A Self-Regulation eHealth Intervention to Increase Healthy Behavior Through General Practice: Protocol and Systematic Development

An Ecological Mobile Momentary Intervention to Support Dynamic Goal Pursuit: Feasibility and Acceptability Study

BACKGROUND

Individuals can experience difficulties pursuing their goals amid multiple competing priorities in their environment. Effective goal dynamics require flexible and generalizable pursuit skills. Supporting successful goal pursuit requires a perpetually adapting intervention responsive to internal states.

OBJECTIVE

The purpose of this study was to (1) develop a flexible intervention that can adapt to an individual's changing short to medium-term goals and be applied to their daily life and (2) examine the feasibility and acceptability of the just-in-time adaptive intervention for goal pursuit.

METHODS

This study involved 3 iterations to test and systematically enhance all aspects of the intervention. During the pilot phase, 73 participants engaged in an ecological momentary assessment (EMA) over 1 month. After week 1, they attended an intervention training session and received just-in-time intervention prompts during the following 3 weeks. The training employed the Capability, Opportunity, Motivation, and Behavior (COM-B) framework for goal setting, along with mental contrasting with implementation intentions (MCII). Subsequent prompts, triggered by variability in goal pursuit, guided the participants to engage in MCII in relation to their current goal. We evaluated feasibility and acceptability, efficacy, and individual change processes by combining intensive (single-case experimental design) and extensive methods.

RESULTS

The results suggest that the digital intervention was feasible and acceptable to participants. Compliance with the intervention was high (n=63, 86%). The participants endorsed high acceptability ratings relating to both the study procedures and the intervention. All participants (N=73, 100%) demonstrated significant improvements in goal pursuit with an average difference of 0.495 units in the outcome (P<.001). The results of the dynamic network modeling suggest that self-monitoring behavior (EMA) and implementing the MCII strategy may aid in goal reprioritization, where goal pursuit itself is a driver of further goal pursuit.

CONCLUSIONS

This pilot study demonstrated the feasibility and acceptability of a just-in-time adaptive intervention among a nonclinical adult sample. This intervention used self-monitoring of behavior, the COM-B framework, and MCII strategies to improve dynamic goal pursuit. It was delivered via an Ecological Momentary Intervention (EMI) procedure. Future research should consider the utility of this approach as an additional intervention element within psychological interventions to improve goal pursuit. Sustaining goal pursuit throughout interventions is central to their effectiveness and warrants further evaluation.

Tools to guide clinical discussions on physical activity, sedentary behaviour, and/or sleep for health promotion between primary care providers and adults accessing care: a scoping review

BACKGROUND

Health care providers have reported low knowledge, skill, and confidence for discussing movement behaviours (i.e., physical activity, sedentary behaviour, and sleep), which may be improved with the use of tools to guide movement behaviour discussions in their practice. Past reviews have examined the psychometric properties, scoring, and behavioural outcomes of physical activity discussion tools. However, the features, perceptions, and effectiveness of discussion tools for physical activity, sedentary behaviour, and/or sleep have not yet been synthesized. The aim of this review was to report and appraise tools for movement behaviour discussions between health care providers and adults 18 + years in a primary care context within Canada or analogous countries.

METHODS

An integrated knowledge translation approach guided this review, whereby a working group of experts in medicine, knowledge translation, communications, kinesiology, and health promotion was engaged from research question formation to interpretation of findings. Three search approaches were used (i.e., peer-reviewed, grey literature, and forward searches) to identify studies reporting on perceptions and/or effectiveness of tools for physical activity, sedentary behaviour, and/or sleep. The quality of included studies was assessed using the Mixed Methods Appraisal Tool.

