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Record W4386157469 · doi:10.2196/49319

Exploring the Needs and Requirements of Informal Caregivers of Older Adults With Cognitive Impairment From Sensor-Based Care Solutions: Multimethod Study

2023· article· en· W4386157469 on OpenAlexvenueno aff
Nikita Sharma, Annemarie Braakman‐Jansen, Harri Oinas‐Kukkonen, Jan Hendrik Croockewit, Julia E.W.C. van Gemert‐Pijnen

Bibliographic record

VenueJMIR Aging · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicTechnology Use by Older Adults
Canadian institutionsnot available
FundersEuropean Commission
KeywordsThematic analysisCognitionPsychologyPopulationCognitive impairmentApplied psychologyGerontologyQualitative researchMedicinePsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: With the increase in the older adult population, sensor-based care solutions that can monitor the deviations in physical, emotional, and physiological activities in real-time from a distance are demanded for prolonging the stay of community-dwelling older adults with cognitive impairment. To effectively develop and implement these care solutions, it is important to understand the current experiences, future expectations, perceived usefulness (PU), and communication needs of the informal caregivers of older adults with cognitive impairment regarding such solutions. OBJECTIVE: This comprehensive study with informal caregivers of older adults with cognitive impairment aims to (1) highlight current experiences with (if any) and future expectations from general sensor-based care solutions, (2) explore PU specifically toward unobtrusive sensing solutions (USSs), (3) determine the information communication (IC) needs and requirements for communicating the information obtained through USSs in different care scenarios (fall, nocturnal unrest, agitation, and normal daily life), and (4) elicit the design features for designing the interaction platform in accordance with the persuasive system design (PSD) model. METHODS: A multimethod research approach encompassing a survey (N=464) and in-depth interviews (10/464, 2.2%) with informal caregivers of older adults with cognitive impairment was used. The insights into past experiences with and future expectations from the sensor-based care solutions were obtained through inductive thematic analysis of the interviews. A convergent mixed methods approach was used to explore PU and gather the IC needs from USSs by using scenario-specific questions in both survey and interviews. Finally, the design features were elicited by using the PSD model on the obtained IC needs and requirements. RESULTS: Informal caregivers expect care infrastructure to consider centralized and empathetic care approaches. Specifically, sensor-based care solutions should be adaptable to care needs, demonstrate trust and reliability, and ensure privacy and safety. Most informal caregivers found USSs to be useful for emergencies (mean 4.09, SD 0.04) rather than for monitoring normal daily life activities (mean 3.50, SD 0.04). Moreover, they display variations in information needs including mode, content, time, and stakeholders involved based on the care scenario at hand. Finally, PSD features, namely, reduction, tailoring, personalization, reminders, suggestions, trustworthiness, and social learning, were identified for various care scenarios. CONCLUSIONS: From the obtained results, it can be concluded that the care scenario at hand drives PU and IC design needs and requirements toward USSs. Therefore, future technology developers are recommended to develop technology that can be easily adapted to diverse care scenarios, whereas designers of such sensor-driven platforms are encouraged to go beyond tailoring and strive for strong personalization while maintaining the privacy of the users.

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)0.000
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.095
Threshold uncertainty score0.878

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.057
GPT teacher head0.324
Teacher spread0.266 · 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 designQualitative
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

Citations5
Published2023
Admission routes1
Has abstractyes

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