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Record W4402348206 · doi:10.1016/j.jth.2024.101901

Understanding the behavioural determinants of active travel among older adults: A mixed methods study

2024· article· en· W4402348206 on OpenAlexafffundabout
Avet Khachatryan, Paula Voorheis, Ignacio Tiznado-Aitken, Michelle Pannor Silver

Bibliographic record

VenueJournal of Transport & Health · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsThe Scarborough HospitalHamilton Health SciencesUniversity of Toronto
FundersUniversity of Toronto Scarborough
KeywordsPsychologyGerontologyDevelopmental psychologyMedicine

Abstract

fetched live from OpenAlex

Active travel is an accessible form of physical activity, associated with physical and mental health benefits, particularly for older adults. This study aimed to identify the behavioural determinants that significantly impact older adults’ engagement in active travel as well as how and why these behavioural determinants occur. This study employed an explanatory sequential mixed methods design informed by the Theoretical Domains Framework (TDF). The study focused on older adults in Scarborough, Canada. In the quantitative phase, a TDF-informed survey was distributed among Scarborough older adults and analyzed using descriptive statistics, Chi-squared tests, and generalized linear models. TDF determinants that significantly impacted older adult participants in active travel were identified. The qualitative phase followed up with select survey participants about their experiences with these TDF determinants. Semi-structured interviews were conducted, and data was analyzed using thematic codebook analysis. To integrate the quantitative and quantitative results, data were interpreted and integrated using a joint display matrix. The survey results suggest that the TDF domains of motivation (i.e., feeling inspired to active travel) and skill (i.e., having the aptitude to active travel) were highly predictive of active travel behaviour among older adults in Scarborough, Canada. Beyond the TDF, the likelihood of active travel increased if participants reported being encouraged and satisfied with their health, fitness, exercise, and well-being in general. The interview results suggest that fostering motivation and skill require a network of interventions that address multiple TDF domains such as promoting action planning, creating commitment, building social connectedness, increasing awareness of active travel options, and building emotional and physical safety. By integrating the quantitative and qualitative results, three overarching themes were presented about older adults’ needs to engage in active travel: building reassurance (i.e., putting safety first); building meaningfulness (i.e., creating joy, fulfillment, and purpose); and building relatedness (i.e., creating awareness and connection). This study suggests several important implications for future research and policy. The results of this study provide evidence to support investment in both built environment improvements (e.g., safer intersections, bike lanes, lower vehicle speed limits, public transit subsidies) as well as tailored activities to promote active travel (e.g., public awareness campaigns, skill development activities, organized walks). The mixed methods study design employed in this study in conjunction with a novel application of the TDF allowed for a more thorough understanding of behavioural determinants of active travel among older adults. • Reassurance, meaningfulness, and relatedness are overarching themes in older adults' active travel (AT). • Health satisfaction, motivation and skill may facilitate AT among older adults. • Building AT motivation and skill requires fostering action planning, commitment, social connectedness, awareness, safety. • Combining a mixed-methods study design with the TDF allowed for thorough understanding of behavioural determinants of AT.

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)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.057
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.117
GPT teacher head0.430
Teacher spread0.312 · 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 teacher head, 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

Citations4
Published2024
Admission routes3
Has abstractyes

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