Smartphone-based gait assessment for multiple sclerosis.

INTRODUCTION: Multiple Sclerosis causes gait alteration, even in the early stages of the disease. Traditional methods to quantify gait impairment, such as performance-based measures, lab-based motion analyses, and self-report, have limited ecological relevance. The Mon4t® app is a digital tool that uses sensors embedded in standard smartphones to measure various gait parameters. OBJECTIVES: To evaluate the use of Mon4t® technology in monitoring MS patients. METHODS: 100 MS patients and age-matched healthy controls were evaluated using both a human rater and the Mon4t Clinic™ app. Three motor tasks were performed: 3m Timed up and go test (TUG), 10m TUG, and tandem walk. The digital markers were used to compare MS vs. HC, MS with EDSS=0 vs. HC, and MS with EDSS=0 vs. MS with EDSS>0. Within the MS EDSS>0 group, correlations between digital gait markers and the EDSS score were calculated. RESULTS: Significant differences were found between MS patients and HC in multiple gait parameters. When comparing MS patients with minimal disability (EDSS=0) and HC: On the 3m TUG task, MS patients took longer to complete the task (mean difference 0.167seconds, p =0.034), took more steps (mean difference 1.32 steps, p =0.003), and had a weaker ML step-to-step correlation (mean difference 0.1, p = 0.001). The combination of features from the three motor tasks allowed distinguishing a nondisabled MS patient from a HC with high confidence (AUC of 85.65 on the ROC). When comparing MS patients with minimal disability (EDSS=0) to those with higher disability (EDSS>0): On the tandem walk task, patients with EDSS>0 took significantly longer to complete 10 steps than those with EDSS=0 (mean difference 4.63 seconds, p < 0.001), showed greater ML sway (mean difference 0.2, p < 0.001), and had larger angular velocity in the SI axis on average (mean difference 2.31 degrees/sec, p = 0.01). A classification model achieved 81.79 ROC AUC. In the subgroup of patients with EDSS>0, gait features significantly correlated with EDSS score in all three tasks. CONCLUSION: The findings demonstrate the potential of digital gait assessment to augment traditional disease monitoring and support clinical decision making. The Mon4t® app provides a convenient and ecologically relevant tool for monitoring MS patients and detecting early changes in gait impairment.

Circadian profile, daytime activity, and the Parkinson's phenotype: A motion sensor pilot study with neurobiological underpinnings.

Circadian rhythm impairment may play a role in Parkinson's disease (PD) pathophysiology. Recent literature associated circadian rhythm features to the risk of developing Parkinson and to its progression through stages. The association between the chronotype and the phenotype should be verified on a clinical and biological point of view. Herein we investigate the chronotype of a sample of 50 PD patients with the Morningness Eveningness Questionnaire and monitor their daily activity with a motion sensor embedded in a smartphone. Fibroblasts were collected from PD patients (n = 5) and from sex/age matched controls (n = 3) and tested for the circadian expression of clock genes (CLOCK, BMAL1, PER1, CRY1), and for cell morphology, proliferation, and death. Our results show an association between the chronotype and the PD phenotype. The most representative clinical chronotypes were "moderate morning" (56%), the "intermediate" (24%) and, in a minor part, the "definite morning" (16%). They differed for axial motor impairment, presence of motor fluctuations and quality of life (p < 0.05). Patients with visuospatial dysfunction and patients with a higher PIGD score had a blunted motor daily activity (p = 0.006 and p = 0.001, respectively), independently by the influence of age and other motor scores. Fibroblasts obtained by PD patients (n = 5) had an impaired BMAL1 cycle compared to controls (n = 3, p = 0.01). Moreover, a PD flat BMAL1 profile was associated with the lowest cell proliferation and the largest cell morphology. This study contributes to the growing literature on CR abnormalities in the pathophysiology of Parkinson's disease providing a link between the clinical and biological patient chronotype and the disease phenomenology.

Static Balance Digital Endpoints with Mon4t: Smartphone Sensors vs. Force Plate.

