A factorial randomised controlled trial to identify efficacious self-regulation techniques in an e- and m-health intervention to target an active lifestyle: study protocol

BACKGROUND

Sufficient physical activity and a limited amount of sedentary behaviour can prevent a range of chronic diseases. However, most adults do not meet the recommendations for physical activity and sedentary behaviour. Effective and engaging interventions are needed to change people's behaviour. E- and m-health interventions are promising, but unfortunately they result in small effects and suffer from high attrition rates. Improvements to intervention content and design are required. Qualitative research has revealed the need for clear and concise interventions. Furthermore, many interventions use a range of behaviour-change techniques, and it is yet unknown whether these techniques are equally important to obtain behaviour change. It may well be that a limited set of these techniques is sufficient. In this study, the aim is to experimentally investigate the efficacy of three behaviour-change techniques (i.e. action planning, coping planning and self-monitoring) on physical activity, sedentary behaviour and related determinants among adults.

METHODS

In a 2 x 2 x 2 factorial trial participants will be randomly allocated to eight groups (including one control group). Each group will receive a different version of the self-regulation-based e- and m-health intervention 'MyPlan 2.0', in which three behaviour-change techniques (i.e. action planning, coping planning, self-monitoring) will be combined in order to achieve self-formulated goals about physical activity or sedentary behaviour. Goal attainment, and levels of physical activity and sedentary behaviour will be measured via self-report questionnaires.

DISCUSSION

This study should provide insight into the role of various behaviour-change techniques in changing health behaviour and its determinants. Its experimental and longitudinal design, with repeated measures of several determinants of behaviour change, allows an in-depth analysis of the processes underlying behaviour change, enabling the authors to provide guidance for the development of future e- and m-health interventions.

TRIAL REGISTRATION

This study is registered as MyPlan 2.0 as a clinical trial (ID number: NCT03274271 ). Release date: 20 October 2017.

How Users Experience and Use an eHealth Intervention Based on Self-Regulation: Mixed-Methods Study

BACKGROUND

eHealth interventions show stronger effects when informed by solid behavioral change theories; for example, self-regulation models supporting people in translating vague intentions to specific actions have shown to be effective in altering health behaviors. Although these theories inform developers about which behavioral change techniques should be included, they provide limited information about how these techniques can be engagingly implemented in Web-based interventions. Considering the high levels of attrition in eHealth, investigating users' experience about the implementation of behavior change techniques might be a fruitful avenue.

OBJECTIVE

The objective of our study was to investigate how users experience the implementation of self-regulation techniques in a Web-based intervention targeting physical activity and sedentary behavior in the general population.

METHODS

In this study, 20 adults from the general population used the intervention for 5 weeks. Users' website data were explored, and semistructured interviews with each of the users were performed. A directed content analysis was performed using NVivo Software.

RESULTS

The techniques "providing feedback on performance," "action planning," and "prompting review of behavioral goals" were appreciated by users. However, the implementation of "barrier identification/problem solving" appeared to frustrate users; this was also reflected by the users' website data-many coping plans were of poor quality. Most users were well aware of the benefits of adopting a more active way of living and stated not to have learned novel information. However, they appreciated the provided information because it reminded them about the importance of having an active lifestyle. Furthermore, prompting users to self-monitor their behavioral change was not sufficiently stimulating to make users actually monitor their behavior.

CONCLUSIONS

Iteratively involving potential end users offers guidance to optimally adapt the implementation of various behavior change techniques to the target population. We recommend creating short interventions with a straightforward layout that support users in creating and evaluating specific plans for action.

Experiences and Opinions of Adults with Type 2 Diabetes Regarding a Self-Regulation-Based eHealth Intervention Targeting Physical Activity and Sedentary Behaviour

Background: Online interventions targeting a healthy lifestyle in adults with type 2 diabetes are more effective when informed by behaviour change theories. Although these theories provide guidance in developing the content of an intervention, information regarding how to present this content in an engaging way is often lacking. Consequently, incorporating users’ views in the creation of eHealth interventions has become an important target. Methods: Via a qualitative interview study with 21 adults with type 2 diabetes who had completed an online self-regulation-based intervention (‘MyPlan 2.0’), we assessed participants’ opinions regarding the usefulness of the implemented self-regulation techniques, the design of the programme as well as their knowledge regarding physical activity and sedentary behaviour. A directed content analysis was performed to synthesize the interview data. Results: Participants experienced difficulties completing the coping planning component. The simple design of the website was considered helpful, and most participants were aware of the beneficial effects of an active lifestyle. Conclusions: ‘MyPlan 2.0’ was well-accepted by the majority of participants. However, the coping planning component will need to be adapted. Based on these findings, recommendations on how to tailor eHealth interventions to the population of adults with type 2 diabetes have been formulated.

The Effect of the eHealth Intervention 'MyPlan 1.0' on Physical Activity in Adults Who Visit General Practice: A Quasi-Experimental Trial

Physical inactivity is one of the major risk factors for poor health in the world. Therefore, effective interventions that promote physical activity are needed. Hence, we developed an eHealth intervention for adults, i.e., 'MyPlan 1.0', which includes self-regulation techniques for behaviour change. This study examined the effect of 'MyPlan 1.0' on physical activity (PA) levels in general practice. 615 adults (≥18 years) were recruited in 19 Flemish general practices, for the intervention group (n = 328) or for the wait-list control group (n = 183). Participants in the intervention group received the web-based intervention 'MyPlan 1.0' and were prompted to discuss their personal advice/action plan with their general practitioner. Participants in the wait-list control group only received general advice from the website. Self-reported physical activity was assessed with the International Physical Activity Questionnaire (IPAQ) at baseline and after one month. A three-level (general practice, adults, time) regression analysis was conducted in MLwiN. Significant intervention effects were found for total PA and moderate to vigorous PA with an increase for the intervention group compared to a decrease in the control condition. However, there was a high dropout rate in the intervention group (76%) and the wait-list control group (57%). Our self-regulation intervention was effective in increasing physical activity levels in adults. Future studies should consider strategies to prevent the large dropout from participants.

