CuPiD was feasible, well-accepted and seemed to be an effective approach to promote gait training, as participants improved equally to controls. This benefit may be ascribed to the real-time feedback, stimulating corrective actions and promoting self-efficacy to achieve optimal performance. Further optimization of the system and adequately-powered studies are warranted to corroborate these findings and determine cost-effectiveness.

Background The traditional evaluation of gait in the laboratory during structured testing has provided important insights, but is limited by its “snapshot” character and observation in an unnatural environment. Wearables enable monitoring of gait in real-world environments over a week. Initial findings show that in-lab and real-world measures differ. As a step towards better understanding these gaps, we directly compared in-lab usual-walking (UW) and dual-task walking (DTW) to daily-living measures of gait. Methods In-lab gait features (e.g., gait speed, step regularity, and stride regularity) derived from UW and DTW were compared to the same gait features during daily-living in 150 elderly fallers (age: 76.5 ± 6.3 years, 37.6% men). In both settings, features were extracted from a lower-back accelerometer. In the real-world setting, subjects were asked to wear the device for 1 week and pre-processing detected 30-s daily-living walking bouts. A histogram of all walking bouts was determined for each walking feature for each subject and then each subject’s typical (percentile 50, median), worst (percentile 10) and the best (percentile 90) values over the week were determined for each feature. Statistics of reliability were assessed using Intra-Class correlations and Bland-Altman plots. Results As expected, in-lab gait speed, step regularity, and stride regularity were worse during DTW, compared to UW. In-lab gait speed, step regularity, and stride regularity during UW were significantly higher (i.e., better) than the typical daily-living values ( p < 0.001) and different ( p < 0.001) from the worst and best values. DTW values tended to be similar to typical daily-living values ( p = 0.205, p = 0.053, p = 0.013 respectively). ICC assessment and Bland-Altman plots indicated that in-lab values do not reliably reflect the daily-walking values. Conclusions Gait values measured during relatively long (30-s) daily-living walking bouts are more similar to the corresponding values obtained in the lab during dual-task walking, as compared to usual walking. Still, gait performance during most daily-living walking bouts is worse than that measured during usual and dual-tasking in the lab. The values measured in the lab do not reliably reflect daily-living measures. That is, an older adult’s typical daily-living gait cannot be estimated by simply measuring walking in a structured, laboratory setting.

Recent studies have suggested that deficits in executive function contribute to freezing of gait (FOG), an episodic disturbance common among patients with Parkinson's disease (PD). To date, most findings provide only indirect evidence of this relationship. Here, we evaluated a more direct link between FOG and frontal lobe dysfunction. Functional, near infrared spectroscopy measured frontal activation, i.e., oxygenated hemoglobin (HbO2) levels in Brodmann area 10 before and during FOG. Eleven patients with PD and eleven healthy older adults were studied. Changes in frontal lobe activation before and during FOG that occurred during turns were determined. Altogether, 49 FOG episodes were observed-28 occurred during turns that were anticipated (i.e., the patient knew in advance that the turn was coming), 21 during unanticipated turns that were performed "abruptly", according to the examiner's request. During anticipated turns, HbO2 increased by 0.22 ± 0.08 µM (p = 0.004) before FOG and by an additional 0.19 ± 0.13 µM (p = 0.072) during FOG. In contrast, during unanticipated turns, HbO2 did not increase before or during FOG. HbO2 decreased by 0.32 ± 0.08 µM (p = 0.004) during turns without FOG; in healthy controls HbO2 did not change during turns. These findings support the existence of an association between FOG episodes and changes in frontal lobe HbO2. Increased activation in Brodmann area 10 before FOG, specifically during anticipated turns, highlights the connections between motor planning, information processing, and FOG. These results support the idea that alterations in executive control play a role in this debilitating motor disturbance.

IntroductionExisting 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 analysisThis 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 disseminationThe 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 numberISRCTN (12246987).

Freezing of gait (FOG) is a disabling symptom that is common among patients with advanced Parkinson’s disease (PD). External cues such as rhythmic auditory stimulation can help PD patients experiencing freezing to resume walking. Wearable systems for automatic freezing detection have been recently developed. However, these systems detect a FOG episode after it has happened. Instead, in this study, a new approach for the prediction of FOG (before it actually happens) is presented. Prediction of FOG might enable preventive cueing, reducing the likelihood that FOG will occur. Moreover, understanding the causes and circumstances of FOG is still an open research problem. Hence, a quantitative characterization of movement patterns just before FOG (the pre-FOG phase) is of great importance. In this study, wearable inertial sensors were used to identify and quantify the characteristics of gait during the pre-FOG phase and compare them with the characteristics of gait that do not precede FOG. The hypothesis of this study is based on the threshold-based model of FOG, which suggests that before FOG occurs, there is a degradation of the gait pattern. Eleven PD subjects were analyzed. Six features extracted from movement signals recorded by inertial sensors showed significant differences between gait and pre-FOG. A classification algorithm was developed in order to test if it is feasible to predict FOG (i.e., detect it before it happens). The aim of the classification procedure was to identify the pre-FOG phase. Results confirm that there is a degradation of gait occurring before freezing. Results also provide preliminary evidence on the feasibility of creating an automatic algorithm to predict FOG. Although some limitations are present, this study shows promising findings for characterizing and identifying pre-FOG patterns, another step toward a better understanding, prediction, and prevention of this disabling symptom.