Towards a Practical Pedestrian Distraction Detection Framework using Wearables

Pedestrian safety continues to be a significant concern in urban communities and pedestrian distraction is emerging as one of the main causes of grave and fatal accidents involving pedestrians. The advent of sophisticated mobile and wearable devices, equipped with high-precision on-board sensors capable of measuring fine-grained user movements and context, provides a tremendous opportunity for designing effective pedestrian safety systems and applications. Accurate and efficient recognition of pedestrian distractions in real-time given the memory, computation and communication limitations of these devices, however, remains the key technical challenge in the design of such systems. Earlier research efforts in pedestrian distraction detection using data available from mobile and wearable devices have primarily focused only on achieving high detection accuracy, resulting in designs that are either resource intensive and unsuitable for implementation on mainstream mobile devices, or computationally slow and not useful for real-time pedestrian safety applications, or require specialized hardware and less likely to be adopted by most users. In the quest for a pedestrian safety system that achieves a favorable balance between computational efficiency, detection accuracy, and energy consumption, this paper makes the following main contributions: (i) design of a novel complex activity recognition framework which employs motion data available from users' mobile and wearable devices and a lightweight frequency matching approach to accurately and efficiently recognize complex distraction related activities, and (ii) a comprehensive comparative evaluation of the proposed framework with well-known complex activity recognition techniques in the literature with the help of data collected from human subject pedestrians and prototype implementations on commercially-available mobile and wearable devices

The Age of Artificial Intelligence: Use of Digital Technology in Clinical Nutrition

Purpose of review Computing advances over the decades have catalyzed the pervasive integration of digital technology in the medical industry, now followed by similar applications for clinical nutrition. This review discusses the implementation of such technologies for nutrition, ranging from the use of mobile apps and wearable technologies to the development of decision support tools for parenteral nutrition and use of telehealth for remote assessment of nutrition. Recent findings Mobile applications and wearable technologies have provided opportunities for real-time collection of granular nutrition-related data. Machine learning has allowed for more complex analyses of the increasing volume of data collected. The combination of these tools has also translated into practical clinical applications, such as decision support tools, risk prediction, and diet optimization. Summary The state of digital technology for clinical nutrition is still young, although there is much promise for growth and disruption in the future

The use of mHealth solutions in active and healthy ageing promotion: an explorative scoping review

The global population aged 60 years and over is expected to almost double between 2015 and 2050 from 12.0% to 22.0%, which will directly impact countries' labor market composition and increase the economic pressure on their healthcare systems. One way to address these challenges is to promote Active and Healthy Ageing (AHA) using mobile Health (mHealth). This research aims to provide an initial overview of the width and the depth of contemporary preventive mHealth solutions that promote AHA among healthy, independent older adults (individuals aged 60 years and over). To do so, an explorative scoping review was applied to search online databases for recent studies (March 2015 - March 2020) addressing the promotion of mHealth solutions targeting healthy and independent older adults. We identified 31 publications that met the inclusion criteria. Most of them utilized either mobile (n=25) and/or wearable (n=11) devices. mHealth solutions mostly promoted AHA by targeting older adults’ active lifestyles or independence. Most of the studies (n=27) did not apply a theoretical framework on which the mHealth promotion was based. User-experience was positive (n=12) when the solution was easy to use but negative (n=11) when the participants were resistant or faced challenges using the device and/or technology. The review concludes that mHealth offers the opportunity to combat the issues faced by an unhealthy and dependent aging population by promoting AHA through focusing on older adults’ Lifestyle, Daily functioning, and Participation. Future research should use multidisciplinary integrated approaches and strong theoretical and methodological foundations to investigate mHealth solutions' impact on AHA behavioral change

Design Principles of Mobile Information Systems in the Digital Transformation of the Workplace - Utilization of Smartwatch-based Information Systems in the Corporate Context

