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Record W4295514731 · doi:10.12678/1089-313x.121522f

Dance, Music, and Social Conversation Program Participation Positively Affects Physical and Mental Health in Community-Dwelling Older Adults: A Randomized Controlled Trial

2022· article· en· W4295514731 on OpenAlexaboutno aff
Jatin P. Ambegaonkar, Holly C. Matto, Emily S. Ihara, Catherine J. Tompkins, Shane V. Caswell, Nelson Cortés, Rick D. Davis, Sarah M. Coogan, Victoria N. Fauntroy, Elizabeth Glass, Judy Lee, Gwen Baraniecki‐Zwil, Niyati Dhokai

Bibliographic record

VenueJournal of Dance Medicine & Science · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicArt Therapy and Mental Health
Canadian institutionsnot available
FundersNational Endowment for the Arts
KeywordsDanceAttendanceMental healthPsychological interventionPsychologyRandomized controlled trialPopulationGerontologyConversationBallroomMedicinePsychiatry

Abstract

fetched live from OpenAlex

INTRODUCTION: As the world population ages, practitioners use community-engaged interventions to help older adults stay healthy. Engaging in arts programs (e.g., dance or music) reportedly improves physical and mental health, but little research exists examining these effects in community-dwelling older adults. Our purposes were to examine how taking part in 10-week, twice per week community arts programs (dance and music) and control (social conversation) affected physical and mental health in community-dwelling older adults and their perceptions after program participation. Methods: In this randomized controlled trial, 64 older adults over 65 years of age (71.3 ± 4.6 years, 166.9 ± 8.3 cm, 78.1 ± 18.1 kg) took part in community-engaged arts programs: ballroom dance (n = 23), music (ukulele-playing, n = 17), or control (social conversation n = 24), two times per week for 10 weeks. Participants' physical health using the Short Physical Performance Battery (SPPB; score 0 = worst to 12 = best) and mental health using the Montreal Cognitive Assessment (MoCA; score = 0 to 30, where less than 26 = normal) were tested three times: 1. before (pre), 2. at the end of 10 weeks (post-1), and 3. 1 month after intervention (post-2). Separate 3 (group) x 3 (time) ANOVAs and adjusted Bonferroni pairwise comparisons as appropriate examined changes across groups and time. Focus group interviews and surveys were audio recorded, transcribed, and analyzed using inductive thematic analyses to examine participants' perceptions. Results: Across all groups, participants had an 87.8% attendance and an 87.5% retention rate. Participants' SPPB performance improved over time (pre = 10.5 ± 1.4, post-1 = 10.7 ± 1.3, post-2 = 11.3 ± 1.0; p < 0.001), but similarly across groups (p = 0.40). Post-hoc analyses revealed that performance improved from pre to post-1 (p = 0.002) and pre to post-2 (p < 0.001). Participants' cognition improved over time (pre = 26.3 ± 2.8, post-1 = 27.3 ± 2.6, post-2 = 27.5 ± 2.5, p < 0.001), and similarly across groups (p = 0.60). Post-hoc analyses revealed that cognition improved from pre- to post-1 (p = 0.002), and pre- to post-2 (p = 0.001). Participants consistently mentioned increased social engagement as the major reason for participation. Conclusions: Overall, taking part in community-engaged arts (dance and music) and social conversation programs positively influenced physical and mental health in older adults. Still, as all groups improved equally, the results may partly be due to participants having normal physical and mental function pre-participation and due to them learning the test over time. These study findings imply that providing fun and free community-engaged programs that empower participants to be more engaged can positively influence physical and mental health and promote successful aging in older adults.

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)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0050.004
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0100.001

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 head0.027
GPT teacher head0.334
Teacher spread0.308 · 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 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".

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Citations22
Published2022
Admission routes1
Has abstractyes

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