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BackgroundMultiple sclerosis (MS) is a chronic, neurodegenerative disease that causes a range of motor, sensory, and cognitive symptoms. Due to these symptoms, people with MS are at a high risk for falls, fall-related injuries, and reductions in quality of life. There is no cure for MS, and managing symptoms and disease progression is important to maintain a high quality of life. Mobile health (mHealth) apps are commonly used by people with MS to help manage their health. However, there are limited health apps for people with MS designed to evaluate fall risk. A fall risk app can increase access to fall risk assessments and improve self-management. When designing mHealth apps, a user-centered approach is critical for improving use and adoption. ObjectiveThe purpose of this study is to undergo a user-centered approach to test and refine the usability of the app through an iterative design process. MethodsThe fall risk app Steady-MS is an extension of Steady, a fall risk app for older adults. Steady-MS consists of 2 components: a 25-item questionnaire about demographics and MS symptoms and 5 standing balance tasks. Data from the questionnaire and balance tasks were inputted into an algorithm to compute a fall risk score. Two iterations of semistructured interviews (n=5 participants per iteration) were performed to evaluate usability. People with MS used Steady-MS on a smartphone, thinking out loud. Interviews were recorded, transcribed, and developed into codes and themes. People with MS also completed the System Usability Scale. ResultsA total of 3 themes were identified: intuitive navigation, efficiency of use, and perceived value. Overall, the participants found Steady-MS efficient to use and useful to learn their fall risk score. There were challenges related to cognitive overload during the balance tasks. Modifications were made, and after the second iteration, people with MS reported that the app was intuitive and efficient. Average System Usability Scale scores were 95.5 in both iterations, representing excellent usability. ConclusionsSteady-MS is the first mHealth app for people with MS to assess their overall risk of falling and is usable by a subset of people with MS. People with MS found Steady-MS to be usable and useful for understanding their risk of falling. When developing future mHealth apps for people with MS, it is important to prevent cognitive overload through simple and clear instructions and present scores that are understood and interpreted correctly through visuals and text. These findings underscore the importance of user-centered design and provide a foundation for the future development of tools to assess and prevent scalable falls for people with MS. Future steps include understanding the validity of the fall risk algorithm and evaluating the clinical utility of the app.

Automated fall detection devices for individuals who use wheelchairs to minimize the consequences of falls are lacking. This study aimed to develop and train a fall detection algorithm to differentiate falls from wheelchair mobility activities using machine learning techniques. Thirty, healthy, ambulatory, young adults simulated falls from a wheelchair and performed other wheelchair-related mobility activities in a laboratory. Neural Network classifiers were used to train the algorithm developed based on data retrieved from accelerometers mounted at the participant's wrist, chest, and head. Results indicate excellent accuracy to differentiate between falls and wheelchair mobility activities. The sensors mounted at the wrist, chest, and head presented with an accuracy of 100%, 96.9%, and 94.8%, respectively, using data from 258 falls and 220 wheelchair mobility activities. This pilot study indicates that a fall detection algorithm developed in a laboratory setting based on fall accelerometer patterns can accurately differentiate wheelchair-related falls and wheelchair mobility activities. This algorithm should be integrated into a wrist-worn devices and tested among individuals who use a wheelchair in the community. [ABSTRACT FROM AUTHOR]

Background: Individuals who use wheelchairs and scooters rarely undergo fall risk screening. Mobile health technology is a possible avenue to provide fall risk assessment. The promise of this approach is dependent upon its usability. Objective: We aimed to determine the usability of a fall risk mobile health app and identify key technology development insights for aging adults who use wheeled devices. Methods: Two rounds (with 5 participants in each round) of usability testing utilizing an iterative design-evaluation process were performed. Participants completed use of the custom-designed fall risk app, Steady-Wheels. To quantify fall risk, the app led participants through 12 demographic questions and 3 progressively more challenging seated balance tasks. Once completed, participants shared insights on the app’s usability through semistructured interviews and completion of the Systematic Usability Scale. Testing sessions were recorded and transcribed. Codes were identified within the transcriptions to create themes. Average Systematic Usability Scale scores were calculated for each round. Results: The first round of testing yielded 2 main themes: ease of use and flexibility of design. Systematic Usability Scale scores ranged from 72.5 to 97.5 with a mean score of 84.5 (SD 11.4). After modifications were made, the second round of testing yielded 2 new themes: app layout and clarity of instruction. Systematic Usability Scale scores improved in the second iteration and ranged from 87.5 to 97.5 with a mean score of 91.9 (SD 4.3). Conclusions: The mobile health app, Steady-Wheels, has excellent usability and the potential to provide adult wheeled device users with an easy-to-use, remote fall risk assessment tool. Characteristics that promoted usability were guided navigation, large text and radio buttons, clear and brief instructions accompanied by representative illustrations, and simple error recovery. Intuitive fall risk reporting was achieved through the presentation of a single number located on a color-coordinated continuum that delineated low, medium, and high risk. [ABSTRACT FROM AUTHOR]

