Advancing Models and Theories for Digital Behavior Change Interventions

Hekler, Eric B.; Michie, Susan; Pavel, Misha; Rivera, Daniel E.; Collins, Linda M.; Jimison, Holly; Garnett, Claire; Parral, Skye; Spruijt‐Metz, Donna

doi:10.1016/j.amepre.2016.06.013

Cited by 157 publications

(129 citation statements)

References 47 publications

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“…Theoretical considerations have served an important function in the design of health interventions [15] by supporting the transference of acquired knowledge between related domains [9]. This is reflected in HCI, where there have been numerous studies engaging with or inspired by behavior theory.…”

Section: Hci and Cognitive Modelsmentioning

confidence: 99%

Designing for Diabetes Decision Support Systems with Fluid Contextual Reasoning

Katz

Price

Holland

et al. 2018

Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems

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Type 1 diabetes is a potentially life-threatening chronic condition that requires frequent interactions with diverse data to inform treatment decisions. While mobile technologies such as blood glucose meters have long been an essential part of this process, designing interfaces that explicitly support decision-making remains challenging. Dual-process models are a common approach to understanding such cognitive tasks. However, evidence from the first of two studies we present suggests that in demanding and complex situations, some individuals approach disease management in distinctive ways that do not seem to fit well within existing models. This finding motivated, and helped frame our second study, a survey (n=192) to investigate these behaviors in more detail. On the basis of the resulting analysis, we posit Fluid Contextual Reasoning to explain how some people with diabetes respond to particular situations, and discuss how an extended framework might help inform the design of user interfaces for diabetes management.

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Section: Hci and Cognitive Modelsmentioning

confidence: 99%

Designing for Diabetes Decision Support Systems with Fluid Contextual Reasoning

Katz

Price

Holland

et al. 2018

Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems

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“…Moreover, adap-tive interventions should permit tailoring for individual differences to be supplemented by "within-person" tailoring as the individual's needs and status change. 15 Context sensing (using mobile or environmental sensors to detect features of the person's current behavior and circumstances) should enable timely delivery of content and notifications tailored to the individual's immediate situation 50 ; for example, activity sensors have been used successfully to detect sedentary behavior and prompt physical activity breaks. Although context sensing should increase engagement by enhancing the perceived attune-ment of the intervention, limited research has yet examined this assumption owing to the novelty of this technology.…”

Section: Promoting Effective Engagementmentioning

confidence: 99%

“…The novel research designs that can support these analyses are discussed in companion papers in this issue. 15,34,38 Psychophysiological measurements, ranging from skin conductance and heart rate to facial expression or fMRI, have been used to measure users' task engagement. 39 Such measures can help identify aspects of the inter-vention that attract attention or evoke emotional arousal, suggesting mechanisms through which DBCI content or design impact shortterm engagement.…”

Section: Conceptualizing Engagementmentioning

confidence: 99%

Understanding and Promoting Effective Engagement With Digital Behavior Change Interventions

Yardley

Spring

Riper

et al. 2016

American Journal of Preventive Medicine

910

865

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This paper is one in a series developed through a process of expert consensus to provide an overview of questions of current importance in research into engagement with digital behavior change interventions, identifying guidance based on research to date and priority topics for future research. The first part of this paper critically reflects on current approaches to conceptualizing and measuring engagement. Next, issues relevant to promoting effective engagement are discussed, including how best to tailor to individual needs and combine digital and human support. A key conclusion with regard to conceptualizing engagement is that it is important to understand the relationship between engagement with the digital intervention and the desired behavior change. This paper argues that it may be more valuable to establish and promote "effective engagement," rather than simply more engagement, with "effective engagement" defined empirically as sufficient engagement with the intervention to achieve intended outcomes. Appraisal of the value and limitations of methods of assessing different aspects of engagement highlights the need to identify valid and efficient combinations of measures to develop and test multidimensional models of engagement. The final section of the paper reflects on how interventions can be designed to fit the user and their specific needs and context. Despite many unresolved questions posed by novel and rapidly changing technologies, there is widespread consensus that successful intervention design demands a user-centered and iterative approach to development, using mixed methods and in-depth qualitative research to progressively refine the intervention to meet user requirements.

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“…Despite the proliferation of mobile digital technologies for health behaviors, few are grounded in behavioral change theories or techniques [42][43][44] and the development of theories for technology-based behavior change is in its infancy [45]. The effective translation of behavioral interventions to interactive digital technologies may be highly dependent upon the fit between the target behaviors, the specific theoretically-grounded behavior change strategies employed in the technology, as well as the relationship between the functional aspects of the technology and user preferences.…”

Section: Applying Intervention Strategies To Interactive Digital Techmentioning

confidence: 99%

Theoretically-Based Emotion Regulation Strategies Using a Mobile App and Wearable Sensor Among Homeless Adolescent Mothers: Acceptability and Feasibility Study (Preprint)

Leonard¹,

Casarjian²,

Fletcher³

et al. 2017

Preprint

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Background: Many adolescent mothers (AMs) are parenting young children under highly stressful conditions as they are managing first-time parenthood, poverty, lack of housing, school/work demands, and challenging peer and familial relationships. Increasing AMs effective emotion regulation strategies using 'just-in-time' mobile technology has the potential to intervene at various points in the emotion regulation process to provide support for more adaptive emotional and behavioral regulation in the course of their daily life. Objective: The goal of this paper is to examine the acceptability, feasibility, use patterns, and mechanisms by which the technology fostered AMs adaptive emotion regulation strategies under real-life conditions. Methods: Participants (N=49) were enrolled in the intervention condition of a randomizedcontrolled pilot study of homeless AMs living in group-based shelters. Intervention activities involved a blended intervention consisting of in-person, provider-delivered intervention sessions and the companion Calm Mom technology. Calm Mom consisted of a mobile app and wrist-worn sensorband for the ambulatory measurement and alerting of increased electrodermal activity (EDA), a physiological measurement of stress. We examined logs of mobile app activity and conducted semi-structured qualitative interviews with a subsample (N= 10) of participants. Qualitative data analysis was guided by the theoretical frames of the intervention and a technology acceptance models and included an analysis of emerging themes and concepts. Results: Participants used one or more elements of the technology on average, 44.11% of days they had the Calm Mom technology (SD = 24.82). Overall, participants indicated that one or more of the elements Calm Mom supported their ability to effectively regulate their emotions in the course of their daily life in ways that were consonant with the intervention's theoretical model. For many AMs, the app became an integral tool for managing stress. Due to technical challenges, fewer participants received sensorband alerts however, those that received alerts reported high levels of acceptability as the technology helped them to identify their emotions and supported them in engaging in more adaptive behaviors during real-life stressful situations with their children, peers, and family members. Conclusions: Calm Mom is a promising technology for providing theoretically driven behavioral intervention strategies during real-life stressful moments among a highly vulnerable population. Future research efforts will involve addressing technology challenges and refining tailoring algorithms for implementation in larger scale studies.

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Advancing Models and Theories for Digital Behavior Change Interventions

Cited by 157 publications

References 47 publications

Designing for Diabetes Decision Support Systems with Fluid Contextual Reasoning

Designing for Diabetes Decision Support Systems with Fluid Contextual Reasoning

Understanding and Promoting Effective Engagement With Digital Behavior Change Interventions

Theoretically-Based Emotion Regulation Strategies Using a Mobile App and Wearable Sensor Among Homeless Adolescent Mothers: Acceptability and Feasibility Study (Preprint)

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