Characteristic Changes of the Stance-Phase Plantar Pressure Curve When Walking Uphill and Downhill: Cross-Sectional Study.

BACKGROUND: Monitoring of gait patterns by insoles is popular to study behavior and activity in the daily life of people and throughout the rehabilitation process of patients. Live data analyses may improve personalized prevention and treatment regimens, as well as rehabilitation. The M-shaped plantar pressure curve during the stance phase is mainly defined by the loading and unloading slope, 2 maxima, 1 minimum, as well as the force during defined periods. When monitoring gait continuously, walking uphill or downhill could affect this curve in characteristic ways. OBJECTIVE: For walking on a slope, typical changes in the stance phase curve measured by insoles were hypothesized. METHODS: In total, 40 healthy participants of both sexes were fitted with individually calibrated insoles with 16 pressure sensors each and a recording frequency of 100 Hz. Participants walked on a treadmill at 4 km/h for 1 minute in each of the following slopes: -20%, -15%, -10%, -5%, 0%, 5%, 10%, 15%, and 20%. Raw data were exported for analyses. A custom-developed data platform was used for data processing and parameter calculation, including step detection, data transformation, and normalization for time by natural cubic spline interpolation and force (proportion of body weight). To identify the time-axis positions of the desired maxima and minimum among the available extremum candidates in each step, a Gaussian filter was applied (σ=3, kernel size 7). Inconclusive extremum candidates were further processed by screening for time plausibility, maximum or minimum pool filtering, and monotony. Several parameters that describe the curve trajectory were computed for each step. The normal distribution of data was tested by the Kolmogorov-Smirnov and Shapiro-Wilk tests. RESULTS: Data were normally distributed. An analysis of variance with the gait parameters as dependent and slope as independent variables revealed significant changes related to the slope for the following parameters of the stance phase curve: the mean force during loading and unloading, the 2 maxima and the minimum, as well as the loading and unloading slope (all P<.001). A simultaneous increase in the loading slope, the first maximum and the mean loading force combined with a decrease in the mean unloading force, the second maximum, and the unloading slope is characteristic for downhill walking. The opposite represents uphill walking. The minimum had its peak at horizontal walking and values dropped when walking uphill and downhill alike. It is therefore not a suitable parameter to distinguish between uphill and downhill walking. CONCLUSIONS: While patient-related factors, such as anthropometrics, injury, or disease shape the stance phase curve on a longer-term scale, walking on slopes leads to temporary and characteristic short-term changes in the curve trajectory.

Inter-joint coordination with and without dopaminergic medication in Parkinson's disease: a case-control study.

BACKGROUND: How the joints exactly move and interact and how this reflects PD-related gait abnormalities and the response to dopaminergic treatment is poorly understood. A detailed understanding of these kinematics can inform clinical management and treatment decisions. The aim of the study was to investigate the influence of different gait speeds and medication on/off conditions on inter-joint coordination, as well as kinematic differences throughout the whole gait cycle in well characterized pwPD. METHODS: 29 controls and 29 PD patients during medication on, 8 of them also during medication off walked a straight walking path in slow, preferred and fast walking speeds. Gait data was collected using optical motion capture system. Kinematics of the hip and knee and coordinated hip-knee kinematics were evaluated using Statistical Parametric Mapping (SPM) and cyclograms (angle-angle plots). Values derived from cyclograms were compared using repeated-measures ANOVA for within group, and ttest for between group comparisons. RESULTS: PD gait differed from controls mainly by lower knee range of motion (ROM). Adaptation to gait speed in PD was mainly achieved by increasing hip ROM. Regularity of gait was worse in PD but only during preferred speed. The ratios of different speed cyclograms were smaller in the PD groups. SPM analyses revealed that PD participants had smaller hip and knee angles during the swing phase, and PD participants reached peak hip flexion later than controls. Withdrawal of medication showed an exacerbation of only a few parameters. CONCLUSIONS: Our findings demonstrate the potential of granular kinematic analyses, including > 1 joint, for disease and treatment monitoring in PD. Our approach can be extended to further mobility-limiting conditions and other joint combinations. TRIAL REGISTRATION: The study is registered in the German Clinical Trials Register (DRKS00022998, registered on 04 Sep 2020).

