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The Active2Gether intervention is an app-based intervention designed to help and encourage young adults to become and remain physically active by means of personalized, real-time activity tracking and context-specific feedback.
The objective of our study was to describe the development and content of the Active2Gether intervention for physical activity promotion.
A systematic and stepwise approach was used to develop the Active2Gether intervention. This included formulating objectives and a theoretical framework, selecting behavior change techniques, specifying the tailoring, pilot testing, and describing an evaluation protocol.
The development of the Active2Gether intervention comprised seven steps: analyzing the (health) problem, developing a program framework, writing (tailored) messages, developing tailoring assessments, developing the Active2Gether intervention, pilot testing, and testing and evaluating the intervention. The primary objective of the intervention was to increase the total time spent in moderate-vigorous physical activity for those who do not meet the Dutch guideline, maintain physical activity levels of those who meet the guideline, or further increase physical activity levels if they so indicated. The theoretical framework is informed by the social cognitive theory, and insights from other theories and evidence were added for specific topics. Development of the intervention content and communication channel resulted in the development of an app that provides highly tailored coaching messages that are framed in an autonomy-supportive style. These coaching messages include behavior change techniques aiming to address relevant behavioral determinants (eg, self-efficacy and outcome expectations) and are partly context specific. A model-based reasoning engine has been developed to tailor the intervention with respect to the type of support provided by the app, send relevant and context-specific messages to the user, and tailor the graphs displayed in the app. For the input of the tailoring, different instruments and sensors are used, such as an activity monitor (Fitbit One), Web-based and mobile questionnaires, and the location services on the user’s mobile phone.
The systematic and stepwise approach resulted in an intervention that is based on theory and input from end users. The use of a model-based reasoning system to provide context-specific coaching messages goes beyond many existing eHealth and mHealth interventions.
Insufficient physical activity (PA) is a risk factor for avoidable burden of disease [
In general, health promotion interventions informed by established health behavior theory have been found to be associated with higher effect sizes than interventions not based on theory [
Besides interventions being informed by theory, interventions are more likely to be effective when established behavior change techniques (BCTs) are incorporated [
Systematic reviews further showed that Information and communications technology (ICT)-supported, individually tailored interventions are superior to generic interventions in promoting PA and user engagement and appreciation [
Several content analyses have been conducted to identify if and how constructs of behavior change theories and BCTs are incorporated in PA promotion apps. Generally, the apps analyzed were lacking applications of behavior change theories and the use of evidence-based BCTs [
In summary, innovative ICT-supported mobile technology-based approaches that are evidence based and include dynamic tailoring using intelligent data interpretation techniques may help to effectively support achievement and maintenance of behavior change in the PA domain. However, both the empirical basis and dynamic tailoring are lacking in current apps. Thus, PA apps that incorporate constructs of behavior change theories and BCTs and provide dynamically tailored feedback are needed. Therefore, we developed the Active2Gether intervention that combines mobile (app-based) technology with dynamically tailored feedback and aims to go beyond existing (mobile) PA interventions. The Active2Gether intervention is an app-based intervention designed to help and encourage young adults to become and remain physically active by focusing on the domains of active transport, stair climbing, and sports participation. To do so, participants of the Active2Gether intervention will be categorized into one of the 3 awareness categories (education, coaching, and feedback). Participants in the education category will receive educational messages on the benefits of PA, whereas participants in the feedback category will receive motivational messages to maintain their active lifestyle. Participants who are in the coaching category will be coached on sports participation, taking the stairs, or active transport. Every week, the participants will be asked to choose their coaching domain and to set a weekly goal. Participants will receive a message with a suggestion for a coaching domain and a weekly goal based on their previous behavior, but the final decision will be up to the user. The participants will receive a Fitbit One activity tracker that can be synced to the Active2Gether app and will allow the participants to monitor their PA behavior through the Active2Gether app. Additionally, participants will receive (daily) coaching messages addressing relevant behavioral determinants. The content of the messages will be tailored to the user’s behavioral determinants, occupational status, and weather. Lastly, the intervention offers the opportunity to monitor and compare the behaviors with those of other Active2Gether participants because the app will display the activity data of the participant, including a graph displaying the activity data of 6 other participants, preferably friends. The graph with the activity data of others will rank the participants based on their step activity and the user preferences for social comparison (ie, upward or downward comparison). Taking this preference into account does influence the effectiveness of social comparison as a behavior change technique [
The Active2Gether intervention focuses on healthy and highly educated young adults aged 18-30 years who have a suitable smartphone running on Android version 4.0 or higher.
We used a 7-step systematic approach to develop and evaluate the intervention (
Description of the stepwise process for the development of Active2Gether.
