Research

Ventricular arrhythmia (VA) is a leading cause of sudden death and health deterioration. Recent advances in predictive analytics and wearable technology for behavior assessment show promise but require further investigation. Yet, previous studies have only assessed other health outcomes and monitored patients for short durations (7–14 days). This study explores how behaviors reported by a consumer wearable can assist VA risk prediction. An exploratory observational study was conducted with participants who had an implantable cardioverter-defibrillator (ICD) and wore a Fitbit Alta HR consumer wearable. Fitbit reported behavioral markers for physical activity (light, fair, vigorous), sleep, and heart rate. A case-crossover analysis using conditional logistic regression assessed the effects of time-adjusted behaviors over 1–8 weeks on VA incidence. Twenty-seven patients (25 males, median age 59 years) were included. Among the participants, ICDs recorded 262 VA events during 8093 days monitored by Fitbit (median follow-up period 960 days). Longer light to fair activity durations and a higher heart rate increased the odds of a VA event (p < 0.001). In contrast, lengthier fair to vigorous activity and sleep durations decreased the odds of a VA event (p < 0.001). Future studies using consumer wearables in a larger population should prioritize these outcomes to further assess VA risk.

Latest known journal performance at submission time: impact factor 4.945, class A (excellent), quartile Q1 in “Medicine, General and Internal” (rank 31/167) and “Health Care Sciences & Services” (rank 15/107), JCR2020.

Energy and fatigue carry important implications for vitality and overall quality of life. Lacking energy and experiencing fatigue can be both burdensome as well as adaptive. This chapter first classifies energy and fatigue and then reviews their measurement. This chapter closes with opportunities for future directions. Energy and fatigue are present under varying conditions including in daily performance, during and after acute physical or mental strain (capacity), and in the context of chronic conditions. Energy and fatigue have been measured both subjectively and objectively. Subjective outcomes can be derived from self-reported scales and prompts; objective outcomes derived from performance and capacity tasks and technology-reported physiological, biological, and behavioural markers. The scales and tasks employed to measure energy have been traditionally validated but may lack daily life context and ecological validity. Prompts and behavioural monitoring methods are emerging as promising alternatives. Energy and fatigue have also been routinely monitored for specific diseases and occupations. However, fewer studies monitor healthy individuals through consumer technology in daily life contexts. More research is needed for an objective, unobtrusive, longitudinal, and contextual measurement of energy and fatigue in the healthy general population, in service of improving health, wellbeing, and quality of life.

Chronic diseases represent a significant share of the burden of disease globally. They are responsible for 86% of premature deaths in Europe. Unhealthy behaviours, such as physical inactivity, insufficient sleep, poor nutrition, and tobacco intake, explain up to 50% of chronic disease risk. However, the evidence is not precise enough to assess the risk for each disease. Human subject studies monitoring behaviours over long periods during daily life using unobtrusive technology can allow human behaviours to unfold. They can quantify the relationships between behaviours, health, and Quality of Life (QoL). This thesis explores two research areas. In the first area, we research the motivation to participate in human subject studies. We propose a presentational model using personalised stories to improve human studies’ participation. We design two unifying frameworks for conducting a wide range of human subject studies (mQoL mobile app, mQoL-Chat chatbot). In the second area, we research the relationships between behaviours, health, and QoL (co-calibration). We present the coQoL computational model for co-calibration. We demonstrate its feasibility in a human subject study on 42 healthy older individuals (a population at risk, appropriate for disease prevention, and having benefited from insufficient co-calibrations). Participants answered questionnaires about their physical activity (IPAQ scale), social support (MSPSS), anxiety and depression (GADS), nutrition (PREDIMED, SelfMNA), memory (MFE), sleep (PSQI), and health-related QoL (EQ-5D-3L). They wore consumer wearables (Fitbit Charge 2) for up to two years. The wearables reported their physical activity, sleep, and heart rate during daily life. We observed new relationships between these outcomes. We described the study’s human factors and data quality. The scientific contributions in both research areas can inform future studies’ design leveraging consumer technology that monitors behaviours to assess and improve health and QoL.

