App and Body Fat Scale in the Management of Overweight Patients

Schizophrenia Clinical Trial

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

Clinical Trial Details — Status: Not yet recruiting

Administrative data

• NCT number: NCT05866107
• Other study ID #: MISP#100150
• Status: Not yet recruiting
• Phase: N/A
• Start date: October 15, 2023
• Est. completion date: December 31, 2024

Study information

• Verified date: August 2023
• Source: Capital Medical University
• Contact: Xiao Le
• Phone: +8613466604224
• Email: xiaole373[@]163.com
• Is FDA regulated: No
• Study type: Interventional

Clinical Trial 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:

1. 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

2. To evaluate the longitudinal association between self-monitoring adherence and percent weight loss.

3. To evaluate the prospective association between monthly % weight loss and the subsequent month of self-monitoring adherence

List the clinical hypotheses:

1. 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.

2. The self-monitoring adherence is associated with greater weight loss.

3. The monthly weight loss is associated with the subsequent month of self-monitoring adherence.

4. The self-weight monitoring using smart body fat scale and health app are feasible by evaluating the compliance and completeness of the data.

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.

Recruitment information / eligibility

• Status: Not yet recruiting
• Enrollment: 200
• Est. completion date: December 31, 2024
• Est. primary completion date: December 31, 2024
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years to 60 Years

Eligibility

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.

Related Conditions & MeSH terms

• Bipolar Disorder
• Metabolic Syndrome
• Overweight
• Schizophrenia

Intervention

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.

Locations

Country	Name	City	State
n/a

Sponsors (2)

Lead Sponsor	Collaborator
Capital Medical University	Merck Sharp & Dohme LLC

References & Publications (9)

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. — View 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 M — View 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. — View 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. — View 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. — View 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. — View 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. — View 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. — View 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):193-20 — View Citation

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	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.	at the end of 1 months
Primary	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.	at the end of 1 months
Primary	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.	at the end of 2 months
Primary	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.	at the end of 2 months
Primary	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.	at the end of 3 months
Primary	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.	at the end of 3 months
Primary	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.	at the end of 6 months
Primary	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.	at the end of 6 months
Secondary	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.	at the end of 1,2,3, and 6 months
Secondary	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.	at the end of 1,2,3, and 6 months
Secondary	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.	at the end of 1,2,3, and 6 months
Secondary	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.	at the end of 1,2,3, and 6 months