Studies
| Study First Submitted Date | 2023-03-06 |
| Study First Posted Date | 2023-05-19 |
| Last Update Posted Date | 2023-05-19 |
| Start Month Year | June 1, 2023 |
| Primary Completion Month Year | December 31, 2024 |
| Verification Month Year | May 2023 |
| Verification Date | 2023-05-31 |
| Last Update Posted Date | 2023-05-19 |
Detailed Descriptions
| Sequence: | 20814563 |
| 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. |
Conditions
| Sequence: | 52408087 | Sequence: | 52408088 | Sequence: | 52408089 |
| Name | Schizophrenia | Name | Bipolar Disorder | Name | Metabolic Syndrome |
| Downcase Name | schizophrenia | Downcase Name | bipolar disorder | Downcase Name | metabolic syndrome |
Id Information
| Sequence: | 40326169 |
| Id Source | org_study_id |
| Id Value | MISP#100150 |
Design Groups
| Sequence: | 55855544 | Sequence: | 55855545 |
| Group Type | Experimental | Group Type | Experimental |
| Title | Block 1 | Title | Block 2 |
| Description | 50 patients with schizophrenia and 50 patients with bipolar disorder | Description | 50 patients with schizophrenia and 50 patients with bipolar disorder |
Interventions
| Sequence: | 52716311 |
| Intervention Type | Device |
| Name | 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. |
| 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. |
Design Outcomes
| Sequence: | 178254804 | Sequence: | 178254805 | Sequence: | 178254806 | Sequence: | 178254807 | Sequence: | 178254808 | Sequence: | 178254809 | Sequence: | 178254810 | Sequence: | 178254811 | Sequence: | 178254812 | Sequence: | 178254813 | Sequence: | 178254814 | Sequence: | 178254815 |
| Outcome Type | primary | Outcome Type | primary | Outcome Type | primary | Outcome Type | primary | Outcome Type | primary | Outcome Type | primary | Outcome Type | primary | Outcome Type | primary | Outcome Type | secondary | Outcome Type | secondary | Outcome Type | secondary | Outcome Type | secondary |
| Measure | 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. | Measure | 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. | Measure | 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. | Measure | 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. | Measure | 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. | Measure | 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. | Measure | 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. | Measure | 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. | Measure | 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. | Measure | 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. | Measure | The association between self-monitoring and monthly weight loss will be evaluated by linear mixed models with random effects of time (month) and participant. | Measure | 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. |
| Time Frame | at the end of 1 months | Time Frame | at the end of 1 months | Time Frame | at the end of 2 months | Time Frame | at the end of 2 months | Time Frame | at the end of 3 months | Time Frame | at the end of 3 months | Time Frame | at the end of 6 months | Time Frame | at the end of 6 months | Time Frame | at the end of 1,2,3, and 6 months | Time Frame | at the end of 1,2,3, and 6 months | Time Frame | at the end of 1,2,3, and 6 months | Time Frame | at the end of 1,2,3, and 6 months |
| 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. | 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. | 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. | 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. | 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. | 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. | 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. | 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. | 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. | 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. | 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. | 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. |
Browse Conditions
| Sequence: | 194386380 | Sequence: | 194386381 | Sequence: | 194386382 | Sequence: | 194386383 | Sequence: | 194386384 | Sequence: | 194386385 | Sequence: | 194386386 | Sequence: | 194386387 | Sequence: | 194386388 | Sequence: | 194386389 | Sequence: | 194386390 | Sequence: | 194386391 | Sequence: | 194386392 | Sequence: | 194386393 | Sequence: | 194386394 |
