Not just cognitive impairment: Using motion‐based technology to examine mobility, balance, and cognition in dementia and MCI
Bibliographic record
Abstract
Abstract Background Dementia and Mild Cognitive Impairment (MCI) are both characterized by impaired cognition, with dementia a more significant impairment that interferes with functioning in daily life. Both groups are also at high risk of falls and reduced physical activity. Yet, little is known about the characterization of this population with regards to balance, mobility, and their relationship to cognition. Method Participants with dementia and MCI (n=28; 53.6% female) were recruited from four community‐based adult day programs and invited to participate in a 10‐week motion‐based technology intervention (i.e., Xbox Kinect bowling) to see whether the intervention impacted their balance, movement confidence, and cognition. As part of the data collection procedures, participants completed a demographic survey, as well as the Montreal Cognitive Assessment (MoCA), and the Mini Balance Evaluation Systems Test (Mini‐BEST) at the start of the study. These baseline data were analyzed using descriptive statistics. Result In addition to confirming participants’ cognitive impairment (mean MoCA: 12.86/30; range: 2‐25), participants reported a high instance of mobility impairment (16/28 = 57.1%), with many participants using either walkers (12/16 = 75.0%) or canes (4/16 = 25.0%). The entire sample demonstrated significant balance impairments according to the Mini‐BEST (mean: 14.58/28 [<19‐point cut‐off]; range: 6‐21), but only one of 28 participants (3.6%) reported ever receiving treatment (e.g., physiotherapy) for a balance problem. Conclusion People with dementia and MCI have a high prevalence of balance and mobility impairments, in addition to their cognitive impairment. Furthermore, there are few instances in which rehabilitation for these physical issues are offered to this population. This may reflect the primary focus on their cognitive impairment but also misconceptions about the capabilities of people with dementia and MCI to engage in rehabilitation. The findings highlight a need for more physical rehabilitation and/or exercise programs for people with dementia and MCI that target balance and mobility, in addition in taking participants’ cognitive impairment into account.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".