Steps | Step description |
Step 1: Analyzing the (health) problem | Describing a theoretical framework on how to promote MVPAa Selecting behavior change techniques based on theory and evidence to address determinants of behavior, based on existing studies and reviews [ |
Step 2: Developing a Program Framework | Identifying relevant physical activity behaviors to increase MVPA. Defining the main and subobjectives of the intervention Describing framework components |
Step 3: Writing (tailored) messages (the order of this step was changed: Step 5)b | Writing tailored messages |
Step 4: Developing tailoring assessments (the order of this step was changed: Step 3)b | Selecting and developing measurements to assess levels of physical activity, behavioral determinants, locations, and connected friends |
Step 5: Developing the Active2Gether intervention (steps were merged)c | Designing tailoring algorithms for the reasoning system Channel of communication: building a Web-based app and system to combine and interpret data and send messages |
Step 6: Pilot testing | Pilot-testing the intervention to detect errors and impracticalities in order to improve the intervention prior to its implementation |
Step 7: Testing and evaluating the intervention | The intervention will be used by a larger group of participants and then analyzed and evaluated with respect to effect, process, and impact |
aMVPA: moderate-vigorous physical activity.
bAccording to the program-planning model by Kreuter et al [
cCreating tailoring algorithms, automating the tailoring process, and developing the communication channel are described in the same step, whereas according to the program-planning model, these are steps 6 and 7, respectively.
Because theory-based interventions are associated with higher effect sizes than interventions not based on theory [
Social cognitive theory (SCT) was adopted as a basis for the theoretical framework as it is one of the most prominent behavior change theories used to inform interventions targeting health behavior change [
We first identified evidence-based and relevant BCTs and linked these with the behavioral determinants of the theoretical framework by means of a review of the relevant literature, based on an existing taxonomy of BCTs [
The structural pathways of Bandura’s social cognitive theory.
The specific theoretical framework that is used for the Active2Gether intervention.
The foundation of the intervention is the definition of the program’s outcomes and objectives [
Based on steps 1 and 2 and the research team’s expertise, a general framework was developed. The aim was to develop a highly tailored intervention that contains a self-monitoring tool, goal setting, social comparison, and motivational and context-specific messages.
Tailoring and personalization of the intervention content is realized in the following 6 ways: determining the personally appropriate type of support (ie, education, coaching, or feedback), selecting the personally relevant and preferred domain of PA for user coaching (ie, sports participation, stair use, or active transport), suggesting a weekly goal, selecting the personally appropriate behavioral determinants for coaching, sending only relevant coaching messages and filtering out nonrelevant messages, and tailoring and personalization of the app content.
Next, we translated the BCTs into actual tailored feedback messages and advice. For each BCT per behavioral determinant (
Creating the message library was an iterative process of brainstorming, writing a set of messages (AM), and providing feedback and suggestions (JM and StV). To test whether the tone of voice and content appealed to the target population, a subset of messages was pilot-tested among 7 people of the target population.
To tailor the messages to the individual users, assessment methods were selected.
First, after considering functionalities, validity, and costs of a range of available activity trackers, the user’s activity was monitored using Fitbit One, which includes monitoring of steps and stairs climbed. Fitbit One was chosen because of its functionalities and small size [
Fitbit allows developers and researchers to access Fitbit data and thus integrate the Fitbit data into health behavior interventions such as Active2Gether. To access Fitbit data, Fitbit offers an application programming interface (API). Fitbit One was validated using smaller time intervals (ie, minutes, hours, and days) relevant for real-time feedback and instant behavioral insights to its users. Healthy young adults (N=34) wore the ActiGraph GT3x+ and a Fitbit One for one week. Detailed information on the methodology can be found elsewhere [
Literature was reviewed for relevant, existing, and validated questionnaires to assess behavioral determinants. Behavioral determinants are assessed by means of a questionnaire with both its long and short versions, which were selected based on validations of such questionnaires. The long version is part of an “intake” questionnaire before the actual intervention and as a point of departure for the tailored intervention, whereas the short version is used repeatedly throughout the intervention period to dynamically tailor the intervention content to the user.
A questionnaire was designed for the purpose of assessing information on significant places. In addition, the Active2Gether app was built in a way that enabled the collection of the user’s location data.
To increase the users’ engagement, we assessed whether users’ friends were also participating in the Active2Gether intervention. Because Facebook is very popular among Dutch young adults—93% of Dutch adults aged 18-24 use Facebook [
To realize such tailored coaching, we developed a system that combines detailed behavior monitoring with intelligent data interpretation and model-based predictions. Thus, by combining data from the different sources, the system enables personalization of the coaching strategies to try to achieve the most positive effect on behavior change. Detailed information on the system and the development of the system can be found elsewhere and is not described in the Results section [
We decided that the communication channel of the Active2Gether intervention should be a smartphone app. The app shows the website in a format that is viewable on smaller screens. Thus, the intervention content was accessible through the app or the website. The research team developed the design template of the smartphone app. Information on the development of the app can be found elsewhere [
To detect possible bugs in the system and to assess user friendliness and appreciation, the app was pilot-tested in two steps. First, the Active2Gether team (AM, JSM, Adnan Manzoor Rajper, SJtV, and MCAK) used the initial version of the Active2Gether app. Bugs, nuisances, etc, were monitored, listed, and fixed accordingly when and where possible. Second, 7 people from the target population (5 women, 21-28 years old, all highly educated, or studying at the bachelor’s or master’s level) were recruited to use the adjusted version of the app, monitor bugs and nuisances, provide feedback in person, and answer a questionnaire regarding use, user friendliness, and appreciation. The app was further adjusted based on that information.
