Home-Based Physical Behavior in Late Stage Parkinson Disease Dementia: Differences between Cognitive Subtypes

Abstract

Background: For the early diagnosis of Parkinson disease dementia (PDD), objective home-based tools are needed to quantify even mild stages of dysfunction of the activities of daily living (ADL). Objectives: In this pilot study, home-based physical behavior was assessed to examine whether it is possible to distinguish mild cognitive impairment (PD-MCI) from PDD. Methods: Fifty-five patients with mild to severe Parkinson disease (PD) participated in this cross-sectional study. Based on comprehensive neuropsychological testing, PD patients were classified as cognitively nonimpaired (PD-NC), PD-MCI or PDD. For physical behavior assessments, patients wore the accelerometer DynaPort® (McRoberts) for 3 days. Ordinal logistic regression models with continuous Y were applied to correct results for motor impairment and depressive symptoms. Results: After excluding 7 patients due to insufficient wearing time, 48 patients with a mean of 2 recorded days were analyzed (17 PD-NC, 22 PD-MCI, 9 PDD). ADL-impaired PDD patients showed fewer sedentary bouts than non-ADL-impaired PD-MCI (p = 0.01, odds ratio [OR] = 8.9, 95% confidence interval [CI] = 1.8-45.2) and PD-NC (p = 0.01, OR = 10.3, CI = 1.6-67.3) patients, as well as a longer sedentary bout length (PD-NC: p = 0.02, OR = 0.1, CI = 0.02-0.65; PD-MCI: p = 0.02, OR = 0.14, CI = 0.03-0.69). These differences were mainly caused by fewer (PD-NC: p = 0.02, OR = 9.6, CI = 1.5-62.4; PD-MCI: p = 0.01, OR = 8.5, CI = 1.5-37.3) but longer sitting bouts (PD-NC: p = 0.03, OR = 0.12, CI = 0.02-0.80; PD-MCI: p = 0.04, OR = 0.19, CI = 0.04-0.93). Tests assessing executive function, visuoconstruction and attention correlated significantly with specific activity parameters (ρ ≥ 0.3; p < 0.05). Conclusion: Objective assessment of physical behavior, in particular the detection of sedentary bouts, is a promising contributor to the discrimination between PD-MCI and PDD.

Using Digital Technology to Quantify Habitual Physical Activity in Community Dwellers With Cognitive Impairment: Systematic Review

“…A total of 14 (42%) papers addressed reasons for loss of data and issues with adherence to protocol, including technical issues (n=5, 36%) [30][31][32][33][34], participants removing or refusing to wear devices (n=6, 43%) [30,33,[35][36][37][38], insufficient data collected (n=8, 57%; <10 hours per day for 8/14 days [39]; <80% daily wear time [40]; <3 days [31]; <10 hours per day for at least 3 days [41]; <7 days [15]; <6 consecutive days [33]; incomplete recorded days [34,40]), lost devices (n=2, 14%) [15,33], organizational issues (n=2, 14%) [15,33], forgetting to wear the device (n=1, 7%) [42], and hospitalization during the data collection period (n=1, 7%) [38].…”

“…Of the 13 (39%) studies reporting HPA in MCI, 8 (62%) characterized volumes [32,34,40,41,[43][44][45][46][47]. The most common volume metric was steps per day (n=5, 39%), with mean/median averages ranging between 3407 and 12,256 steps [40,41,43,44,46,47]. Two (15%) studies reported time spent walking per day, with a mean range between 113 and 150 minutes [41,44].…”

“…The percentage of the day spent active ranged between 3.9% and 8.7% across 2 (15%) studies [40,44], with the same studies reporting a range between 261 and 490 walking bouts per day. Another 2 (15%) studies reported the mean percentage of time spent active, which ranged between 8.69% and 21% [40,45].…”

Wearable GPS and Accelerometer Technologies for Monitoring Mobility and Physical Activity in Neurodegenerative Disorders: A Systematic Review

“…Eleven studies explored associations between objective accelerometer-derived measures and widely used in-person assessments (e.g., TUG) and paper-based tools that assess function (e.g., ALS Functional Rating Scale-Revised), mobility (e.g., Parkinson's Disease Questionnaire (PDQ) mobility scores), or PA (e.g., Physical Activity Scale for the Elderly) [20,[26][27][28]34,36,39,43,46,59]. Furthermore, nine of the studies explored relationships between sensor-derived activity measures and disease stage, duration, or progression [20,27,28,30,34,35,39,40,45]. One study proposed that assessing physical behaviour patterns, particularly sedentary bouts, could potentially be used to discriminate between PD with mild cognitive impairment and PD with dementia [40].…”

“…Furthermore, nine of the studies explored relationships between sensor-derived activity measures and disease stage, duration, or progression [20,27,28,30,34,35,39,40,45]. One study proposed that assessing physical behaviour patterns, particularly sedentary bouts, could potentially be used to discriminate between PD with mild cognitive impairment and PD with dementia [40]. Kim et al reported that, in PD, the number of steps recorded by wrist-worn devices were significantly higher than those recorded by waist-worn devices (p < 0.001).…”