Trend change analysis of postural balance in Parkinson's disease discriminates between medication state.

BACKGROUND: Maintaining static balance is relevant and common in everyday life and it depends on a correct intersegmental coordination. A change or reduction in postural capacity has been linked to increased risk of falls. People with Parkinson's disease (pwPD) experience motor symptoms affecting the maintenance of a stable posture. The aim of the study is to understand the intersegmental changes in postural sway and to apply a trend change analysis to uncover different movement strategies between pwPD and healthy adults. METHODS: In total, 61 healthy participants, 40 young (YO), 21 old participants (OP), and 29 pwPD (13 during medication off, PDoff; 23 during medication on, PDon) were included. Participants stood quietly for 10 s as part of the Short Physical Performance Battery. Inertial measurement units (IMU) at the head, sternum, and lumbar region were used to extract postural parameters and a trend change analysis (TCA) was performed to compare between groups. OBJECTIVE: This study aims to explore the potential application of TCA for the assessment of postural stability using IMUs, and secondly, to employ this analysis within the context of neurological diseases, specifically Parkinson's disease. RESULTS: Comparison of sensors locations revealed significant differences between head, sternum and pelvis for almost all parameters and cohorts. When comparing PDon and PDoff, the TCA revealed differences that were not seen by any other parameter. CONCLUSIONS: While all parameters could differentiate between sensor locations, no group differences could be uncovered except for the TCA that allowed to distinguish between the PD on/off. The potential of the TCA to assess disease progression, response to treatment or even the prodromal PD phase should be explored in future studies. TRIAL REGISTRATION: The research procedure was approved by the ethical committee of the Medical Faculty of Kiel University (D438/18). The study is registered in the German Clinical Trials Register (DRKS00022998).

Turning When Using Smartphone in Persons With and Those Without Neurologic Conditions: Observational Study.

BACKGROUND: Turning during walking is a relevant and common everyday movement and it depends on a correct top-down intersegmental coordination. This could be reduced in several conditions (en bloc turning), and an altered turning kinematics has been linked to increased risk of falls. Smartphone use has been associated with poorer balance and gait; however, its effect on turning-while-walking has not been investigated yet. This study explores turning intersegmental coordination during smartphone use in different age groups and neurologic conditions. OBJECTIVE: This study aims to evaluate the effect of smartphone use on turning behavior in healthy individuals of different ages and those with various neurological diseases. METHODS: Younger (aged 18-60 years) and older (aged >60 years) healthy individuals and those with Parkinson disease, multiple sclerosis, subacute stroke (<4 weeks), or lower-back pain performed turning-while-walking alone (single task [ST]) and while performing 2 different cognitive tasks of increasing complexity (dual task [DT]). The mobility task consisted of walking up and down a 5-m walkway at self-selected speed, thus including 180° turns. Cognitive tasks consisted of a simple reaction time test (simple DT [SDT]) and a numerical Stroop test (complex DT [CDT]). General (turn duration and the number of steps while turning), segmental (peak angular velocity), and intersegmental turning parameters (intersegmental turning onset latency and maximum intersegmental angle) were extracted for head, sternum, and pelvis using a motion capture system and a turning detection algorithm. RESULTS: In total, 121 participants were enrolled. All participants, irrespective of age and neurologic disease, showed a reduced intersegmental turning onset latency and a reduced maximum intersegmental angle of both pelvis and sternum relative to head, thus indicating an en bloc turning behavior when using a smartphone. With regard to change from the ST to turning when using a smartphone, participants with Parkinson disease reduced their peak angular velocity the most, which was significantly different from lower-back pain relative to the head (P<.01). Participants with stroke showed en bloc turning already without smartphone use. CONCLUSIONS: Smartphone use during turning-while-walking may lead to en bloc turning and thus increase fall risk across age and neurologic disease groups. This behavior is probably particularly dangerous for those groups with the most pronounced changes in turning parameters during smartphone use and the highest fall risk, such as individuals with Parkinson disease. Moreover, the experimental paradigm presented here might be useful in differentiating individuals with lower-back pain without and those with early or prodromal Parkinson disease. In individuals with subacute stroke, en bloc turning could represent a compensative strategy to overcome the newly occurring mobility deficit. Considering the ubiquitous smartphone use in daily life, this study should stimulate future studies in the area of fall risk and neurological and orthopedic diseases. TRIAL REGISTRATION: German Clinical Trials Register DRKS00022998; https://drks.de/search/en/trial/DRKS00022998.

