Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: Remote measurement technology (RMT) offers the opportunity to provide insight and support for self-management of health in real-time. These methods could augment healthcare by providing personalised, precise and pre-emptive interventions. However, for successful implementation users need to be engaged in its use. Objective: This paper provides an overview of the key barriers and facilitators to engagement and use. It is timely as mobile technology such as wearable devices and smartphone applications are becoming increasingly available. Methods: A systematic review using PRISMA guidelines involving original studies dating back to the last systematic review published in 2014. They were included if they met the following PICOS entry criteria. Population: people using RMT approaches to aid management of health; Intervention: RMT system; comparison group: no comparison group specified; outcomes: qualitative and or quantitative evaluation of the barriers and facilitators to engagement with this system; study design: randomized controlled trials (RCT), feasibility studies, and observational studies. Five databases were searched: Ovid MEDLINE, IEEE Xplore, EMBASE, Web of Science and the Cochrane Library, from January 2014 to May 2017). Articles were screened by two researchers, independently. Study characteristics were extracted and a content analysis conducted to define emerging themes to synthesise findings. Formal quality assessments were performed to address risk of bias. Results: Thirty-three publications met inclusion criteria, employing either quantitative, qualitative or mixed-methods designs. Studies were conducted in 10 countries, included men and women, ranging from 8-95 years and included both active and passive remote monitoring systems for a diverse range of physical and mental health conditions. However, they were relatively short and had small sample sizes and reporting of usage statistics was inconsistent. Acceptability of RMT according to the average percentage of time used (64-86.5%) and dropout rates (0-44%) were variable. The barriers and facilitators from the content analysis related to: health status, perceived utility and value, motivation, convenience and accessibility, and usability. These led to the development of a model of barriers and facilitators of engagement with RMT. Conclusions: The hypothesised model of processes that could facilitate RMT engagement may drive the development of more person-focussed RMT, especially if it involves co-design with the target audience. However, it needs further testing through carefully designed experimental studies.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: Mobile technology has the potential to provide more accurate, impactful data on the symptoms of depression, which could improve health management or even assist in early detection of relapse. However, for this potential to be achieved, it is essential that patients engage with the technology. While many barriers and facilitators are common across therapeutic areas and technology types, many may be culture and health context-specific. Objective: This study was designed to understand potential barriers and facilitators to engagement with mobile health (mHealth) technology for remote measurement and management of depression across three Western European countries. Methods: Participants with experience of depression took part in five focus groups held in three countries (2 in the UK, 2 in Spain, and 1 in Italy) investigating potential barriers and facilitators to the use of mHealth technology. A systematic thematic analysis was used to extract themes and subthemes. Results: Facilitators and barriers were organised as: (1) health-related factors; (2) user-related factors; and (3) technology-related factors. Fifty-eight subthemes of specific barriers and facilitators or moderators emerged. A core group of themes including motivation, potential impact on mood and anxiety, aspects of inconvenience and ease of use were present across all countries. Conclusions: Similarities in the barriers and facilitators to use of mHealth technology have been found across Spain, Italy, and the UK. These themes provide guidance on ways to promote the design of feasible and acceptable cross-cultural mHealth tools.

Physical mobility is essential to health, and patients often rate it as a high-priority clinical outcome. Digital mobility outcomes (DMOs), such as real-world gait speed or step count, show promise as clinical measures in many medical conditions. However, current research is nascent and fragmented by discipline. This scoping review maps existing evidence on the clinical utility of DMOs, identifying commonalities across traditional disciplinary divides. In November 2019, 11 databases were searched for records investigating the validity and responsiveness of 34 DMOs in four diverse medical conditions (Parkinson’s disease, multiple sclerosis, chronic obstructive pulmonary disease, hip fracture). Searches yielded 19,672 unique records. After screening, 855 records representing 775 studies were included and charted in systematic maps. Studies frequently investigated gait speed (70.4% of studies), step length (30.7%), cadence (21.4%), and daily step count (20.7%). They studied differences between healthy and pathological gait (36.4%), associations between DMOs and clinical measures (48.8%) or outcomes (4.3%), and responsiveness to interventions (26.8%). Gait speed, step length, cadence, step time and step count exhibited consistent evidence of validity and responsiveness in multiple conditions, although the evidence was inconsistent or lacking for other DMOs. If DMOs are to be adopted as mainstream tools, further work is needed to establish their predictive validity, responsiveness, and ecological validity. Cross-disciplinary efforts to align methodology and validate DMOs may facilitate their adoption into clinical practice.

