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Record W4417062284 · doi:10.2196/82092

The Virtual Kitchen Challenge—Version 2: Validation of a Digital Assessment of Everyday Function in Older Adults

2025· article· en· W4417062284 on OpenAlexvenueno aff
Marina Kaplan, Moira McKniff, Stephanie M. Simone, Molly B. Tassoni, Katherine Hackett, Sophia Holmqvist, Rachel Mis, Kimberly Halberstadter, Riya Chaturvedi, Melissa Rosahl, Giuliana Vallecorsa, Mijail D. Serruya, Deborah A. G. Drabick, Takehiko Yamaguchi, Tania Giovannetti

Bibliographic record

VenueJMIR Aging · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicTechnology Use by Older Adults
Canadian institutionsnot available
Fundersnot available
KeywordsFunction (biology)Measure (data warehouse)Everyday lifeStatus quo

Abstract

fetched live from OpenAlex

BACKGROUND: Conventional methods of functional assessment include subjective self- or informant report, which may be biased by personal characteristics, cognitive abilities, and lack of standardization (eg, influence of idiosyncratic task demands). Traditional performance-based assessments offer some advantages over self- or informant reports but are time-consuming to administer and score. OBJECTIVE: This study aims to evaluate the validity and reliability of the Virtual Kitchen Challenge-Version 2 (VKC-2), an objective, standardized, and highly efficient alternative to current functional assessments for older adults across the spectrum of cognitive aging, from preclinical to mild dementia. METHODS: A total of 236 community-dwelling, diverse older adults completed a comprehensive neuropsychological evaluation to classify cognitive status as healthy, mild cognitive impairment, or mild dementia, after adjustment for demographic variables (age, education, sex, and estimated IQ). Participants completed 2 everyday tasks (breakfast and lunch) in a virtual kitchen (VKC-2) using a touchscreen interface to select objects and sequence steps. Automated scoring reflected completion time and performance efficiency (eg, number of screen interactions, percentage of time spent off-screen, interactions with distractor objects). Participants also completed the VKC-2 tasks using real objects (Real Kitchen). All participants and informants for 219 participants completed questionnaires regarding everyday function. A subsample of participants (n=143) performed the VKC-2 again in a second session, 4-6 weeks after the baseline, for retest analyses. Analyses evaluated construct and convergent validity, as well as retest and internal reliability, of VKC-2 automated scores. RESULTS: A principal component analysis showed that the primary VKC-2 automated scores captured a single dimension and could be combined into a composite score reflecting task efficiency. Construct validity was supported by analyses of covariance results showing that participants with healthy cognition obtained significantly better VKC-2 scores than participants with cognitive impairment (all Ps<.001), even after controlling for demographics and general computer visuomotor dexterity. Convergent validity was supported by significant correlations between VKC-2 scores and performance on the Real Kitchen (r=-0.58 to 0.64, Ps<.001), conventional cognitive test scores (r=-0.50 to -0.22, Ps<.001), and self- and informant report questionnaires evaluating everyday function (r=0.25 to 0.43, Ps<.001). Intraclass correlation coefficients (ICCs) indicated moderate to excellent retest reliability (ICC=0.70-0.90) for VKC-2 scores after 4-6 weeks. Reliability improved in analyses including only participants who reported no change in cognitive status between time 1 and time 2 (n=123). Spearman-Brown correlations showed acceptable to good internal consistency between the VKC-2 tasks (breakfast and lunch) for all scores (0.77-0.84), supporting the use of total scores. CONCLUSIONS: The VKC-2 is an efficient, valid, and sensitive measure of everyday function for diverse older adults and holds promise to improve the status quo of functional assessment in aging, particularly when informants are unavailable or unreliable.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.362
Threshold uncertainty score0.231

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 head0.009
GPT teacher head0.302
Teacher spread0.293 · 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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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