RESULTS

In total, 135 studies reporting on 61 tools (i.e., 51 on physical activity, one on sleep, and nine combining two movement behaviours) met inclusion criteria. Included tools served the purposes of assessment (n = 57), counselling (n = 50), prescription (n = 18), and/or referral (n = 12) of one or more movement behaviour. Most tools were used or intended for use by physicians, followed by nurses/nurse practitioners (n = 11), and adults accessing care (n = 10). Most tools were also used or intended to be used with adults without chronic conditions aged 18-64 years (n = 34), followed by adults with chronic conditions (n = 18). The quality of the 116 studies that evaluated tool effectiveness varied.

CONCLUSIONS

Many tools were positively perceived and were deemed effective at enhancing knowledge of, confidence for, ability in, and frequency of movement behaviour discussions. Future tools should guide discussions of all movement behaviours in an integrated manner in line with the 24-Hour Movement Guidelines. Practically, this review offers seven evidence-based recommendations that may guide future tool development and implementation.

An individualized mobile health intervention to promote physical activity in adults with obstructive sleep apnea: An intervention mapping approach

Objective

Combining continuous positive airway pressure with physical activity (PA) might be a promising strategy to treat obstructive sleep apnea (OSA). This article describes how intervention mapping (IM) can guide the development, content, and mechanisms of action of a mobile application to promote PA in adults with OSA.

Methods

To develop the program, the IM approach was followed. This article presents the first three steps of IM to develop a mixed intervention (with interventionists and a digital application) aiming to: (1) assess patients' health problems and needs in a literature review to determine their expectations and perceived facilitators and barriers to PA behavior change and thus select the determinants of behavior; (2) formulate the expected intervention outcomes and objectives to be met to achieve the overall program goals; and (3) select and implement the behavior change techniques (BCTs) to achieve the change objectives.

Results

The literature review identified the relevant determinants (e.g., self-efficacy, coping, planning, and habit) of PA behavior using the health action process approach and multiprocess action control. These results were used to specify the program outcomes for PA adapted to physical, psychological, and social parameters. Overall, 11 performance objectives and 30 change objectives were defined. Lastly, BCTs and practical applications were identified.

Conclusions

The study provides a theoretical and methodological basis for researchers and practitioners given the current paucity of evidence-based PA interventions for adults with OSA. It addresses the lack of BCTs (framing and prompts/cue techniques) and meaningful behavioral determinants (identity and habit) in most interventions.

Using Intervention Mapping to Develop an mHealth Intervention to Support Men Who Have Sex With Men Engaging in Chemsex (Budd): Development and Usability Study

BACKGROUND

Chemsex refers to the intentional use of drugs before or during sex among men who have sex with men (MSM). Engaging in chemsex has been linked to significant negative impacts on physical, psychological, and social well-being. However, no evidence-based support tools have addressed either these harms or the care needs of MSM who engage in chemsex.

OBJECTIVE

The purpose of this paper was to describe the development of a mobile health intervention (named Budd) using the intervention mapping protocol (IMP). Budd aims to support and inform MSM who participate in chemsex, reduce the negative impacts associated with chemsex, and encourage more reasoned participation.

METHODS

The IMP consists of 6 steps to develop, implement, and evaluate evidence-based health interventions. A needs assessment was carried out between September 2, 2019, and March 31, 2020, by conducting a literature study and in-depth interviews. Change objectives were selected based on these findings, after which theory-based intervention methods were selected. The first version of the intervention was developed in December 2020 and pilot-tested between February 1, 2021, and April 30, 2021. Adjustments were made based on the findings from this study. A separate article will be dedicated to the effectiveness study, conducted between October 15, 2021, and February 24, 2022, and implementation of the intervention. The Budd app went live in April 2022.

RESULTS

Budd aims to address individual factors and support chemsex participants in applying harm reduction measures when taking drugs (drug information, drug combination tool, and notebook), preparing for participation in a chemsex session (articles on chemsex, preparation tool, and event-specific checklist), planning sufficient time after a chemsex session to recover (planning tool), seeking support for their chemsex participation (overview of existing local health care and peer support services, reflection, personal statistics, and user testimonials), taking HIV medication or pre-exposure prophylaxis in a timely manner during a chemsex session (preparation tool), and contacting emergency services in case of an emergency and giving first aid to others (emergency information and personal buddy).

