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Record W3092207243 · doi:10.3138/ptc-2019-0115

“Think of It Like a Game”: Older Adults’ and Health Professionals’ Perspectives on Interactive Exercise Technology Design

2020· article· en· W3092207243 on OpenAlexaffvenue
Ainsley Smith, Jessica Belgrave Sookhoo, Caitlin McArthur, Stephen Surlin, Adekunle Akinyemi, Paula Gardner, Αλεξάνδρα Παπαϊωάννου

Bibliographic record

VenuePhysiotherapy Canada · 2020
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsMcMaster University
Fundersnot available
KeywordsThematic analysisFocus groupHealth professionalsUser-centered designPsychologyMedical educationFocus (optics)Applied psychologyTest (biology)MedicineQualitative researchComputer scienceHealth careHuman–computer interaction

Abstract

fetched live from OpenAlex

Purpose: Interactive exercise technology (IET) is an effective and practical way to support physiotherapy for older adults. The purpose of this study was to use design thinking to collect feedback on the first iteration of an IET prototype from older adults and health professionals and to use that feedback to gain an understanding of their needs and values, with the goal of developing recommendations to inform the second iteration of the IET prototype. Method: This study was conducted using three steps of design thinking: (1) test, in which four focus groups were conducted, asking older adults and health professionals about their perspectives on an IET prototype; (2) empathize, in which the focus group discussions were recorded and transcribed and thematic content analysis was conducted; and (3) define, in which the needs and values of the participants were identified. Results: The participants were 19 health professionals and four older adults. Four themes, which represented the values that these groups held regarding IET design, were revealed: instruction, safety, accessibility, and motivation. Conclusions: Older adults and health professionals have specific needs for the design of IET, which should be considered in the development of future IET.

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 score0.313
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.014
GPT teacher head0.313
Teacher spread0.299 · 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
Published2020
Admission routes2
Has abstractyes

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