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A healthy lifestyle, including regular physical activity and a healthy diet, is becoming increasingly important in the treatment of chronic diseases. eHealth interventions that incorporate behavior change techniques (BCTs) and dynamic tailoring strategies could effectively support a healthy lifestyle. E-Supporter 1.0 is an eCoach designed to support physical activity and a healthy diet in people with type 2 diabetes (T2D).
This paper aimed to describe the systematic development of E-Supporter 1.0.
Our systematic design process consisted of 3 phases. The definition phase included the selection of the target group and formulation of intervention objectives, and the identification of behavioral determinants based on which BCTs were selected to apply in the intervention. In the development phase, intervention content was developed by specifying tailoring variables, intervention options, and decision rules. In the last phase, E-Supporter 1.0 integrated in the Diameter app was evaluated using a usability test in 9 people with T2D to assess intervention usage and acceptability.
The main intervention objectives were to stimulate light to moderate-vigorous physical activities or adherence to the Dutch dietary guidelines in people with T2D. The selection of behavioral determinants was informed by the health action process approach and theories explaining behavior maintenance. BCTs were included to address relevant behavioral determinants (eg, action control, self-efficacy, and coping planning). Development of the intervention resulted in 3 types of intervention options, consisting of motivational messages, behavioral feedback, and tailor-made supportive exercises. On the basis of IF-THEN rules, intervention options could be tailored to, among others, type of behavioral goal and (barriers to) goal achievement. Data on these variables could be collected using app data, activity tracker data, and daily ecological momentary assessments. Usability testing revealed that user experiences were predominantly positive, despite some problems in the fixed delivery of content.
The systematic development approach resulted in a theory-based and dynamically tailored eCoach. Future work should focus on expanding intervention content to other chronic diseases and lifestyle behaviors, enhancing the degree of tailoring and evaluating intervention effects on acceptability, use, and cost-effectiveness.
In 2020, nearly 60% of Dutch adults had experienced one or more chronic diseases [
Despite the positive effects of healthy lifestyle on the course of chronic diseases, people often find it challenging to live healthy. For instance, >50% of Dutch adults adhered to the Dutch Physical Activity Guidelines [
eHealth has the potential to support lifestyle self-management [
One source of variability in eHealth effectiveness is differences in the use of behavior change theory [
Systematic reviews showed that tailored eHealth interventions are more effective in promoting healthy behaviors and user engagement than generic interventions [
eHealth interventions are expected to be more beneficial when provided in a blended care setting (ie, combining eHealth and regular health care) [
In summary, innovative eHealth interventions that are theory based, which include dynamic tailoring, and are offered as blended care may help to effectively support health behavior change. However, eHealth interventions that integrate these potential success factors are scarce to date. Therefore, the aim of this paper is to successively describe the systematic development and usability testing of the first version of E-Supporter, a theory-based, dynamically tailored, and blended lifestyle coaching intervention for people with chronic diseases.
Ethics approval was obtained from the Medical Research Ethics Committees United Nieuwegein, the Netherlands (R20.121). Written consent was requested from each patient to participate in the study. All participants gave verbal consent before starting the audio recording of the interviews. Data privacy was protected in accordance with the General Data Protection Regulation standard. Participants were informed in detail about how data were collected, processed, and stored in the subject information sheet. Participants gave explicit consent for the use of their data by signing the informed consent form. Data privacy was protected by offering anonymous preset accounts without personal data to prevent sharing personal data with commercial parties.
The development of E-Supporter is part of the eManager project [
The ABCC tool is already being used during consultations with a health care professional and is developed for several chronic diseases, including chronic obstructive pulmonary disease, asthma, T2D, and chronic heart failure. The ABCC tool maps the patient’s disease burden based on a questionnaire that the patient completes before consultation and based on medical information from the electronic patient file [
eManager chronic diseases. ABCC: Assessment of Burden of Chronic Conditions.
