ABSTRACT
Background
It is unclear if it is appropriate for people with Parkinson's disease (PD) to wear activity monitors on the wrist because of the potential influence of impairments on the data.
Objective
The objective of this study was to determine (1) whether activity monitor data collected from devices worn at the wrist and waist are comparable and (2) the contribution of PD impairments to any differences in step and activity counts at the wrist and waist.
Methods
A total of 46 community‐dwelling people with PD wore an accelerometer at the wrist and waist simultaneously for 1 week. Motor impairments (rigidity, bradykinesia, tremor, dyskinesia) were assessed using the Movement Disorders Society Unified Parkinson's Disease Rating Scale part III (motor examination) and part IV (motor complications).
Results
Higher values were recorded by the wrist monitor for steps/day (wrist, 9236 [standard deviation (SD) 3812]; waist, 5324 [SD 2800]; difference 3912; P < 0.001) and activity counts/day (wrist, 872,590 [SD 349,148]; waist, 186,491 [SD 101,989]; difference 686,099; P < 0.001). However, the wrist and waist values were strongly correlated (steps, r = 0.89; counts, r = 0.74; P ≤ 0.001). Increased tremor and dyskinesia explained 19% of the variation in the difference in average steps/day, and these variables plus reduced bradykinesia explained 24% of the variation in the difference in average activity counts/day.
Conclusion
Wrist monitors are likely to overestimate activity, particularly in people with tremor and dyskinesia. Nonetheless, activity monitors can be worn on the wrist if the aim is to monitor change rather than accurately record activity.
Keywords: Parkinson's disease, exercise, technology, ambulatory monitoring, accelerometry
Physical activity (ie, any bodily activity requiring energy expenditure, including exercise and incidental activity)1 is important for optimal health. Exercise is widely known to bring health benefits in the general population and in people with chronic neurological conditions, such as Parkinson's disease (PD).2, 3 Besides improvements in general health, exercise has been found to reduce falls and improve mobility, balance, and muscle strength in people with PD.4, 5 Conversely, inactivity increases the risk of developing several health conditions and contributes to premature mortality2 and is considered a public health issue in the general population. However, people with PD are even less active than people of the same age without PD6 and do not meet recommended levels of daily physical activity.7
Activity monitors can be used to both measure physical activity and assist people to increase physical activity, as they can utilize behavior change techniques such as self‐monitoring, goal setting, and tailored feedback.8 Monitors have successfully been used as a component of programs designed to increase physical activity in people with PD.9, 10 Although activity monitors can be used at a variety of locations on the body (eg, ankle,11 thigh,12 waist,9, 13 wrist13), recently there has been a move toward wearing monitors on the wrist. This is evident in the context of research with a shift in protocol used for accelerometers in the US National Health and Nutrition Examination Survey where waist‐worn monitors were changed to wrist‐worn monitors to improve wear compliance.14 In addition, there is a trend for commercially available monitors to be predominantly wrist‐worn models, with waist‐worn Fitbit monitors (Fitbit, San Francisco, CA) no longer available.
Although people may prefer to wear activity monitors on the wrist, previous research has shown that wrist‐worn activity monitors record a higher step count than waist‐worn monitors.15, 16, 17 This is likely to be a result of the monitor spuriously recording some upper limb activities as steps. In addition, in people with PD, motor impairments may interfere with readings made by devices worn on the wrist. Wearable sensors worn on the wrist or hand18 have been successfully used to assess the motor impairments of PD, including tremor, dyskinesia, bradykinesia, and motor fluctuations. It therefore seems likely that these types of impairments may influence the data obtained from an activity monitor worn on the wrist of a person with PD. Consequently, it is unclear if data collected from the wrist can be used to monitor physical activity in this population. This study aimed to determine (1) whether activity monitor data collected from devices worn at the wrist and waist are comparable and (2) the contribution of PD impairments to any differences in the step and activity counts measured at the wrist and the waist.
Methods
Design
This cross‐sectional observational study was conducted with 46 participants with PD. Data used for this study were collected as part of 2 studies (ACTRN12616001143415 and ACTRN12615000847516) completed in 2015 to 2017. Methods relevant to the present study are described here. Participants who completed both of these studies contributed only their first set of data to the present study. Assessments were conducted either at The University of Sydney or in participants’ homes while the participant was on their levodopa medication (typically around 1 hour after taking medication). The study was approved by the University of Sydney Human Research Ethics Committee, and all participants gave written informed consent.
