholar

Daneman, M., & Hannon, B. (2007). What do working memory span tasks really measure? In Logie, R. H., Osaka, N., & D’Esposito, M. (Eds.), The cognitive neuroscience of working memory (pp. 21–42). Oxford: Oxford University Press.Google Scholar

DeLoss, D. J., Watanabe, T., & Andersen, G. J. (2015). Improving vision among older adults: Behavioral training to improve sight. Psychological Science, 26(4), 456–466. doi: 10.1177/0956797614567510Google Scholar

Edwards, J. D., Fausto, B. A., Tetlow, A. M., Corona, R. T., & Valdes, E. G. (2018). Systematic review and meta-analyses of useful field of view cognitive training. Neuroscience and Biobehavioral Reviews, 84, 72–91. doi: 10.1016/j.neubiorev.2017.11.004Google Scholar

Edwards, J. D., Ross, L. A., Ackerman, M. L., et al. (2008). Longitudinal predictors of driving cessation among older adults from the ACTIVE clinical trial. Journals of Gerontology, Series B: Psychological Sciences and Social Sciences, 63(1), P6–P12. doi: 10.1093/geronb/63.1.P6Google Scholar

Edwards, J. D., Xu, H., Clark, D. O., et al. (2017). Speed of processing training results in lower risk of dementia. Alzheimer’s and Dementia (NY), 3(4), 603–611. doi: 10.1016/j.trci.2017.09.002Google Scholar

Engvig, A., Fjell, A. M., Westlye, L. T., et al. (2010). Effects of memory training on cortical thickness in the elderly. NeuroImage, 52(4), 1667–1676. doi: 10.1016/j.neuroimage.2010.05.041Google Scholar

Ferreira, L. K., Regina, A. C., Kovacevic, N., et al. (2016). Aging effects on whole-brain functional connectivity in adults free of cognitive and psychiatric disorders. Cerebral Cortex, 26(9), 3851–3865. doi: 10.1093/cercor/bhv190Google Scholar

Fornito, A., Harrison, B. J., Zalesky, A., & Simons, J. S. (2012). Competitive and cooperative dynamics of large-scale brain functional networks supporting recollection. Proceedings of the National Academy of Sciences USA, 109(31), 12788–12793. doi: 10.1073/pnas.1204185109Google Scholar

Foroughi, C. K., Monfort, S. S., Paczynski, M., McKnight, P. E., & Greenwood, P. M. (2016). Placebo effects in cognitive training. Proceedings of the National Academy of Sciences USA, 113(27), 7470–7474. doi: 10.1073/pnas.1601243113Google Scholar

Fox, M. D., Snyder, A. Z., Vincent, J. L., et al. (2005). The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proceedings of the National Academy of Sciences USA, 102(27), 9673–9678. doi: 10.1073/pnas.0504136102Google Scholar

Fukuda, K., Vogel, E., Mayr, U., & Awh, E. (2010). Quantity, not quality: The relationship between fluid intelligence and working memory capacity. Psychonomic Bulletin and Review, 17(5), 673–679. doi: 10.3758/17.5.673Google Scholar

Gazzaley, A., Clapp, W., Kelley, J., et al. (2008). Age-related top-down suppression deficit in the early stages of cortical visual memory processing. Proceedings of the National Academy of Sciences USA, 105(35), 13122–13126. doi: 10.1073/pnas.0806074105Google Scholar

Gazzaley, A., Cooney, J. W., Rissman, J., & D’Esposito, M. (2005). Top-down suppression deficit underlies working memory impairment in normal aging. Nature Neuroscience, 8(10), 1298–1300. doi: 10.1038/nn1543Google Scholar

Geerligs, L., Renken, R. J., Saliasi, E., Maurits, N. M., & Lorist, M. M. (2015). A brain-wide study of age-related changes in functional connectivity. Cerebral Cortex, 25(7), 1987–1999. doi: 10.1093/cercor/bhu012Google Scholar

Gilbert, C. D., & Sigman, M. (2007). Brain states: Top-down influences in sensory processing. Neuron, 54(5), 677–696. doi: 10.1016/j.neuron.2007.05.019Google Scholar

Goh, J. O., An, Y., & Resnick, S. M. (2012). Differential trajectories of age-related changes in components of executive and memory processes. Psychology and Aging, 27(3), 707–719. doi: 10.1037/a0026715Google Scholar

Golland, Y., Golland, P., Bentin, S., & Malach, R. (2008). Data-driven clustering reveals a fundamental subdivision of the human cortex into two global systems. Neuropsychologia, 46(2), 540–553. doi: 10.1016/j.neuropsychologia.2007.10.003Google Scholar

Gottfredson, L. S. (1997). Why g matters: The complexity of everyday life. Intelligence, 24(1), 79–132. doi: 10.1016/S0160-2896(97)90014-3Google Scholar

Grady, C. L., & Craik, F. I. (2000). Changes in memory processing with age. Current Opinion in Neurobiology, 10(2), 224–231. doi: 10.1016/S0959-4388(00)00073-8Google Scholar

Grady, C. L., Maisog, J. M., Horwitz, B., et al. (1994). Age-related changes in cortical blood flow activation during visual processing of faces and location. Journal of Neuroscience, 14, 1450–1462. doi: 10.1523/JNEUROSCI.14-03-01450.1994Google Scholar

Grady, C. L., Sarraf, S., Saverino, C., & Campbell, K. (2016). Age differences in the functional interactions among the default, frontoparietal control, and dorsal attention networks. Neurobiology of Aging, 41, 159–172. doi: 10.1016/j.neurobiolaging.2016.02.020Google Scholar

Greenwood, P. M. (2007). Functional plasticity in cognitive aging: Review and hypothesis. Neuropsychology, 21(6), 657–673. doi: 10.1037/0894-4105.21.6.657Google Scholar

Greenwood, P. M., & Parasuraman, R. (1999). Scale of attentional focus in visual search. Perception and Psychophysics, 61, 837–859. doi: 10.3758/BF03206901Google Scholar

Greenwood, P. M., & Parasuraman, R. (2004). The scaling of spatial attention in visual search and its modification in healthy aging. Perception and Psychophysics, 66, 3–22. doi: 10.3758/BF03194857Google Scholar

Greenwood, P. M., & Parasuraman, R. (2016). The mechanisms of far transfer from cognitive training: Review and hypothesis. Neuropsychology, 30(6), 742–755. doi: 10.1037/neu0000235Google Scholar

Greenwood, P. M., Parasuraman, R., & Alexander, G. E. (1997). Controlling the focus of spatial attention during visual search: Effects of advanced aging and Alzheimer disease. Neuropsychology, 11(1), 3–12. doi.org/10.1037/0894-4105.11.1.3 Google Scholar

Hasher, L., Stoltzfus, E. R., Zacks, R. T., & Rypma, B. (1991). Age and inhibition. Journal of Experimental Psychology: Learning, Memory, and Cognition, 17(1), 163–169. doi: 10.1037//0278-7393.17.1.163Google Scholar

Hasher, L., & Zacks, R. T. (1979). Automatic and effortful processes in memory. Journal of Experimental Psychology: General, 108, 356–388. doi: 10.1037/0096-3445.108.3.356Google Scholar

Hillyard, S. A., & Anllo-Vento, L. (1998). Event-related brain potentials in the study of visual selective attention. Proceedings of the National Academy of Sciences USA, 95, 781–788. doi: 10.1073/pnas.95.3.781Google Scholar

Jaeggi, S. M., Buschkuehl, M., Jonides, J., & Perrig, W. J. (2008). Improving fluid intelligence with training on working memory. Proceedings of the National Academy of Sciences USA, 105(19), 6829–6833. doi: 10.1073/pnas.0801268105Google Scholar

Jobe, J. B., Smith, D. M., Ball, K., et al. (2001). ACTIVE: A cognitive intervention trial to promote independence in older adults. Controlled Clinical Trials, 22(4), 453–479. doi: 10.1016/S0197-2456(01)00139-8Google Scholar

Karbach, J., & Verhaeghen, P. (2014). Making working memory work: A meta-analysis of executive-control and working memory training in older adults. Psychological Science, 25(11), 2027–2037. doi: 10.1177/0956797614548725Google Scholar

Kelly, A. M., & Garavan, H. (2005). Human functional neuroimaging of brain changes associated with practice. Cerebral Cortex, 15(8), 1089–1102. doi: 10.1093/cercor/bhi005Google Scholar

Kelly, M. E., Loughrey, D., Lawlor, B. A., et al. (2014). The impact of cognitive training and mental stimulation on cognitive and everyday functioning of healthy older adults: A systematic review and meta-analysis. Ageing Research Reviews, 15, 28–43. doi: 10.1016/j.arr.2014.02.004Google Scholar

Kraus, N., Bradlow, A. R., Cheatham, M. A., et al. (2000). Consequences of neural asynchrony: A case of auditory neuropathy. Journal of the Association for Research in Otolaryngology, 1(1), 33–45. doi: 10.1007/s101620010004Google Scholar

Kristjansson, A., & Nakayama, K. (2002). The attentional blink in space and time. Vision Research, 42(17), 2039–2050. doi: 10.1016/S0042-6989(02)00129-3Google Scholar

Kuncel, N. R., Hezlett, S. A., & Ones, D. S. (2004). Academic performance, career potential, creativity, and job performance: Can one construct predict them all? Journal of Personality and Social Psychology, 86(1), 148–161. doi: 10.1037/0022-3514.86.1.148Google Scholar

Lawton, M. P., & Brody, E. M. (1969). Assessment of older people: Self-maintaining and instrumental activities of daily living. Gerontologist, 9(3), 179–186. doi: 10.1093/geront/9.3_Part_1.179Google Scholar

Lewis, C. M., Baldassarre, A., Committeri, G., Romani, G. L., & Corbetta, M. (2009). Learning sculpts the spontaneous activity of the resting human brain. Proceedings of the National Academy of Sciences USA, 106(41), 17558–17563. doi: 10.1073/pnas.0902455106Google Scholar

Li, X., Allen, P. A., Lien, M. C., & Yamamoto, N. (2017). Practice makes it better: A psychophysical study of visual perceptual learning and its transfer effects on aging. Psychology and Aging, 32(1), 16–27. doi: 10.1037/pag0000145Google Scholar