Visualization of disease burden measured by the Assessment of Burden of Chronic Conditions (ABCC) tool [
E-Supporter aims to support the patient in daily life in pursuing the earlier established personalized treatment goals. Diabetes professionals can offer E-Supporter to patients in a shared decision-making process. E-Supporter focuses on obtaining and maintaining a healthy lifestyle to reduce the perceived burden of disease. In this paper, the development of the first version of E-Supporter (ie, the components as presented within the
The systematic design of the intervention was guided by program-planning models [
We followed a participatory development process by combining the perspectives of 9 health care professionals and 33 people with T2D. The team of health care professionals consisted of 4 internists, 3 diabetes nurses, and 2 physician researchers. The health care professionals contributed during all phases of the development approach at research group meetings and focus groups by formulating intervention objectives, formulating lifestyle advice for people with T2D, and reviewing and revising the motivational message.
The requirements for digital lifestyle coaching from the patient’s perspective emerged from interviews with 19 people with T2D. The results of these interviews are described elsewhere [
Phase 1: definition
Step 1: target population and behaviors
Selecting a target group and behaviors to promote in the intervention
Step 2: intervention objectives
Defining the main and subobjectives of the intervention
Step 3: selection of behavioral determinants
Selecting relevant behavioral determinants based on existing behavior change models and describing the determinants of behavior to target in the intervention
Step 4: selection of behavior change techniques (BCTs)
Selecting BCTs to address determinants of behavior based on existing studies and reviews
Phase 2: development
Step 1: tailoring variables
Deciding which information concerning the individual will be used for tailoring (ie, to decide when and how to intervene)
Step 2: decision rules
Operationalizing of intervention options by specifying which intervention option to offer to whom
Step 3: intervention options
Developing possible actions (ie, interventions) that might be employed during the intervention period
Step 4: integration of E-Supporter 1.0
Describing the integration of the E-Supporter content in an app for people with type 2 diabetes (T2D)
Phase 3: evaluation
Usability test
Usability testing of the intervention in a mobile app on acceptability and usage in 9 patients with T2D
The program-planning model developed by Kreuter et al [
In accordance with Nuham-Shani’s model [
After being integrated in the Diameter app, E-Supporter 1.0 was evaluated during a 5-week usability study among 9 people with T2D. The Diameter app was used because its purpose closely aligned with the initial design and application of E-Supporter 1.0 to encourage physical activity and a healthy diet in people with T2D. This study was the first to evaluate the E-Supporter integrated into an app. Therefore, the aim of the study was two-fold: (1) to gain insight into intervention use and acceptability of E-Supporter 1.0 integrated in the Diameter app and (2) to identify technical issues in the integration of E-Supporter 1.0 within the Diameter app. In total, 9 patients with T2D visiting the outpatient clinic at the Ziekenhuis Groep Twente Hospital were recruited; they were ≥18 years and were familiar with an Android smartphone (version 5.0 or higher). Patients were not able to participate when they underwent renal replacement therapy, were engaged in drug abuse, or had insufficient proficiency in the Dutch language. Participants used the Diameter app, with E-Supporter content, in combination with the activity tracker Fitbit Inspire 2 [
A mixed methods approach was used to explore intervention use and acceptability of E-Supporter 1.0 and its integration within the Diameter app. Intervention use was exploratively assessed using log data of the E-Supporter 1.0 components (ie, motivational messages and physiological exercises) and Diameter app components (ie, Fitbit, food diary and Freestyle Libre 2). Use of the Diameter app and E-Supporter components was reported by describing frequency and duration of the used functionalities. Log data were also used to identify whether the intervention was delivered as intended and to identify technical integration issues. Open-ended interviews based on the Unified Theory of Acceptance and Use of Technology 2 model [
E-Supporter 1.0 focused on improving physical activity and a healthy diet in people with T2D. Diabetes is one of the four major types of noncommunicable diseases worldwide [
The Dutch health care standard for T2D states that small improvements in physical activity levels can already lead to positive health effects, although being physically active for longer periods more often and more intensively does have additional health benefits [
The Dutch
The health action process approach (HAPA) [
Intervention targets of E-Supporter 1.0.
