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The person's health status fluctuates continually, varying from being in good health to becoming ill and returning to being healthy. To understand the concept of illness prevention, there are two models. First, the health-illness continuum model is a graphic representation of an individual's wellness. It states that a person is considered healthy in the absence of physical disease and the presence of good emotional health.
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Related Experiment Video

Updated: Jan 16, 2026

An Application for Pairing with Wearable Devices to Monitor Personal Health Status

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Published on: February 3, 2022

AI-Enabled, Text-Based Health Coaching and Navigation for Employees to Support Health Outcomes: Pre-Post

Paula Wilbourne^1,2, Susan Mirch-Kretschmann³, Denise Walker⁴

¹Sibly, Inc, 400 Concar Dr., San Mateo, CA, 94402, United States, +1 801 855 6493.

JMIR Formative Research

|September 30, 2025

Summary

This summary is machine-generated.

Digital health coaching using artificial intelligence (AI) and text-based support significantly reduced employee distress and improved well-being and productivity. This scalable solution offers timely mental health support, addressing access and quality challenges.

Keywords:

access artificial intelligence behavioral digital mental health health coaching interviewing machine learning proof of concept quality of life scalability

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Area of Science:

Digital Health
Mental Health Technology
Workplace Wellness

Background:

Limited access to quality mental health care impacts well-being and productivity.
Traditional solutions face challenges due to a shortage of professionals, inconsistent quality, and stigma.
Scalable, science-based solutions are needed to address these mental health access issues.

Purpose of the Study:

To evaluate a novel digital health coaching service using AI-assisted, human-delivered, text-based coaching.
To assess the service's efficacy in delivering mental health support, improving well-being, and enhancing workplace productivity.

Main Methods:

Observational study analyzing operational and self-reported data from employees using a digital health coaching service.
Utilized sentiment analysis via natural language processing and tracked adherence to motivational interviewing.
Assessed pre-post measures of distress, unhealthy days, and presenteeism in a subset of users.

Main Results:

AI-assisted coaching demonstrated rapid response times and increased positive sentiment in conversations.
Significant reductions in reported distress (79%) and unhealthy days were observed.
Self-reported productivity improved by 18%, with high engagement in referred support services.

Conclusions:

AI-enabled, text-based health coaching and navigation offer effective, scalable mental health support in workplaces.
The service successfully reduced distress, improved well-being, and boosted productivity.
Highlights the potential of AI-assisted coaching to overcome traditional mental health service barriers.