Mapping the Dynamics of Inhibitors and Facilitators of Exercise Behavior Within the Transtheoretical Model: Nationwide Cross-Sectional Study Using Text Mining Analysis
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».