Showing papers by "Katarzyna Wac published in 2022"

What possibly affects nighttime heart rate? Conclusions from N-of-1 observational data

Abstract: Background Heart rate (HR), especially at nighttime, is an important biomarker for cardiovascular health. It is known to be influenced by overall physical fitness, as well as daily life physical or psychological stressors like exercise, insufficient sleep, excess alcohol, certain foods, socialization, or air travel causing physiological arousal of the body. However, the exact mechanisms by which these stressors affect nighttime HR are unclear and may be highly idiographic (i.e. individual-specific). A single-case or “n-of-1” observational study (N1OS) is useful in exploring such suggested effects by examining each subject's exposure to both stressors and baseline conditions, thereby characterizing suggested effects specific to that individual. Objective Our objective was to test and generate individual-specific N1OS hypotheses of the suggested effects of daily life stressors on nighttime HR. As an N1OS, this study provides conclusions for each participant, thus not requiring a representative population. Methods We studied three healthy, nonathlete individuals, collecting the data for up to four years. Additionally, we evaluated model-twin randomization (MoTR), a novel Monte Carlo method facilitating the discovery of personalized interventions on stressors in daily life. Results We found that physical activity can increase the nighttime heart rate amplitude, whereas there were no strong conclusions about its suggested effect on total sleep time. Self-reported states such as exercise, yoga, and stress were associated with increased (for the first two) and decreased (last one) average nighttime heart rate. Conclusions This study implemented the MoTR method evaluating the suggested effects of daily stressors on nighttime heart rate, sleep time, and physical activity in an individualized way: via the N-of-1 approach. A Python implementation of MoTR is freely available.

Using Consumer-Wearable Activity Trackers for Risk Prediction of Life-Threatening Heart Arrhythmia in Patients with an Implantable Cardioverter-Defibrillator: An Exploratory Observational Study

Abstract: 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.

Burned-out with burnout? Insights from historical analysis

Abstract: Fierce debates surround the conceptualization and measurement of job-related distress in occupational health science. The use of burnout as an index of job-related distress, though commonplace, has increasingly been called into question. In this paper, we first highlight foundational problems that undermine the burnout construct and its legacy measure, the Maslach Burnout Inventory (MBI). Next, we report on advances in research on job-related distress that depart from the use of the burnout construct. Tracing the genesis of the burnout construct, we observe that (a) burnout’s definition was preestablished rather than derived from a rigorous research process and (b) the MBI has little in the way of a theoretical or empirical foundation. Historical analysis suggests that the burnout construct was cobbled together from unchallenged personal impressions and anecdotal evidence before getting reified by the MBI. This state of affairs may account for many of the disconcerting problems encountered in burnout research. We close our paper by presenting the Occupational Depression Inventory (ODI), a recently developed instrument reflective of a renewed approach to job-related distress. The ODI has demonstrated robust psychometric and structural properties across countries, sexes, age groups, occupations, and languages. The instrument addresses job-related distress both dimensionally and categorically. A dimensional approach can be useful, for instance, in examining the dynamics of etiological processes and symptom development. A categorical approach can serve screening and diagnostic purposes and help clinicians and public health professionals in their decision-making. It is concluded that the ODI offers occupational health specialists a promising way forward.

Less annoying: quality of experience of commonly used mobile applications

Abstract: In recent years, research on the Quality of Experience (QoE) of smartphone applications has received attention from both industry and academia due to the complexity of quantifying and managing it. This paper proposes a smartphone-embedded system able to quantify and notify smartphone users of the expected QoE level (high or low) during their interaction with their devices. We conducted two in the wild studies for four weeks each with Android smartphones users. The first study enabled the collection of the QoE levels of popular smartphone applications' usage rated by 38 users. We aimed to derive an understanding of users' QoE level. From this dataset, we also built our own model that predicts the QoE level for application category. Existing QoE models lack contextual features, such as duration of the user interaction with an application and the user's current physical activity. Subsequently, we implemented our model in an Android application (called expectQoE) for a second study involving 30 users to maximize high QoE level, and we replicated a previous study (2012) on the factors influencing the QoE of commonly used applications. The expectQoE, through emoji-based notifications, presents the expected application category QoE level. This information enable the user's to make a conscious choice about the application to launch. We then investigated whether if expectQoE improved the user's perceived QoE level and affected their application usage. The results showed no conclusive user-reported improvement of their perceived QoE due to expectQoE. Although the participants always had high QoE application usage expectations, the variation in their expectations was minimal and not significant. However, based on a time series analysis of the quantitative data, we observed that expectQoE decreased the application usage duration. Finally, the factors influencing the QoE on smartphone applications were similar to the 2012 findings. However, we observed the emergence of digital wellbeing features as facets of the users' lifestyle choices.

