jfa journal

AND option

OR option



R. Shi1,2,*, W. Hao1,*, W. Zhao1, T. Kimura1, T. Mizuguchi3, S. Ukawa4, K. Kondo5,6, A. Tamakoshi1


1. Department of Public Health, Hokkaido University Graduate School of Medicine, Hokkaido University, Sapporo, Hokkaido, Japan; 2. Haidian Maternal and Child Health Hospital, Beijing, China; 3. Maxell, Ltd. Yokohama, Kanagawa, Japan; 4. Department of Social Welfare Science and Clinical Psychology, Osaka Metropolitan University Graduate School of Human Life and Ecology, Osaka, Osaka, Japan; 5. Department of Social Preventive Medical Sciences, Center for Preventive Medical Sciences, Chiba University, Chiba, Chiba, Japan;
6. Department of Gerontological Evaluation, Center for Gerontology and Social Science, National Center for Geriatrics and Gerontology, Obu, Aichi, Japan; * These authors contributed equally to this work

Corresponding Author: Prof Akiko Tamakoshi MD, PhD, Department of Public Health, Hokkaido University Graduate School of Medicine, N15W7, Kita-ku, Sapporo 060-0812, Japan, Tel: +81 11 7065068; Fax: +81 11 7065068, E-mail: tamaa@med.hokudai.ac.jp

J Frailty Aging 2024;in press
Published online April 10, 2024, http://dx.doi.org/10.14283/jfa.2024.34



BACKGROUND: Finger tapping impairment and frailty share overlapping pathophysiology and symptoms in older adults, however, the relationship between each other has not been previously studied.
OBJECTIVES: To investigate how finger tapping movements correlate with frail status in older Japanese adults.
DESIGN, SETTING, AND PARTICIPANTS: Data were from a cross-sectional study called the Cognition and Activity in Rural Environment of Hokkaido Senior Survey 2018. A total of 244 community-dwelling older adults (mean age 75.3 years) were included.
MEASUREMENTS: Participants underwent physical examinations, gait and finger tapping tests, and completed self-administered questionnaires. Frailty was assessed using Fried’s frailty phenotype, and factor analysis was conducted to extract relevant finger tapping factors. Multinomial logistic regression was employed to analyze associations, generating adjusted odds ratios.
RESULTS: Of the participants, 18 were frail, and 145 pre-frail. Analysis identified three distinct finger tapping patterns: “Range of Motion – Nondominant Hand,” “Variability – Dominant Hand – Anti,” and “Variability – Nondominant Hand – Anti.” These patterns showed significant associations with aspects of Fried’s frailty phenotype, particularly low physical activity (P = 0.002), weakness (P = 0.003), and slowness (P = 0.004). A larger range of motion in the nondominant hand correlated with a lower frailty risk (Odds Ratio: 0.09, 95% CI: 0.02-0.46), while higher variability in the same hand increased the risk of pre-frailty (Odds Ratio: 2.19, 95% CI: 1.09-4.39).
CONCLUSION: Finger tapping movements are significantly associated with frailty status as determined by Fried’s phenotype. The findings underscore the importance of further longitudinal studies to understand the relationship between motor function and frailty.

Key words: Finger tapping, frailty, aged.

Abbreviations: PF: physical frailty; GS: grip strength; FFP: Fried’s Frailty Phenotype; CARE-DO: Cognition and Activity in the Rural Environment of Hokkaido Senior; JAGES: Japan Gerontological Evaluation Study; IADL: Instrumental Activities of Daily Living; BMI: body mass index; OR: odds ratio; CI: confidence interval; ROM: range of motion; NH: non-dominant hand; DH: dominant hand.



