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Mobile health monitoring of children with CHDs

Published online by Cambridge University Press:  10 October 2024

Megan E. LeBlanc*
Affiliation:
Department of Pediatrics, Duke University Medical Center, Durham, NC, USA
Sarah Tallent
Affiliation:
Department of Pediatric Cardiology, Duke University Medical Center, Durham, NC, USA
Christoph P. Hornik
Affiliation:
Department of Pediatric Cardiac Surgery, Duke University Medical Center, Durham, NC, USA
Michael G.W. Camitta
Affiliation:
Department of Pediatric Cardiology, Duke University Medical Center, Durham, NC, USA
Anne C. Schmelzer
Affiliation:
Department of Pediatric Cardiology, Duke University Medical Center, Durham, NC, USA Department of Neonatology, Duke University Medical Center, Durham, NC, USA
Lillian Kang
Affiliation:
Department of Surgery, Duke University Medical Center, Durham, NC, USA
Kevin D. Hill
Affiliation:
Department of Pediatric Cardiology, Duke University Medical Center, Durham, NC, USA
*
Corresponding author: Megan E. LeBlanc; Email: [email protected]
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Abstract

Background:

Mobile health has been shown to improve quality, access, and efficiency of health care in select populations. We sought to evaluate the benefits of mobile health monitoring using the KidsHeart app in an infant CHD population.

Methods:

We reviewed data submitted to KidsHeart from parents of infants discharged following intervention for high-risk CHD lesions including subjects status post stage 1 single ventricle palliation, ductal stent or surgical shunt, pulmonary artery band, or right ventricular outflow tract stent. We report on the benefits of a novel mobile health red flag scoring system, mobile health growth/feed tracking, and longitudinal neurodevelopmental outcomes tracking.

Results:

A total of 69 CHD subjects (63% male, 41% non-white, median age 28 days [interquartile range 20, 75 days]) were included with median mobile health follow-up of 137 days (56, 190). During the analytic window, subjects submitted 5700 mobile health red flag notifications including 245 violations (mean [standard deviation] 3 ± 3.96 per participant) with 80% (55/69) of subjects submitting at least one violation. Violations precipitated 116 interventions including hospital admission in 34 (29%) with trans-catheter evaluation in 15 (13%) of those. Growth data (n = 2543 daily weights) were submitted by 63/69 (91%) subjects and precipitated 31 feed changes in 23 participants. Sixty-eight percent of subjects with age >2 months submitted at least one complete neurodevelopment questionnaire.

Conclusion:

In our initial experience, mobile health monitoring using the KidsHeart app enhanced interstage monitoring permitting earlier intervention, allowed for remote tracking of growth feeding, and provided a means for tracking longitudinal neurodevelopmental outcomes.

Type
Original Article
Copyright
© The Author(s), 2024. Published by Cambridge University Press

Introduction

Mobile health (mHealth) encompasses “a spectrum of digital technologies that leverage mobile devices, wearables, and applications to support the achievement of health objectives”. 1 In its most idealised form, mHealth promises a paradigm shift from reactive to proactive care by empowering patients and their families to actively engage in their own health. In a meta-analysis of 34 studies, mHealth technologies were associated with improved care delivery, access to care, quality of care, and enhanced technical performance, accuracy, and efficacy of workers. Reference Borges do Nascimento, Abdulazeem and Vasanthan2

Currently, there is little data on the role of mHealth in children with heart disease. Childhood heart diseases are often high-risk conditions, and more frequent mHealth monitoring could meaningfully augment care delivery. As an additional benefit, mHealth allows improved tracking of longitudinal outcomes and could fill an unmet need. In a 2007 report on outcome measures of paediatric cardiac diseases, Jacobs et al. concluded that “analysis of outcomes must move beyond mortality, and encompass longer term follow up, including cardiac and non-cardiac morbidities, and importantly, those morbidities impacting health related quality of life”. Reference Jacobs, Wernovsky and Elliott3