Engineering uncertain time for its practical integration in ontologies

Abstract: Ontologies are commonly used as a strategy for knowledge representation. However, they are still presenting limitations to model domains that require broad forms of temporal reasoning. This study is part of the Onto-mQoL project and was motivated by the real need to extend static ontologies with diverse time concepts, relations and properties, which go beyond the commonly used Allen’s Interval Algebra. Therefore, we use the n-ary relations as the basis for temporal structures, which minimally modify the original ontology, and extend these structures with a generic set of time concepts (moments and intervals), time concept properties (precise and uncertain), time relations (interval–interval, interval–moment, and moment–moment), and time relation properties (qualitative and quantitative). We divided the scientific contribution of this study into three parts. Firstly, we present the ontological temporal model (classes and properties) and how it is integrated into static ontologies. Secondly, we discuss the creation of axioms that give the semantics for precise temporal elements. Finally, as our main contribution, these ideas are extended with axioms for uncertain time. All these elements follow the Ontology Web Language (OWL) standards, so this proposal is still compatible with the main ontology editors and reasoners currently available. A case example demonstrates the use of this approach in the nutrition assessment domain.

Designing a Social Robot Companion to Support Homecare: Usability Results

Abstract: Earlier studies show frail seniors often experience loneliness and depression. Moreover, frailty can lead problems with medication and nutrition patterns. The availability of family care and/or nursing care at home is limited. Digital companions, such as social robots, could complement homecare nurses, thereby improving the quality of care to frail seniors. The Guardian project has co- designed with end-users, a social robot providing social company and health support. To assess the digital and co-created solution, usability evaluations have been conducted with 43 participants distributed as fairly as possible between frail seniors, family carers and professional nurses; in three different European areas: The Netherlands, Italy and Switzerland. The goal of this study is to evaluate if the GUARDIAN solution is accepted by the target users and also gather data on how to improve the system for ensuring added-value in home care. The iterative method based on user-centered approach put the end-users at the centre of the usability evaluation. Through thematic analysis of the qualitative datasets, we conclude that a high number of users accept the solution and describe it as useful. End-user needs have been mainly addressed but some new improvements have been pointed out by the participants and some other needs have been uncovered.

Behavioral Data Categorization for Transformers-based Models in Digital Health

Abstract: Transformers are recent deep learning (DL) models used to capture the dependence between parts of sequential data. While their potential was already demonstrated in the natural language processing (NLP) domain, emerging research shows transformers can also be an adequate modeling approach to relate longitudinal multi-featured continuous behavioral data to future health outcomes. As transformers-based predictions are based on a domain lexicon, the use of categories, commonly used in specialized areas to cluster values, is the likely way to compose lexica. However, the number of categories may influence the transformer prediction accuracy, mainly when the categorization process creates imbalanced datasets, or the search space is very restricted to generate optimal feasible solutions. This paper analyzes the relationship between models' accuracy and the sparsity of behavioral data categories that compose the lexicon. This analysis relies on a case example that uses mQoL-Transformer to model the influence of physical activity behavior on sleep health. Results show that the number of categories shall be treated as a further transformer's hyperparameter, which can balance the literature-based categorization and optimization aspects. Thus, DL processes could also obtain similar accuracies compared to traditional approaches, such as long short-term memory, when used to process short behavioral data sequences.

The social robot companion to support homecare nurses: The guardian study protocol

Abstract: The reduction of the older people's self-sufficiency and the increase in the need for help in daily activities has a significant impact on the person and their caregivers. The primary objective of the GUARDIAN project is to enable the older people to live as long as possible at home, by means of the GUARDIAN socio-technical platform.and Analysis: The GUARDIAN platform consists of two connected apps: one dedicated to the caregiver (Caregiver App) and one dedicated to the older people (Senior App), plus a robot (Misty II), to provide coaching in an engaging modality. The study is designed as a technical feasibility pilot to test the GUARDIAN system on a group of older people.The proposed solution reflects the real wants and needs of the older people person, increasing the acceptability of the system. In addition, the GUARDIAN project has the potential to have distinguished two phases of testing, so that changes can be made to the platform between the first and second phases, using data, both qualitative and quantitative, collected after the first phase.The study was approved by the Ethic Committee of the IRCCS INRCA. It was recorded in ClinicalTrials.gov on the number NCT05284292.

Dejà vu: Recurrent Neural Networks for health wearables data forecast

Abstract: Wearable devices are a useful and widely used source of continuous and temporal dependant data. In contrast to the traditional clinical environment, these devices allow time series data collection in an individual’s daily living environment. However, missing data can occur while using them. Many techniques have been applied to solve these data gaps; nonetheless, missing time series data poses extra challenges, such as maintaining the temporal dependency. In this article, we addressed the forecast of sleep trackers data (sleeping heart rate (HR) and time asleep) for 2 main reasons: (1) to design models capable of accurately forecasting missing data from those devices, and (2) to apply those models to empower sleep interventions that may increase its quality, by forecasting future sleep events. We collected wearables data over 290 days (per individual) from 12 participants using a smartwatch and made this dataset publicly available. We then explored several hyperparameters of 2 Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). We further elaborated and compared the performance of 3 approaches to training those RNNs. Although similar performance, slightly more accurate results were obtained after training a GRU network on an entire population’s dataset, which was able to forecast the average, minimum, and maximum sleeping HR with a root-mean-squared error (RMSE) of 4.4 (± 1.4), 4.9 (± 2.6), and 12.1 ( 4.0) beats per minute, respectively. However, the total time ±asleep was impossible to forecast with low error.

Advances in telemedicine using personal mobile devices: One patient at a time

Abstract: This commentary is on the original article by Prelack et al. on pages 1351–1358 of this issue.