CONCLUSIONS

The IMP proved to be a valuable tool in the planning and development of the Budd app. This study provides researchers and practitioners with valuable information that may help them to set up their own health interventions.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR1-10.2196/39678.

Acceptability and feasibility of the mHealth intervention 'MyDayPlan' to increase physical activity in a general adult population

Background

Electronic health (eHealth) and mobile health (mHealth) interventions have the potential to tackle the worldwide problem of physical inactivity. However, they often suffer from large attrition rates. Consequently, feasibility and acceptability of interventions have become important matters in the creation of e- and mHealth interventions. The aim of this study was to evaluate participants’ opinions regarding acceptability and feasibility of a self-regulation, app-based intervention called ‘MyDayPlan’. ‘MyDayPlan’ provides an innovative daily cycle providing several self-regulation techniques throughout the day that guide users towards an active lifestyle via various self-regulation techniques.

Methods

Semi-structured interviews were conducted with 20 adults after using the app for 2 weeks. A directed content analysis was performed using NVivo Software.

Results

‘MyDayPlan’ was well-received and seems to be feasible and acceptable with inactive adults. The straightforward lay out and ease of use of the app were appreciated. Furthermore, the incorporation of the techniques ‘action planning’, and ‘prompting review of behavioral goals’ was positively evaluated. However, the users gave some recommendations: implementation of activity trackers to self-monitor physical activity could be of added value. Furthermore, increasing intuitiveness by minimizing text input and providing more preprogrammed options could further increase the ease of use. Finally, users indicated that they would benefit from more guidance during the “coping planning” component (barrier identification/problem solving), for example by receiving more tailored examples.

Conclusions

Based on these findings, adaptations will be made to the ‘MyDayPlan’ app before evaluating its effectiveness. Furthermore, involving potential end users and evaluating acceptability and feasibility during the development of an e- and mHealth intervention is key. Also, creating interventions with a large ease of use and straightforward layout that provides tailored support during action and coping planning is key.

Development of interventions for an intelligent and individualized mobile health care system to promote healthy diet and physical activity: using an intervention mapping framework

BACKGROUND

The mortality of coronary heart disease can be largely reduced by modifying unhealthy lifestyles. However, the long-term effectiveness of interventions for modifying unhealthy diet and physical inactivity of patients with coronary heart disease remain unsatisfactory worldwide. This study aims to systematically design a set of theory-based and evidence-based, individualized, and intelligent interventions for promoting the adoption and maintenance of a healthy diet and physical activity level in patients with coronary heart disease.

METHODS

The interventions will be delivered by a mobile health care system called Individualized, Intelligent and Integrated Cardiovascular Application for Risk Elimination. Three steps of the intervention mapping framework were used to systematically develop the interventions. Step 1: needs assessment, which was carried out by a literature review, in-depth interviews and focus group discussions. Step 2: development of objective matrix for diet and physical activity changes, based on the intersection of objectives and determinants from the Contemplation-Action-Maintenance behavior change model. Step 3: formulation of evidence-based methods and strategies, and practical applications, through a systematic review of existing literature, research team discussions, and consultation with multidisciplinary expert panels.

RESULTS

Three needs relevant to content of the intervention, one need relevant to presentation modes of the intervention, and four needs relevant to functional features of the application were identified. The objective matrix includes three performance objectives, and 24 proximal performance objectives. The evidence-based and theory-based interventions include 31 strategies, 61 evidence-based methods, and 393 practical applications.

CONCLUSIONS

This article describes the development of theory-based and evidence-based interventions of the mobile health care system for promoting the adoption and maintenance of a healthy diet and physical activity level in a structured format. The results will provide a theoretical and methodological basis to explore the application of intervention mapping in developing effective behavioral mobile health interventions for patients with coronary heart disease.

