brintellex
Home About us Editorial board Ahead of print Current issue Search Archives Submit article Instructions Subscribe Contacts Login 
  • Users Online: 259
  • Home
  • Print this page
  • Email this page

 Table of Contents  
ORIGINAL ARTICLE
Year : 2022  |  Volume : 9  |  Issue : 2  |  Page : 79-86

A study of handwriting sample in geriatric population with cognitive impairment: A cross-sectional observational study


Department of Geriatric Mental Health, King George's Medical University, Lucknow, Uttar Pradesh, India

Date of Submission23-Jul-2022
Date of Decision28-Oct-2022
Date of Acceptance19-Nov-2022
Date of Web Publication20-Jan-2023

Correspondence Address:
Dr. V Suresh Heijebu
Department of Geriatric Mental Health, King George's Medical University, Lucknow - 226 003, Uttar Pradesh
India
Login to access the Email id

Source of Support: None, Conflict of Interest: None


DOI: 10.4103/jgmh.jgmh_39_22

Rights and Permissions
  Abstract 


Objectives: Cognitive impairment in the geriatric population often remains undiagnosed until progressed enough to cause interruptions in activities of daily living. Routine tests are time taking, requiring a specialist. Handwriting function reflects the brain's cognitive capacity by involving it's both halves. It is easy to collect and does not strain the participant, and can aid in the faster diagnosis of cognitive impairment. Materials and Methods: To study handwriting parameters collected with Livescribe Echo Smart Pen and compare them with cognitive scores of Montreal Cognitive Assessment-Hindi (MOCA-H) and Addenbrooke's Cognitive Examination-Hindi (ACE-H) in a cross-sectional observational study. Handwritten parameters differentiating both cognitive groups were identified and analyzed. Results: The mean age of the study population was 66.4 (5.3) years. The mean MOCA score in the cognitively impaired (CI) and noncognitively (NCI) group was 22.67 and 27.00, respectively. The mean ACE-H score in CI and NCI group was 80.68 and 93.05, respectively. In all handwriting tasks (T1-T3), higher scores were obtained on all parameters in the CI group except text width (TW), stroke frequency (SF), and writing speed (WS). In handwriting task 3 (single letter repetition), WC (word count) was found to be higher in the NCI group. Handwriting parameters of the whole task (TOT, PSPS, TW, TH, NOL, and WS) and text line (MTOL and MTOSS) were found to be helpful in group differentiation in all three tasks. There was a moderate degree of positive correlation with handwriting parameters (PSPS, WS, and WC) and a negative correlation with handwriting parameters (NOPS, TOT, TH, NOL, MHOL, MTOL, and MTOSS) across the tasks with MOCA and ACE scores. Conclusion: Inclusion of quantitative handwriting analysis in neuropsychological assessment can be one step forward towards a simple, reliable, and faster diagnosis of geriatric cognitive impairment.

Keywords: Cognition, geriatric, handwriting, neuropsychological


How to cite this article:
Heijebu V S, Singh B, Srivastava S, Singh SK. A study of handwriting sample in geriatric population with cognitive impairment: A cross-sectional observational study. J Geriatr Ment Health 2022;9:79-86

How to cite this URL:
Heijebu V S, Singh B, Srivastava S, Singh SK. A study of handwriting sample in geriatric population with cognitive impairment: A cross-sectional observational study. J Geriatr Ment Health [serial online] 2022 [cited 2023 Feb 2];9:79-86. Available from: https://www.jgmh.org/text.asp?2022/9/2/79/368294




  Introduction Top


Handwriting function of the brain utilizes all components of cognition and can be a marker of the brain's cognitive capacity. It is a coordinated task involving both sides of the brain. Furthermore, hand muscles have the largest motor representation in the cerebral cortex for fine motor movements involved in handwriting. Cognitive assessment on standard neuropsychological tests is usually a time taking process. Handwriting can be an equally reliable and fast quantitative tool in the evaluation of cognition. Handwriting analysis is a novel concept for diagnosing cognitive impairment in the elderly. It is the first such kind of a study in Indian geriatric population.

