Feasibility and usability of a digital health technology system to monitor mobility and assess medication adherence in mild-to-moderate Parkinson's disease

Introduction: Parkinson's disease (PD) is a neurodegenerative disorder which requires complex medication regimens to mitigate motor symptoms. The use of digital health technology systems (DHTSs) to collect mobility and medication data provides an opportunity to objectively quantify the effect of medication on motor performance during day-to-day activities. This insight could inform clinical decision-making, personalise care, and aid self-management. This study investigates the feasibility and usability of a multi-component DHTS to remotely assess self-reported medication adherence and monitor mobility in people with Parkinson's (PwP). Methods: Thirty participants with PD [Hoehn and Yahr stage I (n = 1) and II (n = 29)] were recruited for this cross-sectional study. Participants were required to wear, and where appropriate, interact with a DHTS (smartwatch, inertial measurement unit, and smartphone) for seven consecutive days to assess medication adherence and monitor digital mobility outcomes and contextual factors. Participants reported their daily motor complications [motor fluctuations and dyskinesias (i.e., involuntary movements)] in a diary. Following the monitoring period, participants completed a questionnaire to gauge the usability of the DHTS. Feasibility was assessed through the percentage of data collected, and usability through analysis of qualitative questionnaire feedback. Results: Adherence to each device exceeded 70% and ranged from 73 to 97%. Overall, the DHTS was well tolerated with 17/30 participants giving a score > 75% [average score for these participants = 89%, from 0 (worst) to 100 (best)] for its usability. Usability of the DHTS was significantly associated with age (ρ = −0.560, BCa 95% CI [−0.791, −0.207]). This study identified means to improve usability of the DHTS by addressing technical and design issues of the smartwatch. Feasibility, usability and acceptability were identified as key themes from PwP qualitative feedback on the DHTS. Conclusion: This study highlighted the feasibility and usability of our integrated DHTS to remotely assess medication adherence and monitor mobility in people with mild-to-moderate Parkinson's disease. Further work is necessary to determine whether this DHTS can be implemented for clinical decision-making to optimise management of PwP

A multi-sensor wearable system for the assessment of diseased gait in real-world conditions

Introduction: Accurately assessing people’s gait, especially in real-world conditions and in case of impaired mobility, is still a challenge due to intrinsic and extrinsic factors resulting in gait complexity. To improve the estimation of gait-related digital mobility outcomes (DMOs) in real-world scenarios, this study presents a wearable multi-sensor system (INDIP), integrating complementary sensing approaches (two plantar pressure insoles, three inertial units and two distance sensors). Methods: The INDIP technical validity was assessed against stereophotogrammetry during a laboratory experimental protocol comprising structured tests (including continuous curvilinear and rectilinear walking and steps) and a simulation of daily-life activities (including intermittent gait and short walking bouts). To evaluate its performance on various gait patterns, data were collected on 128 participants from seven cohorts: healthy young and older adults, patients with Parkinson’s disease, multiple sclerosis, chronic obstructive pulmonary disease, congestive heart failure, and proximal femur fracture. Moreover, INDIP usability was evaluated by recording 2.5-h of real-world unsupervised activity. Results and discussion: Excellent absolute agreement (ICC >0.95) and very limited mean absolute errors were observed for all cohorts and digital mobility outcomes (cadence ≤0.61 steps/min, stride length ≤0.02 m, walking speed ≤0.02 m/s) in the structured tests. Larger, but limited, errors were observed during the daily-life simulation (cadence 2.72–4.87 steps/min, stride length 0.04–0.06 m, walking speed 0.03–0.05 m/s). Neither major technical nor usability issues were declared during the 2.5-h acquisitions. Therefore, the INDIP system can be considered a valid and feasible solution to collect reference data for analyzing gait in real-world conditions

Assessing real-world gait with digital technology? Validation, insights and recommendations from the Mobilise-D consortium