TRIAL REGISTRATION

Chinese Clinical Trial Registry: ChiCTR-INR-16010242. Registered 24 December 2016. http://www.chictr.org.cn/index.aspx.

Results of MyPlan 2.0 on Physical Activity in Older Belgian Adults: Randomized Controlled Trial

Background

The beneficial effects of physical activity (PA) for older adults are well known. However, few older adults reach the health guideline of 150 min per week of moderate-to-vigorous PA (MVPA). Electronic health (eHealth) interventions are effective in increasing PA levels in older adults in the short term but, rarely, intermediate-term effects after a period without the support of a website or an app have been examined. Furthermore, current theory-based interventions focus mainly on preintentional determinants, although postintentional determinants should also be included to increase the likelihood of successful behavior change.

Objective

This study aimed to investigate the effect of the theory-based eHealth intervention, MyPlan 2.0, focusing on pre- and postintentional determinants on both accelerometer-based and self-reported PA levels in older Belgian adults in the short and intermediate term.

Methods

This study was a randomized controlled trial with three data collection points: baseline (N=72), post (five weeks after baseline; N=65), and follow-up (three months after baseline; N=65). The study took place in Ghent, and older adults (aged ≥65 years) were recruited through a combination of random and convenience sampling. At all the time points, participants were visited by the research team. Self-reported domain-specific PA was assessed using the International Physical Activity Questionnaire, and accelerometers were used to objectively assess PA. Participants in the intervention group got access to the eHealth intervention, MyPlan 2.0, and used it independently for five consecutive weeks after baseline. MyPlan 2.0 was based on the self-regulatory theory and focused on both pre- and postintentional processes to increase PA. Multilevel mixed-models repeated measures analyses were performed in R (R Foundation for Statistical Computing).

Results

Significant (borderline) positive intervention effects were found for accelerometer-based MVPA (baseline−follow-up: intervention group +5 min per day and control group −5 min per day; P=.07) and for accelerometer-based total PA (baseline−post: intervention group +20 min per day and control group −24 min per day; P=.05). MyPlan 2.0 was also effective in increasing self-reported PA, mainly in the intermediate term. A positive intermediate-term intervention effect was found for leisure-time vigorous PA (P=.02), moderate household-related PA (P=.01), and moderate PA in the garden (P=.04). Negative intermediate-term intervention effects were found for leisure-time moderate PA (P=.01) and cycling for transport (P=.07).

Conclusions

The findings suggest that theory-based eHealth interventions focusing on pre- and postintentional determinants have the potential for behavior change in older adults. If future studies including larger samples and long-term follow-up can confirm and clarify these findings, researchers and practitioners should be encouraged to use a self-regulation perspective for eHealth intervention development.

Trial Registration

Clinicaltrials.gov NCT03194334; https://clinicaltrials.gov/ct2/show/NCT03783611.

Efficacy of a Self-Regulation-Based Electronic and Mobile Health Intervention Targeting an Active Lifestyle in Adults Having Type 2 Diabetes and in Adults Aged 50 Years or Older: Two Randomized Controlled Trials

Background

Adopting an active lifestyle plays a key role in the prevention and management of chronic diseases such as type 2 diabetes mellitus (T2DM). Web-based interventions are able to alter health behaviors and show stronger effects when they are informed by a behavior change theory. MyPlan 2.0 is a fully automated electronic health (eHealth) and mobile health (mHealth) intervention targeting physical activity (PA) and sedentary behavior (SB) based on the Health Action Process Approach (HAPA).

Objective

This study aimed to test the short-term effect of MyPlan 2.0 in altering levels of PA and SB and in changing personal determinants of behavior in adults with T2DM and in adults aged ≥50 years.

