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Record W4403255275 · doi:10.2196/53156

Assessing the Feasibility and Acceptability of Virtual Reality for Remote Group-Mediated Physical Activity in Older Adults: Pilot Randomized Controlled Trial

2024· article· en· W4403255275 on OpenAlexvenueno aff
Kyle Kershner, David J. Morton, Justin Robison, Kindia Williams N'dah, Jason Fanning

Bibliographic record

VenueJMIR Formative Research · 2024
Typearticle
Languageen
FieldComputer Science
TopicVirtual Reality Applications and Impacts
Canadian institutionsnot available
FundersWells Fargo
KeywordsPreprintRandomized controlled trialGroup (periodic table)Virtual realityPhysical therapyMedicineGerontologyPsychologyComputer scienceHuman–computer interactionWorld Wide WebSurgeryPhysics

Abstract

fetched live from OpenAlex

BACKGROUND: Physical inactivity represents a major health concern for older adults. Most social, at-home physical activity (PA) interventions use videoconference, email, or telephone communication for program delivery. However, evidence suggests that these platforms may hinder the social connection experienced by users. Recent advancements in virtual reality (VR) suggest that it may be a rich platform for social, at-home interventions because it offers legitimate options for intervention delivery and PA. OBJECTIVE: This pilot study aims to determine the feasibility and acceptability of VR compared to videoconference as a medium for remote group-mediated behavioral intervention for older adults. The information generated from this investigation will inform the use of VR as a medium for intervention delivery. METHODS: Nine low-active older adults (mean age 66.8, SD 4.8 y) were randomized to a 4-week home-based, group-mediated PA intervention delivered via VR or videoconference. Feasibility (ie, the total number of sessions attended and the number of VR accesses outside of scheduled meetings) and acceptability (ie, the number of participants reporting high levels of nausea, program evaluations using Likert-style prompts with responses ranging from -5=very difficult or disconnected to 5=very easy or connected, and participant feedback on immersion and social connection) are illustrated via descriptive statistics and quotes from open-ended responses. RESULTS: None of the participants experienced severe VR-related sickness before randomization, with a low average sickness rating of 1.6 (SD 1.6) out of 27 points. Attendance rates for group meetings were 98% (59/60) and 96% (46/48) for the VR and videoconference groups, respectively. Outside of scheduled meeting times, participants reported a median of 5.5 (IQR 5.3-5.8, range 0-27) VR accesses throughout the entire intervention. Program evaluations suggested that participants felt personally connected to their peers (VR group: median 3.0, IQR 2.5-3.5; videoconference group: median 3.0, IQR 2.7-3.3), found that goals were easy to accomplish (VR group: median 3.0, IQR 2.8-3.3; videoconference group: median 3.0, IQR 2.6-3.4), and had ease in finding PA options (VR group: median 4.0, IQR 3.5-4.3; videoconference group: median 2.0, IQR 1.6-2.4) and engaging in meaningful dialogue with peers (VR group: median 4.0, IQR 4.0-4.0; videoconference group: median 3.5, IQR 3.3-3.8). Open-ended responses regarding VR use indicated increased immersion experiences and intrinsic motivation for PA. CONCLUSIONS: These findings suggest that VR may be a useful medium for social PA programming in older adults, given it was found to be feasible and acceptable in this sample. Importantly, all participants indicated low levels of VR-related sickness before randomization, and both groups demonstrated very high attendance at meetings with their groups and behavioral coaches, which is promising for using VR and videoconference in future interventions. Modifications for future iterations of similar interventions are provided. Further work using larger samples and longer follow-up durations is needed. TRIAL REGISTRATION: ClinicalTrials.gov NCT04756245; https://www.clinicaltrials.gov/study/NCT04756245.

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)0.017
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.495
Threshold uncertainty score0.599

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0170.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0000.001
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 head0.103
GPT teacher head0.464
Teacher spread0.361 · 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 designRandomized trial
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

Citations8
Published2024
Admission routes1
Has abstractyes

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