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.

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.

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.