Participants
Participants were recruited through advertisements placed in the Parkinson's New South Wales newsletter and through databases of participants from previous studies held at the University of Sydney who had consented to being contacted about opportunities to participate in research.
To be included, participants were older than the age of 40; cognitively intact (Mini‐Mental State Examination score ≥2419); able to walk independently, with or without a walking aid; residing in the community; and proficient in written and spoken English. Participants were excluded if they had any medical conditions that would interfere with the collection of data or interpretation of the results (eg, stroke).
Outcome Measures
Background information, including age, gender, height, weight, and years since diagnosis, was collected. PD disease severity was assessed by a trained assessor using the Movement Disorders Society Unified Parkinson's Disease Rating Scale.20 Part III (motor examination) was used to determine an overall motor examination score, a total tremor score (postural tremor of the hands, kinetic tremor of the hands, rest tremor amplitude, constancy of rest tremor), a bradykinesia score (finger tapping, hand movements, pronation–supination movements of hands, toe tapping, leg agility, global spontaneity of movement) and a rigidity score (neck and upper and lower extremity rigidity scores). Part IV (motor complications) was used to determine an overall motor complication score and a dyskinesia score (time spent with and functional impact of dyskinesias).
Following the assessment, the participants were provided with 2 activity monitors (ActiGraph GT3X+, ActiGraph, Penascola, FL). The ActiGraph is a small (3.8 cm × 3.7 cm × 1.8 cm), lightweight (27 g), water resistant, triaxial accelerometer. The accelerometer detects motion, and the ActiLife software uses a preprogrammed acceleration threshold to determine when a step has been taken. The ActiGraph is reliable21 and is widely used in research in people with and without PD.18, 22 The participants were instructed to wear the ActiGraphs for 7 consecutive days; wearing the ActiGraph at the wrist for 24 hours a day, the ActiGraph at the waist during waking hours, and removing both monitors for showering, bathing, or swimming. Data collected during waking hours were used for the data analysis. The wrist monitor was worn on a strap on the least‐affected hand (or the nondominant hand if both hands were equally affected) to minimize the impact of impairments on the measurements. The waist monitor was worn on an elastic belt over the right hip. To aid in verification and interpretation of ActiGraph data, the participants were asked to complete a daily physical activity logbook.
Outcome measures extracted from the ActiGraph were the average: steps per day; activity counts per day; percent time spent sedentary (≤100 counts per minute); percent time spent in light (101–1951 counts per minute) and in moderate (1952–5737 counts per minute) to vigorous physical activity (≥5738 counts per minute); and percent time spent in walking speed <1.04 m/s, between 1.05 and 1.3 m/s, and > 1.31 m/s. The intensity of ambulatory physical activity (ie, sedentary, light or moderate to vigorous) was determined using Freedson23 cut‐off points. The only measures that were made using algorithms specifically developed for people with PD were the percent time spent in different walking speeds.24
Data Analysis
Accelerometer data were analyzed using ActiLife software (version 6.13.3; ActiGraph). To be consistent with the methods used to determine the cut points, raw data were reintegrated into 60‐second epochs to extract the intensity of ambulatory activity23 and were reintegrated into 15‐second epochs to extract the time spent in each walking‐speed bracket.24 Minimum wear time was set a priori at 540 minutes per day for 4 days.25 Wear‐time validation was performed on each participant's data,26 and the analyzed wear time for the waist and the wrist were matched to ensure that data were from the same time for both devices. In addition, the daily accelerometer data and the physical activity logbook were compared to assist with wear‐time validation. The data were then processed to extract the outcome measures. The difference between ActiGraph‐derived outcome measures at the wrist and the waist were determined via simple deduction.
Statistical analysis was performed using the Statistical Package for the Social Sciences software (version 18; SPSS, Inc, Chicago, IL). To explore the comparability of the data collected from the ActiGraphs at the wrist and at the waist, either paired‐samples t tests (normally distributed data) or Wilcoxon signed‐rank tests (nonnormally distributed data) were conducted. The results were then correlated via Pearson's correlation to determine the strength of association between the wrist and waist measures. Post hoc analysis was used to compare measures collected by ActiGraphs worn at the wrist and waist for participants with worse tremor scores. The sample was dichotomized at the median, and paired‐samples t tests (normally distributed data) or Wilcoxon signed rank tests (nonnormally distributed data) and correlations were conducted separately with the data from the 24 participants with tremor scores ≥5 and the 22 participants with tremor score <5.
