A computational cognitive model of self-efficacy and daily adherence in mHealth

Pirolli, Peter

doi:10.1007/s13142-016-0391-y

Cited by 23 publications

(22 citation statements)

References 35 publications

(36 reference statements)

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“…In recent years, we assisted the first attempts of developing computational models based on self-efficacy theory in order to promote PA [29,30]. Self-efficacy theory is particularly suitable to be modeled because of its nature that is explicitly dynamic (i.e., it takes into account time-varying information such as individual achievements, self-efficacy beliefs and expectations) and, thus, permits adapting the intervention to the individual over the course of the intervention itself [12].…”

Section: Introductionmentioning

confidence: 99%

“…First computational models of self-efficacy focused on different approaches and frameworks. Pirolli [30] proposed a computational model, called ACT-R-DStress, aiming to (i) model interactions among behavioral goals, memories of past experiences, and behavioral performance, and (ii) explains and predict both the dynamics of self-efficacy and the individual performance in an exercise program. For these purposes the ACT-R-DStress exploited the computational neurocognitive architecture that characterizes the ACT-R theory's simulation environment [32].…”

Section: Introductionmentioning

confidence: 99%

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Improving Physical Activity mHealth Interventions: Development of a Computational Model of Self-Efficacy Theory to Define Adaptive Goals for Exercise Promotion

Baretta

Sartori

Greco

et al. 2019

Advances in Human-Computer Interaction

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The practice of regular physical exercise is a protective factor against noncommunicable diseases and premature mortality. In spite of that, large part of the population does not meet physical activity guidelines and many individuals live a sedentary life. Recent technological progresses and the widespread adoption of mobile technology, such as smartphone and wearables, have opened the way to the development of digital behaviour change interventions targeting physical activity promotion. Such interventions would greatly benefit from the inclusion of computational models framed on behaviour change theories and model-based reasoning. However, research on these topics is still at its infancy. The current paper presents a smartphone application and wearable device system called Muoviti! that targets physical activity promotion among adults not meeting the recommended physical activity guidelines. Specifically, we propose a computational model of behaviour change, grounded on the social cognitive theory of self-efficacy. The purpose of the computational model is to dynamically integrate information referring to individuals’ self-efficacy beliefs and physical activity behaviour in order to define exercising goals that adapt to individuals’ changes over time. The paper presents (i) the theoretical constructs that informed the development of the computational model, (ii) an overview of Muoviti! describing the system dynamics, the graphical user interface, the adopted measures and the intervention design, and (iii) the computational model based on Dynamic Decision Network. We conclude by presenting early results from an experimental study.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improving Physical Activity mHealth Interventions: Development of a Computational Model of Self-Efficacy Theory to Define Adaptive Goals for Exercise Promotion

Baretta

Sartori

Greco

et al. 2019

Advances in Human-Computer Interaction

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“…Participants may benefit from just-in-time, scheduled, and self-triggered reminders to consider relevant ERB that they can take to reduce health risks from smoke episodes (Pirolli, 2016;Pirolli et al, 2017). The smartphone platform provides an avenue to enhance social normative learning and to evaluate the impact of risk and behavior messages to those who are affected by smoke.…”

Section: Advances Using Citizen Science and Smartphone Approachesmentioning

confidence: 99%

“…The application provides a platform for testing different behavioral mechanisms such as implementation intentions, which help people translate goal intentions into behaviors. Participants may benefit from just-in-time, scheduled, and self-triggered reminders to consider relevant ERB that they can take to reduce health risks from smoke episodes (Pirolli, 2016;Pirolli et al, 2017). Smartphone applications have been shown to influence human behavior.…”

Section: Advances Using Citizen Science and Smartphone Approachesmentioning

confidence: 99%

Smoke Sense Initiative Leverages Citizen Science to Address the Growing Wildfire‐Related Public Health Problem