Static balance tests are conducted in various clinics for diagnosis and treatment adjustment. As a result of population aging, the accessibility of these tests should be increased, in the clinic, and for remote patient examination. A number of publications have already conducted static balance evaluations using the sensors embedded in a smartphone. This study focuses on the applicability of using smartphone-based balance assessment on a large scale while considering ease of use, safety, and reliability. The Mon4t® app was used to acquire the postural motion using different smartphone devices, different smartphone locations, and various standing postures. The signals derived from the app were compared to the center of pressure displacement derived from a force plate. The results showed moderate to high agreement between the two methods, particularly at the tandem stance (0.69 ≤ r ≤ 0.91). Preliminary data collection was conducted on three healthy participants, followed by 50 additional healthy volunteers, aged 65+. The results demonstrated that the Mon4t app can serve as an accessible and inexpensive static balance assessment tool, both in clinical settings and for remote patient monitoring, which is key for enabling telehealth.

Personalized Exercise Programs Based upon Remote Assessment of Motor Fitness: A Pilot Study among Healthy People Aged 65 Years and Older.

BACKGROUND: The World Health Organization has recently updated exercise guidelines for people aged >65 years, emphasizing the inclusion of multiple fitness components. However, without adequate recognition of individual differences, these guidelines may be applied using an approach that "one-size-fits-all." Within the shifting paradigm toward an increasingly personalized approach to medicine and health, it is apparent that fitness components display a significant age-related increase in variability. Therefore, it is both logical and necessary to perform an accurate individualized assessment of multiple fitness components prior to optimal prescription for a personalized exercise program. OBJECTIVE: The aim of the study was to test the feasibility and effectiveness of a novel tool able to remotely assess balance, flexibility, and strength using smartphone sensors (accelerometer/gyroscope), and subsequently deliver personalized exercise programs via the smartphone. METHODS: We enrolled 52 healthy volunteers (34 females) aged 65+ years, with normal cognition and low fall risk. Baseline data from remote smartphone fitness assessment were analyzed to generate 42 fitness digital markers (DMs), used to guide personalized exercise programs (×5/week for 6 weeks) delivered via smartphone. Programs included graded exercises for upper/lower body, flexibility, strength, and balance (dynamic, static, and vestibular). Participants were retested after 6 weeks. RESULTS: Average age was 74.7 ± 6.4 years; adherence was 3.6 ± 1.7 exercise sessions/week. Significant improvement for pre-/posttesting was observed for 10/12 DMs of strength/flexibility for upper/lower body (sit-to-stand repetitions/duration; arm-lift duration; torso rotation; and arm extension/flexion). Balance improved significantly for 6/10 measures of tandem stance, with consistent (nonsignificant) trends observed across 20 balance DMs of tandem walk and 1 leg stance. Balance tended to improve among the 37 participants exercising ≥3/week. DISCUSSION: These preliminary results provide a proof of concept, with high adherence and improved fitness confirming the benefits of remote fitness assessment for guiding home personalized exercise programs among healthy adults aged >65 years. Further examination of the application within a randomized control study is necessary, comparing the personalized exercise program to general guidelines among healthy older adults, as well as specific populations, such as those with frailty, deconditioning, cognitive, or functional impairment. The study tool offers the opportunity to collect big data, including additional variables, with subsequent utilization of artificial intelligence to optimize the personalized exercise program.

Personalized multicomponent exercise programs using smartphone technology among older people: protocol for a randomized controlled trial.

BACKGROUND: Optimal application of the recently updated World Health Organization (WHO) guidelines for exercise in advanced age necessitates an accurate adjustment for the age-related increasing variability in biological age and fitness levels, alongside detailed recommendations across a range of motor fitness components, including balance, strength, and flexibility. We previously developed and validated a novel tool, designed to both remotely assess these fitness components, and subsequently deliver a personalized exercise program via smartphone. We describe the design of a prospective randomized control trial, comparing the effectiveness of the remotely delivered personalized multicomponent exercise program to either WHO exercise guidelines or no intervention. METHODS: Participants (n = 300) are community dwelling, healthy, functionally independent, cognitively intact volunteers aged ≥65 at low risk for serious fall injuries, assigned using permuted block randomization (age/gender) to intervention, active-control, or control group. The intervention is an 8-week program including individually tailored exercises for upper/lower body, flexibility, strength, and balance (dynamic, static, vestibular); active-controls receive exercising counselling according to WHO guidelines; controls receive no guidance. Primary outcome is participant fitness level, operationalized as 42 digital markers generated from 10 motor fitness measures (balance, strength, flexibility); measured at baseline, mid-trial (4-weeks), trial-end (8-weeks), and follow-up (12-weeks). Target sample size is 300 participants to provide 99% power for moderate and high effect sizes (Cohen's f = 0.25, 0.40 respectively). DISCUSSION: The study will help understand the value of individualized motor fitness assessment used to generate personalized multicomponent exercise programs, delivered remotely among older adults. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT04181983.