The use of technology with digitizer, tablets, iPad, and stylus for handwriting collection is growing research. Handwriting performance was substantially connected with the severity of dementia and several cognitive domains.[1] Nirjon et al. reported that the smartphone app Mobile-Cognition (MOBI-COG) can generate an automatic and instant telemedicine diagnosis of dementia.[2] Zorluoglu et al. developed several cognitive screening tests using various Android apps.[3] Livescribe Smart Pen technology, USA, made tremendous research in learning and handwriting. Mann et al. reported that it looks like a normal ballpoint pen, but can read ink from special patterned paper sheets with Haptic Sensor Touch Technology where machines communicate with humans.[4] Micrometry with Image J, an open-source Java-based software (Original Author and Developers: National Institute of Health and Laboratory for Optical and Computational Instrumentation [LOCI], University of Wisconsin, by Dr. Wayne Rasband. Country of Origin: USA), was exploited for handwriting analysis in our research. Schneider et al. reported that software tools such as ImageJ served as pioneers for the analysis of scientific images.[5]

Rationale for this study

The aim of this research was to study handwriting parameters in the geriatric population with cognitive impairment, as handwriting analysis has many potential advantages. It is a natural functional skill and new learning is not required. It is simpler to collect without much stress on the subject. Hectic patient training and preparation before sessions are not required. There is no need for any medical expert or a professional to administer the handwriting test. The testing session can be shared across several patients at once so that the examiner need not focus only on one subject as in conventional cognitive screening tests. Handwriting can be collected even from a remote distance or at home, bypassing the geographical barriers, and limitations of health-care facilities.


  Materials and Methods Top


The study was conducted on 165 eligible participants in a tertiary health-care setting following approval from the institutional ethics committee with the objective to study and compare handwriting parameters in participants with and without cognitive impairment.

Study design

This was a cross-sectional observational study.

Study duration

The duration of the study was 18 months, starting from August 2020 and ending in February 2022.

Study setting

Subjects visiting a tertiary health-care geriatric specialty center were used for study settings.

Sampling method

This was purposive sampling.

Inclusion criteria

(1) Those who gave informed consent, (2) those in the age group of 60-85 years, (3) those having education of high school certificate and above, (4) those with skilled category occupation and above as per the latest Kuppuswamy Socioeconomic Status Scale, and (5) those who can understand and write in English language.

Exclusion criteria

(1) Noncooperative participants; (2) those who are unable to follow test instructions; (3) the presence of dementia, stroke, epilepsy, uncorrected visual and hearing impairment, head injury, movement disorders, musculoskeletal and connective tissue disorders, psychotic disorders, mood disorders not in remission, and substance use disorders (as per ICD-10 Classification of Mental and Behavioral Disorders: Diagnostic Criteria for Research-WHO), or any other neurological, physical, uncontrolled, or severe medical disorder that can impair cognition or handwriting; (5) those with Montreal Cognitive Assessment-Hindi (MOCA-H) score 18 and below.

Sample size calculation

Using the formula, sample size (N) = (Z@/2) 2*p* (1-p)/d2 where N = sample size, Z = Z statistic. Z value is 1.96 at 95% confidence level and 80% power, p = prevalence per 100 as per previous studies, d = allowable error (relative precision) = 10%-20% of assumed prevalence. The prevalence of cognitive impairment in elderly individuals in India was 10%-30% (prevalence and determinants of cognitive impairment in the elderly population in India, Help Age India Research and Development Journal, 2016). Taking the higher value of range of prevalence (P = 30%) and substituting the values, the final sample size was estimated to be 165.