Livingston, G., Sommerlad, A., Orgeta, V., et al. (2017). Dementia prevention, intervention, and care. Lancet, 390(10113), 2673–2734. doi: 10.1016/S0140-6736(17)31363-6Google Scholar

Luck, S. J., Hillyard, S. A., Mouloua, M., et al. (1994). Effects of spatial cuing on luminance detectability: Psychophysical and electrophysiological evidence for early selection. Journal of Experimental Psychology: Human Perception and Performance, 20, 887–904. doi: 10.1037//0096-1523.20.4.887Google Scholar

Madden, D. J., Whiting, W. L., Cabeza, R., & Huettel, S. A. (2004). Age-related preservation of top-down attentional guidance during visual search. Psychology and Aging, 19(2), 304–309. doi: 10.1037/0882-7974.19.2.304Google Scholar

Mahncke, H. W., Connor, B. B., Appelman, J., et al. (2006). Memory enhancement in healthy older adults using a brain plasticity-based training program: A randomized, controlled study. Proceedings of the National Academy of Sciences USA, 103(33), 12523–12528. doi: 10.1073/pnas.0605194103Google Scholar

McNab, F., Varrone, A., Farde, L., et al. (2009). Changes in cortical dopamine D1 receptor binding associated with cognitive training. Science, 323(5915), 800–802. 10.1126/science.1166102Google Scholar

Melby-Lervag, M., & Hulme, C. (2013). Is working memory training effective? A meta-analytic review. Developmental Psychology, 49(2), 270–291. doi: 10.1037/a0028228Google Scholar

MetLife (2011). The MetLife Study of Elder Financial Abuse: Crimes of Occasion, Desperation, and Predation against America’s Elders. National Committee for the Prevention of Elder Abuse, and Virginia Tech. ltcombudsman.org/uploads/files/issues/mmi-elder-financial-abuse.pdf Google Scholar

Mishra, J., de Villers-Sidani, E., Merzenich, M., & Gazzaley, A. (2014). Adaptive training diminishes distractibility in aging across species. Neuron, 84(5), 1091–1103. doi: 10.1016/j.neuron.2014.10.034Google Scholar

Miyake, A., Friedman, N. P., Emerson, M. J., et al. (2000). The unity and diversity of executive functions and their contributions to complex “frontal lobe” tasks: A latent variable analysis. Cognitive Psychology, 41(1), 49–100. doi: 10.1006/cogp.1999.0734Google Scholar

Mukai, I., Kim, D., Fukunaga, M., et al. (2007). Activations in visual and attention-related areas predict and correlate with the degree of perceptual learning. Journal of Neuroscience, 27(42), 11401–11411. doi: 10.1523/JNEUROSCI.3002-07.2007Google Scholar

Nigbur, R., Ivanova, G., & Sturmer, B. (2011). Theta power as a marker for cognitive interference. Clinical Neurophysiology, 122(11), 2185–2194. doi: 10.1016/j.clinph.2011.03.030Google Scholar

NIH US (2018). US Study to Protect Brain Health through Lifestyle Intervention to Reduce Risk (POINTER). https://clinicaltrials.gov/ct2/show/NCT03688126 Google Scholar

Sadaghiani, S., Poline, J. B., Kleinschmidt, A., & D’Esposito, M. (2015). Ongoing dynamics in large-scale functional connectivity predict perception. Proceedings of the National Academy of Sciences USA, 112(27), 8463–8468. doi: 10.1073/pnas.1420687112Google Scholar

Salminen, T., Kuhn, S., Frensch, P. A., & Schubert, T. (2016). Transfer after dual n-back training depends on striatal activation change. Journal of Neuroscience, 36(39), 10198–10213. doi: 10.1523/JNEUROSCI.2305-15.2016Google Scholar

Sawaki, R., & Luck, S. J. (2013). Active suppression after involuntary capture of attention. Psychonomic Bulletin and Review, 20(2), 296–301. doi: 10.3758/s13423-012-0353-4Google Scholar

Schoups, A., Vogels, R., Qian, N., & Orban, G. (2001). Practising orientation identification improves orientation coding in V1 neurons. Nature, 412(6846), 549–553. doi: 10.1038/35087601Google Scholar

Smith, G. E., Housen, P., Yaffe, K., et al. (2009). A cognitive training program based on principles of brain plasticity: Results from the Improvement in Memory with Plasticity-Based Adaptive Cognitive Training (IMPACT) study. Journal of the American Geriatrics Society, 57(4), 594–603. doi: 10.1111/j.1532-5415.2008.02167.xGoogle Scholar

Söderqvist, S., & Bergman Nutley, S. (2017). Are measures of transfer effects missing the target? Journal of Cognitive Enhancement, 1(4), 508–512. doi: 10.1007/s41465-017-0048-xGoogle Scholar

Soveri, A., Antfolk, J., Karlsson, L., Salo, B., & Laine, M. (2017). Working memory training revisited: A multi-level meta-analysis of n-back training studies. Psychonomic Bulletin and Review, 24(4), 1077–1096. doi: 10.3758/s13423-016-1217-0Google Scholar

Stine-Morrow, E. A., Parisi, J. M., Morrow, D. G., & Park, D. C. (2008). The effects of an engaged lifestyle on cognitive vitality: A field experiment. Psychology and Aging, 23(4), 778–786. doi: 10.1037/a0014341Google Scholar

Strenziok, M., Parasuraman, R., Clarke, E., et al. (2014). Neurocognitive enhancement in older adults: Comparison of three cognitive training tasks to test a hypothesis of training transfer in brain connectivity. NeuroImage, 85, 1027–1039. doi: 10.1016/j.neuroimage.2013.07.069Google Scholar

Suzuki, M., & Gottlieb, J. (2013). Distinct neural mechanisms of distractor suppression in the frontal and parietal lobe. Nature Neuroscience, 16(1), 98–104. doi: 10.1038/nn.3282Google Scholar

Theeuwes, J. (1994). Stimulus-driven capture and attentional set: Selective search for color and visual abrupt onsets. Journal of Experimental Psychology: Human Perception and Performance, 20(4), 799–806. doi: 10.1037//0096-1523.20.4.799Google Scholar

Tomasi, D., & Volkow, N. D. (2012). Aging and functional brain networks. Molecular Psychiatry, 17(5), 471, 549–458. doi: 10.1038/mp.2011.81Google Scholar

Vogel, E. K., McCollough, A. W., & Machizawa, M. G. (2005). Neural measures reveal individual differences in controlling access to working memory. Nature, 438(7067), 500–503. doi: 10.1038/nature04171Google Scholar

Vossel, S., Geng, J. J., & Fink, G. R. (2014). Dorsal and ventral attention systems: Distinct neural circuits but collaborative roles. Neuroscientist, 20(2), 150–159. doi: 10.1177/1073858413494269Google Scholar

Waris, O., Soveri, A., & Laine, M. (2015). Transfer after working memory updating training. PLoS One, 10(9), e0138734. doi: 10.1371/journal.pone.0138734Google Scholar

Whitton, J. P., Hancock, K. E., Shannon, J. M., & Polley, D. B. (2017). Audiomotor perceptual training enhances speech intelligibility in background noise. Current Biology, 27(21), 3237–3247.e6. doi: 10.1016/j.cub.2017.09.014Google Scholar

Wiley, J., Jarosz, A. F., Cushen, P. J., & Colflesh, G. J. (2011). New rule use drives the relation between working memory capacity and Raven’s Advanced Progressive Matrices. Journal of Experimental Psychology: Learning, Memory, and Cognition, 37(1), 256–263. doi: 10.1037/a0021613Google Scholar

References

Anguera, J., Boccanfuso, J., Rintoul, J., et al. (2013). Video game training enhances cognitive control in older adults. Nature, 501, 97–101. http://doi.org/10.1038/nature12486 Google Scholar

Arnett, J. J. (2000). Emerging adulthood: A theory of development from the late teens through the twenties. American Psychologist, 55(5), 469–480. http://doi.org/10.1037//0003-066X.55.5.469 Google Scholar

Ball, K., Berch, D. B., Helmers, K. F., et al. (2002). Effects of cognitive training interventions with older adults: A randomized controlled trial. JAMA, 288(18), 2271–2281. http://doi.org/10.1001/jama.288.18.2271 Google Scholar

Baltes, P. B., Lindenberger, U., & Staudinger, U. M. (2006). Life span theory in developmental psychology. In Lerner, R. M. & Damon, W. (Eds.), Handbook of child psychology: Theoretical models of human development (pp. 569–664). Hoboken, NJ: Wiley.Google Scholar

Bielak, A. A. M. (2010). How can we not “lose it” if we still don’t understand how to “use it”? Unanswered questions about the influence of activity participation on cognitive performance in older age – A mini-review. Gerontology, 56, 507–519. http://doi.org/10.1159/000264918 Google Scholar

Boot, W. R., Simons, D. J., Stothart, C., & Stutts, C. (2013). The pervasive problem with placebos in psychology: Why active control groups are not sufficient to rule out placebo effects. Perspectives on Psychological Science, 8(4), 445–454. http://doi.org/10.1177/1745691613491271 Google Scholar

Bugos, J. A., Perlstein, W. M., McCrae, C. S., Brophy, T. S., & Bedenbaugh, P. H. (2007). Individualized piano instruction enhances executive functioning and working memory in older adults. Aging and Mental Health, 11(4), 464–471. http://doi.org/10.1080/13607860601086504 Google Scholar

Bures, V., Cech, P., Mikulecká, J., Ponce, D., & Kuca, K. (2016). The effect of cognitive training on the subjective perception of well-being in older adults. PeerJ, 4, e2785. http://doi.org/10.7717/peerj.2785 Google Scholar

Cacioppo, J. T., & Hawkley, L. C. (2009). Perceived social isolation and cognition. Trends in Cognitive Sciences, 13(10), 447–454. http://doi.org/10.1016/j.tics.2009.06.005 Google Scholar

Carlson, M. C., Parisi, J. M., Xia, J., et al. (2012). Lifestyle activities and memory: Variety may be the spice of life. The women’s health and aging study II. Journal of the International Neuropsychological Society, 18(2), 286–294. http://doi.org/10.1017/S135561771100169X Google Scholar

