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Record W3172366231 · doi:10.2196/16824

Developing a Management Guide (the DemPower App) for Couples Where One Partner Has Dementia: Nonrandomized Feasibility Study

2021· article· en· W3172366231 on OpenAlexvenueno aff
Reena Lasrado, Therése Bielsten, Mark Hann, James Schumm, Siobhán Reilly, Linda Davies, Caroline Swarbrick, Robyn Dowlen, John Keady, Ingrid Hellström

Bibliographic record

VenueJMIR Aging · 2021
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsnot available
FundersEconomic and Social Research CouncilNational Institute for Health and Care Research
KeywordsDementiaPsychological interventionIntervention (counseling)PsychologyFocus groupMedicineQuality of life (healthcare)GerontologyClinical psychologyNursing

Abstract

fetched live from OpenAlex

BACKGROUND: Promoting the health and well-being of couples where one partner has dementia is an overlooked area of care practice. Most postdiagnostic services currently lack a couple-centered approach and have a limited focus on the couple relationship. To help address this situation, we developed a tablet-based self-management guide (DemPower) focused on helping couples enhance their well-being and relationship quality. OBJECTIVE: The aim of this study is to investigate the feasibility and acceptability of the DemPower app. METHODS: A nonrandomized feasibility design was used to evaluate the DemPower intervention over 3 months among couples where a partner had a diagnosis of dementia. The study recruited 25 couples in the United Kingdom and 19 couples in Sweden. Outcome measures were obtained at baseline and postintervention. The study process and interventions were evaluated at various stages. RESULTS: The study was completed by 48% (21/44) of couples where one partner had dementia, of whom 86% (18/21) of couples accessed all parts of the DemPower app. Each couple spent an average of 8 hours (SD 3.35 hours) using the app during the study period. In total, 90% (19/21) of couples reported that all sections of DemPower were useful in addressing various aspects of daily life and helped to focus on how they interacted in their relationship. Of the 4 core subjects on which the DemPower app was structured, home and neighborhood received the highest number of visits. Couples used activity sections more often than the core subject pages. The perception of DemPower's utility varied with each couple's lived experience of dementia, geographic location, relationship dynamics, and opportunities for social interaction. A 5.2-point increase in the dementia quality of life score for people with dementia and a marginal increase in the Mutuality scale (+1.23 points) for caregiver spouses were found. Design and navigational challenges were reported in the DemPower app. CONCLUSIONS: The findings suggest that the DemPower app is a useful resource for couples where one partner has dementia and that the implementation of the app requires the support of memory clinics to reach couples at early diagnosis. TRIAL REGISTRATION: ISRCTN Registry ISRCTN10122979; http://www.isrctn.com/ISRCTN10122979.

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)0.019
metaresearch head score (Gemma)0.026
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: Non-randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.100

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.026
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
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)0.0040.001

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 head0.077
GPT teacher head0.397
Teacher spread0.319 · 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 designNon-randomized 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

Citations3
Published2021
Admission routes1
Has abstractyes

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