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Abstract

Objective

This study evaluated the effectiveness of age simulation gloves in replicating age-related declines in hand function.

Background

Commercially available age simulation gloves are increasingly used. However, their ability to replicate age-related physical decline remains largely unverified.

Method

Twenty healthy adults (mean age: 26.8 years) completed assessments under three conditions: with no glove, using a Cambridge Simulation Glove (CG), and using the CG combined with tremor simulation (TS). Grip and pinch strength (Biometrics electronic dynamometer and pinch meter), gross (Box and Block Test) and fine (Grooved Pegboard Test) motor dexterity, hand function (Southampton Hand Assessment Procedure), postural tremor (MetaMotionC accelerometers), tactile sensitivity (Semmes-Weinstein Monofilaments), and usability (System Usability Scale) were evaluated. Performance with glove conditions was compared against normative data of older adults if available.

Results

Grip strength and gross and fine motor dexterity declined in both glove conditions, aligning with normative ageing values. However, pinch strength and functional performance did not show consistent replication of normative ageing. Usability scores were below acceptable thresholds for both gloves. While the addition of tremor simulation increased peak frequency consistent with ageing, it did not replicate the rise in amplitude.

Conclusion

Overall, the gloves partially replicated age-related hand function decline. Improvements in pinch force, tremor fidelity, and ergonomic design are needed to enhance realism and usability in educational, clinical, and design contexts.

Application

Findings can guide in selecting or improving age simulation tools to better support age inclusive product development and assessment.

Keywords

ageing simulation hand function ergonomics

Introduction

By 2050, the number of individuals aged over 65 years is estimated to surpass 1.5 billion worldwide (United Nations, 2019). This increase in the older population poses significant challenges and responsibilities for societies, particularly in healthcare and design. To promote active ageing, maintain independence, and ensure equality, it is crucial that environments, services, and products align with the physiological needs of older adults (Portegijs et al., 2023).

Many physiological changes occur with age (Farage et al., 2012; Haigh, 1993). Among the various age-related physiological changes, those affecting the hand are particularly impactful, as the human hand plays a crucial role in maintaining autonomy in daily life, from manipulating small objects to writing, dressing, or using digital devices (Carmeli et al., 2003; Vasylenko et al., 2018). Hand function generally declines due to decreases in strength, range of motion, fine motor control, and sensory integrity, whereas physiological tremors increase (Carmeli et al., 2003; Chen & Chiu, 2025; Morrison et al., 2006; Pennathur et al., 2003). As a result, older adults experience challenges when performing daily activities, diminishing quality of life (Eid et al., 2024). Analysing and addressing age-related changes in the hand and enhancing them through design or interventions fall squarely within the scope of ergonomics (Karwowski, 2005).

Initially introduced in the automotive industry to improve inclusive vehicle design (Gerhardy et al., 2022), age simulation suits, including hand modules, were developed to address the challenges faced by older individuals. These wearable systems simulate common physical and sensory impairments with ageing, such as joint stiffness and reduced strength, by incorporating weights and restrictive elements. Age simulation suits are used in educational settings, healthcare training, and engineering design (Gerhardy et al., 2022; Lauenroth et al., 2017). They foster empathy among students and caregivers, enhance healthcare professionals’ understanding of age-related challenges (Eost-Telling et al., 2021), and support engineers in user-centred product development. Moreover, age simulation suits have the potential to serve as valuable surrogates for identifying usability issues in early-stage testing when direct involvement of older adults is either ethically or logistically challenging due to health-related limitations, transportation difficulties or physical demands of prolonged testing. In addition, early-stage products may lack full safety assurances or be unrefined ergonomically.

Despite these applications and the growing enthusiasm for age simulation suits in various fields, their ability to replicate age-related physical decline remains largely unverified. Most studies on ageing simulation have focused on psychosocial outcomes, such as empathy or attitudinal change, rather than on the physical effectiveness of the simulations (Gerhardy et al., 2022). Moreover, these suits are rarely tested for their accuracy in replicating effective functional decline, and quantitative outcome measures are largely lacking in the literature.

This gap is particularly striking when considering simulation tools for the hand and wrist, which are functionally critical yet underrepresented in simulation research. While many age simulation suits include generic gloves or wrist weights (Lauenroth et al., 2017), few target the specific, multidimensional impairments of the ageing hand. Even fewer have undergone objective validation against clinical norms or standardised assessment protocols. Consequently, it is unclear whether these gloves truly reflect geriatric hand function, or whether they merely offer a symbolic or theatrical representation. Importantly, inaccurate or exaggerated simulations may reinforce negative stereotypes about older adults. Therefore, it is essential to establish whether these tools realistically and proportionately replicate functional limitations that are aligned with established geriatric norms, rather than offering symbolic or arbitrary representations.

The aim of this study was to perform a quantitative, multi-domain assessment of the Cambridge Simulation Glove (CG; Inclusive Design Group, University of Cambridge, Cambridge, UK), a commercially available hand simulation glove developed to replicate age-related functional limitations. Function is restricted by rigid plastic strips positioned on the dorsal side of each finger and secured using Velcro fasteners. The proximal ends of these strips extend towards the wrist and are connected via sewn elastic parts with Velcro attachments that fasten to an adjustable Velcro band secured around the wrist. This allows the strip length to be adjusted according to the individual’s finger length.

The CG was tested both when used alone and when used in combination with a commercially available tremor simulator (TS; Produkt + Projekt, Wolfgang Moll, Germany), which was intended to mimic the increased hand tremors observed in ageing with electrical muscle stimulation. The glove is made of electrically conductive fabric and connects to a control unit via detachable snap-button cables positioned on the dorsal side of the wrist, which deliver electrical muscle stimulation. To simulate age-related tremor, the control unit is adjusted according to the manufacturer’s recommendations, with a pulse width of 120 µs and a pulse rate of 10 Hz. The hypothesis was that the combined CG and TS condition would produce greater reduction in hand function performance compared to only the CG, reflecting a stronger simulation of age-related functional loss.

This is the first study to test both CG and the CG combined with TS (CG + TS) under controlled conditions using a comprehensive and multidimensional assessment protocol, offering objective insight into their simulated age-related effects. To our knowledge, no quantitative data have been publicly disclosed by the manufacturers to support the functional claims of age simulation gloves. This study also established a structured protocol that provides a reproducible framework to guide future simulation glove testing, assistive technology testing, and empathy-based educational interventions.

Material and Methods

This study followed a repeated measures experimental design that was approved by the Imperial College Research Ethics Committee (ICREC Reference: 6652856). The research was conducted in accordance with the Declaration of Helsinki, and written informed consent was obtained from all participants prior to data collection.