In total, 2 key determinants were selected that recur in all phases of the intervention: action control and self-efficacy. Action control comprises three self-regulatory processes: (1) awareness of standards (ie, a self-set goal), (2) self-monitoring that yields information about the attainment of individual’s behavior or goal, and (3) self-regulatory effort to achieve the goal [
Phase-specific determinants were derived for each of the defined phases of behavior change (
The selected BCTs per determinant based on the literature search can be found in
We covered 3 BCTs identified by Abraham and Michie that address self-regulatory processes from the concept of negative feedback control [
Determinants and linked behavior change techniques incorporated in E-Supporter 1.0.
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Action control | Self-efficacy | Knowledge | Risk perception | Outcome expectations | Attitude | Social support | Action planning | Coping planning | Mood management | Habits | Satisfaction | Social influences |
1.1 Goal-setting (behavior) |
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✓ |
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1.2 Problem-solving |
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✓ |
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✓ | ✓ |
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✓ |
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1.4 Action planning |
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✓ |
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✓ | ✓ |
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✓ |
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1.5 Review behavior goal | ✓ |
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2.2 Feedback on behavior | ✓ |
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2.3 Self-monitoring of behavior | ✓ | ✓ |
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2.4 Self-monitoring of outcomes of behavior |
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✓ |
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3.1 Social support, including motivational interviewing |
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✓ |
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✓ |
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✓ |
3.2 Social support (practical) |
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✓ |
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✓ |
4.1 Instruction on how to perform the behavior |
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✓ | ✓ |
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5.1 Information about health consequences |
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✓ | ✓ | ✓ | ✓ |
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5.6 Information about emotional consequences |
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✓ |
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6.3 Information about others’ approval |
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✓ |
7.1 Prompts and cues |
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✓ |
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✓ |
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8.3 Habit formation |
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✓ |
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9.1 Credible source |
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✓ |
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9.2 Pros and cons |
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✓ | ✓ |
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9.3 Comparative imagining of future outcomes |
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✓ |
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11.2 Reduce negative emotions |
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✓ |
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✓ |
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13.2 Framing and reframing |
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✓ |
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15.1 Verbal persuasion about capability |
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✓ |
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15.3 Focus on past success |
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✓ |
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BCTs that previously were shown to be effective in influencing specific determinants of behavior were extracted from several studies [
We specified the main intervention features for E-Supporter, consisting of (1) goal-setting options (ie, step goals, cycling goals, or nutritional goals) and (2) intervention options consisting of motivational messages, feedback, and reinforcement or barrier identification combined with psychological exercises (
Examples of workflow E-Supporter 1.0 per tailoring variable.
Tailoring variables | Decision rule | Decision point | Intervention options | Example intervention option |
Motivational message based on |
Twice a day | Informative, motivational, or advisory motivational message | Action phase, disease-generic, morning-specific, and physical activity goal: “Good morning |
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Motivational message based on |
Twice a day | Informative, motivational, or advisory motivational message | Initiation phase, diabetes-specific, any time of the day, and physical activity goal: “Hi |
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Motivational message based on |
Twice a day | Informative, motivational, or advisory motivational message | Initiation phase, morning-specific, diabetes-specific, and nutrition goal: “Eating breakfast is particularly important. People with diabetes who skip breakfast are on average heavier and have higher blood sugar levels. Your body produces less insulin if you do not eat breakfast.” | |
Motivational message based on |
Twice a day | Informative, motivational, or advisory motivational message | Maintenance phase, physical activity, disease generic, and any time of the day: “Hi |
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Feedback based on |
7 days after setting a goal | Provision of feedback on goal achievement and examination of barriers and recommendation of an exercise | Goal not achieved: “Hi |
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Exercise based on |
7 days after setting a goal | Provision of matching psychological exercise | Mood: “By reflecting on pleasant things that you experience, you become happier. Think of 3 fun things that happened to you today or yesterday. You experienced 3 things that make you happier. If you want, you can make such a list at the end of every day for the next week. Hopefully, this helps you to think positively.” |
In E-Supporter, 6 tailoring variables were applied: duration of intervention use, type of chronic disease, time of day, type of behavior goal, goal achievement, and the identified barrier toward goal achievement.
