Background: After allogeneic hematopoietic cell transplantation (alloHCT), patients often report functional impairments like reduced gait speed and muscle weakness. These impairments can increase the risk of adverse health events similar to elderly populations. However, they have not been quantified in patients after alloHCT (PATs). Methods: We compared fear of falling (Falls Efficacy Scale–International) and temporal gait parameters recorded on a 10-m walkway at preferred and maximum gait speed and under dual-task walking of 16 PATs (aged 31-73 years) with 15 age-matched control participants (CONs) and 17 seniors (SENs, aged >73 years). Results: Groups’ gait parameters especially differed during the maximum speed condition: PATs walked slower and required more steps/10 m than CONs. PATs exhibited greater stride, stance, and swing times than CONs. PATs’ swing time was even longer than SENs’. The PATs’ ability to accelerate their gait speed from preferred to fast was smaller compared with CONs’. PATs reported a greater fear of falling than CONs and SENs. Conclusion: Gait analysis of alloHCT patients has revealed impairments of functional performance. Patients presented a diminished ability to accelerate gait and extending steps possibly related to a notable strength deficit that impairs power-generation abilities from lower extremities. Furthermore, patients reported a greater fear of falling than control participants and even seniors. Slowing locomotion could be a risk-preventive safety strategy. Since functional disadvantages may put alloHCT patients at a higher risk of frailty, reinforcing appropriate physical exercises already during and after alloHCT could prevent adverse health events and reduce the risk of premature functional aging.

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BACKGROUND Balance rehabilitation programs constitute the most common treatment for balance disorders. Nonetheless, lack of resources and of highly expert physiotherapists barriers for patients to have individualized rehabilitation sessions and the required in person monitoring of the rehabilitation program. Therefore, balance rehabilitation programs are often transferred to the home environment, with a high risk of the patient mis-performing the exercises or failing to follow the programme at all. OBJECTIVE Holobalance is a persuasive coaching system with the capacity to offer full-scale rehabilitation services at home. Holobalance comprises of several modules, from rehabilitation program management to Augmented Reality (AR) coach presentation. One of the modules is the scoring module, which assesses the performance of the patient using wearable devices. METHODS The data-driven scoring module described in the present work is based on an extensive dataset (~1300 rehabilitation exercise sessions) collected during the Holobalance pilot study. Its scope is to be used as a training and testing dataset for training machine learning (ML) models, which can infer the scoring components of all physical rehabilitation exercises. RESULTS The results of the trained model improved the performance of the scoring module, in terms of more accurate assessment of a performed exercise, when compared to a rule-based scoring model deployed at an early phase of the system (k-statistic value of 15.9% for the sitting exercises, 20.8% for the standing exercises and 26.8% for the walking exercises). CONCLUSIONS Finally, the resulting performance of the model resembles the threshold of the interobserver variability, enabling the trustworthy usage of the scoring module to the closed-loop chain of the Holobalance coaching system.

