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

Effective Communication Supported by an App for Pregnant Women: Quantitative Longitudinal Study

BACKGROUND

In the medical field of obstetrics, communication plays a crucial role, and pregnant women, in particular, can benefit from interventions improving their self-reported communication behavior. Effective communication behavior can be understood as the correct transmission of information without misunderstanding, confusion, or losses. Although effective communication can be trained by patient education, there is limited research testing this systematically with an app-based digital intervention. Thus, little is known about the success of such a digital intervention in the form of a web-app, potential behavioral barriers for engagement, as well as the processes by which such a web-app might improve self-reported communication behavior.

OBJECTIVE

This study fills this research gap by applying a web-app aiming at improving pregnant women's communication behavior in clinical care. The goals of this study were to (1) uncover the potential risk factors for early dropout from the web-app and (2) investigate the social-cognitive factors that predict self-reported communication behavior after having used the web-app.

METHODS

In this study, 1187 pregnant women were recruited. They all started to use a theory-based web-app focusing on intention, planning, self-efficacy, and outcome expectancy to improve communication behavior. Mechanisms of behavior change as a result of exposure to the web-app were explored using stepwise regression and path analysis. Moreover, determinants of dropout were tested using logistic regression.

RESULTS

We found that dropout was associated with younger age (P=.014). Mechanisms of behavior change were consistent with the predictions of the health action process approach. The stepwise regression analysis revealed that action planning was the best predictor for successful behavioral change over the course of the app-based digital intervention (β=.331; P<.001). The path analyses proved that self-efficacy beliefs affected the intention to communicate effectively, which in turn, elicited action planning and thereby improved communication behavior (β=.017; comparative fit index=0.994; Tucker-Lewis index=0.971; root mean square error of approximation=0.055).

CONCLUSIONS

Our findings can guide the development and improvement of apps addressing communication behavior in the following ways in obstetric care. First, such tools would enable action planning to improve communication behavior, as action planning is the key predictor of behavior change. Second, younger women need more attention to keep them from dropping out. However, future research should build upon the gained insights by conducting similar internet interventions in related fields of clinical care. The focus should be on processes of behavior change and strategies to minimize dropout rates, as well as replicating the findings with patient safety measures.

TRIAL REGISTRATION

ClinicalTrials.gov identifier: NCT03855735; https://classic.clinicaltrials.gov/ct2/show/NCT03855735.

Can smartphone-based response inhibition training elicit sustained changes in appetite, preference, and cravings for energy-dense foods? A free-living randomized controlled trial

BACKGROUND

Food-specific response inhibition training has been implemented as a strategy to modify food choices and reward-related eating behaviours, but short-term studies have produced equivocal findings.

OBJECTIVE

To longitudinally assess the effect of a smartphone-based response inhibition intervention on food reward, hedonic eating drive, and cravings in a free-living setting.

METHODS

84 adults (Mage  = 30.49, SDage  = 13.01, 52 female) with high responsivity to food cues or overweight/obesity were randomly assigned to a response inhibition training intervention (n = 45) or a control game (n = 39) at home during a training week, followed by a week with no training. Primary analyses compared groups on measures of explicit liking and implicit wanting for food of different energy densities, food cravings, and reward-related eating throughout this two-week period.

RESULTS

A reduction was observed in explicit liking and implicit wanting for energy-dense foods from baseline to post-training independent of condition (ps < .001). These changes from baseline were sustained after a 1-week latency period, also independent of condition (ps < .001). These effects coincided with similar observations of hedonic eating drive, tonic cravings, and control over cravings during the observation period (ps < .01).

CONCLUSIONS

Although significant reductions in reward-related appetite were observed, free-living response inhibition training did not offer additional benefit over a control activity. Future intervention studies with observable food intake are needed to investigate which appetitive mechanisms most reliably predict eating behaviour over time.

TRIAL REGISTRATION

Retrospectively registered with ANZCTR [ACTRN12622001502729].

Digital health interventions for all? Examining inclusivity across all stages of the digital health intervention research process

Digital interventions offer many possibilities for improving health, as remote interventions can enhance reach and access to underserved groups of society. However, research evaluating digital health interventions demonstrates that such technologies do not equally benefit all and that some in fact seem to reinforce a "digital health divide." By better understanding these potential pitfalls, we may contribute to narrowing the digital divide in health promotion. The aim of this article is to highlight and reflect upon study design decisions that might unintentionally enhance inequities across key research stages-recruitment, enrollment, engagement, efficacy/effectiveness, and retention. To address the concerns highlighted, we propose strategies including (1) the standard definition of "effectiveness" should be revised to include a measure of inclusivity; (2) studies should report a broad range of potential inequity indicators of participants recruited, randomized, and retained and should conduct sensitivity analyses examining potential sociodemographic differences for both the effect and engagement of the digital interventions; (3) participants from historically marginalized groups should be involved in the design of study procedures, including those related to recruitment, consent, intervention implementation and engagement, assessment, and retention; (4) eligibility criteria should be minimized and carefully selected and the screening process should be streamlined; (5) preregistration of trials should include recruitment benchmarks for sample diversity and comprehensive lists of sociodemographic characteristics assessed; and (6) studies within trials should be embedded to systematically test recruitment and retention strategies to improve inclusivity. The implementation of these strategies would enhance the ability of digital health trials to recruit, randomize, engage, and retain a broader and more representative population in trials, ultimately minimizing the digital divide and broadly improving population health.

Usage and Daily Attrition of a Smartphone-Based Health Behavior Intervention: Randomized Controlled Trial

BACKGROUND

Although most adolescents have access to smartphones, few of them use mobile health (mHealth) apps for health improvement, highlighting the apparent lack of interest in mHealth apps among adolescents. Adolescent mHealth interventions have been burdened with high attrition rates. Research on these interventions among adolescents has frequently lacked detailed time-related attrition data alongside analysis of attrition reasons through usage.

OBJECTIVE

The objective was to obtain daily attrition rates among adolescents in an mHealth intervention to gain a deeper understanding of attrition patterns, including the role of motivational support, such as altruistic rewards, through analysis of app usage data.

METHODS

A randomized controlled trial was conducted with 304 adolescent participants (152 boys and 152 girls) aged 13-15 years. Based on 3 participating schools, participants were randomly assigned to control, treatment as usual (TAU), and intervention groups. Measures were obtained at baseline, continuously throughout the 42-day trial period (research groups), and at the trial end. The mHealth app is called SidekickHealth and is a social health game with the following 3 main categories: nutrition, mental health, and physical health. Primary measures were attrition based on time from launch, and the type, frequency, and time of health behavior exercise usage. Outcome differences were obtained through comparison tests, while regression models and survival analyses were used for attrition measures.

RESULTS

Attrition differed significantly between the intervention and TAU groups (44.4% vs 94.3%; χ²₁=61.220; P<.001). The mean usage duration was 6.286 days in the TAU group and 24.975 days in the intervention group. In the intervention group, male participants were active significantly longer than female participants (29.155 vs 20.433 days; χ²₁=6.574; P<.001). Participants in the intervention group completed a larger number of health exercises in all trial weeks, and a significant decrease in usage was observed from the first to second week in the TAU group (t₁₀₅=9.208; P<.001) but not in the intervention group. There was a significant increase in health exercises in the intervention group from the fifth to sixth week (t₁₀₅=3.446; P<.001). Such a significant increase in usage was not evident in the TAU group. The research group was significantly related to attrition time (hazard ratio 0.308, 95% CI 0.222-0.420), as well as the numbers of mental health exercises (P<.001) and nutrition exercises (P<.001).

