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| Name | Class |
|---|---|
| Merck Sharp & Dohme LLC | INDUSTRY |
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This study tests whether using a health app (Huawei Health) and a smart body fat scale can help overweight patients with schizophrenia or bipolar disorder lose weight and stay engaged in their health.
What We're Testing:
How It Works:
Patients weigh themselves weekly with the scale (auto-syncs to the app) and upload dietary log in Huawei Health app. The app will gives personalized diet/exercise tips and tracks progress. Doctors and nutritionists provide extra support through messages.
Goal:
To see if this digital tool + professional support combo works better for long-term weight management.
The investigators will recruit the patients diagnosed with schizophrenia or bipolar disorder from Beijing Anding Hospital. Participants will use a mobile phone app (Huawei Health) to collect data on daily activities and calorie consumption. The smart body fat scale with high-precision weighing chip (Huawei Scale 2pro) will be used to collect heart rate, weight, BMI, body type, basal metabolic rate, fat rate, fat free body weight, skeletal muscle mass, bone salt content, visceral fat grade, body water (%), body protein rate and body composition, and all data will be uploaded to the app. Participants could also record their daily dietary intake (for calculation of calorie intake) in the health app. This is a 6-month, single-center, stepped wedge-shaped cluster randomized study. It is planned to recruit 204 overweight subjects from 6 units from Beijing Anding Hospital. The six clinical units comprise four inpatient wards, one day rehabilitation unit, and one outpatient department. All units (clusters) were randomly allocated to two batches (3 units each) using a computer-generated sequence. Batch 1 received the intervention from Month 3 to Month 6, while Batch 2 started from Month 5 to Month 6 (total study duration: 6 months). All units underwent baseline observation during Months 1-2, followed by a 2-week transition period for training. All clusters follow usual care prior to their assigned intervention period, with months 1-2 serving as baseline control and months 7-8 for full-intervention observation across all units. Each unit operates as an independent intervention cluster with dedicated staff teams and unit-specific WeChat-based communication groups. Intervention materials, reinforcement messages, counseling content, and technical support are synchronized with each cluster's assigned timeline. Clinical staff will be trained on intervention delivery, digital tool usage, and adherence protocols and are required to adhere strictly to the scheduled rollout sequence. Monitoring and supervision mechanisms will be implemented to track adherence, engagement, and protocol compliance in real-time. Each cluster undergoes a two-week pre-implementation transition period for device distribution, app installation, and training. The intervention combines self-weighing using using smart body fat scale, dietary logging, exercise management, and behavioral reinforcement. Behavioral reinforcement includes weekly personalized feedback messages and real-time alerts for missed self-monitoring via WeChat messages. We assess clinical, functional, and subjective outcomes at baseline and monthly intervals. Socio-demographic and clinical characteristics (age, sex, education, income, medical history) are extracted from EHRs. Body composition metrics (BMI, body fat percentage, etc.) are automatically recorded during weigh-ins. Subjective outcomes will be collected using validated scales at Month 1,2,3 and 6. Smart scale pairing success rate and weekly weigh-in adherence will be used to assess protocol feasibility. To capture user perspectives on mobile app and weighing smart scale, we will conduct 30-minute semi-structured interviews with 30 purposely selected participants (stratified by adherence and diagnosis), exploring app usability, behavioral impacts, and improvement suggestions.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Batch 1 (Intervention from Month 3) | Experimental | 3 clinical units (clusters) receiving the combined digital and multidisciplinary intervention starting at Month 3. Each unit includes approximately 34 patients (total n=102) with schizophrenia or bipolar disorder. |
|
| Batch 2 (Intervention from Month 5) | Experimental | 3 clinical units (clusters) receiving the same intervention starting at Month 5. Each unit includes approximately 34 patients (total n=102) with schizophrenia or bipolar disorder. |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Effectiveness and Feasibility of Self-Monitoring for Weight Management in Individuals with Mental Disorders Using Digital Intervention ("SWIM" trial) | Device | Participants receive a digital-behavioral intervention via Huawei Health App and smart scale:
Implementation: Staggered rollout: Batch 1 (Month 3-6), Batch 2 (Month 5-6). Includes 2-week training. Effectiveness monitored via app metrics and adherence. Routine care maintained. |
| Measure | Description | Time Frame |
|---|---|---|
| Percent weight loss | Proportion of body weight lost, assessed via smart scale synced with app. Factors distinguish those who do/don't lose weight is detected by using machine learning. | At the end of Months 1, 2, 3, and 6 |
| Adherence to self-monitoring | Number of days per week participants complete self-weighing, dietary logging and follow up visits. | At the end of Months 1, 2, 3, and 6 |
| Measure | Description | Time Frame |
|---|---|---|
| Longitudinal adherence to self-monitoringcompliance to app + scale protocol and the participants who have bad compliance is compared by percent weight loss. | Adherence measured as self-monitoring days per week, assessed monthly across the 6-month study. | At the end of Months 1, 2, 3, and 6 |
| Weight loss by adherence level |
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Inclusion Criteria:
Exclusion Criteria:
Participants include approximately 50% with schizophrenia and 50% with bipolar disorder, distributed across all clusters.