Participant recruitment procedure: Participants who match for age, education, and occupational criteria were further screened for dementia by MINI COG scale, and those with a score of <3 were excluded. Participants with involuntary movements of limbs or trunk were then excluded by administration of Abnormal Involuntary Movement Scale (AIMS). The participants were then administered MOCA Scale (v7.1-Hindi) and divided into two groups, i.e., (1) cognitively impaired (CI) group (MOCA score = 19-25) and (2) nonCI (NCI) (MOCA score = 26-30), based on cut-off for normal MOCA score as 26. Participants with MOCA score of 18 and below were excluded. It was followed by administration of Addenbrooke's Cognitive Examination (Hindi version) for confirming MOCA-H scores. Participants who scored <76 on ACE-H were excluded. After this step, handwriting collection was attempted using the smart pen (refer to [Figure 1] and [Figure 2] for study design and processing workflow respectively). Modes of technology: Three modes were used in this study (1) smart pen technology, (2) Anoto dot paper technology, and (3) haptic capture technology through artificial intelligence. The handwriting sample was collected using Livescribe Echo Smart Pen on an A4-sized 6-mm grid ruled smart page. A total of three handwriting tasks in English were given to each participant. The smart pen requires dot paper and Echo Desktop Software to capture, synchronize, transfer, store, and retrieve handwriting data dynamically. The pen was equipped with optics technology which senses the pressure generated by the replaceable ink tip on the paper during writing through a force-resisting sensor (FSR). In an activated state (pen tip in contact with the paper), FSR turns on the infrared (IR) camera inside the pen for tracking the handwriting, and in a deactivated state (pen tip not in contact with the paper), FSR turns off the IR camera and the tracking stops.[6],[7] The microdot pattern on each page is based on Patented Anoto Dot Pattern Technology provided by Anoto Group AB, a Swedish cloud-based software. This dot pattern consists of small dots (100 μ m in diameter) arranged with a spacing of approximately 0.3 mm. The dot pattern overlays an imaginary square grid. The dots are slightly displaced from the grid, each dot in one of four possible positions, forming a pattern. The dot pattern identifies the specific page and locations on the page during writing. The camera at the tip of the smart pen takes a continuous series of photographs of 6 × 6 dots, spaced 0.3 mm apart, and creating images of 1.8 × 1.8 mm grids of 36 dots. The camera takes 72 snapshots per s, sufficient to capture and recreate handwriting. Each picture of these dots was decoded by software in the smart pen to provide an (x, y) coordinate pair, describing the smart pen's exact position on paper at any given point in time, which was then interpreted and mapped by smart pen software. The camera neither recognizes the ink nor the artwork nor the legibility of handwriting. It only precisely tracks and remembers where ink was written by plotting the coordinates of items. Here, the patented dot-positioning system similar to the geographical positioning system on Google Maps comes into the picture where the smart pen precisely tracks its movement on paper. As a result, anything written-a word, number, or drawing can be recognized, analyzed, and stored through artificial intelligence.[8] The obtained handwritten sample was then transferred to ImageJ software and handwriting parameters were extracted at the level of whole task and text lines (Refer to Appendix: Supplemental Table (ST): ST 1 and 2 for the description of handwriting tasks and definition, categorization of handwritten parameters, respectively). Description of study tools: (1) semi-structured pro forma to collect the baseline sociodemographic data; (2) Mini COG: It is a simple and fast screening test to detect dementia in its early stages. It takes just 3 min to identify the cognitive impairment and a score of <3 was an indicator of dementia;[9] (3) AIMS: It stands for Abnormal Involuntary Movement Scale. It is useful to quantify abnormal body movements, particularly induced by psychotropics. It has 12 items rated on a severity scale from 0 to 4. A score greater than two in different body segments indicates dyskinesia;[10],[11] (4) MOCA-H: MOCA-Hindi version to detect mild cognitive impairment (MCI). It should be administered by trained clinicians only. It assesses executive/visuospatial, naming, memory, attention, language, abstraction, delayed recall, and orientation. A score of 26 or above out of 30 is considered normal;[12] (5) ACE-H: Addenbrooke's Cognitive Examination-Hindi version. It is used as a screening tool to detect cognitive impairment. It assesses six cognitive domains – orientation, attention, memory, verbal fluency, language, and visuospatial ability. The total score is 100. A score >88 is considered normal;[13] (6) Livescribe Echo Smart pen, Notebook-11 * 8.5 inches (California, USA, Livescribe Inc.) with Echo desk space dashboard for PC station on MS Windows 8 and above; and (7) ImageJ software: ImageJ is a JAVA-based image processing program developed at the National Institute of Health and Laboratory for Optical and Computational Instrumentation at the University of Wisconsin. The project was developed by Mr. Wayne Rasband, a software developer of the Research Services Branch of the National Institute of Mental Health, in 2010.[14] ImageJ1 1.53e/Java 1.8.0_172 (64 bit) the latest stable release, open source freely available version was used in our study for handwriting analysis with a preset calibration of 6-mm grid equating 22.67 pixels.[15]
Figure 1: Showing the study design for participant recruitment and handwriting collection