CONCLUSIONS

Differences in attrition rates and usage between groups of adolescents were identified. Motivational support is a significant factor for lowering attrition in adolescent mHealth interventions. The results point to sensitivity periods in the completion of diverse health tasks, and emphasis on time-specific attrition, along with the type, frequency, and time of health behavior exercise usage, is likely a fruitful avenue for further research on mHealth interventions for adolescent populations, in which attrition rates remain excessive.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05912439; https://clinicaltrials.gov/study/NCT05912439.

Towards more personalized digital health interventions: a clustering method of action and coping plans to promote physical activity

BACKGROUND

Despite effectiveness of action and coping planning in digital health interventions to promote physical activity (PA), attrition rates remain high. Indeed, support to make plans is often abstract and similar for each individual. Nevertheless, people are different, and context varies. Tailored support at the content level, involving suggestions of specific plans that are personalized to the individual, may reduce attrition and improve outcomes in digital health interventions. The aim of this study was to investigate whether user information relates toward specific action and coping plans using a clustering method. In doing so, we demonstrate how knowledge can be acquired in order to develop a knowledge-base, which might provide personalized suggestions in a later phase.

METHODS

To establish proof-of-concept for this approach, data of 65 healthy adults, including 222 action plans and 204 coping plans, were used and were collected as part of the digital health intervention MyPlan 2.0 to promote PA. As a first step, clusters of action plans, clusters of coping plans and clusters of combinations of action plans and barriers of coping plans were identified using hierarchical clustering. As a second step, relations with user information (i.e. gender, motivational stage, ...) were examined using anova's and chi²-tests.

RESULTS

First, three clusters of action plans, eight clusters of coping plans and eight clusters of the combination of action and coping plans were identified. Second, relating these clusters to user information was possible for action plans: 1) Users with a higher BMI related more to outdoor leisure activities (F = 13.40, P < .001), 2) Women, users that didn't perform PA regularly yet, or users with a job related more to household activities (X² = 16.92, P < .001; X² = 20.34, P < .001; X² = 10.79, P = .004; respectively), 3) Younger users related more to active transport and different sports activities (F = 14.40, P < .001). However, relating clusters to user information proved difficult for the coping plans and combination of action and coping plans.

CONCLUSIONS

The approach used in this study might be a feasible approach to acquire input for a knowledge-base, however more data (i.e. contextual and dynamic user information) from possible end users should be acquired in future research. This might result in a first type of context-aware personalized suggestions on the content level.

TRIAL REGISTRATION

The digital health intervention MyPlan 2.0 was preregistered as a clinical trial (ID:NCT03274271). Release date: 6-September-2017.

Mother and Daughter Perspectives on Genetic Counseling and Testing of Adolescents for Hereditary Breast Cancer Risk

OBJECTIVE

To evaluate the risks, benefits, and utility of testing for adult-onset hereditary breast and ovarian cancer (HBOC) in adolescents and young adults.

STUDY DESIGN

We evaluated interest in genetic testing of adolescents for adult-onset HBOC genes through semistructured interviews with mothers and adolescents who had previously participated in breast cancer research or had pursued (mothers) clinical testing for HBOC.

RESULTS

The majority of mothers (73%) and daughters (75%) were interested in the daughter having genetic testing and were motivated by the future medical utility and current social utility of relieving anxiety and allowing them to prepare. Mothers and daughters both reported that approximately 3 years in the future was the best time to test the daughter regardless of the current age of the daughter. Overall, both mothers and daughters expressed the importance of the involvement of the mother to provide educational and emotional support but ultimately it was the daughter's decision to test. Balancing the independence and maturity of the daughter while reinforcing communication and support within the dyad was a prominent theme throughout the interviews.

CONCLUSIONS

There is interest among some high-risk adolescents and young adults to engage in genetic counseling and undergo testing. Providing pretest and posttest genetic counseling, assessing preferences for parent involvement, and offering psychosocial support may be important if genetic testing for HBOC is offered to adolescents and young adults before age 25 years.

Identifying Personality Characteristics and Indicators of Psychological Well-Being Associated With Attrition in the Motivation Makes the Move! Physical Activity Intervention: Randomized Technology-Supported Trial

BACKGROUND

Data attrition has been a common problem in longitudinal lifestyle interventions. The contributors to attrition in technology-supported physical activity interventions have not been thoroughly studied.

OBJECTIVE

The present study examined the roles of personality characteristics and indicators of psychological well-being in data attrition within a technology-supported, longitudinal intervention study with overweight adults.

METHODS

Participants (N=89) were adults from the Motivation Makes the Move! intervention study. Data attrition was studied after a 3-month follow-up. Participants' personality characteristics were studied using the Short Five self-report questionnaire. Psychological well-being indicators were assessed with the RAND 36-item health survey, Positive and Negative Affect Schedule, and Beck Depression Inventory. Logistic regression analyses were conducted to assess the risk of discontinuing the study. The analyses were adjusted for sex, age, study group, and educational status.

RESULTS

At the 3-month follow-up, 65 of 89 participants (73% of the initial sample) had continued in the study. Participants' personality characteristics and indicators of psychological well-being were not associated with the risk of dropping out of the study (all P values >.05). The results remained the same after covariate controls.

CONCLUSIONS

Participant attrition was not attributable to personality characteristics or psychological well-being in the Motivation Makes the Move! study conducted with overweight adults. As attrition remains a challenge within longitudinal, technology-supported lifestyle interventions, attention should be paid to the potentially dynamic natures of personality and psychological well-being, as well as other elements beyond these.

TRIAL REGISTRATION

ClinicalTrials.gov NCT02686502; https://clinicaltrials.gov/ct2/show/NCT02686502.

Enrollment and retention of participants in remote digital health studies: a scoping review and framework proposal (Preprint)

Background

Objective

Methods

Results

Conclusions

Current status and influencing factors of digital health literacy among community-dwelling older adults in Southwest China: a cross-sectional study

BACKGROUND

The rapid development of digital health has reduced the time and cost of medical treatment, bringing efficient and economical benefits. However, older adults all over the world are deficient in digital health knowledge and skills to varying degrees. This study intends to investigate the current status and influencing factors of digital health literacy among community-dwelling older adults in Southwest China, so as to provide theoretical reference for global digital health researches and the construction of gerontological digital health service models.

METHODS

A cross-sectional survey was conducted from September 2020 to April 2021 in Chongqing, China. 572 community-dwelling older adults (≥ 65 years) were surveyed by stratified sampling. Data on sociodemographic characteristics, Internet usage, attitude towards Internet health information and digital health literacy were collected. Wherein, the digital health literacy assessment adopted the Digital Health Literacy Assessment Scale for community-dwelling older adults, which was developed by the research group, proven to be with good internal consistency (0.941), split-half reliability (0.889), test-retest reliability (0.941), content validity (0.967), criterion validity (0.938) and construct validity. The influencing factors were explored by univariate analysis and multiple linear regression analysis.

