Fine or Gross Motor Index as a Simple Tool for Predicting Cognitive Impairment in Elderly People: Findings from The Irish Longitudinal Study on Ageing (TILDA)

Abstract

Background:

Several kinds of motor dysfunction can predict future cognitive impairment in elderly individuals. However, the ability of the fine motor index (FINEA) and gross motor index (GROSSA) to predict the risk of cognitive impairment has not been assessed.

Objective:

We investigated the associations between FINEA/GROSSA and cognitive impairment.

Methods:

The data of 4,745 participants from The Irish Longitudinal Study on Ageing (TILDA) were analyzed. Cognitive function was assessed using the Mini-Mental State Examination (MMSE). We first assessed the correlation between the FINEA/GROSSA and MMSE in a cross-sectional study. Then, we further investigated the predictive role of the incidence of cognitive impairment in a prospective cohort study.

Results:

We found that both FINEA and GROSSA were negatively correlated with MMSE in both the unadjusted (FINEA: B = –1.00, 95%confidence intervals (CI): –1.17, –0.83, t = –11.53, p < 0.001; GROSSA: B = –0.85, 95%CI: –0.94, –0.76, t = –18.29, p < 0.001) and adjusted (FINEA: B = –0.63, 95%CI: –0.79, –0.47, t = –7.77, p < 0.001; GROSSA: B = –0.57, 95%CI: –0.66, –0.48, t = –12.61, p < 0.001) analyses in a cross-sectional study. In a prospective cohort study, both high FINEA and high GROSSA were associated with an increased incidence of cognitive function impairment (FINEA: adjusted odds ratios (OR) = 2.35, 95%CI: 1.05, 5.23, p = 0.036; GROSSA adjusted OR = 3.00, 95%CI: 1.49, 6.03, p = 0.002) after 2 years of follow-up.

Conclusion:

Higher FINEA and GROSSA scores were both associated with an increased incidence of cognitive impairment. FINEA or GROSSA might be a simple tool for identifying patients with cognitive impairment.

Keywords

Cognitive impairment fine motor index gross motor index the Irish longitudinal study on ageing

INTRODUCTION

Dementia is a syndrome of cognitive impairment that affects memory, cognitive abilities, and behavior and seriously affects human social functioning [1]. Worldwide, approximately 50 million people have dementia, and approximately 10 million new cases are diagnosed each year. Furthermore, the number of people with dementia is estimated to reach 152 million by 2050 [2]. The behavioral and psychological symptoms exhibited by most dementia patients result in a poor quality of life, caregiver burden, precipitous declines in function, and risk of physical abuse [3]. Identifying at-risk people with a noninvasive but effective method that can be used in a family doctor’s office and even in-home has attracted wide attention for several years [5, 6].

Cognitive, perceptual, mechanical, and neurological mechanisms may affect motor actions (fine motor or gross motor), indicating the diagnostic role of motor actions in cognitive impairment [7]. Studies have found that several kinds of motor dysfunction can predict future cognitive impairment in the elderly. For example, Camiciol et al. and Toots et al. showed that gait speed or posture impairment can predict decreased cognitive function in elderly individuals [8, 9]. Another longitudinal cohort study showed that the motor dysfunction of gait slowing occurred up to 7 years prior to the clinical onset of dementia, suggesting strong links between cognitive and motor function in older adults [10].

However, several studies failed to demonstrate a relationship between cognition and motor function (e.g., handgrip strength, finger-tapping test, and rest tremor) [11 –13], suggesting that not all motor actions can predict cognitive impairment. Therefore, more studies need to explore these relationships to identify which motor functions may be effective predictive indicators for dementia detection during the asymptomatic stage. The combined assessment of different motor actions as an evaluation index may represent a better tool for assessing cognitive impairment [14]. The Irish Longitudinal Study on Ageing is a large prospective study investigating social, economic, and health factors in community-dwelling adults. In this cohort, the fine motor index and gross motor index are used as compound scores of motor functions. To date, no studies focused on the association between these motor scores and cognitive function. Therefore, in the present study, we aimed to test the predictive effect of the fine motor index and gross motor index on cognitive impairment.

