Feasibility of a Digital Coaching Program for Improving Mental Well-Being and Emotional Intelligence: Pragmatic Retrospective Cohort Study
Bibliographic record
Abstract
Background: Within the past decade, digital coaching programs (DCPs) have emerged as an evidence-based modality to improve mental well-being and emotional intelligence (EI), although there is limited evidence in real-world contexts. Objective: This pragmatic retrospective cohort study aims to determine the preliminary effectiveness of a DCP in improving mental well-being and EI within a real-world context. We hypothesized that there would be a significant increase in mental well-being and EI. Methods: This study included 588 people who voluntarily enrolled in an 8-week, blended care DCP offered through their employers from October 2021 to August 2024. The DCP included routine check-ins and consultations with certified coaches. Participants completed the World Health Organization-Five Well-Being Index (WHO-5) at baseline and then weekly until the end of the program, as well as the Brief Emotional Intelligence Scale-10 (BEIS-10) at baseline and the end of the program. Multivariable linear mixed models examined changes in WHO-5 (biweekly) and BEIS-10 (pre-post) scores, adjusting for age, gender, program engagement, and program completion. Multivariable logistic regression models evaluated correlates of clinically meaningful improvements on the WHO-5 (ie, at least a 10-point improvement). We calculated a reliable change index (RCI) for the BEIS-10 and the proportion of participants meeting the RCI criterion from baseline to end of treatment. Results: In multivariate linear mixed models adjusting for demographics and program characteristics, we observed a significant increase in WHO-5 scores (baseline x¯=45.6; week 8 x¯=66.3; Cohen's d=1.98; P<.001). Over half of the sample (55.4%) experienced a clinically meaningful improvement on the WHO-5. Multivariable logistic regression found that higher engagement was associated with an increased odds of a clinically meaningful improvement on the WHO-5 (adjusted odds ratio [aOR] 1.002, 95% CI 1.001-1.003), while program noncompletion (aOR 0.27, 95% CI 0.15-0.50) and higher baseline well-being (aOR 0.91, 95% CI 0.89-0.92) were associated with reduced odds. BEIS-10 scores also significantly increased from baseline to the end of the program after adjusting for relevant correlates (baseline x¯=37.6; week 8 x¯=41.2; Cohen's d=1.32; P<.001). The estimated RCI on the BEIS-10 was approximately 5, with 19.7% experiencing a meaningful improvement. Conclusions: These results demonstrate that DCPs can be a viable option for individuals looking to improve their mental well-being. Additional efforts should focus on establishing reliable change metrics for EI measures. Studies using hybrid effectiveness-implementation trial designs are now needed to further evaluate the real-world effectiveness of this program.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".