Click here to view
Figure 2: Shows that processing work flow of handwriting analysis from data acquisition to data analysis

Click here to view



Statistical analysis

Data were analyzed using SPSS version 28 (IBM Corp. Released 2021. IBM SPSS Statistics for Windows, Version 20.0. Armonk, NY, USA: IBM Corp.) for descriptive statistics such as frequency, percentage, mean, and standard deviation. Group differences were analyzed using the Chi-square test for categorical variables and the MannWhitney U test for continuous variables. Spearman's correlation coefficient assessed the correlation between the groups. P < 0.05 was taken as a statistically significant value.


  Results Top


Out of 230 individuals who were screened, 65 participants were excluded due to consent issues (5), medical causes (40), and others (20), leaving a final sample of 165 participants (refer to [Figure 3]). The mean age of the study population was 66.4 ± 5.3 years. The mean years of formal education were 15.6 ± 2.7 years. The mean MOCA-H score in CI and NCI groups was 22.67 (1.34) and 27.00 (1.09), respectively. The mean ACE-H score in CI and NCI groups was 80.68 (3.68) and 93.05 (2.94), respectively. CI and NCI groups had an almost similar number of participants with 53% in CI and 47% in the NCI group. Male participants accounted for 45.5% in the CI group and 41.2% in the NCI group with an age range of 60-69 (35.8% vs. 33.9%) belonging to Hindu religion (49.7% vs. 44.2%), educated up to postgraduation level (23.0% vs. 22.4%), and professional occupational category with upper socioeconomic class (20.6% vs. 23.6%) in CI and NCI groups, respectively. In both the groups, majority of the participants had no family history of psychiatric illness (49.1% vs 41.2%), no comorbid mood disorders (29.7% vs. 32.7%), and had medical comorbidity (31.0% vs. 23.6%). Except for occupational category and socioeconomic status (P < 0.05), there were no statistically significant differences across the two groups with respect to sociodemographic variables [Table 1]. The mean MOCA-H score in CI and NCI groups was 22.67 (1.34) and 27.00 (1.09), respectively. The mean ACE-H score in CI and NCI groups was 80.68 (3.68) and 93.05 (2.94), respectively [Table 2]. The mean scores of both MOCA and ACE were lower in the CI group, and the results were statistically significant (P < 0.005). In all three handwriting tasks, the mean scores of all handwriting parameters except TW, PSPS (SF), and WS were higher in the CI group than in the NCI group. In task 3, WC was higher in the NCI group. HWPs such as TOT, PSPS, TW, TH, NOL, WS, MTOL, and MTOSS were able to differentiate the two groups CI and NCI at statistically significant levels (P < 0.05)in all three handwritten tasks [Table 3], [Table 4], [Table 5]. There was mild-to-moderate degree of positive correlation with handwriting parameters (PSPS, WS, and WC) and negative correlation with handwriting parameters (NOPS, TOT, TH, NOL, MHOL, MTOL, and MTOSS) with respective MOCA and ACE scores, respectively [Table 6].
Figure 3: Flowchart of participant screening, recruitment and exclusion. PSP: Progressive supranuclear palsy, MSA: multiple system atrophy, SUDs: substance use disorders