et al. 2019

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Smoke Sense is a citizen science project with investigative, educational, and action-oriented objectives at the intersection of wildland fire smoke and public health. Participants engage with a smartphone application to explore current and forecast visualizations of air quality, learn about how to protect health from wildfire smoke, and record their smoke experiences, health symptoms, and behaviors taken to reduce their exposures to smoke. Through participation in the project, individuals engage in observing changes in their environment and recording changes in their health, thus facilitating progression on awareness of health effects of air pollution and adoption of desired health-promoting behaviors. Participants can also view what others are reporting. Data from the pilot season (1 August 2017 to 7 January 2018; 5,598 downloads) suggest that there is a clear demand for personally relevant data during wildfire episodes motivated by recognition of environmental hazard and the personal concern for health. However, while participants shared clear perceptions of the environmental hazard and health risks in general, they did not consistently recognize their own personal health risk. The engagement in health protective behavior was driven in response to symptoms rather than as preventive courses of action. We also observed clear differences in the adoption likelihood of various health protective behaviors attributed to barriers and perceived benefits of these actions. As users experience a greater number and severity of symptoms, the perceived benefits of taking health protective actions exceeded the costs associated with the barriers and thus increased adoption of those actions. Based on pilot season data, we summarize key insights which may improve current health risk communications in nudging individuals toward health protective behavior; there is a need to increase personal awareness of risk and compelling evidence that health protective behaviors are beneficial.Plain Language Summary An observed increase in wildfire smoke exposure highlights the need for best practices in promoting protective health behaviors during wildfire smoke events. Understanding perceptions, motivations, and barriers to behavioral change (physical, social, or financial) among impacted individuals can provide insights into how to improve health risk communication and achieve better public health outcomes during smoke events. However, almost nothing is known about how current recommendations are perceived, adopted, and adhered to by individuals impacted by smoke. Smoke Sense is a citizen science project which aims to engage participants by encouraging them to observe their environment and record changes in their health, thus facilitating progression on awareness of health effects of air pollution and adoption of desired health-promoting behaviors. We examined the feasibility of engaging smartphone users in a citizen science study and investigated the range of behavioral responses taken when experiencing smoke and health symptoms. Based on p...

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“…An adaptive intervention that modulated exercise difficulty to user ability increased adherence when compared to statically scheduled controls [17]. The computational model in [30] suggests that the individualized modulation of exercise difficulty affects selfefficacy which in turn determined adherence rates. While these interventions may be effective, they involve major restructuring of systems.…”

Section: Adherence and Attritionmentioning

confidence: 99%

Leveraging Self-Affirmation to Improve Behavior Change: A Mobile Health App Experiment (Preprint)

Springer¹,

Venkatakrishnan²,

Mohan³

et al. 2017

Preprint

Self Cite

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Background:Mobile health (mHealth) interventions can improve the lives of participants by enhancing both physical and mental wellbeing. Unfortunately, mHealth interventions often suffer from low adherence. One possible way to increase adherence is the usage of self-affirmations. Self-affirmations are writing exercises that have been effective in increasing behavior change in many realms. However, current self-affirmations involve a level of writing not amenable to deployment on mobile platforms. Objective:The aim of this experiment was to adapt a self-affirmation exercise to a form better suited for delivery through a mobile application and test effectiveness of differing self-affirmation doses on adherence to behavior change goals over time. Methods:We examine how the self-affirmation doses affect behavior change using a 2x2 factorial design with 127 participants using an mHealth application. Participants were asked to adhere to a daily goal of eating 5 servings of fruits and vegetables. Participants were divided into the 4 total conditions based on 2 independent variables. The first independent variable was whether the participant received an initial self-affirmation exercise as part of their onboarding. The second independent variable was whether the participant received ongoing booster self-affirmations throughout the 28 day study. Results:Analysis of 3556 observations from 127 participants indicates that higher doses of self-affirmation result in improved adherence to mHealth intervention goals, (coefficient = 1.42, SE = 0.71, p = .044). Under our conservative definition of attrition, this increase in adherence does not seem to translate to a decrease in participant attrition (.61 < p's < .96). Finally we examine the mechanisms by which self-affirmation may affect intentions of behavior change; we build a strong model of intention (R 2 =.39, p < .001) but self-affirmation does not directly affect final intentions (.09 < p's < .93). Conclusions:Self-affirmations can successfully increase adherence to recommended diet and health goals using an mHealth application. These self-affirmations are easy to implement and low cost for both users and developers. We recommend that other mHealth applications integrate self-affirmations to examine their effects on adherence and behavior change in the context of other mHealth applications.

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A computational cognitive model of self-efficacy and daily adherence in mHealth

Cited by 23 publications

References 35 publications

Improving Physical Activity mHealth Interventions: Development of a Computational Model of Self-Efficacy Theory to Define Adaptive Goals for Exercise Promotion

Improving Physical Activity mHealth Interventions: Development of a Computational Model of Self-Efficacy Theory to Define Adaptive Goals for Exercise Promotion

Smoke Sense Initiative Leverages Citizen Science to Address the Growing Wildfire‐Related Public Health Problem

Leveraging Self-Affirmation to Improve Behavior Change: A Mobile Health App Experiment (Preprint)

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