Characteristics and Application Value of Handwriting in Elderly Patients with Mild Cognitive Impairment
Bibliographic record
Abstract
Background Handwriting synthesis techniques have been extensively studied in the detection of cognitive impairment in dementia and Parkinson's disease. But handwriting characteristics in older adults with mild cognitive impairment (MCI) still need to be studied further. Objective To explore the differences between the handwriting characteristics of elderly patients with MCI and normal elderly people, and to assess the value of handwriting features in MCI screening. Methods By use of convenience sampling, 33 older adults with MCI were recruited from Huzhou communities from January to April 2022 (observation group), and were compared to age-, sex- and education level-matched 43 community-living older adults with normal cognitive function (control group). The General Information Questionnaire, the Mini-Mental State Examination (MMSE), the Montreal Cognitive Assessment-Basic (MoCA-B), the Activity of Daily Living Scale (ADL), and the 15-item Geriatric Depression Scale (GDS-15) were used to survey subjects. Subjects were invited to complete six handwriting tasks (four are Chinese characters tasks and the other two are graphical drawing tasks) using a dot matrix digital pen to collect their kinematic parameters of handwriting. The classification accuracy, sensitivity and specificity of handwriting characteristics for the diagnosis of MCI were analyzed by discriminant analysis and receiver operating characteristic (ROC) curve, and predictive values of different schemes for MCI were analyzed. Results Compared with the control group, the observation group had higher average pressure in writing (Z=-2.122, P=0.034), longer time in air (Z=-4.302, P<0.001), writing time (Z=-3.663, P<0.001) and total time (t'=-5.565, P<0.001), lower average writing velocity (Z=-2.458, P=0.014), horizontal (Z=-2.950, P=0.003) and vertical (Z=-2.094, P=0.040) average writing velocity and maximum horizontal writing velocity (Z=-2.206, P=0.027), lower average acceleration of writing in horizontal direction (Z=-2.667, P=0.008) and overall score for writing correctness (Z=-3.593, P<0.001) in completing graphical drawing tasks. The observation group had relatively longer time in air (Z=-3.464, P=0.001) and total time (Z=-2.940, P=0.003) in completing Chinese characters tasks. Compared with the total time for completing Chinese characters tasks, the total time for completing graphical drawing tasks had higher specificity (93.0% vs 55.8%) in differentiating between MCI and control groups, with an area under the curve (AUC) of 0.828. The summary of handwriting characteristics for graphical drawing tasks correctly classified 80.3% (61/76) of older adults with MCI, with 87.9% sensitivity and 79.1% specificity, and had higher diagnostic efficacy for those with MCI than the MMSE scale (Z=1.993, P=0.046) and the summary of handwriting characteristics for Chinese characters tasks (Z=2.408, P=0.016) . Conclusion Handwriting characteristics of graphical drawing tasks may have potential application in screening of older adults at risk for MCI, which can be used simultaneously or prior to sets of neuropsychological tests conducted for the diagnosis of MCI in community health care facilities.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".