Background Balance rehabilitation programs represent the most common treatments for balance disorders. Nonetheless, lack of resources and lack of highly expert physiotherapists are barriers for patients to undergo individualized rehabilitation sessions. Therefore, balance rehabilitation programs are often transferred to the home environment, with a considerable risk of the patient misperforming the exercises or failing to follow the program at all. Holobalance is a persuasive coaching system with the capacity to offer full-scale rehabilitation services at home. Holobalance involves several modules, from rehabilitation program management to augmented reality coach presentation. Objective The aim of this study was to design, implement, test, and evaluate a scoring model for the accurate assessment of balance rehabilitation exercises, based on data-driven techniques. Methods The data-driven scoring module is based on an extensive data set (approximately 1300 rehabilitation exercise sessions) collected during the Holobalance pilot study. It can be used as a training and testing data set for training machine learning (ML) models, which can infer the scoring components of all physical rehabilitation exercises. In that direction, for creating the data set, 2 independent experts monitored (in the clinic) 19 patients performing 1313 balance rehabilitation exercises and scored their performance based on a predefined scoring rubric. On the collected data, preprocessing, data cleansing, and normalization techniques were applied before deploying feature selection techniques. Finally, a wide set of ML algorithms, like random forests and neural networks, were used to identify the most suitable model for each scoring component. Results The results of the trained model improved the performance of the scoring module in terms of more accurate assessment of a performed exercise, when compared with a rule-based scoring model deployed at an early phase of the system (k-statistic value of 15.9% for sitting exercises, 20.8% for standing exercises, and 26.8% for walking exercises). Finally, the resulting performance of the model resembled the threshold of the interobserver variability, enabling trustworthy usage of the scoring module in the closed-loop chain of the Holobalance coaching system. Conclusions The proposed set of ML models can effectively score the balance rehabilitation exercises of the Holobalance system. The models had similar accuracy in terms of Cohen kappa analysis, with interobserver variability, enabling the scoring module to infer the score of an exercise based on the collected signals from sensing devices. More specifically, for sitting exercises, the scoring model had high classification accuracy, ranging from 0.86 to 0.90. Similarly, for standing exercises, the classification accuracy ranged from 0.85 to 0.92, while for walking exercises, it ranged from 0.81 to 0.90. Trial Registration ClinicalTrials.gov NCT04053829; https://clinicaltrials.gov/ct2/show/NCT04053829

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Background There is ample evidence that mobility abilities between healthy young and elderly people differ. However, we do not know whether these differences are based on different lower leg motor capacity or instead reveal a general motor condition that could be detected by monitoring upper-limb motor behavior. We therefore captured body movements during a standard mobility task, namely the Timed Up and Go test (TUG) with subjects following different instructions while performing a rapid, repetitive goal-directed arm-movement test (arm-movement test). We hypothesized that we would be able to predict gait-related parameters from arm motor behavior, even regardless of age. Methods Sixty healthy individuals were assigned to three groups (young: mean 26 ± 3 years, middle-aged 48 ± 9, old 68 ± 7). They performed the arm-movement and TUG test under three conditions: preferred (at preferred movement speed), dual-task (while counting backwards), and fast (at fast movement speed). We recorded the number of contacts within 20 s and the TUG duration. We also extracted TUG walking sequences to analyze spatiotemporal gait parameters and evaluated the correlation between arm-movement and TUG results. Results The TUG condition at preferred speed revealed differences in gait speed and step length only between young and old, while dual-task and fast execution increased performance differences significantly among all 3 groups. Our old group’s gait speed decreased the most doing the dual-task, while the young group’s gait speed increased the most during the fast condition. As in our TUG results, arm-movements were significant faster in young than in middle-aged and old. We observed significant correlations between arm movements and the fast TUG condition, and that the number of contacts closely predicts TUG timefast and gait speedfast. This prediction is more accurate when including age. Conclusion We found that the age-related decline in mobility performance that TUG reveals strongly depends on the test instruction: the dual-task and fast condition clearly strengthened group contrasts. Interestingly, a fast TUG performance was predictable by the performance in a fast repetitive goal-directed arm-movements test, even beyond the age effect. We assume that arm movements and the fast TUG condition reflect similarly reduced motor function. Trial registration German Clinical Trials Register (DRKS) number: DRKS00016999, prospectively registered on March, 26, 2019.

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Multiple studies for balance rehabilitation interventions have been accomplished aiming to demonstrate that sensory interventions and cognitive functionality are crucial for postural control and improvement of the quality of patient's daily life. However, none of the existing studies is filling the lack of expert physiotherapists availability. A pilot randomized study was conducted to assess the acceptability of the HOLOBalance telerehabilitation system. HOLOBalance is an interactive AR rehabilitation system which encompasses multisensory training program to enhance balance and cognitive coaching, for older adults at falls risk. In this work, we present a sentiment analysis of the patients participating in this study using the VADER methodology to evaluate and quantify their attitude towards the HOLOBalance system. Our results highlight the importance of findings positive polarity towards the AR interaction, which is based on the use of a holographic virtual physiotherapist. The compound score of 0.185 indicates the valuable positive feedback gained from the user experience.