Participants

Twenty healthy adults (10 females, 10 males; mean age: 26.8 ± 2.7 years) were recruited for the study. Overall and across genders, 90% of participants identified as right-handed. Individuals with hand disorders, a history of hand and/or wrist surgery, neurological or musculoskeletal conditions affecting hand function, interfering scar tissue with glove use, or pregnancy were excluded. Screening was conducted via an online survey (Qualtrics XM, Provo, Utah, USA), and each participant was assigned a unique number to ensure confidentiality.

Experimental Protocol

Each participant attended a single laboratory session lasting approximately 3 hours. Before testing, demographic and anthropometric data were collected. Participants completed all assessments under three conditions applied in randomised order: (1) with no glove (NG), serving as the baseline; (2) wearing the CG; and (3) wearing the CG + TS (CG + TS) (Figure 1). Standardised outcome measures were used to evaluate hand function, motor performance, and usability under different glove conditions.

Figure 1.

Glove conditions and applied tests. (A) No glove (NG); (B) Cambridge Simulation Glove (CG); (C) Cambridge Simulation Glove combined with Tremor Simulator (TS; CG + TS)

The Edinburgh Handedness Inventory–Short Form was administered in person and used to determine the dominant hand using four items rated on a 5-point Likert scale. Scores were converted to values ranging from +100 (always right) to −100 (always left) to calculate a Laterality Quotient (LQ). Participants were classified as right-handed (LQ > 60), left-handed (LQ < −60), or ambidextrous (LQ between −60 and 60) (Thomas & Fitch, 2023; Veale, 2014). In this study, only data from the dominant hand were included in the analysis.

Grip strength was assessed with the Biometrics E-LINK G100 electronic dynamometer (Biometrics Ltd, Gwent, UK, 2006) in conjunction with Biometrics DataLINK software (Deborah & Barnett, 2011; Mathiowetz et al., 1984). Testing setup followed the American Society of Hand Therapists guidelines (Fess & Moran, 1981). After adjusting the handle for comfort, three maximum-effort trials were performed with the dominant hand. Each contraction was held for 3–5 seconds, accompanied by verbal encouragement. A 15-second rest was given between attempts, and the highest value was recorded.

Pinch strength was measured using a Biometrics E-LINK P100 electronic pinch meter (Leszczak et al., 2024). Three pinch types were assessed on the dominant hand: two-point (tip-to-tip/pulp), three-point (tripod/palmer/chuck), and lateral (key) pinch (Mathiowetz et al., 1984; Szekeres et al., 2025). The sensor was positioned consistently on the thumb side. The procedure and participant positioning followed the same guidelines as those for grip strength testing. A 5-minute rest was provided between test conditions to reduce fatigue.

The Box and Block Test was used to quantify gross manual dexterity (Mathiowetz, Volland, et al., 1985). The Grooved Pegboard Test™ (Model 32020, Lafayette Instrument Company, United States), which requires precise eye-hand coordination, was used to quantify fine manual dexterity (Ashendorf et al., 2009; Merker & Podell, 2011).

The Southampton Hand Assessment Procedure (SHAP) was used to evaluate performance in daily life. It was administered following a standardised protocol (Light et al., 2002), including 26 timed tasks: 12 abstract objects and 14 activities of daily living tasks, targeting six classical grip types. A validated open-access linear scoring method was used to calculate the Index of Function and grip sub-scores (Burgerhof et al., 2017).

Postural physiological tremor was assessed using MetaMotionC accelerometers (MMC r0.2, MBIENTLAB, San Jose, USA) attached over the second metacarpal head on the dorsum of each hand, to capture vertical acceleration. Participants maintained a standardised postural position, sitting upright with both arms extended at shoulder height, elbows straight, wrists and fingers in a neutral pronated position, and eyes fixed on the dominant side index finger for 30 seconds. To prevent fatigue in the hand from assessments such as grip strength and pinch tests, the tremor assessment was conducted first. Data were collected at 100 Hz, filtered using a 2–30 Hz bandpass filter, and analysed in both time and frequency domains using MATLAB (MATLAB 2022b; MathWorks, Inc. Natick, USA) (Vial et al., 2019).

Semmes-Weinstein Monofilaments were used to assess mechanoreceptive discriminatory pressure. Participants closed their eyes and indicated when they perceived contact. Each monofilament, differing in thickness and corresponding force level (1.65–6.65), was applied to randomly selected points within the median, ulnar, and radial nerve regions. Testing began with the 2.83 filament, proceeding to thinner or thicker ones as needed. The thinnest perceivable filament was recorded as the sensory threshold (Weinstein, 1993).

System usability was assessed using the System Usability Scale (SUS), a validated 10-item questionnaire with five response options ranging from ‘Strongly agree’ to ‘Strongly disagree’. Participants were asked, ‘If you were using a system to understand the hand function of older people, answer the questions based on your preference for this system’. Scores were calculated by summing item contributions, adjusted per item type, and multiplying the total by 2.5, yielding a final score between 0 and 100. Higher scores indicated better usability perceptions (Brooke, 1996). A score of 70 and above was considered the threshold for acceptable usability (Kortum & Bangor, 2013).

Statistical Methods

A priori power analysis was conducted to determine the minimum required sample size (G*Power software, version 3.1.9.7; Faul et al., 2007) to detect a within-subject effect across three conditions using repeated measures analysis of variance (ANOVA). An effect size of f = 0.4 was employed, based on published recommendations for studies assessing the effectiveness of simulation-based interventions (Vieweg & Schaefer, 2020). With an alpha level of 0.05 and an assumed correlation among repeated measures of 0.50, the analysis indicated that a minimum of 18 participants would be required to achieve a statistical power of 0.95.

All statistical analyses were conducted using IBM SPSS Statistics 29 (IBM Corporation, New York, NY, USA). Normality of data was assessed using skewness, kurtosis, and the Shapiro–Wilk test. Differences across the three glove conditions (NG, CG, CG + TS) were evaluated using repeated measures ANOVA for normally distributed data. When the assumption of sphericity was violated, Greenhouse–Geisser corrections were applied. For data that did not meet normality assumptions, the non-parametric Friedman test was used. When overall effects were significant (p < 0.05), pairwise comparisons were conducted using Bonferroni-adjusted paired t-tests (parametric) or Wilcoxon signed-rank tests with Bonferroni correction (non-parametric). Gender was included as a between-subject factor where relevant. Pairwise results are reported as t/p values for parametric tests and Z/p values for non-parametric tests.