During the last decades, smartwatches emerged as an innovative and promising technology and hit the consumer market due to the accessibility of affordable devices and predominant acceptance caused by the considerable similarity to common wristwatches. With the unique characteristics of permanent availability, unobtrusiveness, and hands-free operation, they can provide additional value in the corporate context. Thus, this thesis analyzes use cases for smartwatches in companies, elaborates on the design of smartwatch-based information systems, and covers the usability of smartwatch applications during the development of smartwatch-based information systems. It is composed of three research complexes. The first research complex focuses on the digital assistance of (mobile) employees who have to execute manual work and have been excluded so far from the benefits of the digitalization since they cannot operate hand-held devices. The objective is to design smartwatch-based information systems to support workflows in the corporate context, facilitate the daily work of numerous employees, and make processes more efficient for companies. During a design science research approach, smartwatch-based software artifacts are designed and evaluated in use cases of production, support, security service, as well as logistics, and a nascent design theory is proposed to complement theory according to mobile information system research. The evaluation shows that, on the one hand, smartwatches have enormous potential to assist employees with a fast and ubiquitous exchange of information, instant notifications, collaboration, and workflow guidance while they can be operated incidentally during manual work. On the other hand, the design of smartwatch-based information systems is a crucial factor for successful long-term deployment in companies, and especially limitations according to the small form-factor, general conditions, acceptance of the employees, and legal regulations have to be addressed appropriately. The second research complex addresses smartwatch-based information systems at the office workplace. This broadens and complements the view on the utilization of smartwatches in the corporate context in addition to the mobile context described in the first research complex. Though smartwatches are devices constructed for mobile use, the utilization in low mobile or stationary scenarios also has benefits due they exhibit the characteristic of a wearable computer and are directly connected to the employee’s body. Various sensors can perceive employee-, environment- and therefore context-related information and demand the employees’ attention with proactive notifications that are accompanied by a vibration. Thus, a smartwatch-based and gamified information system for health promotion at the office workplace is designed and evaluated. Research complex three provides a closer look at the topic of usability concerning applications running on smartwatches since it is a crucial factor during the development cycle. As a supporting element for the studies within the first and second research complex, a framework for the usability analysis of smartwatch applications is developed. For research, this thesis contributes a systemization of the state-of-the-art of smartwatch utilization in the corporate context, enabling and inhibiting influence factors of the smartwatch adoption in companies, and design principles as well as a nascent design theory for smartwatch-based information systems to support mobile employees executing manual work. For practice, this thesis contributes possible use cases for smartwatches in companies, assistance in decision-making for the introduction of smartwatch-based information systems in the corporate context with the Smartwatch Applicability Framework, situated implementations of a smartwatch-based information system for typical use cases, design recommendations for smartwatch-based information systems, an implementation of a smartwatch-based information system for the support of mobile employees executing manual work, and a usability-framework for smartwatches to automatically access usability of existing applications providing suggestions for usability improvement

From Small to Big: Smartwatch Use in Mitigating COVID-19 – Understanding User Experience from Social Media Content Analysis

Smartwatches offer both functions and convenience that can have great potentials for technological interventions. Despite widespread discussion of technological interventions for COVID-19, smartwatch use has received little attention in the literature. This research aims to fill the literature gap by providing a broad understanding of smartwatch use for COVID-19 mitigation. We investigate smartwatch use through content analysis of the data collected from two social media platforms. The method allows us to draw on user experience beyond technological features and functions. In addition to functions, we also identified the concerns of using smartwatches for mitigating COVID-19. Furthermore, we uncovered both similarities and differences between the different social media platforms in terms of functions and concerns of smartwatch use. Our findings have implications for various stakeholders of the smartwatch technology and for mitigating the impact of the pandemic

Mobile health as a primary mode of intervention for women at risk of, or diagnosed with, gestational diabetes mellitus: a scoping review.

OBJECTIVE: The objective of this review was to map the knowledge related to the use of mHealth as a primary mode of intervention for the prevention and management of gestational diabetes mellitus and its long-term implications among women at risk of or diagnosed with gestational diabetes mellitus. We also sought to understand if mHealth for women at risk of or diagnosed with gestational diabetes mellitus incorporated relevant behavior change theory and techniques. INTRODUCTION: Prevention and management of gestational diabetes mellitus and its associated adverse outcomes are important to maternal and infant health. Women with gestational diabetes mellitus report high burden of disease management and barriers to lifestyle change post-delivery, which mHealth interventions may help to overcome. Evidence suggests apps could help gestational diabetes mellitus prevention and management, however, less is known about broader applications of mHealth from preconception to interconception and whether relevant behavior change techniques are incorporated. INCLUSION CRITERIA: Studies published in English that focused on mHealth use as primary mode of intervention for the prevention and management of gestational diabetes mellitus and its long-term implications were considered for inclusion. Telehealth or telemedicine were excluded as these have been reviewed elsewhere. METHODS: Six databases were searched: MEDLINE (Ovid), CINAHL (EBSCO), Embase (Ovid), Cochrane Database (Wiley), Scopus, and TRIP. No limits were applied to database exploration periods to ensure retrieval of all relevant studies. Gray literature sources searched were OpenGrey, ISRCTN Registry, ClinicalTrials.gov, EU Clinical Trials Register, and ANZCTR. Two reviewers independently screened abstracts and assessed full texts against the inclusion criteria. Data were extracted using an adapted version of the JBI data extraction instrument. Data are presented in narrative form accompanied by tables and figures. RESULTS: This review identified 2166 sources, of which 96 full texts were screened. Thirty eligible reports were included, covering 25 different mHealth interventions. Over half (n = 14) were for self-managing blood glucose during pregnancy. Common features included tracking blood glucose levels, real-time feedback, communication with professionals, and educational information. Few (n = 6) mHealth interventions were designed for postpartum use and none for interconception use. Five for postpartum use supported behavior change to reduce the risk of type 2 diabetes and included additional features such as social support functions and integrated rewards. Early development and feasibility studies used mixed methods to assess usability and acceptability. Later stage evaluations of effectiveness typically used randomized controlled trial designs to measure clinical outcomes such as glycemic control and reduced body weight. Three mHealth interventions were developed using behavior change theory. Most mHealth interventions incorporated two behavior change techniques shown to be optimal when combined and those delivering behavior change interventions included a wider range. Nevertheless, only half of the 26 techniques listed in a published behavior change taxonomy were tried. CONCLUSIONS: mHealth for gestational diabetes mellitus focuses on apps to improve clinical outcomes. This focus could be broadened by incorporating existing resources that women value, such as social media, to address needs, such as peer support. Although nearly all mHealth interventions incorporated behavior change techniques, findings suggest future development should consider selecting techniques that target women's needs and barriers. Lack of mHealth interventions for prevention of gestational diabetes mellitus recurrence and type 2 diabetes mellitus suggests further development and evaluation is required

