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Record W4224085466 · doi:10.2196/35511

A Photography-based, Social Media Walking Intervention Targeting Autonomous Motivations for Physical Activity: Semistructured Interviews With Older Women

2022· article· en· W4224085466 on OpenAlexvenueno aff
Michael C Robertson, Maria C. Swartz, Ursela Christopherson, Jason R. Bentley, Karen Basen‐Engquist, Debbe Thompson, Elena Volpi, Elizabeth Lyons

Bibliographic record

VenueJMIR Serious Games · 2022
Typearticle
Languageen
FieldMedicine
TopicPhysical Activity and Health
Canadian institutionsnot available
FundersNational Institute on AgingAgricultural Research ServiceCenter for Energy Balance in Cancer Prevention and SurvivorshipUniversity of Texas MD Anderson Cancer CenterNational Cancer InstituteNational Institutes of HealthU.S. Department of Agriculture
KeywordsPhotographyIntervention (counseling)Social mediaPsychologyPhysical activityApplied psychologyGerontologyMedical educationMultimediaPhysical medicine and rehabilitationNursingVisual artsMedicineComputer scienceArtWorld Wide Web

Abstract

fetched live from OpenAlex

BACKGROUND: Older adult women are at risk for negative health outcomes that engaging in sustained physical activity can help prevent. However, promoting long-term maintenance of physical activity in this population has proven to be a challenge. Increasing autonomous motivations (ie, intrinsic, integrated, and identified regulations) for physical activity may facilitate enduring behavior change. Digitally delivered games for health that take a celebratory technology approach, that is, using technology to create new ways to experience valued behaviors and express valued beliefs, may be a useful way to target autonomous motivations for physical activity. Formative research with the target population is needed to design compelling intervention content. OBJECTIVE: The objective of this study is to investigate older adult women's reactions to and thoughts about a photography-based, social media walking game targeting autonomous motivations for physical activity. METHODS: During an individual semistructured interview, a moderator solicited feedback from 20 older adult women (age range 65-74 years) as part of formative research to develop a social media game featuring weekly walking challenges. The challenges were designed to target autonomous motivations for physical activity. Interviews were audio-recorded and transcribed verbatim. Two reviewers conducted thematic content analysis on interview transcripts. RESULTS: We identified 3 overarching themes in qualitative data analysis. These reflected the playful experiences, value, and acceptability associated with the intervention challenges. Generally, participants understood what the challenges were asking them to do, proffered appropriate example responses, and indicated that the challenges would be enjoyable. Participants reported that the intervention content afforded many and varied playful experiences (eg, competition, discovery, exploration, expression, fellowship, humor, nurture, sensation). Further, participants indicated that the intervention increased their motivation for physical activity, occasioned meaningful shifts in perspective, increased their knowledge of various topics of interest, provided an opportunity to create valued connection with others, and provided health-related benefits. Participants suggested the intervention emphasize local history, nature, and cultural events. CONCLUSIONS: The photography-based, social media walking game with relatively simple game mechanics was well received and judged to be apt to bring about a wide variety of emotive experiences. A clear, geographically specific identity emerged as a key driver of interest for intervention content. Taking a celebratory technology approach holds promise for targeting autonomous motivations for physical activity in older adult women.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.719
Threshold uncertainty score0.776

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.020
GPT teacher head0.311
Teacher spread0.291 · 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 designOther design
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

Citations10
Published2022
Admission routes1
Has abstractyes

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