Background: Falls are the leading cause of injury-related death in older adults. Due to various constraints, objective fall risk screening is seldom performed in clinical settings. Smartphones offer a high potential to provide fall risk screening for older adults in home settings. However, there is limited understanding of whether smartphone technology for falls screening is usable by older adults who present age-related changes in perceptual, cognitive, and motor capabilities. Methods: A fall risk app was developed that consists of a health history questionnaire and 5 progressively challenging mobility tasks to measure individual fall risk. An iterative design-evaluation process of semistructured interviews was performed to determine the usability of the app on a smartphone and tablet. Participants also completed a Systematic Usability Scale (SUS). In the first round of interviews, 6 older adults participated, and in the second round, 5 older adults participated. Interviews were videotaped and transcribed, and the data were coded to create themes. Average SUS scores were calculated for the smartphone and tablet. Results: There were 2 themes identified from the first round of interviews, related to perceived ease of use and perceived usefulness. While instructions for the balance tasks were difficult to understand, participants found it beneficial to learn about their risk for falls, found the app easy to follow, and reported confidence in using the app on their own. Modifications were made to the app, and following the second round of interviews, participants reported high ease of use and usefulness in learning about their risk of falling. Few differences were reported between using a smartphone or tablet. Average SUS scores ranged from 79 to 84. Conclusions: Our fall risk app was found to be highly usable by older adults as reported from interviews and high scores on the SUS. When designing a mHealth app for older adults, developers should include clear and simple instructions and preventative strategies to improve health. Furthermore, if the design accommodates for age-related sensory changes, smartphones can be as effective as tablets. A mobile app to assess fall risk has the potential to be used in home settings by older adults.

Background: Multiple 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. Objective: The 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. Methods: The 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. Results: A 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. Conclusions: Steady-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.

Background: Engaging in sufficient levels of physical activity, guarding against sustained sitting, and maintaining a healthy body weight represent important lifestyle strategies for managing older adults’ chronic pain. Our first Mobile Health Intervention to Reduce Pain and Improve Health (MORPH) randomized pilot study demonstrated that a partially remote group-mediated diet and daylong activity intervention (ie, a focus on moving often throughout the day) can lead to improved physical function, weight loss, less pain intensity, and fewer minutes of sedentary time. We also identified unique delivery challenges that limited the program’s scalability and potential efficacy. Objective: The purpose of the MORPH-II randomized pilot study is to refine the MORPH intervention package based on feedback from MORPH and evaluate the feasibility, acceptability, and preliminary efficacy of this revised package prior to conducting a larger clinical trial. Methods: The MORPH-II study is an iteration on MORPH designed to pilot a refined framework, enhance scalability through fully remote delivery, and increase uptake of the daylong movement protocol through revised education content and additional personalized remote coaching. Older, obese, and low-active adults with chronic multisite pain (n=30) will be randomly assigned to receive a 12-week remote group-mediated physical activity and dietary weight loss intervention followed by a 12-week maintenance period or a control condition. Those in the intervention condition will partake in weekly social cognitive theory–based group meetings via teleconference software plus one-on-one support calls on a tapered schedule. They will also engage with a tablet application paired with a wearable activity monitor and smart scale designed to provide ongoing social and behavioral support throughout the week. Those in the control group will receive only the self-monitoring tools. Results: Recruitment is ongoing as of January 2021. Conclusions: Findings from MORPH-II will help guide other researchers working to intervene on sedentary behavior through frequent movement in older adults with chronic pain. Trial Registration: ClinicalTrials.gov NCT04655001; https://clinicaltrials.gov/ct2/show/NCT04655001 International Registered Report Identifier (IRRID): PRR1-10.2196/29013

Older adults demonstrate gait impairments that increase their risk for falls. These age-related mobility impairments are in part due to declines in muscle mass and strength. High-intensity exercise can improve muscle strength and mobility but may not be tolerable for older adults due to musculoskeletal injury and pain. Blood flow restriction (BFR) with lower-intensity exercise offers a strategy that may be more tolerable for older adults, but whether BFR improves gait and mobility in older adults is unclear. The purpose of this systematic review and meta-analysis was to determine the effect of BFR on gait and mobility in healthy older adults. PubMed, Embase, Cochrane Library, and CINAHL were systematically searched for articles utilizing BFR in older adults. Articles were included if adults were over 60 years, did not have chronic health conditions, had undergone randomized controlled trials, and presented objectively measured gait outcomes. The search identified 1501 studies, of which 9 were included in the systematic review and 8 studies in the meta-analysis. Outcome measures included the Timed Up and Go (TUG), 6-Minute Walk Test (6MWT), 400 m walk test, Short Physical Performance Battery (SPPB), and 10 m walk test. Meta-analyses found improvements in the TUG (mean difference (MD) = −0.71; 95% CI = −1.05, −0.37; p < 0.001) and SPPB (MD = −0.94; 95% CI = −1.48, −0.39; p < 0.001) in BFR compared to no BFR. There were no differences in gait speed (MD = 0.59; 95% CI = −0.22, 1.41; p = 0.16). BFR may be effective for gait and mobility tasks over shorter distances. Clinicians may consider incorporating BFR to improve mobility and gait function in older adults.

Falls are a prevalent and serious health concern across clinical populations. A critical step in falls prevention is identifying modifiable risk factors, but due to time constraints and equipment costs, fall risk screening is rarely performed. Mobile technology offers an innovative approach to provide personalized fall risk screening for clinical populations. To inform future development, this manuscript discusses the development and testing of mobile health fall risk applications for three unique clinical populations [older adults, individuals with Multiple Sclerosis (MS), and wheeled-device users]. We focus on key lessons learned and future directions to improve the field of fall risk mHealth. During the development phase, we first identified fall risk factors specific to each population that are measurable with mobile technology. Second, we determined whether inertial measurement units within smartphones can measure postural control within the target population. Last, we developed the interface of each app with a user-centered design approach with usability testing through iterative semi-structured interviews. We then tested our apps in real-world settings. Our cumulative work demonstrates that mobile technology can be leveraged to provide personalized fall risk screening for different clinical populations. Fall risk apps should be designed and tailored for the targeted group to enhance usefulness and feasibility. In addition, fall risk factors measured with mobile technology should include those that are specific to the population, are measurable with mobile technology, and can accurately measure fall risk. Future work should improve fall risk algorithms and implement mobile technology into fall prevention programs.