Frailty, a common geriatric syndrome characterized by a reduction in physiological reserve and resistance to stressors among older adults, is associated with a higher risk of disability, falls, hospitalization, and death (1). In ultra-aging societies like Japan, the prevalence of pre-frailty and frailty, along with the healthcare and formal long-term care costs related to frailty prevention and treatment, are expected to rise significantly in the future years (2). However, early detection and timely interventions can effectively address and potentially reverse frail conditions, making the early identification of frailty crucial for healthcare and extending healthy life expectancy (3, 4).
The underlying pathophysiology of frailty has not been fully understood, likely due to its complex and multifactorial nature. This clinical condition of frailty may manifest in various phenotypes, including sensorial, physical, social, cognitive, psychological/depressive, and nutritional aspects (5). Within all, physical frailty (PF) is the most studied dimension and is known to predict the onset of other forms of frailty (6). PF results from the cumulative deterioration of multiple functions, including muscle mass loss, diminished motor skills, and weakened cognitive function. Studies have shown that older adults with PF often exhibit motor impairments that requiring multifunctional coordination, such as a slow gait speed (7, 8), decreased grip strength (GS) (9), and limitations in daily activities (10).
Repetitive and coordinated finger tapping is a common test of fine motor function. Normal finger tapping relies on the functional integrity of the corticospinal tract, cerebellar motor circuitry, and proprioceptive pathways, offering a quantitative method for evaluating motor skills (11). Finger tapping movements has been proved as an easy yet sensitive tool of assessing motor neuron lesion, sarcopenia, and a variety of diseases related to motor and cognitive functions (12–14). Notably, a reduction in the speed and regularity of finger tapping is linked to decreased muscle strength, impaired finger joints, and, specifically, brain abnormalities and related neuro deficits in both cross-sectional and longitudinal studies (15, 16). While these conditions are often causative factors or prodromes of PF (17–19). These findings indicate that finger tapping movements could be related to PF and may serve as an early indicator of a pre-PF status. However, the internal relationship between these two factors has not yet been explored so far.
In the current cross-sectional study, we adopted a well-validated magnetic sensing device that can collect multi-dimensional finger-tapping data. Our aim is to explore the comprehensive association with both pre-PF and PF status, as defined by Fried’s Frailty Phenotype (FFP), among community-dwelling older adults. We hypothesize that finger-tapping movements are highly correlated with both pre-PF and PF and could provide deeper insights into the underlying neurophysiological pathways associated with frailty.


Material and methods

This study obtained data from the Cognition and Activity in the Rural Environment of HokkaiDO Senior (CARE-DO) study 2018. The CARE-DO study was conducted after the Japan Gerontological Evaluation Study (JAGES) 2016, which is a large panel study that aimed to understand the association of health with social, and behavioral factors among older adults in Japan. The detail of JAGES has been described elsewhere (20). The baseline CARE-DO study, targeting 5,103 individuals from the 2016 JAGES survey aged 69 to 78 years in six Hokkaido towns, received responses from 569 individuals who agreed to participate in the winter 2017 survey following a mail invitation. The following year, these participants were re-invited to the CARE-DO summer survey 2018. Of these, 262 did not respond, and two requested their partners to participate instead. Ultimately, 309 individuals participated in the 2018 CARE-DO summer survey. From the eligible participants, those who self-reported Parkinson’s disease or cognitive impairment (n=3), were left-handed (n=14), did not finish the finger tapping test (n=24), or could not identify their PF status (n=24) were excluded. Finally, a total of 244 participants were enrolled (Figure 1).

Figure 1. Flowchart of participants’ selection in the current study


During the study, participants first underwent a comprehensive health examination conducted by trained investigators. This examination encompassed a range of assessments including blood pressure, height, weight, body composition (Inbody 430, Japan), sensor-measured gait, GS, finger tapping test, and cognitive evaluation. Cognitive function was assessed through a face-to-face interview using the Japanese version of Montreal Cognitive Assessment (21). At the end of the interview, years of education were recorded. Body mass index was calculated as weight in kilogram divided by height in meters squared. Following these examinations, they underwent a physical activity assessment using the wearable accelerometer (Active Style Pro HJA-350T, Omron Healthcare Co., Ltd.) (22), and completed an at-home self-administered questionnaire that included the information on age, sex, smoking and drinking status (current, past, never), the presence of medical histories (hypertension, heart disease, diabetes, musculoskeletal disease, injuries (fall/fracture) and cancer), and the instrumental activities of daily living (IADL) (23). Participants were instructed to return the questionnaires and accelerometers to the researchers after a period of two weeks.
All participants provided written informed consent for data collection and analysis. The study design was approved by the Institutional Review Committee of the Hokkaido University Graduate School of Medicine for Ethical Issues (No. 18-025).