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| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Le Xiao | Contact | +8613466604224 | xiaole373@163.com |
| Name | Affiliation | Role |
|---|---|---|
| Le Xiao | Capital Medical University | Study Chair |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Beijing Anding Hospital | Recruiting | Beijing | Beijing Municipality | China |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 25962699 | Result | Tek C, Kucukgoncu S, Guloksuz S, Woods SW, Srihari VH, Annamalai A. Antipsychotic-induced weight gain in first-episode psychosis patients: a meta-analysis of differential effects of antipsychotic medications. Early Interv Psychiatry. 2016 Jun;10(3):193-202. doi: 10.1111/eip.12251. Epub 2015 May 12. | |
| 28883731 | Result |
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De-identified participant data (including demographics, clinical outcomes, and body composition metrics) will be released to participants and referring clinicians upon request
Data will be available from 6 months after primary publication for 5 years
Researchers must submit a proposal to the corresponding author, subject to approval by the ethics committee
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| Type | Date | Date Unknown |
|---|---|---|
| Release | Sep 29, 2025 | |
| Reset | Oct 16, 2025 |
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|
Comparison of percent weight loss between high- and low-adherence groups. |
| At the end of Months 1, 2, 3, and 6 |
| Association between adherence and weight loss | Independent variables include diagnosis, treatment, baseline weight, self-monitoring adherence, %WL from the previous month (e.g., %WL at the end of month 2 predicted self-monitoring during month 3), and the interaction between condition and %WL. | At the end of Months 1, 2, 3, and 6 |
| Prediction of future adherence by prior weight loss | Generalized linear mixed models will test whether weight loss in a given month predicts adherence in the following month. | At the end of Months 1, 2, 3, and 6 |
| Dayabandara M, Hanwella R, Ratnatunga S, Seneviratne S, Suraweera C, de Silva VA. Antipsychotic-associated weight gain: management strategies and impact on treatment adherence. Neuropsychiatr Dis Treat. 2017 Aug 22;13:2231-2241. doi: 10.2147/NDT.S113099. eCollection 2017. |
| 32437055 | Result | Brockmann AN, Eastman A, Ross KM. Frequency and Consistency of Self-Weighing to Promote Weight-Loss Maintenance. Obesity (Silver Spring). 2020 Jul;28(7):1215-1218. doi: 10.1002/oby.22828. Epub 2020 May 21. |
| 33624440 | Result | Patel ML, Wakayama LN, Bennett GG. Self-Monitoring via Digital Health in Weight Loss Interventions: A Systematic Review Among Adults with Overweight or Obesity. Obesity (Silver Spring). 2021 Mar;29(3):478-499. doi: 10.1002/oby.23088. |
| 28488834 | Result | Cheatham SW, Stull KR, Fantigrassi M, Motel I. The efficacy of wearable activity tracking technology as part of a weight loss program: a systematic review. J Sports Med Phys Fitness. 2018 Apr;58(4):534-548. doi: 10.23736/S0022-4707.17.07437-0. Epub 2017 May 9. |
| 31144666 | Result | Suen L, Wang W, Cheng KKY, Chua MCH, Yeung JWF, Koh WK, Yeung SKW, Ho JYS. Self-Administered Auricular Acupressure Integrated With a Smartphone App for Weight Reduction: Randomized Feasibility Trial. JMIR Mhealth Uhealth. 2019 May 29;7(5):e14386. doi: 10.2196/14386. |
| 26554314 | Result | Flores Mateo G, Granado-Font E, Ferre-Grau C, Montana-Carreras X. Mobile Phone Apps to Promote Weight Loss and Increase Physical Activity: A Systematic Review and Meta-Analysis. J Med Internet Res. 2015 Nov 10;17(11):e253. doi: 10.2196/jmir.4836. |
| 31556659 | Result | Goldstein SP, Goldstein CM, Bond DS, Raynor HA, Wing RR, Thomas JG. Associations between self-monitoring and weight change in behavioral weight loss interventions. Health Psychol. 2019 Dec;38(12):1128-1136. doi: 10.1037/hea0000800. Epub 2019 Sep 26. |
| 30816851 | Result | Patel ML, Hopkins CM, Brooks TL, Bennett GG. Comparing Self-Monitoring Strategies for Weight Loss in a Smartphone App: Randomized Controlled Trial. JMIR Mhealth Uhealth. 2019 Feb 28;7(2):e12209. doi: 10.2196/12209. |
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| Release Date | Unrelease Date | Unrelease Date Unknown | Reset Date | MCP Release Number |
|---|---|---|---|---|
| Sep 29, 2025 | Oct 16, 2025 |
| ID | Term |
|---|---|
| D012559 | Schizophrenia |
| D001714 | Bipolar Disorder |
| D024821 | Metabolic Syndrome |
| D050177 | Overweight |
| D009765 | Obesity |
| ID | Term |
|---|---|
| D019967 | Schizophrenia Spectrum and Other Psychotic Disorders |
| D001523 | Mental Disorders |
| D000068105 | Bipolar and Related Disorders |
| D019964 | Mood Disorders |
| D007333 | Insulin Resistance |
| D006946 | Hyperinsulinism |
| D044882 | Glucose Metabolism Disorders |
| D008659 | Metabolic Diseases |
| D009750 | Nutritional and Metabolic Diseases |
| D044343 | Overnutrition |
| D009748 | Nutrition Disorders |
| D001835 | Body Weight |
| D012816 | Signs and Symptoms |
| D013568 | Pathological Conditions, Signs and Symptoms |
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