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App and Body Fat Scale in the Management of Overweight Patients

Primary Purpose

Schizophrenia, Bipolar Disorder, Metabolic Syndrome

Status
Not yet recruiting
Phase
Not Applicable
Locations
Study Type
Interventional
Intervention
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.
Sponsored by
Capital Medical University
About
Eligibility
Locations
Arms
Outcomes
Full info

About this trial

This is an interventional treatment trial for Schizophrenia

Eligibility Criteria

18 Years - 60 Years (Adult)All SexesDoes not accept healthy volunteers

Inclusion Criteria: Age 18-60 years old, no gender restriction. 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. Currently using at least one antipsychotic or mood stabilizer (e.g. lithium, magnesium valproate, sodium valproate, lamotrigine). Currently overweight or obese (body mass index ≥ 24kg/m2) and willing to use health app and smart scales to lose weight. The education level of primary school or above, able to understand the content of the scale, and be able to use smart phone proficiently. Understand and voluntarily participate in this study, and sign the informed consent form. Exclusion Criteria: Plan to lose weight by other methods during the study period (such as dieting, inducing vomiting, taking diet pills, surgery). Self-reported weight loss ≥ 7% in the past 6 months. Weight over 150 kg. Other secondary obesity (such as hypothyroidism, Cushing's syndrome, hypothalamic obesity, etc.). Currently pregnant, lactating, < 6 months postpartum or planning to become pregnant during the study period. Self-reported cardiac discomfort or chest pain during activity or at rest. There is a serious medical condition, and the researchers believe that there may be safety risks when participating in sports. Be unable to walk 30 minutes without stopping. 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); Any other conditions deemed inappropriate by the investigator.

Sites / Locations

    Arms of the Study

    Arm 1

    Arm 2

    Arm Type

    Experimental

    Experimental

    Arm Label

    Block 1

    Block 2

    Arm Description

    50 patients with schizophrenia and 50 patients with bipolar disorder

    50 patients with schizophrenia and 50 patients with bipolar disorder

    Outcomes

    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.
    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.
    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.
    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.
    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.
    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.
    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.
    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.
    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.
    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.
    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.
    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.
    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.

    Full Information

    First Posted
    March 6, 2023
    Last Updated
    August 29, 2023
    Sponsor
    Capital Medical University
    Collaborators
    Merck Sharp & Dohme LLC
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    1. Study Identification

    Unique Protocol Identification Number
    NCT05866107
    Brief Title
    App and Body Fat Scale in the Management of Overweight Patients
    Official Title
    The Effectiveness and Feasibility of Health App and Smart Body Fat Scale in the Management of Health Outcomes in the Overweight Patients Treated With Antipsychotics: a Stepped-wedge Cluster Randomized Study
    Study Type
    Interventional

    2. Study Status

    Record Verification Date
    August 2023
    Overall Recruitment Status
    Not yet recruiting
    Study Start Date
    October 15, 2023 (Anticipated)
    Primary Completion Date
    December 31, 2024 (Anticipated)
    Study Completion Date
    December 31, 2024 (Anticipated)

    3. Sponsor/Collaborators

    Responsible Party, by Official Title
    Principal Investigator
    Name of the Sponsor
    Capital Medical University
    Collaborators
    Merck Sharp & Dohme LLC

    4. Oversight

    Studies a U.S. FDA-regulated Drug Product
    No
    Studies a U.S. FDA-regulated Device Product
    No

    5. 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: 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 To evaluate the longitudinal association between self-monitoring adherence and percent weight loss. To evaluate the prospective association between monthly % weight loss and the subsequent month of self-monitoring adherence List the clinical hypotheses: 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. The self-monitoring adherence is associated with greater weight loss. The monthly weight loss is associated with the subsequent month of self-monitoring adherence. The self-weight monitoring using smart body fat scale and health app are feasible by evaluating the compliance and completeness of the data.
    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.