| Mesh Term | Overweight | Mesh Term | Metabolic Syndrome | Mesh Term | Schizophrenia | Mesh Term | Bipolar Disorder | Mesh Term | Schizophrenia Spectrum and Other Psychotic Disorders | Mesh Term | Mental Disorders | Mesh Term | Overnutrition | Mesh Term | Nutrition Disorders | Mesh Term | Body Weight | Mesh Term | Insulin Resistance | Mesh Term | Hyperinsulinism | Mesh Term | Glucose Metabolism Disorders | Mesh Term | Metabolic Diseases | Mesh Term | Bipolar and Related Disorders | Mesh Term | Mood Disorders |
| Downcase Mesh Term | overweight | Downcase Mesh Term | metabolic syndrome | Downcase Mesh Term | schizophrenia | Downcase Mesh Term | bipolar disorder | Downcase Mesh Term | schizophrenia spectrum and other psychotic disorders | Downcase Mesh Term | mental disorders | Downcase Mesh Term | overnutrition | Downcase Mesh Term | nutrition disorders | Downcase Mesh Term | body weight | Downcase Mesh Term | insulin resistance | Downcase Mesh Term | hyperinsulinism | Downcase Mesh Term | glucose metabolism disorders | Downcase Mesh Term | metabolic diseases | Downcase Mesh Term | bipolar and related disorders | Downcase Mesh Term | mood disorders |
| Mesh Type | mesh-list | Mesh Type | mesh-list | Mesh Type | mesh-list | Mesh Type | mesh-list | Mesh Type | mesh-ancestor | Mesh Type | mesh-ancestor | Mesh Type | mesh-ancestor | Mesh Type | mesh-ancestor | Mesh Type | mesh-ancestor | Mesh Type | mesh-ancestor | Mesh Type | mesh-ancestor | Mesh Type | mesh-ancestor | Mesh Type | mesh-ancestor | Mesh Type | mesh-ancestor | Mesh Type | mesh-ancestor |
Sponsors
| Sequence: | 48539791 | Sequence: | 48539792 |
| Agency Class | OTHER | Agency Class | INDUSTRY |
| Lead Or Collaborator | lead | Lead Or Collaborator | collaborator |
| Name | Capital Medical University | Name | Merck Sharp & Dohme LLC |
Overall Officials
| Sequence: | 29408355 |
| Role | Study Chair |
| Name | Xiao Le |
| Affiliation | Capital Medical University |
Central Contacts
| Sequence: | 12071235 |
| Contact Type | primary |
| Name | Xiao Le |
| Phone | +8613466604224 |
| [email protected] | |
| Role | Contact |
Design Group Interventions
| Sequence: | 68469384 | Sequence: | 68469385 |
| Design Group Id | 55855544 | Design Group Id | 55855545 |
| Intervention Id | 52716311 | Intervention Id | 52716311 |
Eligibilities
| Sequence: | 30901801 |
| Gender | All |
| Minimum Age | 18 Years |
| Maximum Age | 60 Years |
| Healthy Volunteers | No |
| 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. |
| Adult | True |
| Child | False |
| Older Adult | False |
Calculated Values
| Sequence: | 254155440 |
| Registered In Calendar Year | 2023 |
| Were Results Reported | False |
| Has Single Facility | False |
| Minimum Age Num | 18 |
| Maximum Age Num | 60 |
| Minimum Age Unit | Years |
| Maximum Age Unit | Years |
| Number Of Primary Outcomes To Measure | 8 |
| Number Of Secondary Outcomes To Measure | 4 |
Designs
| Sequence: | 30647524 |
| Allocation | Randomized |
| Intervention Model | Parallel Assignment |
| Observational Model | |
| Primary Purpose | Treatment |
| Time Perspective | |
| Masking | None (Open Label) |
Responsible Parties
| Sequence: | 29014158 |
| Responsible Party Type | Principal Investigator |
| Name | Le Xiao |
| Title | Chief Physician, Beijing Anding Hospital |
| Affiliation | Capital Medical University |
Study References
| Sequence: | 52319367 | Sequence: | 52319368 | Sequence: | 52319369 | Sequence: | 52319370 | Sequence: | 52319371 | Sequence: | 52319372 | Sequence: | 52319373 | Sequence: | 52319374 | Sequence: | 52319375 |
| Pmid | 25962699 | Pmid | 28883731 | Pmid | 32437055 | Pmid | 33624440 | Pmid | 28488834 | Pmid | 31144666 | Pmid | 26554314 | Pmid | 31556659 | Pmid | 30816851 |
| Reference Type | result | Reference Type | result | Reference Type | result | Reference Type | result | Reference Type | result | Reference Type | result | Reference Type | result | Reference Type | result | Reference Type | result |
| 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. | 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. | 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. | 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. | 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. | 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. | 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. | 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. | 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. |