Finger tapping measurements

The finger tapping performance was assessed using a magnetic-sensing finger tapping device (UB-2, Maxell, Tokyo, Japan), details of which have been described elsewhere (13, 24). As shown in Figure 2, the magnetic sensors were worn on the tips of the thumb and index finger of both hands. Before testing, the handedness of each participant was recorded. The finger tapping test had two tasks in sequence: in-phase and antiphase tasks. Each task involved a short practice session, and the formal test lasted 15 seconds. During the in-phase and antiphase, participants were asked to tap their thumb tips on the index fingertips simultaneously and commutatively in a 4-centimeter opening distance for both hands, the action should be performed as quick as possible while keeping a steady rhythm and without resting their arms on the table.
The sensors were connected to a laptop, and all finger tapping data were exported by the testing application. Forty unimanual parameters (right and left hands) were measured (Supplementary Table 1) and comprised: 7 parameters for the distance domain, 15 for the velocity domain, 10 for the acceleration domain, and 8 for the tapping-interval domain. And four bimanual parameters were measured. In total, (40 left-hand parameters + 40 right hand parameters + 4 bimanual parameters) × 2 phases, that is, 168 variables were recorded for each participant.

Figure 2. Magnetic sensing finger tapping device and task of finger tapping test

(a) Magnetic sensing finger tapping device. (b) In-phase, (c) Anti-phase


PF evaluation

PF status was assessed using the FFP (1) as follows: 1) Shrinking, determined by self-reported weight loss: “Have you lost 3 kg in the past 6 months?”, with a ‘yes’ response meeting the criteria. 2) Exhaustion, based on the Geriatric Depression Scale question: “Do you feel full of energy?”, where a ‘no’ response indicated exhaustion. 3) Low physical activity, measured by weekly calories expended as recorded by a wearable accelerometer (Active Style Pro HJA-350T, Omron Healthcare Co., Ltd.). Participants wore the accelerometer for two weeks, except during bathing and sleeping. Only data from devices worn at least 10 hours daily for more than four days were used. The lowest 20% in calorie consumption, stratified by sex (men <3,166 kcal/week; women <2,782 kcal/week), met the low activity criterion. 4) Weakness, gauged by the average grip strength (GS) of the dominant hand using a JAMAR Plus+ digital hand dynamometer (Sammons Preston). The weakest 20%, stratified by sex and BMI, were classified as weak (details in Supplementary Table 2). 5) Slowness, ascertained from average walking speed over a 6-meter course, recorded with Physilog® (GaitUp). The slowest 20%, stratified by sex and height, were categorized as slow (details in Supplementary Table 3) (25).
The Physical Frailty (PF) score was computed based on the number of criteria met, with a total possible score of 5 points. Participants scoring 3 or higher were classified as PF, those with a score of 1 or 2 as pre-PF, and those with a score of 0 as robust. In cases where a participant missed one criterion, the PF status was determined based on the available PF scores. Records with more than two missing criteria were excluded from the analysis.

Statistical analysis

To avoid over-calculation in later statistical analyses, 13 finger tapping parameters were initially extracted (see Supplementary Table 1). We then conducted an exploratory factor analysis on the remaining 116 finger tapping parameters to identify distinct finger tapping pattern factors. The suitability of factor analysis was assessed using Bartlett’s test and the Kaiser–Meyer–Olkin measure. Parameters with rotated factor loadings of 0.6 or higher were deemed significant contributors to the identified finger tapping pattern factors.
Finally, multinomial logistic regression was conducted to calculate odds ratio (OR) and 95% confidence interval (CI) of each finger tapping pattern factor by tertile, adjusted for sex, age, and the IADL score. Cochran–Armitage trend was calculated using the Wald statistics with finger tapping pattern tertile treated as continuous variables. Statistical significance was set at p < 0.05. All analyses were performed using SAS 9.4 (SAS Institute Inc., Cary, NC, USA).



Of the 244 participants, 123 (50.4%) were women, and the average age was 75.5 (±2.8) years. Eighteen (7.4%) participants were frail, and 145 (59.4%) were pre-frail.
Factor analysis yielded three factors that accounted for 63.3% of variance (Supplementary Table 1). The first factor explained 35.0% of total variance with greater factor loadings of nondominant hand parameters and was named “range of motion (ROM)-non-dominant hand (NH).” The second factor explained 16.5% of total variance with greater factor loadings and was named “Variability-dominant hand (DH)-anti.” The third factor explained 13.2% of total variance with greater factor loadings and was named “Variability-NH-anti.” Based on the parameters included and their vectors, the higher the value for the first pattern and the lower the value for the last two patterns, the better the finger tapping performance.
The basic characteristics and finger tapping patterns by tertile are presented in Supplementary Table 4. Age and IADL scores were similar across tertiles. Men were more likely to be in the higher tertile of the ‘ROM-NH’ pattern.
Table 1 shows the relationship between finger tapping factors and PF criteria in the robust group. Higher tertiles of ‘ROM-NH’ were associated with significantly lower OR for low physical activity, weakness, and slowness. Conversely, higher tertiles of ‘Variability-NH-anti’ were linked to increased OR for weakness and slowness.

