Dataset Information

Adults' Preferences for Behavior Change Techniques and Engagement Features in a Mobile App to Promote 24-Hour Movement Behaviors: Cross-Sectional Survey Study.

ABSTRACT:

Background

There is a limited understanding of components that should be included in digital interventions for 24-hour movement behaviors (physical activity [PA], sleep, and sedentary behavior [SB]). For intervention effectiveness, user engagement is important. This can be enhanced by a user-centered design to, for example, explore and integrate user preferences for intervention techniques and features.

Objective

This study aimed to examine adult users' preferences for techniques and features in mobile apps for 24-hour movement behaviors.

Methods

A total of 86 participants (mean age 37.4 years [SD 9.2]; 49/86, 57% female) completed a Web-based survey. Behavior change techniques (BCTs) were based on a validated taxonomy v2 by Abraham and Michie, and engagement features were based on a list extracted from the literature. Behavioral data were collected using Fitbit trackers. Correlations, (repeated measures) analysis of variance, and independent sample t tests were used to examine associations and differences between and within users by the type of health domain and users' behavioral intention and adoption.

Results

Preferences were generally the highest for information on the health consequences of movement behavior self-monitoring, behavioral feedback, insight into healthy lifestyles, and tips and instructions. Although the same ranking was found for techniques across behaviors, preferences were stronger for all but one BCT for PA in comparison to the other two health behaviors. Although techniques fit user preferences for addressing PA well, supplemental techniques may be able to address preferences for sleep and SB in a better manner. In addition to what is commonly included in apps, sleep apps should consider providing tips for sleep. SB apps may wish to include more self-regulation and goal-setting techniques. Few differences were found by users' intentions or adoption to change a particular behavior. Apps should provide more self-monitoring (P=.03), information on behavior health outcome (P=.048), and feedback (P=.04) and incorporate social support (P=.048) to help those who are further removed from healthy sleep. A virtual coach (P<.001) and video modeling (P=.004) may provide appreciated support to those who are physically less active. PA self-monitoring appealed more to those with an intention to change PA (P=.03). Social comparison and support features are not high on users' agenda and may not be needed from an engagement point of view. Engagement features may not be very relevant for user engagement but should be examined in future research with a less reflective method.

Conclusions

The findings of this study provide guidance for the design of digital 24-hour movement behavior interventions. As 24-hour movement guidelines are increasingly being adopted in several countries, our study findings are timely to support the design of interventions to meet these guidelines.

SUBMITTER: DeSmet A

PROVIDER: S-EPMC6942183 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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Publications

Adults' Preferences for Behavior Change Techniques and Engagement Features in a Mobile App to Promote 24-Hour Movement Behaviors: Cross-Sectional Survey Study.

DeSmet Ann A De Bourdeaudhuij Ilse I Chastin Sebastien S Crombez Geert G Maddison Ralph R Cardon Greet G

JMIR mHealth and uHealth 20191220 12

<h4>Background</h4>There is a limited understanding of components that should be included in digital interventions for 24-hour movement behaviors (physical activity [PA], sleep, and sedentary behavior [SB]). For intervention effectiveness, user engagement is important. This can be enhanced by a user-centered design to, for example, explore and integrate user preferences for intervention techniques and features.<h4>Objective</h4>This study aimed to examine adult users' preferences for techniques ...[more]