Methods

The study comprised two randomized controlled trials (RCTs) with an identical design. RCT 1 was conducted with adults with T2DM. RCT 2 was performed in adults aged ≥50 years. Data were collected via face-to-face assessments. The participants decided either to increase their level of PA or to decrease their level of SB. The participants were randomly allocated with a 2:1 ratio to the intervention group or the waiting-list control group. They were not blinded for their group allocation. The participants in the intervention group were instructed to go through MyPlan 2.0, comprising 5 sessions with an interval of 1 week between each session. The primary outcomes were objectively measured and self-reported PA (ie, light PA, moderate-to-vigorous PA, total PA, number of steps, and domain-specific [eg, transport-related] PA) and SB (ie, sitting time, number of breaks from sitting time, and length of sitting bouts). Secondary outcomes were self-reported behavioral determinants for PA and SB (eg, self-efficacy). Separate linear mixed models were performed to analyze the effects of MyPlan 2.0 in the two samples.

Results

In RCT 1 (n=54), the PA intervention group showed, in contrast to the control group, a decrease in self-reported time spent sitting (P=.09) and an increase in accelerometer-measured moderate (P=.05) and moderate-to-vigorous PA (P=.049). The SB intervention group displayed an increase in accelerometer-assessed breaks from sedentary time in comparison with the control group (P=.005). A total of 14 participants of RCT 1 dropped out. In RCT 2 (n=63), the PA intervention group showed an increase for self-reported total PA in comparison with the control group (P=.003). Furthermore, in contrast to the control group, the SB intervention group decreased their self-reported time spent sitting (P=.08) and increased their accelerometer-assessed moderate (P=.06) and moderate-to-vigorous PA (P=.07). A total of 8 participants of RCT 2 dropped out.

Conclusions

For both the samples, the HAPA-based eHealth and mHealth intervention, MyPlan 2.0, was able to improve only some of the primary outcomes.

Trial Registration

ClinicalTrials.gov NCT03291171; http://clinicaltrials.gov/ct2/show/NCT03291171. ClinicalTrials.gov NCT03799146; http://clinicaltrials.gov/ct2/show/NCT03799146.

International Registered Report Identifier (IRRID)

RR2-10.2196/12413

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.

A Self-Regulation-Based eHealth and mHealth Intervention for an Active Lifestyle in Adults With Type 2 Diabetes: Protocol for a Randomized Controlled Trial

Background

Adoption of an active lifestyle plays an important role in the management of type 2 diabetes. Online interventions targeting lifestyle changes in adults with type 2 diabetes have provided mixed results. Previous research highlights the importance of creating theory-based interventions adapted to the population’s specific needs. The online intervention “MyPlan 2.0” targets physical activity and sedentary behavior in adults with type 2 diabetes. This intervention is grounded in the self-regulation framework and, by incorporating the feedback of users with type 2 diabetes, iteratively adapted to its target population.

Objective

The aim of this paper is to thoroughly describe “MyPlan 2.0” and the study protocol that will be used to test the effectiveness of this intervention to alter patients’ levels of physical activity and sedentary behavior.

Methods

A two-arm superiority randomized controlled trial will be performed. Physical activity and sedentary behavior will be measured using accelerometers and questionnaires. Furthermore, using questionnaires and diaries, patients’ stressors and personal determinants for change will be explored in depth. To evaluate the primary outcomes of the intervention, multilevel analyses will be conducted.

Results

The randomized controlled trial started in January 2018. As participants can start at different moments, we aim to finish all testing by July 2019.

Conclusions

This study will increase our understanding about whether and how a theory-based online intervention can help adults with type 2 diabetes increase their level of physical activity and decrease their sedentary time.

International Registered Report Identifier (IRRID)

DERR1-10.2196/12413

A Mobile App (WhiteTeeth) to Promote Good Oral Health Behavior Among Dutch Adolescents with Fixed Orthodontic Appliances: Intervention Mapping Approach

Background

The insertion of fixed orthodontic appliances increases the risk of dental caries, particularly in adolescents. Caries can be prevented through good oral health behavior. To support adolescents with fixed orthodontic appliances and for promoting oral health behavior, we developed a theory- and evidence-based mHealth program, the WhiteTeeth app.

