Trial reportJMIR mHealth and uHealth2019
Impact of Personal Health Records and Wearables on Health Outcomes and Patient Response: Three-Arm Randomized Controlled Trial.
Trial report in JMIR mHealth and uHealth, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03200223 (A Study of the Management of the Patient's Disease Based on the Life Log Data of Patient With Lifestyle-related Diseases Through a Wearable Device and Mobile App), which is not on this map. Cited by 39 papers, 6 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
A Study of the Management of the Patient's Disease Based on the Life Log Data of Patient With Lifestyle-related Diseases Through a Wearable Device and Mobile App
Who cites it
39 citing papers in PubMed, 6 syntheses or guidelines pooled it.
- From passive monitoring to active engagement: a systematic review and meta-analysis of digital health technologies for improving objective physical activity and cardiorespiratory fitness in patients with obstructive sleep apnea.Frontiers in public health · 2026Pooled it
- Patient-Centered Digital Health Records and Their Effects on Health Outcomes: Systematic Review.Journal of medical Internet research · 2022Pooled it
- The Influence of Wearables on Health Care Outcomes in Chronic Disease: Systematic Review.Journal of medical Internet research · 2022Pooled it
- The Effectiveness of Wearable Devices as Physical Activity Interventions for Preventing and Treating Obesity in Children and Adolescents: Systematic Review and Meta-analysis.JMIR mHealth and uHealth · 2022Pooled it
- Remote Monitoring Systems for Patients With Chronic Diseases in Primary Health Care: Systematic Review.JMIR mHealth and uHealth · 2021Pooled it
- Health wearable devices for weight and BMI reduction in individuals with overweight/obesity and chronic comorbidities: systematic review and network meta-analysis.British journal of sports medicine · 2021Pooled it
- Integrating Personal Health Records to Improve Data Integrity in Trials With Decentralized Elements: A Pilot Study.Clinical and translational science · 2025Trial
- Guidelines for rigor and reproducibility of heart rate variability within human cardiovascular research.American journal of physiology. Heart and circulatory physiology · 2026Review
- Using Health Information Systems to Support Behavioral Interventions in Local Contexts: Scoping Review.JMIR medical informatics · 2026Review
- AI-Driven Wearable Bioelectronics in Digital Healthcare.Biosensors · 2025Review
- Integrating Mobile Health App Data Into Electronic Medical or Health Record Systems and Its Impact on Health Care Delivery and Patient Health Outcomes: Scoping Review.JMIR mHealth and uHealth · 2025Article
- Progress in the management of type 2 diabetes mellitus: a narrative review of telerehabilitation and wearable devices.Annals of pediatric endocrinology & metabolism · 2025Article
- Comparative Effectiveness of Wearable Devices and Built-In Step Counters in Reducing Metabolic Syndrome Risk in South Korea: Population-Based Cohort Study.JMIR mHealth and uHealth · 2025Article
- Participant Contributions to Person-Generated Health Data Research Using Mobile Devices: Scoping Review.Journal of medical Internet research · 2025Article
- NurseVerse: nursing in the metaverse era.Frontiers in medicine · 2025Review
- A Systematic Review of Internet-Based Remote Patient Monitoring Systems for Chronic Disease Management in Asian.Patient preference and adherence · 2025Review
- The Role of Wearable Devices in Chronic Disease Monitoring and Patient Care: A Comprehensive Review.Cureus · 2024Review
- Using explainable machine learning and fitbit data to investigate predictors of adolescent obesity.Scientific reports · 2024Article
- A machine learning approach using conditional normalizing flow to address extreme class imbalance problems in personal health records.BioData mining · 2024Article
- The use of internet-based smartphone apps consistently improved consumers' healthy eating behaviors: a systematic review of randomized controlled trials.Frontiers in digital health · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAlthough using the technologies for a variety of chronic health conditions such as personal health record (PHR) is reported to be acceptable and useful, there is a lack of evidence on the associations between the use of the technologies and the change of health outcome and patients' response to a digital health app.
objectiveThis study aimed to examine the impact of the use of PHR and wearables on health outcome improvement and sustained use of the health app that can be associated with patient engagement.
methodsWe developed an Android-based mobile phone app and used a wristband-type activity tracker (Samsung Charm) to collect data on health-related daily activities from individual patients. Dietary record, daily step counts, sleep log, subjective stress amount, blood pressure, and weight values were recorded. We conducted a prospective randomized clinical trial across 4 weeks on those diagnosed with obstructive sleep apnea (OSA) who had visited the outpatient clinic of Seoul National University Bundang Hospital. The trial randomly assigned 60 patients to 3 subgroups including 2 intervention groups: (1) mobile app and wearable device users (n=20), (2) mobile app-only users (n=20), and (3) controls (n=20). The primary outcome measure was weight change. Body weights before and after the trial were recorded and analyzed during clinic visits. Changes in OSA-related respiratory parameters such as respiratory disturbance, apnea-hypopnea, and oxygenation desaturation indexes and snoring comprised the secondary outcome and were analyzed for each participant.
resultsWe collected the individual data for each group during the trial, specifically anthropometric measurement and laboratory test results for health outcomes, and the app usage logs for patient response were collected and analyzed. The body weight showed a significant reduction in the 2 intervention groups after intervention, and the mobile app-only group showed more weight loss compared with the controls (P=.01). There were no significant changes in sleep-related health outcomes. From a patient response point of view, the average daily step counts (8165 steps) from the app plus wearable group were significantly higher than those (6034 steps) from the app-only group because they collected step count data from different devices (P=.02). The average rate of data collection was not different in physical activity (P=.99), food intake (P=.98), sleep (P=.95), stress (P=.70), and weight (P=.90) in the app plus wearable and app-only groups, respectively.
conclusionsWe tried to integrate PHR data that allow clinicians and patients to share lifelog data with the clinical workflow to support lifestyle interventions. Our results suggest that a PHR-based intervention may be successful in losing body weight and improvement in lifestyle behavior.
trial registrationClinicalTrials.gov NCT03200223; https://clinicaltrials.gov/ct2/show/NCT03200223 (Archived by WebCite at http://www.webcitation.org/74baZmnCX).
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.