Automated detection of missteps during community ambulation in patients with Parkinson’s disease: a new approach for quantifying fall risk in the community setting

Background: Falls are a leading cause of morbidity and mortality among older adults and patients with neurological disease like Parkinson’s disease (PD). Self-report of missteps, also referred to as near falls, has been related to fall risk in patients with PD. We developed an objective tool for detecting missteps under real-world, daily life conditions to enhance the evaluation of fall risk and applied this new method to 3 day continuous recordings. Methods: 40 patients with PD (mean age ± SD: 62.2 ± 10.0 yrs, disease duration: 5.3 ± 3.5 yrs) wore a small device that contained accelerometers and gyroscopes on the lower back while participating in a protocol designed to provoke missteps in the laboratory. Afterwards, the subjects wore the sensor for 3 days as they carried out their routine activities of daily living. An algorithm designed to automatically identify missteps was developed based on the laboratory data and was validated on the 3 days recordings. Results: In the laboratory, we recorded 29 missteps and more than 60 hours of data. When applied to this dataset, the algorithm achieved a 93.1% hit ratio and 98.6% specificity. When we applied this algorithm to the 3 days recordings, patients who reported two falls or more in the 6 months prior to the study (i.e., fallers) were significantly more likely to have a detected misstep during the 3 day recordings (p = 0.010) compared to the non-fallers. Conclusions: These findings suggest that this novel approach can be applied to detect missteps during daily life among patients with PD and will likely help in the longitudinal assessment of disease progression and fall risk

Protocol for the DeFOG trial: A randomized controlled trial on the effects of smartphone-based, on-demand cueing for freezing of gait in Parkinson's disease

Background: Freezing of gait (FOG) is a highly incapacitating symptom that affects many people with Parkinson's disease (PD). Cueing triggered upon real-time FOG detection (on-demand cueing) shows promise for FOG treatment. Yet, the feasibility of implementation and efficacy in daily life is still unknown. Therefore, this study aims to investigate the effectiveness of DeFOG: a smartphone and sensor-based on-demand cueing solution for FOG. Methods: Sixty-two PD patients with FOG will be recruited for this single-blind, multi-center, randomized controlled phase II trial. Patients will be randomized into either the intervention group or the active control group. For four weeks, both groups will receive feedback about their physical activity using the wearable DeFOG system in daily life. In addition, the intervention group will also receive on-demand auditory cueing and instructions. Before and after the intervention, home-based assessments will be performed to evaluate the primary outcome, i.e., “percentage time frozen” during a FOG-provoking protocol. Secondary outcomes include the training effects on physical activity monitored over 7 days and the user-friendliness of the technology. Discussion: The DeFOG trial will investigate the effectiveness of personalized on-demand cueing in a controlled design, delivered for 4 weeks in the patient's home environment. We anticipate that DeFOG will reduce FOG to a greater degree than in the control group and we will explore the impact of the intervention on physical activity levels. We expect to gain in-depth insight into whether and how patients control FOG using cueing methods in their daily lives. Trial registration: Clinicaltrials.gov NCT03978507

Acceptability of wearable devices for measuring mobility remotely: Observations from the Mobilise-D technical validation study

Background This study aimed to explore the acceptability of a wearable device for remotely measuring mobility in the Mobilise-D technical validation study (TVS), and to explore the acceptability of using digital tools to monitor health. Methods Participants (N = 106) in the TVS wore a waist-worn device (McRoberts Dynaport MM + ) for one week. Following this, acceptability of the device was measured using two questionnaires: The Comfort Rating Scale (CRS) and a previously validated questionnaire. A subset of participants (n = 36) also completed semi-structured interviews to further determine device acceptability and to explore their opinions of the use of digital tools to monitor their health. Questionnaire results were analysed descriptively and interviews using a content analysis. Results The device was considered both comfortable (median CRS (IQR; min-max) = 0.0 (0.0; 0–20) on a scale from 0–20 where lower scores signify better comfort) and acceptable (5.0 (0.5; 3.0–5.0) on a scale from 1–5 where higher scores signify better acceptability). Interviews showed it was easy to use, did not interfere with daily activities, and was comfortable. The following themes emerged from participants’ as being important to digital technology: altered expectations for themselves, the use of technology, trust, and communication with healthcare professionals. Conclusions Digital tools may bridge existing communication gaps between patients and clinicians and participants are open to this. This work indicates that waist-worn devices are supported, but further work with patient advisors should be undertaken to understand some of the key issues highlighted. This will form part of the ongoing work of the Mobilise-D consortium

Executive Function and Falls in Older Adults: New Findings from a Five-Year Prospective Study Link Fall Risk to Cognition

