Background E- and m-health interventions are promising to change health behaviour. Many of these interventions use a large variety of behaviour change techniques (BCTs), but it’s not known which BCTs or which combination of BCTs contribute to their efficacy. Therefore, this experimental study investigated the efficacy of three BCTs (i.e. action planning, coping planning and self-monitoring) and their combinations on physical activity (PA) and sedentary behaviour (SB) against a background set of other BCTs. Methods In a 2 (action planning: present vs absent) × 2 (coping planning: present vs absent) × 2 (self-monitoring: present vs absent) factorial trial, 473 adults from the general population used the self-regulation based e- and m-health intervention ‘MyPlan2.0’ for five weeks. All combinations of BCTs were considered, resulting in eight groups. Participants selected their preferred target behaviour, either PA (n = 335, age = 35.8, 28.1% men) or SB (n = 138, age = 37.8, 37.7% men), and were then randomly allocated to the experimental groups. Levels of PA (MVPA in minutes/week) or SB (total sedentary time in hours/day) were assessed at baseline and post-intervention using self-reported questionnaires. Linear mixed-effect models were fitted to assess the impact of the different combinations of the BCTs on PA and SB. Results First, overall efficacy of each BCT was examined. The delivery of self-monitoring increased PA (t = 2.735, p = 0.007) and reduced SB (t = − 2.573, p = 0.012) compared with no delivery of self-monitoring. Also, the delivery of coping planning increased PA (t = 2.302, p = 0.022) compared with no delivery of coping planning. Second, we investigated to what extent adding BCTs increased efficacy. Using the combination of the three BCTs was most effective to increase PA (x2 = 8849, p = 0.003) whereas the combination of action planning and self-monitoring was most effective to decrease SB (x2 = 3.918, p = 0.048). To increase PA, action planning was always more effective in combination with coping planning (x2 = 5.590, p = 0.014; x2 = 17.722, p < 0.001; x2 = 4.552, p = 0.033) compared with using action planning without coping planning. Of note, the use of action planning alone reduced PA compared with using coping planning alone (x2 = 4.389, p = 0.031) and self-monitoring alone (x2 = 8.858, p = 003), respectively. Conclusions This study provides indications that different (combinations of) BCTs may be effective to promote PA and reduce SB. More experimental research to investigate the effectiveness of BCTs is needed, which can contribute to improved design and more effective e- and m-health interventions in the future. Trial registration This study was preregistered as a clinical trial (ID number: NCT03274271). Release date: 20 October 2017.

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

BackgroundeHealth 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.ObjectiveThe 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.MethodsIn 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.ResultsThe 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.ConclusionsIteratively 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.

Background Digital health interventions have gained momentum to change health behaviors such as physical activity (PA) and sedentary behavior (SB). Although these interventions show promising results in terms of behavior change, they still suffer from high attrition rates, resulting in a lower potential and accessibility. To reduce attrition rates in the future, there is a need to investigate the reasons why individuals stop using the interventions. Certain demographic variables have already been related to attrition; however, the role of psychological determinants of behavior change as predictors of attrition has not yet been fully explored. Objective The aim of this study was to examine when, which, and why users stopped using a digital health intervention. In particular, we aimed to investigate whether psychological determinants of behavior change were predictors for attrition. Methods The sample consisted of 473 healthy adults who participated in the intervention MyPlan 2.0 to promote PA or reduce SB. The intervention was developed using the health action process approach (HAPA) model, which describes psychological determinants that guide individuals in changing their behavior. If participants stopped with the intervention, a questionnaire with 8 question concerning attrition was sent by email. To analyze when users stopped using the intervention, descriptive statistics were used per part of the intervention (including pre- and posttest measurements and the 5 website sessions). To analyze which users stopped using the intervention, demographic variables, behavioral status, and HAPA-based psychological determinants at pretest measurement were investigated as potential predictors of attrition using logistic regression models. To analyze why users stopped using the intervention, descriptive statistics of scores to the attrition-related questionnaire were used. Results The study demonstrated that 47.9% (227/473) of participants stopped using the intervention, and drop out occurred mainly in the beginning of the intervention. The results seem to indicate that gender and participant scores on the psychological determinants action planning, coping planning, and self-monitoring were predictors of first session, third session, or whole intervention completion. The most endorsed reasons to stop using the intervention were the time-consuming nature of questionnaires (55%), not having time (50%), dissatisfaction with the content of the intervention (41%), technical problems (39%), already meeting the guidelines for PA/SB (31%), and, to a lesser extent, the experience of medical/emotional problems (16%). Conclusions This study provides some directions for future studies. To decrease attrition, it will be important to personalize interventions on different levels, questionnaires (either for research purposes or tailoring) should be kept to a minimum especially in the beginning of interventions by, for example, using objective monitoring devices, and technical aspects of digital health interventions should be thoroughly tested in advance. Trial Registration ClinicalTrials.gov NCT03274271; https://clinicaltrials.gov/ct2/show/NCT03274271 International Registered Report Identifier (IRRID) RR2-10.1186/s13063-019-3456-7

BackgroundE- and m-health interventions are promising to change health behaviour. Many of these interventions use a large variety of behaviour change techniques (BCTs), but it’s not known which BCTs or which combination of BCTs contribute to their efficacy. Therefore, this study investigated the efficacy of three BCTs (i.e. action planning, coping planning and self-monitoring) and their combinations on physical activity (PA) and sedentary behaviour (SB).MethodsIn a 2(action planning: present vs absent) x2(coping planning: present vs absent) x2(self-monitoring: present vs absent) factorial trial, 473 adults from the general population used the self-regulation based e- and m-health intervention ‘MyPlan2.0’ for five weeks. All combinations of BCTs were considered, resulting in eight groups. Participants selected their preferred target behaviour, either PA (n = 335,age = 35.8,28.1% men) or SB (n = 138,age = 37.8,37.7% men), and were then randomly allocated to the experimental groups. Levels of PA (MVPA in minutes/week) or SB (total sedentary time in hours/day) were assessed at baseline and post-intervention using self-reported questionnaires. Linear mixed-effect models were fitted to assess the impact of the different combinations of the BCTs on PA and SB.ResultsFirst, overall efficacy of each BCT was examined. The delivery of self-monitoring increased PA (t = 2.735,p = 0.007) and reduced SB (t=-2.573,p = 0.012) compared with no delivery of self-monitoring. Also, the delivery of coping planning increased PA (t = 2.302,p = 0.022) compared with no delivery of coping planning. Second, we investigated to what extent adding BCTs increased efficacy. Using the combination of the three BCTs was most effective to increase PA (x2 = 8,849,p = 0.003) whereas the combination of action planning and self-monitoring was most effective to decrease SB (x2 = 3.918,p = 0.048). To increase PA, action planning was always more effective in combination with coping planning (x2 = 5.590,p = 0.014;x2 = 17.722,p < 0.001;x2 = 4.552,p = 0.033) compared with using action planning without coping planning. Of note, the use of action planning alone reduced PA compared with using coping planning alone (x2 = 4.389,p = 0.031) and self-monitoring alone (x2 = 8.858,p = 003), respectively.ConclusionsThis study provides indications that different (combinations of) BCTs may be effective to promote PA and reduce SB. More experimental research to investigate the effectiveness of BCTs is needed, which can contribute to improved design and more effective e- and m-health interventions in the future.Trial registrationThis study was preregistered as a clinical trial (ID number: NCT03274271). Release date: 20 October 2017, http://clinicaltrials.gov/ct2/show/NCT03274271