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Record W2971738955 · doi:10.2196/13570

Perceptions and Experiences of Women Participating in a Digital Technology–Based Physical Activity Intervention (the mPED Trial): Qualitative Study

2019· article· en· W2971738955 on OpenAlexvenueno aff
Teri Lindgren, Julie Hooper, Yoshimi Fukuoka

Bibliographic record

VenueJMIR Public Health and Surveillance · 2019
Typearticle
Languageen
FieldMedicine
TopicPhysical Activity and Health
Canadian institutionsnot available
FundersNational Institute of Nursing ResearchNational Heart, Lung, and Blood Institute
KeywordsPedometerRandomized controlled trialIntervention (counseling)Physical activityMedicinePhysical therapyPsychological interventionQualitative researchDescriptive statisticsPerceptionPsychologyFamily medicineNursing

Abstract

fetched live from OpenAlex

BACKGROUND: Despite the benefits of regular physical activity, women in every age group have lower activity levels than men, and few women meet the recommended levels of physical activity. Digital technologies have been useful in increasing physical activity during the course of an interventional study. However, sustaining that activity once the clinical trial was complete was a major challenge. OBJECTIVE: This study aimed to describe the experiences and perspectives of physically inactive women who completed the mobile phone-based physical activity education (mPED), a randomized controlled trial, at 12 months. METHODS: Of 210 women who were enrolled in the mPED trial, 203 completed a 12-month open-ended exit interview and survey through phone. The participants were asked about their physical activity levels; their digital technology use; what they learned from, liked, and would change about the trial; their motivations to keep active post-trial; and their advice for other women. Interviews were transcribed verbatim and thematically analyzed using the brief survey qualitative description. Descriptive statistics were used to describe the survey data with the significance level set at P<.05. RESULTS: In the 12-month survey, a greater proportion of the participants in the intervention group, compared with the control group, reported that they regularly wore a pedometer or physical activity tracker (49.3%, 66/143 vs 26.1%, 18/69; P=.002) and engaged in brisk walking (54.5%, 73/134 vs 30.4%, 21/69; P=.001). The experiences and perceptions of physical activity of physically inactive women over time were embedded in a complex interplay of internal and external factors. A total of 6 interactive themes emerged as critical in supporting continued engagement in physical activity postintervention: tracking, technology versus personal touch, accountability, resources and environment, motivation, and habit formation. Technology allowed for self-tracking, which supported internal accountability. However, tracking by another person (personal touch) was needed for external accountability. Resources and environment underpinned the relationship among the themes of tracking, technology versus personal touch, accountability, motivation, and habit formation. CONCLUSIONS: Future research is needed to identify the best ways to harness this dynamic process in promoting and sustaining physical activity among inactive women. Digital technology is evolving at an exponential rate and provides new opportunities to transform research into new approaches to promote physical activity. TRIAL REGISTRATION: ClinicalTrials.gov NCT01280812; https://clinicaltrials.gov/ct2/show/NCT01280812. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1186/1471-2485-11-933.

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)0.011
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.015
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0040.004
Scholarly communication0.0020.002
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.072
GPT teacher head0.437
Teacher spread0.365 · 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 designQualitative
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

Citations25
Published2019
Admission routes1
Has abstractyes

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