Table 1. Association between frailty criteria and each finger tapping pattern by tertile

Note: N, number; OR, odds ratio; CI, confidence interval; NH, non-dominant hand; DH, dominant hand; Multinomial logistic regression was conducted to calculate the ORs and 95%CI; *P<0.05


Table 2 details the relationship between finger tapping patterns and pre-PF/PF status, compared to the robust group. A higher ‘ROM-NH’ tertile significantly reduced PF risk (OR: 0.09, 95% CI: 0.02–0.46), with a significant decreasing trend in PF risk as ‘ROM-NH’ scores increased (P for trend: 0.001). The ‘Variability-DH-anti’ pattern showed no significant association with pre-PF/PF. The ‘Variability-NH-anti’ pattern was potentially related to pre-PF, but no significant differences were observed in the middle or highest tertiles compared to the lowest.

Table 2. Association between frailty status and each finger tapping pattern by tertile

Note: N, number; OR: odds ratio; CI: confidence interval; NH: non-dominant hand; DH: dominant hand; Multinomial logistic regression was conducted to calculate the ORs and 95%CI; *P<0.05



The present study discerned three finger tapping patterns: “ROM-NH,” “Variability-DH-anti,” and “Variability-NH-anti”. We found that a better performance of “ROM-NH” pattern was significantly related to lower PF risk, and the higher “Variability-NH-anti” was significantly related to a higher pre-PF risk.
“ROM-NH” denotes a finger tapping pattern predominantly characterized by the tapping range of the non-dominant hand. Older adults exhibiting a superior “ROM-NH” pattern demonstrated longer tap distances in both in-phase and antiphase activities with their non-dominant hand. Significantly, these individuals had a lower prevalence of PF compared to those with shorter tap distances. Based on the strong correlation between the weakness criterion of PF and “ROM-NH” pattern, although no previous study has evaluated PF and finger tapping parameters directly before, the relationship between poor GS/hand muscle strength and fine-motor skill has been demonstrated previously. For instance, using a motor performance series work panel, the GS can predict hand dexterity, including the aimed tapping ability and numbers (26); GS and pinch strength values correlated with the Duruöz and Dreiser indices, which are self-reported scales for assessing hand function (27). Based on this evidence, which could support the current results, we hypothesized that the poor GS of frail older adults might affect their fine-motor skills, and thereby cause lower tapping ROM in the finger tapping test. The difference between frail and robust older adults regarding the “ROM-NH” pattern indicates the possibility of early PF detection.
In contrast, poor GS and poor upper limb strength are common in frailty. Prior research indicated that pre-frailty and frailty in individuals aged 55 years and older were associated with diminished upper limb dexterity and lower power, as measured by the box-and-block test (28). Additionally, older adults with stronger arm-curl strength and better manual coordination demonstrated improved performance in hand function tests (29). In our study, the finger tapping test required participants to keep their arms raised off the table, suggesting that for robust participants, greater upper limb strength could facilitate more effective completion of the test.
Several mechanisms could underlie the link between frailty and reduced finger tapping ROM. This diminished ROM might be affected by hand muscle strength and fatigue. Common PF symptoms, such as sarcopenia and increased fatigability, are significant physical factors contributing to PF (30, 31). Furthermore, neurological, endocrine, and immune dysfunctions associated with PF can disrupt muscle homeostasis, leading to loss of muscle mass and strength (31, 32). A decline in maximal voluntary contractions is often attributed to muscle contractile failure (33). The finger tapping test in our study required participants to tap as quickly as possible while maintaining a specific ROM. Therefore, weaker hand muscle strength may have limited the tapping ROM in frail older adults. The ‘Variability-NH-anti’ pattern, reflecting intraindividual variability during the nondominant hand’s antiphase finger tapping, indicated that individuals with higher variability could not maintain stable tapping speed, suggesting a higher prevalence of pre-PF. The exact mechanism linking individual variability with PF is not fully understood. However, based on previous research, it may be influenced by neurogenic impairment in older adults. This could involve the diminished tactile input and efficiency of motor functional neurobiological substrates with aging, along with a loss of tactile sensory feedback (34–36).
In our study, the ‘ROM-NH’ pattern appeared to reflect impairment in hand muscle strength, while ‘Variability-NH-anti’ seemed to indicate neuronal impairment in pre-PF. These findings provide initial evidence that PF affects finger tapping intraindividual variability, shedding light on the complex interplay between physical and neurological factors in aging.
As mentioned above, PF is defined as a biological syndrome of decreased reserve and resistance to stressors during aging (1). We considered that aging may interact in the association of frailty with finger tapping performance. However, we did not observe a difference between age and each finger tapping pattern in the current study. One reason for this might be the small age range of our participants’, as increasing evidence has demonstrated an association of aging with fine-motor skill impairment. Compared with young adults aged 18–25 years, older adults aged 65–77 years have a significantly slower tapping pace (37). Finger tapping frequency decreases with age (38). Older adults relied more heavily on executive control functions for sequential tapping (39). Hand tactile discrimination decreases and slower finger tapping reaction times are related to aging (40, 41). As the participants in our study were relatively healthy, and their age distribution was within 10 years, we might have underestimated age-related fine-motor decline.
This study had a few limitations. Firstly, as this was a cross-sectional study, the causal relationship between finger tapping and PF remains unclear. Secondly, the response rate for the current Hokkaido-DO study from the original JAGES participants was quite low (less than 10%). This low response rate suggests that the participants in this study may be relatively younger and healthier compared to the broader population. Consequently, this could lead to an underestimation of the prevalence of PF in the study’s findings. Additionally, in this study, FFP was utilized to assess frailty, with a focus primarily on motor functions. While FFP is validated for PF, it lacks comprehensive coverage of other frailty determinants, such as social, cognitive, or psychological aspects. This limitation suggests that despite multi-pathophysiology being considered in the relationship between finger movements and frailty, the observed significance in this study may still be predominantly due to overlapping motor mechanisms. Nevertheless, other studies have reported a strong correlation between finger movements, frailty, and multidimensional deficits. This underscores the need for future research to explore finger movements in conjunction with other aspects of frailty, aiming to achieve a more comprehensive understanding of the underlying mechanisms.