RESULTS

The average score of digital health literacy was 37.10 (SD 18.65). Univariate analysis showed that there were statistically significant differences in the comparison of digital health literacy according to 16 variables, such as different age and education levels. Multiple linear regression analysis showed that education level, marital status, self-rated health status, degree of health concerns, duration of Internet usage, time spent using the Internet per day, frequency of Internet usage, frequency of receiving guidance passively from family members, perceived usefulness, perceived ease of use and perceived reliability were positively correlated with digital health literacy, while age and perceived risk were negatively correlated with digital health literacy.

CONCLUSION

The overall digital health literacy of community-dwelling older adults in Southwest China is relatively low. In the future, health professionals should fully consider the diverse influencing factors of digital health literacy, assess individual differences and provide targeted intervention programs. Meanwhile, global public health authorities should integrate health resources effectively, and seek health service models for older adults in line with the development of the digital age to narrow the digital divide.

Nonusage Attrition of Adolescents in an mHealth Promotion Intervention and the Role of Socioeconomic Status: Secondary Analysis of a 2-Arm Cluster-Controlled Trial

Background

Mobile health (mHealth) interventions may help adolescents adopt healthy lifestyles. However, attrition in these interventions is high. Overall, there is a lack of research on nonusage attrition in adolescents, particularly regarding the role of socioeconomic status (SES).

Objective

The aim of this study was to focus on the role of SES in the following three research questions (RQs): When do adolescents stop using an mHealth intervention (RQ1)? Why do they report nonusage attrition (RQ2)? Which intervention components (ie, self-regulation component, narrative, and chatbot) prevent nonusage attrition among adolescents (RQ3)?

Methods

A total of 186 Flemish adolescents (aged 12-15 years) participated in a 12-week mHealth program. Log data were monitored to measure nonusage attrition and usage duration for the 3 intervention components. A web-based questionnaire was administered to assess reasons for attrition. A survival analysis was conducted to estimate the time to attrition and determine whether this differed according to SES (RQ1). Descriptive statistics were performed to map the attrition reasons, and Fisher exact tests were used to determine if these reasons differed depending on the educational track (RQ2). Mixed effects Cox proportional hazard regression models were used to estimate the associations between the use duration of the 3 components during the first week and attrition. An interaction term was added to the regression models to determine whether associations differed by the educational track (RQ3).

Results

After 12 weeks, 95.7% (178/186) of the participants stopped using the app. 30.1% (56/186) of the adolescents only opened the app on the installation day, and 44.1% (82/186) stopped using the app in the first week. Attrition at any given time during the intervention period was higher for adolescents from the nonacademic educational track compared with those from the academic track. The other SES indicators (family affluence and perceived financial situation) did not explain attrition. The most common reasons for nonusage attrition among participants were perceiving that the app did not lead to behavior change, not liking the app, thinking that they already had a sufficiently healthy lifestyle, using other apps, and not being motivated by the environment. Attrition reasons did not differ depending on the educational track. More time spent in the self-regulation and narrative components during the first week was associated with lower attrition, whereas chatbot use duration was not associated with attrition rates. No moderating effects of SES were observed in the latter association.

Conclusions

Nonusage attrition was high, especially among adolescents in the nonacademic educational track. The reported reasons for attrition were diverse, with no statistical differences according to the educational level. The duration of the use of the self-regulation and narrative components during the first week may prevent attrition for both educational tracks.

Trial Registration

ClinicalTrials.gov NCT04719858; http://clinicaltrials.gov/ct2/show/NCT04719858

Effect of a Lifestyle-Focused Web-Based Application on Risk Factor Management in Patients Who Have Had a Myocardial Infarction: Randomized Controlled Trial

Background

Cardiac rehabilitation is central in reducing mortality and morbidity after myocardial infarction. However, the fulfillment of guideline-recommended cardiac rehabilitation targets is unsatisfactory. eHealth offers new possibilities to improve clinical care.

Objective

This study aims to assess the effect of a web-based application designed to support adherence to lifestyle advice and self-control of risk factors (intervention) in addition to center-based cardiac rehabilitation, compared with cardiac rehabilitation only (usual care).

Methods

All 150 patients participated in cardiac rehabilitation. Patients randomized to the intervention group (n=101) received access to the application for 25 weeks where information about lifestyle (eg, diet and physical activity), risk factors (eg, weight and blood pressure [BP]), and symptoms could be registered. The software provided feedback and lifestyle advice. The primary outcome was a change in submaximal exercise capacity (Watts [W]) between follow-up visits. Secondary outcomes included changes in modifiable risk factors between baseline and follow-up visits and uptake and adherence to the application. Regression analysis was used, adjusting for relevant baseline variables.

Results

There was a nonsignificant trend toward a larger change in exercise capacity in the intervention group (n=66) compared with the usual care group (n=40; +14.4, SD 19.0 W, vs +10.3, SD 16.1 W; P=.22). Patients in the intervention group achieved significantly larger BP reduction compared with usual care patients at 2 weeks (systolic −27.7 vs −16.4 mm Hg; P=.006) and at 6 to 10 weeks (systolic −25.3 vs −16.4 mm Hg; P=.02, and diastolic −13.4 vs −9.1 mm Hg; P=.05). A healthy diet index score improved significantly more between baseline and the 2-week follow-up in the intervention group (+2.3 vs +1.4 points; P=.05), mostly owing to an increase in the consumption of fish and fruit. At 6 to 10 weeks, 64% (14/22) versus 46% (5/11) of smokers in the intervention versus usual care groups had quit smoking, and at 12 to 14 months, the respective percentages were 55% (12/22) versus 36% (4/11). However, the number of smokers in the study was low (33/149, 21.9%), and the differences were nonsignificant. Attendance in cardiac rehabilitation was high, with 96% (96/100) of patients in the intervention group and 98% (48/49) of patients receiving usual care only attending 12- to 14-month follow-up. Uptake (logging data in the application at least once) was 86.1% (87/101). Adherence (logging data at least twice weekly) was 91% (79/87) in week 1 and 56% (49/87) in week 25.

Conclusions

Complementing cardiac rehabilitation with a web-based application improved BP and dietary habits during the first months after myocardial infarction. A nonsignificant tendency toward better exercise capacity and higher smoking cessation rates was observed. Although the study group was small, these positive trends support further development of eHealth in cardiac rehabilitation.

Trial Registration

ClinicalTrials.gov NCT03260582; https://clinicaltrials.gov/ct2/show/NCT03260582

International Registered Report Identifier (IRRID)

RR2-10.1186/s13063-018-3118-1

Weight Loss Trajectories in Healthy Weight Coaching: Cohort Study

Background

As global obesity prevalence continues to increase, there is a need for accessible and affordable weight management interventions, such as web-based programs.

Objective

This paper aims to assess the outcomes of healthy weight coaching (HWC), a web-based obesity management program integrated into standard Finnish clinical care.

Methods

HWC is an ongoing, structured digital 12-month program based on acceptance and commitment therapy. It includes weekly training sessions focused on lifestyle, general health, and psychological factors. Participants received remote one-on-one support from a personal coach. In this real-life, single-arm, prospective cohort study, we examined the total weight loss, weight loss profiles, and variables associated with weight loss success and program retention in 1189 adults (963 women) with a BMI >25 kg/m² among participants of the program between October 2016 and March 2019. Absolute (kg) and relative (%) weight loss from the baseline were the primary outcomes. We also examined the weight loss profiles, clustered based on the dynamic time-warping distance, and the possible variables associated with greater weight loss success and program retention. We compared different groups using the Mann-Whitney test or Kruskal-Wallis test for continuous variables and the chi-squared test for categorical variables. We analyzed changes in medication using the McNemar test.

