Skip to main content Accessibility help
×
Hostname: page-component-586b7cd67f-dsjbd Total loading time: 0 Render date: 2024-11-22T13:55:58.892Z Has data issue: false hasContentIssue false

25 - Tele-Trials, Remote Monitoring, and Trial Technology for Alzheimer’s Disease Clinical Trials

from Section 3 - Alzheimer’s Disease Clinical Trials

Published online by Cambridge University Press:  03 March 2022

Jeffrey Cummings
Affiliation:
University of Nevada, Las Vegas
Jefferson Kinney
Affiliation:
University of Nevada, Las Vegas
Howard Fillit
Affiliation:
Alzheimer’s Drug Discovery Foundation
Get access

Summary

Digital technologies show great promise for moving clinical trials from using in-person approaches that have perpetuated long drug trial timelines, biased sampling and high costs. A review of the current state, however, reveals that technology use has been largely limited to replicating known methods and/or applied to small study samples. Full realization of the potential will require significant investment in validating digital signals into novel metrics fueled by advanced computational methods. These steps, however, will require regulatory guidance, as well as considerations regarding data security and future proofing against rapid technology obsolescence. Despite these challenges, the end-to-end virtual clinical trial is possible today.

Type
Chapter
Information
Alzheimer's Disease Drug Development
Research and Development Ecosystem
, pp. 292 - 300
Publisher: Cambridge University Press
Print publication year: 2022

