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Record W3030348857 · doi:10.2196/15600

A Smart Health Platform for Measuring Health and Well-Being Improvement in People With Dementia and Their Informal Caregivers: Usability Study

2020· article· en· W3030348857 on OpenAlexvenueno aff
Estefanía Fernández, Catherine Blake, L. M. Mackey, Paula Alexandra Silva, Dermot Power, Diarmuid O’Shea, Brian Caulfield

Bibliographic record

VenueJMIR Aging · 2020
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsnot available
FundersHorizon 2020 Framework ProgrammeEuropean CommissionHealth Research Board
KeywordsDementiaUsabilityPsychological interventionMental healthQuality of life (healthcare)DyadActivities of daily livingCaregiver burdenPsychologyGerontologyMedicineApplied psychologyNursingDiseasePsychiatryDevelopmental psychologyComputer science

Abstract

fetched live from OpenAlex

BACKGROUND: Dementia is a neurodegenerative chronic condition characterized by a progressive decline in a person's memory, thinking, learning skills, and the ability to perform activities of daily living. Previous research has indicated that there are many types of technology interventions available in the literature that have shown promising results in improving disease progression, disease management, and the well-being of people with dementia (PwD) and their informal caregiver, thus facilitating dementia care and living. Technology-driven home care interventions, such as Connected Health (CH), could offer a convenient and low-cost alternative to traditional home care, providing an informal caregiver with the support they may need at home while caring for a PwD, improving their physical and mental well-being. OBJECTIVE: This study aimed (1) to create a multidimensional profile for evaluating the well-being progression of the PwD-informal caregiver dyad for a year during their use of a CH platform, designed for monitoring PwD and supporting their informal caregivers at home, and (2) to conduct a long-term follow-up using the proposed well-being profile at different time-interval evaluations. METHODS: The PwD-informal caregiver well-being profile was created based on the World Health Organization International Classification of Functioning considering the following outcomes: functional status, cognitive status, and quality of life for the PwD and mental well-being, sleeping quality, and burden for the informal caregiver. Over a year, comprehensive assessments of these outcomes were conducted every 3 months to evaluate the well-being of PwD-informal caregivers, using international and standardized validated questionnaires. Participants' demographic information was analyzed using descriptive statistics and presented as means and SDs. A nonparametric Friedman test was used to analyze the outcome changes and the progression in the PwD-caregiver dyads and to determine if those changes were statistically significant. RESULTS: There were no significant changes in the well-being of PwD or their caregivers over the year of follow-up, with the majority of the PwD-caregiver dyads remaining stable. The only instances in which significant changes were observed were the functional status in the PwD and sleep quality in their caregivers. In each of these measures, post hoc pairwise comparisons did not indicate that the changes observed were related to the deployment of the CH platform. CONCLUSIONS: The follow-up of this population of PwD and their informal caregivers has shown that disease progression and physical and mental well-being do not change significantly during the time, being a slow and gradual process. The well-being profile created to analyze the potential impact of the CH platform on the PwD-informal caregiver dyad well-being, once validated, could be used as a future tool to conduct the same analyses with other CH technologies for this population. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/13280.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.053
Threshold uncertainty score0.516

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.024
GPT teacher head0.307
Teacher spread0.283 · 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 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

Citations7
Published2020
Admission routes1
Has abstractyes

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