To explore the associations between impairments and any differences between the data collected at the wrist and at the waist for the average steps per day and the average activity counts per day, univariate linear regressions were used. Independent variables where P < 0.1 in the univariate analysis were candidates for inclusion in the multivariate models. A maximum of 3 independent variables were used (to enable at least 15 participants per variable), and where there were more than 3 candidates, or where candidate variables were highly correlated with each other (r > 0.7), the variable with the lowest P value from the univariate analysis was included.
Results
Participant Characteristics
A total of 46 (32 male) participants were enrolled. Participant characteristics are described in Table 1. Overall, the group had mild to moderate PD with a mean Movement Disorders Society Unified Parkinson's Disease Rating Scale motor score of 34.4 (standard deviation 13.2) and motor complications score of 2.2 (standard deviation 3.4).
Table 1.
Demographic and clinical characteristics of study participants (n = 46)
| Characteristic | Mean (SD) or n (%) |
|---|---|
| Male gender, n | 32 (69.6%) |
| Age, y | 68 (7.9) |
| Height, cm | 172 (9.4) |
| Weight, kg | 77 (15) |
| Time since diagnosis, y | 7.6 (6.8) |
| MDS‐UPDRS motor examination totala (0–132) | 34 (13) |
| Rigidity (0–20) | 6.1 (3.3) |
| Bradykinesia (0–44) | 17.3 (6.8) |
| Tremor (0–40) | 5.1 (4.2) |
| MDS‐UPDRS motor complications totala (0–24) | 2.2 (3.4) |
| Dyskinesia (0–8) | 0.5 (0.9) |
| Hoehn and Yahr stagea (0–5) | 2.2 (0.6) |
| Stage 1 | 4 (8.7%) |
| Stage 2 | 33 (71.7%) |
| Stage 3 | 7 (15.2%) |
| Stage 4 | 2 (4.3%) |
Rigidity = sum of all components in item 3.3; bradykinesia = sum of items 3.4–3.8 and 3.14; tremor = sum of items 3.15–3.18. Dyskinesia = sum of items 4.1 and 4.2.
High score is worse.
SD, standard deviation; MDS‐UPDRS, Movement Disorder Society Unified Parkinson's Disease Rating Scale.
Comparability of Data from the Wrist and Waist
Significantly higher values were recorded by the wrist monitor for average steps per day, average activity counts per day, percentage of time in light and in moderate to vigorous physical activity, and the percentage of time walking 1.05 to 1.3 m/s (Table 2). In contrast, the waist recorded higher values for percentage of time spent sedentary and for percentage of time spent walking ≤1.04 m/s or ≥1.31 m/s. However, all wrist and waist measures were moderately to strongly correlated (r = 0.49–0.89, P ≤ 0.001), with average steps per day having the strongest correlation (Table 2).
Table 2.
Paired‐samples t tests (normally distributed data) or Wilcoxon Signed Rank tests (nonnormally distributed data) results comparing measures collected by ActiGraphs worn at the wrist and the waist and correlations between the 2 locations (n = 46)
| Variable | Wrist, Mean (SD) | Waist, Mean (SD) | Mean Difference, Mean (SD) | 95% CI or z Statistic (P Value) | Correlation Between Wrist and Waist (P Value) |
|---|---|---|---|---|---|
| Average steps per day | 9236 (3812) | 5324 (2800) | 3912 (1834) | −5.9 (<0.001)c | 0.89 (<0.001)c |
| Average activity counts per day | 872,590 (349,148) | 186,491 (101,989) | 686,099 (282,404) | 602,235 to 769,963 (<0.001)c |
0.74 (<0.001)c |
| Average % time sedentarya | 38 (13) | 70 (11%) | −33 (9) | −35 to −30 (<0.001)c |
0.75 (<0.001)c |
| Average % time in light physical activitya | 51 (9) | 28 (10) | 24 (9) | 21 to 26 (<0.001)c |
0.57 (<0.001)c |
| Average % time in moderate‐vigorous physical activitya | 11 (8) | 2 (2) | 9 (7) | −5.9 (<0.001)c |
0.49 (0.001)c |
| Average % time spent walking <1.04 m/sb | 71 (11) | 95 (3) | −24 (10) | −5.9 (<0.001)c |
0.58 (<0.001)c |
| Average % time spent walking 1.05‐1.30 m/sb | 29 (11) | 4 (2) | 25 (10) | 22 to 28 (<0.001)c |
0.56 (<0.001)c |
| Average % time spent walking >1.31 m/sb | 0 (0) | 1 (2) | −1 (2) | N/A | N/A |
The Contribution of PD‐Related Variables to the Difference Between Wrist and Waist Data
Results for the univariate and multivariate linear regressions are presented in Table 3. Univariate regression found that increased tremor (P = 0.03), motor complications (P = 0.02), and dyskinesia (P < 0.01) were all significantly associated with increased difference in average steps per day, whereas only increased tremor (P = 0.04) was significantly associated with increased difference in average activity counts per day. The multivariate linear regression model for the difference in average steps per day included tremor and dyskinesia and explained 19% of the variance (adjusted R 2 = 0.19, P < 0.01), with dyskinesia making an independent contribution to the variance. The multivariate model for the difference in average activity counts per day included bradykinesia, tremor, and dyskinesia and explained 24% of the variance (adjusted R 2 = 0.24, P < 0.01). In this model, independent contributions were made by both bradykinesia (P < 0.01) and tremor (P = 0.01).
