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Importance Higher physical activity levels have been suggested as a potential modifiable risk factor for reducing the risk of incident Parkinson's disease (PD). This study uses objective measures of physical activity to investigate the role of reverse causation in the observed associations. Objective To investigate the association between accelerometer-derived daily step count and incident PD, and to assess the impact of reverse causation on this association. Design This prospective cohort study involved a follow-up period with a median duration of 7.9 years, with participants who wore wrist-worn accelerometers for up to 7 days. Setting The study was conducted within the UK Biobank, a large, population-based cohort. Participants The analysis included 94,696 participants aged 43-78 years (56% female) from the UK Biobank who provided valid accelerometer data and did not have PD at baseline. Exposure Daily step counts were derived using machine learning models to determine the median daily step count over the monitoring period. Main Outcomes and Measures The primary outcome was incident PD, identified through hospital admission and death records. Cox proportional hazards regression models estimated hazard ratios (HR) and 95% confidence intervals (CI) for the association between daily step count and incident PD, with adjustments for various covariates and evaluation of reverse causation by splitting follow-up periods. Results During a median follow-up of 7.9 years (IQR: 7.4-8.4), 407 incident PD cases were identified. An inverse linear association was observed between daily step count and incident PD. Participants in the highest quintile of daily steps (>12,369 steps) had an HR of 0.41 (95% CI 0.31-0.54) compared to the lowest quintile (<6,276 steps; HR 1.00; 95% CI 0.84-1.19). A per 1,000 step increase was associated with an HR of 0.92 (95% CI 0.89-0.94). However, after excluding the first six years of follow-up, the association was not significant (HR 0.96, 95% CI 0.92-1.01). Conclusions and Relevance The observed association between higher daily step count and lower incident PD is likely influenced by reverse causation, suggesting changes in physical activity levels occur years before PD diagnosis. While step counts may serve as a predictor for PD, they may not represent a modifiable risk factor. Further research with extended follow-up periods is warranted to better understand this relationship and account for reverse causation.
Importance Higher physical activity levels have been suggested as a potential modifiable risk factor for reducing the risk of incident Parkinson's disease (PD). This study uses objective measures of physical activity to investigate the role of reverse causation in the observed associations. Objective To investigate the association between accelerometer-derived daily step count and incident PD, and to assess the impact of reverse causation on this association. Design This prospective cohort study involved a follow-up period with a median duration of 7.9 years, with participants who wore wrist-worn accelerometers for up to 7 days. Setting The study was conducted within the UK Biobank, a large, population-based cohort. Participants The analysis included 94,696 participants aged 43-78 years (56% female) from the UK Biobank who provided valid accelerometer data and did not have PD at baseline. Exposure Daily step counts were derived using machine learning models to determine the median daily step count over the monitoring period. Main Outcomes and Measures The primary outcome was incident PD, identified through hospital admission and death records. Cox proportional hazards regression models estimated hazard ratios (HR) and 95% confidence intervals (CI) for the association between daily step count and incident PD, with adjustments for various covariates and evaluation of reverse causation by splitting follow-up periods. Results During a median follow-up of 7.9 years (IQR: 7.4-8.4), 407 incident PD cases were identified. An inverse linear association was observed between daily step count and incident PD. Participants in the highest quintile of daily steps (>12,369 steps) had an HR of 0.41 (95% CI 0.31-0.54) compared to the lowest quintile (<6,276 steps; HR 1.00; 95% CI 0.84-1.19). A per 1,000 step increase was associated with an HR of 0.92 (95% CI 0.89-0.94). However, after excluding the first six years of follow-up, the association was not significant (HR 0.96, 95% CI 0.92-1.01). Conclusions and Relevance The observed association between higher daily step count and lower incident PD is likely influenced by reverse causation, suggesting changes in physical activity levels occur years before PD diagnosis. While step counts may serve as a predictor for PD, they may not represent a modifiable risk factor. Further research with extended follow-up periods is warranted to better understand this relationship and account for reverse causation.
