Metabolic Syndrome
Conditions
Brief summary
Increased health risks and diseases are believed to be caused by multilevel interactions of genetic and environmental factors (including lifestyle habits). Considering the recent advancements in genetic analysis, wearable devices, and big data analysis techniques, collecting and analyzing personal genetic information, lifelogs, and environmental data and predicting the exact health risks of individuals could be possible.
Detailed description
These changes enable the development of healthcare solutions that allow users to actively engage in healthcare and provide appropriate care measures to sufficiently delay or prevent chronic diseases, thereby minimizing the financial losses of the individuals and the society. In particular, chronic diseases such as metabolic syndrome can be prevented or delayed through healthy lifestyle habits. Controversy regarding the effectiveness of using wearable devices and mobile health applications for maintaining lifestyle habits and losing weight exists. Additionally, a study assessing the health of the participants and the disease prevention care for the participants using the genetic data and the lifelog data by the wearable device has not been conducted yet. Therefore, the objective is to operate a lifestyle correction program for examinees who visited a family medicine and health checkup center to develop a user-participation health and disease prevention care system using genetic data and lifelog data. From adults who visited a family medicine and health checkup center, the following should be performed: (1) collect a variety of clinical information, including lifestyle data, physical information, metabolic parameters, genetic information, and metagenomes, which can affect chronic diseases; (2) establish a service model that combines lifestyle data, examination data, and genetic data and analyzes and identifies one's health level through a smartphone application; and (3) examine the effects on the metabolic parameters, lifestyle data, and metagenome (gut microbiome) after using the device (smart healthcare).This pilot study aimed to provide personalized my data by linking clinical data and personal lifestyle patterns of the participants who visited a family medicine and examination center. From adults who visited a family medicine and health check-up center, the following should be performed: 1. Collect a variety of clinical information, including lifestyle data, physical information, metabolic parameters, genetic information, and metagenomes, which can affect chronic diseases 2. Establish a service model that combines lifestyle data, examination data, and genetic data and analyzes and identifies one's health level through a smartphone application 3. Examine the effects on the metabolic parameters, lifestyle data, and metagenome (gut microbiome) after using the device (smart healthcare)
Interventions
DEVICEWearing a Wearable device (Fitbit inspire HR)
'Wearing a wearable device (the smart watch) for 8 weeks The smart watch to be used in this study is Fitbit Inspire HR. This is a device that has a high worldwide use rate and has active research on its accuracy. It is worn like a normal watch, and it can check heart rate, exercise level, energy consumed, and sleep quality. The values can be checked in real-time on a smartphone application.'
Sponsors
Gangnam Severance Hospital
Study design
Allocation
NA
Intervention model
SINGLE_GROUP
Primary purpose
PREVENTION
Masking
NONE
Eligibility
Sex/Gender
ALL
Age
20 Years to 65 Years
Healthy volunteers
No
Inclusion criteria
1. Korean adults aged between 20 and 65 years who visited a family medicine and health checkup center 2. Individuals who understand and are fluent in Korean language 3. Individuals who provided consent for inclusion in the study 4. Individuals who can complete the study without participating in other intervention studies (medicine, diet, exercise) during the whole research duration 5. Individuals who have no restrictions on their participation in physical activities during the whole research duration
Exclusion criteria
1. Individuals who did not provide consent for inclusion in the study or individuals who did not provide complete data 2. Individuals with cognitive dysfunction or inability in using wearable devices 3. Individuals with history of infectious brain diseases, head trauma, thyroid diseases, and substance abuse 4. Individuals with vision, hearing, and speech impairments that can affect examinations 5. Individuals considered unfit to participate in this study by researchers 6. Individuals who do not use smartphones
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Changes in metabolic parameter (HOMA-IR) at 8 weeks after smart healthcare intervention | 8 weeks after smart healthcare intervention | Changes in HOMA-IR (calculated using fasting glucose and insulin level) at 8 weeks after smart healthcare intervention. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Changes in other metabolic parameters, inflammatory parameters, metagenome related to the gut microbiome, and sleep quality, diet, and exercise at 8 weeks after smart healthcare intervention | 8 weeks after smart healthcare intervention | Changes in lipid profile (total cholesterol, TG, LDL-C, HDL-C levels) at 8 weeks after smart healthcare intervention. |
Countries
South Korea
Contacts
Primary ContactJiwon Lee, Professor
Outcome results
None listed