Table 3.
Results of the univariate and multivariate linear regression analyses to explore contribution of disease severity and motor impairments to the difference in average step counts and average activity counts per day (n = 46)
| Difference in Average Step Counts per Day | Difference in Average Activity Counts per Day | |||
|---|---|---|---|---|
| Predictor Variable from MDS‐UPDRS* | Univariate Coefficient (95% CI), P | Multivariate Coefficient (95% CI), P ** | Univariate Coefficient (95% CI), P | Multivariate Coefficient (95% CI), P *** |
| Motor examination total (0–132) | 5 (−37 to 47), 0.80 | −4073 (−10,443 to 2,298), 0.2 | ||
| Rigidity (0–20) | −30 (−198 to 137), 0.72 | −12,000 (−37,574 to 13,573), 0.3 | ||
| Bradykinesia (0–44) | −16 (−97 to 65), 0.70 | −11,976 (−23,966 to 14), 0.05 | −16,536 (−27,648 to −5423), <0.01a | |
| Tremor (0–40) | 140 (12 to 267), 0.03a | 109 (−12 to 230), 0.08 | 21,051 (1393 to 40,710), 0.04a | 23,969 (5442 to 42,496), 0.01a |
| Motor complications total (0–24) | 187 (33 to 342), 0.02a | 14,243 (−10,764 to 39,250), 0.3 | ||
| Dyskinesia (0–8) | 808 (266 to 1350), <0.01a | 717 (179 to 1255), 0.01 | 81,828 (−6339 to 169,997), 0.07 | 76,605 (−3970 to 157,180), 0.06 |
Rigidity = sum of all components in item 3.3; bradykinesia = sum of items 3.4–3.8 and 3.14; tremor = sum of items 3.15–3.18. Dyskinesia = sum of items 4.1 and 4.2.
High score is worse.
Multivariate regression: adjusted R 2 = 0.194, P = 0.004.
Multivariate regression: adjusted R 2 = 0.244, P = 0.002.
Indicates statistical significance.
MDS‐UPDRS, Movement Disorder Society Unified Parkinson's Disease Rating Scale; CI, confidence interval.
Post Hoc Analysis Assessing the Comparability of Data from the Wrist and Waist in Participants with Worse Tremor
As tremor was the only variable to be significantly associated with the difference in average steps per day and activity counts per day in univariate analyses, participants were dichotomized at the median tremor score to create a group with worse tremor (tremor score ≥5, n = 24) and a group with less tremor (tremor score <5, n = 22). Additional univariate analyses found that tremor was associated with the difference in both steps and activity counts in the group with worse tremor (difference in average steps per day unstandardized coefficient = 283; 95% confidence interval [CI], 61–505; P = 0.015; difference in average activity counts per day unstandardized coefficient = 37,661; 95% CI, 5139–70,182; P = 0.025), but not in the group with less tremor (difference in average steps per day unstandardized coefficient = −31; 95% CI, −459 to 396; P = 0.9; difference in average activity counts per day unstandardized coefficient = 2300; 95% CI, −71,662 to 76,263; P = 0.9). Despite this, the difference in measures taken at the wrist and the waist for each group were similar (Tables S1 and S2). In addition, both groups continued to have significant differences between wrist and waist measures and moderate to strong correlations between wrist and waist measures, with the average steps per day retaining the strongest correlation.