The study’s findings distinctly link comprehensive finger movements with both pre-PF and PF. This significant association points to the potential of finger motor movement as an effective tool for early frailty assessment. In the future, longitudinal cohort research with larger sample size and assessing multi-dimensional frailty are needed to elucidate the internal causality and pathophysiology underlying these associations.


Acknowledgements: We would like to thank all participants and investigators for their collaboration in this study. And we would like to thank Editage (www.editage.com) for English language editing.

Formatting of funding sources: The study was supported by the Japan Society for the Promotion of Science’s Grants-in-Aid for Scientific Research (B): Influence of Indoor Temperature Distribution on Health of elderly in Cold Climate [17H04129] and Challenging Research (Exploratory): Research on the effect of gait parameters and modifiers on cognitive function of older adults by using wearable devices [18H05389].

Conflicts of Interest: Dr. Tamakoshi reports non-financial support from Maxell, Tokyo, during the conduct of the study.

Ethical standard: All participants provided written informed consent for data collection and analysis. The study design was approved by the Institutional Review Committee of the Hokkaido University Graduate School of Medicine for Ethical Issues (No. 18-025).





1. Fried LP, Tangen CM, Walston J, et al. Frailty in older adults: evidence for a phenotype. J Gerontol A Biol Sci Med Sci 2001;56:M146-5610.1093/gerona/56.3.m146
2. Kasajima M, Eggleston K, Kusaka S, et al. Projecting prevalence of frailty and dementia and the economic cost of care in Japan from 2016 to 2043: a microsimulation modelling study. The Lancet Public Health 2022;7:e458–e46810.1016/S2468-2667(22)00044-5
3. Puts MTE, Toubasi S, Andrew MK, et al. Interventions to prevent or reduce the level of frailty in community-dwelling older adults: a scoping review of the literature and international policies. Age Ageing 2017;46:383–39210.1093/ageing/afw247
4. Kidd T, Mold F, Jones C, et al. What are the most effective interventions to improve physical performance in pre-frail and frail adults? A systematic review of randomised control trials. BMC Geriatr 2019;19:18410.1186/s12877-019-1196-x
5. Panza F, Solfrizzi V, Giannini M, Seripa D, Pilotto A, Logroscino G. Nutrition, frailty, and Alzheimer’s disease. Front Aging Neurosci 2014;6:22110.3389/fnagi.2014.00221
6. Nagai K, Tamaki K, Kusunoki H, et al. Physical frailty predicts the development of social frailty: a prospective cohort study. BMC Geriatrics 2020;20:40310.1186/s12877-020-01814-2
7. Binotto MA, Lenardt MH, Rodríguez-Martínez MDC. Physical frailty and gait speed in community elderly: a systematic review. Rev Esc Enferm USP 2018;52:e0339210.1590/S1980-220X2017028703392
8. O’Donoghue PJ, Claffey P, Rice C, et al. Association between gait speed and the SHARE Frailty Instrument in a Falls and Syncope Clinic. Eur Geriatr Med 2021;12:1101–110510.1007/s41999-021-00509-0
9. Syddall H, Cooper C, Martin F, Briggs R, Aihie Sayer A. Is grip strength a useful single marker of frailty? Age and Ageing 2003;32:650–65610.1093/ageing/afg111