Background Although digital mobility outcomes (DMOs) can be readily calculated from real-world data collected with wearable devices and ad-hoc algorithms, technical validation is still required. The aim of this paper is to comparatively assess and validate DMOs estimated using real-world gait data from six different cohorts, focusing on gait sequence detection, foot initial contact detection (ICD), cadence (CAD) and stride length (SL) estimates. Methods Twenty healthy older adults, 20 people with Parkinson’s disease, 20 with multiple sclerosis, 19 with proximal femoral fracture, 17 with chronic obstructive pulmonary disease and 12 with congestive heart failure were monitored for 2.5 h in the real-world, using a single wearable device worn on the lower back. A reference system combining inertial modules with distance sensors and pressure insoles was used for comparison of DMOs from the single wearable device. We assessed and validated three algorithms for gait sequence detection, four for ICD, three for CAD and four for SL by concurrently comparing their performances (e.g., accuracy, specificity, sensitivity, absolute and relative errors). Additionally, the effects of walking bout (WB) speed and duration on algorithm performance were investigated. Results We identified two cohort-specific top performing algorithms for gait sequence detection and CAD, and a single best for ICD and SL. Best gait sequence detection algorithms showed good performances (sensitivity > 0.73, positive predictive values > 0.75, specificity > 0.95, accuracy > 0.94). ICD and CAD algorithms presented excellent results, with sensitivity > 0.79, positive predictive values > 0.89 and relative errors < 11% for ICD and < 8.5% for CAD. The best identified SL algorithm showed lower performances than other DMOs (absolute error < 0.21 m). Lower performances across all DMOs were found for the cohort with most severe gait impairments (proximal femoral fracture). Algorithms’ performances were lower for short walking bouts; slower gait speeds (< 0.5 m/s) resulted in reduced performance of the CAD and SL algorithms. Conclusions Overall, the identified algorithms enabled a robust estimation of key DMOs. Our findings showed that the choice of algorithm for estimation of gait sequence detection and CAD should be cohort-specific (e.g., slow walkers and with gait impairments). Short walking bout length and slow walking speed worsened algorithms’ performances

Mobilise-D insights to estimate real-world walking speed in multiple conditions with a wearable device

This study aimed to validate a wearable device’s walking speed estimation pipeline, considering complexity, speed, and walking bout duration. The goal was to provide recommendations on the use of wearable devices for real-world mobility analysis. Participants with Parkinson’s Disease, Multiple Sclerosis, Proximal Femoral Fracture, Chronic Obstructive Pulmonary Disease, Congestive Heart Failure, and healthy older adults (n = 97) were monitored in the laboratory and the real-world (2.5 h), using a lower back wearable device. Two walking speed estimation pipelines were validated across 4408/1298 (2.5 h/laboratory) detected walking bouts, compared to 4620/1365 bouts detected by a multi-sensor reference system. In the laboratory, the mean absolute error (MAE) and mean relative error (MRE) for walking speed estimation ranged from 0.06 to 0.12 m/s and − 2.1 to 14.4%, with ICCs (Intraclass correlation coefficients) between good (0.79) and excellent (0.91). Real-world MAE ranged from 0.09 to 0.13, MARE from 1.3 to 22.7%, with ICCs indicating moderate (0.57) to good (0.88) agreement. Lower errors were observed for cohorts without major gait impairments, less complex tasks, and longer walking bouts. The analytical pipelines demonstrated moderate to good accuracy in estimating walking speed. Accuracy depended on confounding factors, emphasizing the need for robust technical validation before clinical application. Trial registration: ISRCTN – 12246987

Comparison of walking protocols and gait assessment systems for machine learning-based classification of Parkinson’s disease

Early diagnosis of Parkinson’s diseases (PD) is challenging; applying machine learning (ML) models to gait characteristics may support the classification process. Comparing performance of ML models used in various studies can be problematic due to different walking protocols and gait assessment systems. The objective of this study was to compare the impact of walking protocols and gait assessment systems on the performance of a support vector machine (SVM) and random forest (RF) for classification of PD. 93 PD and 103 controls performed two walking protocols at their normal pace: (i) four times along a 10 m walkway (intermittent walk-IW), (ii) walking for 2 minutes on a 25 m oval circuit (continuous walk-CW). 14 gait characteristics were extracted from two different systems (an instrumented walkway—GAITRite; and an accelerometer attached at the lower back—Axivity). SVM and RF were trained on normalized data (accounting for step velocity, gender, age and BMI) and evaluated using 10-fold cross validation with area under the curve (AUC). Overall performance was higher for both systems during CW compared to IW. SVM performed better than RF. With SVM, during CW Axivity significantly outperformed GAITRite (AUC: 87.83 ± 7.81% vs. 80.49 ± 9.85%); during IW systems performed similarly. These findings suggest that choice of testing protocol and sensing system may have a direct impact on ML PD classification results and highlight the need for standardization for wide scale implementation

Predicting first fall in newly diagnosed Parkinson's disease: Insights from a fall-naïve cohort

