Real-World Outcomes of a Digital Behavioral Coaching Intervention to Improve Employee Health Status: Retrospective Observational Study
Bibliographic record
Abstract
BACKGROUND: Chronic noncommunicable diseases (NCDs) account for major disability and premature mortality worldwide, with low- and middle-income countries being disproportionately burdened. Given the negative impact of NCDs on employee performance and work productivity, there is a rising need for stakeholders to identify effective workplace solutions that can improve employee health outcomes. As the workplace becomes more dispersed post pandemic, digital behavioral coaching offers a scalable, personalized, and cost-effective method of managing chronic disease risk factors among employees. OBJECTIVE: This study aimed to retrospectively evaluate the impact of a digital behavioral coaching program on year-to-year changes in employee health status in a cohort of Indonesian employees. METHODS: This retrospective real-world exploratory analysis of secondary health data followed 774 employees of an Indonesian company who completed company-sponsored health screenings between 2021 and 2022 and were given access to Naluri (Naluri Hidup Sdn Bhd), a holistic digital therapeutics platform offering digital behavioral health coaching and self-help tools. Participants were retrospectively classified as those who received active coaching (n=177), passive coaching (n=108), and no coaching (n=489). Linear mixed-effects models were used to evaluate the year-to-year changes in health outcomes across the 3 employee groups, with post hoc analyses evaluating within-group differences between the 2 time points and between-group differences at follow-up. RESULTS: =-11.45, P<.001) for employees in the Active Coaching group between 2021 and 2022, with the other 2 groups reporting deteriorations in multiple health outcomes throughout the 2 time points. At follow-up, those who received active coaching between 2021 and 2022 reported significantly lower body weight (P<.001), BMI (P=.001), low-density lipoprotein (P=.045), and total cholesterol (P<.001) than the No Coaching group. CONCLUSIONS: This study demonstrates real-world outcomes and implications supporting the use of workplace digital behavioral coaching in improving employee health status. Given the rising burden of NCDs in the Southeast Asian region, our findings underscore the role that workplace digital health interventions can play in preventing and managing chronic disease risk factors.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".