A Deep Learning Approach for Gait Event Detection from a Single Shank-Worn IMU: Validation in Healthy and Neurological Cohorts.

Many algorithms use 3D accelerometer and/or gyroscope data from inertial measurement unit (IMU) sensors to detect gait events (i.e., initial and final foot contact). However, these algorithms often require knowledge about sensor orientation and use empirically derived thresholds. As alignment cannot always be controlled for in ambulatory assessments, methods are needed that require little knowledge on sensor location and orientation, e.g., a convolutional neural network-based deep learning model. Therefore, 157 participants from healthy and neurologically diseased cohorts walked 5 m distances at slow, preferred, and fast walking speed, while data were collected from IMUs on the left and right ankle and shank. Gait events were detected and stride parameters were extracted using a deep learning model and an optoelectronic motion capture (OMC) system for reference. The deep learning model consisted of convolutional layers using dilated convolutions, followed by two independent fully connected layers to predict whether a time step corresponded to the event of initial contact (IC) or final contact (FC), respectively. Results showed a high detection rate for both initial and final contacts across sensor locations (recall ≥92%, precision ≥97%). Time agreement was excellent as witnessed from the median time error (0.005 s) and corresponding inter-quartile range (0.020 s). The extracted stride-specific parameters were in good agreement with parameters derived from the OMC system (maximum mean difference 0.003 s and corresponding maximum limits of agreement (-0.049 s, 0.051 s) for a 95% confidence level). Thus, the deep learning approach was considered a valid approach for detecting gait events and extracting stride-specific parameters with little knowledge on exact IMU location and orientation in conditions with and without walking pathologies due to neurological diseases.

Scoring the Sit-to-Stand Performance of Parkinson's Patients with a Single Wearable Sensor.

Monitoring disease progression in Parkinson's disease is challenging. Postural transfers by sit-to-stand motions are adapted to trace the motor performance of subjects. Wearable sensors such as inertial measurement units allow for monitoring motion performance. We propose quantifying the sit-to-stand performance based on two scores compiling kinematics, dynamics, and energy-related variables. Three groups participated in this research: asymptomatic young participants (n = 33), senior asymptomatic participants (n = 17), and Parkinson's patients (n = 20). An unsupervised classification was performed of the two scores to differentiate the three populations. We found a sensitivity of 0.4 and a specificity of 0.96 to distinguish Parkinson's patients from asymptomatic subjects. In addition, seven Parkinson's patients performed the sit-to-stand task "ON" and "OFF" medication, and we noted the scores improved with the patients' medication states (MDS-UPDRS III scores). Our investigation revealed that Parkinson's patients demonstrate a wide spectrum of mobility variations, and while one inertial measurement unit can quantify the sit-to-stand performance, differentiating between PD patients and healthy adults and distinguishing between "ON" and "OFF" periods in PD patients is still challenging.

Reliability of IMU-Derived Temporal Gait Parameters in Neurological Diseases.

