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Record W2318681754 · doi:10.1249/jes.0000000000000014

Adding Depth to the Next Generation of Physical Activity Models

2014· review· en· W2318681754 on OpenAlexafffundabout
Ryan E. Rhodes

Bibliographic record

VenueExercise and Sport Sciences Reviews · 2014
Typereview
Languageen
FieldPsychology
TopicBehavioral Health and Interventions
Canadian institutionsUniversity of Victoria
FundersCanadian Institutes of Health Research
KeywordsTheory of planned behaviorSocial cognitive theoryPsychologyCognitive psychologyPaceTranstheoretical modelBehavior changeCognitionCognitive scienceSocial psychologyComputer scienceArtificial intelligence

Abstract

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In the mid-1990s, an explosion of behavioral theory testing in the physical activity domain began and has continued at a strong pace, as researchers attempt to explain why some people are active and others are not. Theory of planned behavior, social cognitive theory, and the transtheoretical model have comprised the bulk of this research on theory testing (9), yet recent reviews and meta-analyses have not been favorable for their utility in physical activity behavior change efforts (2,7,11). These original theories, applied from social psychology and allied disciplines, also have shown some limitations in terms of scope and missing variables important to physical activity. Thus, recent calls have been made to adapt and create the next generation of models that may better serve physical activity behavior (5,9,11). The Integrated Behavior-Change (IBC) Model for Physical Activity proposed by Hagger and Chatzisarantis (4) takes up this challenge by offering a blend of the theory of planned behavior and self-determination theory under careful consideration of previous limitations of both these theories. Avoiding construct redundancy is a major challenge in theory integration, as many theories use different labels for similar concepts (1). Hagger and Chatzisarantis have performed this task admirably in their IBC Model, as the constructs of the theory of planned behavior have replaced the self-determination theory’s regulation constructs, which are somewhat redundant with the theory of planned behavior and less advanced/distinct in terms of past theory testing. This creates a basis for theory of planned behavior cognitions via the self-determination theory’s need for autonomy while using the depth of the theory of planned behavior’s structure as proximal antecedents of physical activity. The IBC Model, like several other next-generation models (6,8,10,12), also uses a phased structure (motivational, volitional) and considers the role of deliberative and implicit determination of physical activity that has been a shortcoming of the early physical activity theories. There still are some areas where the IBC may be further refined. For example, the model may lack consideration of the affective domain in physical activity behavior over reasoned approaches to motivation (3). The self-determination theory’s intrinsic regulation construct, omitted from their model, comprised this affective domain better than the general attitude construct within the theory of planned behavior. Furthermore, the role of social-ecological context goes unmentioned, and the implicit constructs within the IBC Model have a relatively underdeveloped antecedent structure in comparison with the deliberative constructs. Still, Hagger and Chatzisarantis are open to modification of the model if researchers can establish an overlooked explanatory process or mechanism. This suggests that the IBC Model can be viewed as a master template, which is very helpful to ongoing theory testing. Overall, the model has had successful tests to support its structure via mediation of autonomy to behavior using passive designs. As the authors point out, the critical next step will be to establish its utility in behavior change — a feature within its title — and overcome the applications of early models, like the theory of planned behavior, that failed to gain traction with experimental tests. Ryan E. Rhodes Behavioural Medicine Laboratory Faculty of Education University of Victoria, Victoria British Columbia, Canada R.E. Rhodes is supported by a Canadian Cancer Society Senior Scientist Award and through funds from the Social Sciences and Humanities Research Council of Canada, The Canadian Cancer Society, and the Canadian Institutes for Health Research.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.010
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.002
Scholarly communication0.0050.008
Open science0.0030.005
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0120.002

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 head0.481
GPT teacher head0.508
Teacher spread0.027 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreReview

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".

Quick stats

Citations6
Published2014
Admission routes3
Has abstractyes

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