Inactivity, lack of sleep, and poor nutrition predispose individuals to health risks. Patient-Reported Outcomes (PROs) assess physical behaviours and psychological states but are subject of self-reporting biases. Conversely, wearables are an increasingly accurate source of behavioural Technology-Reported Outcomes (TechROs). However, the extent to which PROs and TechROs provide convergent information is unknown. We propose the coQoL PRO-TechRO co-calibration method and report its feasibility, reliability, and human factors influencing data quality. Thirty-nine seniors provided 7.4 ± 4.4 PROs for physical activity (IPAQ), social support (MSPSS), anxiety/depression (GADS), nutrition (PREDIMED, SelfMNA), memory (MFE), sleep (PSQI), Quality of Life (EQ-5D-3L), and 295 ± 238 days of TechROs (Fitbit Charge 2) along two years. We co-calibrated PROs and TechROs by Spearman rank and reported human factors guiding coQoL use. We report high PRO—TechRO correlations (rS ≥ 0.8) for physical activity (moderate domestic activity—light+fair active duration), social support (family help—fair activity), anxiety/depression (numeric score—sleep duration), or sleep (duration to sleep—sleep duration) at various durations (7–120 days). coQoL feasibly co-calibrates constructs within physical behaviours and psychological states in seniors. Our results can inform designs of longitudinal observations and, whenever appropriate, personalized behavioural interventions.

Latest known journal performance at submission time: impact factor 4.433, class A (excellent), quartile Q1 in “Medicine, General and Internal” (rank 24/165) and “Health Care Sciences & Services” (rank 10/102), JCR2019.

Unhealthy behaviours account for 30-50% health risk and affect Quality of Life. Prior studies quantified behaviours and life quality in the young, clinical context, or short-term (1-7 days). Little research assessed such relations in healthy seniors long-term (7-120 days). We co-calibrated PRO Quality of Life (QoL) (EQ-5D-3L scale) and TechRO behaviours (Fitbit Charge 2) in seniors. We measured PRO mobility, self-care, usual activities, pain/discomfort, anxiety/depression, and health; TechRO (absolute) energy, steps, distance, duration of sedentary, physical activity (PA), sleep, and resting heart rate; TechRO (relative) centred log-ratios of the compositions of sedentary, active, and sleep duration on a day. We aligned PROs with preceding 7-120 days TechRO intervals and co-calibrated them by Spearman test. N = 31 seniors (Hungary, Spain), age 70.66 ± 3.15 provided 54 EQ-5D-3L answers (1.72 ± 1.12 / person) and 9.150 Fitbit days (295.16 ± 247.25 / person). In all seniors, we found the following correlations (p < 0.05): pain / discomfort vs relative fair activity (rs = 0.69) and sleep (-0.58); health state vs relative light activity (0.63) and sleep (0.73). In the healthy, we found: pain/discomfort vs absolute sedentary (0.69), mobility vs absolute sedentary (-0.57), and health state vs heart rate (-0.56). In seniors with mild disease, we found: mobility vs steps (0.71), distance (0.71), absolute sedentary duration (-0.67); anxiety/depression vs steps (-0.57), distance (-0.62). The measurement of the entire day (TechRO PA and sleep) uncovered correlations invisible otherwise. Our results facilitate observational and interventional designs targeting seniors.

A leading risk factor for chronic disease is physical inactivity. In efforts to assess physical activity and inform designs for prevention, health professionals currently use inexpensive, but subjective validated scales, or objective, but expensive research-grade wearables. In the meanwhile, individuals increasingly use affordable consumer-friendly wearable devices that can objectively monitor behaviours while daily life unfolds. However, the relationships between their outcomes and the validated scales are yet to be calibrated. We report our results from a study on 31 seniors from Hungary and Spain (mean age 70.6 +/- 3.2). Our study quantified the relations between physical activity outcomes, as patient-reported through 53 answers (1.71 +/- 0.96 / person) on the International Physical Activity Questionnaire (IPAQ) […], and 5615 days (mean 181.1 +/- 179.2 days collected / person) technology-reported by Fitbit Charge 2. The wearables monitored […] physical activity and sleep for long durations (7 to 120 days). We found strong Spearman correlations between light and moderate IPAQ physical activity in the domestic activity domain, and light-fair intensity Fitbit physical activity (e.g., rS = 0.88, p < 0.005). We also found negative moderate-strong correlations between Fitbit sedentary duration and all IPAQ physical activity domains and intensities (e.g., rS = 0.64, p < 0.005). We obtained increasingly stronger relationships across all IPAQ domains and Fitbit intensities by monitoring physical activity beyond the scale recall period, quantifying physical activity relative to all activities of the day, and including sleep. Our findings inform the design of longitudinal observations and […] interventions for physical activity in seniors.