Background Falls are common and associated with reduced independence and mortality in Parkinson's disease. Previous research has been conducted on falls-prevalent or advanced disease cohorts. Objective This study identifies risk factors for first fall for 36 months in a newly diagnosed, falls-naïve cohort. Methods A total of 121 consecutive Parkinson's disease patients were recruited. Falls data were collected prospectively during 36 months from diagnosis via monthly falls diaries and telephone follow-up for 117 participants. Assessment comprised a comprehensive battery of clinical, gait, and cognitive measures. Significant predictors were identified from decision-tree analysis and survival analysis with time to first fall during 36 months as the dependent variable. Findings At baseline, 26 (22%) participants reported retrospective falls. At 36 months, the remaining cohort (n = 91) comprised 47 fallers (52%) and 30 (33%) nonfallers and 14 (15%) participants with incomplete diaries. Fallers presented with a significantly higher disease severity, poorer ability to stand on one leg, slower gait speed, increased stance time variability, and higher swing time asymmetry. Median time to first fall was 847 days. Gait speed, stance time, and Hoehn & Yahr III stage emerged as significant predictors of first fall, hazard ratio 3.44 (95% confidence interval [CI] 1.58 to 7.48), 3.31(95% CI 1.40 to 7.80), and 2.80 (95% CI 1.38 to 5.65), respectively. The hazard ratio for risk factors combined was 7.82 (CI 2.80 to 21.84). Conclusions Interventions that target gait deficit and postural control in early Parkinson's disease may limit the potential for first fall. © 2016 International Parkinson and Movement Disorder Societ

Gait progression over 6 years in Parkinson’s disease: Effects of age, medication, and pathology

Background: Gait disturbance is an early, cardinal feature of Parkinson’s disease (PD) associated with falls and reduced physical activity. Progression of gait impairment in Parkinson’s disease is not well characterized and a better understanding is imperative to mitigate impairment. Subtle gait impairments progress in early disease despite optimal dopaminergic medication. Evaluating gait disturbances over longer periods, accounting for typical aging and dopaminergic medication changes, will enable a better understanding of gait changes and inform targeted therapies for early disease. This study aimed to describe gait progression over the first 6 years of PD by delineating changes associated with aging, medication, and pathology. Methods: One-hundred and nine newly diagnosed PD participants and 130 controls completed at least two gait assessments. Gait was assessed at 18-month intervals for up to 6 years using an instrumented walkway to measure sixteen spatiotemporal gait characteristics. Linear mixed-effects models assessed progression. Results: Ten gait characteristics significantly progressed in PD, with changes in four of these characteristics attributable to disease progression. Age-related changes also contributed to gait progression; changes in another two characteristics reflected both aging and disease progression. Gait impairment progressed irrespective of dopaminergic medication change for all characteristics except step width variability. Conclusions: Discrete gait impairments continue to progress in PD over 6 years, reflecting a combination of, and potential interaction between, disease-specific progression and age-related change. Gait changes were mostly unrelated to dopaminergic medication adjustments, highlighting limitations of current dopaminergic therapy and the need to improve interventions targeting gait decline

Cholinergic dysfunction contributes to gait disturbance in early Parkinson's disease

