What is the role of lifestyle behaviour change associated with non-communicable disease risk in managing musculoskeletal health conditions with special reference to chronic pain?
Bibliographic record
Abstract
BACKGROUND: Other than activity and exercise, lifestyle practices such as not smoking and healthy nutrition, well established for preventing and managing lifestyle-related non-communicable diseases (i.e., heart disease, cancer, hypertension, stroke, obstructive lung disease, diabetes, and obesity), are less emphasized in the physical therapy guidelines for addressing chronic pain, e.g., back pain. This state-of-the-art review examines the relationships between lifestyle behaviours and musculoskeletal health, with special reference to chronic pain, and their clinical and research implications. DISCUSSION: A state-of-the-art review was conducted to synthesize evidence related to lifestyle factors (not smoking, healthy diet, healthy weight, optimal sleep and manageable stress, as well as physical activity) and musculoskeletal health, with special reference to chronic pain. The findings support that health behaviour change competencies (examination/assessment and intervention/treatment) may warrant being included in first-line management of chronic pain, either independently or in conjunction with conventional physical therapy interventions. To address knowledge gaps in the literature however three lines of clinical trial research are indicated: 1) to establish the degree to which traditional physical therapy interventions prescribed for chronic pain augment the benefits of lifestyle behaviour change; 2) to establish the degree to which adopting healthier lifestyle practices, avoids or reduces the need for conventional physical therapy; and 3) to establish whether patients/clients with healthier lifestyles and who have chronic pain, respond more favourably to conventional physical therapy interventions than those who have less healthy lifestyles. SUMMARY: Lifestyle behaviour change is well accepted in addressing lifestyle-related non-communicable diseases. Compelling evidence exists however supporting the need for elucidation of the role of negative lifestyle behaviours on the incidence of chronic pain, and the role of positive lifestyle behaviours on its incidence and effective management. Addressing lifestyle behaviour change in patients/clients with chronic pain, e.g., back pain, as a first-line intervention might not only constitute a novel approach, but also reduce the socioeconomic burden related to chronic pain as well as non-communicable diseases.
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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 | 0.013 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| 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".