Carlson, M. C., Saczynski, J. S., Rebok, G. W., et al. (2008). Exploring the effects of an “everyday” activity program on executive function and memory in older adults: Experience Corps®. Gerontologist, 48, 793–801. http://doi.org/10.1093/geront/48.6.793 Google Scholar

Chan, M. Y., Haber, S., Drew, L. M., & Park, D. C. (2016). Training older adults to use tablet computers: Does it enhance cognitive functioning? Gerontologist, 56(3), 475–484. http://doi.org/10.1093/geront/gnu057 Google Scholar

Charness, N., & Boot, W. R. (2009). Aging and information technology use: Potential and barriers. Current Directions in Psychological Science, 18(5), 253–258. https://doi.org/10.1111/j.1467-8721.2009.01647.x Google Scholar

Clark, D. O., Xu, H., Unverzagt, F. W., & Hendrie, H. (2016). Does targeted cognitive training reduce educational disparities in cognitive function among cognitively normal older adults? International Journal of Geriatric Psychiatry, 31(7), 809–817. https://doi.org/10.1002/gps.4395 Google Scholar

Czaja, S. J., & Sharit, J. (2003). Practically relevant research: Capturing real world tasks, environments, and outcomes. Gerontologist, 43 (Suppl. 1), 9–18. https://doi.org/10.1093/geront/43.suppl_1.9 Google Scholar

Darling-Hammond, L., Wilhoit, G., & Pittenger, L. (2014). Accountability for college and career readiness: Developing a new paradigm. Education Policy Analysis Archives, 22(86), 1–34. http://dx.doi.org/10.14507/epaa.v22n86.2014 Google Scholar

Depp, C. A., & Jeste, D. V. (2006). Definitions and predictors of successful aging: A comprehensive review of larger quantitative studies. American Journal of Geriatric Psychiatry, 14, 6–20. http://doi.org/10.1097/01.jgp.0000192501.03069.bc Google Scholar

Deveau, J., Jaeggi, S. M., Zordan, V., Phung, C., & Seitz, A. R. (2015). How to build better memory training games. Frontiers in Systems Neuroscience, 8, 243. https://doi.org/10.3389/fnsys.2014.00243 Google Scholar

Dodge, H. H., Du, Y., Saxton, J. A., & Ganguli, M. (2006). Cognitive domains and trajectories of functional independence in non-demented elderly. Journals of Gerontology, Series A: Biological Sciences and Medical Sciences, 61(12), 1330–1337. https://doi.org/10.1093/gerona/61.12.1330 Google Scholar

Ennis, G. E., Hess, T. M., & Smith, B. T. (2013). The impact of age and motivation on cognitive effort: Implications for cognitive engagement in older adulthood. Psychology and Aging, 28, 495–504. http://doi.org/10.1037/a0031255 Google Scholar

Fried, L. P., Carlson, M. C., McGill, S., et al. (2013). Experience Corps: A dual trial to promote the health of older adults and children’s academic success. Contemporary Clinical Trials, 36, 1–13. http://doi.org/10.1016/j.cct.2013.05.003 Google Scholar

Goldberg, J., & Kiernan, M. (2004). Innovative techniques to address retention in a behavioral weight-loss trial. Health Education Research, 20(4), 439–447. https://doi.org/10.1093/her/cyg139 Google Scholar

Grady, C. (2012). The cognitive neuroscience of ageing. Nature Reviews Neuroscience, 13(7), 491–505. http://doi.org/10.1038/nrn3256 Google Scholar

Hastings, E. C., & West, R. L. (2009). The relative success of a self-help and a group-based memory training program for older adults. Psychology and Aging, 24, 586–594. http://doi.org/10.1037/a0016951 Google Scholar

Hertzog, C., Kramer, A. F., Wilson, R. S., & Lindenberger, U. (2009). Enrichment effects on adult cognitive development: Can the functional capacity of older adults be preserved and enhanced? Psychological Science in the Public Interest, 9(1), 1–65. http://doi.org/10.1111/j.1539-6053.2009.01034.x Google Scholar

Hertzog, C., & Nesselroade, J. R. (2003). Assessing psychological change in adulthood: An overview of methodological issues. Psychology and Aging, 18(4), 639–657. http://doi.org/10.1037/0882-7974.18.4.639 Google Scholar

Hess, T. M., (2014). Selective engagement of cognitive resources: Motivational influences on older adults’ cognitive functioning. Perspectives on Psychological Science, 9, 388–407. http://doi.org/10.1177/1745691614527465 Google Scholar

Hess, T. M., Growney, C. M., O’Brien, E. L., Neupert, S. D., & Sherwood, A. (2018). The role of cognitive costs, attitudes about aging, and intrinsic motivation in predicting engagement in everyday activities. Psychology and Aging, 33(6), 953–964. https://doi.org/10.1037/pag0000289 Google Scholar

Hopkins, C. D., Roster, C. A., & Wood, C. M. (2006). Making the transition to retirement: Appraisals, post-transition lifestyle, and changes in consumption patterns. Journal of Consumer Marketing, 23(2), 87–99. https://doi.org/10.1108/07363760610655023 Google Scholar

Hughes, T. F. (2010). Promotion of cognitive health through cognitive activity in the aging population. Aging and Health, 6(1), 111–121. http://doi.org/10.2217/ahe.09.89 Google Scholar

Hultsch, D. F., Hertzog, C., Small, B. J., & Dixon, R. A. (1999). Use it or lose it: Engaged lifestyle as a buffer of cognitive decline in aging? Psychology and Aging, 14(2), 245–263. http://doi.org/10.1037/0882-7974.14.2.245 Google Scholar

Ihle, A., Oris, M., Fagot, D., et al. (2015). The association of leisure activities in middle adulthood with cognitive performance in old age: The moderating role of educational level. Gerontology, 61(6), 543–550. http://doi.org/10.1159/000381311 Google Scholar

Jackson, J. J., Hill, P. L., Payne, B. R., et al. (2012). Can an old dog learn (and want to experience) new tricks? Cognitive training increases openness to experience in older adults. Psychology and Aging, 27(2), 286–292. http://doi.org/10.1037/a0025918 Google Scholar

Jaeggi, S. M., Buschkuehl, M., Jonides, J., & Perrig, W. J. (2008). Improving fluid intelligence with training on working memory. Proceedings of the National Academy of Sciences USA, 105, 6829–6833. http://doi.org/10.1073/pnas.0801268105 Google Scholar

Jaeggi, S. M., Buschkuehl, M., Shah, P., & Jonides, J. (2014). The role of individual differences in cognitive training and transfer. Memory and Cognition, 42, 464–480. http://doi.org/10.1073/pnas.0801268105 Google Scholar

Karbach, J., Konen, T., & Spengler, M. (2017). Who benefits the most? Individual differences in the transfer of executive control training across the life span. Journal of Cognitive Enhancement, 1, 394–405. http://doi.org/10.1007/s41465-017-0054-z Google Scholar

Karp, A., Paillard-Borg, S., Wang, H. X., et al. (2006). Mental, physical and social components in leisure activities equally contribute to decrease dementia risk. Dementia and Geriatric Cognitive Disorders, 21(2), 65–73. http://doi.org/10.1159/000089919 Google Scholar

Katz, B., Jaeggi, S., Buschkuehl, M., Stegman, A., & Shah, P. (2014). Differential effect of motivational features on training improvements in school-based cognitive training. Frontiers in Human Neuroscience, 8, 242. http://doi.org/10.3389/fnhum.2014.00242 Google Scholar

Katz, B., Jones, M. R., Shah, P., Buschkuehl, M., & Jaeggi, S. M. (2016). Individual differences and motivational effects in cognitive training research. In Strobach, T. & Karbach, J. (Eds.), Cognitive training: An overview of features and applications (pp. 157–166). Berlin: Springer.Google Scholar

Kim, S., McMaster, M., Torres, S., et al. (2018). Protocol for a pragmatic randomized controlled trial of Body Brain Life – General Practice and a lifestyle modification programme to decrease dementia risk exposure in a primary care setting. BMJ Open, 8, e019329. http://doi.org/10.1136/bmjopen-2017-019329 Google Scholar

Kramer, A. F., Bherer, L., Colcombe, S. J., Dong, W., & Greenough, W. T. (2004). Environmental influences on cognitive and brain plasticity during aging. Journals of Gerontology, Series A: Biological Sciences and Medical Sciences, 59(9), M940–M957. http://doi.org/10.1093/gerona/59.9.M940 Google Scholar

Lachman, M. E., Andreoletti, C., & Pearman, A. (2006). Memory control beliefs: How are they related to age, strategy use, and memory improvement? Social Cognition, 24, 359–385. http://doi.org/10.1521/soco.2006.24.3.359 Google Scholar

Lachman, M. E., Lipsitz, L., Lubben, J., Castaneda-Sceppa, C., & Jette, A. M. (2018). When adults don’t exercise: Behavioral strategies to increase physical activity in sedentary middle-aged and older adults. Innovation in Aging, 2(1), 1–12. http://doi.org/10.1093/geroni/igy007 Google Scholar

Langbaum, J. B., Rebok, G. W., Bandeen-Roche, K., & Carlson, M. C. (2009). Predicting memory training response patterns: Results from ACTIVE. Journals of Gerontology, Series B: Psychological Sciences and Social Sciences, 64, 14–23. http://dx.doi.org/10.1093/geronb/gbn026 Google Scholar

Leanos, S., Coons, J., Rebok, G. W., Ozer, D. J., & Wu, R. (2019). Development of the Broad Learning Adult Questionnaire (BLAQ). International Journal of Aging and Human Development, 88(3), 286–311. http://doi.org/10.1177/0091415018784695 Google Scholar

Leanos, S., Kurum, E., Strickland-Hughes, C., Ditta, A., Nguyen, G., Felix, M., Yum, H., Rebok, G. W., & Wu, R. (2019). The impact of learning multiple real-world skills on cognitive abilities and functional independence in healthy older adults. Journals of Gerontology: Series B Psychological Sciences, gbz084. http://doi.org/10.1093/geronb/gbz084 Google Scholar