Results

Strength, Dexterity, and Function

Grip strength was affected by glove condition (F (1.21, 21.83) = 31.91, < 0.001, η² = 0.639, observed power > 0.999, Table 1). All analyses were conducted on a group basis, and gender-specific results are provided solely to enable descriptive comparisons with the literature. There was no interaction between glove condition and gender, F (1.21, 21.83) = 0.164, p = 0.73, η² = 0.009 suggesting that the change across conditions was similar for both males and females. However, there was an effect of gender, F (1,18) = 34.21, p < 0.001, η² = 0.655 showing that overall grip strength differed between males and females, with males producing higher scores (Table 2). Pairwise comparisons revealed that grip strength for the NG condition was greater than that of both the CG and CG + TS conditions (p < 0.001, Table 1). No difference was observed between CG and CG + TS conditions (p = 0.06), with similar reduction patterns across both genders (Table 2). This suggests that adding TS did not produce further significant decline in grip strength. To compare with published normative data, the age-related reduction in mean maximum grip strength between younger adults (25–29 and 30–34 years) and older adults (65–69, 70–74, and 75+ years) was calculated (Mathiowetz, Kashman, et al., 1985). This corresponded to 33–46% for females and 25–47% for males, demonstrating comparable reductions with glove use. In females, grip strength decreased by approximately 27% for the CG condition and 33% for the CG + TS condition. In males, the reduction was around 19% when wearing the CG and 24% when wearing CG + TS. These suggest that both gloves were effective in simulating age-related decline in grip strength.

Table 1.

Strength and dexterity test results across conditions

Test	Glove conditions (20 participants)					Pairwise comparison
Test	NG	CG	CG + TS	χ²/F (df)	Sig-p	NG vs CG	NG vs CG + TS	CG vs CG + TS
Grip Strength (kg)	36.26 (±10.87)	28.47 (±9.99)	26.31 (±9.58)	F (1.21,21.83) = 31.91	<0.001 * ‡	<0.001	<0.001	0.060
Two-point pinch strength (kg)	5.68 (±1.90)	5.03 (±1.74)	6.26 (±2.33)	F (2,36) = 8.93	<0.001 * ⁂	0.02	0.33	<0.001
Three-point pinch strength (kg)	8.21 (±2.76)	7.63 (±2.66)	7.47 (±2.57)	F (2,36) = 1.40	0.260	-	-	-
Lateral pinch strength (kg)	7.65 (±2.00)	7.31 (±2.08)	7.03 (±2.13)	F (2,36) = 3.41	0.044	0.646	0.086	0.382
Box and Block (number of blocks)	79.70 (±9.45)	67.95 (±8.46)	61.15 (±8.12)	F (2,36) = 68.60	<0.001 * ‡ ⁂	<0.001	<0.001	0.002
Grooved Pegboard (seconds)	55.61 (±7.68)	77.92 (±16.71)	111.13 (96.68-120.33)	χ² (2) = 38.10	<0.001 * ‡ ⁂	<0.001	<0.001	<0.001

Mean (± one standard deviation) is presented for normally distributed data and median (± interquartile range) is presented for non-normally distributed data. NG = No Glove; CG = Cambridge Simulation Glove; CG + TS = Cambridge Simulation Glove with Tremor Simulator; χ² = Friedman test Chi-square; F = repeated measures analysis of variance F-value; df = degrees of freedom; Sig-p = significance level (p-value); * = significant difference between NG and CG; ‡ = significant difference between NG and CG + TS; ⁂ = significant difference between CG and CG + TS.

Table 2.

Gender-specific strength and dexterity test results across conditions compared to normative values from literature for young and older (A) female and (B) male adults

(A)	Female (10 participants)			Normative values from literature
Test	NG	CG	CG + TS	Young females	Older females
Grip strength (kg)	28.28 (±4.56)	20.94 (±6.25)	19.08 (±6.98)	33.79 (±6.30) for 25–29 years	22.49 (±4.39) for 65–69 years
				35.69 (±8.70) for 30–34 years (Mathiowetz, Kashman, et al., 1985)	22.49 (±5.30) for 70–74 years
					19.32 (±4.98) over 75+ years (Mathiowetz, Kashman, et al., 1985)
Two-point pinch strength (kg)	4.70 (±1.47)	3.73 (±0.83)	4.64 (3.61–5.68)	5.39 (±0.81) for 25–29 years	4.80 (±0.90) for 65–69 years
				5.71 (±1.36) for 30–34 years (Mathiowetz, Kashman, et al., 1985)	4.58 (±1.17) for 70–74 years
					4.35 (±1.27) over 75+ years (Mathiowetz, Kashman, et al., 1985)
Three-point pinch strength (kg)	6.72 (±1.83)	5.62 (±1.08)	5.82 (±1.67)	8.02 (±1.45) for 25–29 years	6.44 (±1.40) for 65–69 years
				8.75 (±2.26) for 30–34 years (Mathiowetz, Kashman, et al., 1985)	6.53 (±1.17) 70–74 years
					5.44 (±1.17) over 75+ years (Mathiowetz, Kashman, et al., 1985)
Lateral pinch strength (kg)	6.14 (±1.15)	5.81 (±1.12)	5.50 (±1.24)	8.02 (±0.91) for 25–29 years	6.80 (±1.17) for 65–69 years
				8.48 (±1.36) for 30–34 years (Mathiowetz, Kashman, et al., 1985)	6.57 (±1.31) 70–74 years
					5.71 (±1.04) over 75+ years (Mathiowetz, Kashman, et al., 1985)
Box and Block (number of blocks)	78.8 (±6.2)	66.8 (±9.5)	59.7 (±6.4)	86.00 (±7.40) for 25–29 years	72.0 (±6.20) for 65–69 years
				85.20 (±7.40) for 30–34 years (Mathiowetz, Volland, et al., 1985)	68.6 (±7.00) for 70–74 years
					65.0 (±7.11) after 75+ years (Mathiowetz, Volland, et al., 1985)
Grooved pegboard (seconds)	53.0 (50.4–59.0)	72.8 (67.0–90.9)	109.2 (±17.9)	-	83.9 (±18.1) for 65–74 years (Ashendorf et al., 2009)

