Variations in rest-activity rhythm are associated with clinically measured disease severity in Parkinson’s disease

ABSTRACT

The continuous, longitudinal nature of accelerometry monitoring is well-suited to capturing the regular 24-hour oscillations in human activity across the day, the cumulative effect of our circadian rhythm and behavior. Disruption of the circadian rhythm in turn disrupts rest-activity rhythms. Although circadian disruption is a major feature of Parkinson’s disease (PD), rest-activity rhythms and their relationship with disease severity have not been well characterized in PD. 13 PD participants (Hoehn & Yahr Stage [H&Y] 1–3) wore a Philips Actiwatch Spectrum PRO continuously for two separate weeks. Rest-activity rhythms were quantified by fitting an oscillating 24-hour cosinor model to each participant-day of activity data. One-way ANOVAs adjusted for demographics revealed significant variation in the amount (MESOR, F = 12.76, p < .01), range (Amplitude, F = 9.62, p < .01), and timing (Acrophase, F = 2.7, p = .05) of activity across H&Y Stages. Those with higher H&Y Stages were significantly more likely to be active later in the day, where-as those who shifted between H&Y Stages during the study were significantly more active than those who did not change H&Y Stage. Being active later in the day was also significantly associated with higher scores on the Movement Disorder Society’s Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) Section III (motor symptom severity, p = .02), Section II (self-reported impact of motor symptoms on daily living, p = .01), and Total Score (p = .01) in an adjusted linear regression model; significant associations between MDS-UPDRS scores and activity levels were observed only in the unadjusted model. These findings demonstrate that continuous actigraphy is capable of detecting rest-activity disruption in PD, and provides preliminary evidence that rest-activity rhythms are associated with motor symptom severity and H&Y Stage.

KEYWORDS:

• Parkinson’s disease
• actigraphy
• rest-activity rhythm
• cosinor
• rhythmometry

Acknowledgments

The authors wish to acknowledge Robert Koehler and Craig Detheridge for their contributions to the performance of the study and processing of actigraphy data, as well as the 20 participants who volunteered to participate in the study.

Declaration of interests statement

The authors declare no conflicts of interest that influence the content of this article. This study was accomplished as a collaborative effort supported by the Neuroscience Research Unit of Pfizer Inc.

Supplementary material

Supplemental data for this article can be accessed here.

Notes

1. Actiware uses a proprietary algorithm to automatically extract AC from raw accelerometry data, which are not directly accessible.

Additional information

Funding

This study was supported by the Neuroscience Research Unit of Pfizer Inc.