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Record W4323061023 · doi:10.2196/43411

Motivational Profiles and Associations With Physical Activity Before, During, and After the COVID-19 Pandemic: Retrospective Study

2023· article· en· W4323061023 on OpenAlexaffvenueabout
Kayla Nuss, Wuyou Sui, Ryan E. Rhodes, Sam Liu

Bibliographic record

VenueJMIR Formative Research · 2023
Typearticle
Languageen
FieldPsychology
TopicMotivation and Self-Concept in Sports
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsSelf-determination theoryPandemicAutonomyCompetence (human resources)PsychologyIntrinsic motivationCoronavirus disease 2019 (COVID-19)Physical activityAmotivationDevelopmental psychologyClinical psychologyGerontologySocial psychologyMedicineDiseasePhysical therapyPolitical scienceInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

BACKGROUND: In March 2020, the World Health Organization declared the worldwide COVID-19 outbreak a pandemic, triggering many countries, including Canada, to issue stay-at-home orders to their citizens. Research indicates that these stay-at-home orders are associated with a decline in physical activity (PA), a behavior that can reduce disease risk and improve the quality of life. Many behavioral change theories, such as the self-determination theory (SDT) of motivation, state that PA engagement is mediated by psychological constructs, such as motivation. According to the SDT, motivation exists on a continuum from more controlled (external or coerced) to more autonomous (volitional) regulatory forms. Individuals move along the continuum from more controlled to more autonomous forms through the fulfillment of 3 psychological needs: autonomy, competence, and relatedness. Research indicates that moderate-to-vigorous physical activity (MVPA) is positively associated with the autonomous regulatory form of motivation. Recently, researchers have speculated that a better method to describe motivation than movement along the continuum is to generate motivational profiles, which represent combinations of differing levels of controlled and autonomous regulation existing simultaneously. OBJECTIVE: We aimed to identify distinct motivational profiles and determine their association with MVPA before, during, and after the COVID-19 pandemic. METHODS: Using a cross-sectional, retrospective design, we surveyed 977 Canadian adults. We assessed motivation for PA using the Behavioral Regulations in Exercise Questionnaire-3 (BREQ-3). We assessed PA pre-, during, and post-COVID-19 stay-at-home orders in Canada using the International Physical Activity Questionnaire (IPAQ). We derived motivational profiles using latent profile analysis (LPA). Using motivational profiles as an independent variable, we assessed their effect on PA at all 3 time points with multilevel models that included the participant ID as a random variable. RESULTS: We identified 4 profiles: high controlled and high autonomous (HCHA), low overall motivation (LOM), high autonomous and introjected (HAI), and high amotivation and external (HAE). The HCHA profile had the highest levels of weekly MVPA minutes at all 3 time points, followed by the HAI profile. CONCLUSIONS: Our results suggest that a combination of both autonomous and controlled regulatory forms may be more effective in influencing MVPA than the controlled or autonomous forms alone, particularly during times of high stress, such as a worldwide pandemic. Although the odds of another global pandemic are low, these results may also be applied to other times of stress, such as job transitions, relationship changes (eg, change in marital status), or the death of a loved one. We suggest that clinicians and practitioners consider developing PA interventions that seek to increase both controlled and autonomous regulatory forms instead of aiming to reduce controlled forms.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.738
Threshold uncertainty score0.527

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.077
GPT teacher head0.437
Teacher spread0.360 · 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 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

Citations6
Published2023
Admission routes3
Has abstractyes

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