Significant health disparities exist between the general population and people with disabilities, particularly with respect to chronic health conditions. Mobile healthcare—the delivery of healthcare via mobile communication devices—is witnessing tremendous growth and has been touted as an important new approach for management of chronic health conditions. At present, little is known about the current state of mobile healthcare for people with disabilities. Early evidence suggests they are not well represented in the growth of mobile healthcare, and particularly the proliferation of mobile health software applications (mHealth apps) for smartphones. Their omission in mHealth could lead to further health disparities. This article describes our research investigating the current state of mHealth apps targeting people with disabilities. Based on a multi-modal approach (literature review, Internet search, survey of disabled smartphone users), we confirm that people with disabilities are under-represented in the growth of mHealth. We identify several areas of future research and development needed to support the inclusion of people with disabilities in the mHealth revolution.

This article serves as the introduction to this special issue on Mobile Health and Mobile Rehabilitation for People with Disabilities. Social, technological and policy trends are reviewed. Needs, opportunities and challenges for the emerging fields of mobile health (mHealth, aka eHealth) and mobile rehabilitation (mRehab) are discussed. Healthcare in the United States (U.S.) is at a critical juncture characterized by: (1) a growing need for healthcare and rehabilitation services; (2) maturing technological capabilities to support more effective and efficient health services; (3) evolving public policies designed, by turns, to contain cost and support new models of care; and (4) a growing need to ensure acceptance and usability of new health technologies by people with disabilities and chronic conditions, clinicians and health delivery systems. Discussion of demographic and population health data, healthcare service delivery and a public policy primarily focuses on the U.S. However, trends identified (aging populations, growing prevalence of chronic conditions and disability, labor shortages in healthcare) apply to most countries with advanced economies and others. Furthermore, technologies that enable mRehab (wearable sensors, in-home environmental monitors, cloud computing, artificial intelligence) transcend national boundaries. Remote and mobile healthcare delivery is needed and inevitable. Proactive engagement is critical to ensure acceptance and effectiveness for all stakeholders.

Numerous societal trends are compelling a transition from inpatient to outpatient venues of care for medical rehabilitation. While there are advantages to outpatient rehabilitation (e.g., lower cost, more relevant to home and community function), there are also challenges including lack of information about how patient progress observed in the outpatient clinic translates into improved functional performance at home. At present, outpatient providers must rely on patient-reported information about functional progress (or lack thereof) at home and in the community. Information and communication technologies (ICT) offer another option—data collected about the patient’s adherence, performance and progress made on home exercises could be used to help guide course corrections between clinic visits, enhancing effectiveness and efficiency of outpatient care. In this article, we describe our efforts to explore use of sensor-enhanced home exercise and big data analytics in medical rehabilitation. The goal of this work is to demonstrate how sensor-enhanced exercise can improve rehabilitation outcomes for patients with significant neurological impairment (e.g., from stroke, traumatic brain injury, and spinal cord injury). We provide an overview of big data analysis and explain how it may be used to optimize outpatient rehabilitation, creating a more efficient model of care. We describe our planned development efforts to build advanced analytic tools to guide home-based rehabilitation and our proposed randomized trial to evaluate effectiveness and implementation of this approach.

Mobile health and mobile rehabilitation (mHealth and mRehab) services and technologies have attracted considerable interest from healthcare providers, technology vendors, rehabilitation engineers, investors and policy makers in recent years. Successful adoption and use of mHealth/mRehab requires clinician support and engagement, including the ability to identify appropriate use cases and possible barriers to use for themselves and their patients, and acquire adequate knowledge and confidence using mHealth/mRehab interventions. This article reports results from a survey of rehabilitation clinicians in the United States on their attitudes, experience, expectations and concerns regarding mHealth/mRehab interventions and technologies. Over 500 clinicians in physical, occupational, speech, recreation and psychological therapy professions, among others, participated in the survey. Respondents reported that an overwhelming majority of their patients need additional therapy after discharge from inpatient environments, and over half of outpatients need additional therapy between visits. A large majority reported prescribing specific exercises and interventions for patients to work on outside of the clinic. However, only 51% reported being comfortable integrating mRehab technology into their practice; and only 23% feel knowledgeable about rehabilitation technology currently available. Technologies to support mRehab are maturing rapidly. Clinicians recognize the need for mRehab, but their knowledge and confidence prescribing mRehab represents a significant barrier to adoption.

The principal aim of this study was to investigate whether a caregiver-inclusive assistive technology intervention improved older care recipients’ functional autonomy and decreased the perceived burden of their family caregivers compared to customary care. The study was a single-blind, mixed-methods, randomized controlled trial with baseline data collection and follow-ups at 6-, 22-, and 58-weeks after baseline evaluation, which was prospectively registered ( ClinicalTrials.gov Identifier: NCT01640470. Registered 11/21/2011). Dyads comprising a care recipient and family caregiver were randomly assigned to either a caregiver-inclusive experimental group (N = 44) or a customary-care comparison group (N = 46). Eligible care recipients were aged ≥55 years and had one or more limitations with mobility or daily activities, and family caregivers provided at least four hours per week of assistance. Outcome measures were administered to both groups at baseline and at the three follow-up time points. The data collectors were blinded regarding participants’ intervention group. The primary outcome measures were the Functional Autonomy Measurement System to assess care recipients’ functional performance, and the Caregiver Assistive Technology Outcome Measure to assess caregivers’ burden. Qualitative interviews examined participants’ perceptions of the caregiver-inclusive and customary care interventions. The experimental intervention addressed significantly more dyad-identified problematic activities, but caregiver involvement was evident in both groups and outcomes were not significantly different over time. In both groups, care recipients’ functional autonomy declined significantly (P < .01), and caregivers’ activity-specific and overall burden decreased significantly (P < .01). Given the unintended congruence between the caregiver-inclusive and customary care interventions, the overall findings lend support for the provision of assistive technology to reduce caregiver burden.

This paper presents data and analysis from survey research conducted by the Rehabilitation Engineering Research Center on Information and Communication Technology Access for Mobile Rehabilitation (mRehab RERC) on the use and unmet needs for mHealth mobile apps by people with disabilities in the United States. Quantitative and qualitative data are reported on user experiences with mHealth apps to map the behavior, interests and needs of people with specific types of disability (physical, cognitive, sensory, emotional/psychological, and speech). Summary results are presented for all respondents and each disability type. Slightly more than half of the participants in this sample (53.2%) reported using mHealth apps. Fitness and exercise apps were the mHealth apps most used by respondents with disabilities, followed by hospital/clinical portal apps. Symptom and disease management apps are the least commonly used, even though these would seem to be important for people with chronic conditions. Text-based responses regarding unmet needs for mHealth apps can be sorted into accessibility needs and functionality needs. In general, respondents with sensory limitations were more likely to identify accessibility needs. However, all disability groups identified both types of unmet needs. These results can help inform research and development efforts to provide mHealth apps that meet the needs of people with disabilities.