Results

Among those having reached the 12-month time point (n=173), the mean weight loss was 4.6% (SE 0.5%), with 43% (n=75) achieving clinically relevant weight loss (≥5%). Baseline BMI ≥40 kg/m² was associated with a greater weight loss than a lower BMI (mean 6.6%, SE 0.9%, vs mean 3.2%, SE 0.6%; P=.02). In addition, more frequent weight reporting was associated with greater weight loss. No significant differences in weight loss were observed according to sex, age, baseline disease, or medication use. The total dropout rate was 29.1%. Dropouts were slightly younger than continuers (47.2, SE 0.6 years vs 49.2, SE 0.4 years; P=.01) and reported their weight less frequently (3.0, SE 0.1 entries per month vs 3.3, SE 0.1 entries per month; P<.001).

Conclusions

A comprehensive web-based program such as HWC is a potential addition to the repertoire of obesity management in a clinical setting. Heavier patients lost more weight, but weight loss success was otherwise independent of baseline characteristics.

Investigating When, Which, and Why Users Stop Using a Digital Health Intervention to Promote an Active Lifestyle: Secondary Analysis With A Focus on Health Action Process Approach–Based Psychological Determinants

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

The Results of the OutsidePlay-ECE Study: Randomized Controlled Trial to Evaluate a Web-Based Intervention to Influence Early Childhood Educators’ Attitudes and Supportive Behaviors Toward Outdoor Play (Preprint)

Background

Outdoor play is critical to children’s healthy development and well-being. Early learning and childcare centers (ELCCs) are important venues for increasing children’s outdoor play opportunities, and early childhood educators’ (ECE) perception of outdoor play can be a major barrier to outdoor play. The OutsidePlay-ECE risk-reframing intervention is a fully automated and open access web-based intervention to reframe ECEs’ perceptions of the importance of outdoor play and risk in play and to promote a change in their practice in supporting it in ELCC settings. We grounded the intervention in social cognitive theory and behavior change techniques.

Objective

The aim of this study is to evaluate the effectiveness of the OutsidePlay-ECE web-based risk-reframing intervention.

Methods

We conducted a single-blind randomized controlled trial in Canada between December 2020 and June 2021 to test the OutsidePlay-ECE risk-reframing intervention for ECEs. We recruited participants using social media and mass emails through our partner and professional networks. We invited ECEs and administrators working in an ELCC, who can speak, read, and understand English. We randomized consented participants to the intervention or control condition. The participants allocated to the intervention condition received a link to the OutsidePlay-ECE intervention. Participants allocated to the control condition read the Position Statement on Active Outdoor Play, a 4-page document on research and recommendations for action in addressing barriers to outdoor play. The primary outcome was a change in tolerance of risk in play. The secondary outcome was goal attainment. We collected data on the web via REDCap (Vanderbilt University) at baseline and 1 week and 3 months after intervention.

Results

A total of 563 participants completed the baseline survey, which assessed their demographics and tolerance of risk in play. They were then randomized: 281 (49.9%) to the intervention and 282 (50.1%) to the control condition. Of these, 136 (48.4%) and 220 (78%) participants completed the baseline requirements for the intervention and control conditions, respectively. At 1 week after intervention, 126 (44.8%) and 209 (74.1%) participants completed follow-up assessments, respectively, and at 3 months after intervention, 119 (42.3%) and 195 (69.1%) participants completed the assessments, respectively. Compared with participants in the control condition, participants in the intervention group had significantly higher tolerance of risk in play at 1 week (β=.320; P=.001) and 3 months after intervention (β=.251; P=.009). Intention-to-treat analyses replicated these findings (β=.335; P<.001 and β=.271; P=.004, respectively). No significant intervention effect was found for goal attainment outcomes (odds ratio 1.124, 95% CI 0.335-3.774; P=.85).

Conclusions

The results of this randomized controlled trial demonstrated that the OutsidePlay-ECE intervention was effective and had a sustained effect in increasing ECEs’ and administrators’ tolerance of risk in play. It was not effective in increasing goal attainment.

Trial Registration

ClinicalTrials.gov NCT04624932; https://clinicaltrials.gov/ct2/show/NCT04624932

International Registered Report Identifier (IRRID)

RR2-10.2196/31041

Use and Appreciation of a Web-Based, Computer-Tailored Diet and Physical Activity Intervention Based on the Self-determination Theory: Evaluation Study of Process and Predictors

Background

eHealth is a promising tool for promoting lifestyle behaviors such as a healthy diet and physical activity (PA). However, making people use interventions is a crucial and challenging problem in eHealth. More insight into use patterns and predicting factors is needed to improve future interventions.

Objective

This study aims to examine the use, predictors of use, and appreciation of a web-based, computer-tailored, dietary and PA promotion intervention, MyLifestyleCoach, which is based on the self-determination theory. First, we depict the participants’ flow in the intervention and identify moments when they are likely to discontinue use. Second, we investigate whether demographic, motivational, and program-related characteristics predict the use of several intervention elements. Finally, we report the appreciation scores for the intervention and the participant and program characteristics associated with these scores.

Methods

This study was based on data from web-based self-report questionnaires. Here, objectively assessed intervention use data were analyzed from participants randomized to the intervention condition. Multiple stepwise (logistic) regression analyses were conducted to examine the predictors of intervention use and evaluation scores.

Results

Our findings indicate a low full completion rate for the intervention among those who chose and completed the diet module (49/146, 33.6%), the PA module (2/12, 17%), and both modules (58/273, 21.2%). Several points in the intervention where participants were likely to stop using the intervention were identified. Autonomous and intrinsic motivation toward diet were related to the completion of the initial sessions of the intervention (ie, the opening session in which participants could choose which module to follow and the first session of the diet module). In contrast, controlled motivation was linked to the completion of both modules (initial and follow-up sessions). Appreciation scores were somewhat positive. Appreciation was predicted by several motivational constructs, such as amotivation and basic psychological needs (eg, competence) and program-related features (eg, number of completed sessions).

Conclusions

This study adds meaningful information on the use and appreciation of a web-based, computer-tailored dietary and PA intervention, MyLifestyleCoach. The results indicate that different types of motivations, such as extrinsic and intrinsic motivation, are at play at the points when people are likely to stop using the intervention. The intervention was appreciated fairly well, and several motivational constructs and fulfillment of basic psychological needs were associated with appreciation. Practical implications of these findings have been provided in this study.

A Web-Based Intervention (Germ Defence) to Increase Handwashing During a Pandemic: Process Evaluations of a Randomized Controlled Trial and Public Dissemination

Background

Washing hands helps prevent transmission of seasonal and pandemic respiratory viruses. In a randomized controlled trial (RCT) during the swine flu outbreak, participants with access to a fully automated, digital intervention promoting handwashing reported washing their hands more often and experienced fewer respiratory tract infections than those without access to the intervention. Based on these findings, the intervention was adapted, renamed as “Germ Defence,” and a study was designed to assess the preliminary dissemination of the intervention to the general public to help prevent the spread of seasonal colds and flu.

