AI-Enabled, Text-Based Health Coaching and Navigation for Employees to Support Health Outcomes: Pre-Post Observational Study
Notice bibliographique
Résumé
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.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,002 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».