10. Sakari R, Era P, Rantanen T, Leskinen E, Laukkanen P, Heikkinen E. Mobility performance and its sensory, psychomotor and musculoskeletal determinants from age 75 to age 80. Aging Clin Exp Res 2010;22:47–5310.1007/BF03324815
11. Shirani A, Newton BD, Okuda DT. Finger tapping impairments are highly sensitive for evaluating upper motor neuron lesions. BMC Neurology 2017;17:5510.1186/s12883-017-0829-y
12. Clark BC, Carson RG. Sarcopenia and Neuroscience: Learning to Communicate. J Gerontol A Biol Sci Med Sci 2021;76:1882–189010.1093/gerona/glab098
13. Sano Y, Kandori A, Shima K, et al. Quantifying Parkinson’s disease finger-tapping severity by extracting and synthesizing finger motion properties. Med Biol Eng Comput 2016;54:953–96510.1007/s11517-016-1467-z
14. Suzumura S, Osawa A, Kanada Y, et al. Finger Tapping Test for Assessing the Risk of Mild Cognitive Impairment. Hong Kong Journal of Occupational Therapy 2022;35:137–14510.1177/15691861221109872
15. Rabinowitz I, Lavner Y. Association between Finger Tapping, Attention, Memory, and Cognitive Diagnosis in Elderly Patients. Percept Mot Skills 2014;119:259–27810.2466/10.22.PMS.119c12z3
16. Zhang L, Lei L, Zhao Y, et al. Finger Tapping Outperforms the Traditional Scale in Patients With Peripheral Nerve Damage. Frontiers in Physiology 2018;910.3389/fphys.2018.01361
17. Pozo N, Romero C, Andrade M, et al. Exploring the relationship between frailty and executive dysfunction: the role of frontal white matter hyperintensities. Frontiers in Aging Neuroscience 2023;1510.3389/fnagi.2023.1196641
18. Bunce D, Batterham PJ, Mackinnon AJ. Long-term Associations Between Physical Frailty and Performance in Specific Cognitive Domains. The Journals of Gerontology: Series B 2019;74:919–92610.1093/geronb/gbx177
19. Waite SJ, Maitland S, Thomas A, Yarnall AJ. Sarcopenia and frailty in individuals with dementia: A systematic review. Archives of Gerontology and Geriatrics 2021;92:10426810.1016/j.archger.2020.104268
20. Kondo K, Rosenberg M, Organization WH. Advancing universal health coverage through knowledge translation for healthy ageing: lessons learnt from the Japan gerontological evaluation study. 2018. World Health Organization
21. Fujiwara Y, Suzuki H, Yasunaga M, et al. Brief screening tool for mild cognitive impairment in older Japanese: Validation of the Japanese version of the Montreal Cognitive Assessment. Geriatrics & Gerontology International 2010;10:225–23210.1111/j.1447-0594.2010.00585.x
22. Sasaki S, Ukawa S, Okada E, et al. Comparison of a new wrist-worn accelerometer with a commonly used triaxial accelerometer under free-living conditions. BMC Research Notes 2018;11:74610.1186/s13104-018-3849-9
23. Koyano W, Shibata H, Nakazato K, Haga H, Suyama Y. Measurement of competence: reliability and validity of the TMIG Index of Competence. Arch Gerontol Geriatr 1991;13:103–11610.1016/0167-4943(91)90053-s
24. Suzumura S, Osawa A, Nagahama T, Kondo I, Sano Y, Kandori A. Assessment of finger motor skills in individuals with mild cognitive impairment and patients with Alzheimer’s disease: Relationship between finger-to-thumb tapping and cognitive function. Japanese Journal of Comprehensive Rehabilitation Science 2016;7:19–2810.11336/jjcrs.7.19