The first tailoring variable was the
The decision rules were operationalized to provide the right type of intervention option tailored to the user circumstances. The decision rules were based on IF-THEN rules specifying the situation (IF) with the cutoff point of a given situation (eg, if a goal is reached or not) and the characteristics of an intervention option (THEN). There were three types of decision rules: (1) rules that triggered the type of motivational message, (2) rules that triggered feedback on goal achievement, and (3) rules that triggered a type of psychological exercise.
Motivational messages were designed for one-way communication, delivered as push notifications, and written in the Dutch language at the Common European Framework of Reference for Languages B1-level [
To be able to tailor motivational messages to the
During the focus groups with health care professionals, 74.6% (208/279) of messages were directly approved. Furthermore, 20.1% (56/279) of motivational messages were revised by reformulating texts and were approved afterward. In addition, 5.4% (15/279) of messages were excluded from the database. The main reasons for adaptation or exclusion were that messages (1) were too difficult to understand, (2) contained information that only health care professionals are allowed to give, and (3) raised unrealistic expectations of the effects of a healthy lifestyle.
Feedback based on
When a goal was not sufficiently reached and motivation, self-efficacy, mood, stress, or planning was the
Exercises comprised a dialog between the user and conversational agent using motivational interviewing techniques (
The content of E-Supporter 1.0 was (partly) integrated into two apps: (1)
Diameter is a Dutch app for people with T2D who aim to improve their glucose regulation through lifestyle changes. To date, the Diameter app has been used in research as a blended care intervention in secondary hospital care. All monitoring and coaching components of E-Supporter were integrated into Diameter app. Motivational messages were integrated as push notifications that could be closed by liking, disliking, or the closing button. Goal setting and weekly exercises that focused on barrier identification and problem-solving were integrated in the form of an interactive dialog with a conversational agent (
Screenshots from the Diameter app. A: home screen with overview of goal achievement; B: example of motivational message as push notification; C: example of start screen of an exercise.
In addition to be coached via the E-Supporter content, individuals using the Diameter app could monitor physical activity (ie, with an activity tracker and manually), nutrition (ie, with an electronic food diary), and real-time glucose levels (ie, by using a Freestyle Libre 2 glucose sensor) [
MiGuide is an app for people with T2D with the aim to improve lifestyle. The MiGuide app is available in both the Google Play Store and AppStore, is available in Dutch, and is offered in a blended care setting in primary care. Only the weekly psychological exercises aimed at barrier identification and problem-solving were integrated from E-Supporter 1.0 into the MiGuide app. MiGuide uses its own goal-setting options, self-monitoring tools, and short messages similar to E-Supporter. In the app, previously set goals, physical activity (ie, using an activity tracker and manually), nutrition (ie, using an electronic food diary), and glucose levels (ie, entering measured values manually) could be monitored. Coaching was offered through short messages developed by MiGuide and the psychological exercises from E-Supporter. The MiGuide app could be linked to different General Practitioner Information Systems (in Dutch: Huisarts Informatie Systeem). This allowed data to be exchanged between the MiGuide app and the Huisartsen Informatie Systeem. Patients could view their medical file via the app, and health care providers could gain insights into the measurements of their patients so that more personalized care could be offered.