Objective

This study compares the process evaluations of the RCT and Germ Defence dissemination to examine (1) how web-based research enrollment procedures affected those who used the intervention, (2) intervention usage in the 2 contexts, and (3) whether increased intentions to wash hands are replicated once disseminated.

Methods

The RCT ran between 2010 and 2012 recruiting participants offline from general practices, with restricted access to the intervention (N=9155). Germ Defence was disseminated as an open access website for use by the general public from 2016 to 2019 (N=624). The process evaluation plan was developed using Medical Research Council guidance and the framework for Analyzing and Measuring Usage and Engagement Data. Both interventions contained a goal-setting section where users self-reported current and intended handwashing behavior across 7 situations.

Results

During web-based enrolment, 54.3% (17,511/32,250) of the RCT participants dropped out of the study compared to 36.5% (358/982) of Germ Defence users. Having reached the start of the intervention, 93.8% (8586/9155) of RCT users completed the core section, whereas 65.1% (406/624) of Germ Defence users reached the same point. Users across both studies selected to increase their handwashing in 5 out of 7 situations, including before eating snacks (RCT mean difference 1.040, 95% CI 1.016-1.063; Germ Defence mean difference 0.949, 95% CI 0.766-1.132) and after blowing their nose, sneezing, or coughing (RCT mean difference 0.995, 95% CI 0.972-1.019; Germ Defence mean difference 0.842, 95% CI 0.675-1.008).

Conclusions

By comparing the preliminary dissemination of Germ Defence to the RCT, we were able to examine the potential effects of the research procedures on uptake and attrition such as the sizeable dropout during the RCT enrolment procedure that may have led to a more motivated sample. The Germ Defence study highlighted the points of attrition within the intervention. Despite sample bias in the trial context, the intervention replicated increases in intentions to handwash when used “in the wild.” This preliminary dissemination study informed the adaptation of the intervention for the COVID-19 health emergency, and it has now been disseminated globally.

Trial Registration

ISRCTN Registry ISRCTN75058295; https://www.isrctn.com/ISRCTN75058295

A Cluster Randomized Controlled Trial Feasibility Study of a WhatsApp-Delivered Intervention to Promote Healthy Eating Habits in Male Firefighters

This cluster randomized controlled trial (CRCT)-designed study aimed to explore the feasibility of a promotion pamphlet and/or WhatsApp as a suitable mode of delivery to promote healthy eating habits with fruit and vegetables (F&V) among firefighters. Convenience and snowball sampling methods were used. Forty-five firefighters from 23 fire stations were recruited and they all received the printed pamphlet, while the intervention group participants (n = 20) received additional teaching material through WhatsApp every two weeks for eight weeks. Feasibility outcomes included retention, practicality, and implementation. The participants reported high levels of satisfaction with the intervention. There were significant improvements in the mean numbers of days consuming F&V (p = 0.002; p = 0.031) in the intervention group, and for fruit consumption (p = 0.033) in the control group between the baseline (T₀) and 3 months after completion of intervention (T₁). High levels of participants’ satisfaction with the intervention revealed that a full-scale CRCT of the WhatsApp-delivered intervention promoting healthy eating could be feasible, especially as a means of increasing the numbers of days they consumed F&V and the numbers of servings of these consumed per day.

A Web-Based Computer-Tailored Program to Improve Treatment Adherence in Patients With Type 2 Diabetes: Randomized Controlled Trial

Background

Adherence to core type 2 diabetes mellitus (T2DM) treatment behaviors is suboptimal, and nonadherence is generally not limited to one treatment behavior. The internet holds promise for programs that aim to improve adherence. We developed a computer-tailored eHealth program for patients with T2DM to improve their treatment adherence, that is, adherence to both a healthy lifestyle and medical behaviors.

Objective

The objective of this study is to examine the effectiveness of the eHealth program in a randomized controlled trial.

Methods

Patients with T2DM were recruited by their health professionals and randomized into either the intervention group, that is, access to the eHealth program for 6 months, or a waiting-list control group. In total, 478 participants completed the baseline questionnaire, of which 234 gained access to the eHealth program. Of the 478 participants, 323 were male and 155 were female, the mean age was 60 years, and the participants had unfavorable BMI and HbA_1c levels on average. Outcome data were collected through web-based assessments on physical activity (PA) levels, caloric intake from unhealthy snacks, and adherence to oral hypoglycemic agents (OHAs) and insulin therapy. Changes to separate behaviors were standardized and summed into a composite change score representing changes in the overall treatment adherence. Further standardization of this composite change score yielded the primary outcome, which can be interpreted as Cohen d (effect size). Standardized change scores observed in separate behaviors acted as secondary outcomes. Mixed linear regression analyses were conducted to examine the effectiveness of the intervention on overall and separate treatment behavior adherence, accommodating relevant covariates and patient nesting.

Results

After the 6-month follow-up assessment, 47.4% (111/234) of participants in the intervention group and 72.5% (177/244) of participants in the control group were retained. The overall treatment adherence improved significantly in the intervention group compared with the control group, reflected by a small effect size (d=0.27; 95% CI 0.032 to 0.509; P=.03). When considering changes in separate treatment behaviors, a significant decrease was observed only in caloric intake from unhealthy snacks in comparison with the control group (d=0.36; 95% CI 0.136 to 0.584; P=.002). For adherence to PA (d=−0.14; 95% CI −0.388 to 0.109; P=.27), OHAs (d=0.27; 95% CI −0.027 to 0.457; P=.08), and insulin therapy (d=0.35; 95% CI −0.066 to 0.773; P=.10), no significant changes were observed. These results from the unadjusted analyses were comparable with the results of the adjusted analyses, the per-protocol analyses, and the sensitivity analyses.

Conclusions

Our multibehavior program significantly improved the overall treatment adherence compared with the control group. To further enhance the impact of the intervention in the personal, societal, and economic areas, a wide-scale implementation of our eHealth intervention is suggested.

Trial Registration

Netherlands Trial Register NL664; https://www.trialregister.nl/trial/6664

Examining moderators of the effectiveness of a web- and video-based computer-tailored physical activity intervention

Understanding for whom behaviour change interventions work is important, however there is a lack of studies examining potential moderators in such interventions. This study investigated potential moderators on the effectiveness of a computer-tailored intervention to increase physical activity among Australian adults. People who had <150 min of moderate-vigorous physical activity (MVPA) a week, able to speak and read English, aged ≥18 years, lived in Australia, and had internet access were eligible to participate. Participants recruited through social media, emails, and third-party databases, were randomly assigned to either the control (n = 167) or intervention groups (n = 334). Physical activity was measured objectively by ActiGraph GT3X and also by self-report at baseline and three months. Three-way interaction terms were tested to identify moderators (i.e., demographic characteristics, BMI, and perceived neighbourhood walkability). The results showed that the three-way interaction was marginally significant for sex on accelerometer measured MVPA/week (p = 0.061) and steps/day (p = 0.047). The intervention appeared to be more effective for women compared to men. No significant three-way interactions were found for the other potential moderators. Strategies to improve levels of personalisation may be needed so that physical activity interventions can be better tailored to different subgroups, especially sex, and therefore improve intervention effectiveness.

Cancer Patients' Perceived Value of a Smartphone App to Enhance the Safety of Home-Based Chemotherapy: Feasibility Study

BACKGROUND

Oral anticancer therapies can be self-administered by patients outside the hospital setting, which poses challenges of adherence to a drug plan and monitoring of side effects. Modern information technology may be developed and implemented to address these pertinent issues.

