Evaluating Mobility in Advanced Dementia: A Scoping Review and Feasibility Analysis
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Mobility decline is a symptom of advanced dementia that affects function, safety, caregiving, and quality of life. Monitoring mobility status is essential for initiating timely and targeted interventions aimed at preventing excess disability in people with dementia (PWD). The physical, cognitive, and behavioral symptoms of dementia however, present unique challenges for mobility assessment. The goals of this review were to (a) identify and describe measures of mobility used for PWD and (b) assess measures' feasibility for use in people with advanced dementia; a group whose degree of cognitive impairment results in severe functional deficits. RESEARCH DESIGN AND METHODS: Electronic searches of Medline, Embase, CINAHL, and PsychInfo databases were conducted using keywords related to dementia, mobility, measurement, and validation. Descriptive characteristics were extracted and measures coded for mobility components. Tools were also evaluated for feasibility of use in advanced dementia and those deemed feasible, screened for psychometric strength. RESULTS: Thirty-eight measures were included and 68% of these tools were performance-based. Elements of mobility evaluated were walking (53% of measures), postural transitions (42%), standing (40%), mobility-related behavioral/psychological symptoms (24%), transfers (10%), bed mobility (5%), and wheeled mobility (3%). 36% of studies included people with advanced dementia. Only 18% of tools received high scores for feasibility. DISCUSSION AND IMPLICATIONS: Existing measures provide only partial information regarding mobility and few target elements that become relevant as dementia progresses. Most measures are not feasible for people with advanced dementia, and the psychometric evaluation of these measures is limited. Further research is needed to develop a comprehensive, dementia-specific, mobility assessment tool.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.069 | 0.207 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.013 |
| Bibliometrics | 0.034 | 0.030 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".