Characteristics and Application Value of Handwriting in Elderly Patients with Mild Cognitive Impairment
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».