MATERIALS AND METHODS

Study sample

We used data from The Irish Longitudinal Study on Ageing (TILDA) for the analyses in this study. Anonymized TILDA data are available for researchers for scientific use if they meet the criteria for access from the Interuniversity Consortium for Political and Social Research at the University of Michigan and the Irish Science Data Archive at University College Dublin (https://tilda.tcd.ie/). The TILDA is a nationally representative study of the population of Ireland aged 49 years or older. The TILDA aims to understand the influence of health, social and financial circumstances on the aging process in the older Irish population. The TILDA includes the following three data collection waves: wave 1 (from October 2009 to July 2011), wave 2 (from February 2012 to March 2013), and wave 3 (from March 2014 to October 2015). The details of the TILDA are published elsewhere [15].

Our study included subjects who completed questionnaires and health examinations during wave 1, which can provide sufficient baseline data for analyzing the associations between the fine motor index or gross motor index and Mini-Mental State Examination (MMSE) scores; subjects with complete fine motor index or gross motor index data in wave 2; and subjects with complete MMSE score data in both waves 2 and 3.

Demographic, clinical, and comorbidity data were recorded. The educational levels were defined as primary, secondary, and high. Smoking status was classified as never smoked, past smoker, or current smoker. Alcohol consumption was classified as “yes” or “no”. Physical activity levels were divided into three groups using the short form eight-item version of the International Physical Activity Questionnaire as follows: low, moderate, or high. The baseline self-reported doctor-diagnosed diseases included in this analysis were high blood pressure or hypertension, angina, congestive heart failure, heart attack, diabetes or high blood sugar, stroke, ministroke, and high cholesterol.

Fine motor index and gross motor index assessments

In TILDA, both the fine motor index and gross motor index were measured by counting the limitations that subjects failed to accomplish. The limitations used in the fine motor index evaluation included picking up a small coin from a table, eating (such as cutting up food), and dressing. The limitations used in the gross motor index evaluation included walking 100 meters, walking across a room, climbing one flight of stairs without resting for long periods, getting in or out of bed, bathing or showering. The higher the index, the more serious the motor function decreased.

Outcomes of cognitive impairment

In our study, cognitive functioning was assessed using the MMSE, which is a screening tool widely used to assess the severity of cognitive decline. Briefly, the MMSE consists of 12 items used to assess orientation to time and place, attention, memory, language, and visual construction. The MMSE yields a single total score ranging from 0 to 30, with lower scores denoting more impaired cognition [18]. An MMSE score less than 24 is considered indicative of cognitive impairment.

Statistical analyses

The data were analyzed using SPSS Statistics Version 25.0 (IBM SPSS Statistics, IBM Corporation, Chicago, IL, USA) for Windows. The continuous variables are expressed as the means with standard deviations (SD) for the normally distributed data or medians with interquartile ranges (IQRs) for the nonnormally distributed data. The normality of the data was analyzed using the Kolmogorov-Smirnov (K.S.) test combined with Q-Q plots. The differences between the groups in the continuous variables were compared using unpaired Student’s t-tests (normal distribution) or Wilcoxon-Mann-Whitney tests (nonnormal distributions). The categorical variables, which are reported as counts and percentages, were compared using χ² tests.

We first assessed the association between the fine motor index (or gross motor index) and MMSE score in a cross-sectional study using wave 2 data. A univariate linear regression analysis was used to analyze the correlation between the MMSE scores and the fine motor index or gross motor index in wave 2. Then, a multivariate linear regression analysis was performed to identify the independent factors of the MMSE scores.

Then, we further investigated the data by combining waves 2 and 3 as prospective cohort studies. During this prospective cohort study, subjects with MMSE scores less than 24 in wave 3 were defined as having impaired cognitive function. First, we excluded subjects with MMSE scores less than 24 in wave 2. Then, we divided the individuals into two groups according to their fine motor index in wave 2 (fine motor index = 0 and fine motor index = 1–3). Then, we analyzed the incidence of cognitive function impairment after 2 years of follow-up (wave 3) stratified by the fine motor index groups (wave 2). We categorized the subjects into two groups according to their gross motor index in wave 2 (gross motor index = 0 and gross motor index = 1–5). Then, we analyzed the incidence of cognitive function impairment after 2 years of follow-up (wave 3) stratified by the gross motor index groups (wave 2). The odds ratios (ORs) and 95%confidence intervals (CIs) were used to test the significance of the differences between the groups. p≤0.05 was considered statistically significant in all analyses.