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

Food and Drug Administration. Digital Health Innovation Action Plan. Available at: www.fda.gov/media/106331/download (accessed 2018).Google Scholar
Food and Drug Administration. Software as a medical device (SAMD): clinical evaluation. Available at: www.fda.gov/regulatory-information/search-fda-guidance-documents/software-medical-device-samd-clinical-evaluation (accessed 2017).Google Scholar
FDA–NIH Biomarker Working Group. BEST (Biomarkers, Endpoints, and Other Tools) Resource. Silver Spring, MD and Bethesda, MD: Food and Drug Administration and National Institutes of Health; 2016.Google Scholar
Chinner, A, Blane, J, Lancaster, C, Hinds, C, Koychev, I. Digital technologies for the assessment of cognition: a clinical review. Evid-Based Ment Health 2018; 21: 6771.CrossRefGoogle ScholarPubMed
Koo, BM, Vizer, LM. Mobile technology for cognitive assessment of older adults: a scoping review. Innov Aging 2018; 3: igy038.Google Scholar
Lussier, M, Lavoie, M, Giroux, S, et al. Early detection of mild cognitive impairment with in-home monitoring sensor technologies using functional measures: a systematic review. IEEE J Biomed Health Inform 2019; 23: 838–47.Google Scholar
Thabtah, F, Mampusti, E, Peebles, D, Herradura, R, Varghese, J. A mobile-based screening system for data analyses of early dementia traits detection. J Med Syst 2019; 44: 24.Google Scholar
Piau, A, Wild, K, Mattek, N, Kaye, J. Current state of digital biomarker technologies for real-life, home-based monitoring of cognitive function for mild cognitive impairment to mild Alzheimer disease and implications for clinical care: systematic review. J Med Internet Res 2019, 21: e12785.Google Scholar
Kourtis, LC, Regele, OB, Wright, JM, Jones, GB. Digital biomarkers for Alzheimer’s disease: the mobile/wearable devices opportunity. NPJ Digit Med 2019; 2: 9.CrossRefGoogle ScholarPubMed
Piers, RJ, Devlin, KN, Ning, B, et al. Age and graphomotor decision making assessed with the digital clock drawing test: the Framingham Heart Study. J Alzheimers Dis 2017; 60: 1611–20.Google Scholar
Thomas, JA, Burkhardt, HA, Chaudhry, S, et al. Assessing the utility of language and voice biomarkers to predict cognitive impairment in the Framingham Heart Study cognitive aging cohort data. J Alzheimers Dis 2020; 76: 905–22.CrossRefGoogle ScholarPubMed
Moon, S, Song, HJ, Sharma, VD, et al. Classification of Parkinson’s disease and essential tremor based on balance and gait characteristics from wearable motion sensors via machine learning techniques: a data-driven approach. J Neuroeng Rehabil 2020; 17: 125.CrossRefGoogle ScholarPubMed
Leach, JM, Mancini, M, Kaye, JA, Hayes, TL, Horak, FB. Day-to-day variability of postural sway and its association with cognitive function in older adults: a pilot study. Front Aging Neurosci 2018; 10: 126.Google Scholar
Tulipani, LJ, Meyer, B, Larie, D, Solomon, AJ, McGinnis, RS. Metrics extracted from a single wearable sensor during sit–stand transitions relate to mobility impairment and fall risk in people with multiple sclerosis. Gait Post 2020; 80: 361–6.Google Scholar
Dorsey, ER, Omberg, L, Waddell, E. Deep phenotyping of Parkinson’s disease. J Parkinsons Dis 2020; 10: 855–73.Google Scholar
Hunfalvay, M, Roberts, CM, Murray, NP. Vertical smooth pursuit as a diagnostic marker of traumatic brain injury. Concussion 2020; 5: CNC69.Google Scholar
Regalia, G, Gerboni, G, Migliorini, M, et al. Sleep assessment by means of a wrist actigraphy-based algorithm: agreement with polysomnography in an ambulatory study on older adults. Chronobiol Int 2021; 38: 400–14.Google Scholar
Kuwabara, M, Harada, K, Hishiki, Y, Kario, K. Validation of two watch-type wearable blood pressure monitors according to the ANSI/AAMI/ISO81060-2:2013 guidelines: Omron HEM-6410 T-ZM and HEM-6410 T-ZL. J Clin Hypertens (Greenwich) 2019; 21: 853–8.Google Scholar
Collier, SR, McCraw, C, Campany, M, et al. Withings body cardio versus gold standards of pulse-wave velocity and body composition. J Pers Med 2020; 10: 17.Google Scholar
Mosnaim, GS, Stempel, DA, Gonzalez, C, et al. The impact of patient self-monitoring via electronic medication monitor and mobile app plus remote clinician feedback on adherence to inhaled corticosteroids: a randomized controlled trial. J Allergy Clin Immunol Pract 2021; 9: 1586–94.Google Scholar
Jafri, RZ, Balliro, CA, El-Khatib, F, A three-way accuracy comparison of the Dexcom G5, Abbott Freestyle Libre Pro, and Senseonics Eversense continuous glucose monitoring devices in a home-use study of subjects with type 1 diabetes. Diabetes Technol Therapeut 2020; 22: 846–52.Google Scholar
Vandenberk, T, Storms, V, Lanssens, D, et al. A vendor-independent mobile health monitoring platform for digital health studies: development and usability study. JMIR mHealth uHealth 2019; 7: e12586.CrossRefGoogle ScholarPubMed
Seelye, A, Mattek, N, Sharma, N. Weekly observations of online survey metadata obtained through home computer use allow for detection of changes in everyday cognition before transition to mild cognitive impairment. Alzheimers Dement 2018; 14: 187–94.Google Scholar
Sano, M, Zhu, CW, Kaye, J, et al. A randomized clinical trial to evaluate home-based assessment of people over 75 years old. Alzheimers Dement 2019; 15: 615–24.Google Scholar
Thomas, N, Beattie, Z, Marcoe, J, et al. An ecologically valid, longitudinal, and unbiased assessment of treatment efficacy in Alzheimer disease (the EVALUATE-AD trial): proof-of-concept study. JMIR Res Protoc 2020; 9: e17603.Google Scholar
Ahamed, F, Shahrestani, S, Cheung, H. Internet of things and machine learning for healthy ageing: identifying the early signs of dementia. Sensors (Basel) 2020; 20: E6031.Google Scholar
Cavuoto, MG, Kinsella, GJ, Ong, B, Pike, KE, Nicholas, CL. Naturalistic measurement of sleep in older adults with amnestic mild cognitive impairment: anxiety symptoms do not explain sleep disturbance. Curr Alzheimer Res 2019; 16: 233–42.Google Scholar
Øksnebjerg, L, Woods, B, Ruth, K, et al. A tablet app supporting self-management for people with dementia: explorative study of adoption and use patterns. JMIR mHealth uHealth 2020; 8: e14694.CrossRefGoogle ScholarPubMed
Buchman, AS, Dawe, RJ, Leurgans, SE. Different combinations of mobility metrics derived from a wearable sensor are associated with distinct health outcomes in older adults. J Gerontol A Biol Sci Med Sci 2020; 75: 1176–83.CrossRefGoogle ScholarPubMed
Mueller, A, Hoefling, HA, Muaremi, A, et al. Continuous digital monitoring of walking speed in frail elderly patients: noninterventional validation study and longitudinal clinical trial. JMIR mHealth uHealth 2019; 7: e15191.Google Scholar
De Vito, AN, Sawyer, RJ 2nd, LaRoche, A, et al. Acceptability and feasibility of a multicomponent telehealth care management program in older adults with advanced dementia in a residential memory care unit. Gerontol Geriatr Med 2020; 6: 2333721420924988.Google Scholar
Seelye, A, Leese, MI, Dorociak, K, et al. Feasibility of in-home sensor monitoring to detect mild cognitive impairment in aging military veterans: prospective observational study. JMIR Form Res 2020; 4: e16371.CrossRefGoogle ScholarPubMed
Lyons, BE, Austin, D, Seelye, A, et al. Pervasive computing technologies to continuously assess Alzheimer’s disease progression and intervention efficacy. Front Aging Neurosci 2015; 7: 102.Google ScholarPubMed
Dodge, HH, Zhu, J, Mattek, NC, et al. Use of high-frequency in-home monitoring data may reduce sample sizes needed in clinical trials. PloS One 2015; 10: e0138095.Google Scholar
Food and Drug Administration. Digital Health Center of Excellence. Available at: www.fda.gov/medical-devices/digital-health-center-excellence (accessed November 2020).Google Scholar
Xue, C, Karjadi, C, Paschalidis, IC, Au, R, Kolachalama, VB. Detection of dementia on voice recordings using deep learning: a Framingham Heart Study. Alzheimers Res Ther 2021; 13: 146.Google Scholar
Inan, OT, Tenaerts, P, Prindiville, SA, et al. Digitizing clinical trials. NPJ Digit Med 2020; 3: 17.Google Scholar
Steinhubl, SR, Wolff-Hughes, DL, Nilsen, W, Iturriaga, E, Califf, RM. Digital clinical trials: creating a vision for the future. NPJ Digit Med 2019; 2: 13.Google Scholar
National Academies of Sciences, Engineering, and Medicine. The Role of Digital Health Technologies in Drug Development: Proceedings of a Workshop. Washington, DC: The National Academies Press; 2020.Google Scholar

Save book to Kindle

To save this book to your Kindle, first ensure [email protected] is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×