Understanding People’s Experience for Physical Activity Planning and Exploring the Impact of Historical Records on Plan Creation and Execution

Xu, Kefan; Yan, Xinghui; Newman, Mark

doi:10.1145/3491102.3501997

Cited by 7 publications

(4 citation statements)

References 53 publications

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“…One of the challenges is the identification and definition of unexpected events. Existing studies focus on capturing common contextualized data (eg, weather, location, temporal factors, and activity data) as historical records [99,100]. These records serve as a reference for identifying deviations from the norm or unusual data, enabling the inference of unexpected events.…”

Section: Forming Intention and Action Controlmentioning

confidence: 99%

Digital Behavior Change Intervention Designs for Habit Formation: Systematic Review

Zhu,

Long,

Wang

et al. 2024

J Med Internet Res

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Background With the development of emerging technologies, digital behavior change interventions (DBCIs) help to maintain regular physical activity in daily life. Objective To comprehensively understand the design implementations of habit formation techniques in current DBCIs, a systematic review was conducted to investigate the implementations of behavior change techniques, types of habit formation techniques, and design strategies in current DBCIs. Methods The process of this review followed the PRISMA (Preferred Reporting Item for Systematic Reviews and Meta-Analyses) guidelines. A total of 4 databases were systematically searched from 2012 to 2022, which included Web of Science, Scopus, ACM Digital Library, and PubMed. The inclusion criteria encompassed studies that used digital tools for physical activity, examined behavior change intervention techniques, and were written in English. Results A total of 41 identiﬁed research articles were included in this review. The results show that the most applied behavior change techniques were the self-monitoring of behavior, goal setting, and prompts and cues. Moreover, habit formation techniques were identified and developed based on intentions, cues, and positive reinforcement. Commonly used methods included automatic monitoring, descriptive feedback, general guidelines, self-set goals, time-based cues, and virtual rewards. Conclusions A total of 32 commonly design strategies of habit formation techniques were summarized and mapped to the proposed conceptual framework, which was categorized into target-mediated (generalization and personalization) and technology-mediated interactions (explicitness and implicitness). Most of the existing studies use the explicit interaction, aligning with the personalized habit formation techniques in the design strategies of DBCIs. However, implicit interaction design strategies are lacking in the reviewed studies. The proposed conceptual framework and potential solutions can serve as guidelines for designing strategies aimed at habit formation within DBCIs.

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Section: Forming Intention and Action Controlmentioning

confidence: 99%

Digital Behavior Change Intervention Designs for Habit Formation: Systematic Review

Zhu,

Long,

Wang

et al. 2024

J Med Internet Res

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“…This phenomenon is called the intention-behavior gap . Planning plays a considerable role in bridging the gap between intention and behavior for implementing health behaviors (Ziegelmann et al, 2006;Xu et al, 2022). Planning is the understanding of the mental simulation of behavior to facilitate the conditions for attainment behavior and an economical and effective method to achieve the goal.…”

Section: Volitional Phasementioning

confidence: 99%

“…There are two types of planning: action planning and coping planning. Action planning determines the when, where, and how to act, and coping planning predicts dangerous situations and the mental simulation of obstacles and readiness for successful adaptation (Sniehotta et al, 2005;Xu et al, 2022). The findings of various researchers show that behavioral intention has a significant effect on action planning and coping planning (Hagger et al, 2017;Brown et al, 2018;Strong et al, 2018;Lin et al, 2020).…”

Section: Volitional Phasementioning

confidence: 99%

Uptake of climate-smart agricultural technologies and practices: a three-phase behavioral model

Khademi Noshabadi,

Omidi Najafabadi,

Mirdamadi

2024

Front. Sustain. Food Syst.