Evaluating gait is part of every neurological movement disorder assessment. Generally, the physician assesses the patient based on their experience, but nowadays inertial measurement units (IMUs) are also often integrated in the assessment. Instrumented gait analysis has a longstanding tradition and temporal parameters are used to compare patient groups or trace disease progression over time. However, the day-to-day variability needs to be considered especially in specific patient cohorts. The aim of the study was to examine day-to-day variability of temporal gait parameters of two experimental conditions in a cohort of neurogeriatric patients using data extracted from a lower back-worn IMU. We recruited 49 participants (24 women (age: 78 years ± 6 years, BMI = 25.1 kg/m2 and 25 men (age: 77 years ± 6 years, BMI = 26.5 kg/m2)) from the neurogeriatric ward. Two gait distances (4 m and 20 m) were performed during the first session and repeated the following day. To evaluate reliability, the Intraclass Correlation Coefficient (ICC2,k) and minimal detectable change (MDC) were calculated for the number of steps, step time, stride time, stance time, swing time, double limb support time, double limb support time variability, stride time variability and stride time asymmetry. The temporal gait parameters showed poor to moderate reliability with mean ICC and mean MDC95% values of 0.57 ± 0.18 and 52% ± 53%, respectively. Overall, only four out of the nine computed temporal gait parameters showed high relative reliability and good absolute reliability values. The reliability increased with walking distance. When only investigating steady-state walking during the 20 m walking condition, the relative and absolute reliability improved again. The most reliable parameters were swing time, stride time, step time and stance time. Study results demonstrate that reliability is an important factor to consider when working with IMU derived gait parameters in specific patient cohorts. This advocates for a careful parameter selection as not all parameters seem to be suitable when assessing gait in neurogeriatric patients.

Validation of IMU-based gait event detection during curved walking and turning in older adults and Parkinson's Disease patients.

BACKGROUND: Identification of individual gait events is essential for clinical gait analysis, because it can be used for diagnostic purposes or tracking disease progression in neurological diseases such as Parkinson's disease. Previous research has shown that gait events can be detected from a shank-mounted inertial measurement unit (IMU), however detection performance was often evaluated only from straight-line walking. For use in daily life, the detection performance needs to be evaluated in curved walking and turning as well as in single-task and dual-task conditions. METHODS: Participants (older adults, people with Parkinson's disease, or people who had suffered from a stroke) performed three different walking trials: (1) straight-line walking, (2) slalom walking, (3) Stroop-and-walk trial. An optical motion capture system was used a reference system. Markers were attached to the heel and toe regions of the shoe, and participants wore IMUs on the lateral sides of both shanks. The angular velocity of the shank IMUs was used to detect instances of initial foot contact (IC) and final foot contact (FC), which were compared to reference values obtained from the marker trajectories. RESULTS: The detection method showed high recall, precision and F1 scores in different populations for both initial contacts and final contacts during straight-line walking (IC: recall [Formula: see text] 100%, precision [Formula: see text] 100%, F1 score [Formula: see text] 100%; FC: recall [Formula: see text] 100%, precision [Formula: see text] 100%, F1 score [Formula: see text] 100%), slalom walking (IC: recall [Formula: see text] 100%, precision [Formula: see text] 99%, F1 score [Formula: see text]100%; FC: recall [Formula: see text] 100%, precision [Formula: see text] 99%, F1 score [Formula: see text]100%), and turning (IC: recall [Formula: see text] 85%, precision [Formula: see text] 95%, F1 score [Formula: see text]91%; FC: recall [Formula: see text] 84%, precision [Formula: see text] 95%, F1 score [Formula: see text]89%). CONCLUSIONS: Shank-mounted IMUs can be used to detect gait events during straight-line walking, slalom walking and turning. However, more false events were observed during turning and more events were missed during turning. For use in daily life we recommend identifying turning before extracting temporal gait parameters from identified gait events.