Objective

The objective of our paper was to describe the systematic development and content of the WhiteTeeth app.

Methods

For systematic development of the program, we used the intervention mapping (IM) approach. In this paper, we present the results of applying the first 5 steps of IM to the design of an mHealth program: (1) identifying target behaviors and determinants through problem analysis, including a literature search, a survey study, and semistructured interviews, to explore adolescent oral health behavior during orthodontic therapy; (2) defining program outcomes and objectives; (3) selecting theoretical methods and translating them into practical strategies for the program design; (4) producing the program, including a pilot test with 28 adolescents testing the acceptability and usability of the WhiteTeeth app; and (5) planning implementation and adoption.

Results

On the basis of our literature search, we identified fluoride use and control of dental plaque levels (eg, tooth brushing and proxy brush usage) as target behaviors for preventing caries. Next, we identified important and changeable determinants of oral health behavior that fitted the theoretical concepts of the Health Action Process Approach (HAPA) theory. The HAPA theory, the self-regulation theory, and the results of the semistructured interviews were used to define the program objectives, that is, the performance and change objectives. After defining the objectives, we identified multiple behavior change techniques that could be used to achieve these objectives, such as providing oral health information and feedback, prompting self-monitoring, coaching of set actions and coping plans, and sending reminders. We translated these methods into practical strategies, such as videos and a brushing timer. Next, we combined these strategies into a single program resulting in the WhiteTeeth app (which is available on both iTunes and Google Play stores as “Witgebit”). Adolescents with fixed orthodontic appliances and dental professionals were included in the development process to increase the success of implementation. The pilot test revealed that the app users appreciated and liked the app. The WhiteTeeth app can be integrated into current orthodontic care.

Conclusions

IM allowed us to identify multiple techniques that have been shown to be the most effective in initiating behavior change, but have not yet been incorporated into existing orthodontic apps. The WhiteTeeth app contains all these techniques, which makes it a unique and promising home-based app for promoting oral health in adolescents with fixed orthodontic appliances.

Process Evaluation of an eHealth Intervention Implemented into General Practice: General Practitioners' and Patients' Views

(1) Background: It has been shown that online interventions can be enhanced by providing additional support; accordingly, we developed an implementation plan for the use of an eHealth intervention targeting physical activity and healthy nutrition in collaboration with general practitioners (GPs). In this study, GPs and patients evaluated the actual implementation; (2) Methods: Two hundred and thirty two patients completed the feasibility questionnaire regarding the implementation of "MyPlan 1.0" in general practice. Individual interviews were conducted with 15 GPs who implemented "MyPlan 1.0" into their daily work flow; (3) Results: The majority of the patients indicated that general practice was an appropriate setting to implement the online intervention. However, patients were not personally addressed by GPs and advice/action plans were not discussed with the GPs. The GPs indicated that this problem was caused by the severe time restrictions in general practice. GPs also seemed to select those patients who they believed to be able to use (e.g., highly educated patients) and to benefit from the intervention (e.g., patients with overweight); (4) Conclusions: Although GPs were involved in the development of the online intervention and its implementation plan, the programme was not used in general practice as intended.

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.

Users' thoughts and opinions about a self-regulation-based eHealth intervention targeting physical activity and the intake of fruit and vegetables: A qualitative study

Purpose

EHealth interventions are effective in changing health behaviours, such as increasing physical activity and altering dietary habits, but suffer from high attrition rates. In order to create interventions that are adapted to end-users, in-depth investigations about their opinions and preferences are required. As opinions and preferences may vary for different target groups, we explored these in two groups: the general population and a clinical sample.

Methods

Twenty adults from the general population (mean age = 42.65, 11 women) and twenty adults with type 2 diabetes (mean age = 64.30, 12 women) performed ‘MyPlan 1.0’, which is a self-regulation-based eHealth intervention designed to increase physical activity and the intake of fruit and vegetables in the general population. The opinions and preferences of end-users were explored using a think aloud procedure and a questionnaire. During a home visit, participants were invited to think aloud while performing ‘MyPlan 1.0’. The thoughts were transcribed verbatim and inductive thematic analysis was applied.

