Psychological predictors for chronic musculoskeletal pain
Are depression, anxiety, and fear-avoidance predictors of outcomes after digital care for chronic musculoskeletal pain?
Published in PAIN Reports, the official journal of the International Association for the Study of Pain (IASP), this peer-reviewed study examined whether depression, anxiety, and fear-avoidance predict outcomes after fully remote digital MSK care.
Why this study matters
MSK recovery isn’t “one size fits all”. Two people can start with the same pain level and end up with very different outcomes. Understanding what drives those differences is critical for employers and health plans investing in outcomes-based MSK solutions. If we can predict who may struggle early, we can intervene sooner.
This study examined whether baseline psychological factors, specifically depression, anxiety, and fear-avoidance beliefs, were associated with pain outcomes after completing fully remote digital MSK care.
What we found
Baseline fear-avoidance and depression were consistent predictors of worse pain outcomes after using digital care across different demographics and clinical subgroups. In practical terms, members who entered care with higher levels of fear around movement or elevated depressive symptoms were more likely to experience slower or less complete pain reduction.
These findings create an opportunity. Instead of reacting to poor progress after it occurs, digital MSK programs can identify higher-risk members at the start of care and provide additional support earlier in the journey.
Key insight for healthcare buyers and leaders
For health plans and employers, this research signals that Sword’s AI Pain Care is evolving beyond standardized pathways toward predictive personalization. When psychological risk factors are measured and incorporated into care models, programs can proactively tailor support intensity. That strengthens clinical performance and improves the likelihood of durable outcomes.
When Sword’s clinically robust approach is combined with our outcomes-based accountability, the result is improved health for members and consistent savings for health plans and employers.
Why this evidence is credible
- Large, real-world sample (~4,700) across all 50 states
- Advanced modelling (SEM) to reduce common analytic bias
- Tested outcomes in multiple ways to ensure robustness
This is what it looks like when digital MSK care is built on clinical science, not guesswork and assumptions.