(B)	Male (10 participants)			Normative values from literature
Test	NG	CG	CG + TS	Young males	Older males
Grip strength (kg)	44.25 (±9.33)	36.00 (±6.74)	33.55 (±5.37)	54.79 (±10.43) for 25–29 years	41.32 (±9.34) for 65–69 years
				55.24 (±10.16) for 30–34 years (Mathiowetz, Kashman, et al., 1985)	34.15 (±9.75) for 70–74 years
					29.80 (±9.52) over 75+ years (Mathiowetz, Kashman, et al., 1985)
Two-point pinch strength (kg)	6.66 (±1.82)	6.34 (±1.40)	7.88 (±2.13)	8.3 (±1.99) for 25–29 years	7.71 (±1.90) for 65–69 years
				7.98 (±3.03) for 30–34 years (Mathiowetz, Kashman, et al., 1985)	6.25 (±1.17) for 70–74 years
					6.35 (±1.54) over 75+ years (Mathiowetz, Kashman, et al., 1985)
Three-point pinch strength	9.70 (±2.79)	9.64 (±2.20)	9.11 (±2.28)	11.79 (±1.95) for 25–29 years	9.70 (1.36) for 65–69 years
				11.20 (±2.13) for 30–34 years (Mathiowetz, Kashman, et al., 1985)	8.21 (±1.54) for 70–74 years
					8.48 (±1.90) over 75+ years (Mathiowetz, Kashman, et al., 1985)
Lateral pinch strength (kg)	9.15 (±1.44)	8.80 (±1.70)	8.57 (±1.70)	12.11 (±2.22) for 25–29 years	10.61 (±1.76) for 65–69 years
				11.97 (±2.17) for 30–34 years (Mathiowetz, Kashman, et al., 1985)	8.75 (±1.08) for 70–74 years
					9.29 (±2.0) over 75+ years (Mathiowetz, Kashman, et al., 1985)
Box and Block (number of blocks)	80.6 (±12.2)	69.1 (±7.7)	62.6 (±9.7)	85.0 (±7.5) for 25–29 years	68.4 (±7.1) for 65–69 years
				81.9 (±9.0) for 30–34 years (Mathiowetz, Volland, et al., 1985)	66.3 (±9.2) for 70–74 years
					63.0 (±7.1) over 75+ years (Mathiowetz, Volland, et al., 1985)
Grooved Pegboard (seconds)	56.0 (±8.4)	75.9 (±14.6)	111.4 (96.4–119.5)	-	90.0 (±19.5) for 65–74 years (Ashendorf et al., 2009)

Mean (± one standard deviation) are presented for normally distributed data and median (± interquartile range) are presented for non-normally distributed data. Grip and pinch reference values were converted from pounds to kg from the reference source. NG = No Glove; CG = Cambridge Simulation Glove; CG + TS = Cambridge Simulation Glove with Tremor Simulator.

Two-point pinch strength also was affected by the glove conditions (F (2,36) = 8.93, < 0.001, η² = 0.332, observed power = 0.961, Table 1). There was no interaction between glove conditions and gender, F (2,36) = 2.387, p = 0.10, η² = 0.117 suggesting that the pattern of change across conditions was similar for both males and females. However, there was an effect of gender, F (1,18) = 19.33, p < 0.001, η² = 0.518 indicating that males generally produced higher two-point pinch scores compared to females (Table 2). Pairwise comparisons revealed that CG + TS condition yielded higher scores than both NG and CG conditions (Table 1). These findings may indicate a compensatory mechanism of pinch grip under tremor simulation. Three-point pinch strength showed no differences across conditions. For lateral pinch strength, although the overall effect of the conditions was significant, post-hoc correction did not reveal any differences across conditions (p > 0.05), indicating that the glove conditions had a limited impact on pinch strength performance.

Box and Block Test scores differed between all glove conditions (F (2.36) = 68.60, < 0.001, η² = 0.792, observed power > 0.999, Table 1); NG was superior to both CG and CG + TS indicating a consistent decline in gross motor dexterity across conditions (p < 0.001, Table 1). There was no interaction between glove conditions and gender, F (2,36) = 0.05, p = 0.943, η² = 0.003 suggesting that the change across conditions was similar for both males and females; no effect of gender was found, F (1,18) = 0.44, p = 0.514, η² = 0.024. When comparing to normative reference values, the baseline scores of our young adult participants were below the reported means values for both genders (Table 2). This may be related to occupational or lifestyle factors. Despite this, the age-related decline in gross motor dexterity reported in the literature between younger adults (25–29 and 30–34 years) and older adults (65–69, 70–74, and 75+ years) (Mathiowetz, Kashman, et al., 1985) was approximately 15–25% for females and 16–26% for males, which align with our results. In females, gross motor dexterity declined by 15% in the CG condition and 24% in the CG + TS condition. In males, the corresponding reductions were 14% and 23%, respectively. These results suggested that both gloves effectively reduced performance in line with age-related decline, with the combined glove and tremor simulator condition (CG + TS) producing the most pronounced effect.

Completion times for the Grooved Pegboard Test™ increased across glove conditions with NG < CG < CG + TS (Table 1). For females, completion times with the CG + TS combination (mean = 109.19 sec) were nearly double those of the NG condition (mean = 53.01 sec). Males showed a similar pattern, with CG + TS again producing the longest times, confirming that tremor simulation impairs fine motor performance (Table 2). This may indicate that the combined use of the CG + TS imposed functional restrictions that exceed typical age-related declines, potentially compromising the realism of the simulations.

SHAP sub-scores showed differences across glove conditions (p < 0.001) (Table 3). Post-hoc analyses (Wilcoxon signed-rank, Bonferroni-adjusted α = .017) indicated that SHAP performance declined from NG to CG in all sub-scores except Power sub-tasks (p < 0.01). Comparisons between NG and CG + TS showed a broader decline, with differences across all sub-scores (p < 0.02). The CG vs CG + TS comparison further confirmed the effect of the TS, particularly in Tripod, Lateral, Extension, Tip, and Weighted scores. These results suggest that wearing the glove alone moderately impairs functional hand performance, while adding the tremor simulation leads to further deterioration, especially in tasks requiring precision grip, coordination, and sustained control (Table 3).

Table 3.

Southampton Hand Assessment Procedure (SHAP) sub-score results across conditions for 20 participants

SHAP sub-score out of 100	NG	CG	CG + TS	χ² (df = 2)	Sig-p (Friedman)
Power	99.22 (96.78–99.53)	97.24 (±1.89)	96.84 (94.52–98.07)	10.00	0.007 ‡ ⁂
Spherical	97.77 (±1.02)	97.34 (95.67–97.87)	97.11 (95.17–97.79)	21.90	<0.001 * ‡
Tripod	96.07 (±2.13)	92.79 (90.76–96.54)	91.99 (89.85–93.21)	19.90	<0.001 * ‡ ⁂
Lateral	99.72 (98.94–100.00)	98.74 (97.54–99.66)	98.19 (96.85–99.43)	22.33	<0.001 * ‡ ⁂
Extension	98.97 (97.02–99.63)	96.48 (±2.49)	93.91 (±3.12)	16.93	<0.001 * ‡ ⁂
Tip	97.52 (±1.66)	95.40 (93.23–98.38)	94.65 (92.66–96.03)	21.70	<0.001 * ‡ ⁂
Weighted	98.38 (96.66–99.07)	96.31 (±2.19)	94.89 (±2.70)	21.10	<0.001 * ‡ ⁂

Mean (± one standard deviation) are presented for normally distributed data and median (± interquartile range) are presented for non-normally distributed data. NG = No Glove; CG = Cambridge Simulation Glove; CG + TS = Cambridge Simulation Glove with Tremor Simulator; χ² = Friedman test Chi-square; df = degrees of freedom; Sig-p = significance level (p-value); * = significant difference between NG and CG; ‡ = significant difference between NG and CG + TS; ⁂ = significant difference between CG and CG + TS.

