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.

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.

<b><i>Background:</i></b> The World Health Organization has recently updated exercise guidelines for people aged &#x3e;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. <b><i>Objective:</i></b> 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. <b><i>Methods:</i></b> 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. <b><i>Results:</i></b> 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. <b><i>Discussion:</i></b> 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 &#x3e;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.

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.

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