Discussion
This study showed that there were significant differences in the activity monitor data collected from the wrist and the waist. However, the data were moderately to strongly correlated, with the strongest correlation for average steps per day, suggesting that steps may be the best variable to use when measuring physical activity in people with PD. This means that overall, as step counts recorded at the waist increased, step counts recorded at the wrist also increased and vice versa. This relationship remained true for the group with worse tremor scores. Although the difference in wrist and waist measures suggests that one or both measures are inaccurate, the correlation results suggest that individuals with PD, including those with tremor, can monitor changes in their physical activity using monitors worn on either the waist or the wrist, providing the location of wear is consistent and providing the aim is to monitor change rather than accurately measure activity. However, further research is required to explore how well these measures can be used to monitor change at the group level to determine their suitability for use in trials of interventions designed to increase physical activity in people with PD. The large SD recorded here suggest that large sample sizes may be required to show group‐level changes in these measures.
We found that the monitor worn on the wrist detected a higher number of steps and activity counts as well as more percentage of time in physical activity and more percentage of time walking at moderate speed (1.05–1.3 m/s) when compared with the monitor worn on the waist. Similar results have been found in adults in the general population, where higher step counts have been reported from devices worn on the wrist when compared with the waist.15, 16, 17 Notably, studies with younger adults have reported smaller differences in the step count (median age 31 years, step difference 138115/median age 27 years, step difference 255816) when compared with the present study (mean age 68 years, step difference 3912) and a study conducted with older participants (mean age 72 years, step difference 472917). The reason for this increase in difference in older people is unclear and may be the result of age‐related factors, including increased sedentary time producing more activity at the wrist compared with the waist. This possibility is supported in the present study by the increased sedentary time recorded by the waist monitor.
The largest difference for the percentage of time spent in different intensities of activity was in the time spent sedentary, with 33% more sedentary time recorded at the waist than the wrist. Sedentary behavior is categorized as activity at a low intensity (<1.5 metabolic equivalents) in a sitting or lying posture during waking hours.27 Previous research with office workers found that waist‐worn ActiGraph monitors were poor at distinguishing between occupational sitting and standing time.28 It is therefore possible that the waist monitor in the present study was overestimating sedentary time by including standing time. Further research exploring sedentary behavior in people with PD could consider using a device worn on the thigh, such as the activPAL (PAL Technologies Ltd, Glasgow, UK), which is more likely to accurately detect sitting and lying time.29
PD impairments contributed to the difference in step and activity counts. The multivariate regression analysis identified that more tremor and dyskinesia accounted for 19% of the variability in the difference in step counts. Similarly, more tremor and dyskinesia, along with less bradykinesia, accounted for 24% of the variability in the difference in activity counts. The increased step and activity counts at the wrist in participants with tremor and dyskinesia is likely to be the result of these impairments leading to increased upper limb movements, which are erroneously recorded as steps and activity counts by the activity monitor. In addition, people with less bradykinesia may have more and faster spontaneous upper limb movements, leading to increased activity counts measured at the wrist. This result is not surprising given that wearable sensors are used to measure tremor, dyskinesia, and bradykinesia in people with PD.18 It seems likely that much of the remaining variability may be the result of similar factors as in the general population, such as an overestimation of counts at the wrist because of the use of the hands in activities that are performed while seated.16 However, given that the post hoc analysis showed that participants with more tremor had a similar magnitude of difference in step and activity counts when compared with participants with less tremor and that approximately 80% of the variability in the difference in step and activity counts remains unaccounted for, further research is warranted to explore other factors, such as time spent sitting, that may contribute to this difference.
In addition to the monitor at the wrist overestimating counts, the activity monitor at the waist may also be underestimating counts. A recent study comparing the accuracy of activity monitors worn on the wrist and the waist by people with PD performing short walks in a laboratory environment found that although the waist‐worn devices were more accurate, all devices underestimated step counts.13 Similarly, another study found that ActiGraphs worn on the waist were less than 80% accurate when participants walked at speeds between 0.8 and 1.2 m/s, with this accuracy decreasing further at slower walking speeds, possibly because of insufficient acceleration at the waist with slower swing‐through of the leg.30 Notably, in the present study, more time was recorded in slow walking (<1.04 m/s) by the waist monitor than the wrist monitor, suggesting that both monitors may be underestimating the amount of walking undertaken at slow speeds. However, this result should be interpreted in the knowledge that the algorithm for the walking speed cut points in people with PD was developed under laboratory conditions and has not been validated under free‐living conditions.24 In addition, in the present study we did not enable the low‐frequency extension filter in ActiLife, meaning that lower amplitude movements may not have been recorded. Nonetheless, taken together, research to date suggests that current algorithms for devices worn on the wrist or waist do not provide accurate data in people with PD. This is problematic as commercially available devices are predominantly designed for wear on the wrist. Although the present study suggests that these devices can be useful as they provide step‐count information that can be used to monitor changes in activity over time, the inaccuracies mean that people will not know their actual step count, and the ability of people to gain motivation31 through comparing their step count with others is limited. Developers of commercially available activity monitors should strive to produce a device that provides accurate feedback when worn by people with impaired mobility, including people with PD.