Levkoff, S., & Sanchez, H. (2003). Lessons learned about minority recruitment and retention from the Centers on Minority Aging and Health Promotion. Gerontologist, 43, 8–26. http://doi.org/10.1093/geront/43.1.18 Google Scholar

Levy, B. R., Ferrucci, L., Zonderman, A. B., Slade, M. D., Troncoso, J., & Resnick, S. M. (2016). A culture–brain link: Negative age stereotypes predict Alzheimer’s-disease biomarkers. Psychology and Aging, 31(1), 82–88. http://doi.org/10.1037/pag0000062 Google Scholar

Locke, E. A., & Latham, G. P. (2002). Building a practically useful theory of goal setting and task motivation: A 35-year odyssey. American Psychologist, 57(9), 705–717. http://doi.org/10.1037//0003-066X.57.9.705Google Scholar

Mani, A., Mullainathan, S., Shafir, E., & Zhao, J. (2013). Poverty impedes cognitive function. Science, 341(6149), 976–980. http://doi.org/10.1126/science.1238041 Google Scholar

McGillivray, S., Murayama, K., & Castel, A. D. (2015). Thirst for knowledge: The effects of curiosity and interest in memory in younger and older adults. Psychology and Aging, 30(4), 835–841. http://doi.org/10.1037/a0039801 Google Scholar

Michie, S., & Abraham, C. (2004). Interventions to change health behaviours: Evidence-based or evidence-inspired? Psychology and Health, 19(1), 29–49. http://doi.org/10.1080/0887044031000141199 Google Scholar

Michie, S., Johnston, M., Francis, J., Hardeman, W., & Eccles, M. (2008). From theory to intervention: Mapping theoretically derived behavioural determinants to behavior change techniques. Applied Psychology: An International Review, 57, 660–680. http://doi.org/10.1111/j.1464-0597.2008.00341.x Google Scholar

Michie, S., van Stralen, M. M., & West, R. (2011). The behaviour change wheel: A new method for characterising and designing behaviour change interventions. Implementation Science, 6, 42. http://doi.org/10.1186/1748-5908-6-42 Google Scholar

Middleton, K. R., Anton, S. D., & Perri, M. G. (2013). Long-term adherence to health behavior change. American Journal of Lifestyle Medicine, 7(6), 395–404. http://doi.org/10.1177/1559827613488867 Google Scholar

Nguyen, C., Leanos, S., Natsuaki, M. N., Rebok, G. W., & Wu, R. (2018). Adaptation via learning new skills as a means to long-term functional independence in older adulthood: Insights from emerging adulthood. Gerontologist. October 12 [Epub ahead of print]. http://doi.org/10.1093/geront/gny128 Google Scholar

Noice, T., Noice, H., & Kramer, A. F. (2014). Participatory arts for older adults: A review of benefits and challenges. Gerontologist, 54(5), 741–753. http://doi.org/10.1093/geront/gnt138 Google Scholar

Noom, M. J., Dekovic, M., & Meeus, W. (2001). Conceptual analysis and measurement of adolescent autonomy. Journal of Youth and Adolescence, 30, 577–595. http://doi.org/10.1023/A:1010400721676 Google Scholar

Parisi, J. M., Gross., A. L., Marsiske, M., Willis, S. L., & Rebok, G. W. (2017). Control beliefs and cognition over a 10-year period: Findings from the ACTIVE trial. Psychology and Aging, 32(1), 69–75. http://doi.org/10.1037/pag0000147 Google Scholar

Park, D. C., Gutchess, A., Meade, M., & Stine-Morrow, E. (2007). Improving cognitive function in older adults: Nontraditional approaches. Journals of Gerontology, Series B: Psychological Sciences and Social Sciences, 62, 45–52. http://doi.org/10.1093/geronb/62.special_issue_1.45 Google Scholar

Park, D. C., Lodi-Smith, J., Drew, L. M., et al. (2014). The impact of sustained engagement on cognitive function in older adults: The Synapse Project. Psychological Science, 25(1), 103–112. http://doi.org/10.1177/0956797613499592 Google Scholar

Park, D. C., & Reuter-Lorenz, P. (2009). The adaptive brain: Aging and neurocognitive scaffolding. Annual Review of Psychology, 60, 173–196. http://dx.doi.org/10.1146/annurev.psych.59.103006.093656 Google Scholar

Raz, N., & Rodrigue, K. M. (2006). Differential aging of the brain: Patterns, cognitive correlates and modifiers. Neuroscience and Biobehavioral Reviews, 30(6), 730–748. http://doi.org/10.1016/j.neubiorev.2006.07.001 Google Scholar

Rebok, G. W., Ball, K., Guey, L. T., et al. (2014). Ten-year effects of the ACTIVE cognitive training trial on cognition and everyday functioning in older adults. Journal of the American Geriatrics Society, 62(1), 16–24. http://doi.org/10.1111/jgs.12607 Google Scholar

Rebok, G. W., Carlson, M. C., Barron, J. S., et al. (2011). Experience Corps®: A civic engagement-based public health intervention in the public schools. In Hartman-Stein, P. E. & La Rue, A. (Eds.), Enhancing cognitive fitness in adults: A guide to the use and development of community-based programs (pp. 469–487). New York: Springer.Google Scholar

Rebok, G. W., Langbaum, J. B. S., Jones, R. N., et al. (2013). Memory training in the ACTIVE study: How much is needed and who benefits? Journal of Aging and Health, 25, 21S–42S. http://doi.org/10.1177/0898264312461937 Google Scholar

Reuter-Lorenz, P. A., & Park, D. C. (2014). How does it STAC up? Revisiting the scaffolding theory of aging and cognition. Neuropsychology Review, 24(3), 355–370. http://doi.org/10.1007/s11065-014-9270-9 Google Scholar

Robertson, D. A., King-Kallimanis, B. L., & Kenny, R. A. (2016). Negative perceptions of aging predict longitudinal decline in cognitive function. Psychology and Aging, 31, 71–81. http://doi.org/10.1037/pag0000061 Google Scholar

Roth, T. L., Lubin, F. D., Funk, A. J., & Sweatt, D. (2009). Lasting epigenetic influence of early-life adversity on the BDNF gene. Biological Psychiatry, 65(9), 760–769. http://doi.org/10.1016/j.biopsych.2008.11.028 Google Scholar

Rowe, J. W., & Kahn, R. L. (1997). Successful aging. Gerontologist, 37(4), 433–440. http://doi.org/10.1093/geront/37.4.433 Google Scholar

Salthouse, T. A. (2006). Mental exercise and mental aging. Evaluating the validity of the “use it or lose it” hypothesis. Perspectives on Psychological Science, 1(1), 68–87. http://doi.org/10.1111/j.1745-6916.2006.00005.x Google Scholar

Shipstead, Z., Redick, T. S., & Engle, R. W. (2012). Is working memory training effective? Psychological Bulletin, 138(4), 628–654. http://doi.org/10.1037/a0027473 Google Scholar

Simons, D. J., Boot, W. R., Charness, N., et al. (2016).Do “brain-training” programs work? Psychological Science in Public Interest, 17(3), 103–186. http://doi.org/10.1177/1529100616661983 Google Scholar

Stephan, Y., Fouquereau, E., & Fernandez, A. (2008). The relation between self-determination and retirement satisfaction among active retired individuals. International Journal of Aging and Human Development, 66(4), 329–345. http://doi.org/10.2190/AG.66.4.d Google Scholar

Stine-Morrow, E. A. L., Parisi, J. M., Morrow, D. G., & Park, D. C. (2008). The effects of an engaged lifestyle on cognitive vitality: A field experiment. Psychology and Aging, 23(4), 778–786. http://doi.org/10.1037/a0014341 Google Scholar

Stine-Morrow, E. A. L., Payne, B. R., Roberts, B. W., et al. (2014). Training versus engagement as paths to cognitive enrichment with aging. Psychology and Aging, 29(4), 891–906. http://doi.org/10.1037/a0038244 Google Scholar

Thøgersen-Ntoumani, C., & Ntoumanis, N. (2006). The role of self-determined motivation in the understanding of exercise-related behaviours, cognitions, and physical self-evaluations. Journal of Sports Science, 24(4), 393–404. http://doi.org/10.1080/02640410500131670 Google Scholar

Torres, W. J., & Beier, M. E. (2018). Adult development in the wild: The determinants of autonomous learning in a Massive Open Online Course. Learning and Individual Differences, 65, 207–217. https://doi.org/10.1016/j.lindif.2018.06.003 Google Scholar

Tzuang, M., Owusu, J., Spira, A. P., Albert, M. S., & Rebok, G. W. (2018). Cognitive training for ethnic minority older adults in the US: A review. Gerontologist, 58(5), e311–e324. http://doi.org/10.1093/geront/gnw260 Google Scholar

van der Bij, A. K., Laurant, M. G., & Wensing, M. (2002). Effectiveness of physical activity interventions for older adults: A review. American Journal of Preventive Medicine, 22, 120–133. http://doi.org/10.1016/S0749-3797(01)00413-5 Google Scholar

Willis, S. L., & Caskie, G. I. L. (2013). Reasoning training in the ACTIVE study: How much is needed and who benefits? Journal of Aging and Health, 25 (Suppl. 8), 43–64. http://doi.org/10.1177/0898264313503987 Google Scholar

Willis, S. L., & Schaie, K. W. (1986). Training the elderly on the ability factors of spatial orientation and inductive reasoning. Psychology and Aging, 1(3), 239–247. http://doi.org/10.1037/0882-7974.1.3.239 Google Scholar

Wilson, R. S., Scherr, P. A., Schneider, J. A., Tang, Y., & Bennett, D. A. (2007). Relation of cognitive activity to risk of developing Alzheimer disease. Neurology, 69(20), 1911–1920. http://doi.org/10.1212/01.wnl.0000271087.67782.cb Google Scholar

Wolfgang, M. E., & Dowling, W. D. (1981). Differences in motivation of adult and younger undergraduates. Journal of Higher Education, 52(6), 640–648. http://doi.org/10.1080/00221546.1981.11778136 Google Scholar

Wu, R., Rebok, G. W., & Lin, F. V. (2017). A novel theoretical life course framework for triggering cognitive development across the lifespan. Human Development, 59(6), 342–365. http://doi.org/10.1159/000458720 Google Scholar