Human subject studies with mobile users are widely used to understand, and model, human aspects such as behaviours and preferences, in the lab and in the wild. These studies usually employ mixed methods, collecting data by active participation and passive sensing using interactive, mobile, wearable, and ubiquitous devices. Researchers rely on a software platform to design and execute their studies, but existing solutions require a steep learning curve, allow little control, and offer limited guarantees. Our research lab built the mQoL Lab platform using open source technologies, and evolved it to a durable and reliable software ecosystem in over ten mobile subject studies along eight years across three countries. In this paper, we share the acquired experience via tangible artifacts such as requirements, architecture, design, step-by-step support, configuration scripts, and recommendations for researchers to construct a software platform supporting mobile subject studies. The paper is especially relevant for researchers embracing short-term to longitudinal, observational or intervention-based studies, leveraging mixed methods, including multiple devices, and tens to hundreds of simultaneous participants.

There is substantial evidence on the relevant factors that motivate participation in human subject studies and the expectations of participants when sharing their health data for research. However, most human subject studies focus on participant eligibility and data collection, omitting even a rudimentary use of the factors that motivate participation. We illustrate an approach to use motivation to construct personalized stories and exemplify it by using a chatbot under development towards monitoring, analyzing, and influencing health study participation, engagement, and retention. Additionally, we discuss the new advantages, challenges, and unexplored avenues for research stemming from our approach.

Latest known workshop performance at submission time: class A (excellent), associated with a conference of class A* (flagship), CORE2018.

Individuals increasingly use mobile, wearable, and ubiquitous devices capable of unobtrusive collection of vast amounts of scientifically rich personal data over long periods (months to years), and in the context of their daily life. However, numerous human and technological factors challenge longitudinal data collection, often limiting research studies to very short data collection periods (days to weeks), spawning recruitment biases, and affecting participant retention over time. This workshop is designed to bring together researchers involved in longitudinal data collection studies to foster an insightful exchange of ideas, experiences, and discoveries to improve the studies’ reliability, validity, and perceived meaning of longitudinal mobile, wearable, and ubiquitous data collection for the participants.

Latest known workshop performance at submission time: class A (excellent), associated with a conference of class A* (flagship), CORE2018.

Nowadays, the app stores host a variety of mobile health solutions. Smartphone users can choose from tens of thousands of applications, designed to prevent or manage certain diseases, or induce behavior change to improve health and life quality in general. However, the value of most applications remains unclear, as they stop short from documenting adherence to medical evidence. We review the fundamental mobile health challenges and propose Mobile Quality of Life Lab (mQoL), a mobile health platform which addresses the identified challenges and leverages recent developments to facilitate the deployment of much-needed longitudinal, multidimensional, evidence-based studies that are minimally obtrusive for the participants, yet provide high value in terms of the collected datasets, as well as potential for behavior change towards improving Quality of Life.

Latest known workshop performance at submission time: class A (excellent), associated with a conference of class A* (flagship), CORE2018.