Step Length Is a Promising Progression Marker in Parkinson's Disease.

Current research on Parkinson's disease (PD) is increasingly concerned with the identification of objective and specific markers to make reliable statements about the effect of therapy and disease progression. Parameters from inertial measurement units (IMUs) are objective and accurate, and thus an interesting option to be included in the regular assessment of these patients. In this study, 68 patients with PD (PwP) in Hoehn and Yahr (H&Y) stages 1-4 were assessed with two gait tasks-20 m straight walk and circular walk-using IMUs. In an ANCOVA model, we found a significant and large effect of the H&Y scores on step length in both tasks, and only a minor effect on step time. This study provides evidence that from the two potentially most important gait parameters currently accessible with wearable technology under supervised assessment strategies, step length changes substantially over the course of PD, while step time shows surprisingly little change in the progression of PD. These results show the importance of carefully evaluating quantitative gait parameters to make assumptions about disease progression, and the potential of the granular evaluation of symptoms such as gait deficits when monitoring chronic progressive diseases such as PD.

Proposed Mobility Assessments with Simultaneous Full-Body Inertial Measurement Units and Optical Motion Capture in Healthy Adults and Neurological Patients for Future Validation Studies: Study Protocol.

Healthy adults and neurological patients show unique mobility patterns over the course of their lifespan and disease. Quantifying these mobility patterns could support diagnosing, tracking disease progression and measuring response to treatment. This quantification can be done with wearable technology, such as inertial measurement units (IMUs). Before IMUs can be used to quantify mobility, algorithms need to be developed and validated with age and disease-specific datasets. This study proposes a protocol for a dataset that can be used to develop and validate IMU-based mobility algorithms for healthy adults (18-60 years), healthy older adults (>60 years), and patients with Parkinson's disease, multiple sclerosis, a symptomatic stroke and chronic low back pain. All participants will be measured simultaneously with IMUs and a 3D optical motion capture system while performing standardized mobility tasks and non-standardized activities of daily living. Specific clinical scales and questionnaires will be collected. This study aims at building the largest dataset for the development and validation of IMU-based mobility algorithms for healthy adults and neurological patients. It is anticipated to provide this dataset for further research use and collaboration, with the ultimate goal to bring IMU-based mobility algorithms as quickly as possible into clinical trials and clinical routine.

Gait analysis with wearables predicts conversion to parkinson disease.

OBJECTIVE: Quantification of gait with wearable technology is promising; recent cross-sectional studies showed that gait characteristics are potential prodromal markers for Parkinson disease (PD). The aim of this longitudinal prospective observational study was to establish gait impairments and trajectories in the prodromal phase of PD, identifying which gait characteristics are potentially early diagnostic markers of PD. METHODS: The 696 healthy controls (mean age = 63 ± 7 years) recruited in the Tubingen Evaluation of Risk Factors for Early Detection of Neurodegeneration study were included. Assessments were performed longitudinally 4 times at 2-year intervals, and people who converted to PD were identified. Participants were asked to walk at different speeds under single and dual tasking, with a wearable device placed on the lower back; 14 validated clinically relevant gait characteristics were quantified. Cox regression was used to examine whether gait at first visit could predict time to PD conversion after controlling for age and sex. Random effects linear mixed models (RELMs) were used to establish longitudinal trajectories of gait and model the latency between impaired gait and PD diagnosis. RESULTS: Sixteen participants were diagnosed with PD on average 4.5 years after first visit (converters; PDC). Higher step time variability and asymmetry of all gait characteristics were associated with a shorter time to PD diagnosis. RELMs indicated that gait (lower pace) deviates from that of non-PDC approximately 4 years prior to diagnosis. INTERPRETATION: Together with other prodromal markers, quantitative gait characteristics can play an important role in identifying prodromal PD and progression within this phase. ANN NEUROL 2019;86:357-367.