Wu, R., & Strickland-Hughes, C. (2019). Adaptation for growth as a common goal throughout the lifespan: Why and how. Psychology of Learning and Motivation, 71, 387-414. http://doi.org/10.1016/bs.plm.2019.07.005 Google Scholar

Zhao, X., Xu, Y., Fu, J., & Maes, J. H. R. (2018). Are training and transfer effects of working memory updating training modulated by achievement motivation? Memory and Cognition, 46, 398–409. http://doi.org/10.3758/s13421-017-0773-5 Google Scholar

Zinke, K., Zeintl, M., Rose, N. S., et al. (2014). Working memory training and transfer in older adults: Effects of age, baseline performance, and training gains. Developmental Psychology, 50, 304–315. http://doi.org/10.1037/a0032982 Google Scholar

References

Agmon, M., Belza, B., Nguyen, H. Q., Logsdon, R. G., & Kelly, V. E. (2014). A systematic review of interventions conducted in clinical or community settings to improve dual-task postural control in older adults. Clinical Interventions in Aging, 9, 477–492. https://doi.org/10.2147/CIA.S54978 Google Scholar

Al-Yahya, E., Dawes, H., Smith, L., et al. (2011). Cognitive motor interference when walking: A systematic review and meta-analysis. Neuroscience and Biobehavioral Reviews, 35(3), 715–728. https://doi.org/10.1016/j.neubiorev.2010.08.008 Google Scholar

Allali, G., Ayers, E. I., & Verghese, J. (2016). Motoric cognitive risk syndrome subtypes and cognitive profiles. Journals of Gerontology, Series A: Biological Sciences and Medical Sciences, 71(3), 378–384. https://doi.org/10.1093/gerona/glv092 Google Scholar

Atkinson, H. H., Rosano, C., Simonsick, E. M., et al. (2007). Cognitive function, gait speed decline, and comorbidities: The health, aging and body composition study. Journals of Gerontology, Series A: Biological Sciences and Medical Sciences, 62(8), 844–850. https://doi.org/10.1093/gerona/62.8.844 Google Scholar

Bandinelli, S., Pozzi, M., Lauretani, F., et al. (2006). Adding challenge to performance-based tests of walking: The Walking InCHIANTI Toolkit (WIT). American Journal of Physical Medicine and Rehabilitation, 85(12), 986–991. https://doi.org/10.1097/01.phm.0000233210.69400.d4 Google Scholar

Blazer, D. G., Yaffe, K., & Liverman, C. T. (Eds.) (2015). Cognitive aging: Progress in understanding and opportunities for action. Washington: The National Academies Press.Google Scholar

Blondell, S. J., Hammersley-Mather, R., & Veerman, J. L. (2014). Does physical activity prevent cognitive decline and dementia? A systematic review and meta-analysis of longitudinal studies. BMC Public Health, 14(510). https://doi.org/10.1186/1471-2458-14-510 Google Scholar

Buracchio, T., Dodge, H. H., Howieson, D., Wasserman, D., & Kaye, J. (2010). The trajectory of gait speed preceding mild cognitive impairment. Archives of Neurology, 67(8), 980–986. https://doi.org/10.1001/archneurol.2010.159 Google Scholar

Camicioli, R., Howieson, D., Oken, B, Sexton, G., & Kaye, J. (1998). Motor slowing precedes cognitive impairment in the oldest old. Neurology, 50(5), 1496–1498. https://doi.org/10.1212/WNL.50.5.1496 Google Scholar

Centers for Disease Control and Prevention (2017). Healthy brain initiative. www.cdc.gov/aging/healthybrain/index.htm Google Scholar

Chertkow, H., Feldman, H. H., Jacova, C., & Massoud, F. (2013). Definitions of dementia and predementia states in Alzheimer’s disease and vascular cognitive impairment: Consensus from the Canadian conference on diagnosis of dementia. Alzheimer’s Research and Therapy, 5(Suppl. 1), S2. https://doi.org/10.1186/alzrt198 Google Scholar

Chhetri, J. K., Chan, P., Vellas, B., & Cesari, M. (2017). Motoric cognitive risk syndrome: Predictor of dementia and age-related negative outcomes. Frontiers in Medicine, 4, 166. https://doi.org/10.3389/fmed.2017.00166 Google Scholar

Cohen, J. A., Verghese, J., & Zwerling, J. L. (2016). Cognition and gait in older people. Maturitas, 93, 73–77. https://doi.org/10.1016/j.maturitas.2016.05.005 Google Scholar

de Andrade, L. P., Gobbi, L. T. B., Coelho, F. G. M., et al. (2013). Benefits of multimodal exercise intervention for postural control and frontal cognitive functions in individuals with Alzheimer’s disease: A controlled trial. Journal of the American Geriatrics Society, 61(11), 1919–1926. https://doi.org/10.1111/jgs.12531 Google Scholar

de Melo Borges, S., Radanovic, M., & Forlenza, O. V. (2012). Dual tasking and functional mobility in Alzheimer’s disease, mild cognitive impairment and normal aging: Correlation with executive function. Alzheimer’s and Dementia, 8(4), P131. https://doi.org/10.1016/j.jalz.2012.05.348 Google Scholar

de Melo Borges, S., Radanovic, M., & Forlenza, O. V. (2015). Functional mobility in a divided attention task in older adults with cognitive impairment. Journal of Motor Behavior, 47(5), 378–385. https://doi.org/10.1080/00222895.2014.998331 Google Scholar

de Melo Borges, S., Radanovic, M., & Forlenza, O. V. (2018). Correlation between functional mobility and cognitive performance in older adults with cognitive impairment. Aging, Neuropsychology, and Cognition, 25(1), 23–32. https://doi.org/10.1080/13825585.2016.1258035 Google Scholar

Demnitz, N., Esser, P., Dawes, H., et al. (2016). A systematic review and meta-analysis of cross-sectional studies examining the relationship between mobility and cognition in healthy older adults. Gait and Posture, 50, 164–174. https://doi.org/10.1016/j.gaitpost.2016.08.028 Google Scholar

Demnitz, N., Zsoldos, E., Mahmood, A., et al. (2017). Associations between mobility, cognition, and brain structure in healthy older adults. Frontiers in Aging Neuroscience, 9, 155. https://doi.org/10.3389/fnagi.2017.00155 Google Scholar

Forte, R., Boreham, C. A. G., Leite, J. C., et al. (2013). Enhancing cognitive functioning in the elderly: Multicomponent vs resistance training. Clinical Interventions in Aging, 2013(8), 19–27. https://doi.org/10.2147/CIA.S36514 Google Scholar

Fritz, N., Cheek, F., & Nichols-Larsen, D. (2015). Motor-cognitive dual-task training in neurologic disorders: A systematic review. Journal of Neurologic Physical Therapy, 39(3), 142–153. https://doi.org/10.1097/NPT.0000000000000090 Google Scholar

Gandolfi, M., Geroin, C., Picelli, A., Smania, N., & Bartolo, M. (2018). Assessment of balance disorders. In Sandrini, G., Smania, N., Homberg, V., Pedrocchi, A., & Saltuari, L. (Eds.), Advanced technologies for the rehabilitation of gait and balance disorders (pp. 47–68). Cham, Switzerland: Springer.Google Scholar

Global Council on Brain Health (2016). The brain-body connection: GCBH recommendations on physical activity and brain health. https://doi.org/10.26419/pia.00013.001 Google Scholar

Hackett, R. A., Davies-Kershaw, H., Cadar, D., Orrell, M., & Steptoe, A. (2018). Walking speed, cognitive function, and dementia risk in the English longitudinal study of ageing. Journal of the American Geriatrics Society, 66(9), 1670–1675. https://doi.org/10.1111/jgs.15312 Google Scholar

Harada, C. N., Natelson Love, M. C., & Triebel, K. L. (2013). Normal cognitive aging. Clinics in Geriatric Medicine, 29(4), 737–752. https://doi.org/10.1016/j.cger.2013.07.002 Google Scholar

Härlein, J., Dassen, T., Halfens, R. J. G., & Heinze, C. (2009). Fall risk factors in older people with dementia or cognitive impairment: A systematic review. Journal of Advanced Nursing, 65(5), 922–933. https://doi.org/10.1111/j.1365-2648.2008.04950.x Google Scholar

Hausdorff, J. M., Schweiger, A., Herman, T., Yogev-Seligmann, G., & Giladi, N. (2008). Dual-task decrements in gait: Contributing factors among healthy older adults. Journals of Gerontology, Series A: Biological Sciences and Medical Sciences, 63(12), 1335–1343. https://doi.org/10.1093/gerona/63.12.1335 Google Scholar

Hausdorff, J. M., Yogev, G., Springer, S., Simon, E. Y., & Giladi, N. (2005). Walking is more like catching than tapping: Gait in the elderly as a complex task. Experimental Brain Research, 164(4), 541–548. https://doi.org/10.1007/s00221-005-2280-3 Google Scholar

Hollman, J. H., Kovash, F. M., Kubik, J. J., & Linbo, R. A. (2006). Age-related differences in spatio-temporal markers of gait stability during dual task walking. Gait and Posture, 26(1), 113–119. https://doi.org/10.1016/j.gaitpost.2006.08.005 Google Scholar

Holtzer, R., Verghese, J., Xue, X., & Lipton, R. B. (2006). Cognitive processes related to gait velocity: Results from the Einstein aging study. Neuropsychology, 20(2), 215–223. https://doi.org/10.1037/0894-4105.20.2.215 Google Scholar

Inzitari, M., Baldereschi, M., Di Carlo, A., et al. (2007). Impaired attention predicts motor performance decline in older community-dwellers with normal baseline mobility: Results from the Italian longitudinal study on aging (ILSA). Journals of Gerontology, Series A: Biological Sciences and Medical Sciences, 62(8), 837–843. https://doi.org/10.1093/gerona/62.8.837 Google Scholar

Jehu, D., Paquet, N., & Lajoie, Y. (2017). Balance and mobility training with or without concurrent cognitive training does not improve posture, but improve reaction time in healthy older adults. Gait and Posture, 52, 227–232. https://doi.org/10.1016/j.gaitpost.2016.12.006 Google Scholar

