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Record W4402926009 · doi:10.2196/50710

Health Perceptions and Practices of a Telewellness Fitness Program: Exploratory Case Study

2024· article· en· W4402926009 on OpenAlexvenueno aff
Verónica Ahumada-Newhart, Taffeta Wood, Noriko Satake, James P. Marcin

Bibliographic record

VenueJMIR Formative Research · 2024
Typearticle
Languageen
FieldMedicine
TopicPhysical Activity and Health
Canadian institutionsnot available
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Center for Advancing Translational SciencesNational Center for Research ResourcesNational Institute of Child Health and Human DevelopmentOffice of the President, University of CaliforniaCancer Research Coordinating CommitteeNational Institutes of HealthNational Science Foundation
KeywordsPreprintExploratory researchPerceptionPsychologyApplied psychologyGerontologySociologyComputer scienceMedicineWorld Wide WebSocial science

Abstract

fetched live from OpenAlex

Background: During the COVID-19 pandemic, many people lacked access to group fitness opportunities due to elevated risk of infection, lockdown, and closure of exercise facilities. Additionally, many people experienced higher than average rates of mental health burden (eg, anxiety and stress). To help address these needs, an existing in-person community exercise class, taught by a faculty member from an academic medical center, transitioned to an online synchronous (OS) physical fitness class via the Zoom (Zoom Video Communications) videoconferencing platform. As such, the instructor advertised the OS fitness classes through an existing email list of community members and university faculty, staff, students, or alumni email listservs. This telewellness intervention sought to create a sense of community, build social support, and promote physical and mental wellness during the COVID-19 pandemic. Objective: Our aim was to determine the perceived mental and physical health benefits of attending an OS fitness class for community members, including health care workers. We also assessed the use and functionality of related technologies necessary for delivering and attending the fitness classes. Methods: An online survey questionnaire was created and tested to collect quantitative and qualitative data for an exploratory study. Data were collected to evaluate the fitness class, motivation, perceived health benefits, and related technologies. A convenience sample of people who had participated in the OS fitness classes was recruited for this study via an emailed recruitment flyer. Results: A total of 51 participants accessed and completed the survey questionnaire. Survey participants consisted of 28 of 51 (55%) with a university affiliation, 17 of 51 (33%) with no university affiliation, and 6 of 51 (12%) who declined to state. The largest group of participants reporting full-time employment (18/51, 35%) also reported university affiliation with the academic medical center. In this group, 13 of 51 (25%) participants reported full-time employment, university affiliation, and doctoral degrees. High overall exercise class satisfaction was observed in the survey responses (mean 4.0, SD 1). Data analyses revealed significant perceived value of both mental and physical health benefits as motivating factors for participating in the OS fitness class. Challenges were identified as not being able to receive individual feedback from the instructor and the inability of some participants to see if they were in sync with the rest of the class. Conclusions: Results provide preliminary support for the use of online videoconferencing fitness platforms to promote wellness and facilitate group exercise in the community during times of high infection risk. Future studies should continue to explore perceived benefits, mental and physical wellness, best practices, and the design of related technologies.

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)0.003
metaresearch head score (Gemma)0.006
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 score0.010
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0090.003
Scholarly communication0.0020.002
Open science0.0020.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.246
GPT teacher head0.578
Teacher spread0.333 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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