Development and Validation of the FSIQ-RMS: A New Patient-Reported Questionnaire to Assess Symptoms and Impacts of Fatigue in Relapsing Multiple Sclerosis

Objectives: A new patient-reported outcome (PRO) instrument to measure fatigue symptoms and impacts in relapsing multiple sclerosis (RMS) was developed in a qualitative stage, followed by psychometric validation and migration from paper to an electronic format. Methods: Adult patients with relapsing-remitting multiple sclerosis (RRMS) were interviewed to elicit fatigue-related symptoms and impacts. A draft questionnaire was debriefed in cognitive interviews with further RRMS patients, and revised. Content confirmation interviews were conducted with patients with progressive-relapsing multiple sclerosis (PRMS) and relapsing secondary-progressive multiple sclerosis (RSPMS). Psychometric analyses used data from adult patients with different RMS subtypes and matched non-RMS controls in a multicenter, observational study. After item reduction, the final instrument was migrated to a smartphone (eDiary) and usability was confirmed in interviews with additional adult RMS patients. Results: The qualitative stage included 37 RRMS, 5 PRMS, and 5 RSPMS patients. Saturation of concepts was reached during concept elicitation. Cognitive interviews confirmed that participants understood the instructions, items, and response options of the instrument—named FSIQ-RMS—as intended. Psychometric validation included 164 RMS and 74 control patients. Internal consistency and test–retest reliability were demonstrated. The symptoms domain discriminated along the RMS symptom-severity continuum and between patients and controls. Patients were able to attribute fatigue-related symptoms to RMS. Usability and conceptual equivalence of the eDiary were confirmed (n = 10 participants). Conclusions: With 7 symptom items and 13 impact items (in 3 impacts subdomains: physical, cognitive and emotional, and coping) after item reduction, the FSIQ-RMS is a comprehensive, valid, and reliable measure of fatigue-related symptoms and impacts in RMS patients

Digital Traces of the Mind::Using Smartphones to Capture Signals of Well-Being in Individuals

General context and questions Adolescents and young adults typically use their smartphone several hours a day. Although there are concerns about how such behaviour might affect their well-being, the popularity of these powerful devices also opens novel opportunities for monitoring well-being in daily life. If successful, monitoring well-being in daily life provides novel opportunities to develop future interventions that provide personalized support to individuals at the moment they require it (just-in-time adaptive interventions). Taking an interdisciplinary approach with insights from communication, computational, and psychological science, this dissertation investigated the relation between smartphone app use and well-being and developed machine learning models to estimate an individual’s well-being based on how they interact with their smartphone. To elucidate the relation between smartphone trace data and well-being and to contribute to the development of technologies for monitoring well-being in future clinical practice, this dissertation addressed two overarching questions:RQ1: Can we find empirical support for theoretically motivated relations between smartphone trace data and well-being in individuals? RQ2: Can we use smartphone trace data to monitor well-being in individuals?Aims The first aim of this dissertation was to quantify the relation between the collected smartphone trace data and momentary well-being at the sample level, but also for each individual, following recent conceptual insights and empirical findings in psychological, communication, and computational science. A strength of this personalized (or idiographic) approach is that it allows us to capture how individuals might differ in how smartphone app use is related to their well-being. Considering such interindividual differences is important to determine if some individuals might potentially benefit from spending more time on their smartphone apps whereas others do not or even experience adverse effects. The second aim of this dissertation was to develop models for monitoring well-being in daily life. The present work pursued this transdisciplinary aim by taking a machine learning approach and evaluating to what extent we might estimate an individual’s well-being based on their smartphone trace data. If such traces can be used for this purpose by helping to pinpoint when individuals are unwell, they might be a useful data source for developing future interventions that provide personalized support to individuals at the moment they require it (just-in-time adaptive interventions). With this aim, the dissertation follows current developments in psychoinformatics and psychiatry, where much research resources are invested in using smartphone traces and similar data (obtained with smartphone sensors and wearables) to develop technologies for detecting whether an individual is currently unwell or will be in the future. Data collection and analysis This work combined novel data collection techniques (digital phenotyping and experience sampling methodology) for measuring smartphone use and well-being in the daily lives of 247 student participants. For a period up to four months, a dedicated application installed on participants’ smartphones collected smartphone trace data. In the same time period, participants completed a brief smartphone-based well-being survey five times a day (for 30 days in the first month and 30 days in the fourth month; up to 300 assessments in total). At each measurement, this survey comprised questions about the participants’ momentary level of procrastination, stress, and fatigue, while sleep duration was measured in the morning. Taking a time-series and machine learning approach to analysing these data, I provide the following contributions: Chapter 2 investigates the person-specific relation between passively logged usage of different application types and momentary subjective procrastination, Chapter 3 develops machine learning methodology to estimate sleep duration using smartphone trace data, Chapter 4 combines machine learning and explainable artificial intelligence to discover smartphone-tracked digital markers of momentary subjective stress, Chapter 5 uses a personalized machine learning approach to evaluate if smartphone trace data contains behavioral signs of fatigue. Collectively, these empirical studies provide preliminary answers to the overarching questions of this dissertation.Summary of results With respect to the theoretically motivated relations between smartphone trace data and wellbeing (RQ1), we found that different patterns in smartphone trace data, from time spent on social network, messenger, video, and game applications to smartphone-tracked sleep proxies, are related to well-being in individuals. The strength and nature of this relation depends on the individual and app usage pattern under consideration. The relation between smartphone app use patterns and well-being is limited in most individuals, but relatively strong in a minority. Whereas some individuals might benefit from using specific app types, others might experience decreases in well-being when spending more time on these apps. With respect to the question whether we might use smartphone trace data to monitor well-being in individuals (RQ2), we found that smartphone trace data might be useful for this purpose in some individuals and to some extent. They appear most relevant in the context of sleep monitoring (Chapter 3) and have the potential to be included as one of several data sources for monitoring momentary procrastination (Chapter 2), stress (Chapter 4), and fatigue (Chapter 5) in daily life. Outlook Future interdisciplinary research is needed to investigate whether the relationship between smartphone use and well-being depends on the nature of the activities performed on these devices, the content they present, and the context in which they are used. Answering these questions is essential to unravel the complex puzzle of developing technologies for monitoring well-being in daily life.<br/