Postural transitions detection and characterization in healthy and patient populations using a single waist sensor.

BACKGROUND: Sit-to-stand and stand-to-sit transitions are frequent daily functional tasks indicative of muscle power and balance performance. Monitoring these postural transitions with inertial sensors provides an objective tool to assess mobility in both the laboratory and home environment. While the measurement depends on the sensor location, the clinical and everyday use requires high compliance and subject adherence. The objective of this study was to propose a sit-to-stand and stand-to-sit transition detection algorithm that works independently of the sensor location. METHODS: For a location-independent algorithm, the vertical acceleration of the lower back in the global frame was used to detect the postural transitions in daily activities. The detection performance of the algorithm was validated against video observations. To investigate the effect of the location on the kinematic parameters, these parameters were extracted during a five-time sit-to-stand test and were compared for different locations of the sensor on the trunk and lower back. RESULTS: The proposed detection method demonstrates high accuracy in different populations with a mean positive predictive value (and mean sensitivity) of 98% (95%) for healthy individuals and 89% (89%) for participants with diseases. CONCLUSIONS: The sensor location around the waist did not affect the performance of the algorithm in detecting the sit-to-stand and stand-to-sit transitions. However, regarding the accuracy of the kinematic parameters, the sensors located on the sternum and L5 vertebrae demonstrated the highest reliability.

Wearable Health Technology to Quantify the Functional Impact of Peripheral Neuropathy on Mobility in Parkinson's Disease: A Systematic Review.

The occurrence of peripheral neuropathy (PNP) is often observed in Parkinson's disease (PD) patients with a prevalence up to 55%, leading to more prominent functional deficits. Motor assessment with mobile health technologies allows high sensitivity and accuracy and is widely adopted in PD, but scarcely used for PNP assessments. This review provides a comprehensive overview of the methodologies and the most relevant features to investigate PNP and PD motor deficits with wearables. Because of the lack of studies investigating motor impairments in this specific subset of PNP-PD patients, Pubmed, Scopus, and Web of Science electronic databases were used to summarize the state of the art on PNP motor assessment with wearable technology and compare it with the existing evidence on PD. A total of 24 papers on PNP and 13 on PD were selected for data extraction: The main characteristics were described, highlighting major findings, clinical applications, and the most relevant features. The information from both groups (PNP and PD) was merged for defining future directions for the assessment of PNP-PD patients with wearable technology. We established suggestions on the assessment protocol aiming at accurate patient monitoring, targeting personalized treatments and strategies to prevent falls and to investigate PD and PNP motor characteristics.

Quantification of Arm Swing during Walking in Healthy Adults and Parkinson's Disease Patients: Wearable Sensor-Based Algorithm Development and Validation.

Neurological pathologies can alter the swinging movement of the arms during walking. The quantification of arm swings has therefore a high clinical relevance. This study developed and validated a wearable sensor-based arm swing algorithm for healthy adults and patients with Parkinson's disease (PwP). Arm swings of 15 healthy adults and 13 PwP were evaluated (i) with wearable sensors on each wrist while walking on a treadmill, and (ii) with reflective markers for optical motion capture fixed on top of the respective sensor for validation purposes. The gyroscope data from the wearable sensors were used to calculate several arm swing parameters, including amplitude and peak angular velocity. Arm swing amplitude and peak angular velocity were extracted with systematic errors ranging from 0.1 to 0.5° and from -0.3 to 0.3°/s, respectively. These extracted parameters were significantly different between healthy adults and PwP as expected based on the literature. An accurate algorithm was developed that can be used in both clinical and daily-living situations. This algorithm provides the basis for the use of wearable sensor-extracted arm swing parameters in healthy adults and patients with movement disorders such as Parkinson's disease.

Gait asymmetries after fibular free flap harvest: A cross-sectional observational study.