Kao, C. C., Chiu, H. L., Liu, D., et al. (2018). Effect of interactive cognitive motor training on gait and balance among older adults: A randomized controlled trial. International Journal of Nursing Studies, 82, 121–128. https://doi.org/10.1016/J.IJNURSTU.2018.03.015 Google Scholar

Knopman, D. S., & Petersen, R. C. (2014). Mild cognitive impairment and mild dementia: A clinical perspective. Mayo Clinic Proceedings, 89(10), 1452–1459. https://doi.org/10.1016/j.mayocp.2014.06.019 Google Scholar

Laatar, R., Kachouri, H., Borji, R., Rebai, H., & Sahli, S. (2018). Combined physical-cognitive training enhances postural performances during daily life tasks in older adults. Experimental Gerontology, 107, 91–97. https://doi.org/10.1016/J.EXGER.2017.09.004 Google Scholar

Lee, Y., Kim, J. H, Lee, K. J., Han, G., & Kim, J. L. (2006). Association of cognitive status with functional limitation and disability in older adults. Aging Clinical and Experimental Research, 17(1), 20–28. https://doi.org/10.1007/BF03337716 Google Scholar

Maquet, D., Lekeu, F., Warzee, E., et al. (2010). Gait analysis in elderly adult patients with mild cognitive impairment and patients with mild Alzheimer’s disease: Simple versus dual task: A preliminary report. Clinical Physiology and Functional Imaging, 30(1), 51–56. https://doi.org/10.1111/j.1475-097X.2009.00903.x Google Scholar

Marquis, S., Moore, M. M., Howieson, D. B., et al. (2002). Independent predictors of cognitive decline in healthy elderly persons. Neurology, 58(4), 601–606. https://doi.org/10.1001/archneur.59.4.601 Google Scholar

McGuire, L. C., Ford, E. S., & Ajani, U. A. (2006). Cognitive functioning as a predictor of functional disability in later life. American Journal of Geriatric Psychiatry, 14(1), 36–42. https://doi.org/10.1097/01.JGP.0000192502.10692.D6 Google Scholar

Mielke, M. M., Roberts, R. O., Savica, R., et al. (2012). Assessing the temporal relationship between cognition and gait: Slow gait predicts cognitive decline in the Mayo Clinic study of aging. Journals of Gerontology, Series A: Biological Sciences and Medical Sciences, 68(8), 929–937. https://doi.org/10.1093/gerona/gls256 Google Scholar

Montero-Odasso, M., Almeida, Q. J., Bherer, L., et al. (2018). Consensus on shared measures of mobility and cognition: From the Canadian Consortium on Neurodegeneration in Aging (CCNA). Journals of Gerontology, Series A: Biological Sciences and Medical Sciences, 74(6), 897–909. https://doi.org/10.1093/gerona/gly148 Google Scholar

Montero-Odasso, M., Bherer, L., Studenski, S., et al. (2015). Mobility and cognition in seniors. Report from the 2008 Institute of Aging (CIHR) mobility and cognition workshop. Canadian Geriatrics Journal, 18(3), 159–167. https://doi.org/10.5770/cgj.18.188 Google Scholar

Montero-Odasso, M., Islam, A., Anton-Rodrigo, I., et al. (2015). Cognition predicts frailty status: Results from the “Gait & Brain Study.” Gerontologist, 55(S2), 570. https://doi.org/10.1093/geront/gnv281.04 Google Scholar

Montero-Odasso, M., & Speechley, M. (2018). Falls in cognitively impaired older adults: Implications for risk assessment and prevention. Journal of the American Geriatrics Society, 66(2), 367–375. https://doi.org/10.1111/jgs.15219 Google Scholar

Montero-Odasso, M., Verghese, J., Beauchet, O., & Hausdorrf, J. M. (2012). Gait and cognition: A complementary approach to understanding brain function and the risk of falling. Journal of the American Geriatrics Society, 60(11), 2127–2136. https://doi.org/10.1111/j.1532-5415.2012.04209.x Google Scholar

Morris, R., Lord, S., Bunce, J., Burn, D., & Rochester, L. (2016). Gait and cognition: Mapping the global and discrete relationships in ageing and neurodegenerative disease. Neuroscience and Behavioral Reviews, 64, 326–345. https://doi.org/10.1016/j.neubiorev.2016.02.012 Google Scholar

Musich, S., Wang, S. S., Ruiz, J., Hawkins, K., & Wicker, E. (2018). The impact of mobility limitations on health outcomes among older adults. Geriatric Nursing, 39(2), 162–169. https://doi.org/10.1016/j.gerinurse.2017.08.002 Google Scholar

Nardone, A., & Turcato, A. M. (2018). An overview of the physiology and pathophysiology of postural control. In Sandrini, G., Homberg, V., Saltuari, L., Smania, N., & Pedrocchi, A. (Eds.), Advanced technologies for the rehabilitation of gait and balance disorders (pp. 3–28). Cham, Switzerland: Springer International Publishing.Google Scholar

National Institute on Aging (2017). What is mild cognitive impairment? www.nia.nih.gov/health/what-mild-cognitive-impairment Google Scholar

Panzer, V. P., Wakefield, D. B., Hall, C. B., & Wolfson, L. I. (2011). Mobility assessment: Sensitivity and specificity of measurement sets in older adults. Archives of Physical Medicine and Rehabilitation, 92(6), 905–912. https://doi.org/10.1016/j.apmr.2011.01.004 Google Scholar

Persad, C. C., Jones, J. L., Ashton-Miller, J. A., Alexander, N. B., & Giordani, B. (2008). Executive function and gait in older adults with cognitive impairment. Journals of Gerontology, Series A: Biological Sciences and Medical Sciences, 63(12), 1350–1355. https://doi.org/10.1093/gerona/63.12.1350 Google Scholar

Powell, L. E., & Myers, A. M. (1995). The Activities-Specific Balance Confidence (ABC) Scale. Journals of Gerontology, Series A: Biological Sciences and Medical Sciences, 50(1), M28–M34. https://doi.org/10.1093/gerona/50A.1.M28 Google Scholar

Rankin, J. K., Woollacott, M. H., Shumway-Cook, A., & Brown, L. A. (2000). Cognitive influence on postural stability: A neuromuscular analysis in young and older adults. Journals of Gerontology, Series A: Biological Sciences and Medical Sciences, 55(3), M112–M119. https://doi.org/10.1093/gerona/55.3.M112 Google Scholar

Rantakokko, M., Portegijs, E., Viljanen, A., Iwarsson, S., & Rantanen, T. (2013). Life-space mobility and quality of life in community-dwelling older people. Journal of the American Geriatrics Society, 61(10), 1830–1832. https://doi.org/10.1111/jgs.12473 Google Scholar

Ross, L. A., Schmidt, E. L., & Ball, K. (2013). Interventions to maintain mobility: What works? Accident Analysis and Prevention, 61, 167–196. https://doi.org/10.1016/j.aap.2012.09.027 Google Scholar

Royall, D. R., Palmer, R., Chiodo, L. K., & Polk, M. J. (2004). Declining executive control in normal aging predicts change in functional status: the Freedom House Study. Journal of the American Geriatrics Society, 52(3), 346–352. https://doi.org/10.1111/j.1532-5415.2004.52104.x Google Scholar

Salzman, B. (2010). Gait and balance disorders in older adults. American Family Physician, 82(1), 61–68. www.aafp.org/afp/2010/0701/p61.html Google Scholar

Satariano, W. A., Guralnik, J. M., Jackson, R. J., et al. (2012). Mobility and aging: New directions for public health action. American Journal of Public Health, 102(8), 1508–1515. https://doi.org/10.2105/AJPH.2011.300631 Google Scholar

Sherrington, C., Whitney, J. C., Lord, S. R., et al. (2008). Effective exercise for the prevention of falls: A systematic review and meta-analysis. Journal of the American Geriatrics Society, 56(12), 2234–2243. https://doi.org/10.1111/j.1532-5415.2008.02014.x Google Scholar

Shumway-Cook, A., & Woollacott, M. (2000). Attentional demands and postural control: The effect of sensory context. Journals of Gerontology, Series A: Biological Sciences and Medical Sciences, 55(1), M10–M16. https://doi.org/10.1093/gerona/55.1.M10 Google Scholar

Smith-Ray, R. L., Hughes, S. L., Prohaska, T. R., et al. (2013). Impact of cognitive training on balance and gait in older adults. Journals of Gerontology, Series B: Psychological Sciences and Social Sciences, 70(3), 357–366. https://doi.org/10.1093/geronb/gbt097 Google Scholar

Springer, S., Giladi, N., Peretz, C., et al. (2006). Dual-tasking effects on gait variability: The role of aging, falls, and executive function. Movement Disorders, 21(7), 950–957. https://doi.org/10.1002/mds.20848 Google Scholar

Srygley, J. M., Mirelman, A., Herman, T., Giladi, N., & Hausdorff, J. M. (2009). When does walking alter thinking? Age and task related findings. Brain Research, 1253, 92–99. https://doi.org/10.1016/j.brainres.2008.11.067 Google Scholar

Steinmetz, J., & Federspiel, C. (2014). The effects of cognitive training on gait speed and stride variability in older adults: Findings from a pilot study. Aging Clinical and Experimental Research, 26(6), 635–643. https://doi.org/10.1007/s40520-014-0228-9 Google Scholar

Sudarsky, L. (2001). Neurologic disorders of gait. Current Neurology and Neuroscience Reports, 1(4), 350–356. https://doi.org/10.1007/s11910-001-0089-4 Google Scholar

Valkanova, V., & Ebmeier, K. P. (2017). What can gait tell us about dementia? Review of epidemiological and neuropsychological evidence. Gait and Posture, 53, 215–223. https://doi.org/10.1016/j.gaitpost.2017.01.024 Google Scholar

Verbrugge, L. M., & Jette, A. M. (1994). The disablement process. Social Science and Medicine, 38(1), 1–14. https://doi.org/10.1016/0277-9536(94)90294-1 Google Scholar

Verghese, J., Lipton, R. B., Hall, C. B., et al. (2002). Abnormality of gait as a predictor of non-Alzheimer’s dementia. New England Journal of Medicine, 347(22), 1761–1768. https://doi.org/10.1056/NEJMoa020441 Google Scholar