Click here to view
Table 1: Distribution of sociodemographic factors in cognitively impaired and non-cognitively impaired groups

Click here to view
Table 2: Descriptive and association statistics of Montreal Cognitive Assessment and Addenbrooke's Cognitive Examination in cognitively impaired and noncognitively impaired groups

Click here to view
Table 3: Descriptive and comparative statistics of task 1 handwriting parameters in cognitively impaired and noncognitively impaired groups

Click here to view
Table 4: Descriptive and comparative statistics of task 2 handwriting parameters in cognitively impaired and noncognitively impaired groups

Click here to view
Table 5: Descriptive and comparative statistics of task 3 handwriting parameters in cognitively impaired and noncognitively impaired groups

Click here to view
Table 6: Correlation between handwriting parameters and cognitive test scores

Click here to view



  Discussion Top


Handwriting analysis in the elderly is essential for an easy and faster way of cognitive assessment as many of them have multiple medical comorbid conditions and live in a residential environment mostly unaccompanied by their children. Handwriting assessment facilitates early diagnosis and timely therapeutic intervention in the elderly.

There was only one study conducted by Kawa et al. in Poland in 2017 with Livescribe Echo Smartpen, which first attempted to analyze the spatial and dynamic aspects of handwriting in the elderly population. Regarding the sociodemographic factors, the mean age in our study was 65.7 (3.3) years in the CI group and 67.81 (4.0) years in the NCI group. It contrasts with the previous study of Kawa et al.,[16] where the mean age in the CI and NCI group was 76.1 (5.8) years and 74.8 (5.7) years, respectively. It shows in our study that cognitive impairment was evident at a relatively earlier age. This was due to more number of older adults visiting our tertiary health center for regular cognitive screening at a relatively higher frequency due to the greater availability of geriatric specialty services at our institution. With regard to gender distribution, the majority of them were males (87%). These results were similar to the findings of Kawa et al., but contradictory to those in Schroter et al.,[17] Werner et al.,[18] and Yan et al.[19]

These differences in the Indian context could be explained by the fact that women were less likely to seek medical attention for their cognitive disturbances, unless more profound to disturb their activities of daily living. They are more restricted to household chores, taking care of their domestic issues, children, and grandchildren. Regarding education, the majority of participants in our study were graduates, with approximately 16 years of formal education. These findings were similar to previously mentioned studies. On similar lines, the majority of the study population (32%) belonged to the higher occupational category. A study by Gheysen et al. reported that older adults with cognitively challenging jobs as of teachers, doctors, music, drivers, pilots, and those with a greater degree of baseline education often carry or retain a higher level of cognitive functions into their later life. These traits have a protective effect on processing speed, memory, and intelligence and influence both fluid and crystallized cognition.[20] It also influences the overall socioeconomic status. In our study, the majority of the study population (36%) belonged to the upper socioeconomic class.

With regard to MOCA-H scores, it was found to be 22.67 (1.34) in the CI group and 27.00 (1.09) in the NCI group. ACE-H scores were 80.68 (3.60) in the CI group and 93.05 (2.94) in the NCI group [Table 2]. The results were similar to that of Kawa et al. In the study by Schroter et al. and Yan et al., the respective mean MMSE scores were higher across similar cohorts.[21],[22],[23],[24] The relatively lower cognitive scores in our study group were probably related to the use of better cognitive screening tools with higher sensitivity and specificity than MMSE. The use of such tools yields more precise results in evaluating cognitive impairment. The degree of correlation was higher in MOCA (0.68) than ACE H (0.43), implying that MOCA was better to ACE-H in picking up the cognitive disturbances in the earlier stages, which was consistent with studies of Trzepacz et al.,[25] and Wang et al.[26]

