Developing Health Care Service System Based on Participatory Big Data
Overview
- Phase
- Not Applicable
- Intervention
- Not specified
- Conditions
- Metabolic Syndrome
- Sponsor
- Gangnam Severance Hospital
- Enrollment
- 384
- Locations
- 1
- Primary Endpoint
- Changes in metabolic parameter (HOMA-IR) at 8 weeks after smart healthcare intervention
- Last Updated
- 5 years ago
Overview
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) "
Investigators
Ji-Won Lee
Professor,Yonsei University College of Medicine,
Gangnam Severance Hospital
Eligibility Criteria
Inclusion Criteria
- •Korean adults aged between 20 and 65 years who visited a family medicine and health checkup center
- •Individuals who understand and are fluent in Korean language
- •Individuals who provided consent for inclusion in the study
- •Individuals who can complete the study without participating in other intervention studies (medicine, diet, exercise) during the whole research duration
- •Individuals who have no restrictions on their participation in physical activities during the whole research duration
Exclusion Criteria
- •Individuals who did not provide consent for inclusion in the study or individuals who did not provide complete data
- •Individuals with cognitive dysfunction or inability in using wearable devices
- •Individuals with history of infectious brain diseases, head trauma, thyroid diseases, and substance abuse
- •Individuals with vision, hearing, and speech impairments that can affect examinations
- •Individuals considered unfit to participate in this study by researchers
- •Individuals who do not use smartphones
Outcomes
Primary Outcomes
Changes in metabolic parameter (HOMA-IR) at 8 weeks after smart healthcare intervention
Time Frame: 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 Outcomes
- 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)