Randomized Controlled Trial of the Smart Online-to-Offline Model Development for Chronic Diseases Management Through Digital Health in Real World Setting

Objectives The aim of this intervention is to assess how well and how efficiently the O2O Service Model works in delivering primary healthcare and managing chronic diseases for patients living in Pyeongchang-gun at the local government level.

Trial design This is a randomized controlled trial with restricted, batch, blocked and individual random assignment. Investigators will allocate 1,000 participants from Pyeongchang-gun into two groups: one with diabetes (and pre-diabetes) and one with hypertension (including borderline cases), focusing on poorly controlled patients. As participants visit local health centers, their data will be sent to the research team for blocked randomization based on disease type, severity, and region. Assignment results will be communicated back to the centers and patients.

Study setting The intervention will take place in Pyeongchang-gun, Kangwon-do, South Korea. The Smart Healthcare Center affiliated to Public Medical Center of Pyeongchang will primarily manage the O2O service by providing necessary education and human resources while receiving and monitoring data from each participant and local medical centers. The overall study (including the design of trial) is managed by Seoul National University College of Medicine while the provision of O2O service and data collection are primarily in charge of Smart Healthcare Center of Pyeongchang.

Intervention in O2O Service Model:

After initial health exams, each patient gets a custom care plan, classifying their disease and care level. The Pyeongchang Smart Healthcare Center, with doctors, tracks patients via an online platform, sending automated health messages. High-risk patients receive a two-week glucose monitor and guidance on lifestyle adjustments. Doctors access patient health records through the participant's personal online datastore (POD), with consent, integrating health and prescription history from national insurance, and can assess heart disease risk.

Sample size The trial's sample size is determined using the MDES(Minimum Detectable Effect Size), with 1,000 participants divided equally between diabetes and hypertension groups. Each group is then split into treatment and control subgroups through random allocation. The MDES calculation, accounting for an explained outcome variation of 50% due to blocking and controls, and based on 500 subjects per group with equal allocation, aims for a high-accuracy effect size of 0.216 at a 5% significance level and 80% power.

Assignment of interventions: allocation Participants will be randomly placed into groups by a computer program. This study uses a specific randomization method that is restricted and organized by individual characteristics. The research team will tell the centers and patients about their group assignment via phone and email. Neither the healthcare staff nor the participants will know the group allocation beforehand. The research team is in charge of the randomization process and informing everyone involved. The staff at Pyeongchang's Smart Healthcare Center will sign up participants and manage their placement into the assigned intervention groups. There will be no blinding after participants are assigned to their groups.

List of Data For the study, the investigators have created questionnaires and interview formats to collect various data types, including random assignment records, baseline information, outcomes, service usage and costs, demographic and health details, physician-collected health records, life-log data from apps or web, clinical exam results, and service effectiveness and efficiency from the perspective of medical staff and administrators.

Statistical methods For the analysis of primary and secondary outcomes, the investigators will employ ITT(Intention-to-Treat) and Regression-Adjusted Impact Estimate. When data allows (especially data from control group), the investigators will also conduct timeseries effect analysis. To investigate the time-series changes in program effects across multiple time points, the dependent variable (outcome variable) should be measured at each time point to estimate program effects repeatedly.

Data management and monitoring The data management for the study will be conducted using electronic systems for secure and accurate data entry, coding, and storage, with robust checks and backups to ensure data quality, which will be archived for three years in compliance with bioethics standards before disposal according to privacy regulations. Participant confidentiality is prioritized through secure data collection and restricted access, with sharing only to authorized research team members and regulators using password protection or encryption. Additionally, a dedicated Data Monitoring Committee will oversee trial progress and adherence to protocols, while the Smart Healthcare Center collaborates with healthcare workers to manage patient services, with minimal adverse events expected.