Remote smartphone gait monitoring and fall prediction in Parkinson's disease during the COVID-19 lockdown.

BACKGROUND: Falls could be serious events in Parkinson's disease (PD). Patient remote monitoring strategies are on the raise and may be an additional aid in identifying patients who are at risk of falling. The aim of the study was to evaluate if balance and timed-up-and-go data obtained by a smartphone application during COVID-19 lockdown were able to predict falls in PD patients. METHODS: A cohort of PD patients were monitored for 4 weeks during the COVID-19 lockdown with an application measuring static balance and timed-up-and-go test. The main outcome was the occurrence of falls (UPDRS-II item 13) during the observation period. RESULTS: Thirty-three patients completed the study, and 4 (12%) reported falls in the observation period. The rate of falls was reduced with respect to patient previous falls history (24%). The stand-up time and the mediolateral sway, acquired through the application, differed between "fallers" and "non-fallers" and related to the occurrence of new falls (OR 1.7 and 1.6 respectively, p < 0.05), together with previous falling (OR 7.5, p < 0.01). In a multivariate model, the stand-up time and the history of falling independently related to the outcome (p < 0.01). CONCLUSIONS: Our study provides new data on falls in Parkinson's disease during the lockdown. The reduction of falling events and the relationship with the stand-up time might suggest that a different quality of falls occurs when patient is forced to stay home - hence, clinicians should point their attention also on monitoring patients' sit-to-stand body transition other than more acknowledged features based on step quality.

Parkinson's Disease Remote Patient Monitoring During the COVID-19 Lockdown.

Objective: To evaluate the feasibility of a smartphone remote patient monitoring approach in a real-life Parkinson's disease (PD) cohort during the Italian COVID-19 lockdown. Methods: Fifty-four non-demented PD patients who were supposed to attend the outpatient March clinic were recruited for a prospective study. All patients had a known UPDRS-III and a modified Hoehn and Yahr (H&Y) score and were provided with a smartphone application capable of providing indicators of gait, tapping, tremor, memory and executive functions. Different questionnaires exploring non-motor symptoms and quality of life were administered through phone-calls. Patients were asked to run the app at least twice per week (i.e., full compliance). Subjects were phone-checked weekly throughout a 3-week period for compliance and final satisfaction questionnaires. Results: Forty-five patients (83.3%) ran the app at least once; Twenty-nine (53.7%) subjects were half-compliant, while 16 (29.6%) were fully compliant. Adherence was hindered by technical issues or digital illiteracy (38.7%), demotivation (24%) and health-related issues (7.4%). Ten patients (18.5%) underwent PD therapy changes. The main factors related to lack of compliance included loss of interest, sadness, anxiety, the absence of a caregiver, the presence of falls and higher H&Y. Gait, tapping, tremor and cognitive application outcomes were correlated to disease duration, UPDRS-III and H&Y. Discussion: The majority of patients were compliant and satisfied by the provided monitoring program. Some of the application outcomes were statistically correlated to clinical parameters, but further validation is required. Our pilot study suggested that the available technologies could be readily implemented even with the current population's technical and intellectual resources.

Smartphone Based Timed Up and Go Test Can Identify Postural Instability in Parkinson's Disease.

BACKGROUND: There is a need for standardized and objective methods to measure postural instability (PI) and gait dysfunction in Parkinson's disease (PD) patients. Recent technological advances in wearable devices, including standard smartphones, may provide such measurements. OBJECTIVES: To test the feasibility of smartphones to detect PI during the Timed Up and Go (TUG) test. METHODS: Ambulatory PD patients, divided by item 30 (postural stability) of the motor Unified Parkinson's Disease Rating Scale (UPDRS) to those with a normal (score = 0, PD-NPT) and an abnormal (score ≥ 1, PD-APT) test and a group of healthy controls (HC) performed a 10-meter TUG while motion sensor data was recorded from a smartphone attached to their sternum using the EncephaLog application. RESULTS: In this observational study, 44 PD patients (21 PD-NPT and 23 PD-APT) and 22 HC similar in age and gender distribution were assessed. PD-APT differed significantly in all gait parameters when compared to PD-NPT and HC. Significant difference between PD-NPT and HC included only turning time (P < 0.006) and step-to-step correlation (P < 0.05). CONCLUSIONS: While high correlations were found between EncephaLog gait parameters and axial UPDRS items, the pull test was least correlated with EncephaLog measures. Motion sensor data from a smartphone can detect differences in gait and balance measures between PD with and without PI and HC.

