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Record W770505135

Mobility in Aging: Travel Behavior and Implications for Physical Activity

2014· dissertation· en· W770505135 on OpenAlexfundaboutno aff
Moniruzzaman Khan M

Bibliographic record

VenueMacSphere (McMaster University) · 2014
Typedissertation
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsnot available
FundersMcMaster University
KeywordsPhysical activityPsychologyTravel behaviorGerontologyTransport engineeringEngineeringMedicinePhysical medicine and rehabilitation
DOInot available

Abstract

fetched live from OpenAlex

Rapid demographic aging in countries around the world has prompted an interest in understanding the mobility patterns of seniors. While much research has been conducted in terms of motorized modes, the promotion of healthy aging argues for new research to investigate the multi-modal travel behavior of seniors including active travel. It is generally agreed that walking is a convenient, safe, and adequate activity for all ages and particularly for seniors, because it places the right amount of stress on their joints. It also is an inexpensive mode of transportation under a wide range of circumstances and can help achieve physical activity guidelines without imposing additional time demands. The objectives of this dissertation are fourfold. The first two objectives investigate the factors that influence use, length, frequency of two motorized (transit and car) and one active mode of transportation (walking) of seniors. The third objective is to introduce a concept of Compliance Potential Mapping (CPM) that produces maps to show spatial variation in percentage of physical activity requirements seniors obtain from their regular walking for transport. Finally, the dissertation implements a street segment sampling approach and investigates the attributes of walkable environments from the perspective of seniors. A joint discrete-continuous modeling framework was used to model mode use and trip length simultaneously and, on the other hand, a trivariate ordered probit model was used for estimating the multi-modal trip generation of seniors. CPM concept used simple map algebra operations on maps of spatial variations in trip length and frequency in order to produce potential maps of physical activity compliance. Lastly, the street sampling approach used multinomial spatial scan statistic to detect cluster of street segments where walkability audits can be conducted. Data were drawn from Montreal’s Household Travel Survey of 2008. A broad array of covariates related to personal, mobility tools (possession of driver’s licence and automobile), neighborhood, and accessibility variables were considered in the models of mode use, trip length, and trip frequency for the Montreal Island. The results of the analyses reveal a significant degree of geographical variability in the travel behavior of seniors in the Island. In particular, estimates for seniors with different socio- demographic profiles show substantial intra-urban variability in walking behavior, and the role of neighborhood design attributes and accessibility in influencing the mobility of seniors. Demonstration of CPM indicates that seniors in the central parts of Montreal Island obtain higher percentage of physical activity guidelines from walking, but with variations according to gender, income, possession of driver’s licence and vehicle. The results of the walkability analysis suggest that, other factors being equal, walking is more prevalent in street segments with marked cross–walks, horizontal and vertical mixtures in land uses, and low traffic volume. Other factors being equal, walking was less prevalent in segments with unmarked cross–walks, single residential and/or vacant land use, and high traffic volume.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.647
Threshold uncertainty score1.000

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.025
GPT teacher head0.292
Teacher spread0.267 · 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.

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
Published2014
Admission routes2
Has abstractyes

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