RESULTS

Cross-sectional study

In total, 7,207 subjects were recruited for wave 2 of the TILDA study. After excluding 2,462 subjects with missing data (N = 2,052 for BMI, N = 405 for alcohol use, and N = 5 for fine or gross motor index), 4,745 subjects were included in the cross-sectional study. A flowchart of the selection of eligible individuals from TILDA is shown in Fig. 1. The basic characteristics of the included subjects are presented in Table 1. The mean age was 64 years. In total, 45%were males, and most patients were drinkers (77.4%) and smokers (53.5%). More than half of the individuals had moderate or high physical activity levels (72%), and hypertension and high cholesterol were the most common comorbidities.

Fig. 1

Flowchart of the participants included in the cross-sectional study and prospective cohort study of the relationship between the fine/gross motor index and cognitive decline in the participants included in The Irish Longitudinal Study on Ageing (TILDA).

Table 1

Baseline characteristics of the subjects included in the cross-sectional study

Variables	N%or Mean±SD (N = 4,745)
Age (y)	64.3±8.9
Sex (%male)	2,134 (45.0%)
BMI (kg/m²)	27.7±3.
Education, (%)
Lower	1,079 (22.7%)
Secondary	2,832 (59.7%)
High	834 (17.6%)
Smoking status, n (%)
Never	2,206 (46.5%)
Past	1,840 (38.8%)
Current	699 (14.7%)
Drink alcohol, n (%yes)	3,675 (77.4%)
Levels of physical activity, n (%)
Low	1,330 (28.0%)
Moderate	1,690 (35.6%)
High	1,725 (36.4%)
Comorbidities, n (%)
Hypertension	1,565 (33.0%)
Angina	218 (4.6%)
Heart attack	185 (3.9%)
Congestive heart failure	34 (0.7%)
Diabetes or high blood sugar	275 (5.8%)
Stroke	66 (1.4%)
Ministroke or TIA	91 (1.9%)
High cholesterol	1,901 (40.1%)

BMI, body mass index; TIA, transient cerebral ischemia. Levels of physical activity: low: do not meet the criteria for either the moderate or high activity categories; moderate: spent 3 or more days performing 20 minutes of vigorous activity/spent 30 minutes or more walking and performing moderate exercise on at least 5 days/respondents who engage in any activity on 5 or more days for a total of more than 600 met minutes; high: spent 3 or more days on vigorous activity for a total of 1,500 or more met minutes/respondents who spent 7 or more days on all activities for a total of more than 3,000 met minutes.

Correlation between the fine motor index and MMSE score

We used a linear regression to explore the correlation between the fine motor index and MMSE score. As shown in Table 2, we found that the motor index was negatively correlated with the MMSE scores (B = –1.00, 95%CI: –1.17, –0.83, t = –11.53, p < 0.001) in the unadjusted analysis. To further evaluate the correlation between the fine motor index and MMSE scores, a multiple linear regression analysis was conducted. The results remained significant after adjusting for age, sex, education level, BMI, alcohol drinking, levels of physical activity, comorbidities: hypertension, angina, heart attack, congestive heart failure, diabetes or high blood sugar, stroke, ministroke or TIA, high cholesterol (B = –0.63, 95%CI: –0.79, –0.47, t = –7.77, p < 0.001), suggesting that the fine motor index was independently inversely correlated with the MMSE score (Table 2).

Table 2

Univariate and multivariate linear regression analysis of the fine motor index groups or gross motor index groups with Mini-Mental State Examinations

Variable	FINEA	GROSSA
	B (95%CI)	B (95%CI)
	t	t
	p	p
Unadjusted	–1.00 (–1.17, –0.83)	–0.85 (–0.94, –0.76)
	–11.53	–18.29
	p < 0.001	p < 0.001
Model 1	–0.73 (–0.90, –0.57)	–0.68 (–0.77, –0.59)
	–8.80	–14.91
	p < 0.001	p < 0.001
Model 2	–0.63 (–0.79, –0.47)	–0.57 (–0.66, –0.48)
	–7.77	–12.61
	p < 0.001	p < 0.001

Model 1 was adjusted for age and sex; Model 2 adjusted for age, sex, education level, BMI, alcohol drinking, levels of physical activity, comorbidities: hypertension, angina, heart attack, congestive heart failure, diabetes or high blood sugar, stroke, ministroke or TIA, high cholesterol. p≤0.05 is statistically significant. FINEA, fine motor index; GROSSA, gross motor index; CI, confident interval.