There are currently only a few examples in the literature of mHealth/telehealth monitoring in CHD. Reference Bingler, Erickson and Reid4Reference Vergales, Peregoy, Zalewski and Plummer8 Given the potential of mHealth to augment care, and the unmet need/need for improvement in longitudinal outcome tracking in childhood heart disease, we developed the KidsHeart mHealth application to track family-reported outcomes in patients with high-risk heart disease following neonatal heart surgery. The KidsHeart platform collects longitudinal outcomes with measures including vital signs, weight gain, medication compliance, and incidence/severity of red flag symptom events, and it delivers age-appropriate neurodevelopmental surveys. Here, we report initial feasibility, participant response rates, and the clinical and research utility of the KidsHeart mHealth platform.

Materials and methods

The KidsHeart app (Figure 1) was developed with parent input via a series of focus groups. Focus groups included six female participants ages 32 through 39 (75% identifying as white, 25% as black, one hispanic ethnicity). A focus group summary and full-text comments about it are included in the on-line supplement. The KidsHeart app was developed on the Pattern HealthTM mobile app platform (Pattern Health Technologies, Inc., Durham, NC). Features include educational materials in both video and PDF modalities, customisable centre information (e.g. clinic locations, contact information, provider names), a diagnostic summary, and a chat feature accessible to both parents and providers. Parents were encouraged to input daily growth and feeding data, vital signs (oxygen saturations and heart rates), and medication compliance. They can track progress using the app’s graphical features and receive rewards and reminders via the app to encourage appropriate data submission. One of the design objectives of the KidsHeart app was that it would augment home monitoring of the high-risk infants including single ventricle patients in the “interstage” period. However, the app was intended to augment and not replace traditional home monitoring protocols. Therefore, all patients continued with regularly scheduled out-patient cardiology appointments as well as traditional primary care follow-up.

Figure 1. Images of KidsHeart app interface.

The KidsHeart app incorporates two longitudinal survey mechanisms, a red flag monitoring survey and a neurodevelopmental survey. The red flag monitoring survey is a custom-designed single ventricle red flag questionnaire that we developed for remote interstage monitoring. The questionnaire is delivered to parents daily and is scored on a scale from 1 to 30 based on symptom severity (Table 1). The scoring system was designed by our investigative team with input from content experts and sought to evaluate a spectrum of clinical concerns that might indicate early clinical deterioration. Any questionnaire with a red flag event (any score above 0) triggered an alert to the care team. Parents were then contacted directly to further evaluate symptoms and to triage needs. One of the aims of this study was to assess and validate this red flag scoring system’s ability to accurately triage severity of interventions based on the score. The neurodevelopmental survey is the “Survey of Well-being of Young ChildrenTM”, a parent-report instrument for remote neurodevelopment assessment. Reference Sheldrick and Perrin9 The Survey of Well-being of Young Children is a validated survey of neurodevelopmental progression Reference Medicine10 and was chosen for its quality and because it is used in our Primary Paediatric Clinics. The Survey of Well-being of Young ChildrenTM is automatically delivered via the KidsHeart app to parents at 2, 4, 6, 9, 12, and 18 months. In response to an abnormal Survey of Well-being of Young Children survey, families are contacted to schedule more in-depth neurodevelopmental assessment and families are referred to local resources (psychologic, speech, and occupational therapies, etc.) when indicated. Survey of Well-being of Young Children data are summarised in this manuscript as a proof of principle that neurodevelopmental surveys can be delivered remotely to families; however, because comprehensive evaluation of neurodevelopment requires longer-term follow-up, we have deferred more comprehensive analysis of the impact of Survey of Well-being of Young Children surveys on neurodevelopmental outcomes.