Background: Recent findings suggest that executive function (EF) plays a critical role in the regulation of gait in older adults, especially under complex and challenging conditions, and that EF deficits may, therefore, contribute to fall risk. The objective of this study was to evaluate if reduced EF is a risk factor for future falls over the course of 5 years of follow-up. Secondary objectives were to assess whether single and dual task walking abilities, an alternative window into EF, were associated with fall risk. Methodology/Main Results We longitudinally followed 256 community-living older adults (age: 76.4±4.5 yrs; 61% women) who were dementia free and had good mobility upon entrance into the study. At baseline, a computerized cognitive battery generated an index of EF, attention, a closely related construct, and other cognitive domains. Gait was assessed during single and dual task conditions. Falls data were collected prospectively using monthly calendars. Negative binomial regression quantified risk ratios (RR). After adjusting for age, gender and the number of falls in the year prior to the study, only the EF index (RR: .85; CI: .74–.98, p = .021), the attention index (RR: .84; CI: .75–.94, p = .002) and dual tasking gait variability (RR: 1.11; CI: 1.01–1.23; p = .027) were associated with future fall risk. Other cognitive function measures were not related to falls. Survival analyses indicated that subjects with the lowest EF scores were more likely to fall sooner and more likely to experience multiple falls during the 66 months of follow-up (p<0.02). Conclusions/Significance: These findings demonstrate that among community-living older adults, the risk of future falls was predicted by performance on EF and attention tests conducted 5 years earlier. The present results link falls among older adults to cognition, indicating that screening EF will likely enhance fall risk assessment, and that treatment of EF may reduce fall risk

Technical validation of real-world monitoring of gait : a multicentric observational study

Introduction: Existing mobility endpoints based on functional performance, physical assessments and patient self-reporting are often affected by lack of sensitivity, limiting their utility in clinical practice. Wearable devices including inertial measurement units (IMUs) can overcome these limitations by quantifying digital mobility outcomes (DMOs) both during supervised structured assessments and in real-world conditions. The validity of IMU-based methods in the real-world, however, is still limited in patient populations. Rigorous validation procedures should cover the device metrological verification, the validation of the algorithms for the DMOs computation specifically for the population of interest and in daily life situations, and the users’ perspective on the device. Methods and analysis: This protocol was designed to establish the technical validity and patient acceptability of the approach used to quantify digital mobility in the real world by Mobilise-D, a consortium funded by the European Union (EU) as part of the Innovative Medicine Initiative, aiming at fostering regulatory approval and clinical adoption of DMOs. After defining the procedures for the metrological verification of an IMU-based device, the experimental procedures for the validation of algorithms used to calculate the DMOs are presented. These include laboratory and real-world assessment in 120 participants from five groups: healthy older adults; chronic obstructive pulmonary disease, Parkinson’s disease, multiple sclerosis, proximal femoral fracture and congestive heart failure. DMOs extracted from the monitoring device will be compared with those from different reference systems, chosen according to the contexts of observation. Questionnaires and interviews will evaluate the users’ perspective on the deployed technology and relevance of the mobility assessment. Ethics and dissemination: The study has been granted ethics approval by the centre’s committees (London—Bloomsbury Research Ethics committee; Helsinki Committee, Tel Aviv Sourasky Medical Centre; Medical Faculties of The University of Tübingen and of the University of Kiel). Data and algorithms will be made publicly available. Trial registration number ISRCTN (12246987)

Technical validation of real-world monitoring of gait: a multicentric observational study

Introduction: Existing mobility endpoints based on functional performance, physical assessments and patient self-reporting are often affected by lack of sensitivity, limiting their utility in clinical practice. Wearable devices including inertial measurement units (IMUs) can overcome these limitations by quantifying digital mobility outcomes (DMOs) both during supervised structured assessments and in real-world conditions. The validity of IMU-based methods in the real- world, however, is still limited in patient populations. Rigorous validation procedures should cover the device metrological verification, the validation of the algorithms for the DMOs computation specifically for the population of interest and in daily life situations, and the users’ perspective on the device. Methods and analysis: This protocol was designed to establish the technical validity and patient acceptability of the approach used to quantify digital mobility in the real world by Mobilise-D, a consortium funded by the European Union (EU) as part of the Innovative Medicine Initiative, aiming at fostering regulatory approval and clinical adoption of DMOs. After defining the procedures for the metrological verification of an IMU-based device, the experimental procedures for the validation of algorithms used to calculate the DMOs are presented. These include laboratory and real-world assessment in 120 participants from five groups: healthy older adults; chronic obstructive pulmonary disease, Parkinson’s disease, multiple sclerosis, proximal femoral fracture and congestive heart failure. DMOs extracted from the monitoring device will be compared with those from different reference systems, chosen according to the contexts of observation. Questionnaires and interviews will evaluate the users’ perspective on the deployed technology and relevance of the mobility assessment. Ethics and dissemination: The study has been granted ethics approval by the centre’s committees (London—Bloomsbury Research Ethics committee; Helsinki Committee, Tel Aviv Sourasky Medical Centre; Medical Faculties of The University of Tübingen and of the University of Kiel). Data and algorithms will be made publicly available