A reliable fall detection device is crucial to minimize long-term consequences of falls among wheelchair users. This study examines the sensitivity of Apple Watch to detect intentional falls from a wheelchair. Twenty-five able bodied (age: 21.9 ± 2.5 years) participated in a protocol in which they intentionally fell out of a wheelchair in a laboratory setting. Each participant wore an Apple Watch Series 5 and performed 3 falls in the forward, right and left sideways, and backward directions onto a crash pad totaling 12 falls each. The Apple Watch was manually checked after each fall to determine if the device registered a fall. From 300 fall trials captured, the Apple Watch detected 14 falls showing a sensitivity of 4.7%, a false negative rate of 95.3%. Logistic regression showed that participant's height, impact force, lower limb functioning, and fall direction are parameters that may influence the ability of the Apple Watch to detect falls from a wheelchair. The Apple Watch fall detection feature presented with a very poor sensitivity to detect intentional falls from a wheelchair among able bodied young adults. Due to the high incidence and consequences of falls, a reliable fall detection device specific for wheelchair users is warranted. [ABSTRACT FROM AUTHOR]

Falls are common, detrimental events among ambulatory individuals with spinal cord injury (SCI). Following SCI, changes to lower limb function are probable and likely to impact an individual's fall risk, yet no comprehensive review has been completed on the topic. This study systematically reviewed data on the relationship between lower limb function and fall prevalence in ambulatory individuals with SCI. A literature search was conducted in PubMed, Web of Science, Scopus, and CINAHL. Two independent reviewers screened abstracts/titles and then full articles. Study details, participants' characteristics, lower limb function assessed, and fall-related data were extracted from the studies. A qualitative analysis of the relationship between lower limb function and fall prevalence was performed. The risk of bias was evaluated using the Newcastle-Ottawa Quality Assessment Scale. The search yielded 1553 articles. Eight prospective, two retrospective, and three cross-sectional studies met the eligibility criteria. These studies ranged from low to high risk of bias. Overall, the qualitative analysis provided little evidence to support the relationship between lower limb function recorded by clinical measures and fall prevalence. This review highlights the inconsistent relationship between lower limb function and falls prevalence in ambulatory adults with SCI. Greater uniformity in methodology and consistent categorization of fallers and nonfallers among researchers is necessary to move the field forward. Investigating additional factors such as behavior traits, assistive device use, and environmental risk factors may be appropriate in understanding fall prevalence in this population. [ABSTRACT FROM AUTHOR]

Purpose of Review: Walking impairments are highly prevalent in persons with multiple sclerosis (PwMS) and are associated with reduced quality of life. Walking is traditionally quantified with various measures, including patient self-reports, clinical rating scales, performance measures, and advanced lab-based movement analysis techniques. Yet, the majority of these measures do not fully characterize walking (i.e., gait quality) nor adequately reflect walking in the real world (i.e., community ambulation) and have limited timescale (only measure walking at a single point in time). We discuss the potential of wearable sensors to provide sensitive, objective, and easy-to-use assessment of community ambulation in PwMS. Recent Findings: Wearable technology has the ability to measure all aspects of gait in PwMS yet is under-studied in comparison with other populations (e.g., older adults). Within the studies focusing on PwMS, half that measure pace collected free-living data, while only one study explored gait variability in free-living conditions. No studies explore gait asymmetry or complexity in free-living conditions. Summary: Wearable technology has the ability to provide objective, comprehensive, and sensitive measures of gait in PwMS. Future research should investigate this technology's ability to accurately assess free-living measures of gait quality, specifically gait asymmetry and complexity. [ABSTRACT FROM AUTHOR]

• Each neurodegenerative disease (NDD) group displayed unique compensatory neural correlate activity during motor learning. • People with NDD use neural compensation to maintain motor learning but are not always successful. • Neural compensation is linked to the progression of the NDD, associated lesions and the interaction with the learning type. Chronic progressive neurodegenerative diseases (NDD) cause mobility and cognitive impairments that disrupt quality of life. The learning of new motor skills, motor learning, is a critical component of rehabilitation efforts to counteract these chronic progressive impairments. In people with NDD, there are impairments in motor learning which appear to scale with the severity of impairment. Compensatory cortical activity plays a role in counteracting motor learning impairments in NDD. Yet, the functional and structural brain alterations associated with motor learning have not been synthesized in people with NDD. The purpose of this scoping review is to explore the neural alterations of motor learning in NDD. Thirty-five peer-reviewed original articles met the inclusion criteria. Participant demographics, motor learning results, and brain imaging results were extracted. Distinct motor learning associated compensatory processes were identified across NDD populations. Evidence from this review suggests the success of motor learning in NDD populations depends on the neural alterations and their interaction with motor learning networks, as well as the progression of disease. [ABSTRACT FROM AUTHOR]