Table 1. Red flag scoring system

The KidsHeart app was first tested by a small number of collaborators and then rolled out to patient groups for this initial pilot study with the opportunity to make further adjustments if needed in the future based on findings. For the purposes of this pilot study, parents of children admitted for a birth hospitalisation and fulfilling any of the following diagnostic criteria were eligible for inclusion: (1) any single ventricle heart disease status post stage 1 palliation, (2) biventricular CHD status post ductal stent or surgical shunt, (3) biventricular CHD status post pulmonary artery band, and (4) biventricular CHDs with right ventricular outflow tract stent. Parents were approached for study participation following transition from the paediatric cardiac ICU to our step-down unit. They were given access to the app via the care team and provided digital consent upon downloading the app. Parents were allowed to enter data into the app during the remainder of their in-patient stay to allow for troubleshooting and teaching. Our objectives for this analysis were to report on initial feasibility, participant response rates, and the clinical and research utility of the KidsHeart mHealth platform. We collected demographic data from the app-enrolled study population and assessed both the clinical and longitudinal outcomes tracking utility of red flag monitoring, growth/feeding monitoring, and neurodevelopmental surveys. To assess red flag monitoring, we evaluated four key intervention types collected via review of the electronic medical record: (1) any adjustment to the child’s feeding or medication regimen, (2) any non-elective out-patient or emergency department evaluation, (3) any unplanned admission, and (4) any unplanned admission with cardiac intervention (trans-catheter). With respect to the neurodevelopmental assessments, long-term neurodevelopmental outcomes data are not yet compiled; therefore, analyses focus on the initial feasibility of mHealth neurodevelopmental surveying. All KidsHeart data were prospectively collected, and data presented in this manuscript cover the 18-month period spanning from 04-01-2021 to 09-30-2023, though data collection is ongoing. Study data were downloaded from the on-line tracking platform into a study database and combined with data collected manually from the electronic health record.

Statistical analysis

Descriptive statistics, including n (%), median (interquartile range), and mean (standard deviation) where appropriate, were used to describe cohort characteristics and study outcomes. T-tests were used to compare red flag severity scores by intervention and to evaluate weight gain documented via app versus the electronic medical record. A two-tailed p value <0.05 was considered statistically significant.

Results

Study population

A total of 72 participants (63% male, 41% non-white) consented to study participation. Of these, 3 were excluded from analysis after transitioning care to other centres. Table 2 summarises patient demographic and diagnostic information. Median (interquartile range) length of mHealth follow-up at the time of data lock was 137 days (56, 190) with 100% of enrolled participants submitting some longitudinal follow-up data following birth hospital discharge; 90% (n = 28) of those status post stage II palliation continued to submit data through the time of their stage II hospitalisation.

Table 2. Demographics

IQR = interquartile range.

Remote red flag monitoring and interstage interventions

A total of 5700 red flag surveys were completed (82.6 ± 71.5 per participant) (Figure 2). Of these, 245 represented red flag violations (any score >0). Red flag violations were submitted by 80% (55/69) of enrolled subjects (average 3.0 ± 3.96 red flag violations per participant). Survey scores ranged from 0 to 15 with a median per-patient highest severity score of 4 (interquartile range 2, 5). During the study analytic window, 219 interventions were performed in the study cohort with 53% (116/219) of all interventions initiated following a remote, mHealth red flag monitoring violation. Interventions precipitated by these violations are summarised in Figure 3 and included hospital admission in 34 (29%) with 15 (13%) of the admissions including trans-catheter evaluation. Higher-level interventions (out-patient/emergency department evaluation or hospitalisation/intervention) had significantly higher red flag scores when compared to red flag submission without intervention or with lower-level (medication or feeding change) interventions (3.1 ± 1.4 vs. 4.9 ± 2.8 respectively, p < 0.0001). Our response rates were analysed for those who had completed the interstage period (i.e. had completed their second stage intervention surgery). Daily compliance with app survey response was 65 ± 38 % of daily responses with nearly half of participants responding >70% of the time. There were five deaths (6.9%), all in interstage (pre-stage II surgery) single ventricle patients. Of these five deaths, one was a “classic interstage death” with the patient presenting to the emergency department in extremis with failed resuscitation. This patient had not submitted any red flag surveys in the 7 days preceding death. The remaining four deaths occurred during in-patient re-admissions, all of >2 months duration. None presented in extremis although two of the four were admitted in response to red flag events received via the KidsHeart app.

Figure 2. Number of red flag surveys submitted by participants.

