A case for theoretical integration: combining constructs from the theory of planned behavior and the extended parallel process model to predict exercise intentions
Bibliographic record
Abstract
The present research investigated whether constructs of the Theory of Planned Behavior (TPB) and the Extended Parallel Process Model can be integrated into a model that can better explain intentions to exercise than the TPB constructs alone. A total of 336 participants completed measures of constructs from both theories and past exercise behavior. Hierarchical regression analyses revealed that attitudes, subjective norms, susceptibility, self-efficacy, and past behavior contributed unique variance to a model that predicted intentions to exercise. This model explained a greater proportion of the variance in exercise intentions than the TPB alone. Relationships between key variables of both models were also highlighted. Implications for theory and practice are discussed. It is widely acknowledged that regular exercise is associated with significant physiological and psychological benefits (Warburton, Nicol, & Bredin, 2006; Williams, 2001). However, in many westernized countries, less than half the population meet the minimum recommended physical activity requirements to achieve these health benefits (e.g., Australian Institute of Health and Welfare, 2010; Canadian Fitness and Lifestyle Research Institute, 2004; U.S. Department of Health and Human Services, 2003). As such, investigating ways of increasing exercise is a concern within health psychology (e.g., Hagger & Armitage, 2004; Hagger, Chatzisarantis, & Biddle, 2002a; Jones, Sinclair, Rhodes, & Courneya, 2004; Rhodes,& Nasuti,2011). The focus of this research is to identify the socio-cognitive factors that predict exercise intentions and behavior and provide a psychological account of how these factors determine behavior. Advancing theories in this way is important as successful manipulation of these factors via health promotion and communication may be useful in increasing the rates of exercise (Armitage & Conner, 2000). Several models have been proposed to explain health behaviors. Two prominent models are the Theory of Planned Behavior (TPB; Ajzen, 1985, 1987) and the Extended Parallel Process Model (EPPM; Witte, 1992a). Both these models have been utilized to explain health behavior and intentions to engage in health behavior. Intention refers to the strength of the motivation or desire to engage in a particular behavior (Ajzen, 1991). Embedded in both models is the assumption that an individual’s intention to engage in a particular health behavior is a proximal predictor of engagement in that behavior (Ajzen, 1991; Witte, 1994). As such, intentions are often used as the dependent variable of interest rather than actual behavior change (e.g., Abraham, Sheeran, & Henderson, 2011; Armitage & Conner, 2000; Godin & Kok, 1996; Hagger & Armitage, 2004; Hagger et al., 2002a). However, meta-analytic reviews suggest that neither model can explain all or even most of the variance in either behavioral intentions or health behavior change (e.g., Armitage & Conner, 2001; Floyd, Prentice-Dunn, & Rogers, 2000; Hagger et al., 2002a; Milne, Sheeran, & Orbell, 2000; Witte & Allen, 2000). The literature primarily focuses on testing and utilizing existing theory to predict intentions and health behavior. Most often, one theory is selected to guide the choice of explanatory and outcome variables as if the other theories did not exist (Weinstein, 1993). Several researchers have lamented that there is a lack of research comparing competing theories or augmenting existing theories (Noar & Zimmerman, 2005; Ogden, 2003; Weinstein, 1993, 2007; Weinstein & Rothman, 2005; see Conner & Armitage, 1998; Dodge, Stock, & Litt, 2013; Dolman & Chase, 1996; Godin & Kok, 1996; Hagger et al., 2002a; Murray-Johnson et al., 2006, for some notable exceptions). Failing to compare or adjust existing theory means that it fails to naturally evolve, and as such our understanding of the socio-cognitive factors which determine health behavior change (and the mediators of health behavior change) does not improve (Weinstein & Rothman, 2005). Several researchers have advocated taking a broader approach to predicting health behavior change by utilizing constructs from several theoretical perspectives—namely, theoretical integration (e.g., Hagger, 2009, 2010; Noar & Zimmerman, 2005). Bringing together the constructs with the most research support into a single model may yield a model which can explain a larger proportion of the variance than any single model alone. In many cases, the similarities between models of health behavior outweigh the differences (Hagger, 2009, 2010; Weinstein, 2007). Therefore, to reduce redundancy, only dissimilar models should be integrated. Two models which stand out as being different from one another, while still explaining a large proportion of the variance in health behavior change, are the TPB and EPPM.
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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 | 0.120 | 0.146 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.004 | 0.026 |
| Scholarly communication | 0.015 | 0.033 |
| Open science | 0.008 | 0.018 |
| Research integrity | 0.007 | 0.023 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".