    6. Conditions and Keywords

    Primary Disease or Condition Being Studied in the Trial, or the Focus of the Study
    Schizophrenia, Bipolar Disorder, Metabolic Syndrome

    7. Study Design

    Primary Purpose
    Treatment
    Study Phase
    Not Applicable
    Interventional Study Model
    Parallel Assignment
    Masking
    None (Open Label)
    Allocation
    Randomized
    Enrollment
    200 (Anticipated)

    8. Arms, Groups, and Interventions

    Arm Title
    Block 1
    Arm Type
    Experimental
    Arm Description
    50 patients with schizophrenia and 50 patients with bipolar disorder
    Arm Title
    Block 2
    Arm Type
    Experimental
    Arm Description
    50 patients with schizophrenia and 50 patients with bipolar disorder
    Intervention Type
    Device
    Intervention Name(s)
    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.
    Intervention Description
    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.
    Primary Outcome Measure Information:
    Title
    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.
    Description
    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.
    Time Frame
    at the end of 1 months
    Title
    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.
    Description
    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.
    Time Frame
    at the end of 1 months
    Title
    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.
    Description
    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.
    Time Frame
    at the end of 2 months
    Title
    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.
    Description
    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.
    Time Frame
    at the end of 2 months
    Title
    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.
    Description
    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.
    Time Frame
    at the end of 3 months
    Title
    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.
    Description
    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.
    Time Frame
    at the end of 3 months
    Title
    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.
    Description
    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.
    Time Frame
    at the end of 6 months
    Title
    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.
    Description
    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.
    Time Frame
    at the end of 6 months
    Secondary Outcome Measure Information:
    Title
    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.
    Description
    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.
    Time Frame
    at the end of 1,2,3, and 6 months
    Title
    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.
    Description
    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.
    Time Frame
    at the end of 1,2,3, and 6 months
    Title
    The association between self-monitoring and monthly weight loss will be evaluated by linear mixed models with random effects of time (month) and participant.
    Description
    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.
    Time Frame
    at the end of 1,2,3, and 6 months
    Title
    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.
    Description
    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.
    Time Frame
    at the end of 1,2,3, and 6 months

    10. Eligibility

    Sex
    All
    Minimum Age & Unit of Time
    18 Years
    Maximum Age & Unit of Time
    60 Years
    Accepts Healthy Volunteers
    No
    Eligibility Criteria
    Inclusion Criteria: Age 18-60 years old, no gender restriction. 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. Currently using at least one antipsychotic or mood stabilizer (e.g. lithium, magnesium valproate, sodium valproate, lamotrigine). Currently overweight or obese (body mass index ≥ 24kg/m2) and willing to use health app and smart scales to lose weight. The education level of primary school or above, able to understand the content of the scale, and be able to use smart phone proficiently. Understand and voluntarily participate in this study, and sign the informed consent form. Exclusion Criteria: Plan to lose weight by other methods during the study period (such as dieting, inducing vomiting, taking diet pills, surgery). Self-reported weight loss ≥ 7% in the past 6 months. Weight over 150 kg. Other secondary obesity (such as hypothyroidism, Cushing's syndrome, hypothalamic obesity, etc.). Currently pregnant, lactating, < 6 months postpartum or planning to become pregnant during the study period. Self-reported cardiac discomfort or chest pain during activity or at rest. There is a serious medical condition, and the researchers believe that there may be safety risks when participating in sports. Be unable to walk 30 minutes without stopping. 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); Any other conditions deemed inappropriate by the investigator.
    Central Contact Person:
    First Name & Middle Initial & Last Name or Official Title & Degree
    Xiao Le
    Phone
    +8613466604224
    Email
    xiaole373@163.com
    Overall Study Officials:
    First Name & Middle Initial & Last Name & Degree
    Xiao Le
    Organizational Affiliation
    Capital Medical University
    Official's Role
    Study Chair

    12. IPD Sharing Statement

    Citations:
    PubMed Identifier
    25962699
    Citation
    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.
    Results Reference
    result
    PubMed Identifier
    28883731
    Citation
    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.
    Results Reference
    result
    PubMed Identifier
    32437055
    Citation
    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.
    Results Reference
    result
    PubMed Identifier
    33624440
    Citation
    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.
    Results Reference
    result
    PubMed Identifier
    28488834
    Citation
    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.
    Results Reference
    result
    PubMed Identifier
    31144666
    Citation
    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.
    Results Reference
    result
    PubMed Identifier
    26554314
    Citation
    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.
    Results Reference
    result
    PubMed Identifier
    31556659
    Citation
    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.
    Results Reference
    result
    PubMed Identifier
    30816851
    Citation
    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.
    Results Reference
    result

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    App and Body Fat Scale in the Management of Overweight Patients

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