Figure 3. Percent of patients in cohort undergoing intervention by type. (For the purposes of this figure, admissions and admissions with interventions represent mutually exclusive groups; thereby, any admission with a trans-catheter intervention is not also represented within the admission group).

Remote feeding and growth tracking

Growth/feeding data were digitally submitted by 63/69 (91%) patients with a total of 5253 weight data points submitted by all patients (76.1 ± 67.24 submitted weights per patient). Median daily weight gain was 18 g/day (12, 20) and was the same as chart-documented weight gain obtained from clinic visits (but with more frequent monitoring via the app). Daily weight gain was not statistically different for those using the app (n = 63) compared to those that did not (n = 6), although numbers are too small for accurate comparison. Home app-monitoring precipitated 31 feed changes in 23 participants, which included addition or subtraction of fortifiers (formula caloric content, mct oil, etc.), adjustment of feed volumes, and changes in formula.

Remote neurodevelopmental tracking

Neurodevelopmental questionnaires were provided at age-appropriate intervals. At the time of data lock, 68% of app-enrolled subjects with age >2 months had submitted at least one complete developmental questionnaire.

Discussion

We report our experience using the KidsHeart mHealth app to monitor for high-risk “red flag” violations, to track feeding and growth, and to evaluate longitudinal outcomes. In this pilot analysis, patient engagement was good with most patients submitting daily red flag scores and daily weights/feeding data. More than half of all interstage interventions were precipitated by an app-generated red flag notification. In addition, we remotely tracked interstage weight gain and intervened with app-precipitated feeding and nutrition changes in more than a third of all enrolled participants. Finally, mHealth proved a useful means for tracking longitudinal post-discharge outcomes such as neurodevelopmental scores.

Mobile health improves both access to care and quality of care Reference Borges do Nascimento, Abdulazeem and Vasanthan2 and has been shown to offer clinical impact across multiple populations. Reference Borges do Nascimento, Abdulazeem and Vasanthan2,Reference Alhussein and Hadjileontiadis11Reference Webb, Joseph, Yardley and Michie17 MHealth may be especially valuable for rare diseases and conditions, such as children with CHDs, where care is primarily delivered at regional referral centres. An estimated 25% of CHD patients live >100 miles from their surgical centre. Reference Welke, Pasquali and Lin18 Although more intensive monitoring improves outcomes in these patients, Reference Rudd, Ghanayem and Hill7 geographic isolation can be a major impediment. In other paediatric patient populations with complex medical needs, mHealth improves caregiver involvement and our experiences were similar with 100% of enrolled subjects submitting post-discharge data via the app. Reference Bird, Li, Ouellette, Hopkins, McGillion and Carter19

Our data add to a growing body of pilot data demonstrating the value of mHealth technology for interstage monitoring in children with single ventricle heart disease. Reference Blair, Vergales, Peregoy, Seegal and Keim‐Malpass20Reference Kauw, Koole and van Dorth23 The interstage period represents the time between the first and second stage of single ventricle surgical palliation. During this high-risk period, intensive surveillance monitoring significantly decreases the incidence of interstage mortality and other poor outcomes. Reference Anderson, Beekman and Kugler24Reference Staehler, Schaeffer and Wasner29 Our work expands this literature by performing interstage monitoring digitally as well as the addition of longitudinal remote neurodevelopmental tracking. Although we did not rely solely on mHealth interstage monitoring and continued our standard interstage monitoring programmes, over half of our interstage interventions were precipitated by an mHealth alert. These interventions included 34 hospital admissions with 15 of these leading to trans-catheter evaluation. These interventions’ clinical acuity suggests that our mHealth monitoring is not simply leading to increased detection of non-significant concerns. Our red flag monitoring checklist was custom designed for the KidsHeart app. While prior interstage monitoring studies have provided reasons to contact the medical team Reference Hartman, Ebenroth and Farrell22 or a list of action plans, Reference Anderson, Beekman and Kugler24 our red flag daily questionnaires automated the process to facilitate prompt intervention with an associated severity score. Our data validates the checklist with higher scores associated with higher acuity interventions.