Only one study in the literature reported normative SHAP data for older adults aged 66–75 years, providing a weighted score of 92, without sub-score details (Metcalf et al., 2008). In comparison, the young adult weighted scores reported in the same study were 99 and 98, values that closely match the NG condition observed in our group (Table 3). CG and CG + TS conditions produced higher weighted scores than 92, indicating that although performance declined with glove use, neither condition replicated the functional level observed in older adults.

Tremor

Tremor parameters were compared across the three conditions (Table 4). The Friedman test revealed no difference in peak amplitude (χ² (2) = 4.333, p = 0.115). For peak frequency, the Friedman test showed an effect (χ² (2) = 7.729, p = 0.021). However, the only difference was between CG and CG + TS (Z = −3.079, p = 0.002), indicating a marked increase in tremor frequency with tremor simulation.

Table 4.

Tremor parameters (peak amplitude and peak frequency) across conditions for 18 participants.

	NG	CG	CG + TS	Sig-p
Peak amplitude (m/s²)	0.5127 (±0.1714)	0.4780 (±0.1768)	0.6641 (0.4544–0.1.1482)	0.115
Peak frequency (Hz)	3.5 (2.0–9.5)	3.0 (2.0–8.2)	10.5 (2.93–11.0)	0.021 ⁂

Sensation

Differences in monofilament thresholds were found across glove conditions for the ulnar (p < 0.001), median (p < 0.001), and radial nerves (p < 0.001) (Table 5). Post-hoc Wilcoxon tests revealed that sensory thresholds in the CG + TS condition were higher than in both NG and CG across all nerves (p < 0.001), indicating reduced tactile sensitivity with the tremor simulation glove. No differences were observed between NG and CG for ulnar and median nerves (p > 0.05). These findings suggest that tremor simulation notably impairs mechanoreceptive discrimination, while CG glove use alone has minimal effect. Results were interpreted relative to standard threshold ranges rather than specific age matched values because of the absence of age-stratified normative data.

Table 5.

Monofilament sensory threshold values for the ulnar, median, and radial nerves across three glove conditions for 20 participants

Nerve	NG	CG	CG + TS	χ² (df = 2)	Sig-p (Friedman)	Normative values
Ulnar	2.44 (1.65–2.83)	2.44 (1.65–2.83)	3.61 (2.44–4.17)	34.57	<0.001 ‡ ⁂	1.65–2.83 (Suda et al., 2021)
Median	2.44 (1.65–2.83)	2.44 (1.65–2.83)	3.61 (2.44–4.17)	32.939	<0.001 ‡ ⁂
Radial	2.44 (1.65–3.22)	2.83 (1.65–3.61)	3.61 (2.83–4.17)	34.33	<0.001 ‡ ⁂

Median (± interquartile range) are presented for non-normally distributed data. NG = No glove; CG = Cambridge Simulation Glove; CG + TS = Cambridge Simulation Glove with Tremor Simulator; χ² = Friedman test Chi-square; df = degrees of freedom; Sig-p = significance level (p-value); * = significant difference between NG and CG; ‡ = significant difference between NG and CG + TS; ⁂ = significant difference between CG and CG + TS.

SUS

Comparisons between the two glove conditions were conducted using paired samples t-tests. The CG condition yielded higher SUS scores (Mean = 57.50, SD = 19.25) compared to the CG + TS condition ((Mean = 50.00, SD = 20.15), t (19) = 2.63, p = .016). Neither reached the threshold for acceptable usability.

Discussion

Previous studies of age simulation suits primarily focused on psychological outcomes, while their impact on physical functioning, such as strength, remained untested. This study examined two commercially available age-simulating gloves.

Fine motor dexterity scores, assessed using the Grooved Pegboard test, showed that the CG condition alone yielded better performance than normative values for the 65–74 year old age group, while the CG + TS condition caused additional performance decline. In the assessment of the physical effectiveness of age simulation suits (Gerhardy et al., 2022) only one study has included a specific assessment of the hand component (Vieweg & Schaefer, 2020). In that study, fine motor dexterity was assessed using the Purdue Pegboard test. The authors reported that performance with the GERT suit matched that of individuals in their late 70s to early 80s. The KINdReD disability suit was designed not solely to simulate the effects of ageing, but to represent a broader spectrum of disabilities or physical conditions leading to functional impairments. However, in a study evaluating its effects, the Nine Hole Peg Test was used to assess fine motor dexterity, enabling comparisons between suit-induced impairments and those observed in various clinical populations as well as healthy individuals (Armstrong et al., 2015). The completion time for the dominant hand increased from 19.5 seconds (no suit) to 28.9 seconds (with the suit). Although results were not reported by gender, normative Nine Hole Peg Test values typically observed in adults aged 65 years old and above are 25–27 seconds (Mathiowetz, Weber, et al., 1985), indicating that the suit effectively simulated an age-related decline in fine motor dexterity. This suggests that both the GERT suit hand component and the combined glove with tremor simulation provide more effective representation of advanced age-related decline in fine motor dexterity, while the KINdReD disability suit and the CG better represent a less aged cohort.

Another study using the GERT age simulation suit assessed grip strength using a Jamar dynamometer, both with and without the suit (Gerhardy et al., 2024). For young adult females, grip strength decreased from 30.7 ± 5.3 kg to 25.7 ± 5.1 kg when wearing the suit. According to their reference source (Werle et al., 2009), this reduction corresponded to normative values for individuals aged 70–79 years. However, based on our reference values (Mathiowetz, Kashman, et al., 1985), the resulting grip strength fell below the 65-year old age category, indicating that the simulation did not produce a sufficient decline in grip strength. In our study, the grip strength of female participants decreased to within the normative ranges for individuals over 75 years old. Moreover, while use of the GERT age simulation suit resulted in no decline in grip strength for young adult males, our results showed a decrease to levels which aligned with normative values for individuals aged 70–74 years old.

