Using Passive Smartphone Sensing for Improved Risk Stratification of Patients With Depression and Diabetes: Cross-Sectional Observational Study
| العنوان: | Using Passive Smartphone Sensing for Improved Risk Stratification of Patients With Depression and Diabetes: Cross-Sectional Observational Study |
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| المؤلفون: | Suresh Munuswamy, Shubhankar Sarda, Vinod Subramanian, Archana Sarda |
| المصدر: | JMIR mHealth and uHealth JMIR mHealth and uHealth, Vol 7, Iss 1, p e11041 (2019) |
| بيانات النشر: | JMIR Publications, 2019. |
| سنة النشر: | 2019 |
| مصطلحات موضوعية: | Adult, Male, medicine.medical_specialty, Health Informatics, Information technology, smartphone, law.invention, Randomized controlled trial, Diabetes management, law, Diabetes mellitus, Surveys and Questionnaires, medicine, Diabetes Mellitus, Humans, Depression (differential diagnoses), Univariate analysis, Original Paper, diabetes, business.industry, Depression, risk assessment, Middle Aged, medicine.disease, T58.5-58.64, Comorbidity, Mental health, Mobile Applications, comorbidity, passive sensing, machine learning, Cross-Sectional Studies, classification, mHealth, Physical therapy, Observational study, Female, Public aspects of medicine, RA1-1270, business, mental health |
| الوصف: | BackgroundResearch studies are establishing the use of smartphone sensing to measure mental well-being. Smartphone sensor information captures behavioral patterns, and its analysis helps reveal well-being changes. Depression in diabetes goes highly underdiagnosed and underreported. The comorbidity has been associated with increased mortality and worse clinical outcomes, including poor glycemic control and self-management. Clinical-only intervention has been found to have a very modest effect on diabetes management among people with depression. Smartphone technologies could play a significant role in complementing comorbid care. ObjectiveThis study aimed to analyze the association between smartphone-sensing parameters and symptoms of depression and to explore an approach to risk-stratify people with diabetes. MethodsA cross-sectional observational study (Project SHADO—Analyzing Social and Health Attributes through Daily Digital Observation) was conducted on 47 participants with diabetes. The study’s smartphone-sensing app passively collected data regarding activity, mobility, sleep, and communication from each participant. Self-reported symptoms of depression using a validated Patient Health Questionnaire-9 (PHQ-9) were collected once every 2 weeks from all participants. A descriptive analysis was performed to understand the representation of the participants. A univariate analysis was performed on each derived sensing variable to compare behavioral changes between depression states—those with self-reported major depression (PHQ-9>9) and those with none (PHQ-9≤9). A classification predictive modeling, using supervised machine-learning methods, was explored using derived sensing variables as input to construct and compare classifiers that could risk-stratify people with diabetes based on symptoms of depression. ResultsA noticeably high prevalence of self-reported depression (30 out of 47 participants, 63%) was found among the participants. Between depression states, a significant difference was found for average activity rates (daytime) between participant-day instances with symptoms of major depression (mean 16.06 [SD 14.90]) and those with none (mean 18.79 [SD 16.72]), P=.005. For average number of people called (calls made and received), a significant difference was found between participant-day instances with symptoms of major depression (mean 5.08 [SD 3.83]) and those with none (mean 8.59 [SD 7.05]), P |
| اللغة: | English |
| تدمد: | 2291-5222 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9943083419bb103a1336132d3d520b81 http://europepmc.org/articles/PMC6371066 |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....9943083419bb103a1336132d3d520b81 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 22915222 |
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