PMID: 31859680

Similar Datasets

Project description:BackgroundThe Canadian 24-Hour Movement Guidelines for adults provide specific recommendations for levels of physical activity, sedentary behavior, and sleep (ie, the movement behaviors) required for optimal health. Performance of the movement behaviors is associated with improved mental well-being. However, most postsecondary students do not meet the movement behavior recommendations within the Canadian 24-Hour Movement Guidelines and experience increased stress and declining well-being, suggesting the need for an intervention targeting students' movement behaviors.ObjectiveWe aimed to develop and implement a theory-informed intervention intended to improve the movement behaviors and mental well-being of first-year postsecondary students.MethodsThe Intervention Mapping protocol was applied in the development and implementation of the intervention. Intervention Mapping entailed performing a needs assessment, determining the intervention outcomes, selecting theory- and evidence-based change methods and applications, preparing and producing intervention plans and materials, developing the implementation plan, and finally developing an evaluation plan. The Theoretical Domains Framework and the Behavior Change Wheel were also used in conjunction with the Intervention Mapping protocol to ensure a solid theoretical basis for the intervention. This protocol led to the development and implementation of a 6-week, theory-informed ParticipACTION app-based intervention aimed at helping first-year postsecondary students improve their movement behaviors and mental well-being. The developed app content provided students with information on each of the movement behaviors and behavioral strategies (ie, goal setting, action planning, monitoring, and coping planning). The use of Intervention Mapping allowed for the continuous involvement of various multidisciplinary partners and end users, ensuring that the intervention design and implementation was appropriate for the target audience. The feasibility, acceptability, and potential impact of the intervention will be examined in a subsequent proof-of-concept study at 2 Canadian university campuses.ResultsParticipant recruitment occurred during September 2021, and the intervention was conducted from October to December 2021. The deadline for completion of the postintervention questionnaire by participants was mid-December 2021. The analysis of data examining the feasibility, acceptability, and potential impact of the intervention began in January 2022, with the publication of the proof-of-concept evaluation expected in 2023.ConclusionsIntervention Mapping with the Theoretical Domains Framework and Behavior Change Wheel was a useful approach to combine evidence and theoretical concepts to guide the design and implementation of a ParticipACTION app-based intervention targeting postsecondary students' movement behaviors and mental well-being. This process may serve as an example for other researchers developing multiple behavior change app-based interventions. Should the forthcoming evaluation demonstrate the intervention's acceptability, feasibility, and potential impact, the intervention may provide a scalable method of improving postsecondary students' movement behaviors and mental well-being.International registered report identifier (irrid)RR1-10.2196/39977.

Project description:Background Physical activity (PA), sedentary behaviors (SB), and sleep are interrelated behavior components of a 24-hour day. Research interests continue to increase in examining the inter-relationship of three behaviors and their combined effects on health. The purpose of this study was to develop a comprehensive instrument to measure 24-hour movement behaviors for Chinese college students. Methods The 24-hour movement behaviors questionnaire (24HMBQ) was developed based on a literature review and expert review. The target population (Chinese college students) and an expert panel assessed the face and content validity. After the final revision of the questionnaire, the participants (n = 229) were asked to complete the 24HMBQ twice to examine test-retest reliability. Convergent validity was evaluated using Spearman’s rho, by comparing the 24HMBQ estimates of sleep, SB, and PA with results derived from the Pittsburgh Sleep Quality Index (PSQI), the Adult Sedentary Behaviors Questionnaire in China (ASBQC), and the International Physical Activity Questionnaire - Short Form (IPAQ-SF). Results The 24HMBQ exhibited good face validity and high acceptability to respondents. Regarding content validity, the S-CVI/UA and S-CVI/Ave were 0.88 and 0.97, respectively. As indicated by ICC, the test-retest reliability was considered moderate to excellent, ranging from 0.68 to 0.97 (P < 0.01). Regarding the convergent validity, correlations were 0.32 for the duration of sleep per day, 0.33 for total time of physical activity per day, and 0.43 for the duration of sedentary behaviors per day. Conclusion The 24HMBQ is a feasible questionnaire with suitable validity and moderate to excellent test-retest reliability of all items. It is a promising tool to investigate 24-hour movement behaviors of Chinese college students. The 24HMBQ can be administrated in epidemiological studies. Supplementary Information The online version contains supplementary material available at 10.1186/s12889-023-15393-5.