BACKGROUND Average daily steps (avDS) could be a valuable indicator of real-world ambulation in people with Parkinson’s disease (PwPD) and previous studies reported the validity and reliability of this measure. Nonetheless, no study to date has considered disease phenotype, stage and severity when assessing reliability of consumer wrist-worn devices to estimate daily step count in unsupervised, free-living conditions in PwPD. OBJECTIVE To assess and compare the reliability of a consumer wrist-worn smartwatch (Garmin Vivosmart 4) in counting avDS in PwPD in unsupervised, free-living conditions among disease phenotypes, stages, and severity groups. METHODS One-hundred-four PwPD were monitored through Garmin Vivosmart 4 for 5 consecutive days. Total daily steps for each day were recorded and avDS were calculated. PwPD were dichotomized into tremor dominant (TD) (N=39) or postural instability and gait disorder (PIGD) (N=65), presence (N=57) or absence (N=47) of tremor, and mild (N=65) or moderate (N=39) disease severity. Based on modified Hoeh and Yahr scale (mHY), PwPD were further dichotomized into earlier (mHY 1-2) (N=68) or intermediate (mHY 2.5-3) (N=36) disease stage. Intraclass correlation coefficient (ICC) (3, k), standard error of measurement (SEM) and minimum detectable change (MDC) were used to evaluate the reliability of avDS for each subgroup. The threshold for acceptability was set at an ICC ≥ 0.8 with a lower bound of 95% confidence interval (CI) ≥ 0.75. Student’s t-tests for independent groups and analysis of 83.4% CI overlap were used to compare ICC between each group pair. RESULTS Reliability of avDS measured through Garmin Vivosmart 4 for 5 consecutive days in unsupervised, free-living conditions was acceptable in the overall population with an ICC of 0.89 (0.85-0.92), SEM% below 10% and an MDC of 1580 steps/day (27% of criterion). In all investigated subgroups, reliability of avDS was also acceptable (ICC range 0.84-0.94). However, ICCs were significantly lower in PwPD with tremor (P=.030), with mild severity (P=.040) and earlier stage (P=.003). Moreover, SEM% was below 10% in PwPD with PIGD phenotype, without tremor, moderate disease severity and intermediate disease stage, with a MDC ranging from 1148 to 1687 steps/day (18-25% of criterion). Conversely, in PwPD with TD phenotype, tremor, mild disease severity and earlier disease stage, SEM was >10% of criterion and MDC values ranged from 1401 to 2263 steps/day (30- 33% of the criterion). CONCLUSIONS In mild-to-moderate PwPD, avDS measured through a consumer smartwatch in unsupervised, free-living conditions for 5 consecutive days are reliable irrespective of disease phenotype, stage, and severity. However, in PwPD with TD phenotype, tremor, mild disease severity and earlier disease stages, reliability could be lower. These findings could facilitate a broader and informed implementation of avDS as an index of ambulatory activity in PwPD.
Background: Dyskinesias and freezing of gait are episodic disorders in Parkinson’s disease, characterized by a fluctuating and unpredictable nature. This cross-sectional study aims to objectively monitor Parkinsonian patients experiencing dyskinesias and/or freezing of gait during activities of daily living and assess possible changes in spatiotemporal gait parameters. Methods: Seventy-one patients with Parkinson’s disease (40 with dyskinesias and 33 with freezing of gait) were continuously monitored at home for a minimum of 5 days using a single wearable sensor. Dedicated machine-learning algorithms were used to categorize patients based on the occurrence of dyskinesias and freezing of gait. Additionally, specific spatiotemporal gait parameters were compared among patients with and without dyskinesias and/or freezing of gait. Results: The wearable sensor algorithms accurately classified patients with and without dyskinesias as well as those with and without freezing of gait based on the recorded dyskinesias and freezing of gait episodes. Standard spatiotemporal gait parameters did not differ significantly between patients with and without dyskinesias or freezing of gait. Both the time spent with dyskinesias and the number of freezing of gait episodes positively correlated with the disease severity and medication dosage. Conclusions: A single inertial wearable sensor shows promise in monitoring complex, episodic movement patterns, such as dyskinesias and freezing of gait, during daily activities. This approach may help implement targeted therapeutic and preventive strategies for Parkinson’s disease.
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