Mapping the Dynamics of Inhibitors and Facilitators of Exercise Behavior Within the Transtheoretical Model: Nationwide Cross-Sectional Study Using Text Mining Analysis
Bibliographic record
Abstract
Background: The transtheoretical model (TTM) explains behavior changes through sequential stages influenced by the balance of perceived benefits and barriers. Although previous studies have identified the inhibitors and facilitators of exercise behavior, only a few have elucidated how these factors vary across the stages of behavior change. Objective: This study aimed to identify the inhibitors and facilitators of each stage of behavior change using text mining. Methods: A nationwide cross-sectional study was conducted using an internet-based questionnaire with a panel of approximately 2 million members. From this panel, 93,460 individuals were randomly selected and invited to participate via email and app push notifications. A total of 1500 valid responses were included in the analysis through stratified sampling based on sex, age group, and geographic region. The participants self-assessed their stages of change. Two open-ended questions captured the perceptions of inhibitors and facilitators of exercise behavior. Text responses were analyzed in a 4-step process: morphological analysis to extract frequently used words, correspondence analysis to visualize relationships between frequently used words and the 5 change stages, conceptual categorization with coding rules, and creation of heat maps to illustrate stage-specific categories in inhibitors and facilitators. Results: Out of 1500 respondents, 754 (50.3%) were males and 355 (23.7%) individuals were in the 50-59 age group. Stage percentages were precontemplation 24.3% (365/1500), contemplation 23.5% (352/1500), preparation 21.3% (320/1500), action 5% (75/1500), and maintenance 25.9% (388/1500). The inhibitors and facilitators were described using 9893 words and 8372 words, respectively. Inhibitors clustered into 7 categories; most frequent were time (408/1500, 27.2%), motivation (253/1500, 16.9%), health (189/1500, 12.6%), and working (158/1500, 10.5%). Facilitators formed 8 categories; most frequent were subjectivity (155/1500, 10.3%), relationship (93/1500, 6.2%), opportunity (84/1500, 5.6%), reward (78/1500, 5.2%), and record (78/1500, 5.2%). Stage-specific patterns emerged: inhibitors shifted from motivation and health (precontemplation) to family, time, and working (contemplation and preparation), opportunity (action), and weather and health (maintenance). Facilitators of reward, health, and record rose progressively from precontemplation to maintenance. Conclusions: This study enhances our understanding of the dynamic mechanisms underlying exercise behavior change by identifying how specific inhibitors and facilitators vary across behavioral stages. The findings underscore the need to tailor interventions based on individuals' readiness to change, rather than relying on one-size-fits-all strategies. For both practitioners and policymakers, incorporating behavioral stage frameworks into assessments and interventions, such as those conducted during health checkups or workplace programs, may improve the effectiveness and sustainability of physical activity promotion.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".