Trial reportJMIR mHealth and uHealth2020
Use of a Smartphone-Based Mobile App for Weight Management in Obese Minority Stroke Survivors: Pilot Randomized Controlled Trial With Open Blinded End Point.
Trial report in JMIR mHealth and uHealth, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT02531074 (Swipe Out Stroke), which is not on this map. Cited by 27 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.
Swipe Out Stroke (SOS): Feasibility of Using a Consumer Based Electronic Application to Improve Compliance With Weight Loss in Obese Minority Stroke Patients
Who cites it
27 citing papers in PubMed, 6 syntheses or guidelines pooled it, 41 citations in OpenAlex.
- Digital Health Technologies for Risk Factor Management and Secondary Prevention After Stroke: A Systematic Review and Meta-Analysis.Neurology. Clinical practice · 2026Pooled it
- Effectiveness of Digital Lifestyle Interventions on Depression, Anxiety, Stress, and Well-Being: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Health-promotion interventions targeting multiple behaviors: A meta-analytic review of general and behavior-specific processes of change.Psychological bulletin · 2024Pooled it
- Effectiveness of mobile application interventions for stroke survivors: systematic review and meta-analysis.BMC medical informatics and decision making · 2024Pooled it
- Mobile App Interventions for Parkinson's Disease, Multiple Sclerosis and Stroke: A Systematic Literature Review.Sensors (Basel, Switzerland) · 2023Pooled it
- Factors Influencing Adherence to mHealth Apps for Prevention or Management of Noncommunicable Diseases: Systematic Review.Journal of medical Internet research · 2022Pooled it
- FisioCare Neurogroup: A new application for home monitoring of individuals with neurological diseases.Hong Kong physiotherapy journal : official publication of the Hong Kong Physiotherapy Association Limited = Wu li chih liao · 2026Article
- Weight Management Post-Stroke: A Scoping Review.American journal of lifestyle medicine · 2026Review
- Chinese Version of the mHealth App Usability Questionnaire for Health Care Providers: Cross-Sectional Translation and Validation Study.JMIR mHealth and uHealth · 2026Article
- Dietary and lifestyle interventions for overweight and obesity in post-acute stroke rehabilitation: a scoping review.BMJ open · 2026Article
- Mobile Health Interventions Across the Stroke Care Continuum: A Scoping Review.Journal of clinical medicine · 2026Review
- A mini-program for high-risk of stroke population management using the intervention mapping approach: design, development process, and usability study.BMC public health · 2026Article
- Calorie-Counting Apps for Monitoring and Managing Calorie Intake in Adults Living With Weight-Related Chronic Diseases: Decade-Long Scoping Review (2013-2024).JMIR mHealth and uHealth · 2026Article
- Weathering the storm: Effect of climate change on acute stroke care and stroke rehabilitation.PM & R : the journal of injury, function, and rehabilitation · 2026Review
- Introducing and Validating a Minimum Data Set and Core Functionalities for Remote Poststroke Home Monitoring: "A Cross-Sectional Study".Health science reports · 2026Article
- Current Status of Information and Communication Technologies Utilization, Education Needs, Mobile Health Literacy, and Self-Care Education Needs of a Population of Stroke Patients.Healthcare (Basel, Switzerland) · 2025Article
- Evaluation of Intermittent Restricted Eating Using the Interval Weight Loss Online Platform in an Everyday Setting.Nutrients · 2025Article
- Review
- 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
- Mobile Technology-Based Interventions for Stroke Self-Management Support: Scoping Review.JMIR mHealth and uHealth · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 4 institutions in 1 country.
Funding
Abstract
backgroundMinorities have an increased incidence of early-onset, obesity-related cerebrovascular disease. Unfortunately, effective weight management in this vulnerable population has significant barriers.
objectiveOur objective was to determine the feasibility and preliminary treatment effects of a smartphone-based weight loss intervention versus food journals to monitor dietary patterns in minority stroke patients.
methodsSwipe out Stroke was a pilot prospective randomized controlled trial with open blinded end point. Minority stroke patients and their caregivers were screened for participation using cluster enrollment. We used adaptive randomization for assignment to a behavior intervention with (1) smartphone-based self-monitoring or (2) food journal self-monitoring. The smartphone group used Lose it! to record meals and communicate with us. Reminder messages (first 30 days), weekly summaries plus reminder messages on missed days (days 31-90), and weekly summaries only (days 91-180) were sent via push notifications. The food journal group used paper diaries. Both groups received 4 in-person visits (baseline and 30, 90, and 180 days), culturally competent counseling, and educational materials. The primary outcome was reduced total body weight.
resultsWe enrolled 36 stroke patients (n=23, 64% African American; n=13, 36% Hispanic), 17 in the smartphone group, and 19 in the food journal group. Mean age was 54 (SD 9) years; mean body mass index was 35.7 (SD 5.7) kg/m2; education, employment status, and family history of stroke or obesity did not differ between the groups. Baseline rates of depression (Patient Health Questionnaire-9 [PHQ-9] score median 5.5, IQR 3.0-9.5), cognitive impairment (Montreal Cognitive Assessment score median 23.5, IQR 21-26), and inability to ambulate (5/36, 14% with modified Rankin Scale score 3) were similar. In total, 25 (69%) stroke survivors completed Swipe out Stroke (13/17 in the smartphone group, 12/19 in the food journal group); 1 participant in the smartphone group died. Median weight change at 180 days was 5.7 lb (IQR -2.4 to 8.0) in the smartphone group versus 6.4 lb (IQR -2.2 to 12.5; P=.77) in the food journal group. Depression was significantly lower at 30 days in the smartphone group than in the food journal group (PHQ-9 score 2 vs 8; P=.03). Clinically relevant depression rates remained in the zero to minimal range for the smartphone group compared with mild to moderate range in the food journal group at day 90 (PHQ-9 score 3.5 vs 4.5; P=.39) and day 180 (PHQ-9 score 3 vs 6; P=.12).
conclusionsIn a population of obese minority stroke survivors, the use of a smartphone did not lead to a significant difference in weight change compared with keeping a food journal. The presence of baseline depression (19/36, 53%) was a confounding variable, which improved with app engagement. Future studies that include treatment of poststroke depression may positively influence intervention efficacy.
trial registrationClinicalTrials.gov NCT02531074; https://www.clinicaltrials.gov/ct2/show/NCT02531074.
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Registered trials
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.