Reframing physical activity barriers in individuals with multiple sclerosis: a proof-of-concept study
Bibliographic record
Abstract
Purpose To explore how a brief reframing intervention influences physical activity (PA) behaviors and cognitive biases in individuals with Multiple Sclerosis (MS).Materials and methods In this mixed-method, single-arm study, 22 individuals with MS received a one-on-one, ∼15-min reframing session targeting negative exercise-related thinking patterns. Participants completed surveys at four time points (pre-, post-, 1-week, and 4-weeks post-intervention) assessing cognitive errors and PA. A follow-up interview at 1 week explored individual experiences and perceived changes. Interview data were analyzed using inductive content analysis; Repeated Measures ANOVA assessed changes in outcomes.Findings Cognitive errors decreased from pre-reframing (M = 5.32) to 1-week post reframing (M = 4.52, p = 0.013, partial eta = 0.258). Light PA significantly increased from pre-reframing (M = 2.26) to 1-week post reframing (M = 3.14, p = 0.029, partial eta = 0.218). While moderate-vigorous PA significantly increased from pre reframing (M = 2.76) to 4-weeks post reframing (M = 5.76, p = < 0.01, partial eta = 0.460). Changes to PA at all levels were sustained at the 4-week follow-up. Interviews contextualized these changes, revealing shifts in motivation and self-talk.Conclusion Reframing may be an effective strategy to increase physical activity and decrease cognitive biases for individuals with MS. Findings provide preliminary support for a future randomized pilot trial to evaluate efficacy.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".