Chronic diseases are the top contributor to mortality worldwide. Their risk decreases with a healthy lifestyle, determined by daily life behavior. Reference chronic illness risk models combine patient performance-based reports with self-reports. Performance reports, obtained at the doctor’s office, are collected with clinically approved devices to ensure high accuracy, but the process is expensive, momentary, and occurs outside of the patient’s daily life. Self-reports are affordable, can be contextual and recur in time, but introduce perception bias and are prone to socially acceptable answers. Meanwhile, the market for mHealth, i.e., personalized devices that monitor daily life through behavioral markers (e.g., exercise or sleep), is gaining acceptance. Although many do not promise medical accuracy yet, the sheer data collected may provide useful insights into patterns. A caveat is that the mHealth space is fragmented. Numerous researchers design, build, deploy, and maintain applications that focus on a single experiment, a marker, or a disease. Such apps prove too narrow to address participant Quality of Life from a holistic perspective. In addition, researchers report back gaps in use (and data) from the apps. Faced with these data collection challenges ourselves and aiming at holistic QoL assessment, we are operationalizing the Mobile Quality of Life Lab. Our app aims at serving as an ecosystem of digital health exploration for participants and researchers. With personalized, contextual, and graphical content for participants to monitor, observe, and reflect upon daily life as a whole, we hypothesize that it can serve as a useful tool to make sense of behavior and life quality and potentially enable behavior change in the long term. By reusing its data collection and exploration apparatus on top of well-known frameworks such as ResearchKit, HealthKit, Open mHealth, or AWARE, our app enables to obtain performance-reported outcomes by measurement of behavioral markers in time and context. Also, we include general self-reports on demographics and Quality of Life as well as domain-specific self-reports in the explorations. Researchers like ourselves can now focus only on collecting approved, consented, anonymized, contextual, and chronologic data, while providing participants timely, personalized, contextual, and beneficial information from their investigations. We intend to leverage the app in Europe within the WellCo H2020 project where we aim to manage cardiovascular disease risk. We have 300 participants planned for 2018.

Within elder care, an increased distribution of care poses strong requirements on the ability of health providers, to coordinate their activities across organizational boundaries. However, existing care administration systems do not offer sufficient support for collaboration and coordination among a heterogeneous ensemble of care providers. In this paper, we present findings from a field study of coordinative work in distributed elder care in Denmark. The purpose of this study is to further our understanding of the coordinative challenges of distributed elder care, and to inform design of new care administration systems. Overall, we found that caregivers at the care center fulfill a crucial role in prioritizing the scheduled activities in cases where opposed interests occur, which indicates a need for a care administration system, that not only supports the meticulous planning of activities, that are needed by the healthcare system, but also the work involved in balancing priorities.

Latest known conference performance at submission time: class A (excellent), CORE2014.

In Denmark, elder care is currently distributed across various care providers, including a care center where the elderly citizens live, and numerous specialized care providers. This poses strong requirements on the abilities of the involved care providers to align and coordinate their activities in order to support the life, care, and comfort of the residents. At the care center in this study, planning of the residents’ activities is currently supported by a care administration system that allows caregivers to maintain schedules of the residents’ activities. However, in practice, this system mainly supports local planning and not coordination of care, which is arguably necessary among the members of the distributed ensemble of care providers that constitute elder care. In this paper we present preliminary findings from a project aimed at designing improved IT-support of collaboration elder care. By studying how the care administration was appropriated in use, currently unsupported communication and collaboration needs became visible, including a lack of support for caregivers in prioritizing activities in cases where the care providers have opposed interests and expectations.

Latest known workshop performance at submission time: class C (basic), associated with a conference of class B (good to very good), CORE2014.

In Denmark, care of elderly people involves numerous and relatively autonomous care providers, including care centers, activity centers, physiotherapists, doctors, and other specialists. However, due to a poor coordination of activities, many elderly experience a lack of continuity of care, missed appointments, and general discomfort. In this poster we report on preliminary findings from a project aimed at creating improved IT support for coordination of care for the elderly in a Danish municipality. We propose that in order to successfully support heterogeneous collaboration, our system must address the disruptions in the existing routines, minimize the inherent articulation work, and coherently unify their coordination mechanisms.

Latest known conference performance at submission time: class A (excellent), CORE2014.

Virtual Reality is becoming more than a part of our everyday life, helping us to quickly identify the elements of the environment or to better entertain us. The purpose of this paper is to present an application framework that we are developing, in order to help others implement their own applications. In the first section the focus is set on the Augmented Reality basic concepts and the necessity of developing such a framework. The prototype that we present in the second part of this paper comes to demonstrate how our framework can be used to achieve our targeted application for Augmented Reality. It also contains some future works to highlight the capabilities of the AR API.

Latest known conference performance at submission time: class C (basic), ERA2010.