Handwriting parameters such as the number of pen strokes (NOPS), total time of task (TOT), text height (TH), the mean height of text line (MHOL), and the number of lines (NOL) were seen to be consistently higher in CI group across all tasks whereas stroke frequency (pen strokes per second) and writing speed (WS) were consistently lower in CI group, indicating poor handwriting fluency [Table 3], [Table 4], [Table 5]. In the study by Kawa et al., the predominant findings included increased time of task in capital letter writing and single letter multiply because it requires more planning and visuospatial abilities than required for cursive writing. In this study, the handwriting was collected from 37 participants each of MCI and healthy controls with Livescribe Echo Pen by incorporating three writing tasks of cursive, capital letter writing, and single capital letter repetition. It showed that subjects with cognitive impairment required more time to complete the first task (median 119.5s vs. 95.1s in healthy controls, at P < 0.05) and the second task (median 84.2s vs. 53.7s in healthy controls) showing that they write slowly due to lower processing speed and dysfunctional hand motor coordination, resulting in more time taken to complete a single stroke of handwritten text. The written text also appeared to be grossly oversized in the CI group in all three handwriting tasks (the median height of text in the second task was 22.3 mm vs. 20.2 mm in the control group). Similar findings were observed in our study with longer pauses between strokes in the first and second tasks as evidenced by the increased meantime of single stroke. Capital letter task writing revealed many of the differentiating features. Moreover, our study revealed a mild-to-moderate degree of correlation of handwritten parameters with the neuropsychological test scores [Table 6]. This compared to previous studies displayed a weak correlation. It was related to the amount of data variability, differences in the shapes of two distributions, and sample characteristics.

Aging brain has early loss of gray matter and shrinkage of white matter, particularly in the prefrontal cortex and precentral gyrus with decreasing synaptic density connections and increasing deposits of beta-amyloid in specific brain regions (hippocampus, temporal lobes, and entorhinal cortex). Language, vocabulary, and implicit (nondeclarative) memory remain largely spared or resist age-related degeneration, according to the literature review findings of Harada et al.[27] These results were in synchrony with the physiological and neuroanatomical cognitive changes of aging brain, corresponding to decreased fluid intelligence, processing speed, attention, and memory.

Strengths of study

Digital collection with semiautomatic offline analysis of handwriting through a preinstalled software (ImageJ) on a PC (personal computer). The method of handwriting collection can be easy, flexible, quick, and even from a remote distance, where health-care facilities are limited. It also eliminates the need for a specialist.

Limitations of study

Handwriting analysis cannot be carried out in those who are illiterate and those with musculoskeletal and physical abnormalities of their hands. Given the nature of a cross-sectional study in an urban setting on participants attending from a tertiary health-care center, the study findings cannot be generalized to the whole community.


  Conclusion Top


Simple registration system with a readily available smart pen through three handwriting tasks revealed handwriting features (NOPS, TOT, PSPS, WS, and MTOSS) that could distinguish CI from cognitively normal subjects. These parameters correspond to cognitive domains particularly attention, fluency, processing speed, memory recall, and visuoconstructive skills and can be thus be considered sensitive enough to reflect changes in the cognitive status of an individual. Overall cognitively deteriorated individuals write slowly, with higher pauses, less fluently, with more text lines, bigger text size, and with higher number of penstrokes, and having more difficulty with capital letter writing. The cognitively intact subjects took lesser time, and showed greater fluency and speed of handwriting, with smaller letters and a lesser number of text lines giving a compact appearance.

Future directions

There is a need to study handwriting kinematics in broader clinical settings with different neuropsychiatric disorders for analyzing sensory-motor dysfunction in neurodegenerative research. Handwriting provides a quantitative estimate of cognition, which can be exploited by psychiatrists. Handwritten parameters can also be used as a prognostic marker to study the response to drug treatment protocols in cognitive neurology and cognitive psychology. Handwriting can thus serve as a potential disease biomarker for cognitive disorders, with a role to become a new diagnostic tool. It also serves a significant role in occupational therapy for the elderly.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.