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

Supplementary Material for: The Potential of Transcranial Alternating Current Stimulation to Alleviate Dual-Task Gait Costs in Older Adults: Insights from a Double-Blinded Pilot Study

Background: The performance of an attention-demanding task while walking, i.e., dual-tasking, leads to dual-task costs (e.g., reduced gait speed) in older adults. Previous studies have shown that dual-task costs in gait are associated with future falls and cognitive decline. According to the communication through coherence hypothesis, transcranial alternating current stimulation (tACS) might help alleviate this problem. Objective: The aim of this study was to examine the effects of a single session of theta-tACS targeting the left fronto-parietal network (L-FPN) on dual-task walking and cognitive function compared to sham stimulation and transcranial direct current stimulation (tDCS) targeting the left dorsolateral prefrontal cortex, a node within the L-FPN. Methods: Twenty older adults completed a four-visit, double-blinded, within-subject, cross-over study in which usual-walking, dual-task walking, and cognitive function were evaluated before and immediately after 20 min of tACS, tDCS, or sham (order randomized) stimulation. Dual-task costs to gait speed (primary outcome) and other measures were analyzed. Results: The dual-task cost to gait speed tended to be lower (i.e., better) after tACS (p = 0.067, Cohen’s d = 0.433∼small); tDCS significantly reduced this dual-task cost (p = 0.012, Cohen’s d = 0.618∼medium), and sham stimulation had no effect (p = 0.467). tACS significantly reduced the dual-task cost to step length (p = 0.037, Cohen’s d = 0.502∼medium); a trend was seen after tDCS (p = 0.069, Cohen’s d = 0.443∼small). No statistical differences were found for other measures of gait or cognitive function. Conclusions: The positive effects of tACS on dual-task gait speed and step length were roughly similar to those seen with tDCS. These results suggest that tACS affects the fronto-parietal network and, similar to tDCS, tACS may improve dual-tasking. Nonetheless, to achieve larger benefits and differentiate the effects of tACS and tDCS on brain function and dual-task walking in older adults, other stimulation montages and protocols should be tested

Protocol for the DeFOG trial: A randomized controlled trial on the effects of smartphone-based, on-demand cueing for freezing of gait in Parkinson's disease

BACKGROUND: Freezing of gait (FOG) is a highly incapacitating symptom that affects many people with Parkinson's disease (PD). Cueing triggered upon real-time FOG detection (on-demand cueing) shows promise for FOG treatment. Yet, the feasibility of implementation and efficacy in daily life is still unknown. Therefore, this study aims to investigate the effectiveness of DeFOG: a smartphone and sensor-based on-demand cueing solution for FOG. METHODS: Sixty-two PD patients with FOG will be recruited for this single-blind, multi-center, randomized controlled phase II trial. Patients will be randomized into either the intervention group or the active control group. For four weeks, both groups will receive feedback about their physical activity using the wearable DeFOG system in daily life. In addition, the intervention group will also receive on-demand auditory cueing and instructions. Before and after the intervention, home-based assessments will be performed to evaluate the primary outcome, i.e., “percentage time frozen” during a FOG-provoking protocol. Secondary outcomes include the training effects on physical activity monitored over 7 days and the user-friendliness of the technology. DISCUSSION: The DeFOG trial will investigate the effectiveness of personalized on-demand cueing in a controlled design, delivered for 4 weeks in the patient's home environment. We anticipate that DeFOG will reduce FOG to a greater degree than in the control group and we will explore the impact of the intervention on physical activity levels. We expect to gain in-depth insight into whether and how patients control FOG using cueing methods in their daily lives. TRIAL REGISTRATION: Clinicaltrials.gov NCT03978507

Survival curves illustrating the percent of subjects who did not fall as a function of time and executive function (EF).

<p>Differences were found in ‘time to first fall’ between the highest (indicated as 1^st on the graph) and lowest EF (indicated as 4^th on the graph) quartile (P = 0.017) and time to second fall (P = 0.023). Subjects with in lowest quartile were more likely to fall sooner (LEFT) and more likely to become multi-fallers sooner (RIGHT) than those in highest quartile. Note when performing similar analyses on those subjects who reported no falls in the year prior to the study, subjects with lowest quartile of EF were also more likely to fall during the follow-up period, similar to what is observed if the entire cohort is included in the analysis. EF quartile was defined based on the ranking of EF scores obtained at baseline using the computerized cognitive battery. By definition, subjects in the lowest quartile had the lowest (i.e., relatively worst) EF scores, whereas subjects in the highest quartile had the highest (i.e., best) scores.</p