OBJECTIVE

The aim of this study was to explore how a smartphone app developed through a stepwise, iterative process can help patients using oral chemotherapy to take their drug, and to report adherence and side effects in a reliable and verifiable manner.

METHODS

Fourteen patients starting capecitabine treatment were included in this study and used the smartphone app in addition to regular follow up of capecitabine treatment. Nine of these patients fulfilled the treatment plan and were interviewed based on a semistructured interview guide and the System Usability Scale (SUS). In addition, two focus groups were completed with 7 oncologists and 7 oncology nurses, respectively. Interview data were analyzed in accordance with the principles of systematic text condensation. Features of the app were also assessed.

RESULTS

The smartphone app provided the patients with a feeling of reassurance regarding correct adherence of their oral chemotherapy treatment. They used the app as a memory tool about their treatment and possible serious side effects, as well as for treatment education. Patients expressed concerns about using the app to report side effects that were not considered to be obviously serious, fearing overreporting. The health personnel expressed an overall positive attitude to integrate this new tool in their everyday work.

CONCLUSIONS

Patients on oral chemotherapy treatment at home felt safe and found the app to be helpful. The app promoted learning about their treatment and made the patients more independent of the cancer clinic, reducing the need for the clinic's limited resources for follow up of patients on oral anticancer medications.

Factors associated with adherence to a web-based alcohol intervention among college students

Abstract This study aimed to evaluate the association between student characteristics and recruitment strategies in the adherence of college students to a web-based alcohol intervention. Participants were 46,329 Brazilian students aged from 18 to 30, who consumed alcohol during the past three months. Three recruitment strategies were implemented: open invitations, and personally-addressed invitations with or without non-monetary incentives. We evaluated the educational, sociodemographic, motivational, and alcohol consumption effects on adherence using logistic regression models. Women (aOR = 1.09 [1.04; 1.14]), students with higher income (aOR = 1.32 [1.21; 1.45]), and more motivated students (aOR = 1.04 [1.03; 1.05]) were more adherent to the intervention, as well as those reporting binge drinking (aOR = 1.26 [1.19; 1.33]) and alcohol hazardous use (aOR = 1.11 [1.05; 1.18]). The use of incentives was the main factor associated with adherence (aOR = 3.69 [2.46; 5.55]). Our results may help the development of future web-based interventions related to alcohol use.

Influence of attrition, missing data, compliance, and related biases and analyses strategies on treatment effects in randomized controlled trials in rehabilitation: a methodological review

INTRODUCTION

Attrition, missing data, compliance, and related biases are three interrelated concepts. Previous research has found that these biases can affect the treatment estimates of randomized trials (RCTs). The extent to which the effects of attrition, missing data, compliance and related biases influence effect size estimates in rehabilitation as well as the effect of analytic strategies to mitigate these biases is unknown.

EVIDENCE ACQUISITION

To compile and synthetize the empirical evidence regarding the effects of attrition and compliance related biases on treatment effect estimates in rehabilitation RCTs. Electronic searches were conducted. Studies were included if they investigated the effects of attrition, missing data, compliance and related biases on treatment estimates. The seven studies meeting inclusion criteria were coded for type of biases and summarized using a narrative and/or quantitative approach when appropriate.

EVIDENCE SYNTHESIS

Findings demonstrated that trials reporting higher levels of attrition (differences in ES: 0.18 [95%CI: 0.15, 0.22 ]), exclusion of participants from analyses (differences in ES: 0.13 [95% CI: -0.03, 0.29]), lack of good control of incomplete outcome data (differences in ES: 0.14 [95%CI: -0.02, 0.30]) and analysis by "as treated"(differences in ES:-0.39 [95%CI: -0.99, 0.2]) or "per protocol" (differences in ES:-0.46 [95%CI: -0.92, 0]) analyses were more likely to have higher effects than those that did not.

CONCLUSIONS

These findings suggest that attrition, missing data, compliance, and related biases have an influence in treatment effect estimates in rehabilitation trials. Therefore, these results should be taken into consideration when designing, conducting and reporting trials in the rehabilitation field.

Are web-based personally tailored physical activity videos more effective than personally tailored text-based interventions? Results from the three-arm randomised controlled TaylorActive trial

Objectives

Some online, personally tailored, text-based physical activity interventions have proven effective. However, people tend to ‘skim’ and ‘scan’ web-based text rather than thoroughly read their contents. In contrast, online videos are more engaging and popular. We examined whether web-based personally tailored physical activity videos were more effective in promoting physical activity than personally tailored text and generic information.

Methods

501 adults were randomised into a video-tailored intervention, text-tailored intervention or control. Over a 3-month period, intervention groups received access to eight sessions of web-based personally tailored physical activity advice. Only the delivery method differed between intervention groups: tailored video versus tailored text. The primary outcome was 7-day ActiGraph-GT3X+ measured moderate-to-vigorous physical activity (MVPA) assessed at 0, 3 and 9 months. Secondary outcomes included self-reported MVPA and website engagement. Differences were examined using generalised linear mixed models with intention-to-treat and multiple imputation.

Results

Accelerometer-assessed MVPA increased 23% in the control (1.23 (1.06, 1.43)), 12% in the text-tailored (1.12 (0.95, 1.32)) and 28% in the video-tailored (1.28 (1.06, 1.53)) groups at the 3-month follow-up only, though there were no significant between-group differences. Both text-tailored (1.77 (1.37, 2.28]) and video-tailored (1.37 (1.04, 1.79)) groups significantly increased self-reported MVPA more than the control group at 3 months only, but there were no differences between video-tailored and text-tailored groups. The video-tailored group spent significantly more time on the website compared with text-tailored participants (90 vs 77 min, p=0.02).

Conclusions

The personally tailored videos were not more effective than personally tailored text in increasing MVPA. The findings from this study conflict with pilot study outcomes and previous literature. Process evaluation and mediation analyses will provide further insights.

Trial registration number

ACTRN12615000057583

Which behaviour change techniques are effective to promote physical activity and reduce sedentary behaviour in adults: a factorial randomized trial of an e- and m-health intervention

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.

Effects of Incentives on Adherence to a Web-Based Intervention Promoting Physical Activity: Naturalistic Study

Background

Despite many advantages of web-based health behavior interventions such as wide accessibility or low costs, these interventions are often accompanied by high attrition rates, particularly in usage under real-life conditions. It would therefore be helpful to implement strategies such as the use of financial incentives to motivate program participation and increase adherence.

Objective

This naturalistic study examined real-life usage data of a 12-week web-based physical activity (PA) intervention (Fitness Coach) among insurants who participated in an additional incentive program (incentive group) and those who did not (nonincentive group). Users in the incentive group had the perspective of receiving €30 (about US $33) cash back at the end of the intervention.

Methods

Registration and real-life usage data as part of routine data management and evaluation of the Fitness Coach were analyzed between September 2016 and June 2018. Depending on the duration of use and the weekly recording of tasks, 4 adherence groups (low, occasional, strong, and complete adherence) were defined. Demographic characteristics were collected by a self-reported questionnaire at registration. We analyzed baseline predictors and moderators of complete adherence such as participation in the program, age, gender, and BMI using binary logistic regressions.

