An I-Change approach to understanding exercise determinants among black individuals with hypertension
Bibliographic record
Abstract
OBJECTIVE: Black populations are disproportionately affected by hypertension and are less likely to engage in blood pressure-lowering activities, such as exercise, compared to non-Hispanic White populations. There is a lack of theory-informed approaches to understand how individual and environmental racial disparities impact exercise participation among Black individuals with hypertension. The I-Change Model, an integrated behavior change framework, combines concepts from social and health psychology to explain the interaction between awareness, motivation, and action in adopting and maintaining health behaviors. This study aims to apply an augmented version of the I-Change Model to enhance our understanding of racial disparities in exercise participation. METHODS: Individuals with self-reported doctor-diagnosed hypertension ( N = 370), comprising Black ( n = 142) and White ( n = 228) adults who were recruited via an online recruitment platform, completed a survey with validated theoretical constructs at baseline and at 4 weeks. Structural equation modeling with race set as a group variable was used to among both races, intention predicted exercise behavior model path effects. RESULTS: Among both races, intention positively influenced exercise behavior, whereas psychological barriers reduced the likelihood of engaging in the behavior. However, notable racial disparities among Black participants included environmental barriers (e.g., safety, accessibility to a gym) that hindered exercise behavior and affective attitudes that did not facilitate intention. CONCLUSIONS: An exercise promotion program that fosters I-Change determinants and creates an accessible, supportive environment would enhance equitable exercise opportunities for Black individuals with hypertension. Additional recommendations for designing such a program are provided.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".