Digital innovation in Multiple Sclerosis Management

Due to innovation in technology, a new type of patient has been created, the e-patient, characterized by the use of electronic communication tools and commitment to participate in their own care. The extent to which the world of digital health has changed during the COVID-19 pandemic has been widely recognized. Remote medicine has become part of the new normal for patients and clinicians, introducing innovative care delivery models that are likely to endure even if the pendulum swings back to some degree in a post-COVID age. The development of digital applications and remote communication technologies for patients with multiple sclerosis has increased rapidly in recent years. For patients, eHealth apps have been shown to improve outcomes and increase access to care, disease information, and support. For HCPs, eHealth technology may facilitate the assessment of clinical disability, analysis of lab and imaging data, and remote monitoring of patient symptoms, adverse events, and outcomes. It may allow time optimization and more timely intervention than is possible with scheduled face-to-face visits. The way we measure the impact of MS on daily life has remained relatively unchanged for decades, and is heavily reliant on clinic visits that may only occur once or twice each year.These benefits are important because multiple sclerosis requires ongoing monitoring, assessment, and management.The aim of this Special Issue is to cover the state of knowledge and expertise in the field of eHealth technology applied to multiple sclerosis, from clinical evaluation to patient education

Quantifying Quality of Life

Describes technological methods and tools for objective and quantitative assessment of QoL Appraises technology-enabled methods for incorporating QoL measurements in medicine Highlights the success factors for adoption and scaling of technology-enabled methods This open access book presents the rise of technology-enabled methods and tools for objective, quantitative assessment of Quality of Life (QoL), while following the WHOQOL model. It is an in-depth resource describing and examining state-of-the-art, minimally obtrusive, ubiquitous technologies. Highlighting the required factors for adoption and scaling of technology-enabled methods and tools for QoL assessment, it also describes how these technologies can be leveraged for behavior change, disease prevention, health management and long-term QoL enhancement in populations at large. Quantifying Quality of Life: Incorporating Daily Life into Medicine fills a gap in the field of QoL by providing assessment methods, techniques and tools. These assessments differ from the current methods that are now mostly infrequent, subjective, qualitative, memory-based, context-poor and sparse. Therefore, it is an ideal resource for physicians, physicians in training, software and hardware developers, computer scientists, data scientists, behavioural scientists, entrepreneurs, healthcare leaders and administrators who are seeking an up-to-date resource on this subject

Quantifying Quality of Life

Describes technological methods and tools for objective and quantitative assessment of QoL Appraises technology-enabled methods for incorporating QoL measurements in medicine Highlights the success factors for adoption and scaling of technology-enabled methods This open access book presents the rise of technology-enabled methods and tools for objective, quantitative assessment of Quality of Life (QoL), while following the WHOQOL model. It is an in-depth resource describing and examining state-of-the-art, minimally obtrusive, ubiquitous technologies. Highlighting the required factors for adoption and scaling of technology-enabled methods and tools for QoL assessment, it also describes how these technologies can be leveraged for behavior change, disease prevention, health management and long-term QoL enhancement in populations at large. Quantifying Quality of Life: Incorporating Daily Life into Medicine fills a gap in the field of QoL by providing assessment methods, techniques and tools. These assessments differ from the current methods that are now mostly infrequent, subjective, qualitative, memory-based, context-poor and sparse. Therefore, it is an ideal resource for physicians, physicians in training, software and hardware developers, computer scientists, data scientists, behavioural scientists, entrepreneurs, healthcare leaders and administrators who are seeking an up-to-date resource on this subject