Results

A total of 18,613 eligible persons registered for the intervention. Of these, 15,482 users chose to participate in the incentive program (incentive group): mean age 42.4 (SD 14.4) years, mean BMI 24.5 (SD 4.0) kg/m², median (IQR) BMI 23.8 (21.7-26.4) kg/m²; 65.12% (10,082/15,482) female; and 3131 users decided not to use the incentive program (nonincentive group): mean age 40.7 (SD 13.4) years, mean BMI 26.2 (SD 5.0) kg/m², median BMI 25.3 (IQR 22.6-28.7) kg/m²; 72.18% (2260/3131) female. At the end of the intervention, participants in the incentive program group showed 4.8 times higher complete adherence rates than those in the nonincentive program group (39.2% vs 8.1%), also yielding significantly higher odds to complete the intervention (odds ratio [OR] 12.638) for the incentive program group. Gender significantly moderated the effect with men in the incentive group showing higher odds to be completely adherent than women overall and men in the nonincentive group (OR 1.761). Furthermore, older age and male gender were significant predictors of complete adherence for all participants, whereas BMI did not predict intervention completion.

Conclusions

This is the first naturalistic study in the field of web-based PA interventions that shows the potential of even small financial incentives to increase program adherence. Male users, in particular, seem to be strongly motivated by incentives to complete the intervention. Based on these findings, health care providers can use differentiated incentive systems to increase regular participation in web-based PA interventions.

Predictors of Self-Determined Module Choice in a Web-Based Computer-Tailored Diet and Physical Activity Intervention: Secondary Analysis of Data From a Randomized Controlled Trial

Background

Tailoring an online intervention to participant preferences (eg, by giving participants a choice which modules to follow) may increase engagement in the intervention, motivation for behavioral change, and possibly intervention effects. So far, little is known about what characteristics predict these module choices. Filling this knowledge gap is useful for optimizing program engagement.

Objective

We investigated participant choice for a dietary and/or physical activity (PA) promotion module in our web-based computer-tailored intervention based on self-determination theory (SDT) and motivational interviewing (MI). Furthermore, we investigated which demographic characteristics, current behavior, psychosocial constructs and constructs from SDT and MI, and program-related variables such as advice on which module to follow were associated with these choices.

Methods

Observational data were used from the randomized controlled trial MyLifestyleCoach of participants who were randomized into the intervention condition, completed the baseline questionnaire, and made a module choice in the opening session of the intervention. Here, they received advice on their own dietary and PA behavior. At the session’s end, they chose which lifestyle modules they would like to follow (both, diet, PA, or no module). Measurements included demographic information; self-reported diet and PA; and several psychosocial, SDT, and MI constructs. In total, data from 619 Dutch adults (59.6% women; mean age was 51.9 [SD 13.5] years) were analyzed. A stepwise multinomial logistic regression analysis was conducted to investigate which characteristics are related to module choice; the diet module served as reference category as almost everyone was advised to follow this module.

Results

Of this sample, 54.8% (339/619) chose to do both the diet and PA module, 25.4% (157/619) chose to follow the diet module, 17.8% (110/619) preferred to follow no module, and 2.1% (13/619) chose to do the PA module only. Furthermore, it was found that older people, those who consumed more fruit, and those who scored lower on importance to change their current diet were more likely to choose no module compared to the diet module. People who had more motivation to change their current PA and those who received strong advice compared with slight advice to follow the diet module were more likely to choose both modules compared with the diet module only.

Conclusions

The results show that more than half of the sample was interested in following both the diet and PA module in this online lifestyle intervention. Several characteristics were found to be related to module choice. A future challenge is to examine how this knowledge can be used to improve future interventions, such as tailoring (messages or content) on specific groups or examining where and how MI could be used to motivate people to make a certain module choice.

Trial Registration

Netherlands Trial Register NL7333; https://www.trialregister.nl/trial/7333

Mobile Health Apps in Pediatric Obesity Treatment: Process Outcomes From a Feasibility Study of a Multicomponent Intervention

BACKGROUND

Multicomponent family interventions underline current best practice in childhood obesity treatment. Mobile health (mHealth) adjuncts that address eating and physical activity behaviors have shown promise in clinical studies.

OBJECTIVE

This study aimed to describe process methods for applying an mHealth intervention to reduce the rate of eating and monitor physical activity among children with obesity.

METHODS

The study protocol was designed to incorporate 2 mHealth apps as an adjunct to usual care treatment for obesity. Children and adolescents (aged 9-16 years) with obesity (BMI ≥98th centile) were recruited in person from a weight management service at a tertiary health care center in the Republic of Ireland. Eligible participants and their parents received information leaflets, and informed consent and assent were signed. Participants completed 2 weeks of baseline testing, including behavioral and quality of life questionnaires, anthropometry, rate of eating by Mandolean, and physical activity level using a smart watch and the myBigO smartphone app. Thereafter, participants were randomized to the (1) intervention (usual clinical care+Mandolean training to reduce the rate of eating) or (2) control (usual clinical care) groups. Gender and age group (9.0-12.9 years and 13.0-16.9 years) stratifications were applied. At the end of a 4-week treatment period, participants repeated the 2-week testing period. Process evaluation measures included recruitment, study retention, fidelity parameters, acceptability, and user satisfaction.

RESULTS

A total of 20 participants were enrolled in the study. A web-based randomization system assigned 8 participants to the intervention group and 12 participants to the control group. Attrition rates were higher among the participants in the intervention group (5/8, 63%) than those in the control group (3/12, 25%). Intervention participants undertook a median of 1.0 training meal using Mandolean (25th centile 0, 75th centile 9.3), which represented 19.2% of planned intervention exposure. Only 50% (9/18) of participants with smart watches logged physical activity data. Significant differences in psychosocial profile were observed at baseline between the groups. The Child Behavior Checklist (CBCL) mean total score was 71.7 (SD 3.1) in the intervention group vs 57.6 (SD 6.6) in the control group, t-test P<.001, and also different among those who completed the planned protocol compared with those who withdrew early (CBCL mean total score 59.0, SD 9.3, vs 67.9, SD 5.6, respectively; t-test P=.04).

CONCLUSIONS

A high early attrition rate was a key barrier to full study implementation. Perceived task burden in combination with behavioral issues may have contributed to attrition. Low exposure to the experimental intervention was explained by poor acceptability of Mandolean as a home-based tool for treatment. Self-monitoring using myBigO and the smartwatch was acceptable among this cohort. Further technical and usability studies are needed to improve adherence in our patient group in the tertiary setting.

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.

Determinants of eHealth Literacy among Adults in China

eHealth can empower patients to make informed health decisions. However, inaccurate and misleading health information is not uncommon on the Internet, which requires users' competencies to both utilize eHealth technologies and evaluate eHealth credibilities. Therefore, this study investigates the determinants of both self-efficacy in utilizing eHealth and frequency of eHealth information evaluation. An Internet-based survey of 923 Chinese adults who are residing in China aged from 21 to 55 years old was conducted. Path analysis was adopted to examine sociodemographic variables, Internet literacy, and health information evaluation as determinants of eHealth literacy variables. Findings demonstrated that Internet literacy positively predicted only self-efficacy in utilizing eHealth. In contrast, health information orientation positively predicted both self-efficacy in utilizing eHealth and frequency of eHealth information evaluation. In addition, Internet literacy and health information orientation mediated the predicted effects of sociodemographic factors on the two eHealth variables. The findings imply that Internet literacy is no longer the primary determinant of eHealth competencies for adults who are tech-savvy users. Instead, interests in health information play a crucial role in improving eHealth competencies.