Project description:BackgroundRecently, research focus has shifted to the combination of all 24-h movement behaviors (physical activity, sedentary behavior and sleep) instead of each behavior separately. Yet, no reliable and valid proxy-report tools exist to assess all these behaviors in 0-4-year-old children. By involving end-users (parents) and key stakeholders (researchers, professionals working with young children), this mixed-methods study aimed to 1) develop a mobile application (app)-based proxy-report tool to assess 24-h movement behaviors in 0-4-year-olds, and 2) examine its content validity.MethodsFirst, we used concept mapping to identify activities 0-4-year-olds engage in. Parents (n = 58) and professionals working with young children (n = 21) generated a list of activities, sorted related activities, and rated the frequency children perform these activities. Second, using multidimensional scaling and cluster analysis, we created activity categories based on the sorted activities of the participants. Third, we developed the My Little Moves app in collaboration with a software developer. Finally, we examined the content validity of the app with parents (n = 14) and researchers (n = 6) using focus groups and individual interviews.ResultsThe app has a time-use format in which parents proxy-report the activities of their child, using eight activity categories: personal care, eating/drinking, active transport, passive transport, playing, screen use, sitting/lying calmly, and sleeping. Categories are clarified by providing examples of children's activities. Additionally, 1-4 follow-up questions collect information on intensity (e.g., active or calm), posture, and/or context (e.g., location) of the activity. Parents and researchers considered filling in the app as feasible, taking 10-30 min per day. The activity categories were considered comprehensive, but alternative examples for several activity categories were suggested to increase the comprehensibility and relevance. Some follow-up questions were considered less relevant. These suggestions were adopted in the second version of the My Little Moves app.ConclusionsInvolving end-users and key stakeholders in the development of the My Little Moves app resulted in a tailored tool to assess 24-h movement behaviors in 0-4-year-olds with adequate content validity. Future studies are needed to evaluate other measurement properties of the app.

Project description:BackgroundMobile health (mHealth) apps represent a promising approach for improving health outcomes in patients with chronic illness, but surprisingly few mHealth interventions have investigated the association between user engagement and health outcomes. We aimed to examine the efficacy of a recommended, commercially available gout self-management app for improving self-care behaviors and to assess self-reported user engagement of the app in a sample of adults with gout.ObjectiveOur objective was to examine differences in self-reported user engagement between a recommended gout app (treatment group) and a dietary app (active control group) over 2 weeks as well as to examine any differences in self-care behaviors and illness perceptions.MethodsSeventy-two adults with gout were recruited from the community and three primary and secondary clinics. Participants were randomized to use either Gout Central (n=36), a self-management app, or the Dietary Approaches to Stop Hypertension Diet Plan (n=36), an app based on a diet developed for hypertension, for 2 weeks. The user version of the Mobile Application Rating Scale (uMARS, scale: 1 to 5) was used after the 2 weeks to assess self-reported user engagement, which included an open-ended question. Participants also completed a self-report questionnaire on self-care behaviors (scale: 1-5 for medication adherence and diet and 0-7 for exercise) and illness perceptions (scale: 0-10) at baseline and after the 2-week trial. Independent samples t tests and analysis of covariance were used to examine differences between groups at baseline and postintervention.ResultsParticipants rated the gout app as more engaging (mean difference -0.58, 95% CI -0.96 to -0.21) and more informative (mean difference -0.34, 95% CI -0.67 to -0.01) than the dietary app at the 2-week follow-up. The gout app group also reported a higher awareness of the importance of gout (mean difference -0.64, 95% CI -1.27 to -0.003) and higher knowledge/understanding of gout (mean difference -0.70, 95% CI -1.30 to -0.09) than the diet app group at follow-up. There were no significant differences in self-care behaviors between the two groups postintervention. The gout app group also demonstrated stronger negative beliefs regarding the impact of gout (mean difference -2.43, 95% CI -3.68 to -1.18), stronger beliefs regarding the severity of symptoms (mean difference -1.97, 95% CI -3.12 to -0.82), and a stronger emotional response to gout (mean difference -2.38, 95% CI -3.85 to -0.90) at follow-up. Participant feedback highlighted the importance of tracking health-related information, customizing to the target group/individual, providing more interactive features, and simplifying information.ConclusionsParticipants found the commercially available gout app more engaging. However, these findings did not translate into differences in self-care behaviors. The gout app group also demonstrated stronger negative illness perceptions at the follow-up. Overall, these findings suggest that the development of gout apps would benefit from a user-centered approach with a focus on daily, long-term self-care behaviors as well as modifying illness beliefs.Trial registrationAustralian New Zealand Clinical Trials Registry ACTRN12617001052325; https://anzctr.org.au/Trial/Registration/TrialReview.aspx?id=373217.

Dataset Information

Adults' Preferences for Behavior Change Techniques and Engagement Features in a Mobile App to Promote 24-Hour Movement Behaviors: Cross-Sectional Survey Study.

Background

Objective

Methods

Results

Conclusions

Publications

Adults' Preferences for Behavior Change Techniques and Engagement Features in a Mobile App to Promote 24-Hour Movement Behaviors: Cross-Sectional Survey Study.

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