Stifled Motivation, Systemic Neglect: A Cross-Sectional Analysis of Inactivity in Post-Chemotherapy Cancer Survivors in the Middle East and North Africa Region
Bibliographic record
Abstract
Background: Physical activity provides substantial survival and quality-of-life benefits for cancer survivors, yet participation remains suboptimal globally, particularly in the Middle East and North Africa (MENA) regions. This study represents the first comprehensive examination of physical activity barriers and facilitators among Tunisian cancer survivors. Methods: This cross-sectional study recruited 120 cancer survivors ≥3 months post-chemotherapy completion from University Hospital Farhat Hached, Sousse, Tunisia (October–December 2024). Participants completed validated questionnaires via structured telephone interviews: the International Physical Activity Questionnaire Short Form (IPAQ-SF), the Physical Activity Barriers After Cancer scale (PABAC), the Fatigue Assessment Scale (FAS), and the Patient Activation Measure (PAM-13). Statistical analyses included descriptive statistics, receiver operating characteristic (ROC) analysis, correlation analyses, and multivariable regression modeling with Bonferroni correction for multiple comparisons. Results: Participants (mean age 51.89 ± 10.2 years, 73.9% female) demonstrated significant physical activity declines post-chemotherapy: moderate activity decreased from 31.1% to 1.7% (p < 0.001), median intensity declined from 297 to 44 MET-min/week (p < 0.001). Mean PABAC score was 29.72 ± 5.13, with cognitive barriers predominating (2.85 ± 0.58). Fatigue was universal (100%), with 21% reporting severe fatigue (FAS ≥ 35). Only 26.1% received exercise guidance from healthcare professionals. PABAC demonstrated excellent predictive performance for physical inactivity (AUC = 0.805, 95%CI: 0.724–0.887). Independent predictors of higher barriers included fatigue severity (β = 0.466, p < 0.001), low patient activation (β = −0.091, p = 0.010), and advanced cancer stage (β = 1.932, p = 0.008). Conclusions: Tunisian cancer survivors experience substantial, multidimensional barriers to physical activity, with inadequate healthcare guidance representing a critical system-level gap. Findings support the development of culturally adapted, multidisciplinary interventions that target modifiable cognitive and symptom-related barriers, while enhancing patient activation and healthcare provider engagement.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".