To our knowledge, this the first study to explore the relationship between activity monitors worn at the wrist and waist in free‐living physical activity in people with PD and the contribution of PD impairments on the difference in step and activity counts. However, caution should be taken when applying these results to the general PD population. We recruited community‐dwelling people with mild to moderate PD without significant cognitive impairment. The results therefore cannot be generalized to people in residential care or with greater disease severity or impaired cognition. In addition, we only measured PD impairments during the participants’ on medication phase, and the participants wore the activity monitors during their on and off phases through the week. The measurement of impairments during the off medication phase and records of on and off times through the week of activity monitoring would assist in determining if impairments made a larger contribution to the difference in activity monitor measures during off time. Our sample size of 46 participants limits the ability of this study to explore other possible contributors to the difference in wrist and waist measures. To further analyze the effects of motor impairments, future studies could focus on attaining a larger sample, including participants with more severe impairments. This may also allow exploration to determine the contributions of PD impairments to the difference in step and activity counts in people with different clinical phenotypes of PD.30
In conclusion, there are differences in activity monitor data collected at the waist and the wrist in people with PD, with these differences similar to those seen in the general population. Despite the contribution of impairments including tremor and dyskinesia to this difference, step and activity count measures from the 2 locations are strongly correlated. Therefore, activity monitors worn on the wrist may be used to monitor changes in physical activity over time in people with mild to moderate PD, provided the aim is to monitor change rather than to accurately record activity.
Author Roles
(1) Research Project: A. Conception, B. Organization, C. Execution; (2) Statistical Analysis: A. Design, B. Execution, C. Review and Critique; (3) Manuscript Preparation: A. Writing of the First Draft, B. Review and Critique.
D.W.K.: 1C, 2B, 3A
L.M.H.: 1A, 2A, 2C, 3B
V.N.: 1B, 1C, 2C, 3B
N.E.A.: 1A, 1B, 1C, 2A, 2B, 2C, 3B
Disclosures
Ethical Compliance Statement
This study was approved by the University of Sydney Human Research Ethics Committee (2015‐574 and 2016‐593). Written informed consent was obtained from all participants prior to testing. We confirm that we have read the journal's position on issues involved in ethical publication and affirm that this work is consistent with those guidelines.
Funding Sources and Conflict of Interest
This work was supported in part by a Parkinson's New South Wales Bendigo Bank Parkinson's Research Grant (ACTRN12615000847516). All authors declare that there are no conflicts of interest relevant to this work.
Financial Disclosures for the Previous 12 Months
L.M.H. has received research grant support from Australian National Health and Medical Research Council, Australian Medical Research Future Fund, University of Sydney. and Prince of Wales Hospital Foundation. N.E.A. has received research grant support from The University of Sydney and Parkinson's New South Wales. D.W.K. and V.N. have no disclosures to report.
Supporting information
Table S1. Paired‐samples t tests or Wilcoxon signed‐rank tests results comparing measures collected by Actigraphs worn at the wrist and the waist and correlations between the 2 locations for participants with worse tremor scores.
Table S2. Paired‐samples t tests or Wilcoxon signed‐rank tests results comparing measures collected by Actigraphs worn at the wrist and the waist and correlations between the 2 locations for participants with lower tremor scores.
Acknowledgment
We acknowledge the people with Parkinson's disease who participated in this study as well as Parkinson's New South Wales for their assistance with recruitment.
Relevant disclosures and conflicts of interest are listed at the end of this article.
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Associated Data
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Supplementary Materials
Table S1. Paired‐samples t tests or Wilcoxon signed‐rank tests results comparing measures collected by Actigraphs worn at the wrist and the waist and correlations between the 2 locations for participants with worse tremor scores.
Table S2. Paired‐samples t tests or Wilcoxon signed‐rank tests results comparing measures collected by Actigraphs worn at the wrist and the waist and correlations between the 2 locations for participants with lower tremor scores.