App and Body Fat Scale in the Management of Overweight Patients
Study Details
Study Description
Brief Summary
Primary objective:
To examine the impact of the sustained use of the health app and smart body fat scale on weight management and patient engagement
Secondary objectives:
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To compare the difference in weight loss between the participants who have good compliance to app + scale protocol and the participants who have bad compliance
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To evaluate the longitudinal association between self-monitoring adherence and percent weight loss.
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To evaluate the prospective association between monthly % weight loss and the subsequent month of self-monitoring adherence
List the clinical hypotheses:
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At least 50% of participants will achieve 7% weight reduction compared with baseline by self-weight monitoring using smart body fat scale and health app.
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The self-monitoring adherence is associated with greater weight loss.
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The monthly weight loss is associated with the subsequent month of self-monitoring adherence.
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The self-weight monitoring using smart body fat scale and health app are feasible by evaluating the compliance and completeness of the data.
| Condition or Disease | Intervention/Treatment | Phase |
|---|---|---|
|
N/A |
Detailed Description
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 sleep log, 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 200 overweight subjects, including 100 patients with schizophrenia and 100 patients with bipolar disorder, who are receiving antipsychotics,. Interventions included self-monitoring of weight using smart body fat scale, dietary management, and exercise management. The follow-up team consists of a psychiatrist, nutrition instructor, and exercise instructor who set weight loss goals and implemented a plan. The patients themselves use the health APP and smart body fat scale to record health data such as body weight; psychiatrists evaluate the patient's condition and conduct laboratory tests; nutrition instructors conduct dietary education and formulate individualized energy-limited balanced diet prescriptions; exercise instructors conduct behavioral ways and sports education, and individualized exercise prescriptions.
Study Design
Arms and Interventions
| Arm | Intervention/Treatment |
|---|---|
| Experimental: Block 1 50 patients with schizophrenia and 50 patients with bipolar disorder |
Device: self-monitoring of weight using smart body fat scale with high-precision weighing chip (Huawei Scale 2pro), a mobile phone app (Huawei Health), dietary management, and exercise management.
Participants will use a mobile phone app (Huawei Health) to collect data on sleep log, 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; psychiatrists evaluate the patient's condition and conduct laboratory tests; nutrition instructors conduct dietary education and formulate individualized energy-limited balanced diet prescriptions; exercise instructors conduct behavioral ways and sports education, and individualized exercise prescriptions.
|
| Experimental: Block 2 50 patients with schizophrenia and 50 patients with bipolar disorder |
Device: self-monitoring of weight using smart body fat scale with high-precision weighing chip (Huawei Scale 2pro), a mobile phone app (Huawei Health), dietary management, and exercise management.
Participants will use a mobile phone app (Huawei Health) to collect data on sleep log, 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; psychiatrists evaluate the patient's condition and conduct laboratory tests; nutrition instructors conduct dietary education and formulate individualized energy-limited balanced diet prescriptions; exercise instructors conduct behavioral ways and sports education, and individualized exercise prescriptions.
|
Outcome Measures
Primary Outcome Measures
- The impact of the sustained use of the health app and smart body fat scale on weight management. Factors distinguish those who do/don't lose weight is detected by using machine learning. [at the end of 1 months]
The impact of the sustained use of the health app and smart body fat scale on weight management is examined by percent weight loss. Factors distinguish those who do/don't lose weight is detected by using machine learning.
- The impact of the sustained use of the health app and smart body fat scale on patient engagement. Factors distinguish those who do/don't lose weight is detected by using machine learning. [at the end of 1 months]
The impact of the sustained use of the health app and smart body fat scale on patient engagement is examined by summing the adherent days per week of each month. Factors distinguish those who do/don't lose weight is detected by using machine learning.
- The impact of the sustained use of the health app and smart body fat scale on weight management. Factors distinguish those who do/don't lose weight is detected by using machine learning. [at the end of 2 months]
The impact of the sustained use of the health app and smart body fat scale on weight management is examined by percent weight loss. Factors distinguish those who do/don't lose weight is detected by using machine learning.
- The impact of the sustained use of the health app and smart body fat scale on patient engagement. Factors distinguish those who do/don't lose weight is detected by using machine learning. [at the end of 2 months]
The impact of the sustained use of the health app and smart body fat scale on patient engagement is examined by summing the adherent days per week of each month. Factors distinguish those who do/don't lose weight is detected by using machine learning.