In a study with a similar design to ours, the aim was to evaluate the effectiveness of a simulation glove in mimicking hand impairments caused by rheumatoid arthritis using two gloves, one that simulated stiffness alone and a second that also simulated joint pain (Hall et al., 2010). Grip strength measurements were collected using a Jamar dynamometer. Although grip strength results were not reported by gender, average values decreased from 32.8 ± 9.5 kg (no glove) to 20.1 ± 6.8 kg (stiffness only) and 19.3 ± 7.3 kg (stiffness and pain). These values are comparable to those observed in our female group under the CG and CG + TS conditions, which also aligned with normative values for adults 75 years old and above. The findings of Hall et al. (2010) show that simulation gloves can successfully reduce function to levels consistent with age-related impairments, even if designed to mimic impairment due to neuromuscular pathologies. The parallel reductions in grip strength observed across both studies reinforce the potential of wearable simulation tools to mimic diverse types of hand impairments. These findings indicate that CG, CG + TS glove conditions, and the gloves by Hall et al., provide a more realistic simulation of strength reduction than the GERT age simulation suit for both genders. By simulating realistic hand strength profiles, such gloves can support ergonomic research and design of tools and environments intended for older adults.

There is some evidence to suggest that in older adults, postural physiological tremor amplitude is increased, especially in the hand and fingers, with a shift in peak power from 2–4 Hz toward the 8–12 Hz range (Morrison et al., 2006). In the NG and CG conditions, the dominant frequency was in 2–4 Hz, while the addition of TS shifted the peak to 8–12 Hz, consistent with age-related tremor. This suggests that the TS component effectively replicated the frequency characteristics of physiological ageing. However, we did not observe a corresponding increase in peak amplitude. Thus, while the simulation successfully mirrored age-related tremor frequency, it did not reproduce the amplitude changes typically associated with ageing. Tremor in healthy older adults is known to be highly variable and influenced by factors such as posture, fatigue, stress, and sensorimotor noise (Deuschl et al., 2022; Raethjen et al., 2000). Given this variability, simulating tremor in a standardised, reproducible way remains challenging. Although peak amplitude did not change, the observed performance decline may be related to the material properties of the tremor glove. The conductive textile fully covers the hand, which may have reduced sensory input at the fingertips and interfered with stable, controlled manipulation.

The CG + TS configuration involved full-hand coverage combined with tremor-inducing electrical stimulation, both of which may interfere with cutaneous receptor function through mechanical obstruction. Although sensory decline with age is commonly accepted, the literature lacks robust normative data specifically quantifying changes in mechanical sensory thresholds among older adults. This gap limits the ability to determine whether the observed impairment in CG + TS accurately mirrors geriatric sensory decline. However, being the first study to assess changes in tactile sensation with age simulation glove use, and specifically the response of mechanoreceptors in the hand, this research offers a new perspective for future glove designs. These insights may help developers to determine how simulation materials affect somatosensory feedback and guide improvements in the design of realistic ageing simulation tools.

Furthermore, this study also examined the gloves’ usability, to assess how acceptable they were from the user’s perspective. Although the CG condition yielded higher usability scores compared to the CG + TS condition, both gloves received mean SUS scores below the commonly ‘acceptable’ threshold of 70 (Kortum & Peres, 2015; P. T. Kortum & Bangor, 2013). Despite the CG performing relatively better, neither glove achieved satisfactory levels of perceived usability. Several factors may account for this. Participants reported that the CG glove Velcro fasteners frequently detached during movement tasks, and the rigid plastic strips caused discomfort with repeated hand motions. To improve comfort, rubber fasteners could be used as an alternative to Velcro, and resistive bands could be used instead of rigid plastic strips. For the CG + TS condition, additional usability challenges were noted. The tremor simulator component, while designed to mimic physiological tremor, produced tingling sensations likely due to electrical stimulation, which may have confounded user experience. Moreover, in tasks requiring bilateral hand use, participants encountered short-circuiting. These usability concerns highlight that ergonomically designed simulation tools are as important as the accuracy of the simulated impairments.

Regarding grip strength, the effect size was η² = 0.639, indicating a large effect; observed power was over 0.999, with glove conditions accounting for approximately 64% of the change in grip strength. This confirmed the adequacy of the sample size to detect differences across conditions. For two-point pinch, η² = 0.332 also represented a large effect, with an observed power of 0.961. In lateral pinch, η² = 0.16 reflected a medium-large effect, with a power of 0.60, suggesting that while the effect was meaningful, caution is needed in interpretation. For the Box and Block test, η² = 0.792 indicated a large effect, with glove conditions accounting for approximately 80% of the change in performance and observed power greater than 0.999. Although the Grooved Pegboard test was analysed using non-parametric tests, the significance indicated a strong effect of glove condition. These results collectively supported the robustness of the findings and validated the sample size used in this study for detecting the outcomes.

This study had limitations. First, although measures were taken to mitigate potential fatigue, such as the randomised order of the conditions, rest breaks between strength trials, this study did not measure fatigue related to the duration of the experiment or the tests. The reference normative data for the Grooved Pegboard Test do not include young and individuals over 75 years of age. While the CG + TS condition in our study resulted in performance values exceeding those of the 65–74 year age group, the exact age-matched performance could not be determined due to the lack of data for older age ranges. A similar limitation applies to the monofilament test. There is currently no published normative dataset describing how tactile sensation changes with age; available guidelines only indicate whether values fall within normal ranges. As a result, age-based comparisons of tactile sensitivity could not be performed, and interpretations were limited to whether the observed values fell within the normal ranges defined in the literature. In contrast to full-body suits, which address general impairments, the glove-based systems in our study offer a more targeted and consistent reduction in hand performance. Factors such as limited elbow range of motion could have indirectly affected hand performance for those wearing an age simulation suit, making direct comparisons with our glove-only setups less straightforward.

While previous studies have used grip strength, fine motor dexterity, or functional tests independently to evaluate glove effectiveness, our study is the first to combine grip and pinch strength, gross and fine motor dexterity, tactile sensation, and hand function assessments in a single protocol specifically designed to evaluate glove performance. Furthermore, this study represents the first attempt to quantitatively evaluate a glove developed to simulate physiological tremor associated with ageing. This comprehensive approach represents a key strength of our study. The integration of motor, sensory, and functional assessments highlights the multidisciplinary nature of ergonomics, providing evidence-based insights into age-related design considerations (Farage et al., 2012; Haigh, 1993).

The findings of this study highlight the importance of designing component-specific simulation elements to represent aged performance as accurately as possible. They underscore the need for future simulation glove designs to account for the sensory interaction of mechanical and electrical components. Additionally, empirical research on age-related sensory thresholds is needed. Simulation gloves should accurately replicate age-related impairments, but without causing additional discomfort or usability problems due to their design. As well as physiological ageing, they have the potential to simulate pathological tremor profiles, such as those seen in Parkinson’s disease in the future. Our findings further support the use of condition-specific glove designs tailored to the targeted impairment, whether pathological or age-related, for improving realism and accuracy in functional simulation.

