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Record W2908546638 · doi:10.2196/11599

Evaluating the Impact of Music & Memory’s Personalized Music and Tablet Engagement Program in Wisconsin Assisted Living Communities: Pilot Study

2019· article· en· W2908546638 on OpenAlexvenueno aff
James H. Ford, Debby Dodds, Julie Hyland, Michael Potteiger

Bibliographic record

VenueJMIR Aging · 2019
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsnot available
Fundersnot available
KeywordsDementiaPsychological interventionGerontologyQuality of life (healthcare)Music therapyMedicinePopulationPsychologyDiseasePsychiatryNursingEnvironmental health

Abstract

fetched live from OpenAlex

BACKGROUND: Individuals with Alzheimer disease or related dementia represent a significant and growing segment of the older adult (aged 65 years and above) population. In addition to physical health concerns, including comorbid medical conditions, these individuals often exhibit behavioral and psychological symptoms of dementia (BPSD). The presence of BPSD in long-term care residential facilities can disrupt resident's care and impact staff. Nonpharmacological interventions such as personalized music and tablet engagement maintain cognitive function, improve quality of life (QOL), and mitigate BPSD for older adults with dementia. Evidence of the impact of such interventions in assisted living communities (ALCs) is needed for widespread adoption and sustainment of these technologies. OBJECTIVE: The aim of this study was to assess the impact of Music & Memory's personalized music and tablet engagement (PMATE) program on QOL, agitation, and medication use for residents living in 6 Wisconsin ALCs. METHODS: The data collected were on the utilization of iPods and iPads by the residents. Residents' outcomes were assessed using the Pittsburgh Agitation Scale, the Quality of Life in Late Stage Dementia scale, and self-reported medication use. A mixed-methods approach was utilized to examine the impact of the PMATE program on these outcomes. Descriptive statistics were calculated. A paired t test explored changes in residents' QOL. A 1-way analysis of variance was utilized to examine changes in resident's agitation and QOL based on the resident's utilization of the PMATE program. Qualitative interviews were conducted with the individuals responsible for PMATE implementation in the ALC. Residents excluded from the analysis were those who passed away, were discharged, or refused to participate. RESULTS: =3.76, P=.02). High utilizers of the PMATE program (>2500 min over 3 months) showed greater improvements in QOL as compared with low utilizers (a change of -5.90 points vs an increase of 0.43 points). The difference was significant (P=.03). Similar significant findings were found between the high- and midutilizers. CONCLUSIONS: The study is one of the first to explore the impact of Music & Memory's PMATE program on residents living in ALCs. Findings suggest that higher utilization over time improves residents' QOL. However, a more comprehensive study with improved data collection efforts across multiple ALCs is needed to confirm these preliminary findings.

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)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.187
GPT teacher head0.457
Teacher spread0.270 · 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 designObservational
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

Citations15
Published2019
Admission routes1
Has abstractyes

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