The intervention, the Active2Gether app, will be evaluated for its efficacy to change weekly levels of MVPA in young adults and for the usability of the app.
As a result of Step 1, a theoretical framework was built based on the relevant scientific literature (please see further details below). The theoretical framework was subsequently used to develop the content of the intervention and predict the PA behavior of the users so that the intervention content could be tailored to each individual user.
Self-efficacy, a key construct within SCT (and in other health behavior theories) [
In line with SCT, we also recognized that the social environment influences behavior through social norms and that performing certain behaviors can evoke social reactions, both positive and negative [
Content analysis showed that the apps available to date generally lack sufficient incorporation of evidence-based BCTs [
Step 2 resulted in the decision to make the following the primary objective of the Active2Gether intervention: increase total time spent in MVPA for those who do not meet the Dutch guideline, maintain PA levels of those who meet the guideline, or further increase PA levels if they so indicated. The secondary aims were defined as follows: to increase the underlying specific categories of MVPA (ie, minutes of weekly sports participation, weekly numbers of stairs climbed, and weekly minutes of active transport) and to enhance the underlying determinants of the PA behaviors.
The framework contained information on the levels of tailoring and an outline of the steps taken to deliver tailored messages. Detailed information on the framework components can be found in
In line with Self-Determination Theory [
A pilot test of a subset of messages among 7 female bachelor’s and master’s students indicated that the messages were friendly, motivational, and empathic; some were perceived as autocratic, whereas some were not. Some minor changes were made to the messages.
Further decisions were made on how to measure the characteristics for tailoring messages.
Our test of the validity of the Fitbit One indicated that Fitbit can be considered a valid device to assess step activity for real-time minute-by-minute self-monitoring, although an overestimation of 677 steps per day by Fitbit was seen compared with the ActiGraph [
Participants need to give permission once for the application to access their activity data. These then can be collected regularly, and a summarized version of the data is stored in the Active2Gether database. These data are utilized in the following several ways: for presenting the activity level (ie, number of steps and number of stairs climbed) to the user, for determining the type of coaching, and for tailoring coaching messages.
We decided to assess behavioral determinants by means of a questionnaire with both its long and short versions, which were selected based on the validations of such questionnaires. The long version is based on existing questionnaires that have previously been validated (ie, Neighborhood Quality of Life Survey and Self-efficacy scales) or questions used in previous studies and were translated and adapted where necessary [
We also included questions about the participants’ significant places (eg, home address, parental home, sports location, university, work location) in the intake questionnaire. These questions focus on travel options from their home to significant locations, thus information about the active and nonactive transportation options. Additionally, information about the number of stairs available at each location and the maximum number of stairs the participant is willing to climb in one go is assessed as well.
The user’s location (GPS coordinates) is collected using Google’s location services that can be linked with the Active2Gether app. The location data are used to determine whether the user visited his or her significant locations (eg, home, study or work place, and sports club) and to derive information about transport and travels that have been made. In addition, information about the characteristics of locations is used for personalized coaching messages to the user. For instance, if a person is being coached on using the stairs more often at their place of work or study, it is only useful to suggest this when the option to climb the stairs is indeed present at the worksite or university.
Information regarding the participants’ friends is collected using the Facebook API. Users are asked to provide access to their Facebook ID and their connections by logging into Facebook once and giving permission for this. It is important to note that Facebook does not provide personal information about someone’s Facebook connections but only a list of Facebook IDs of their connections. This information can be used to see whether any Active2Gether users are connected on Facebook. If two participants of the current intervention are connected on Facebook, they see a ranking within the app that shows achievements of both users. In this way, the users only share their achievements with a closed group and not with “everybody,” according to the preferences stated in the focus group discussions.
The Active2Gether app shows a nonpersonalized, generic avatar with a welcome message that mentions the user’s current weekly goal. The app displays the current number of daily steps and stairs climbed. In addition, the app shows the following 4 graphs: a bar chart with the step progress toward 70,000 steps per week, a ranking with 6 other Active2Gether users—where possible Facebook friends—based on the step activity over the last seven days, the activity data for each weekday for the current coaching domain (ie, minutes of sport activity, numbers of stair climbed, or minutes of active transport), and the step activity for each weekday. The third and fourth graphs display the user’s own data and the average data assessed within Active2Gether. Moreover, these graphs can be adjusted according to the user’s preferences, that is, they can show data for the last week, last month, or from the first use.
