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Record W3119850514 · doi:10.21203/rs.3.rs-70750/v1

I Sit but I Don’t Know Why: Integrating Controlled and Automatic Motivational Precursors Within a Socioecological Approach to Predict Sedentary Behaviors

2020· preprint· en· W3119850514 on OpenAlexaff
Silvio Maltagliati, Philippe Sarrazin, Sandrine Isoard‐Gautheur, Ryan E. Rhodes, Matthieu P. Boisgontier, Boris Cheval

Bibliographic record

VenueResearch Square (Research Square) · 2020
Typepreprint
Languageen
FieldPsychology
TopicBehavioral Health and Interventions
Canadian institutionsUniversity of OttawaUniversity of Victoria
FundersSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungNational Science Foundation
KeywordsPsychologyHuman–computer interactionComputer science

Abstract

fetched live from OpenAlex

Abstract Background. Precursors driving leisure-time sedentary behaviors remain poorly investigated, despite their detrimental consequences. This study aimed to investigate the predictive validity of controlled and automatic motivational precursors toward reducing sedentary behaviors and being physically active on leisure-time sedentary behaviors. The influence of demographic, physical, socio-professional, interpersonal, and environmental variables on leisure-time sedentary behaviors was also examined and compared with the associations of motivational precursors. Methods. 125 adults completed questionnaires measuring controlled motivational precursors (i.e., attitudes, intentions, perceived competence), demographical (i.e., sex and age), physical (i.e., body mass index), and interpersonal (i.e., number of children) variables. Regarding automatic motivational precursors, habit strength and approach-avoidance tendencies were captured using the Self-Report Behavioral Automaticity Index and a manikin task. Leisure time, time and physical activity at work were computed as socio-professional variables, days of the week and weather conditions were recorded as environmental precursors. Participants wore an accelerometer for seven days and leisure time was identified thanks to notebooks. Associations between the different precursors and the leisure-time sedentary behaviors were examined in linear mixed effect models. Results. Intention to be physically active (b = -1.089, p = .028) and habit strength toward physical activity (b = -0.943, p = .019) were negatively associated with leisure-time sedentary behaviors. However, motivational precursors toward reducing sedentary behaviors were not associated with the dependent variable ( ps. > .098). Demographical (b = 5.043, p = .002 for sex and b = 0.493, p = .011 for body mass index), socio-professional (b = -1.318, p =.040 for leisure time and b= 1.861, p = .005 for time at work), interpersonal (b = -2.037, p = .002 for the number of children), and environmental (i.e., p = .028 for the global effect of the day of the week and p < .001 for the global effect of the weather conditions) precursors were more strongly associated with leisure-time sedentary behaviors. Conclusion. Our findings show that, in comparison with demographical, socio-professional, interpersonal and environmental variables, the influence of motivational precursors on leisure-time sedentary behaviors is limited. This study lends support for the adoption of a broad-spectrum of precursors when predicting sedentary behaviors.

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 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)0.017
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesResearch integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.530
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0170.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.002
Science and technology studies0.0020.002
Scholarly communication0.0010.000
Open science0.0020.004
Research integrity0.0020.014
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.180
GPT teacher head0.483
Teacher spread0.304 · 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; both teacher heads agree on what is shown here.

Study designObservational
Domainnot available
GenreEmpirical

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

Citations0
Published2020
Admission routes1
Has abstractyes

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