Most digital health platforms rely on self-reported surveys, intake forms, and one-time assessments. Yet decades of behavioral research show that what people say they intend to do often fails to predict what they actually do over time.
Organizations like the CDC, NIH, and RAND have consistently highlighted that effective health programs require ongoing observation, adaptation, and reinforcement — not static inputs collected at the start.
Sources:
CDC Workplace Health Model: https://www.cdc.gov/workplace-health-promotion/php/model/index.html
NIH Research Matters (Behavior Change): https://www.nih.gov/news-events/nih-research-matters
RAND Workplace Wellness Programs: https://www.rand.org/pubs/research_reports/RR254.html
This gap between intention and action contributes to widespread underutilization of health programs and benefits across both clinical and employer settings.
Learning Behavior, Not Just Capturing Data
CoachLinq was designed to address this gap by learning from real behavior in real life. Instead of relying on outdated assessments, Linq continuously learns through natural interactions — how often someone engages, when friction appears, what language they use, and how their patterns evolve over time.
Behavioral signals Linq learns from include:
- interaction timing and frequency
- friction and hesitation points
- micro-engagement patterns
- emotional language cues
- consistency over time
- alignment between intention and follow-through
These inputs create a dynamic behavioral profile that adapts as a person’s life changes.
Why This Matters
Research from the Kaiser Family Foundation and SHRM shows that employers invest heavily in health benefits that often go underused, not because the offerings lack value, but because systems fail to adapt to human behavior.
Sources:
Kaiser Family Foundation: https://www.kff.org
SHRM Benefits Insights: https://www.shrm.org
CoachLinq closes this gap by understanding how people behave, not just how they report their goals.
Bottom line: Linq doesn’t ask people to be motivated. It learns what motivates them — and adapts accordingly.
Clinical research supporting this approach is available at:
https://www.inhealthonline.com/clinical-research