Psychiatric Patients on Neuroleptics: Evaluation of Parkinsonism and Quantified Assessment of Gait.

OBJECTIVES: We aimed to characterize parkinsonian features and gait performance of psychiatric patients on neuroleptics (PPN) and to compare them to Parkinson's disease (PD) and healthy controls (HC). METHODS: Hospitalized PPN (n = 27) were recruited, examined, and rated for parkinsonian signs according to the motor part of the Movement Disorders Society Unified Parkinson's Disease Rating Scale and performed a 10-m "timed-up-and-go" (TUG) test with a smartphone-based motion capture system attached to their sternum. Gait parameters and mUPDRS scores were compared to those of consecutive age-matched PD patients (n = 18) and HC (n = 27). RESULTS: Psychiatric patients on neuroleptics exhibited parkinsonism (mUPDRS score range: 8-44) but less than that of PD patients (18.2 ± 9.2 vs 29.8 ± 10.3, P = 0.001). TUG times were slower for PPN and PD versus HC (total: 30.6 ± 7.6 seconds vs 30.0 ± 7.3 seconds vs 20.0 ± 3.2 seconds, straight walking: 10.6 ± 2.7 seconds vs 10.6 ± 2.4 seconds vs 6.8 ± 1.2 seconds) (P < 0.001), and cadence and step length were similar among PPN and PD and different from HC as well. Although their gait speed was slower than HC but similar to PD, PPN had lower mediolateral sway (4.3 ± 1.1 cm vs 6.7 ± 2.9 cm vs 6.9 ± 2.9 cm, respectively, P < 0.001) than both. CONCLUSIONS: Parkinsonism is very common in hospitalized PPN, but usually milder than that of PD. It seems that wearable sensor-based technology for assessing gait and balance may present a more sensitive and quantitative tool to detect clinical aspects of neuroleptic-induced parkinsonism than standard clinical ratings.

Pilot Study of the EncephaLog Smartphone Application for Gait Analysis.

Gait disorders and falls are common in elders and in many clinical conditions, yet they are typically infrequently and subjectively evaluated, limiting prevention and intervention. Completion-time of the Timed-Up-and-Go (TUG) test is a well-accepted clinical biomarker for rating mobility and prediction of falls risk. Using smartphones' integral accelerometers and gyroscopes, we already demonstrated that TUG completion-time can be accurately measured via a smartphone app. Here we present an extended app, EncephaLogTM, which provides gait analysis in much more detail, offering 9 additional gait biomarkers on top of the TUG completion-time. In this pilot, four healthy adults participated in a total of 32 TUG tests; simultaneously recorded by EncephaLog and motion sensor devices used in movement labs: motion capture cameras (MCC), pressure mat; and/or wearable sensors. Results show high agreement between EncephaLog biomarkers and those measured by the other devices. These preliminary results suggest that EncephaLog can provide an accurate, yet simpler, instrumented TUG (iTUG) platform than existing alternatives, offering a solution for clinics that cannot afford the cost or space required for a dedicated motion lab and for monitoring patients at their homes. Further research on a larger study population with pathologies is required to assess full validity.

Reduced synchronization persistence in neural networks derived from atm-deficient mice.

Many neurodegenerative diseases are characterized by malfunction of the DNA damage response. Therefore, it is important to understand the connection between system level neural network behavior and DNA. Neural networks drawn from genetically engineered animals, interfaced with micro-electrode arrays allowed us to unveil connections between networks' system level activity properties and such genome instability. We discovered that Atm protein deficiency, which in humans leads to progressive motor impairment, leads to a reduced synchronization persistence compared to wild type synchronization, after chemically imposed DNA damage. Not only do these results suggest a role for DNA stability in neural network activity, they also establish an experimental paradigm for empirically determining the role a gene plays on the behavior of a neural network.