Verghese, J., Mahoney, J., Ambrose, A. F., Wang, C., & Holtzer, R. (2010). Effect of cognitive remediation on gait in sedentary seniors. Journals of Gerontology, Series A: Biological Sciences and Medical Sciences, 65(12), 1338–1343. http://dx.doi.org/10.1093/gerona/glq127 Google Scholar

Verghese, J., Wang, C., Lipton, R. B., Holtzer, R., & Xue, X. (2007). Quantitative gait dysfunction and risk of cognitive decline and dementia. Journal of Neurology, Neuroscience, and Psychiatry, 78, 929–935. http://doi.org/10.1136/jnnp.2006.106914 Google Scholar

Verghese, J., Wang, C., Lipton, R. B., Holtzer, R. (2013). Motoric cognitive risk syndrome and the risk of dementia. (2013). Journals of Gerontology, Series A: Biological Sciences and Medical Sciences, 68(4), 412–418. https://doi.org/10.1093/gerona/gls191 Google Scholar

Waite, L. M., Grayson, D. A., Piguest, O., et al. (2005). Gait slowing as a predictor of incident dementia: 6-year longitudinal data from the Sydney Older Persons Study. Journal of the Neurological Sciences, 229, 89–93. https://doi.org/10.1016/j.jns.2004.11.009 Google Scholar

Webber, S. C., Porter, M. M., & Menec, V. H. (2010). Mobility in older adults: A comprehensive framework. Gerontologist, 50(4), 443–450. https://doi.org/10.1093/geront/gnq013 Google Scholar

Wollesen, B., Voelcker-Rehage, C., Regenbrecht, T., & Mattes, K. (2016). Influence of a visual–verbal Stroop test on standing and walking performance of older adults. Neuroscience, 318(24), 166–177. https://doi.org/10.1016/j.neuroscience.2016.01.031 Google Scholar

Woollacott, M., & Shumway-Cook, A. (2002). Attention and the control of posture and gait: A review of an emerging area of research. Gait and Posture, 16(1), 1–14. https://doi.org/10.1016/S0966-6362(01)00156-4 Google Scholar

Yogev-Seligmann, G., Hausdorff, J. M., & Giladi, N. (2008). The role of executive function and attention in gait. Movement Disorders, 23(3), 329–342. https://doi.org/10.1002/mds.21720 Google Scholar

References

Anderson, M. (2015). Technology device ownership. Washington: Pew Research Center.Google Scholar

Anderson, M., & Perrin, A. (2017). Tech adoption climbs among older adults. Washington: Pew Research Center.Google Scholar

Atzori, L., Iera, A., & Morabito, G. (2010). The internet of things: A survey. Computer Networks, 54(15), 2787–2805. https://doi.org/10.1016/j.comnet.2010.05.010 Google Scholar

Baltes, P. B., & Baltes, M. M. (1990). Psychological perspectives on successful aging: The model of selective optimization with compensation. In Baltes, P. B. & Baltes, M. M. (Eds.), Successful aging: Perspectives from the behavioral sciences (pp. 1–34). New York: Cambridge University Press.Google Scholar

Berkowsky, R. W., Sharit, J., & Czaja, S. J. (2018). Factors predicting decisions about technology adoption among older adults. Innovation in Aging, 1(3), 1–12. https://doi.org/10.1093/geroni/igy002 Google Scholar

Blackman, S., Matlo, C., Bobrovitskiy, C., et al. (2016). Ambient assisted living technologies for aging well: A scoping review. Journal of Intelligent Systems, 25(1), 55–69. https://doi.org/10.1515/jisys-2014-0136 Google Scholar

Blocker, K. A., Insel, K. C., Lee, J. K., et al. (2018). User insights for design of an antihypertensive medication management application. In Proceedings of the Human Factors and Ergonomics Society 62nd Annual Meeting (pp. 1077–1081). Santa Monica, CA: Human Factors and Ergonomics Society.Google Scholar

Brooke, J. (1996). SUS – A quick and dirty usability scale. Usability Evaluation in Industry, 189(194), 4–7.Google Scholar

Chen, K., & Chan, A. H. S. (2014). Gerontechnology acceptance by elderly Hong Kong Chinese: A senior technology acceptance model (STAM). Ergonomics, 57(5), 635–652. https://doi.org/10.1080/00140139.2014.895855 Google Scholar

Chin, J., Moeller, D. D., Johnson, J., et al. (2018). A multi-faceted approach to promote comprehension of online health information among older adults. Gerontologist, 58, 686–695. https://doi.org/10.1093/geront/gnw254 Google Scholar

Confente, I., & Vigolo, V. (2018). Online travel behaviour across cohorts: The impact of social influences and attitude on hotel booking intention. International Journal of Tourism Research, 20(5), 660–670. https://doi.org/10.1002/jtr.2214 Google Scholar

Consel, C. (2018). Assistive computing: A human-centered approach to developing computing support for cognition. In ICSE-SEIS’18: 40th International Conference on Software Engineering: Software Track (pp. 23–32). New York: ACM.Google Scholar

Consel, C., Dupuy, L., & Sauzéon, H. (2017, July). HomeAssist: An assisted living platform for aging in place based on an interdisciplinary approach. In International Conference on Applied Human Factors and Ergonomics (pp. 129–140). Cham, Switzerland: Springer.Google Scholar

Cornejo, R., Favela, J., & Tentori, M. (2010). Ambient displays for integrating older adults into social networking sites. In International Conference on Collaboration and Technology (pp. 321–336). Berlin: Springer.Google Scholar

Cotten, S. R., Yost, E., Berkowsky, R. W., Winstead, V., & Anderson, W. A. (2016). Designing technology training for older adults in continuing care retirement communities. Boca Raton, FL: CRC Press.Google Scholar

Craik, F. I. M. (1986). A functional account of age differences in memory. In Klix, F. & Hagendorf, H. (Eds.), Human memory and cognitive capabilities, mechanisms, and performances (pp. 409–422). Amsterdam: Elsevier Science.Google Scholar

Czaja, S. J., Boot, W. R., Charness, N., & Rogers, W. A. (2019). Designing for older adults: Principles and creative human factors approaches, 3rd ed. Boca Raton, FL: CRC PressGoogle Scholar

Czaja, S. J., Boot, W. R., Charness, N., Rogers, W. A., & Sharit, J. (2017).Improving social support for older adults through technology: Findings from the PRISM randomized controlled trial. Gerontologist, 58(3), 467–477. https://doi.org/10.1093/geront/gnw249 Google Scholar

Czaja, S. J., Boot, W. R., Charness, N., et al. (2015). The Personalized Reminder Information and Social Management System (PRISM) trial: Rationale, methods and baseline characteristics. Contemporary Clinical Trials, 40, 35–46. https://doi.org/10.1016/j.cct.2014.11.004 Google Scholar

Czaja, S. J., Charness, N., Fisk, A. D., et al. (2006). Factors predicting the use of technology: Findings from the Center for Research and Education on Aging and Technology Enhancement (CREATE). Psychology and Aging, 21(2), 333–352. https://doi.org/10.1037/0882-7974.21.2.333 Google Scholar

Dijkstra, K., Charness, N., Yordon, R., & Fox, M. (2009). Changes in physiological and self-reported stress in younger and older adults after exposure to a stressful task. Aging, Neuropsychology and Cognition, 16, 338–356. https://doi.org/10.1080/13825580902773859 Google Scholar

Dupuy, L., Consel, C., & Sauzéon, H. (2016). Self determination-based design to achieve acceptance of assisted living technologies for older adults. Computers in Human Behavior, 65, 508–521. https://doi.org/10.1016/j.chb.2016.07.042 Google Scholar

Durick, J., Robertson, T., Brereton, M., Vetere, F., & Nansen, B. (2013). Dispelling ageing myths in technology design. In Proceedings of the 25th Australian Computer-Human Interaction Conference: Augmentation, Application, Innovation, Collaboration (pp. 467–476). New York: ACM.Google Scholar

Fasola, J., & Matarić, M. J. (2013). A socially assistive robot exercise coach for the elderly. Journal of Human-Robot Interaction, 2(2), 3–32. https://doi.org/10.5898/JHRI.2.2.Fasola Google Scholar

Grindrod, K. A., Li, M., & Gates, A. (2014). Evaluating user perceptions of mobile medication management applications with older adults: A usability study. JMIR mHealth and uHealth, 2(1), e11. https://doi.org/10.2196/mhealth.3048 Google Scholar

Huber, L. L., Shankar, K., Caine, K., et al. (2013). How in-home technologies mediate caregiving relationships in later life. International Journal of Human-Computer Interaction, 29(7), 441–455. https://doi.org/10.1080/10447318.2012.715990 Google Scholar

Khosla, R., Chu, M. T., & Nguyen, K. (2013). Enhancing emotional well being of elderly using assistive social robots in Australia. In Proceedings of the 2013 International Conference on Biometrics and Kansei Engineering (ICBAKE) (pp. 41–46). New York: IEEE.Google Scholar

Lee, Y., Lee, J., & Hwang, Y. (2015). Relating motivation to information and communication technology acceptance: Self-determination theory perspective. Computers in Human Behavior, 51, 418–428. https://doi.org/10.1016/j.chb.2015.05.021 Google Scholar

Lindenberger, U., Lövdén, M., Schellenbach, M., Li, S. C., & Krüger, A. (2008). Psychological principles of successful aging technologies: A mini-review. Gerontology, 54(1), 59–68. https://doi.org/10.1159/000116114 Google Scholar

Lindley, S. E. (2012). Shades of lightweight: Supporting cross-generational communication through home messaging. Universal Access in the Information Society, 11(1), 31–43. https://doi.org/10.1007/s10209-011-0231-2 Google Scholar

Ludden, G. D., van Rompay, T. J., Kelders, S. M., & van Gemert-Pijnen, J. E. (2015). How to increase reach and adherence of web-based interventions: A design research viewpoint. Journal of Medical Internet Research, 17(7), e172. https://doi.org/10.2196/jmir.4201 Google Scholar

Martinson, M., & Berridge, C. (2015). Successful aging and its discontents: A systematic review of social gerontology literature. Gerontologist, 55, 51–57. https://doi.org/10.1093/geront/gnu037 Google Scholar