Screenshots from the MiGuide app. A: activity screen within MiGuide; B: examples of motivational messages within MiGuide; C: example of an exercise to increase motivation.
In total, 9 participants (n=7, 78% male) were included in the usability study. Participants were on average 65.2 (SD 8.7) years old and obese (mean BMI 31.7, SD 3.29 kg/m2). On average, the participants had been diagnosed with T2D for 17 years. Diabetes-related complications were present in 67% (6/9) of participants. In total, 44% (4/9) of participants had used an app to track their physical activity or diet previously at the time of inclusion in the study.
All (9/9, 100%) participants scanned the Freestyle Libre more than the requested 3 times per day with an average of 11 (SD 2.7) scans per day. Data loss occurred in 11% (1/9) of participants because the Freestyle Libre detached prematurely (after a week) from the upper arm; 11% (1/9) of participants experienced problems with synchronizing the Fitbit with the app. The participants without synchronization problems had an average of data loss of 24.8% (range 0%-66.4%). In addition, 67% (6/9) of participants logged activities in addition to using Fitbit. The food diary was completed by 89% (8/9) of participants for the requested 6 days.
Of the 9 participants, 7 (78%) participants experienced problems receiving the 2 daily motivational messages; 1 (11%) participant received no messages at all, which could be explained by a human error made by the researcher. The other 6 participants received the messages very irregularly (eg, 1 instead of 2 messages per day or no messages at all for a day). The log data showed that the push notifications for the messages were sent from the app but that participants mentioned not to receive a notification on their phone. This issue was probably caused by phone settings or notification blockers (ie, push notifications marked as spam are killed by the system). Of the motivational messages of which participants did receive a push notification, 97.1% (298/307) were read. Participants liked 73.8% (220/298) and disliked 12.1% (36/298) of the content of the motivational messages. The content of the remaining messages was perceived as neutral (42/298, 14.1%). No major differences were found between participants. In total, 43% (17/40) of the psychological exercises were completed, with a broad variety in completion rates between participants (range 0%-100%). Furthermore, it was noted in the log data that missing data (eg, missing Fitbit data and missed EMAs) were included in the weekly feedback by stating that a goal was not achieved on the days when there were missing data.
Experiences with E-Supporter 1.0 integrated within the Diameter app were reduced to two major themes: (1)
Participants experienced the goal-setting functionalities as motivating because it gave them a concrete purpose to work on. No areas for improvement were identified for the goal-setting functions. Motivational messages were mostly positively rated. Participants called the coaching messages fun, motivating, and informative and believed that it also contributed to lifestyle improvements. The content of the intervention options matched their preferences regarding the type of physical activities and diet well, but the participants felt that the content could be further tailored. Preferably, the participants would also like to receive real-time feedback on their actual behavior (eg, feedback whether a certain amount of physical activity is sufficient). Opinions were divided about the psychological exercises (ie, conversational agent). Some participants saw the added value of these exercises and thought it made them think about how to achieve their goals as participant 6 mentioned the following:
I like the online coach. Then you become more aware of your own behavior. I mainly use it to think about how I want to take more steps and the coach does make you think about that.
However, other participants found the purpose of the weekly exercises unclear which demotivated them to complete the exercises. Participant 3 explained why the conversational agent was not of added value for her:
I think that is a bit of a nagging of “the coach wants to talk to you” [push notification to complete the psychological exercise]. I do not need that. Then I have that message again and then I think quickly write in some answers and then we are done with it. I don’t see the added value in that, because I can also think for myself why I will or will not achieve my goal.
The participants considered the self-monitoring functionalities of the Diameter app to be valuable because they provided new insights into their own lifestyle. For example, several participants indicated that through self-reporting and tracking physical activities they learned that they were not as active as they thought. In addition, all the participants indicated that the self-monitoring functionalities gave them insight into the effects of lifestyle on glucose levels as participant 2 said the following:
The Diameter provides insight, for example which activities you have undertaken, which food you have eaten and what impact that has had on glucose values. That is extremely useful.