Results

Both groups had similar opinions regarding health behaviours and ‘MyPlan 1.0’. Participants generally liked the website, but often experienced it as time-consuming. Furthermore, they regularly mentioned that a mobile application would be useful to remind them about their goals on a daily basis. Finally, users’ ideas about how to pursue health behaviours often hindered them to correctly use the website.

Conclusions

Although originally created for the general population, ‘MyPlan 1.0’ can also be used in adults with type 2 diabetes. Nevertheless, more adaptations are needed to make the eHealth intervention more convenient and less time-consuming. Furthermore, users’ ideas regarding a healthy lifestyle should be taken into account when designing online interventions.

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.

The insights of health and welfare professionals on hurdles that impede economic evaluations of welfare interventions

BACKGROUND

Four hurdles associated with economic evaluations in welfare interventions were identified and discussed in a previous published literature review. These hurdles include (i) 'Ignoring the impact of condition-specific outcomes', (ii) 'Ignoring the impact of QoL externalities', (iii) 'Calculation of costs from a too narrow perspective' and (iv) 'The lack of well-described & standardized interventions'. This study aims to determine how healthcare providers and social workers experience and deal with these hurdles in practice and what solutions or new insights they would suggest.

METHODS

Twenty-two professionals of welfare interventions carried out in Flanders, were interviewed about the four described hurdles using a semi-structured interview. A thematic framework was developed to enable the qualitative analysis. The analysis of the semi-structured interviews was facilitated through the use of the software program QRS NVivo 10.

RESULTS

The interviews revealed a clear need to tackle these hurdles. The interviewees confirmed that further study of condition-specific outcomes in economic evaluations are needed, especially in the field of mental health and stress. The proposed dimensions for the condition-specific questionnaires varied however between the groups of interviewees (i.e. general practitioners vs social workers). With respect to QoL externalities, the interviewees confirmed that welfare interventions have an impact on the social environment of the patient (friends and family). There was however no consensus on how this impact of QoL externalities should be taken into account in welfare interventions. Professionals also suggested that besides health care costs, the impact of welfare interventions on work productivity, the patients' social life and other items should be incorporated. Standardization appears to be of limited added value for most of the interviewees because they need a certain degree of freedom to interpret the intervention. Furthermore, the target population of the interventions is diverse which requires a tailor-made approach.

CONCLUSION

This qualitative research demonstrated that these hurdles occur in practice. The proposed solutions for these hurdles can contribute to the improvement of the methodological quality of economic evaluations of welfare interventions.

Effectiveness of the self-regulation eHealth intervention 'MyPlan1.0.' on physical activity levels of recently retired Belgian adults: a randomized controlled trial

The study purpose was to test the effectiveness of the self-regulation eHealth intervention 'MyPlan1.0.' to increase physical activity (PA) in recently retired Belgian adults. This study was a randomized controlled trial with three points of follow-up/modules (baseline to 1-week to 1-month follow-up). In total, 240 recently retired adults (intervention group [IG]: n = 89; control group [CG]: n = 151) completed all three modules. The IG filled in evaluation questionnaires and received 'MyPlan1.0.', an intervention focusing on both pre- and post-intentional processes for behavioural change. The CG only filled in evaluation questionnaires. Self-reported PA was assessed using the long International Physical Activity Questionnaire, usual week version. Repeated-measures multivariate analysis of variances were conducted in SPSS 22.0. On the short-term (baseline to 1 week), the intervention significantly increased walking for transport (IG: +11 min/week, CG: -6 min/week; P < 0.01). On the intermediate-term (baseline to 1 month), the intervention increased transport-related walking (IG: +14 min/week, CG: +6 min/week; P < 0.01), leisure-time walking (IG: +26 min/week, CG: -14 min/week; P < 0.10), leisure-time vigorous PA (IG: +16 min/week, CG: -4 min/week; P < 0.01), moderate-intensity gardening (IG: +4 min/week, CG: -34 min/week; P < 0.10) and voluntary work-related vigorous PA (IG: +28 min/week, CG: +13 min/week; P < 0.10). Results show that our eHealth intervention is effective in recently retired adults. Future studies should include long-term follow-up to examine whether the effects persist over a longer period.

