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Record W2012777693 · doi:10.1159/000355669

Measuring Life Space in Older Adults with Mild-to-Moderate Alzheimer's Disease Using Mobile Phone GPS

2013· article· en· W2012777693 on OpenAlexaff
James Tung, Rhiannon V. Rose, Emnet Gammada, Isabel Lam, Éric Roy, Sandra E. Black, Pascal Poupart

Bibliographic record

VenueGerontology · 2013
Typearticle
Languageen
FieldHealth Professions
TopicOlder Adults Driving Studies
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsGlobal Positioning SystemDementiaGeriatric Depression ScaleConstruct validityGerontologyPsychologyQuality of life (healthcare)CognitionMedicineDiseasePsychometricsDevelopmental psychologyComputer sciencePsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: As an indicator of physical and cognitive functioning in community-dwelling older adults, there is increasing interest in measuring life space, defined as the geographical area a person covers in daily life. Typically measured through questionnaires, life space can be challenging to assess in amnestic dementia associated with Alzheimer's disease (AD). While global positioning system (GPS) technology has been suggested as a potential solution, there remains a lack of data validating GPS-based methods to measure life space in cognitively impaired populations. OBJECTIVE: The purpose of the study was to evaluate the construct validity of a GPS system to provide quantitative measurements of global movement for individuals with mild-to-moderate AD. METHODS: Nineteen community-dwelling older adults with mild-to-moderate AD (Mini-Mental State Examination score 14-28, age 70.7 ± 2.2 years) and 33 controls (CTL; age 74.0 ± 1.2 years) wore a GPS-enabled mobile phone during the day for 3 days. Measures of geographical territory (area, perimeter, mean distance from home, and time away from home) were calculated from the GPS log. Following a log-transformation to produce symmetrical distributions, group differences were tested using two-sample t tests. Construct validity of the GPS measures was tested by examining the correlation between the GPS measures and indicators of physical function [steps/day, gait velocity, and Disability Assessment for Dementia (DAD)] and affective state (Apathy Evaluation Scale and Geriatric Depression Scale). Multivariate regression was performed to evaluate the relative strength of significantly correlated factors. RESULTS: GPS-derived area (p < 0.01), perimeter (p < 0.01), and mean distance from home (p < 0.05) were smaller in the AD group compared to CTL. The correlation analysis found significant associations of the GPS measures area and perimeter with all measures of physical function (steps/day, DAD, and gait velocity; p < 0.01), symptoms of apathy (p < 0.01), and depression (p < 0.05). Multivariate regression analysis indicated that gait velocity and dependence were the strongest variables associated with GPS measures. CONCLUSION: This study demonstrated that GPS-derived area and perimeter: (1) distinguished mild-to-moderate AD patients from CTL and (2) were strongly correlated with physical function and affective state. These findings confirm the ability of GPS technology to assess life space behaviour and may be particularly valuable to continuously monitor functional decline associated with neurodegenerative disease, such as AD.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.097
GPT teacher head0.366
Teacher spread0.269 · 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 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

Citations118
Published2013
Admission routes1
Has abstractyes

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