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Record W7064564276

A case for theoretical integration: combining constructs from the theory of planned behavior and the extended parallel process model to predict exercise intentions

2014· article· en· W7064564276 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueNOVA (University of Newcastle, Australia) · 2014
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
Fundersnot available
KeywordsTheory of planned behaviorVariance (accounting)Multilevel modelProcess (computing)Regression analysisPopulationHealth promotionPsychological TheoryBehavior changeExplained variation
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.642
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.081
GPT teacher head0.311
Teacher spread0.230 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it