The Effect of Dual-Task Testing on Balance and Gait Performance in Adults with Type 1 or Type 2 Diabetes Mellitus: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Individuals with diabetes mellitus (DM) are susceptible to balance, gait and cognitive impairments. Importantly, diabetes affects executive function, a set of cognitive processes critical to everyday cortical function and mobility. Reduced executive function is a risk factor for falls in people with DM. Dual-task testing, the completion of two tasks at once, enables the examination of the cognitive-mobility relationship. A synthesis of the literature on the effects of dual- task testing on the balance and gait of individuals with DM has not been performed. OBJECTIVE: To systematically review the literature on the effect of dual-task testing on balance and gait in people with DM. METHODS: Databases EMBASE, CINAHL, MEDLINE, PsycINFO, Scopus and Web of Science were searched (inception-April 2020). INCLUSION CRITERIA: participants were adults with a diagnosis of DM, instrumented dual-task balance and/or gait was assessed, and articles were published in English. RESULTS: Ten articles met inclusion criteria- three examined dual-task balance and seven dual-task gait. In people with DM with or without peripheral neuropathy, dual-task resulted in larger sway velocities during standing tests. Individuals with DM and peripheral neuropathy had impaired dual-- task gait; specifically, and more consistently, reduced pace and rhythm compared to controls or people with DM without peripheral neuropathy. CONCLUSION: The findings support a compromise in the cognitive-mobility relationship of people with DM, and especially in those with peripheral neuropathy. Future research should continue to examine the cognitive-mobility relationship in order to understand the increased prevalence of falls in this population.
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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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".