 
  References Top

1.
Yoon JH, Suh MK, Jeong Y, Ahn HJ, Moon SY, Chin J, et al. Agraphia in Korean patients with early onset Alzheimer's disease. Int Psychogeriatr 2011;23:1317-26.  Back to cited text no. 1
    
2.
Nirjon S, Emi IA, Mondol MA, Salekin A, Stankovic JA. MOBI-COG: A mobile application for instant screening of dementia using the mini-cog test. In: Proceedings of the Wireless Health 2014 on National Institutes of Health. New York, NY, United States: Association for Computing Machinery; 2014. p. 1-7.  Back to cited text no. 2
    
3.
Zorluoglu G, Kamasak ME, Tavacioglu L, Ozanar PO. A mobile application for cognitive screening of dementia. Comput Methods Programs Biomed 2015;118:252-62.  Back to cited text no. 3
    
4.
McMaster KL, Kunkel A, Shin J, Jung PG, Lembke E. Early writing intervention: A best evidence synthesis. J Learn Disabil 2018;51:363-80.  Back to cited text no. 4
    
5.
Schneider CA, Rasband WS, Eliceiri KW. NIH image to ImageJ: 25 years of image analysis. Nat Methods 2012;9:671-5.  Back to cited text no. 5
    
6.
Mann AM, Hinrichs U, Quigley A. Digital pen technology's suitability to support handwriting learning. In: The impact of pen and touch technology on education. Springer, cham: Part of the Human–Computer Interaction Series book series (HCIS); 2015. p. 7-22.  Back to cited text no. 6
    
7.
Bouck EC, Flanagan S, Miller B, Bassette L. Technology in action. J Spec Educ Technol 2012;27:47-57.  Back to cited text no. 7
    
8.
Lind L, Berglund A, Berglund E, Bång M, Hägglund S. Effortless data capture for ambient e-services with digital pen and paper technology. In: Digital Literacy: Concepts, Methodologies, Tools, and Applications. Linköping University & Santa Anna IT Research Institute, Sweden: IGI Global; 2013. p. 522-40.  Back to cited text no. 8
    
9.
Lessig MC, Scanlan JM, Nazemi H, Borson S. Time that tells: Critical clock-drawing errors for dementia screening. Int Psychogeriatr 2008;20:459-70.  Back to cited text no. 9
    
10.
Stacy M, Sajatovic M, Kane JM, Cutler AJ, Liang GS, O'Brien CF, et al. Abnormal involuntary movement scale in tardive dyskinesia: Minimal clinically important difference. Mov Disord 2019;34:1203-9.  Back to cited text no. 10
    
11.
Gharabawi GM, Bossie CA, Lasser RA, Turkoz I, Rodriguez S, Chouinard G. Abnormal Involuntary Movement Scale (AIMS) and Extrapyramidal Symptom Rating Scale (ESRS): Cross-scale comparison in assessing tardive dyskinesia. Schizophr Res 2005;77:119-28.  Back to cited text no. 11
    
12.
Gupta M, Gupta V, Nagar Buckshee R, Sharma V. Validity and reliability of Hindi translated version of Montreal cognitive assessment in older adults. Asian J Psychiatr 2019;45:125-8.  Back to cited text no. 12
    
13.
Bajpai S, Upadhyay A, Sati H, Pandey RM, Chaterjee P, Dey AB. Hindi version of Addenbrooke's cognitive examination III: Distinguishing cognitive impairment among older Indians at the lower cut-offs. Clin Interv Aging 2020;15:329-39.  Back to cited text no. 13
    
14.
Rasband WS. ImageJ, us National Institutes of Health. Maryland, USA: Bethesda; 2011. Available from: http://imagej. Nih. gov/ij/. [Last accessed on 2022 Dec 04].  Back to cited text no. 14
    