BACKGROUND: The ability to walk safely after head and neck reconstruction with fibular free flaps in tumor surgery is a high priority for patients. In addition, surgeons and patients require objective knowledge of the functional donor-site morbidity. However, the effects of fibular free flap surgery on gait asymmetries have only been studied for step length and stance duration. This study analyses whether patients who have undergone fibular free flap reconstruction have enduring gait asymmetries compared to age-matched controls. METHODS: Patients who underwent head and neck reconstruction with fibular free flaps between 2019 and 2023 were recruited, as well as age-matched controls. Participants walked on an instrumented treadmill at 3 km/h. The primary outcome measures were 22 gait asymmetry metrics. Secondary outcome measures were the associations of gait asymmetry with the length of the harvested fibula, and with the time after surgery. FINDINGS: Nine out of 13 recruited patients completed the full assessment without holding on to the handrail on the treadmill. In addition, nine age-matched controls were enrolled. Twenty out of the 22 gait asymmetry parameters of patients were similar to healthy controls, while push-off peak force (p = 0.008) and medial impulse differed (p = 0.003). Gait asymmetry did not correlate with the length of the fibula harvested. Seven gait asymmetry parameters had a strong correlation with the time after surgery. INTERPRETATION: On the long-term, fibular free flap reconstruction has only a limited effect on the asymmetry of force-related and temporal gait parameters while walking on a treadmill.

Long-term continuous instrumented insole-based gait analyses in daily life have advantages over longitudinal gait analyses in the lab to monitor healing of tibial fractures.

Introduction: Monitoring changes in gait during rehabilitation allows early detection of complications. Laboratory-based gait analyses proved valuable for longitudinal monitoring of lower leg fracture healing. However, continuous gait data recorded in the daily life may be superior due to a higher temporal resolution and differences in behavior. In this study, ground reaction force-based gait data of instrumented insoles from longitudinal intermittent laboratory assessments were compared to monitoring in daily life. Methods: Straight walking data of patients were collected during clinical visits and in between those visits the instrumented insoles recorded all stepping activities of the patients during daily life. Results: Out of 16 patients, due to technical and compliance issues, only six delivered sufficient datasets of about 12 weeks. Stance duration was longer (p = 0.004) and gait was more asymmetric during daily life (asymmetry of maximal force p < 0.001, loading slope p = 0.001, unloading slope p < 0.001, stance duration p < 0.001). Discussion: The differences between the laboratory assessments and the daily-life monitoring could be caused by a different and more diverse behavior during daily life. The daily life gait parameters significantly improved over time with union. One of the patients developed an infected non-union and showed worsening of force-related gait parameters, which was earlier detectable in the continuous daily life gait data compared to the lab data. Therefore, continuous gait monitoring in the daily life has potential to detect healing problems early on. Continuous monitoring with instrumented insoles has advantages once technical and compliance problems are solved.

Objectively assessed sleep quality parameters in Multiple Sclerosis at home: Association to disease, disease severity and physical activity.

BACKGROUND: Multiple Sclerosis (MS) is a chronic inflammatory autoimmune, neurodegenerative disease that affects regular mobility and leads predominantly to physical disability. Poor sleep quality, commonly reported in MS patients, impacts their physical activity (PA). Accelerometers monitor 24-h activity patterns, offering insights into disease progression in daily life. OBJECTIVE: To test if the sleep quality variables of MS patients, as assessed with wrist-worn accelerometers, differ from those of controls and are associated with PA and disease severity variables. METHODS: Seven-day raw accelerometer data collected from 40 MS patients and 24 controls was processed using an open-source GGIR package, from which variables of sleep quality (sleep efficiency, wake after sleep onset (WASO), sleep regularity index (SRI), intradaily variability (IV)) and PA (of different intensities: inactivity, light (LPA), moderate (MPA), vigorous (VPA)) were analyzed. The variables were compared between the two study groups and in MS patients, correlation tested associations among the variables of sleep quality, PA, and disease severity (assessed with the Expanded Disability Status Scale, EDSS). RESULTS: Sleep efficiency was the only variable that differed significantly between MS patients and controls (lower in MS, p = 0.01). Both SRI (positively) and IV (negatively) correlated with the time spent in LPA and MPA. WASO correlated negatively with inactivity. CONCLUSION: This is one of the few studies with a wrist-worn accelerometer that shows a difference in sleep efficiency between MS patients and controls and, in MS, an association of sleep quality variables with PA variables.

