Fall Prediction and Prevention Systems: Recent Trends, Challenges, and Future Research Directions.

Fall prediction is a multifaceted problem that involves complex interactions between physiological, behavioral, and environmental factors. Existing fall detection and prediction systems mainly focus on physiological factors such as gait, vision, and cognition, and do not address the multifactorial nature of falls. In addition, these systems lack efficient user interfaces and feedback for preventing future falls. Recent advances in internet of things (IoT) and mobile technologies offer ample opportunities for integrating contextual information about patient behavior and environment along with physiological health data for predicting falls. This article reviews the state-of-the-art in fall detection and prediction systems. It also describes the challenges, limitations, and future directions in the design and implementation of effective fall prediction and prevention systems

Mobile recommender apps with privacy management for accessible and usable technologies

The paper presents the preliminary results of an ongoing survey of the use of computers and mobile devices, interest in recommender apps and knowledge and concerns about privacy issues amongst English and Italian speaking disabled people. Participants were found to be regular users of computers and mobile devices for a range of applications. They were interested in recommender apps for household items, computer software and apps that met their accessibility and other requirements. They showed greater concerns about controlling access to personal data of different types than this data being retained by the computer or mobile device. They were also willing to make tradeoffs to improve device performance

Evaluating the impact of physical activity apps and wearables: interdisciplinary review

Background: Although many smartphone apps and wearables have been designed to improve physical activity, their rapidly evolving nature and complexity present challenges for evaluating their impact. Traditional methodologies, such as randomized controlled trials (RCTs), can be slow. To keep pace with rapid technological development, evaluations of mobile health technologies must be efficient. Rapid alternative research designs have been proposed, and efficient in-app data collection methods, including in-device sensors and device-generated logs, are available. Along with effectiveness, it is important to measure engagement (ie, users’ interaction and usage behavior) and acceptability (ie, users’ subjective perceptions and experiences) to help explain how and why apps and wearables work. Objectives: This study aimed to (1) explore the extent to which evaluations of physical activity apps and wearables: employ rapid research designs; assess engagement, acceptability, as well as effectiveness; use efficient data collection methods; and (2) describe which dimensions of engagement and acceptability are assessed. Method: An interdisciplinary scoping review using 8 databases from health and computing sciences. Included studies measured physical activity, and evaluated physical activity apps or wearables that provided sensor-based feedback. Results were analyzed using descriptive numerical summaries, chi-square testing, and qualitative thematic analysis. Results: A total of 1829 abstracts were screened, and 858 articles read in full. Of 111 included studies, 61 (55.0%) were published between 2015 and 2017. Most (55.0%, 61/111) were RCTs, and only 2 studies (1.8%) used rapid research designs: 1 single-case design and 1 multiphase optimization strategy. Other research designs included 23 (22.5%) repeated measures designs, 11 (9.9%) nonrandomized group designs, 10 (9.0%) case studies, and 4 (3.6%) observational studies. Less than one-third of the studies (32.0%, 35/111) investigated effectiveness, engagement, and acceptability together. To measure physical activity, most studies (90.1%, 101/111) employed sensors (either in-device [67.6%, 75/111] or external [23.4%, 26/111]). RCTs were more likely to employ external sensors (accelerometers: P=.005). Studies that assessed engagement (52.3%, 58/111) mostly used device-generated logs (91%, 53/58) to measure the frequency, depth, and length of engagement. Studies that assessed acceptability (57.7%, 64/111) most often used questionnaires (64%, 42/64) and/or qualitative methods (53%, 34/64) to explore appreciation, perceived effectiveness and usefulness, satisfaction, intention to continue use, and social acceptability. Some studies (14.4%, 16/111) assessed dimensions more closely related to usability (ie, burden of sensor wear and use, interface complexity, and perceived technical performance). Conclusions: The rapid increase of research into the impact of physical activity apps and wearables means that evaluation guidelines are urgently needed to promote efficiency through the use of rapid research designs, in-device sensors and user-logs to assess effectiveness, engagement, and acceptability. Screening articles was time-consuming because reporting across health and computing sciences lacked standardization. Reporting guidelines are therefore needed to facilitate the synthesis of evidence across disciplines

mHealth: providing a mindfulness app for women with chronic pelvic pain in gynaecology outpatient clinics: qualitative data analysis of user experience and lessons learnt

OBJECTIVES: To determine whether a pre-existing smartphone app to teach mindfulness meditation is acceptable to women with chronic pelvic pain (CPP) and can be integrated into clinical practice within the National Health Service (NHS) CPP pathways, and to inform the design of a potential randomised clinical trial. DESIGN: A prestudy patient and public involvement (PPI) group to collect feedback on the acceptability of the existing app and study design was followed by a three-arm randomised feasibility trial. In addition, we undertook interviews and focus groups with patients and staff to explore app usability and acceptability. We also obtained participant comments on the research process, such as acceptability of the study questionnaires. SETTING: Two gynaecology clinics within Barts Health NHS, London, UK. PARTICIPANTS: Patients with CPP lasting ≥6 months with access to smartphone or personal computer and understanding of basic English. INTERVENTION: The intervention was mindfulness meditation content plus additional pain module delivered by a smartphone app. Active controls received muscle relaxation content from the same app. Passive (waiting list) controls received usual care. MAIN OUTCOME MEASURES: Themes on user feedback, app usability and integration, and reasons for using/not using the app. RESULTS: The use of the app was low in both active groups. Patients in the prestudy PPI group, all volunteers, were enthusiastic about the app (convenience, content, portability, flexibility, ease of use). Women contributing to the interview or focus group data (n=14), from a 'real world' clinic (some not regular app users), were less positive, citing as barriers lack of opportunities/motivation to use the app and lack of familiarity and capabilities with technology. Staff (n=7) were concerned about the potential need for extra support for them and for the patients, and considered the app needed organisational backing and peer acceptance. CONCLUSION: The opinions of prestudy PPI volunteers meeting in their private time may not represent those of patients recruited at a routine clinic appointment. It may be more successful to codesign/codevelop an app with typical users than to adapt existing apps for use in real-world clinical populations. TRIAL REGISTRATION NUMBER: ISRCTN10925965