- The impact of the sustained use of the health app and smart body fat scale on weight management. Factors distinguish those who do/don't lose weight is detected by using machine learning. [at the end of 3 months]
The impact of the sustained use of the health app and smart body fat scale on weight management is examined by percent weight loss. Factors distinguish those who do/don't lose weight is detected by using machine learning.
- The impact of the sustained use of the health app and smart body fat scale on patient engagement. Factors distinguish those who do/don't lose weight is detected by using machine learning. [at the end of 3 months]
The impact of the sustained use of the health app and smart body fat scale on patient engagement is examined by summing the adherent days per week of each month. Factors distinguish those who do/don't lose weight is detected by using machine learning.
- The impact of the sustained use of the health app and smart body fat scale on weight management. Factors distinguish those who do/don't lose weight is detected by using machine learning. [at the end of 6 months]
The impact of the sustained use of the health app and smart body fat scale on weight management is examined by percent weight loss. Factors distinguish those who do/don't lose weight is detected by using machine learning.
- The impact of the sustained use of the health app and smart body fat scale on patient engagement. Factors distinguish those who do/don't lose weight is detected by using machine learning. [at the end of 6 months]
The impact of the sustained use of the health app and smart body fat scale on patient engagement is examined by summing the adherent days per week of each month. Factors distinguish those who do/don't lose weight is detected by using machine learning.
Secondary Outcome Measures
- The impact of the sustained use of the health app and smart body fat scale on patient engagement is examined by summing the adherent days per week of each month. [at the end of 1,2,3, and 6 months]
The impact of the sustained use of the health app and smart body fat scale on patient engagement is examined by summing the adherent days per week of each month.
- The difference in weight loss between the participants who have good compliance to app + scale protocol and the participants who have bad compliance is compared by percent weight loss. [at the end of 1,2,3, and 6 months]
The difference in weight loss between the participants who have good compliance to app + scale protocol and the participants who have bad compliance is compared by percent weight loss.
- The association between self-monitoring and monthly weight loss will be evaluated by linear mixed models with random effects of time (month) and participant. [at the end of 1,2,3, and 6 months]
Independent variables include diagnosis, treatment, baseline weight, self-monitoring adherence, and physical activity et al. The dependent variable is calculated as %WL during each month, using baseline weight as a reference point.
- The prospective association between monthly weight loss and adherence to self-monitoring will be evaluated by generalized linear mixed models with random effects of time (month) and participant. [at the end of 1,2,3, and 6 months]
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.
Eligibility Criteria
Criteria
Inclusion Criteria:
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Age 18-60 years old, no gender restriction.
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According to ICD-10 to diagnose bipolar disorder or schizophrenia, the researcher judges that the patient is currently in remission, or the condition is stable and can cooperate with the research.
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Currently using at least one antipsychotic or mood stabilizer (e.g. lithium, magnesium valproate, sodium valproate, lamotrigine).
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Currently overweight or obese (body mass index ≥ 24kg/m2) and willing to use health app and smart scales to lose weight.
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The education level of primary school or above, able to understand the content of the scale, and be able to use smart phone proficiently.
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Understand and voluntarily participate in this study, and sign the informed consent form.
Exclusion Criteria:
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Plan to lose weight by other methods during the study period (such as dieting, inducing vomiting, taking diet pills, surgery).
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Self-reported weight loss ≥ 7% in the past 6 months.
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Weight over 150 kg.
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Other secondary obesity (such as hypothyroidism, Cushing's syndrome, hypothalamic obesity, etc.).
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Currently pregnant, lactating, < 6 months postpartum or planning to become pregnant during the study period.
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Self-reported cardiac discomfort or chest pain during activity or at rest.
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There is a serious medical condition, and the researchers believe that there may be safety risks when participating in sports.
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Be unable to walk 30 minutes without stopping.
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There are problems that may affect compliance with the protocol (eg, end-stage disease, planning to move travel to the field, history of substance abuse, other uncontrolled or untreated medical conditions);
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Any other conditions deemed inappropriate by the investigator.
Contacts and Locations
Locations
No locations specified.Sponsors and Collaborators
- Capital Medical University
- Merck Sharp & Dohme LLC
Investigators
- Study Chair: Xiao Le, Capital Medical University
Study Documents (Full-Text)
None provided.More Information
Publications
None provided.- MISP#100150