Effects of Communicating Genetic Risk of Type 2 Diabetes and Wearable Technologies on Behavioral Outcomes in East Asians: Statistical Analysis Protocol for a Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Evidence suggests that the communication of type 2 diabetes (T2D) genetic risk alone has limited effectiveness on facilitating behavioral changes among individuals of European descent. Although the use of wearable devices has been associated with changes in behavior, the effects of combining personalized precision medicine with wearable devices on behaviors related to T2D prevention remain unclear. This study aims to assess the novel effects of T2D genetic risk communication and wearable device functions on objectively measured moderate to vigorous physical activity (MVPA) time among East Asian individuals with overweight or obesity. OBJECTIVE: The objectives of this study are to (1) investigate the effects of communicating T2D genetic risk and (2) examine the effects of combining T2D genetic risk communication with wearable device functions such as step goal setting and activity prompts on objectively measured MVPA time among East Asians with overweight or obesity. METHODS: In this parallel-group randomized controlled trial, 355 East Asians with overweight or obesity aged between 40 and 60 years are allocated to 1 of 3 groups: 1 control and 2 intervention groups. Blood samples are used for estimation of T2D genetic risk and tested for metabolic risk markers. T2D genetic risk is estimated based on 113 single nucleotide polymorphisms associated with T2D among East Asians using an established method. All 3 groups receive a Fitbit device. Both intervention groups will receive T2D genetic risk estimates along with lifestyle advice, but one of the intervention groups will receive additional Fitbit functions: step goal setting and prompt functions. The intervention materials are delivered weekly using WhatsApp and monthly via email. The primary outcome is MVPA time, which is objectively measured with the built-in accelerometer of the Fitbit Inspire 3 and will be assessed at baseline, immediately after the intervention, 12 months after the intervention, and at 6-month follow-up. Secondary outcomes include other parameters, such as sedentary time, BMI, systolic and diastolic blood pressure, 5 metabolic risk markers, handgrip strength, sleep, activity calories, self-reported physical activity, self-reported fruit and vegetable consumption, smoking status, and psychological variables. RESULTS: This study was funded in January 2023. Data collection for baseline assessments began in February 2023. Formal data analysis started in April 2025 after the 6-month follow-up assessments were completed. CONCLUSIONS: To the best of our knowledge, this study will be the first randomized controlled trial to combine T2D genetic risk communication with wearable device functions in any population. Novel findings will be used to inform future lifestyle modification strategies for T2D. We plan to provide a comprehensive report on this study by publishing this analysis plan before the completion of data collection. TRIAL REGISTRATION: ClinicalTrials.gov NCT05524909; https://www.clinicaltrials.gov/study/NCT05524909. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/65012.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.039 | 0.053 |
| Meta-epidemiology (narrow) | 0.008 | 0.003 |
| Meta-epidemiology (broad) | 0.017 | 0.010 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.074 | 0.010 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".