The Digital Health Revolution and People with Disabilities: Perspective from the United States

Jones, Mike; DeRuyter, Frank; Morris, John

doi:10.3390/ijerph17020381

Cited by 38 publications

(30 citation statements)

References 23 publications

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“…51 Telephone and webbased consultations for physiotherapy or musculoskeletal based assessment/outpatient triage has been in use for several decades, 52 and the use of 'wearables'-personal activity devices to monitor and motivate activity-account for much contemporary digital discussion in therapeutic literature. [53][54][55] A vast array of digital technologies has been harnessed to support all aspects of MS care. 56 Examples include those which focus particularly on physical activity via web-based units of self-directed learning combined with exercise programmes prescribed by a physiotherapist and a texting or email service, 57 in-clinic prescribed exercise programmes combined with home-based automated prompt systems delivering reminders, 58 web-displayed physiotherapy-prescribed exercise routines alongside telephone calls or web-based asynchronous communication with physiotherapists.…”

Section: Discussionmentioning

confidence: 99%

Web-based physical activity intervention for people with progressive multiple sclerosis: application of consensus-based intervention development guidance

et al. 2021

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ObjectivesPeople with progressive multiple sclerosis (PwPMS) report that they recognise the benefits of activity on their physical and psychological health but need support to achieve their physical activity goals. We aimed to systematically develop a theoretically informed intervention that would enable PwPMS to more readily engage in regular physical activity.DesignWe used an intervention mapping approach to inform intervention development.SettingWe conducted semistructured interviews with PwPMS and their families/carers and physiotherapists recruited from secondary care settings.ParticipantsFourteen PwPMS with an Expanded Disability Status Scale score of between 6 and 8 and 7 of their families/carers and 13 physiotherapists and 1 physiotherapy technician participated.ResultsInterview data suggested that the development of supportive coaching relationships with physiotherapists could promote the ability of PwPMS to achieve a desirable and achievable physical activity plan. These interview data informed the prototype ‘Lifestyle Exercise and Activity Package for Multiple Sclerosis’ (LEAP-MS) consisting of a secure multiuser web-based platform (with an education and activity suite, interactive components enabling selection of exercises, goal setting and activity logging), up to six flexible face-to-face or web-based physiotherapy coaching sessions and remote support via an embedded web-based messaging function that all together draw on specific theory-based methods to achieve physical activity behaviour change, namely active learning, reinforcement, modelling, feedback, facilitation, goal setting and guided practice. Implementation is within a multiuser platform accessible to participants, trained physiotherapists and researchers.ConclusionsWe have followed an inclusive, systematic and transparent process to develop the LEAP-MS intervention that enables detailed description of components, context and guiding principles to inform ongoing evaluation. Importantly, PwPMS expressed the need for autonomy in developing physical activity plans. This has been achieved through the embedding of self-management principles in the design and delivery of the LEAP-MS intervention.

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Section: Discussionmentioning

confidence: 99%

Web-based physical activity intervention for people with progressive multiple sclerosis: application of consensus-based intervention development guidance

et al. 2021

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“…Scenarios, where FL would be most beneficial, are varied, and getting these added benefits from FL will require sustained research and development efforts before it can be properly streamlined [186], [187], [188]. The protocols and frameworks discussed attempt to alleviate at least some of FL's drawbacks, but systems heterogeneity is still a critical challenge for FL that still needs to be addressed.…”

Section: Discussionmentioning

confidence: 99%

Federated Learning: A Survey on Enabling Technologies, Protocols, and Applications

et al. 2020

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This paper provides a comprehensive study of Federated Learning (FL) with an emphasis on enabling software and hardware platforms, protocols, real-life applications and use-cases. FL can be applicable to multiple domains but applying it to different industries has its own set of obstacles. FL is known as collaborative learning, where algorithm(s) get trained across multiple devices or servers with decentralized data samples without having to exchange the actual data. This approach is radically different from other more established techniques such as getting the data samples uploaded to servers or having data in some form of distributed infrastructure. FL on the other hand generates more robust models without sharing data, leading to privacy-preserved solutions with higher security and access privileges to data. This paper starts by providing an overview of FL. Then, it gives an overview of technical details that pertain to FL enabling technologies, protocols, and applications. Compared to other survey papers in the field, our objective is to provide a more thorough summary of the most relevant protocols, platforms, and real-life use-cases of FL to enable data scientists to build better privacy-preserving solutions for industries in critical need of FL. We also provide an overview of key challenges presented in the recent literature and provide a summary of related research work. Moreover, we explore both the challenges and advantages of FL and present detailed service use-cases to illustrate how different architectures and protocols that use FL can fit together to deliver desired results.

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“…Societal trends are converging that will increase the demand for and expected cost of rehabilitation services. These demands are likely to accelerate the shift from inpatient to outpatient venues of care [33]. In addition to lower overall cost, outpatient care offers the potential advantage of tailoring therapies to the home and community setting where the patient resides.…”

Section: Discussionmentioning

confidence: 99%

Big Data Analytics and Sensor-Enhanced Activity Management to Improve Effectiveness and Efficiency of Outpatient Medical Rehabilitation

Jones

Collier

Reinkensmeyer

et al. 2020

IJERPH

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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.

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The Digital Health Revolution and People with Disabilities: Perspective from the United States

Cited by 38 publications

References 23 publications

Web-based physical activity intervention for people with progressive multiple sclerosis: application of consensus-based intervention development guidance

Web-based physical activity intervention for people with progressive multiple sclerosis: application of consensus-based intervention development guidance

Federated Learning: A Survey on Enabling Technologies, Protocols, and Applications

Big Data Analytics and Sensor-Enhanced Activity Management to Improve Effectiveness and Efficiency of Outpatient Medical Rehabilitation

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