25. Hao W, Zhao W, Kimura T, et al. Association of gait with global cognitive function and cognitive domains detected by MoCA-J among community-dwelling older adults: a cross-sectional study. BMC Geriatrics 2021;21:52310.1186/s12877-021-02467-5
26. Martin JA, Ramsay J, Hughes C, Peters DM, Edwards MG. Age and grip strength predict hand dexterity in adults. PLoS One 2015;10:e011759810.1371/journal.pone.0117598
27. Incel NA, Sezgin M, As I, Cimen OB, Sahin G. The geriatric hand: correlation of hand-muscle function and activity restriction in elderly. Int J Rehabil Res 2009;32:213–21810.1097/MRR.0b013e3283298226
28. Tay LB, Chua MP, Tay EL, et al. Multidomain Geriatric Screen and Physical Fitness Assessment Identify Prefrailty/Frailty and Potentially Modifiable Risk Factors in Community-Dwelling Older Adults. Ann Acad Med Singap 2019;48:171–180
29. Liu C-J, Marie D, Fredrick A, Bertram J, Utley K, Fess EE. Predicting hand function in older adults: evaluations of grip strength, arm curl strength, and manual dexterity. Aging Clin Exp Res 2017;29:753–76010.1007/s40520-016-0628-0
30. Morley JE, Malmstrom TK, Miller DK. A simple frailty questionnaire (FRAIL) predicts outcomes in middle aged African Americans. J Nutr Health Aging 2012;16:601–60810.1007/s12603-012-0084-2
31. Wilson D, Jackson T, Sapey E, Lord JM. Frailty and sarcopenia: The potential role of an aged immune system. Ageing Res Rev 2017;36:1–1010.1016/j.arr.2017.01.006
32. Clegg A, Young J, Iliffe S, Rikkert MO, Rockwood K. Frailty in elderly people. Lancet 2013;381:752–76210.1016/S0140-6736(12)62167-9
33. Bigland-Ritchie B, Woods JJ. Changes in muscle contractile properties and neural control during human muscular fatigue. Muscle Nerve 1984;7:691–69910.1002/mus.880070902
34. Catalan MJ, Honda M, Weeks RA, Cohen LG, Hallett M. The functional neuroanatomy of simple and complex sequential finger movements: a PET study. Brain 1998;121 ( Pt 2):253–26410.1093/brain/121.2.253
35. Cousins MS, Corrow C, Finn M, Salamone JD. Temporal measures of human finger tapping: effects of age. Pharmacol Biochem Behav 1998;59:445–44910.1016/s0091-3057(97)00443-7
36. Manning H, Tremblay F. Age differences in tactile pattern recognition at the fingertip. Somatosens Mot Res 2006;23:147–15510.1080/08990220601093460
37. Aoki T, Fukuoka Y. Finger tapping ability in healthy elderly and young adults. Med Sci Sports Exerc 2010;42:449–45510.1249/MSS.0b013e3181b7f3e1
38. Bowden JL, McNulty PA. The magnitude and rate of reduction in strength, dexterity and sensation in the human hand vary with ageing. Exp Gerontol 2013;48:756–76510.1016/j.exger.2013.03.011
39. Fraser SA, Li KZH, Penhune VB. Dual-task performance reveals increased involvement of executive control in fine motor sequencing in healthy aging. J Gerontol B Psychol Sci Soc Sci 2010;65:526–53510.1093/geronb/gbq036
40. Vieira AI, Nogueira D, de Azevedo Reis E, da Lapa Rosado M, Vânia Nunes M, Castro-Caldas A. Hand tactile discrimination, social touch and frailty criteria in elderly people: A cross sectional observational study. Arch Gerontol Geriatr 2016;66:73–8110.1016/j.archger.2016.04.012
41. Shim JS, Kim KI, Lim JY, Kim KW, Kim WS, Paik NJ. Finger tap reaction time as an independent prognostic factor for functional outcome in older adults. Annals of Geriatric Medicine and Research 2017;21:64–6910.4235/agmr.2017.21.2.64

© Serdi 2024