Matarić, M. J., & Scassellati, B. (2016). Socially assistive robotics. In Springer handbook of robotics (pp. 1973–1994). Cham, Switzerland: Springer.Google Scholar

McGlynn, S. A., Koon, L. M., Blocker, K. A., Shishegar, N., & Rogers, W. A. (2018). Investigating the potential of digital home assistants to promote physical activity and social engagement for older adults. Presented at the Cognitive Aging Conference (CAC), Atlanta, GA, May.Google Scholar

McMahon, S. K., Lewis, B., Oakes, M., et al. (2016). Older adults’ experiences using a commercially available monitor to self-track their physical activity. JMIR mHealth and uHealth, 4(2), e35. https://doi.org/10.2196/mhealth.5120 Google Scholar

Mercer, K., Giangregorio, L., Schneider, E., et al. (2016). Acceptance of commercially available wearable activity trackers among adults aged over 50 and with chronic illness: A mixed-methods evaluation. JMIR mHealth and uHealth, 4(1), e7. https://doi.org/10.2196/mhealth.4225 Google Scholar

Mitzner, T. L., Boron, J. B., Fausset, C. B., et al. (2010). Older adults talk technology: Technology usage and attitudes. Computers in Human Behavior, 26(6), 1710–1721. https://doi.org/10.1016/j.chb.2010.06.020 Google Scholar

Mitzner, T. L., Sanford, J. A., & Rogers, W. A. (2018). Closing the capacity-ability gap: Using technology to support aging with disability. Innovation in Aging, 2(1), 1–8. https://doi.org/10.1093/geroni/igy008 Google Scholar

Mitzner, T. L., Stuck, R., Hartley, J. Q., Beer, J. M., & Rogers, W. A. (2017). Acceptance of televideo technology by adults aging with a mobility impairment for health and wellness interventions. Journal of Rehabilitation and Assistive Technologies Engineering, 4, 1–2. https://doi.org/10.1177/2055668317692755 Google Scholar

Morrow, D. G., & Rogers, W. A. (2008). Environmental support: An integrative framework. Human Factors, 50, 589–613. https://doi.org/10.1518/001872008X312251 Google Scholar

Neves, B. B., Franz, R. L., Munteanu, C., Baecker, R., & Ngo, M. (2015). My hand doesn’t listen to me!: Adoption and evaluation of a communication technology for the “oldest old.” In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (pp. 1593–1602). New York: ACM.Google Scholar

Preusse, K. C., Mitzner, T. L., Fausset, C. B., & Rogers, W. A. (2017). Older adults’ acceptance of activity trackers. Journal of Applied Gerontology, 36(2), 127–155. https://doi.org/10.1177/0733464815624151 Google Scholar

Quan-Haase, A., Mo, G. Y., & Wellman, B. (2017). Connected seniors: How older adults in East York exchange social support online and offline. Information, Communication and Society, 20(7), 967–983. https://doi.org/10.1080/1369118X.2017.1305428 Google Scholar

Rashidi, P., & Mihailidis, A. (2013). A survey on ambient-assisted living tools for older adults. IEEE Journal of Biomedical and Health Informatics, 17(3), 579–590. https://doi.org/10.1109/JBHI.2012.2234129 Google Scholar

Reuter-Lorenz, P. A., & Park, D. C. (2014). How does it STAC up? Revisiting the scaffolding theory of aging and cognition. Neuropsychological Review, 24, 355–370. https://doi.org/10.1007/s11065-014-9270-9 Google Scholar

Rowan, J., & Mynatt, E. D. (2005). Digital family portrait field trial: Support for aging in place. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 521–530). New York: ACM.Google Scholar

Rowe, J. W., & Kahn, R. L. (1987). Human aging: Usual and successful. Science, 237(4811), 143–149. https://doi.org/10.1145/1054972.1055044 Google Scholar

Rowe, J. W., & Kahn, R. L. (2015). Successful aging 2.0: Conceptual expansions for the 21st century. Journals of Gerontology, Series B: Psychological Sciences and Social Sciences, 70(4), 593–596. https://doi.org/10.1093/geronb/gbv025 Google Scholar

Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55(1), 68–78. https://doi.org/10.1037/0003-066X.55.1.68 Google Scholar

Seligman, M. E. (2011). Flourish: A visionary new understanding of happiness and well-being. Policy, 27(3), 60–61.Google Scholar

Shen, Z., & Wu, Y. (2016). Investigation of practical use of humanoid robots in elderly care centres. In Proceedings of the Fourth International Conference on Human Agent Interaction (pp. 63–66). New York: ACM.Google Scholar

Shibata, T., & Wada, K. (2011). Robot therapy: A new approach for mental healthcare of the elderly – A mini-review. Gerontology, 57(4), 378–386. https://doi.org/10.1159/000319015 Google Scholar

Smarr, C.-A., Mitzner, T. L., Beer, J. M., et al. (2014). Domestic robots for older adults: Attitudes, preferences, and potential. International Journal of Social Robotics, 6(2), 229–247. https://doi.org/10.1007/s12369-013-0220-0 Google Scholar

Smith, A., & Anderson, M. (2017). Automation in everyday life. Washington: Pew Research Center.Google Scholar

Souders, D. J., Boot, W. R., Blocker, K., et al. (2017). Evidence for narrow transfer after short-term cognitive training in older adults. Frontiers in Aging Neuroscience, 9, 41. https://doi.org/10.3389/fnagi.2017.00041 Google Scholar

Stahl, B. C. (2011). What does the future hold? A critical view of emerging information and communication technologies and their social consequences. In Chiasson, M., Henfridsson, O., Karsten, H., & DeGross, J. I. (Eds.), Researching the future in information systems (pp. 59–76). Berlin: Springer.Google Scholar

Stern, I. (2012). Cognitive reserve in ageing and Alzheimer’s disease. Lancet Neurology, 11(11), 1006–1012. https://doi.org/10.1016/S1474-4422(12)70191-6 Google Scholar

Stuck, R. E., Chong, A. W., Mitzner, T. L., & Rogers, W. A. (2017). Medication management apps: Usable by older adults? In Proceedings of the Human Factors and Ergonomics Society 61st Annual Meeting (pp. 1141–1144). Santa Monica, CA: Human Factors and Ergonomics Society.Google Scholar

Stuck, R. E., & Rogers, W. A. (2018). Older adults’ perceptions of supporting factors of trust in a robot care provider. Journal of Robotics, 2018, 1–11. https://doi.org/10.1155/2018/6519713 Google Scholar

Venkatesh, V., Thong, J. Y., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157–178. https://doi.org/10.2307/41410412 Google Scholar

Ware, J. E., Jr., & Sherbourne, C. D. (1992). The MOS 36-item short-form health survey (SF-36): I. Conceptual framework and item selection. Medical Care, 30(6), 473–483. https://doi.org/10.1097/00005650-199206000-00002 Google Scholar

Ziefle, M., Rocker, C., & Holzinger, A. (2011). Medical technology in smart homes: Exploring the user’s perspective on privacy, intimacy and trust. In 2011 IEEE 35th Annual Computer Software and Applications Conference Workshops (COMPSACW) (pp. 410–415). New York: IEEE.Google Scholar

References

Foster, J. L., Harrison, T. L., Hicks, K. L., et al. (2017). Do the effects of working memory training depend on baseline ability level? Journal of Experimental Psychology: Learning, Memory, and Cognition, 43(11), 1677–1689. http://dx.doi.org.ezproxy.library.tufts.edu/10.1037/xlm0000426 Google Scholar

Giraudeau, C., & Bailly, N. (2019). Intergenerational programs: What can school-age children and older people expect from them? A systematic review. European Journal of Ageing, 16(3), 363–376. https://doi.org/10.1007/s10433-018-00497-4 Google Scholar

Hall, S. S. (2003). The quest for a smart pill. Scientific American, 289(3), 54–65.Google Scholar

Kessler, E.-M., & Staudinger, U. M. (2007). Intergenerational potential: Effects of social interaction between older adults and adolescents. Psychology and Aging, 22(4), 690–704. https://doi.org/10.1037/0882-7974.22.4.690 Google Scholar

Lilienfeld, S. O., Lynn, S. J., Ruscio, J., & Beyerstein, B. L. (2010). 50 great myths of popular psychology: Shattering widespread misconceptions about human behavior. Chichester, UK: Wiley-Blackwell.Google Scholar

Northey, J. M., Cherbuin, N., Pumpa, K. L., Smee, D. J., & Rattray, B. (2018). Exercise interventions for cognitive function in adults older than 50: A systematic review with meta-analysis. British Journal of Sports Medicine, 52(3), 154–160. https://doi.org/10.1136/bjsports-2016–096587 Google Scholar

Redick, T. S. (2019). The hype cycle of working memory training. Current Directions in Psychological Science, 28(5), 423–429. https://doi.org/10.1177/0963721419848668 Google Scholar

Sakurai, R., Ishii, K., Sakuma, N., et al. (2018). Preventive effects of an intergenerational program on age-related hippocampal atrophy in older adults: The REPRINTS study. International Journal of Geriatric Psychiatry, 33(2), e264–e272. https://doi.org/10.1002/gps.4785 Google Scholar

Simons, D. J., Boot, W. R., Charness, N., et al. (2016). Do “brain-training” programs work? Psychological Science in the Public Interest, 17(3), 103–186. https://doi.org/10.1177/1529100616661983 Google Scholar

Suzuki, H., Kuraoka, M., Yasunaga, M., et al. (2014). Cognitive intervention through a training program for picture book reading in community-dwelling older adults: A randomized controlled trial. BMC Geriatrics, 14(1). https://doi.org/10.1186/1471-2318-14-122 Google Scholar

Tang, W., Kannaley, K., Friedman, D. B., et al. (2017). Concern about developing Alzheimer’s disease or dementia and intention to be screened: An analysis of national survey data. Archives of Gerontology and Geriatrics, 71, 43–49. https://doi.org/10.1016/j.archger.2017.02.013 Google Scholar

Book contents

Part V - Later Life and Interventions

Access options

References

References

References

References

References

References

References

References

References

References

Save book to Kindle

Save book to Dropbox

Save book to Google Drive