Correlation between the gross motor index and MMSE score

As shown in Table 2, the gross motor index was negatively correlated with the MMSE score (B = –0.85, 95%CI: –0.94, –0.76, t = –18.29, p < 0.001) in the unadjusted analysis. Consistently, the results remained significant after adjusting for age, sex, education level, BMI, alcohol drinking, levels of physical activity, comorbidities: hypertension, angina, heart attack, congestive heart failure, diabetes or high blood sugar, stroke, ministroke or TIA, high cholesterol (B = –0.57, 95%CI: –0.66, –0.48, t = –12.61, p < 0.001), suggesting that the gross motor index was independently and inversely correlated with the MMSE score.

Prospective cohort study

The above cross-sectional study showed an inverse correlation between the fine motor index or gross motor index and MMSE score at baseline. To further clarify their association with cognitive impairment, we further analyzed the relationship after 2 years of follow-up. A cutoff MMSE score below 24 was considered indicative of cognitive function impairment events. Therefore, subjects with an MMSE score less than 24 (N = 115) at baseline were excluded. We further excluded individuals who were lost to follow-up (N = 429) or who had missing data (N = 8), eventually resulting in 4,193 subjects in the prospective study. The details of the subject selection are shown in Fig. 1. The basic characteristics of the included individuals are presented in Table 3.

Table 3

Baseline characteristics of the subjects included in the prospective cohort study

Variables	FINEA = 0 (N = 3,996)	FINEA = 1–3 (N = 197)	p	GROSSA = 0 (N = 3,879)	GROSSA = 1–5 (N = 314)	p
Age (y)	63.4±8.5	69.0±9.2	< 0.001	63.4±8.5	67.8±9.5	< 0.001
Sex (%male)	1,775 (44.4%)	92 (46.7%)	0.529	1,751 (45.1%)	116 (36.9%)	0.005
BMI (kg/m²)	27.7±3.6	28.0±3.9	0.283	27.6±3.6	28.6±3.9	< 0.001
Education, (%)			0.001			< 0.001
Lower	800 (20.0%)	59 (29.9%)	0.001	749 (19.3%)	110 (35.0%)	< 0.001
Secondary	2,460 (61.6%)	113 (57.4%)	0.237	2,396 (61.8%)	177 (56.4%)	0.059
High	736 (18.4%)	25 (12.4%)	0.042	734 (18.9%)	27 (8.6%)	< 0.001
Smoking status, n (%)			0.001			< 0.001
Never	1,896 (47.4%)	72 (36.5%)	0.003	1,870 (48.2%)	98 (31.2%)	< 0.001
Past	1,553 (38.9%)	86 (43.7%)	0.179	1,495 (38.5%)	144 (45.9%)	0.011
Current	547 (13.7%)	39 (19.8%)	0.016	514 (13.3%)	72 (22.9%)	< 0.001
Drink alcohol, n (%)	3,158 (79.0%)	137 (69.5%)	0.002	3,075 (79.3%)	220 (70.1%)	< 0.001
Levels of physical activity, n (%)			< 0.001			< 0.001
Low	1,048 (26.2%)	82 (41.6%)	< 0.001	981 (25.3%)	149 (47.5%)	< 0.001
Moderate	1,445 (36.2%)	67 (34.0%)	0.539	1,414 (36.5%)	98 (31.2%)	0.063
High	1,503 (37.6%)	48 (24.4%)	< 0.001	1,484 (38.3%)	67 (21.3%)	< 0.001
Comorbidities, n (%)
Hypertension	1,250 (31.3%)	97 (49.2%)	< 0.001	1,189 (30.7%)	158 (50.3%)	< 0.001
Angina	155 (3.9%)	16 (8.1%)	0.003	136 (3.5%)	35 (11.1%)	< 0.001
Heart attack	133 (3.3%)	14 (7.1%)	0.005	122 (3.1%)	25 (8.0%)	< 0.001
Congestive heart failure	30 (0.8%)	1 (0.5%)	1	24 (0.6%)	7 (2.2%)	0.004
Diabetes or high blood sugar	203 (5.1%)	29 (14.7%)	< 0.001	196 (5.1%)	36 (11.5%)	< 0.001
Stroke	42 (1.1%)	8 (4.1%)	0.001	34 (0.9%)	16 (5.1%)	< 0.001
Ministroke or TIA	60 (1.5%)	9 (4.6%)	0.003	56 (1.4%)	13 (4.1%)	< 0.001
High cholesterol	1,593 (39.9%)	95 (48.2%)	0.020	1,537 (39.6%)	151 (48.1%)	0.003

