AI-Enabled, Text-Based Health Coaching and Navigation for Employees to Support Health Outcomes: Pre-Post Observational Study
Bibliographic record
Abstract
Background: Limited, timely access to quality mental health treatment harms well-being and quality of life while costing individuals and organizations millions in increased medical spending and reduced productivity. Too few qualified professionals, inconsistent quality, and stigma thwart traditional solutions, creating the need for scalable, science-based solutions. Objective: This report provides an overview of a novel digital health coaching service that consists of artificial intelligence (AI)-assisted, human-delivered, text-based health coaching. This report provides data evaluating the efficacy of this service for delivering mental health support, improving well-being, and enhancing workplace productivity. Methods: This observational study analyzed operational and self-reported health data from employees of subscribing organizations who used Sibly's digital health coaching service. Data included response times, changes in expressed member sentiment, conversation topics, and adherence to motivational interviewing. A subset of members (n=38) provided pre-post self-reported assessment measures of distress, unhealthy days, and presenteeism, having engaged in at least 4 coaching conversations over a minimum of 14 days. Sentiment was evaluated using a natural language processing tool. Results: Sibly provided quick access to interactive human coaching, with a median response time of 132 seconds. Sentiment analysis showed that 57% (878/1540) of conversations increased in positive emotions. The coaches maintained strong fidelity to the techniques of motivational interviewing, with adherence exceeding 90% (387/430). The proportion of users reporting severe distress declined from 33.3% (10/30) at baseline to 6.7% (2/30) at follow-up, representing a 79% relative reduction (P<.001). Participants also reported a reduction in the number of unhealthy days per month, decreasing from 19.57 to 15.87 per month (P=.02). Self-reported productivity improved by 18% during the study period (P<.001). Additionally, 61% (47/77) of users who received referrals to additional employer-sponsored benefits engaged with those resources, demonstrating effective care navigation to relevant support services. Conclusions: This report provides an overview of novel mental health support and navigation services that use AI-enabled, text-based health coaching and care navigation. Data suggest that the services provide effective, scalable mental health support in workplace settings. The platform helped reduce distress, improve well-being, and boost productivity by offering immediate access to trained coaches and personalized guidance. These results are consistent with existing research on digital mental health services. They highlight the potential of AI-assisted coaching to improve access to care. Future research should include larger, diverse populations and more rigorous randomized controlled trials. This formative report provides data that describes and demonstrates a proof of concept for an innovative technology-enabled service that addresses the problems of scalability, access, quality, and stigma that challenge the provision of traditional mental health services.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".