These kinds of insights convinced the participants that an improvement in lifestyle can lead to improved glucose regulation. For several reasons, participants expected that the Diameter app could be a valuable addition to regular care. First, participants stated that the Diameter app could support the transition to a healthy lifestyle by complementing the information and advice of health care professionals during the consultations in the hospital. Second, participants thought that it could be beneficial if health care providers could also access the collected data. Participants believed that insight into these data could allow health care professionals to provide more personalized lifestyle advice. Furthermore, some participants noted that sharing data with health care providers could also lead to more proactive care as participant 6 outlined the following:
If healthcare providers have insight into my data, they can act much more proactively. This means that I do not have a consultation with the doctor every few months, but that consultations will be planned, when necessary, for example if my blood sugars are poorly regulated. But also, regular consultations will be omitted if everything goes well.
Participants indicated that they experienced a high degree of user-friendliness because each component (ie, physical activity, nutrition, and glucose levels) had its own tab and there were a limited number of buttons. The biggest point of criticism regarding ease of use was filling in the food diary. It was difficult to find certain foods or these were not available at all in the food diary. This was particularly experienced by participants who often eat dishes from foreign cuisine. As a result, participants had to look for alternative foods that are similar. This took a lot of time, and participants felt that this gave a distorted picture of their diet. Other frequently mentioned disadvantages were related to the way E-Supporter 1.0 was integrated within the Diameter app. It was not possible to read the motivational messages again once they had closed the message as participant 8 echoed the following:
Sometimes I wanted to read the information from the messages again later or I wanted to look up the hyperlink to a website again. That was not possible now. That is a pity because then I cannot do anything with it anymore.
Regarding the weekly psychological exercises, participants found it inconvenient that the exercises came at a fixed day and time in the week and could not be postponed to another moment. In addition, the exercises popped up automatically on their screen, making it impossible to perform another action within the app (eg, filling in the food diary) without completing the exercise. Participants would like to be able to choose at what time they perform the exercises so that they could also take the time to go through it carefully.
All detected bugs, inconveniences, and so on were listed and fixed by the app developers accordingly whenever possible.
This study describes the development of E-Supporter 1.0, a lifestyle monitoring and coaching intervention, using a systematic and participatory 3-phase design approach. The aim of E-Supporter 1.0 was to encourage people with T2D to be physically active and to follow national dietary guidelines. The HAPA model and theories explaining behavioral maintenance were used to select determinants of behavior and identified BCTs that were presumed to affect the targeted determinants. Thereafter, the intervention was developed by (1) selecting targets to tailor the intervention, (2) operationalizing decision rules to provide the right type of intervention option to an individual, and (3) creating intervention options to influence health behavior.
Regarding intervention development, we ensured a systematic and participatory design approach. The use of program-planning models provided detailed guidance on how to develop E-Supporter 1.0. This approach increased transparency in the design process by providing a comprehensive description of the intervention rationale and development of intervention components. This contributes to a better interpretation of results and the replicability of the intervention [
So far, most of the eHealth interventions to promote health behaviors have shown positive short-term effects [
In addition to individual factors, behavior is largely influenced by the (social) living environment [
A feature that distinguishes E-Supporter from other eHealth interventions is that E-Supporter content can be integrated in different eHealth interventions and settings. Although this version aimed to improve physical activity and diet in people with T2D, we expect that the content can be used to promote other lifestyle behaviors in people with other chronic diseases with simple adjustments owing to about 80% of the content consisting of nondisease-specific health information. Moreover, often the same behavioral determinants influence the behavior change process (eg, self-efficacy).
The strength of this study is that our intervention combines several potentially effective elements for eHealth interventions, including the application of behavior change theory and dynamic tailoring, the deployment of the intervention in a blended care setting, and early end user involvement in both intervention development and evaluation. Another strong point of E-Supporter is that the content can be built into different apps so that it can be used in different contexts and possibly also on a larger scale.