FINEA, fine motor index; GROSSA, gross motor index; BMI, body mass index; TIA, transient cerebral ischemia. Levels of physical activity: low: do not meet the criteria for either the moderate or high activity categories; moderate: spent 3 or more days performing 20 minutes of vigorous activity/spent 30 minutes or more walking and performing moderate exercise on at least 5 days/respondents who engage in any activity on 5 or more days for a total of more than 600 met minutes; High: spent 3 or more days performing vigorous activity for a total of 1,500 or more met minutes/respondents who spent 7 or more days on all activities for a total of more than 3,000 met minutes.

Associations between the baseline fine motor index or gross motor index and cognitive impairment after two years of follow-up

A comparative analysis of cognitive function was performed between the two groups (baseline fine motor index = 0 and baseline fine motor index = 1–3). Our results showed that a high fine motor index was associated with an increased incidence of cognitive function impairments (adjusted OR = 2.35, 95%CI: 1.05, 5.23, p = 0.036). A significant association was also found with a high gross motor index (adjusted OR = 3.00, 95%CI: 1.49, 6.03, p = 0.002) after two years of follow-up (Table 4).

Table 4

Logistic regression analysis of the fine motor index or gross motor index and risk of cognitive decline

	FINEA (N = 4,193)				GROSSA (N = 4,193)
	0 (N = 3996)	1–3 (N = 197)	Unadjusted OR (95%CI)	Adjusted OR (95%CI)^a	0 (N = 3879)	1–5 (N = 314)	Unadjusted OR (95%CI)	Adjusted OR (95%CI)^a
Outcome
Cognitive impairment	39 (1.0%)	9 (4.6%)	4.85 (2.31,10.17)	2.35 (1.05, 5.23)	33 (0.9%)	15 (4.8%)	5.84 (3.14,10.88)	3.00 (1.49, 6.03)
			p < 0.001	p = 0.036			p < 0.001	p = 0.002

^aadjusted for age, sex, education level, BMI, alcohol drinking, levels of physical activity, comorbidities: hypertension, angina, heart attack, congestive heart failure, diabetes or high blood sugar, stroke, ministroke or TIA, high cholesterol; FINEA, fine motor index; GROSSA, gross motor index; OR, odds ratio; CI, confident interval.

DISCUSSION

Using a large sample of community-dwelling older individuals from The Irish Longitudinal Study on Ageing cohort, the present study showed an inverse correlation between the fine motor index or gross motor index and cognitive function at baseline. Furthermore, the individuals with a higher fine motor index or gross motor index exhibited a 2.35-fold or 3.00-fold increased risk of cognitive impairment after a follow-up of 2 years, respectively. These findings introduce a simple screening score for the prediction of cognitive impairment in older populations. Both fine and gross motor function have been studied to identify children with developmental disorders. Nevertheless, no study attempted to analyze their correlation with cognitive impairment in elderly individuals by combining these functions into a score index [19]. Therefore, our results potentially provide a simple and effective tool for the early detection of cognitive impairment.

The positive relationship between motor dysfunction and cognitive impairment in our study is consistent with several prospective studies [10, 20]. In the cross-sectional portion of this study, we found that both the fine motor and gross motor index were independently negatively correlated with the MMSE score in both sex subgroups (result not shown). Moreover, in our prospective study, the incidences of cognitive function impairment after two years were significantly increased in the groups with a high fine motor index or gross motor index. The results mentioned above indicate that motor function might be an independent predictive marker of cognitive impairment if used appropriately. How can we interpret “appropriately”? Primary dementia includes Alzheimer’s disease (AD), vascular dementia, dementia with Lewy bodies, and frontotemporal dementia, and these conditions are caused by different mechanisms [2]. Different types of dementia may cause different leading changes in motor function. For example, a decrease of 1 SD (0.204 m/s) in gait speed was associated with a 47%increased risk of incident AD, and an abnormal gait status predicted the development of vascular dementia (hazard ratio, 3.46 [95%CI 1.86 to 6.42]) [21]. Speech and bradykinesia were associated with incident dementia in idiopathic Parkinson’s disease [22]. Therefore, combining several motor functions that change earlier and more typically in each dementia type may improve the early detection of dementia or cognitive impairment.