Gait Analysis to Monitor Fracture Healing of the Lower Leg.

Fracture healing is typically monitored by infrequent radiographs. Radiographs come at the cost of radiation exposure and reflect fracture healing with a time lag due to delayed fracture mineralization following increases in stiffness. Since union problems frequently occur after fractures, better and timelier methods to monitor the healing process are required. In this review, we provide an overview of the changes in gait parameters following lower leg fractures to investigate whether gait analysis can be used to monitor fracture healing. Studies assessing gait after lower leg fractures that were treated either surgically or conservatively were included. Spatiotemporal gait parameters, kinematics, kinetics, and pedography showed improvements in the gait pattern throughout the healing process of lower leg fractures. Especially gait speed and asymmetry measures have a high potential to monitor fracture healing. Pedographic measurements showed differences in gait between patients with and without union. No literature was available for other gait measures, but it is expected that further parameters reflect progress in bone healing. In conclusion, gait analysis seems to be a valuable tool for monitoring the healing process and predicting the occurrence of non-union of lower leg fractures.

Reduced Range of Gait Speed: A Parkinson's Disease-Specific Symptom?

Reduced range of gait speed (RGS) may lead to decreased environmental adaptability in persons with Parkinson's disease (PwPD). Therefore, lab-measured gait speed, step time, and step length during slow, preferred, and fast walking were assessed in 24 PwPD, 19 stroke patients, and 19 older adults and compared with 31 young adults. Only PwPD, but not the other groups, showed significantly reduced RGS compared to young adults, driven by step time in the low and step length in the high gait speed range. These results suggest that reduced RGS may occur as a PD-specific symptom, and different gait components seem to contribute.

Effects of age, body height, body weight, body mass index and handgrip strength on the trajectory of the plantar pressure stance-phase curve of the gait cycle.

The analysis of gait patterns and plantar pressure distributions via insoles is increasingly used to monitor patients and treatment progress, such as recovery after surgeries. Despite the popularity of pedography, also known as baropodography, characteristic effects of anthropometric and other individual parameters on the trajectory of the stance phase curve of the gait cycle have not been previously reported. We hypothesized characteristic changes of age, body height, body weight, body mass index and handgrip strength on the plantar pressure curve trajectory during gait in healthy participants. Thirty-seven healthy women and men with an average age of 43.65 ± 17.59 years were fitted with Moticon OpenGO insoles equipped with 16 pressure sensors each. Data were recorded at a frequency of 100 Hz during walking at 4 km/h on a level treadmill for 1 minute. Data were processed via a custom-made step detection algorithm. The loading and unloading slopes as well as force extrema-based parameters were computed and characteristic correlations with the targeted parameters were identified via multiple linear regression analysis. Age showed a negative correlation with the mean loading slope. Body height correlated with Fmeanload and the loading slope. Body weight and the body mass index correlated with all analyzed parameters, except the loading slope. In addition, handgrip strength correlated with changes in the second half of the stance phase and did not affect the first half, which is likely due to stronger kick-off. However, only up to 46% of the variability can be explained by age, body weight, height, body mass index and hand grip strength. Thus, further factors must affect the trajectory of the gait cycle curve that were not considered in the present analysis. In conclusion, all analyzed measures affect the trajectory of the stance phase curve. When analyzing insole data, it might be useful to correct for the factors that were identified by using the regression coefficients presented in this paper.