Background: Despite the increasing use of remote measurement technologies (RMT) such as wearables or biosensors in health care programs, challenges associated with selecting and implementing these technologies persist. Many health care programs that use RMT rely on commercially available, “off-the-shelf” devices to collect patient data. However, validation of these devices is sparse, the technology landscape is constantly changing, relative benefits between device options are often unclear, and research on patient and health care provider preferences is often lacking. Objective: To address these common challenges, we propose a novel device selection framework extrapolated from human-centered design principles, which are commonly used in de novo digital health product design. We then present a case study in which we used the framework to identify, test, select, and implement off-the-shelf devices for the Remote Assessment of Disease and Relapse-Central Nervous System (RADAR-CNS) consortium, a research program using RMT to study central nervous system disease progression. Methods: The RADAR-CNS device selection framework describes a human-centered approach to device selection for mobile health programs. The framework guides study designers through stakeholder engagement, technology landscaping, rapid proof of concept testing, and creative problem solving to develop device selection criteria and a robust implementation strategy. It also describes a method for considering compromises when tensions between stakeholder needs occur. Results: The framework successfully guided device selection for the RADAR-CNS study on relapse in multiple sclerosis. In the initial stage, we engaged a multidisciplinary team of patients, health care professionals, researchers, and technologists to identify our primary device-related goals. We desired regular home-based measurements of gait, balance, fatigue, heart rate, and sleep over the course of the study. However, devices and measurement methods had to be user friendly, secure, and able to produce high quality data. In the second stage, we iteratively refined our strategy and selected devices based on technological and regulatory constraints, user feedback, and research goals. At several points, we used this method to devise compromises that addressed conflicting stakeholder needs. We then implemented a feedback mechanism into the study to gather lessons about devices to improve future versions of the RADAR-CNS program. Conclusions: The RADAR device selection framework provides a structured yet flexible approach to device selection for health care programs and can be used to systematically approach complex decisions that require teams to consider patient experiences alongside scientific priorities and logistical, technical, or regulatory constraints.

Introduction Advances in wearable sensor technology now enable frequent, objective monitoring of real-world walking. Walking-related digital mobility outcomes (DMOs), such as real-world walking speed, have the potential to be more sensitive to mobility changes than traditional clinical assessments. However, it is not yet clear which DMOs are most suitable for formal validation. In this review, we will explore the evidence on discriminant ability, construct validity, prognostic value and responsiveness of walking-related DMOs in four disease areas: Parkinson’s disease, multiple sclerosis, chronic obstructive pulmonary disease and proximal femoral fracture. Methods and analysis Arksey and O’Malley’s methodological framework for scoping reviews will guide study conduct. We will search seven databases (Medline, CINAHL, Scopus, Web of Science, EMBASE, IEEE Digital Library and Cochrane Library) and grey literature for studies which (1) measure differences in DMOs between healthy and pathological walking, (2) assess relationships between DMOs and traditional clinical measures, (3) assess the prognostic value of DMOs and (4) use DMOs as endpoints in interventional clinical trials. Two reviewers will screen each abstract and full-text manuscript according to predefined eligibility criteria. We will then chart extracted data, map the literature, perform a narrative synthesis and identify gaps. Ethics and dissemination As this review is limited to publicly available materials, it does not require ethical approval. This work is part of Mobilise-D, an Innovative Medicines Initiative Joint Undertaking which aims to deliver, validate and obtain regulatory approval for DMOs. Results will be shared with the scientific community and general public in cooperation with the Mobilise-D communication team. Registration Study materials and updates will be made available through the Center for Open Science’s OSFRegistry (https://osf.io/k7395).

Background: Remote measurement technologies (RMT) such as mobile health devices and apps are increasingly used by those living with chronic neurological and mental health conditions. RMT enables real-world data collection and regular feedback, providing users with insights about their own conditions. Data visualizations are an integral part of RMT, although little is known about visualization design preferences from the perspectives of those living with chronic conditions. Objective: The aim of this review was to explore the experiences and preferences of individuals with chronic neurological and mental health conditions on data visualizations derived from RMT to manage health. Methods: In this systematic review, we searched peer-reviewed literature and conference proceedings (PubMed, IEEE Xplore, EMBASE, Web of Science, Association for Computing Machinery Computer-Human Interface proceedings, and the Cochrane Library) for original papers published between January 2007 and September 2021 that reported perspectives on data visualization of people living with chronic neurological and mental health conditions. Two reviewers independently screened each abstract and full-text article, with disagreements resolved through discussion. Studies were critically appraised, and extracted data underwent thematic synthesis. Results: We identified 35 eligible publications from 31 studies representing 12 conditions. Coded data coalesced into 3 themes: desire for data visualization, impact of visualizations on condition management, and visualization design considerations. Data visualizations were viewed as an integral part of users’ experiences with RMT, impacting satisfaction and engagement. However, user preferences were diverse and often conflicting both between and within conditions. Conclusions: When used effectively, data visualizations are valuable, engaging components of RMT. They can provide structure and insight, allowing individuals to manage their own health more effectively. However, visualizations are not “one-size-fits-all,” and it is important to engage with potential users during visualization design to understand when, how, and with whom the visualizations will be used to manage health.