Improving chronic pain management with eHealth and mHealth: study protocol for a randomised controlled trial

INTRODUCTION: Chronic pain has become a matter of public health concern due to its high prevalence and because public costs associated with treatment and disability increase each year. Research suggests that limitations in the traditional assessment of chronic pain patients limit the effectiveness of current medical treatments. The use of technology might serve change patient traditional monitoring into ecological momentary assessments, which might be visualised by physicians live. This study describes a randomised control trial designed to test the utility of a technology-based solution for pain telemonitoring consisting of a smartphone app for patients and a web application for physicians. The goal of this study will be to explore whether this combination of eHealth and mHealth improves the effectiveness of existing pain treatments. METHODS AND ANALYSIS: Participants will be 250 patients randomly assigned to one of these two conditions: treatment-as-usual (TAU) and TAU +app+¿web. All participants will receive the usual treatment for their pain. Only the TAU +app+¿web group use Pain Monitor app, which generates alarms that are sent to the physicians in the face of previously established undesired events. Physicians will be able to monitor app reports using a web application, which might result in an adjustment of treatment. We anticipate that the use of Pain Monitor plus the therapist web will result in a reduction of pain intensity and side effects of the medication. Improvements on secondary outcomes, namely fatigue, mood, pain interference, rescue medication use and quality of life, are also expected. Mixed repeated-measure multivariate analyses of variances will be conducted to investigate whether there are differences between preassessment and postassessment scores as a function of the experimental condition. ETHICS AND DISSEMINATION: Ethical approval from the Hospital General Universitari de Castellon was obtained. The findings will be published in peer-reviewed journals

Ecological momentary intervention to enhance emotion regulation in healthcare workers via smartphone: a randomized controlled trial protocol

Background: CUIDA-TE is an APP that offers transdiagnostic cognitive behavioral therapy focused on enhancing emotion regulation. As a novelty, it incorporates ecological momentary interventions (EMI), which can provide psychological support in real time, when suffering arises. The main goal of the study is to evaluate the efficacy of CUIDA-TE to improve emotion regulation in healthcare workers, a population that has been particularly emotionally impacted by the COVID-19 pandemic. Methods: In this three-arm, randomized controlled trial (RCT) the study sample will be composed of a minimum of 174 healthcare workers. They will be randomly assigned to a 2-month EMI group (CUIDA-TE APP, n = 58), a 2-month ecological momentary assessment (EMA) only group (MONITOR EMOCIONAL APP, n = 58), or a wait-list control group (no daily monitoring nor intervention, n = 58). CUIDA-TE will provide EMI if EMA reveals emotional problems, poor sleep quality/quantity, burnout, stress, or low perceived self-efficacy when regulating emotions. Depression will be the primary outcome. Secondary outcomes will include emotion regulation, quality of life, and resilience. Treatment acceptance and usability will also be measured. Primary and secondary outcomes will be obtained at pre- and post-intervention measurements, and at the 3-month follow-up for all groups. Discussion: To our knowledge, this is the first RCT that evaluates the efficacy of an APP-based EMI to improve emotion regulation skills in healthcare workers. This type of intervention might ultimately help disseminate treatments and reach a larger number of individuals than traditional face-to-face individual therapies. Trial registration: ClinicalTrial.gov: NCT04958941 Registered 7 Jun 2021. Study status: Participant recruitment has not started

Exploring the Touch and Motion Features in Game-Based Cognitive Assessments

Early detection of cognitive decline is important for timely intervention and treatment strategies to prevent further deterioration or development of more severe cognitive impairment, as well as identify at risk individuals for research. In this paper, we explore the feasibility of using data collected from built-in sensors of mobile phone and gameplay performance in mobile-game-based cognitive assessments. Twenty-two healthy participants took part in the two-session experiment where they were asked to take a series of standard cognitive assessments followed by playing three popular mobile games in which user-game interaction data were passively collected. The results from bivariate analysis reveal correlations between our proposed features and scores obtained from paper-based cognitive assessments. Our results show that touch gestural interaction and device motion patterns can be used as supplementary features on mobile game-based cognitive measurement. This study provides initial evidence that game related metrics on existing off-the-shelf games have potential to be used as proxies for conventional cognitive measures, specifically for visuospatial function, visual search capability, mental flexibility, memory and attention