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Climate change due to human activities is a reality and a growing threat to global food security. The trend of increasing temperature and decreasing rainfall in Iran shows climate warming, the result of climate change in Iran. Climate-smart agriculture (CSA) is an approach to developing new agricultural practices that support food security against climate change. The purpose of this study was to design a behavioral model for using CSA technologies in three phases: cognitive, motivational, and volitional, using the theories of planned behavior, self-determination, and social cognition. This research is the first to combine behavioral models to understand the application of CSA technologies. The research method in this study was quantitative and non-experimental. The statistical population included 800 wheat farmers from the city of Nazarabad in the Alborz province of Iran. A sample size of 260 people was determined using the Cochran formula. Appropriate stratified sampling was selected as the sampling method. Wheat farmers were then randomly selected in each village through proportional assignment. The research data was collected through a cross-sectional survey and a questionnaire. The models and hypotheses were tested using structural equation modeling. The statistical results confirmed the theoretical model and 15 research hypotheses. Additionally, the estimated field model explained about 62% of the variance in the behavior of using CSA technologies. Therefore, cognition, motivation, and volition effectively shape the behavior of using CSA technologies. In the cognitive phase, environmental concerns have the greatest impact on attitude toward the behavior of using CSA technologies (β = 0.714). In the motivational phase, personal norms have the most impact on the intention to use CSA technologies (β = 0.643). Finally, in the volitional phase, the intention to use CSA technologies has the highest impact on the behavior of using CSA technologies (β = 0.386). The increase in the guaranteed purchase price of wheat and paying soft and long-term loans by the policymakers will lead to an increase in the intention of wheat farmers to use CSA technologies.

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“…Our findings further suggest that such assistance should be offered at the moment of perceiving behavioral support, as users are unlikely to attend to the intervention message later. Researchers and designers may consider adding and improving interaction features to support quick planning, identifying opportune moments with the help of calendars [53], and brainstorming possible adjusted behaviors that are "close" to suggested ones (eg, "If you do not have extra time for a walk outside, you can do chores or gardening to get more steps!"). These features may enhance users' ability to integrate behavioral support into their daily lives.…”

Section: Facilitating In-the-moment Decision-making In Response To Be...mentioning

confidence: 99%

Identifying Design Opportunities for Adaptive mHealth Interventions That Target General Well-Being: Interview Study With Informal Care Partners

Yan,

Newman,

Park

et al. 2023

JMIR Form Res

Self Cite

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Background Mobile health (mHealth) interventions can deliver personalized behavioral support to users in daily contexts. These interventions have been increasingly adopted to support individuals who require low-cost and low-burden support. Prior research has demonstrated the feasibility and acceptability of an mHealth intervention app (CareQOL) designed for use with informal care partners. To further optimize the intervention delivery, we need to investigate how care partners, many of whom lack the time for self-care, react and act in response to different behavioral messages. Objective The goal of this study was to understand the factors that impact care partners’ decision-making and actions in response to different behavioral messages. Insights from this study will help optimize future tailored and personalized behavioral interventions. Methods We conducted semistructured interviews with participants who had recently completed a 3-month randomized controlled feasibility trial of the CareQOL mHealth intervention app. Of the 36 participants from the treatment group of the randomized controlled trial, 23 (64%) participated in these interviews. To prepare for each interview, the team first selected representative behavioral messages (eg, targeting different health dimensions) and presented them to participants during the interview to probe their influence on participants’ thoughts and actions. The time of delivery, self-reported perceptions of the day, and user ratings of a message were presented to the participants during the interviews to assist with recall. Results The interview data showed that after receiving a message, participants took various actions in response to different messages. Participants performed suggested behaviors or adjusted them either immediately or in a delayed manner (eg, sometimes up to a month later). We identified 4 factors that shape the variations in user actions in response to different behavioral messages: uncertainties about the workload required to perform suggested behaviors, concerns about one’s ability to routinize suggested behaviors, in-the-moment willingness and ability to plan for suggested behaviors, and overall capability to engage with the intervention. Conclusions Our study showed that care partners use mHealth behavioral messages differently regarding the immediacy of actions and the adaptation to suggested behaviors. Multiple factors influence people’s perceptions and decisions regarding when and how to take actions. Future systems should consider these factors to tailor behavioral support for individuals and design system features to support the delay or adaptation of the suggested behaviors. The findings also suggest extending the assessment of user adherence by considering the variations in user actions on behavioral support (ie, performing suggested or adjusted behaviors immediately or in a delayed manner). International Registered Report Identifier (IRRID) RR2-10.2196/32842

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Understanding People’s Experience for Physical Activity Planning and Exploring the Impact of Historical Records on Plan Creation and Execution

Cited by 7 publications

References 53 publications

Digital Behavior Change Intervention Designs for Habit Formation: Systematic Review

Digital Behavior Change Intervention Designs for Habit Formation: Systematic Review

Uptake of climate-smart agricultural technologies and practices: a three-phase behavioral model

Identifying Design Opportunities for Adaptive mHealth Interventions That Target General Well-Being: Interview Study With Informal Care Partners

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