Tailored messages and short questions are sent via push messages through the app. After the user reads the messages, they are displayed at the bottom of the app. Only the 5 messages sent most recently are displayed in the app.
The app was adjusted based on the feedback of the 7 participants who pilot-tested the app; for example, the timing of the different steps in the tailoring process (ie, determining the type of feedback, the coaching domain, the weekly goal, and the most promising behavioral determinants) did not originally account for exceptional cases in which a user takes very long to complete a step, causing the next step to be skipped. In the adjusted version, multiple checks and safety mechanisms were implemented to make sure that the tailoring process could still be finished correctly in such conditions. Also, automated messages to remind users to charge their Fitbit and to synchronize their data were added to the system because of the observation that participants in the pilot study sometimes did not notice when it was necessary to do so.
Screenshot of the Active2Gether app.
After developing the intervention, an evaluation study was conducted for which data have been collected between March 2016 and September 2016 and data cleaning and initial analyses are now being conducted. A three-arm quasi-experimental trial—with two active control groups—with a baseline and two follow-up assessments at 6 and 12 weeks was conducted to examine the effectiveness of the Active2Gether intervention. This trial is registered in the Dutch trial registry, No. NTR5630. A detailed description of the study protocol can be found in
This study describes the development and content of Active2Gether, an app-based intervention, which was developed using a systematic and stepwise approach. The aim of the Active2Gether intervention is to empower young adults to become and remain physically active by providing them with app-based tailored coaching and feedback. Active2Gether makes use of an activity tracker and personalized, context-specific feedback. It focuses on 3 PA domains, builds on established behavior theory, and applies evidence-based BCTs and a model-based reasoning system to provide individually tailored coaching messages based on current scores on the behavioral determinants.
The development and content creation of Active2Gether was a stepwise process. The program-planning model proposed by Kreuter et al [
To date, mobile phones and personal digital assistants have been used to monitor PA with either smartphone apps or external devices, deliver feedback, provide information, and offer a support system to the participants [
So far, the majority of the app-based interventions to promote PA showed positive short-term effects [
Active2Gether is ambitious and innovative and incorporates certain risks, for example, the intervention highly relies on input from the activity monitor and location sensor and thus on the user to turn on and synchronize the tracker with the server. Furthermore, it relies on responses from the users on repeated questionnaires. If they do not provide input at all or if they do not provide true and honest answers, the coaching messages that are informed by this information may become irrelevant and nontailored. Moreover, if a participant is not a Facebook user or has no appropriate contacts, the personalization could be limited. Finally, if technical problems are encountered, this may result in errors in synchronization and sending messages late or not at all. To limit the burden for the participants and minimize their input to reduce potential technical problems, future research could make use of smartphone sensors to assess the participant’s behavior.
The overall effectiveness of Active2Gether thus needs to be, and is being, evaluated in a quasi-experimental trial with a 12-week follow-up. However, because app-based interventions offer the possibility to deliver just-in-time interventions that are relevant for the user’s situation for that particular moment, a study is needed to examine the possible effectiveness of specific real-time feedback and advice moments [
Because the intervention has been developed with an early consideration for the preferences of the target population, it is more likely to meet the expectations of the target population. Consequently, the intervention is more likely to be adopted by the target population. However, the intervention might be prone to technical errors, and a significant input from the user is needed to provide tailored feedback. This might be a burden for the participants, leading to a lower adoption rate. We conducted a small pilot study to test the Active2Gether app and to detect bugs and technical errors; ideally, the pilot study would have been conducted with a larger sample. The current version of the Active2Gether intervention has been developed for young adults with higher education owning a smartphone running on Android version 4.0 or higher. The content needs to be adjusted before offering the intervention to other target populations.
This paper describes the systematic development of an intervention that is based on theory and input from end users. The use of a model-based reasoning system to provide context-specific coaching messages goes beyond many existing eHealth and mHealth interventions.
Overview of the behavior change techniques (BCTs) that were selected to target the behavioral determinants of the theoreticalframework and how they were applied within the intervention.
Components and flow chart of the tailored intervention.
Overview of the questions used for the short and long version of the questionnaire.
Study protocol.
application programming interface
behavior change technique
Information and communications technology
moderate-vigorous physical activity
physical activity
social cognitive theory
We want to thank Adnan Manzoor Rajper for his contribution to the project. This research is supported by Philips and Technology Foundation STW, Nationaal Initiatief Hersenen en Cognitie NIHC under the Partnership program Healthy Lifestyle Solutions (grant no. 12014).
None declared.