A Self-Regulation-Based eHealth Intervention to Promote a Healthy Lifestyle: Investigating User and Website Characteristics Related to Attrition

BACKGROUND

eHealth interventions can reach large populations and are effective in increasing physical activity (PA) and fruit and vegetable intake. Nevertheless, the effects of eHealth interventions are overshadowed by high attrition rates. Examining more closely when users decide to leave the intervention can help eHealth developers to make informed decisions about which intervention components should be reshaped or simply removed. Investigating which users are more likely to quit an intervention can inform developers about whether and how their intervention should be adapted to specific subgroups of users.

OBJECTIVE

This study investigated the pattern of attrition in a Web-based intervention to increase PA, fruit, and vegetable intake. The first aim was to describe attrition rates according to different self-regulation components. A second aim was to investigate whether certain user characteristics are predictors for start session completion, returning to a follow-up session and intervention completion.

METHODS

The sample consisted of 549 adults who participated in an online intervention, based on self-regulation theory, to promote PA and fruit and vegetable intake, called "MyPlan 1.0." Using descriptive analysis, attrition was explored per self-regulation component (eg, action planning and coping planning). To identify which user characteristics predict completion, logistic regression analyses were conducted.

RESULTS

At the end of the intervention program, there was an attrition rate of 78.2% (330/422). Attrition rates were very similar for the different self-regulation components. However, attrition levels were higher for the fulfillment of questionnaires (eg, to generate tailored feedback) than for the more interactive components. The highest amount of attrition could be observed when people were asked to make their own action plan. There were no significant predictors for first session completion. Yet, two subgroups had a lower chance to complete the intervention, namely male users (OR: 2.24, 95% CI=1.23-4.08) and younger adults (OR: 1.02, 95% CI=1.00-1.04). Furthermore, younger adults were less likely to return to the website for the first follow-up after one week (OR: 1.03, 95% CI=1.01-1.04).

CONCLUSIONS

This study informs us that eHealth interventions should avoid the use of extensive questionnaires and that users should be provided with a rationale for several components (eg, making an action plan and completing questions). Furthermore, future interventions should focus first on motivating users for the behavior change before guiding them through action planning. Though, this study provides no evidence for removal of one of the self-regulation techniques based on attrition rates. Finally, strong efforts are needed to motivate male users and younger adults to complete eHealth interventions.

Effect of the Web-Based Intervention MyPlan 1.0 on Self-Reported Fruit and Vegetable Intake in Adults Who Visit General Practice: A Quasi-Experimental Trial

Background

Web-based interventions typically have small intervention effects on adults’ health behavior because they primarily target processes leading to an intention to change leaving individuals in an intention-behavior gap, they often occur without contact with health care providers, and a limited amount of feedback is provided only at the beginning of these interventions, but not further on in the behavior change process. Therefore, we developed a Web-based intervention (“MyPlan 1.0”) to promote healthy behavior in adults. The intervention was based on a self-regulation perspective that also targets postintentional processes and guides individuals during all phases of behavior change.

Objective

The study investigated the effectiveness of MyPlan1.0 on fruit and vegetable intake of Flemish adults visiting general practice (3 groups: control group, intervention group recruited by researchers, and intervention group recruited and guided by general practitioners [GPs]). Second, it examined whether there was a larger intervention effect for the intervention group guided by GPs compared to the intervention group recruited by researchers.

Methods

Adults (≥18 years) were recruited in 19 Flemish general practices. In each general practice, patients were systematically allocated by a researcher either for the intervention group (researchers’ intervention group) or the waiting-list control group that received general advice. In a third group, the GP recruited adults for the intervention (GPs intervention group). The two intervention groups filled in evaluation questionnaires and received MyPlan 1.0 for a behavior of choice (fruit, vegetable, or physical activity). The waiting-list control group filled in the evaluation questionnaires and received only general information. Self-reported fruit and vegetable intake were assessed at baseline (T0), 1 week (T1), and 1 month (T2) postbaseline. Three-level (general practice, adults, time) linear regression models were conducted in MLwiN.

Results

A total of 426 adults initially agreed to participate (control group: n=149; GPs’ intervention group: n=41; researchers’ intervention group: n=236). A high attrition rate was observed in both intervention groups (71.8%, 199/277) and in the control group (59.1%, 88/149). In comparison to no change in the control group, both the GPs’ intervention group (fruit: χ²₁=10.9, P=.004; vegetable: χ²₁=5.3, P=.02) and the researchers’ intervention group (fruit: χ²₁=18.0, P=.001; vegetable: χ²₁=12.8, P<.001) increased their intake of fruit and vegetables.

Conclusions

A greater increase in fruit and vegetable intake was found when the Web-based intervention MyPlan 1.0 was used compared to usual care of health promotion in general practice (ie, flyers with general information). However, further investigation on which (or combinations of which) behavior change techniques are effective, how to increase response rates, and the influence of delivery mode in routine practice is required.

Trial Registration

ClinicalTrials.gov NCT02211040; https://clinicaltrials.gov/ct2/show/NCT02211040 (Archived by WebCite® at http://www.webcitation.org/6f8yxTRii)