Optimal feeding and growth are similarly important during the interstage period in patients with CHD. A meta-analysis including >1,400 pre-stage II single ventricle patients showed, on average, reduction of >1 z-score in both and weight during the interstage period. Reference Van den Eynde, Bartelse and Rijnberg30 Growth concerns adversely impact both neurodevelopment and surgical outcomes, Reference Williams, Zak and Ravishankar31 and the Joint Council on Congenital Heart Disease Quality Improvement Task Force highlighted optimising nutritional status as a key target for high-risk neonates. Reference Kugler, Beekman Iii and Rosenthal32 In our pilot data, weight trajectory was measured reliably compared to EMR-documented growth but with more frequent monitoring, more data points, and automated notifications when weight gain is not meeting goals (20–30 g per day). This allows earlier intervention with more than one third of our patient population receiving feeding adjustments following an app notification. It also allowed for closer weight and feeding monitoring during the formula shortage.

Finally, our pilot data demonstrate a proof of principle that mHealth can function as a useful means for tracking longitudinal post-discharge outcomes. This is an unmet need in CHD care which has historically relied on robust in-patient registries yet lacks data on the longer-term post-discharge outcomes that matter most to families. In our analysis, 100% of KidsHeart app study participants submitted post-discharge data, thereby verifying vital status and providing information on important longitudinal outcomes such as growth and neurodevelopment. Our KidsHeart app incorporates a unique patient identifier that, in theory, can be used to link back to existing registry data, thereby providing a rich data source for longitudinal outcomes analyses.

There are important limitations to this analysis. As a pilot study, the sample size is small and overall duration of app usage relatively low. Our data was not intended to be directly compared to current standard of care but rather to augment existing care models. Moreover, we have limited data on longer-term neurodevelopment and growth outcomes which are needed to accurately validate the utility of mHealth neurodevelopmental surveys and feeding interventions. The KidsHeart app will continue to enrol permitting these analyses to be completed in the future. While mHealth offers an opportunity for equity of access to health resources in remote communities, there are limitations to this as well, including socio-economic barriers that may impact feasibility. Reference Wali, Remtulla Tharani and Balmer-Minnes33

In conclusion, the KidsHeart app pilot data demonstrates the utility of mHealth in CHD population and augments the clinical benefits of remote monitoring with success in red flag monitoring and subsequent implementation of interventions, growth monitoring, and longitudinal outcomes assessment. In terms of future directions, we aim to collect more robust and longer-term neurodevelopmental outcomes and to validate these data with standardised neurodevelopmental assessments. We intend to assess socio-economic barriers to mHealth monitoring and to develop a linkage methodology allowing mHealth longitudinal outcomes to be linked back to existing registry data. Accomplishing these goals will extend the value of mHealth technology by supporting research as well as clinical care.

Supplementary material

The supplementary material for this article can be found at https://doi.org/10.1017/S1047951124026222.

Acknowledgements

We would like to thank Stefany Olague for her project leadership.

Financial support

Supported by a research grant from Duke Clinical Research Institute Innovation Campfire.

Competing interests

Author 1 declares no competing interests.

Author 2 declares no competing interests.

Author 3 declares no competing interests.

Author 4 declares no competing interests.

Author 5 declares no competing interests.

Author 6 declares no competing interests.

Author 7, Kevin Hill, is a consultant for Actelion Pharmaceuticals.

