Accelerometer output and its association with energy expenditure in persons with mild-to-moderate Parkinson’s disease

Abstract

Objective This study examined the association between ActiGraph accelerometer output and energy expenditure across different speeds of walking in persons with Parkinson’s disease (PD), and further generated cut-points that represent a metric for quantifying time spent in moderate-to-vigorous physical activity (MVPA) among persons with PD. Methods The sample included 30 persons with mild-to-moderate PD (Hoehn and Yahr stages 2–3) and 30 adults without PD matched by sex and age. All participants completed 5 minutes of quiet, seated rest and then underwent three, 6-minute bouts of walking on a treadmill at three different speeds relative to the individual’s self-selected pace. Activity counts were measured using an ActiGraph accelerometer worn at the waist level on the least affected side for persons with PD and the dominant side for controls. The rate of oxygen consumption, or energy expenditure, was measured using a portable, open-circuit spirometry system. Results Our results indicated a strong association between activity counts and energy expenditure for persons with PD (R2 = 0.87) and controls (R2 = 0.89). However, the significant difference in slopes resulted in a lower cut-point of 1,354 counts·min-1 for persons with PD than the cut-point of 2,010 counts·min-1 for controls. Conclusion Our results support the application of the disease-specific cut-point for quantifying the amount of time spent in MVPA using ActiGraph accelerometers among persons with mild-to-moderate PD. Such an application may provide accurate estimates of MVPA in this population, and better inform future research examining the possible determinants and consequences of physical activity as well as testing of interventions for changing MVPA in PD.

Wrist Accelerometer Estimates of Physical Activity Intensity During Walking in Older Adults and People Living With Complex Health Conditions: Retrospective Observational Data Analysis Study

“…Cut points facilitate the classification of wrist movement into activity intensity categories based on predefined activity count (AVM) thresholds. Although there is value in the ease of use and application to large population studies as a quick, low-burden way to track free-living activity, cut points are specific to the age, fitness, and health status (among other variables) of the sample in which they were developed [ 65 , 66 ]. Applications to dissimilar populations or confounding factors such as the presence of disease affect the validity of this approach.…”

Exercising with a robotic exoskeleton can improve memory and gait in people with Parkinson’s disease by facilitating progressive exercise intensity

“…Heart rate measures are a common proxy measure of exercise intensity 2,49 , but may not be appropriate for accurate real-time measurement 50 . Actigraphy using small, unobtrusive wearable sensors is a portable and low-cost alternative to indirect calorimetry 42,51,52 , therefore was ideal for the purpose of quantifying exercise intensity while participants performed the exercise sessions.…”

“…After the conclusion of the study the raw files for the 27 participants with up to 16 sessions each were exported as activity count data to csv files and further processed with custom written Matlab algorithms to calculate activity counts per minute (ACPM) by summing the activity counts across the time interval of the session (with leading and trailing noise removed) and dividing by the total time in minutes. Consistent with the recommendation from Jeng et al 42 we included only the vertical channel accelerations. ACPM data were further normalized to body mass (ACPM/kg) to account for the mass being moved (ie.…”

“suMus,” a novel digital system for arm movement metrics and muscle energy expenditure

“…Movement data derived from accelerometers can also extrapolate energy expenditure (EE) and values of physical activity. Once again, most of the studies focused on lower limbs (e.g., treadmill, running trials, cycling) ( Freedson et al, 2005 ; Crouter et al, 2006 ; Dooley et al, 2017 ; Zhang et al, 2019 ; Jeng et al, 2020 ). It is not feasible to apply these data for assessing upper limb function, since regression equations based on lower limb parameters underestimate EE quantification in non-locomotive activities ( Bassett et al, 2000 ; Murakami et al, 2019 ; Fernández-Verdejo et al, 2021 ).…”