The Reliability and Validity of Short Online Questionnaires to Measure Fruit and Vegetable Intake in Adults: The Fruit Test and Vegetable Test

The first aim of this study was to investigate the stability of the Fruit Test and Vegetable Test over time and whether the Fruit Test and Vegetable Test are capable of measuring fruit and vegetable intake with consistency. Second, the study aimed to examine criterion (concurrent) validity of the Fruit Test and Vegetable Test by testing their agreement with 7-day food diary-derived measures of fruit and vegetable intake. In total 58 adults (31% male, mean age = 30.0±12.09y) completed the Flemish Fruit and Vegetable test by indicating the frequency of days that they ate fruit and vegetables and the number of portions during the past week. Validity was tested by using a 7-day food diary as a golden standard. Adults were asked to register their fruit and vegetable intake daily in a diary during one week. Spearman correlations were measured to compare total intake reported in the Fruit and Vegetable Test and in the 7-day diary. Agreement plots were used to illustrate absolute agreement. Test-retest reliability was evaluated by having participants completing the Fruit Test and Vegetable Test twice. The Fruit Test (ICC = 0.81) and Vegetable Test (ICC = 0.78) showed excellent and substantial reliability. The Fruit Test (ρ = 0.73) and Vegetable Test showed good validity. Agreement plots showed modest variability in differences between vegetable and fruit intake as measured by the Vegetable and Fruit Test and the 7-day food diary. Also a small underestimation of fruit intake in the Fruit test and vegetable intake in the Vegetable test against the 7-day food diary was shown. Based on the results, it is suggested to include portion size pictures and consumption of mixed vegetables to prevent underestimation. To prevent overestimation, it is concluded to add a moderate number of representative fruit and vegetable items, questions on portion size, household sizes with sufficient detail and food items highly tailored to the dietary behaviors and local food items of the population surveyed. The questionnaires can easily be adapted for the use in other diets (e.g. Asian diet), but reliability and validity should then be examined again. Also, validity remains to be tested in other population groups (i.e. low socio economic status groups, other age groups).

How a Fully Automated eHealth Program Simulates Three Therapeutic Processes: A Case Study

Background

eHealth programs may be better understood by breaking down the components of one particular program and discussing its potential for interactivity and tailoring in regard to concepts from face-to-face counseling. In the search for the efficacious elements within eHealth programs, it is important to understand how a program using lapse management may simultaneously support working alliance, internalization of motivation, and behavior maintenance. These processes have been applied to fully automated eHealth programs individually. However, given their significance in face-to-face counseling, it may be important to simulate the processes simultaneously in interactive, tailored programs.

Objective

We propose a theoretical model for how fully automated behavior change eHealth programs may be more effective by simulating a therapist’s support of a working alliance, internalization of motivation, and managing lapses.

Methods

We show how the model is derived from theory and its application to Endre, a fully automated smoking cessation program that engages the user in several “counseling sessions” about quitting. A descriptive case study based on tools from the intervention mapping protocol shows how each therapeutic process is simulated.

Results

The program supports the user’s working alliance through alliance factors, the nonembodied relational agent Endre and computerized motivational interviewing. Computerized motivational interviewing also supports internalized motivation to quit, whereas a lapse management component responds to lapses. The description operationalizes working alliance, internalization of motivation, and managing lapses, in terms of eHealth support of smoking cessation.

Conclusions

A program may simulate working alliance, internalization of motivation, and lapse management through interactivity and individual tailoring, potentially making fully automated eHealth behavior change programs more effective.

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)