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Passively Captured Interpersonal Social Interactions and Motion From Smartphones for Predicting Decompensation in Heart Failure: Observational Cohort Study

التفاصيل البيبلوغرافية
العنوان: Passively Captured Interpersonal Social Interactions and Motion From Smartphones for Predicting Decompensation in Heart Failure: Observational Cohort Study
المؤلفون: Ayse S Cakmak, Erick A Perez Alday, Samuel Densen, Gabriel Najarro, Pratik Rout, Christopher J Rozell, Omer T Inan, Amit J Shah, Gari D Clifford
المصدر: JMIR Formative Research, Vol 6, Iss 8, p e36972 (2022)
بيانات النشر: JMIR Publications, 2022.
سنة النشر: 2022
المجموعة: LCC:Medicine
مصطلحات موضوعية: Medicine
الوصف: BackgroundHeart failure (HF) is a major cause of frequent hospitalization and death. Early detection of HF symptoms using smartphone-based monitoring may reduce adverse events in a low-cost, scalable way. ObjectiveWe examined the relationship of HF decompensation events with smartphone-based features derived from passively and actively acquired data. MethodsThis was a prospective cohort study in which we monitored HF participants’ social and movement activities using a smartphone app and followed them for clinical events via phone and chart review and classified the encounters as compensated or decompensated by reviewing the provider notes in detail. We extracted motion, location, and social interaction passive features and self-reported quality of life weekly (active) with the short Kansas City Cardiomyopathy Questionnaire (KCCQ-12) survey. We developed and validated an algorithm for classifying decompensated versus compensated clinical encounters (hospitalizations or clinic visits). We evaluated models based on single modality as well as early and late fusion approaches combining patient-reported outcomes and passive smartphone data. We used Shapley additive explanation values to quantify the contribution and impact of each feature to the model. ResultsWe evaluated 28 participants with a mean age of 67 years (SD 8), among whom 11% (3/28) were female and 46% (13/28) were Black. We identified 62 compensated and 48 decompensated clinical events from 24 and 22 participants, respectively. The highest area under the precision-recall curve (AUCPr) for classifying decompensation was with a late fusion approach combining KCCQ-12, motion, and social contact features using leave-one-subject-out cross-validation for a 2-day prediction window. It had an AUCPr of 0.80, with an area under the receiver operator curve (AUC) of 0.83, a positive predictive value (PPV) of 0.73, a sensitivity of 0.77, and a specificity of 0.88 for a 2-day prediction window. Similarly, the 4-day window model had an AUC of 0.82, an AUCPr of 0.69, a PPV of 0.62, a sensitivity of 0.68, and a specificity of 0.87. Passive social data provided some of the most informative features, with fewer calls of longer duration associating with a higher probability of future HF decompensation. ConclusionsSmartphone-based data that includes both passive monitoring and actively collected surveys may provide important behavioral and functional health information on HF status in advance of clinical visits. This proof-of-concept study, although small, offers important insight into the social and behavioral determinants of health and the feasibility of using smartphone-based monitoring in this population. Our strong results are comparable to those of more active and expensive monitoring approaches, and underscore the need for larger studies to understand the clinical significance of this monitoring method.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 2561-326X
88537854
Relation: https://formative.jmir.org/2022/8/e36972; https://doaj.org/toc/2561-326X
DOI: 10.2196/36972
URL الوصول: https://doaj.org/article/d146f88537854a38936102f09af6f44e
رقم الأكسشن: edsdoj.146f88537854a38936102f09af6f44e
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:2561326X
88537854
DOI:10.2196/36972