Facilitated Health Coaching Improves Activity Level and Chronic Low back Pain Symptoms
Bibliographic record
Abstract
ABSTRACT Introduction Behavioral treatments, like increasing physical activity (PA), are recommended for chronic low back pain (CLBP). Finding methods for promoting behavior change with potential for translatability as well as effective behavioral targets remains challenging. Purpose This randomized controlled pilot study evaluated the effectiveness of low-dose facilitated health coaching with activity monitors to improve PA and sedentary behaviors (SB), symptoms, and Patient Global Impression of Change (PGIC) in CLBP. A secondary purpose was identifying behaviors associated with symptom change to inform future trials. Methods Seventy-one adults with CLBP were randomized to receive a wearable activity monitor alone (WAM) or with one primary session of health coaching and two check-ins based on motivational interviewing (WAM + HC) or a wait-list control (WLC) condition for 12 wk. Moderate and vigorous PA (MVPA), light PA (LPA), total and prolonged SB (i.e., sedentary >60 min per bout), and pain (Short Form McGill Pain Questionnaire (MPQ)) were assessed before and after intervention along with PGIC. Regression analyses examined group differences in MVPA, LPA, total and prolonged SB, MPQ, and PGIC after intervention as well as behavioral predictors of symptom improvement (change in MPQ). Results WAM + HC improved MPQ scores ( β = −0.25, P = 0.02), LPA ( β = 0.23, P = 0.04), MVPA ( β = 0.21, P = 0.03), and total SB ( β = −0.24, P = 0.03) compared with WLC over time. Both WAM + HC and WAM had significantly higher PGIC compared with WLC ( P < 0.05). Change in prolonged SB was the only significant predictor of change in MPQ ( β = 0.48, P = 0.01). Conclusions WAM + HC may be effective for changing activity-related behaviors and improving CLBP. Furthermore, reducing prolonged SB may be a meaningful target for future interventions in CLBP. Research focused on reducing prolonged SB including larger samples, and examining changes in symptoms will be important for identifying optimal translational treatment strategies for CLBP.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".