References

WHO. Global Strategy on Digital Health 2020–2025. World Health Organization, Geneva, 2021.Google Scholar
Borges do Nascimento, I, Abdulazeem, HM, Vasanthan, LT et al. The global effect of digital health technologies on health workers’ competencies and health workplace: an umbrella review of systematic reviews and lexical-based and sentence-based meta-analysis. Lancet Digit Health 2023; 5:e534e544.CrossRefGoogle ScholarPubMed
Jacobs, JP, Wernovsky, G, Elliott, MJ. Analysis of outcomes for congenital cardiac disease: can we do better?. Cardiol Young 2007; 17:145158.CrossRefGoogle ScholarPubMed
Bingler, M, Erickson, LA, Reid, KJ et al. Interstage outcomes in infants with single ventricle heart disease comparing home monitoring technology to three-ring binder documentation: a randomized crossover study. World J Pediatr Congenit Heart Surg 2018; 9:305314.CrossRefGoogle ScholarPubMed
Cross, R, Steury, R, Randall, A, Fuska, M, Sable, C. Single-ventricle palliation for high-risk neonates: examining the feasibility of an automated home monitoring system after stage I palliation. Future Cardiol 2012; 8:227235.CrossRefGoogle ScholarPubMed
Foster, CC, Steltzer, M, Snyder, A et al. Integrated multimodality telemedicine to enhance in-home care of infants during the interstage period. Pediatr Cardiol 2021; 42:349360.CrossRefGoogle ScholarPubMed
Rudd, NA, Ghanayem, NS, Hill, GD et al. Interstage home monitoring for infants with single ventricle heart disease: education and management: a scientific statement from the American Heart Association. J Am Heart Assoc 2020; 9: e014548,CrossRefGoogle ScholarPubMed
Vergales, J, Peregoy, L, Zalewski, J, Plummer, ST. Use of a digital monitoring platform to improve outcomes in infants with a single ventricle. World J Pediatr Congenit Heart Surg 2020; 11: 753759.CrossRefGoogle ScholarPubMed
Sheldrick, RC, Perrin, EC. Evidence-based milestones for surveillance of cognitive, language, and motor development. Acad Pediatr 2013; 13: 577586.CrossRefGoogle ScholarPubMed
Alhussein, G, Hadjileontiadis, L. Digital health technologies for long-term self-management of osteoporosis: systematic review and meta-analysis. JMIR Mhealth Uhealth 2022; 10: e32557 CrossRefGoogle ScholarPubMed
Liu, S, Feng, W, Chhatbar, PY, Liu, Y, Ji, X, Ovbiagele, B. Mobile health as a viable strategy to enhance stroke risk factor control: a systematic review and meta-analysis. J Neurol Sci 2017; 378: 140145.CrossRefGoogle ScholarPubMed
Martinez-Millana, A, Zettl, A, Floch, J et al. The potential of self-management mHealth for pediatric cystic fibrosis: mixed-methods study for health care and app assessment. JMIR Mhealth Uhealth 2019; 7: e13362.CrossRefGoogle Scholar
Mitsuya, M., et al., An mHealth App for the Non-contact Measurement of Pulmonary Function Using the Smartphone’s Built-in Depth Sensor. Annu Int Conf IEEE Eng Med Biol Soc, 2022 : p. 3357-3360.CrossRefGoogle Scholar
Schliemann, D, Tan, MM, Hoe, WMK et al mHealth Interventions to Improve Cancer Screening and Early Detection: Scoping Review of Reviews.. J Med Internet Res 2022; 24: e36316.CrossRefGoogle ScholarPubMed
Tripoliti, EE, Karanasiou, GS, Kalatzis, FG, Naka, KK, Fotiadis, DI. The evolution of mHealth solutions for heart failure management. Adv Exp Med Biol 2018; 1067: 353371.CrossRefGoogle ScholarPubMed
Webb, TL, Joseph, J, Yardley, L, Michie, S. Using the internet to promote health behavior change: a systematic review and meta-analysis of the impact of theoretical basis, use of behavior change techniques, and mode of delivery on efficacy. J Med Internet Res 2010; 12 : e4.CrossRefGoogle Scholar
Welke, KF, Pasquali, SK, Lin, P et al. Hospital distribution and patient travel patterns for congenital cardiac surgery in the United States. Ann Thorac Surg 2019; 107 :574581.CrossRefGoogle ScholarPubMed