15.
Rajwa B, McNally HA, Varadharajan P, Sturgis J, Robinson JP. AFM/CLSM data visualization and comparison using an open-source toolkit. Microsc Res Tech 2004;64:176-84.  Back to cited text no. 15
    
16.
Kawa J, Bednorz A, Stępień P, Derejczyk J, Bugdol M. Spatial and dynamical handwriting analysis in mild cognitive impairment. Comput Biol Med 2017;82:21-8.  Back to cited text no. 16
    
17.
Schröter A, Mergl R, Bürger K, Hampel H, Möller HJ, Hegerl U. Kinematic analysis of handwriting movements in patients with Alzheimer's disease, mild cognitive impairment, depression and healthy subjects. Dementia and geriatric cognitive disorders 2003;15:132-42.  Back to cited text no. 17
    
18.
Werner P, Rosenblum S, Bar-On G, Heinik J, Korczyn A. Handwriting process variables discriminating mild Alzheimer's disease and mild cognitive impairment. J Gerontol B Psychol Sci Soc Sci 2006;61:P228-36.  Back to cited text no. 18
    
19.
Yan JH, Rountree S, Massman P, Doody RS, Li H. Alzheimer's disease and mild cognitive impairment deteriorate fine movement control. J Psychiatr Res 2008;42:1203-12.  Back to cited text no. 19
    
20.
Gheysen F, Poppe L, DeSmet A, Swinnen S, Cardon G, De Bourdeaudhuij I, et al. Physical activity to improve cognition in older adults: Can physical activity programs enriched with cognitive challenges enhance the effects? A systematic review and meta-analysis. Int J Behav Nutr Phys Act 2018;15:63.  Back to cited text no. 20
    
21.
Williams KN, Kemper S. Interventions to reduce cognitive decline in aging. J Psychosoc Nurs Ment Health Serv 2010;48:42-51.  Back to cited text no. 21
    
22.
Kemper S, Sumner A. The structure of verbal abilities in young and older adults. Psychol Aging 2001;16:312-22.  Back to cited text no. 22
    
23.
DeDe G, Caplan D, Kemtes K, Waters G. The relationship between age, verbal working memory, and language comprehension. Psychol Aging 2004;19:601-16.  Back to cited text no. 23
    
24.
Mortensen L, Meyer AS, Humphreys GW. Age-related effects on speech production: A review. Lang Cog Process 2006;21:238-90.  Back to cited text no. 24
    
25.
Trzepacz PT, Hochstetler H, Wang S, Walker B, Saykin AJ, Alzheimer's Disease Neuroimaging Initiative. Relationship between the Montreal cognitive assessment and mini-mental state examination for assessment of mild cognitive impairment in older adults. BMC Geriatr 2015;15:107.  Back to cited text no. 25
    
26.
Wang BR, Zheng HF, Xu C, Sun Y, Zhang YD, Shi JQ. Comparative diagnostic accuracy of ACE-III and MoCA for detecting mild cognitive impairment. Neuropsychiatr Dis Treat 2019;15:2647-53.  Back to cited text no. 26
    
27.
Harada CN, Natelson Love MC, Triebel KL. Normal cognitive aging. Clin Geriatr Med 2013;29:737-52.  Back to cited text no. 27
    


    Figures

  [Figure 1], [Figure 2], [Figure 3]
 
 
    Tables

  [Table 1], [Table 2], [Table 3], [Table 4], [Table 5], [Table 6]



 

Top
 
 
  Search
 
Similar in PUBMED
   Search Pubmed for
   Search in Google Scholar for
 Related articles
Access Statistics
Email Alert *
Add to My List *
* Registration required (free)

 
  In this article
Abstract
Introduction
Materials and Me...
Results
Discussion
Conclusion
References
Article Figures
Article Tables

 Article Access Statistics
    Viewed66    
    Printed0    
    Emailed0    
    PDF Downloaded17    
    Comments [Add]    

Recommend this journal


[TAG2]
[TAG3]
[TAG4]