Self-Regulatory Processes, Motivation to Conserve Resources and Activity Levels in People With Chronic Pain: A Series of Digital N-of-1 Observational Studies
Bibliographic record
Abstract
OBJECTIVES: Motivational and self-regulatory processes during goal pursuit may account for activity patterns in people with chronic pain. This article describes a series of N-of-1 observational studies designed to investigate the influence of goal-related factors on fluctuations in motivation to conserve resources and objectively measured activity levels. METHODS: Four participants with chronic pain who attended a formal pain management program (PMP; 41-59 years old; three female) were recruited and completed digital daily diaries for 11-12 weeks. The daily dairies, delivered via text message, measured self-regulatory fatigue, goal self-efficacy, goal striving, perceived demands, pain, and motivation to conserve resources. Continuously worn accelerometers measured physical activity and sedentary time. Analyses were conducted individually for each participant. The effects of self-regulatory fatigue, goal self-efficacy, goal striving, perceived demands, and pain on motivation to conserve resources, physical activity and sedentary time were assessed with dynamic regression modeling. RESULTS: Different patterns of associations between the predictors and outcomes were observed across participants. Most associations occurred concurrently (e.g., on the same day). Perceived demand was the only variable to predict motivation to conserve resources, physical activity, and sedentary time. Motivation to conserve resources and sedentary time were most frequently predicted by goal striving and perceived demand. Self-regulatory fatigue and pain intensity both predicted motivation to conserve resources in two participants and sedentary time in one participant. Motivation to conserve resources predicted sedentary time in two participants. CONCLUSION: This study was the first to examine the impact of fluctuations in self-regulatory processes on motivation to conserve resources and objective activity levels within individuals with chronic pain. The results generally supported recent affective-motivational views of goal pursuit in chronic pain. This study demonstrated that N-of-1 observational studies can be conducted with patients during a PMP using digital technologies. The use of these approaches may facilitate the application of personalized medicine.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".