The Design and Development of a Personalized Leisure Time Physical Activity Application Based on Behavior Change Theories, End-User Perceptions, and Principles From Empirical Data Mining

Sporrel, Karlijn; Boer, R. De; Wang, Shihan; Nibbeling, Nicky; Simons, Monique; Deutekom, Marije; Ettema, Dick; Castro, Paula Costa; Dourado, Victor Zuniga; Kröse, B.J.A.

doi:10.3389/fpubh.2020.528472

Cited by 24 publications

(54 citation statements)

References 74 publications

(151 reference statements)

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“…Consequently, interviews can have a broad range of goals. Examples include identifying points of improvement for a current situation such as treatment of forensic psychiatric patients [ 96 ]; analyzing target or risk groups in, for example, tick bites [ 100 ]; identifying points of improvement for prototypes or existing (eHealth) interventions according to end users or design experts [ 83 , 84 ]; identifying potential barriers and facilitators for implementation later in the process, such as high costs and required skills training [ 78 , 96 ]; describing a current behavior and its determinants [ 91 ]; collecting experiences of participants after letting them try out an app in real life [ 83 ]; and generating or validating values and requirements [ 15 , 28 , 98 , 103 ].…”

Section: Resultsmentioning

confidence: 99%

“…They were never formulated at the beginning of the development process because they should be based on outcomes of earlier activities. The included studies showed that requirements can be based on data generated by 1 method, such as interviews, or on a combination of data from different methods and scientific literature [ 91 ]. Requirements can be based on values, where values serve as a bridge between the previously conducted research and the specific requirements [ 129 ].…”

Section: Resultsmentioning

confidence: 99%

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Methods for Human-Centered eHealth Development: Narrative Scoping Review

Kip¹,

Keizer²,

Silva³

et al. 2022

J Med Internet Res

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Background Thorough holistic development of eHealth can contribute to a good fit among the technology, its users, and the context. However, despite the availability of frameworks, not much is known about specific research activities for different aims, phases, and settings. This results in researchers having to reinvent the wheel. Consequently, there is a need to synthesize existing knowledge on research activities for participatory eHealth development processes. Objective The 3 main goals of this review are to create an overview of the development strategies used in studies based on the CeHRes (Center for eHealth Research) Roadmap, create an overview of the goals for which these methods can be used, and provide insight into the lessons learned about these methods. Methods We included eHealth development studies that were based on the phases and/or principles of the CeHRes Roadmap. This framework was selected because of its focus on participatory, iterative eHealth design in context and to limit the scope of this review. Data were extracted about the type of strategy used, rationale for using the strategy, research questions, and reported information on lessons learned. The most frequently mentioned lessons learned were summarized using a narrative, inductive approach. Results In the included 160 papers, a distinction was made between overarching development methods (n=10) and products (n=7). Methods are used to gather new data, whereas products can be used to synthesize previously collected data and support the collection of new data. The identified methods were focus groups, interviews, questionnaires, usability tests, literature studies, desk research, log data analyses, card sorting, Delphi studies, and experience sampling. The identified products were prototypes, requirements, stakeholder maps, values, behavior change strategies, personas, and business models. Examples of how these methods and products were applied in the development process and information about lessons learned were provided. Conclusions This study shows that there is a plethora of methods and products that can be used at different points in the development process and in different settings. To do justice to the complexity of eHealth development, it seems that multiple strategies should be combined. In addition, we found no evidence for an optimal single step-by-step approach to develop eHealth. Rather, researchers need to select the most suitable research methods for their research objectives, the context in which data are collected, and the characteristics of the participants. This study serves as a first step toward creating a toolkit to support researchers in applying the CeHRes Roadmap to practice. In this way, they can shape the most suitable and efficient eHealth development process.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Methods for Human-Centered eHealth Development: Narrative Scoping Review

Kip¹,

Keizer²,

Silva³

et al. 2022

J Med Internet Res

View full text Add to dashboard Cite

show abstract

“…The development of the PAUL app was conducted based on both theoretical foundations of behavior change [ 20 ] and a focus-group study with our target users [ 21 ]. The design process and practical implementation of this smartphone exercise application can be found in [ 22 ]. In this subsection, we mainly describe the integration of a reinforcement learning model in the PAUL app.…”

Section: Methodsmentioning

confidence: 99%

Reinforcement Learning to Send Reminders at Right Moments in Smartphone Exercise Application: A Feasibility Study