Conclusion

Both CG and CG + TS gloves caused measurable reductions in grip strength and gross and fine manual dexterity, similar to age-related changes observed in older adults. However, inconsistent reductions in pinch strength and function, and the lack of amplitude replication in tremor, highlight limitations in the current designs’ abilities to reproduce age-related changes in hand function. Additionally, neither glove met usability standards, revealing design issues that could hinder practical application. While these devices have potential for illustrating some aspects of ageing-related hand function decline, future improvements should focus on pinch strength performance, execution of functional tasks, tremor realism, ergonomic comfort, and overall usability to better support educational, clinical, and design purposes.

Key Points

• Cambridge Simulation Glove reduced grip and dexterity to ageing normative values

• Tremor simulating gloves replicated postural tremor in frequency but not amplitude.

• Tested gloves showed low usability, indicating the need for design improvements.

• First to combine strength, dexterity, tremor, sensation, usability, and functional assessments.

Footnotes

Acknowledgements

The authors would like to thank K. Wanglertpanich and T. West for their help with MATLAB coding during the analysis of tremor data.

ORCID iDs

Esma Hidayet Lüleci

Angela E. Kedgley

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Esma Hidayet Lüleci was funded by the Republic of Türkiye Ministry of National Education.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Author Biographies

Esma Hidayet Lüleci is a PhD student at Imperial College London, Department of Bioengineering. She received her Master’s degree in biomedical engineering from the University of Strathclyde in 2021.

Angela E. Kegley is a Reader in Orthopaedic Biomechanics at Imperial College London. She received her PhD in mechanical and materials engineering from the University of Western Ontario in 2010.

References

Armstrong

Stone

Immel

Hunter-Zaworski

(2015). A validation study of disability simulation suit usage as a proxy for customer need statements from persons with disabilities. https://doi.org/10.1115/DETC2015-47679.V007T06A046

Ashendorf

Vanderslice-Barr

J. L.

McCaffrey

R. J.

(2009). Motor tests and cognition in healthy older adults. Applied Neuropsychology, 16(3), 171–176. https://doi.org/10.1080/09084280903098562

Brooke

(1996). SUS: A “quick and dirty” usability scale. In Jordan

P. W.

Thomas

McClelland

I. L.

Weerdmeester

(Ed.), Usability evaluation in industry (pp. 189–194). Taylor & Francis. https://doi.org/10.1201/9781498710411

Burgerhof

J. G. M.

Vasluian

Dijkstra

P. U.

Bongers

R. M.

van der Sluis

C. K.

(2017). The Southampton Hand Assessment Procedure revisited: A transparent linear scoring system, applied to data of experienced prosthetic users. Journal of Hand Therapy, 30(1), 49–57. https://doi.org/10.1016/j.jht.2016.05.001

Carmeli

Patish

Coleman

(2003). The aging hand. Journal of Gerontology. Series A, Biological Sciences and Medical Sciences, 58(2), 146–152. https://doi.org/10.1093/gerona/58.2.m146

Chen

Y. L.

Chiu

P. Y.

(2025). Age and sex differences in wrist range of motion: A population-based study of young and elderly Taiwanese adults. International Journal of Industrial Ergonomics, 108(1), Article 103773. https://doi.org/10.1016/j.ergon.2025.103773

Deborah

Barnett

(2011). Reliability and validity of an electronic dynamometer for measuring grip strength. International Journal of Therapy and Rehabilitation, 18(5), 258–264. https://doi.org/10.12968/ijtr.2011.18.5.258

Deuschl

Becktepe

J. S.

Dirkx

Haubenberger

Hassan

Helmich

R. C.

Muthuraman

Panyakaew

Schwingenschuh

Zeuner

K. E.

Elble

R. J.

(2022). The clinical and electrophysiological investigation of tremor. Clinical Neurophysiology, 136(1), 93–129. https://doi.org/10.1016/j.clinph.2022.01.004

Eid

Ahmed

H. I.

Hassan

M. M.

(2024). Activities of daily living and it’s effect on quality of life among older adults. Egyptian Journal of Health Care, 15(2), 650–659. https://doi.org/10.21608/ejhc.2024.356408

10.

Eost-Telling

Kingston

Taylor

Emmerson

(2021). Ageing simulation in health and social care education: A mixed methods systematic review. Journal of Advanced Nursing, 77(1), 23–46. https://doi.org/10.1111/jan.14577

11.

Farage

M. A.

Miller

K. W.

Ajayi

Hutchins

(2012). Design principles to accommodate older adults. Global Journal of Health Science, 4(2), 2–25. https://doi.org/10.5539/gjhs.v4n2p2

12.

Faul

Erdfelder

Lang

A. G.

Buchner

(2007). G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods, 39(2), 175–191. https://doi.org/10.3758/bf03193146

13.

Fess

E. E.

Moran

(1981). Clinical assessment recommendations. American Society of Hand Therapists, 6-8 .

14.

Gerhardy

T. H.

Schlomann

Wahl

H. W.

Mombaur

Sloot

L. H.

Schmidt

L. I.

(2024). Aging in 10 minutes: Do age simulation suits mimic physical decline in old age? Comparing experimental data with established reference data. Experimental Aging Research, 50(5), 692–704. https://doi.org/10.1080/0361073X.2023.2256630

15.

Gerhardy

T. H.

Schlomann

Wahl

H.-W.

Schmidt

L. I.

(2022). Effects of age simulation suits on psychological and physical outcomes: A systematic review. European Journal of Ageing, 19(4), 953–976. https://doi.org/10.1007/s10433-022-00722-1

16.

Haigh

(1993). The ageing process: A challenge for design. Applied Ergonomics, 24(1), 9–14. https://doi.org/10.1016/0003-6870(93)90153-z

17.

Hall

T. C.

Nixon

M. F.

Dias

J. J.

Graham

Cook

(2010). How accurately does a simulation glove reflect function compared to rheumatoid arthritis sufferers? Annals of the Royal College of Surgeons of England, 92(7), 605–609. https://doi.org/10.1308/rcsann.2010.92.7.605

18.

Karwowski

(2005). Ergonomics and human factors: The paradigms for science, engineering, design, technology and management of human-compatible systems. Ergonomics, 48(5), 436–463. https://doi.org/10.1080/00140130400029167

19.

Kortum

Peres

S. C.

(2015). Evaluation of home health care devices: Remote usability assessment. JMIR Human Factors, 2(1), Article e10. https://doi.org/10.2196/HUMANFACTORS.4570

20.