Some limitations are worth noting at this stage of the research. The weekly feedback on goal achievement and whether to offer psychological exercises relies highly on input from the user. Lifestyle goals other than step goals were actively monitored through daily EMAs, which rely on an individual’s daily response. Therefore, work is being done on this issue by making more use of passive assessment tools that require minimal user input (eg, using activity trackers to track cycling goals). In addition, if individuals provided insufficient input, the Diameter app based the tailored feedback on incomplete information. This led to individuals receiving inappropriate feedback that they had not sufficiently achieved their goals, which can be demotivating to use the intervention. The Diameter app was technically adjusted so that no feedback will be given if there is insufficient user input to provide valid feedback.
We had little influence over the design and user interface of the Diameter app into which the E-Supporter content was integrated. The design and interface of the intervention can influence the user experience and use of the app both positively and negatively [
During the usability study, we encountered some challenges in the technical integration of E-Supporter 1.0 into the Diameter app (eg, receiving motivational messages irregularly). These challenges led to the intervention not being delivered as intended, which negatively influenced the results regarding use and acceptability of E-Supporter. To improve the integration, we recorded all detected technical problems and discussed them with the app developer to solve them. Lessons learned from this usability test will be used to make recommendations regarding the integration of E-Supporter content in apps to promote a positive user experience. For example, it is necessary to test what influence missing data has on the feedback initiated by the app (eg, as described in the first limitation).
We have planned several follow-up activities to further improve E-Supporter. First, the intervention content and intervention period will be expanded so that E-Supporter 1.0 can better facilitate behavioral maintenance. Literature states that behavior maintenance is reached when an individual can maintain the desired behavior for at least 6 months [
Second, researching the use of BCTs in other delivery modes (eg, videos and voice messages) than textual coaching is suggested. The content of the intervention options can remain unchanged but will be offered via a different delivery mode. Other delivery modes can make the intervention options more attractive to increase acceptance [
Third, we intend to tailor our intervention more dynamically by applying data science techniques because higher degrees of tailoring can contribute to improved user engagement and effectiveness [
Fourth, our intervention can be improved by aligning intervention content of the ABCC tool and E-Supporter (
To obtain more information about the intervention use and acceptability of our intervention, we plan to evaluate the E-Supporter content in other apps, such as MiGuide [
This paper describes the systematic and participatory development of a theory-based, dynamically tailored lifestyle coaching intervention to support physical activity and a healthy diet in people with T2D. Program-planning models and behavior change theory were used complementarily during the development of the intervention. The intervention was evaluated in a small usability study which provided insights into intervention use and acceptability. Future work should focus on improving the degree of tailoring and evaluating its effects on acceptability, use, and cost-effectiveness.
Methods E-Supporter health expert review.
Methods E-Supporter usability study.
E-Supporter determinants and behavior change techniques (BCTs) including examples.
E-Supporter psychological exercises.
Assessment of Burden of Chronic Conditions
behavior change technique
ecological momentary assessment
health action process approach
quality of life
type 2 diabetes
We thank the health care providers and patients who participated in the development phase and usability study for their active participation and input. In addition, we thank Anouk ten Voorde and Elise Fokkema for their contribution to writing and improving the motivational messages.
This study was funded by the Netherlands Organization for Health Research and Development (grant 104006001). The funders in the study had no role in the study design, data collection, analysis and interpretation of the data, or writing of the report.
EAGH mostly contributed to drafting the manuscript and worked closely together with AM, PvE, KP, and AAJK during the design, development, and usability testing of the intervention. WON-d'H and LKS contributed significantly to the development of intervention content. GDL and MMRV-H provided ongoing feedback related to the methods, results, and discussion of this paper. All authors critically evaluated and approved the definitive version of the paper.
None declared.