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Background: Collecting data on daily habits across a population of individuals is challenging. Mobile-based circadian ecological momentary assessment (cEMA) is a powerful frame for observing the impact of daily living on long-term health.
Objective: In this paper, we (1) describe the design, testing, and rationale for specifications of a mobile-based cEMA app to collect timing of eating and sleeping data and (2) compare cEMA and survey data collected as part of a 6-month observational cohort study. The ultimate goal of this paper is to summarize our experience and lessons learned with the Daily24 mobile app and to highlight the pros and cons of this data collection modality.
Methods: Design specifications for the Daily24 app were drafted by the study team based on the research questions and target audience for the cohort study. The associated backend was optimized to provide real-time data to the study team for participant monitoring and engagement. An external 8-member advisory board was consulted throughout the development process, and additional test users recruited as part of a qualitative study provided feedback through in-depth interviews.
Results: After ≥4 days of at-home use, 37 qualitative study participants provided feedback on the app. The app generally received positive feedback from test users for being fast and easy to use. Test users identified several bugs and areas where modifications were necessary to in-app text and instructions and also provided feedback on the engagement strategy. Data collected through the mobile app captured more variability in eating windows than data collected through a one-time survey, though at a significant cost.
Conclusions: Researchers should consider the potential uses of a mobile app beyond the initial data collection when deciding whether the time and monetary expenditure are advisable for their situation and goals.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8367152 | PMC |
http://dx.doi.org/10.2196/26297 | DOI Listing |
ACS Sens
September 2025
Institute of Applied Mechanics, National Taiwan University, Taipei 106, Taiwan.
In recent AI-driven disease diagnosis, the success of models has depended mainly on extensive data sets and advanced algorithms. However, creating traditional data sets for rare or emerging diseases presents significant challenges. To address this issue, this study introduces a direct-self-attention Wasserstein generative adversarial network (DSAWGAN) designed to improve diagnostic capabilities in infectious diseases with limited data availability.
View Article and Find Full Text PDFJ Orthop Res
September 2025
Department of Kinesiology, College of Health Sciences, University of Rhode Island, Kingston, Rhode Island, USA.
Arthroplasty surgery is a common and successful end-stage intervention for advanced osteoarthritis. Yet, postoperative outcomes vary significantly among patients, leading to a plethora of measures and associated measurement approaches to monitor patient outcomes. Traditional approaches rely heavily on patient-reported outcome measures (PROMs), which are widely used, but often lack sensitivity to detect function changes (e.
View Article and Find Full Text PDFBMC Health Serv Res
September 2025
Center for Health Services Research, Brandenburg Medical School Theodor Fontane, Seebad 82/83, Rüdersdorf, 15562, Germany.
Background: Hypertension remains a critical public health issue in Germany, affecting millions of individuals. Mobile health applications (mHealth apps) offer promising solutions for improving patient outcomes and adherence in hypertension management. Despite their advantages in healthcare, the adoption of mHealth apps by general practitioners (GPs) in Germany remains limited to date.
View Article and Find Full Text PDFJMIR Form Res
September 2025
Department of Medicine, Duke Global Health Institute, Duke University, Durham, NC, United States.
Background: The global penetration of mobile phones has offered novel opportunities for communicating health-related information to individuals. A low-cost system that facilitates autonomous communication with individuals via mobile phones holds potential for expanding the reach of health messaging in settings with human resource and infrastructure limitations.
Objective: We sought to design a flexible, low-code system using open-source software that could be adapted to different contexts and technical environments and accommodate a wide range of automation needs.
Neural Netw
September 2025
Department of Computer Science and Engineering, Hanyang University ERICA, 15588, South Korea. Electronic address:
In 6G mobile communication systems, various AI-based network functions and applications have been standardized. Federated learning (FL) is adopted as the core learning architecture for 6G systems to avoid privacy leakage from mobile user data. However, in FL, users with non-independent and identically distributed (non-IID) datasets can deteriorate the performance of the global model because the convergence direction of the gradient for each dataset is different, thereby inducing a weight divergence problem.
View Article and Find Full Text PDF