Gait disturbance is an early feature in Parkinson’s disease. Its pathophysiology is poorly understood; however, cholinergic dysfunction may be a non-dopaminergic contributor to gait. Short-latency afferent inhibition is a surrogate measure of cholinergic activity, allowing the contribution of cholinergic dysfunction to gait to be evaluated. We hypothesized that short-latency afferent inhibition would be an independent predictor of gait dysfunction in early Parkinson’s disease. Twenty-two participants with Parkinson’s disease and 22 age-matched control subjects took part in the study. Gait was measured objectively using an instrumented walkway (GAITRite), and subjects were asked to walk at their preferred speed for 2 min around a 25-m circuit. Spatiotemporal characteristics (speed, stride length, stride time and step width) and gait dynamics (variability described as the within subject standard deviation of: speed, stride time, stride length and step width) were determined. Short-latency afferent inhibition was measured by conditioning motor evoked potentials, elicited by transcranial magnetic stimulation of the motor cortex, with electrical stimuli delivered to the contralateral median nerve at intervals ranging from N20 (predetermined) to N20 + 4 ms. Short-latency afferent inhibition was determined as the percentage difference between test and conditioned response for all intervals and was described as the group mean. Participants were optimally medicated at the time of testing. Participants with Parkinson’s disease had significantly reduced gait speed (P = 0.002), stride length (P = 0.008) and stride time standard deviation (P = 0.001). Short-latency afferent inhibition was also significantly reduced in participants with Parkinson’s disease (P = 0.004). In participants with Parkinson’s disease, but not control subjects, significant associations were found between gait speed, short-latency afferent inhibition, age and postural instability and gait disorder score (Movement Disorders Society Unified Parkinson’s Disease Rating Scale) and attention, whereas global cognition and depression were marginally significant. No other gait variables were associated with short-latency afferent inhibition. A multiple hierarchical regression model explored the contribution of short-latency afferent inhibition to gait speed, controlling for age, posture and gait symptoms (Postural Instability and Gait Disorder score—Movement Disorders Society Unified Parkinson’s Disease Rating Scale), attention and depression. Regression analysis in participants with Parkinson’s disease showed that reduced short-latency afferent inhibition was an independent predictor of slower gait speed, explaining 37% of variability. The final model explained 72% of variability in gait speed with only short-latency afferent inhibition and attention emerging as independent determinants. The results suggest that cholinergic dysfunction may be an important and early contributor to gait dysfunction in Parkinson’s disease. The findings also point to the contribution of non-motor mechanisms to gait dysfunction. Our study provides new insights into underlying mechanisms of non-dopaminergic gait dysfunction, and may help to direct future therapeutic approaches

Cholinergic deficits contribute to impaired postural control in early Parkinson's disease

[Poster] Objective: To determine whether impaired postural control is associated with cholinergic dysfunction in early Parkinson's disease (PD). Background: Impaired postural control is present even in early PD, and is an important determinant of impaired mobility and falls risk. Postural impairment is underpinned by complex, multisystem pathophysiology. While basal ganglia pathology and associated dopaminergic denervation are key contributors, recent work also implicates cholinergic degeneration in impaired postural control. Methods: Short latency afferent inhibition (SAI), a proxy measure of cholinergic activity, and postural control were measured in 42 subjects with early PD (mean age 70.1 ± 9.9 years) and 38 age-matched controls (68.7 ± 8.4 years) as part of the ICICLE-PD study. SAI was determined by conditioning motor evoked potentials, elicited by transcranial magnetic stimulation of the motor cortex, with electrical stimuli delivered to the contralateral median nerve at intervals ranging from N20 (predetermined) to N20+4ms. Postural control was measured during two minutes of quiet standing with eyes open. Force plates were used to quantify sway, from which mean speed of the centre of pressure (CoP) in the anterior-posterior (AP) and medio-lateral (ML) direction were determined. Partial correlations, controlling for age and cognition, were used to explore relationships between SAI and postural control. Results: SAI was significantly reduced in PD participants compared to controls (76.7 + 28.8% vs. 58.5 + 22.4%), indicating greater cholinergic dysfunction. There was no difference in postural control outcomes between the groups. In PD but not control participants, increased speed of movement of the CoP in the AP and ML direction (impaired postural control) was significantly associated with reduced SAI (cholinergic dysfunction), and this remained significant after controlling for age and cognition (r=0.565, p=0.008; r=0.632, p=0.002, respectively). Conclusions: Our findings suggest that in PD cholinergic dysfunction contributes to postural impairment even in early disease. This study provides insights into non-dopaminergic mechanisms and suggests that therapies targeting acetylcholine may be useful for the treatment of impaired postural control in PD

Short latency afferent inhibition: A biomarker for mild cognitive impairment in Parkinson's disease?

Background Mild cognitive impairment in Parkinson's disease (PD) is common and predicts those at risk of dementia. Cholinergic dysfunction may contribute to its pathophysiology and can be assessed using short latency afferent inhibition. Methods Twenty-two patients with PD (11 cognitively normal; 11 with mild cognitive impairment) and 22 controls participated. Short latency afferent inhibition was measured by conditioning motor evoked potentials, which were elicited by transcranial magnetic stimulation of the motor cortex with electrical stimuli delivered to the contralateral median nerve at varying interstimulus intervals. Results There was no significant difference between cognitively normal PD and controls for short latency afferent inhibition (62.8±30.3% vs. 55.7±21.7%; P=0.447). The PD-mild cognitive impairment group had significantly less inhibition (88.4±25.8%) than both cognitively normal PD (P=0.021) and controls (P=0.01). Conclusions Cholinergic dysfunction occurs early in those with PD-mild cognitive impairment. Short latency afferent inhibition may be a useful biomarker of increased risk of dementia in PD patients