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Record W4389212013 · doi:10.2196/48668

Attitudes, Barriers, and Motivators Toward Daily Walking and a Mobile App to Increase Walking Among Women: Web-Based Anonymous Survey

2023· article· en· W4389212013 on OpenAlexvenueno aff
Catherine Jones, Shikha Chandarana, Amita Vyas, Melissa A. Napolitano

Bibliographic record

VenueJMIR Formative Research · 2023
Typearticle
Languageen
FieldMedicine
TopicPhysical Activity and Health
Canadian institutionsnot available
FundersMilken Institute School of Public Health, George Washington UniversityGeorge Washington University
KeywordsMobile appsWeb surveySmartphone appPsychologyInternet privacyApplied psychologyGerontologyComputer scienceWorld Wide WebMedicine

Abstract

fetched live from OpenAlex

BACKGROUND: There are disparities in the prevalence of physical activity (PA) with women engaging in less PA than men, a gap which widens during midlife. Walking is a generally accepted form of PA among women and should be encouraged. Motivations, barriers, and attitudes to engaging in walking change with age, but the influencing factors are not well understood nor are the features of mobile apps that facilitate daily walking. OBJECTIVE: This study explores the relationship between age and women's self-reported motivations, barriers, attitudes, and beliefs toward daily walking. It further assesses attitudes toward features of a mobile app designed to sync with a wearable step tracker to increase and maintain levels of daily walking among women. METHODS: A web-based anonymous survey was completed by 400 women, aged 21-75 years. The 31-item survey captured women's perceived barriers and motivators toward daily walking and attitudes toward mobile apps to support and maintain daily walking. For analysis, responses to the survey were grouped into 2 categories of women: ages 21-49 years and ages 50-75 years. Bivariate analyses were conducted through SPSS (IBM Corp) for each of the survey questions using chi-square for dichotomous variables and 1-tailed t tests for scales and continuous variables to identify significant differences between the groups. One-tailed t tests were run for scaled variables to identify significant differences between the 10-year age increments. RESULTS: Significant barriers to daily walking were observed in the 21-49-year group for personal and work responsibilities, motivational and psychosocial factors, and physical and environmental factors. Motivators to walk daily in the 21- 49-year group were significantly higher to reduce stress and anxiety, and motivators to walk daily in the 50-75-year group were significantly higher to help manage or lose weight and to reduce the risk of chronic illness. Women's walking preferences, beliefs around their walking behaviors, and their perceived importance of the features of a future mobile app for walking designed specifically for women showed significant variation according to age. When asked about the importance of features for a mobile app, women aged 21-49 years indicated a significantly higher number of positive responses for the following features: digital community support, rewards or point system, and seeing a daily or weekly or monthly progress chart. CONCLUSIONS: Our findings indicate that barriers, motivators, and beliefs around daily walking and the importance of preferred features of a mobile app vary according to women's ages. Messaging and app features should be tailored to different age groups of women. These study results can be viewed as a foundation for future research and development of mobile health interventions to effectively increase daily walking among women of all ages.

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)0.003
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 score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.059
GPT teacher head0.399
Teacher spread0.341 · 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

Citations4
Published2023
Admission routes1
Has abstractyes

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