How do we engage with activity trackers?

Gouveia, Rúben; Karapanos, Evangelos; Hassenzahl, Marc

doi:10.1145/2750858.2804290

Cited by 173 publications

(118 citation statements)

References 38 publications

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“…This is in contrast to previous work on adoption patterns of the technology, which showed that users earlier in the process of behavior change are more likely to adopt and keep using a PI application (Gouveia, Karapanos, & Hassenzahl, 2015). This suggests that although users look for support from PI tools more often in early stages, the applications currently available support users best in later stages.…”

Section: Cautious Optimismcontrasting

confidence: 93%

Personal Informatics, Self-Insight, and Behavior Change: A Critical Review of Current Literature

Dijk

Westerink

Beute

et al. 2017

Human–Computer Interaction

117

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Personal informatics (PI) systems allow users to collect and review personally relevant information. The purpose commonly envisioned for these systems is that they provide users with actionable, data-driven selfinsight to help them change their behavioral patterns for the better. Here, we review relevant theory as well as empirical evidence for this selfimprovement hypothesis. From a corpus of 6,568, only 24 studies met the selection criteria of being a peer-reviewed empirical study reporting on actionable, data-driven insights from PI data, using a "clean" PI system with no other intervention techniques (e.g., additional coaching) on a nonclinical population. First results are promising-many of the selected articles report users gaining actionable insights-but we do note a number of methodological issues that make these results difficult to interpret. We conclude that more work is needed to investigate the self-improvement hypothesis and provide a set of recommendations for future work.

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Section: Cautious Optimismcontrasting

confidence: 93%

Personal Informatics, Self-Insight, and Behavior Change: A Critical Review of Current Literature

Dijk

Westerink

Beute

et al. 2017

Human–Computer Interaction

117

View full text Add to dashboard Cite

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“…QS applications generally suffer from lack of user engagement and motivation [1,3,9], to which, Crowdwalk, for example, offers a solution by providing insights about the user and his or her actions. We suggest that QS applications can perform automated analyses, seeking for significant correlations among users' behaviors, contextual factors, and psychological states, and thus lowering the motivational demands for the user.…”

Section: Related Workmentioning

confidence: 99%

Impact of mood changes on application selection

Visuri

Sarsenbayeva

Gonçalves

et al. 2016

Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct

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Users of quantified self applications habitually log and track personal information, such as mood. Attempts to automate the procedure of logging mood have been made, but applications themselves rarely provide insights into the user's mental well-being. In this paper we explore data from two small scale studies related to mobile device usage and mood tracking. We analyse associations between user's mood throughout the day and the use of smartphone applications from different categories. Our analysis provides insights into the user's behaviour based on their device usage. These insights mean that QS applications can independently use simple analysis tools to provide similar insights for the user.

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“…The use of instant feedback and positive reinforcement from learning theories are common use in mHealth applications [29,47]; Health Belief Model (HBM) has been used in mHealth interventions for self-management and health promotion [52][53][54]; the Transtheoretical Model (TTM) has been used in mobile solutions for smoke cessation and other addictive behaviors [55][56][57][58]; physical activity and fitness interventions use the Theory of Planned Behavior (TPB) [29,50,59], as well as self-regulation theories [29,[60][61][62][63]. The basis for Social Cognitive Theories (SCT) can be found in many interventions using health apps for disease management [64][65][66] and goal setting is very often used in mHealth apps [60,67].…”

Section: Behavioral Changementioning

confidence: 99%

3MD for Chronic Conditions, a Model for Motivational mHealth Design: Embedded Case Study (Preprint)

Giunti¹

2018

Preprint

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Chronic conditions are the leading cause of death in the world. Major improvements in acute care and diagnostics have created a tendency towards the chronification of formerly terminal conditions, requiring people with these conditions to learn how to self-manage. Mobile technologies hold promise as self-management tools due to their ubiquity and costeffectiveness.The delivery of health-related services through the use of mobile technologies (mHealth) has grown exponentially in recent years. However, only a fraction of these solutions takes into consideration the views of relevant stakeholders like healthcare professionals or even patients. The use of behavioral change models (BCM) has proven important in developing successful health solutions, yet engaging patients remains a challenge. There is a trend in mHealth solutions called gamification that attempts to use game elements to drive user behavior and increase engagement. As it stands, designers of mHealth solutions for behavioral change in chronic conditions have no clear way of deciding what factors are relevant to consider.The focus of this work is to discover factors for the design of mHealth solutions for chronic patients; to do so, negotiations between medical knowledge, BCM, and gamification were explored through an embedded case study research methodology. The data obtained was thematically analyzed to create the Model for Motivational Mobile-health Design for chronic conditions (3MD).The 3MD model guides the design of condition-oriented gamified behavioral change mHealth solutions. The main components are: 1) Condition-specific, which describe factors that need to be adjusted and adapted for each particular chronic condition; 2) Motivation-related; which are factors that address how to influence behaviors in an engaging manner; and 3) Technologybased, which are factors that are directly connected to the technical capabilities of mobile technologies. 3MD also provides a series of high level illustrative design questions for designers to use and consider during the design process.The present work addresses a recognized gap in research and practice, and proposes a unique model that could be of use in the generation of new solutions to help chronic patients.

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How do we engage with activity trackers?

Cited by 173 publications

References 38 publications

Personal Informatics, Self-Insight, and Behavior Change: A Critical Review of Current Literature

Personal Informatics, Self-Insight, and Behavior Change: A Critical Review of Current Literature

Impact of mood changes on application selection

3MD for Chronic Conditions, a Model for Motivational mHealth Design: Embedded Case Study (Preprint)

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