Bird, M, Li, L, Ouellette, C, Hopkins, K, McGillion, MH, Carter, N. Use of synchronous digital health technologies for the care of children with special health care needs and their families: scoping review. JMIR Pediatr Parent 2019; 2 e15106.CrossRefGoogle ScholarPubMed
Blair, L, Vergales, J, Peregoy, L, Seegal, H, Keim‐Malpass, J. Acceptability of an interstage home monitoring mobile application for caregivers of children with single ventricle physiology: toward technology-integrated family management. J Spec Pediatr Nurs 2022; 27: e12372.CrossRefGoogle ScholarPubMed
Erickson, LA, Emerson, A, Russell, CL. Parental mobile health adherence to symptom home monitoring for infants with congenital heart disease during the single ventricle interstage period: a concept analysis. J Spec Pediatr Nurs 2020; 25: e12303.CrossRefGoogle ScholarPubMed
Hartman, D, Ebenroth, E, Farrell, A. Utilizing technology to expand home monitoring to high-risk infants with CHD. Cardiol Young 2023; 33 : 11241128.CrossRefGoogle ScholarPubMed
Kauw, D, Koole, MAC, van Dorth, JR et al. eHealth in patients with congenital heart disease: a review. Expert Rev Cardiovasc Ther 2018;16 : 627634.CrossRefGoogle ScholarPubMed
Anderson, JB, Beekman, RH, Kugler, JD et al. Improvement in interstage survival in a national pediatric cardiology learning network. Circ Cardiovasc Qual Outcomes 2015; 8 : 428436.CrossRefGoogle Scholar
Castellanos, DA, Herrington, C, Adler, S, Haas, K, Ram Kumar, S, Kung, GC. Home monitoring program reduces mortality in high-risk sociodemographic single-ventricle patients. Pediatr Cardiol 2016; 37 : 15751580.CrossRefGoogle ScholarPubMed
Ghanayem, NS, Hoffman, GM, Mussatto, KA et al. Home surveillance program prevents interstage mortality after the Norwood procedure. J Thorac Cardiovasc Surg 2003; 126 :13671377.CrossRefGoogle ScholarPubMed
Hehir, DA, Ghanayem, NS. Single-ventricle infant home monitoring programs: outcomes and impact. Curr Opin Cardiol 2013; 28 :97102.CrossRefGoogle ScholarPubMed
Oster, ME, Ehrlich, A, King, E et al. Association of interstage home monitoring with mortality, readmissions, and weight gain: a multicenter study from the national pediatric cardiology quality improvement collaborative. Circulation 2015; 132 :502508.CrossRefGoogle ScholarPubMed
Staehler, H, Schaeffer, T, Wasner, J et al. Impact of home monitoring program on interstage mortality after the norwood procedure. Front Cardiovasc Med 2023; 10: 123947,CrossRefGoogle ScholarPubMed
Van den Eynde, J, Bartelse, S, Rijnberg, FM et al. Somatic growth in single ventricle patients: a systematic review and meta-analysis. Acta Paediatr 2023; 112:, 186199.CrossRefGoogle ScholarPubMed
Williams, RV, Zak, V, Ravishankar, C et al. Factors affecting growth in infants with single ventricle physiology: a report from the pediatric heart network infant single ventricle trial. J Pediatr 2011; 159 :10171022.e2.CrossRefGoogle ScholarPubMed
Kugler, JD, Beekman Iii, RH, Rosenthal, GL et al . Development of a pediatric cardiology quality improvement collaborative: from inception to implementation. From the Joint Council on Congenital Heart Disease Quality Improvement Task Force.. Congenit Heart Dis 2009; 4 :318328.CrossRefGoogle ScholarPubMed
Wali, S, Remtulla Tharani, A, Balmer-Minnes, D et al. Exploring the use of a digital therapeutic intervention to support the pediatric cardiac care journey: qualitative study on clinician perspectives. PLOS Digit Health 2023; 2 : e0000371.CrossRefGoogle ScholarPubMed
Figure 0

Figure 1. Images of KidsHeart app interface.

Figure 1

Table 1. Red flag scoring system

Figure 2

Table 2. Demographics

Figure 3

Figure 2. Number of red flag surveys submitted by participants.

Figure 4

Figure 3. Percent of patients in cohort undergoing intervention by type. (For the purposes of this figure, admissions and admissions with interventions represent mutually exclusive groups; thereby, any admission with a trans-catheter intervention is not also represented within the admission group).

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