Kortum

P. T.

Bangor

(2013). Usability ratings for everyday products measured with the system usability scale. International Journal of Human-Computer Interaction, 29(2), 67–76. https://doi.org/10.1080/10447318.2012.681221

21.

Lauenroth

Schulze

Ioannidis

Simm

Schwesig

(2017). Effect of an age simulation suit on younger adults’ gait performance compared to older adults’ normal gait. Research in Gerontological Nursing, 10(5), 227–233. https://doi.org/10.3928/19404921-20170831-04

22.

Leszczak

Pniak

Drużbicki

Guzik

(2024). The reliability of a biometrics device as a tool for assessing hand grip and pinch strength, in a Polish cohort–A prospective observational study. PLoS ONE, 19(5), Article e0303648. https://doi.org/10.1371/journal.pone.0303648

23.

Light

C. M.

Chappell

P. H.

Kyberd

P. J.

(2002). Establishing a standardized clinical assessment tool of pathologic and prosthetic hand function: Normative data, reliability, and validity. Archives of Physical Medicine and Rehabilitation, 83(6), 776–783. https://doi.org/10.1053/apmr.2002.32737

24.

Mathiowetz

Kashman

Volland

Weber

Dowe

Rogers

(1985). Grip and pinch strength: Normative data for adults. Archives of Physical Medicine and Rehabilitation, 66(2), 69–74.

25.

Mathiowetz

Volland

Kashman

Weber

(1985). Adult norms for the Box and Block Test of manual dexterity. The American Journal of Occupational Therapy: Official Publication of the American Occupational Therapy Association, 39(6), 386–391. https://doi.org/10.5014/ajot.39.6.386

26.

Mathiowetz

Weber

Kashman

Volland

(1985). Adult norms for the nine hole peg test of finger dexterity. The Occupational Therapy Journal of Research, 5(1), 24–38. https://doi.org/10.1177/153944928500500102

27.

Mathiowetz

Weber

Volland

Kashman

Milwaukee

(1984). Reliability and validity of grip and pinch strength evaluations. The Journal of Hand Surgery, 9(2), 222–226. https://doi.org/10.1016/s0363-5023(84)80146-x

28.

Merker

Podell

(2011). Grooved pegboard test. In Encyclopedia of clinical neuropsychology (pp. 1176–1178). Springer.

29.

Metcalf

C. D.

Woodward

Wright

H Chappell

H Burridge

T Yule

(2008). Changes in hand function with age and normative unimpaired scores when measured with the Southampton hand assessment procedure. The British Journal of Hand Therapy, 13(3), 79–83. https://doi.org/10.1177/175899830801300302

30.

Morrison

Mills

Barrett

(2006). Differences in multiple segment tremor dynamics between young and elderly persons. Journal of Gerontology. Series A, Biological Sciences and Medical Sciences, 61(9), 982–990. https://doi.org/10.1093/gerona/61.9.982

31.

Pennathur

Contreras

L. R.

Arcaute

Dowling

(2003). Manual dexterity of older Mexican American adults: A cross-sectional pilot experimental investigation. International Journal of Industrial Ergonomics, 32(6), 419–431. https://doi.org/10.1016/j.ergon.2003.07.001

32.

Portegijs

Lee

Zhu

(2023). Activity-friendly environments for active aging: The physical, social, and technology environments. Frontiers in Public Health, 10(1), Article 1080148. https://doi.org/10.3389/fpubh.2022.1080148

33.

Raethjen

Pawlas

Lindemann

Wenzelburger

Deuschl

(2000). Determinants of physiologic tremor in a large normal population. Clinical Neurophysiology, 111(10), 1825–1837. https://doi.org/10.1016/s1388-2457(00)00384-9

34.

Suda

Kawakami

Okuyama

Ishii

Oshima

Hijikata

Nakamura

Oka

Kondo

Liu

(2021). Validity and reliability of the Semmes-Weinstein monofilament test and the thumb localizing test in patients with stroke. Frontiers in Neurology, 11(1), Article 625917. https://doi.org/10.3389/fneur.2020.625917

35.

Szekeres

Aspinall

Kulick

Sajid

Dabbagh

MacDermid

(2025). Reliability, validity, and responsiveness of pinch strength assessment: A systematic review. Disability and Rehabilitation, 47(7), 1631–1643. https://doi.org/10.1080/09638288.2024.2382907

36.

Thomas

C. L.

Fitch

S. B.

(2023). Handedness and test anxiety: An examination of mixed-handed and consistent-handed students. Experimental Results, 4(1), 1–8. https://doi.org/10.1017/exp.2023.14

37.

United Nations, Department of Economic and Social Affairs Population Division . (2019). World Population Prospects 2019: Highlights (ST/ESA/SER.A/423).

38.

Vasylenko

Gorecka

M. M.

Rodríguez-Aranda

(2018). Manual dexterity in young and healthy older adults. 1. Age- and gender-related differences in unimanual and bimanual performance. Developmental Psychobiology, 60(4), 407–427. https://doi.org/10.1002/dev.21619

39.

Veale

J. F.

(2014). Edinburgh Handedness Inventory - Short Form: A revised version based on confirmatory factor analysis. Laterality, 19(2), 164–177. https://doi.org/10.1080/1357650X.2013.783045

40.

Vial

Kassavetis

Merchant

Haubenberger

Hallett

(2019). How to do an electrophysiological study of tremor. Clinical Neurophysiology Practice, 4(1), 134–142. https://doi.org/10.1016/j.cnp.2019.06.002

41.

Vieweg

Schaefer

(2020). How an age simulation suit affects motor and cognitive performance and self-perception in younger adults. Experimental Aging Research, 46(4), 273–290. https://doi.org/10.1080/0361073X.2020.1766299

42.

Weinstein

(1993). Fifty years of somatosensory research: From the Semmes-Weinstein Monofilaments to the Weinstein Enhanced Sensory Test. Journal of Hand Therapy, 6(1), 11–22. https://doi.org/10.1016/S0894-1130(12)80176-1

43.

Werle

Goldhahn

Drerup

Simmen

B. R.

Sprott

Herren

D. B.

(2009). Age- and gender-specific normative data of grip and pinch strength in a healthy adult Swiss population. Journal of Hand Surgery, European Volume, 34(1), 76–84. https://doi.org/10.1177/1753193408099829

Evaluating the Effectiveness of Age Simulation Gloves

Abstract

Objective

Background

Method

Results

Conclusion

Application

Keywords

Introduction

Material and Methods

Participants

Experimental Protocol

Statistical Methods

Results

Strength, Dexterity, and Function

Tremor

Sensation

SUS

Discussion

Conclusion

Key Points

Footnotes

Acknowledgements

ORCID iDs

Funding

Declaration of Conflicting Interests

Author Biographies

References