To recognize early-stage cognitive impairment, we recommend conducting a routine test of motor function in a clinical setting among elderly individuals [5]. However, specialized highly-sensitive motor function combinations predictive of all subtypes of cognitive decline need to be further investigated. Moreover, race, the age range and other basic characteristics need to be considered when testing item combinations.

Several possible mechanisms might explain our findings. First, both cognitive impairment and motor abnormalities result from an age-related gradual brain mass loss process, leading to white matter and gray matter volume decline [23]. Second, motor dysfunction may represent an early symptom of cognitive decline as discovered in children [19] and gait speed studies in the elderly [10]. Third, motor accomplishments rely on coordinated motor sequence integrality as follows: abnormally folded amyloid-β and tau protein accumulation in amyloid plaques and neuronal tangles in AD [24], neurovascular dysfunction and microvascular thrombosis diffuse in vascular cognitive decline [25], and neocortical amyloid-β deposition in motor dysfunction (postural instability and gait difficulty) featuring Parkinson’s disease [26] causes neuronal damage or dysfunction involved in coordinated motor sequences [27]. Motor dysfunction can result from neuronal damage as mentioned above, and exercise (motor) can protect against neurodegenerative disease development via multiple mechanisms [28]. Therefore, the positive interaction between motor function and cognition needs to be explored and may represent an effective diagnostic and treatment therapy for cognitive impairment in the future.

Strengths and limitations

Our study has several strengths. We first introduced a fine motor or gross index score for the prediction of the development of cognitive impairment. Second, our subjects were obtained from a well-designed and population-based sampling study with a large sample size. The participants were sampled in geographic clusters with an equal probability of being invited to participate in the survey, achieving excellent representativeness of the Irish elderly.

Our study has several limitations. First, the number of cases of cognitive impairment was relatively small (N = 48), which might be limited by the short follow-up period. The association between the fine motor index or gross motor index and cognitive impairment should be further confirmed in more longitudinal studies with larger cohorts. Second, cognitive impairment was assessed using the MMSE questionnaire. However, whether this score can predict more serious cognitive impairment, such as dementia, remains unclear. Third, studies have shown that other behaviors (such as gait speed), genetics (such as the spondin-1 rs11023139-A allele and episodic memory-weighted polygenic risk scores) and biomarkers (such as peripheral blood telomere length and plasma amyloid-β) effectively predict cognitive decline [10 , 29–32]. However, we cannot adjust these variables in our multivariance analysis due to data restrictions. Therefore, whether the fine motor index or gross motor index predicts cognitive impairment independently of the above predictors needs to be further studied. Fourth, our sample was European, and whether our results are generalizable to other populations, such as Americans and Asian, remains unknown.

Conclusion

Higher fine motor index or gross motor index scores were associated with an increased risk of cognitive impairment. Fine motor index or gross motor index might represent simple tools for the prediction of cognitive impairment.

Footnotes

ACKNOWLEDGMENTS

We thank TILDA (The Irish Longitudinal Study on Ageing) investigators for conducting this trial and making these data available.

We acknowledge the grant support from the Guangzhou Science Technology Bureau (202102010007).

This work was supported by the National Natural Science Foundation of China (J.F-W, 82070237; Y.L-Z, 81870170; Y.X-C, 81970200; and Y.X-C, 81770229), Natural Science Foundation of Guangdong Province (Y.L-Z, 2019A1515011682), National High Technology Research and Development Program of Guangzhou (J.F-W, 20180304001; J.F-W, 2019GZR110406004; and Y.L-Z, 201704020044), China Postdoctoral Science Foundation (Z.Y-C, 2020M683123), Guangdong Basic and Applied Basic Research Foundation (M.X-W, 2019A1515110129), and Guangdong Medical Science and Technology Research Foundation (Y-J, A2021006). No funding source had any role in the design, methods, subject recruitment, data collection, analysis or preparation of the paper.

Authors’ disclosures available online ().

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