Emotional self-regulation of individuals with autism spectrum disorders: smartwatches for monitoring and interaction

In this paper, we analyze the needs of individuals with Autism Spectrum Disorders (ASD) to have a pervasive, feasible and non-stigmatizing form of assistance in their emotional self-regulation, in order to ease certain behavioral issues that undermine their mental health throughout their life. We argue the potential of recent widespread wearables, and more specifically smartwatches, to achieve this goal. Then, a smartwatch system that implements a wide range of self-regulation strategies and infers outburst patterns from physiological signals and movement is presented, along with an authoring tool for smartphones that is to be used by caregivers or family members to create and edit these strategies, in an adaptive way. We conducted an intensive experiment with two individuals with ASD who showed varied, representative behavioral responses to their emotional dysregulation. Both users were able to employ effective, customized emotional self-regulation strategies by means of the system, recovering from the majority of mild stress episodes and temper tantrums experienced in the nine days of experiment in their classroomThis work has been partially funded by the projects “e-Training y e-Coaching para la integración socio—laboral” (TIN2013-44586-R) and “eMadrid-CM: Investigación y Desarrollo de Tecnologías Educativas en la Comunidad de Madrid” (S2013/ICE-2715). It has been also funded by Fundación Orange during the early stages of the project “Tic-Tac-TEA: Sistema de asistencia para la autorregulación emocional en momentos de crisis para personas con TEA mediante smartwatches

Development of a digital biomarker and intervention for subclinical depression: study protocol for a longitudinal waitlist control study

Background Depression remains a global health problem, with its prevalence rising worldwide. Digital biomarkers are increasingly investigated to initiate and tailor scalable interventions targeting depression. Due to the steady influx of new cases, focusing on treatment alone will not suffice; academics and practitioners need to focus on the prevention of depression (i.e., addressing subclinical depression). Aim With our study, we aim to (i) develop digital biomarkers for subclinical symptoms of depression, (ii) develop digital biomarkers for severity of subclinical depression, and (iii) investigate the efficacy of a digital intervention in reducing symptoms and severity of subclinical depression. Method Participants will interact with the digital intervention BEDDA consisting of a scripted conversational agent, the slow-paced breathing training Breeze, and actionable advice for different symptoms. The intervention comprises 30 daily interactions to be completed in less than 45 days. We will collect self-reports regarding mood, agitation, anhedonia (proximal outcomes; first objective), self-reports regarding depression severity (primary distal outcome; second and third objective), anxiety severity (secondary distal outcome; second and third objective), stress (secondary distal outcome; second and third objective), voice, and breathing. A subsample of 25% of the participants will use smartwatches to record physiological data (e.g., heart-rate, heart-rate variability), which will be used in the analyses for all three objectives. Discussion Digital voice- and breathing-based biomarkers may improve diagnosis, prevention, and care by enabling an unobtrusive and either complementary or alternative assessment to self-reports. Furthermore, our results may advance our understanding of underlying psychophysiological changes in subclinical depression. Our study also provides further evidence regarding the efficacy of standalone digital health interventions to prevent depression. Trial registration Ethics approval was provided by the Ethics Commission of ETH Zurich (EK-2022-N-31) and the study was registered in the ISRCTN registry (Reference number: ISRCTN38841716, Submission date: 20/08/2022)