Wang

Sporrel

Hoof

et al. 2021

IJERPH

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Just-in-time adaptive intervention (JITAI) has gained attention recently and previous studies have indicated that it is an effective strategy in the field of mobile healthcare intervention. Identifying the right moment for the intervention is a crucial component. In this paper the reinforcement learning (RL) technique has been used in a smartphone exercise application to promote physical activity. This RL model determines the ‘right’ time to deliver a restricted number of notifications adaptively, with respect to users’ temporary context information (i.e., time and calendar). A four-week trial study was conducted to examine the feasibility of our model with real target users. JITAI reminders were sent by the RL model in the fourth week of the intervention, while the participants could only access the app’s other functionalities during the first 3 weeks. Eleven target users registered for this study, and the data from 7 participants using the application for 4 weeks and receiving the intervening reminders were analyzed. Not only were the reaction behaviors of users after receiving the reminders analyzed from the application data, but the user experience with the reminders was also explored in a questionnaire and exit interviews. The results show that 83.3% reminders sent at adaptive moments were able to elicit user reaction within 50 min, and 66.7% of physical activities in the intervention week were performed within 5 h of the delivery of a reminder. Our findings indicated the usability of the RL model, while the timing of the moments to deliver reminders can be further improved based on lessons learned.

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“…The goal of PAUL is to motivate Dutch inactive urban residents to increase their PA through walking, running, and strength exercises in urban spaces (such as parks) with the use of a theoretically and empirically driven app: the PAUL app. The development of the PAUL app arises from interdisciplinary research from the fields of psychology, movement sciences, computer science, and artificial intelligence (26). Three pillars are at the basis of the development of the PAUL app.…”

Section: Paul Projectmentioning

confidence: 99%

A Focus Group Study Among Inactive Adults Regarding the Perceptions of a Theory-Based Physical Activity App

Nibbeling

Simons

Sporrel

et al. 2021

Front. Public Health

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Background: Despite the increasing attention for the positive effects of physical activity (PA), nearly half of the Dutch citizens do not meet the national PA guidelines. A promising method for increasing PA are mobile exercise applications (apps), especially if they are embedded with theoretically supported persuasive strategies (e.g., goal setting and feedback) that align with the needs and wishes of the user. In addition, it is argued that the operationalization of the persuasive strategies could increase the effectiveness of the app, such as the actual content or visualization of feedback. Although much research has been done to examine the preferences for persuasive strategies, little is known about the needs, wishes, and preferences for the design and operationalization of persuasive strategies.Objective: The purpose of this study was to get insight in the needs, wishes, and preferences regarding the practical operationalization of persuasive strategies in a mobile application aimed at promoting PA in healthy inactive adults.Methods: Five semistructured focus groups were performed. During the focus groups, the participants were led into a discussion about the design and operationalization of six predefined theory-based persuasive strategies (e.g., self-monitoring, feedback, goal setting, reminders, rewards, and social support) directed by two moderators. The audio-recorded focus groups were transcribed verbatim and analyzed following the framework approach.Results: Eight men and 17 women between 35 and 55 years (mean age, 49.2) participated in the study. Outcomes demonstrated diverse preferences for implementation types and design characteristics of persuasive strategies in mobile applications. Basic statistics (such as distance, time and calories), positive feedback based on easy-to-achieve goals that relate to health guidelines, and motivating reminders on a relevant moment were preferred. Participants had mixed preferences regarding rewards and a social platform to invite other users to join PA.Conclusions: Findings indicated that in mHealth applications for healthy but inactive adults, persuasive strategies should be designed and implemented in a way that they relate to health guidelines. Moreover, there is a need for an app that can be adapted or can learn based on personal preferences as, for example, preferences with regard to timing of feedback and reminders differed between people.

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The Design and Development of a Personalized Leisure Time Physical Activity Application Based on Behavior Change Theories, End-User Perceptions, and Principles From Empirical Data Mining

Cited by 24 publications

References 74 publications

Methods for Human-Centered eHealth Development: Narrative Scoping Review

Methods for Human-Centered eHealth Development: Narrative Scoping Review

Reinforcement Learning to Send Reminders at Right Moments in Smartphone Exercise Application: A Feasibility Study

A Focus Group Study Among Inactive Adults Regarding the Perceptions of a Theory-Based Physical Activity App

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