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/

Spartan Daily, February 19, 1981

Volume 76, Issue 17https://scholarworks.sjsu.edu/spartandaily/6721/thumbnail.jp

Spartan Daily, February 19, 1981

Volume 76, Issue 17https://scholarworks.sjsu.edu/spartandaily/6721/thumbnail.jp

A Prediction-Based Framework to Reduce Procrastination in Adaptive Learning Systems

Procrastination and other types of dilatory behaviour are common in online learning, especially in higher education. While procrastination is associated with worse performance and discomfort, positive forms of delay can be used as a deliberate strategy without any such consequences. Although dilatory behaviour has received attention in research, it has to my knowledge never been included as an integral part of an adaptive learning system. Differentiating between different types of delay within such a system would allow for tailored interventions to be provided in the future without alienating students who use delay as a successful strategy. In this thesis, I present four studies that provide the basis for such an endeavour. I first discuss the results of two studies that focussed on the prediction of the extent of dilatory behaviour in online assignments. The results of both studies revealed an advantage of objective predictors based on log data over subjective variables based on questionnaires. The predictive performance slightly improved when both sets of predictors were combined. In one of these studies, we implemented Bayesian multilevel models while the other aimed at comparing various machine learning algorithms to determine the best candidates for a future inclusion in real-time predictive models. The results reveal that the most suitable algorithm depended on the type of predictor, implying that multiple models should be implemented in the field, rather than selecting just one. I then present a framework for an adaptive learning system based on the other two studies, where I highlight how dilatory behaviour can be incorporated into such a system, in light of the previously discussed results. I conclude this thesis by providing an outlook into the necessary next steps before an adaptive learning system focussing on delay can be established

Poetical potentials: the value of poems in social impact education

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.For the technologist it is easy to remain in safe technological enclaves with a bespoke language, a community of like minds and a familiar knowledge base. However, progress requires pushing the boundaries, thinking beyond the traditional and the ordinary, and questioning accepted norms. It requires opening of minds. It may surprise the reader that poetry can offer the key to unlock the closed mind. This potential is explored through a variety of poems dealing, in a novel manner, with the social impact of technology

Human Use of Computers Framework: Assessment Using the Computer Procrastination Problem

As more and more human living involves interacting with various computer systems, it becomes increasingly important to understand the full picture of what is involved in such computer use. Without such an understanding, we will be limited in our ability to either design systems and practices which maximize benefit and human flourishing, or to recognize, understand, and address dysfunction where it occurs. Basden’s (2008) Human Use of Computers Framework provides a structure for considering many facets of computer use. Based on the philosophy of Herman Dooyeweerd, Basden’s framework considers computer use as the simultaneous functioning of a.) humans interacting with the computer, b.) engaging with the content, and c.) living with computers in their everyday lives. Each of the three categories of functioning can be analysed in each of Dooyeweerd’s 15 aspects of reality. This framework is a promising structure for providing rich understanding, but its ability to provide useful insight had not been tested or verified. The original contribution to knowledge of this thesis is an assessment of the framework and a demonstration that it does indeed provide insight when used to analyse various computer use situations, including complex or problematic situations. It demonstrates this through an analysis of the problem of computer procrastination, which makes a suitable test case because it is complex, interdisciplinary, and understudied. In addition, the thesis extends the framework by providing an understanding of how normativity and responsibility flow between the simultaneous functionings

CatAlyst: Domain-Extensible Intervention for Preventing Task Procrastination Using Large Generative Models

CatAlyst uses generative models to help workers' progress by influencing their task engagement instead of directly contributing to their task outputs. It prompts distracted workers to resume their tasks by generating a continuation of their work and presenting it as an intervention that is more context-aware than conventional (predetermined) feedback. The prompt can function by drawing their interest and lowering the hurdle for resumption even when the generated continuation is insufficient to substitute their work, while recent human-AI collaboration research aiming at work substitution depends on a stable high accuracy. This frees CatAlyst from domain-specific model-tuning and makes it applicable to various tasks. Our studies involving writing and slide-editing tasks demonstrated CatAlyst's effectiveness in helping workers swiftly resume tasks with a lowered cognitive load. The results suggest a new form of human-AI collaboration where large generative models publicly available but imperfect for each individual domain can contribute to workers' digital well-being.Comment: Accepted by ACM CHI Conference on Human Factors in Computing Systems (CHI '23

Learning and procrastination: A review of publications from 2005 to 2015

Aprendizagem e procrastinação são dois fenômenos inter-relacionados. Além disso, tanto aprendizagem quanto procrastinação parecem impactar todas as esferas da vida social moderna. O objetivo deste artigo é identificar evidências científicas do período 2005 a 2015 sobre as relações conceituais entre os dois fenômenos. Por meio de revisão sistemática da literatura é possível identificar três categorias de conceitos centrais envolvendo esses dois construtos (autoeficácia, autorregulação e o papel das emoções e dos valores do indivíduo), além do uso de ferramentas para redução da procrastinação no ambiente da aprendizagem. O artigo apresenta uma síntese das proposições elaboradas nos estudos prévios levantados sobre as relações dessas categorias com a aprendizagem e a procrastinação. Para auxiliar na apreensão das proposições apresentadas, é elaborado um mapa conceitual das relações entre os conceitos investigados pela revisão. Conclui-se que há viés epistemológico nas visões sobre os dois fenômenos, sugerindo a oportunidade de exploração do tema por meio de novas abordagensLearning and procrastination are two interrelated phenomena. In addition, both learning and procrastination appear to impact all domains of modern social life. The paper aims at identifying scientific evidences of publications between the years of 2005 and 2015 on the conceptual relationship between both phenomena. The systematic literature review method presents three categories of core concepts involving those two constructs (self-efficacy, self-regulation and the role of individual emotions and values), and the use of tools to reduce procrastination in learning environments. The paper presents an overview of the statements presented on the previous studies about the relationship of these categories with learning and procrastination. In order to help the apprehension of the presented statements, it is developed a conceptual map of the relationships between the concepts investigated by the review. We conclude that there is a bias in the epistemological views on the two phenomena, suggesting an opportunity to explore new approache

An exploration of problematic smartphone use among Chinese university students: associations with academic anxiety, academic procrastination, self-Regulation and subjective wellbeing

Over the past decade, there have been an increasing number of studies that have investigated problematic and/or 'addictive' smartphone use. The present study explored the prevalence and correlates of problematic smartphone use (PSU) among Chinese university students. Few studies have investigated relationships between PSU and factors such as academic anxiety, academic procrastination, self-regulation, and subjective wellbeing. The present study proposed and tested a hypothetical model of relationships between PSU and these factors. A total of 475 Chinese university students completed a paper-based survey during class breaks. The survey comprised a battery of psychometric scales translated into Chinese translations examining the study variables (i.e., academic anxiety, academic procrastination, self-regulation, life satisfaction, and PSU). Path analysis was applied to test the hypothetical model. A good model fit was found (CFI = 1.00, RMSEA = 0.008), in which PSU predicted academic procrastination (β = 0.21, p < 0.001) and academic anxiety (β = 0.18, p < 0.01). Also, self-regulation predicted PSU (β = − 0.35, p < 0.001), academic anxiety (β = − 0.29, p < 0.001), academic procrastination (β = 0.23, p < 0.001) and life satisfaction (β = 0.23, p < 0.001). PSU mediated the relationships between self-regulation, and both academic anxiety and academic procrastination. The present study enhances our understanding of the role of problematic smartphone use in relation to academic behaviour, mental health and wellbeing of college students