Impact of a Mobile Phone App to Increase Vegetable Consumption and Variety in Adults: Large-Scale Community Cohort Study

Background

Large-scale initiatives to improve diet quality through increased vegetable consumption have had small to moderate success. Digital technologies have features that are appealing for health-related behavior change interventions.

Objective

This study aimed to describe the implementation and evaluation of a mobile phone app called VegEze, which aims to increase vegetable intake among Australian adults.

Methods

To capture the impact of this app in a real-world setting, the Reach, Effectiveness, Adoption, Implementation, and Maintenance framework was utilized. An uncontrolled, quantitative cohort study was conducted, with evaluations after 21 and 90 days. The app was available in the Apple App Store and was accompanied by television, radio, and social media promotion. Evaluation surveys were embedded into the app using ResearchKit. The primary outcomes were vegetable intake (servings per day) and vegetable variety (types per day). Psychological variables (attitudes, intentions, self-efficacy, and action planning) and app usage were also assessed. Descriptive statistics and multiple linear regression were used to describe the impact of the app on vegetable intake and to determine the characteristics associated with the increased intake.

Results

Data were available from 5062 participants who completed the baseline survey; 1224 participants completed the 21-day survey, and 273 completed the 90-day survey. The participants resided across Australia and were mostly women (4265/5062, 84.3%) with a mean age of 48.2 years (SD 14.1). The mean increase in intake was 0.48 servings, from 3.06 servings at baseline to 3.54 servings at the end of the 21-day challenge (t₁₂₂₃=8.71; P<.001). The variety of vegetables consumed also increased by 0.35 types per day (t₁₁₂₃=9.59; P<.001). No changes in intake and variety were found from day 21 to the 90-day follow-up. Participants with the highest app usage increased their vegetable intake by 0.63 (SD 2.02) servings per day compared with 0.32 (SD 1.69) servings per day for those with the lowest app usage. On the basis of multiple linear regression, gender; age; BMI; psychological variables of self-efficacy, attitudes, intentions, and action planning specific to vegetable intake; baseline vegetable intake; and active days of app usage accounted for 23.3% of the variance associated with the change in intake (F_9,1208=42.09; P<.001). Baseline vegetable intake was the strongest predictor of change in intake (beta=−.495; P<.001), with lower baseline intake associated with a greater change in intake. Self-efficacy (beta=.116; P<.001), action planning (beta=.066; P=.02), BMI (beta=.070; P=.01), and app usage (beta=.081; P=.002) were all significant predictors of the change in intake.

Conclusions

The VegEze app was able to increase intake by half a serving in a large sample of Australian adults. Testing the app in a real-world setting and embedding the consent process allowed for greater reach and an efficient, robust evaluation. Further work to improve engagement is warranted.

The Effect of Active Plus, a Computer-Tailored Physical Activity Intervention, on the Physical Activity of Older Adults with Chronic Illness(es)-A Cluster Randomized Controlled Trial

eHealth interventions aimed at improving physical activity (PA) can reach large populations with few resources and demands on the population as opposed to centre-based interventions. Active Plus is a proven effective computer-tailored PA intervention for the older adult population focusing on PA in daily life. This manuscript describes the effects of the Active Plus intervention (N = 260) on PA of older adults with chronic illnesses (OACI), compared to a waiting list control group (N = 325). It was part of a larger randomized controlled trial (RCT) on the effects of the Active Plus intervention on cognitive functioning. OACI (≥65 years) with at least one chronic illness were allocated to one of the conditions. Intervention group participants received PA advice. Baseline and follow-up measurements were assessed after 6 and 12 months. Intervention effects on objectively measured light PA (LPA) and moderate-to-vigorous PA (MVPA) min/week were analysed with multilevel linear mixed-effects models adjusted for the clustered design. Intervention effects on self-reported MVPA min/week on common types of PA were analysed with two-part generalized linear mixed-effects models adjusted for the clustered design. The dropout rate was 19.1% after 6 months and 25.1% after 12 months. Analyses showed no effects on objectively measured PA. Active Plus increased the likelihood to perform self-reported cycling and gardening at six months and participants who cycled increased their MVPA min/week of cycling. Twelve months after baseline the intervention increased the likelihood to perform self-reported walking and participants who cycled at 12 months increased their MVPA min/week of cycling. Subgroup analyses showed that more vulnerable participants (higher degree of impairment, age or body mass index) benefitted more from the intervention on especially the lower intensity PA outcomes. In conclusion, Active Plus only increased PA behaviour to a limited extent in OACI 6 and 12 months after baseline measurements. The Active Plus intervention may yet be not effective enough by itself in OACI. A blended approach, where this eHealth intervention and face-to-face contact are combined, is advised to improve the effects of Active Plus on PA in this target group.

Engagement with a Web-Based Health Promotion Intervention among Vocational School Students: A Secondary User and Usage Analysis

Engagement with web-based interventions is both generally low and typically declining. Visits and revisits remain a challenge. Based on log data of a web-based cluster randomized controlled trial conducted in vocational schools, the present secondary analysis aimed to identify influencing factors on initially logging in to a health promotion platform among young adults and to examine the engagement over the course of an eight-week intervention. Data of 336 students (62.2% female, age span 18–25) from two intervention arms (web-based intervention and web-based intervention with an additional initial face-to-face contact) was included. Binary logistic regression and log-data visualization were performed. An additional initial face-to-face contact (odds ratio (OR) = 2.971, p = 0.005), female sex (OR = 2.237, p = 0.046) and the health-related skill “dealing with health information” (OR = 2.179, p = 0.030) significantly increased the likelihood of initially logging in. Other variables showed no influence. 16.6% of all potential users logged in at least once, of which 57.4% revisited the platform. Most logins were tracked at the beginning of the intervention and repeated engagement was low. To increase the engagement with web-based interventions, health-related skills should be fostered. In addition, a strategy could be to interlink comparable interventions in vocational schools more regularly with everyday teaching through multi-component interventions.

A Self-management Approach for Dietary Sodium Restriction in Patients With CKD: A Randomized Controlled Trial

RATIONALE & OBJECTIVE

Patients with chronic kidney disease (CKD) are particularly sensitive to dietary sodium. We evaluated a self-management approach for dietary sodium restriction in patients with CKD.

STUDY DESIGN

Randomized controlled trial.

SETTING & PARTICIPANTS

Nephrology outpatient clinics in 4 Dutch hospitals. 99 adults with CKD stages 1 to 4 or a functioning (estimated glomerular filtration rate≥25mL/min/1.73m²) kidney transplant, hypertension, and sodium intake>130mmol/d.

INTERVENTION

Routine care was compared with routine care plus a web-based self-management intervention including individual e-coaching and group meetings implemented over a 3-month intervention period, followed by e-coaching over a 6-month maintenance period.

OUTCOMES

Primary outcomes were sodium excretion after the 3-month intervention and after the 6-month maintenance period. Secondary outcomes were blood pressure, proteinuria, costs, quality of life, self-management skills, and barriers and facilitators for implementation.

RESULTS

Baseline estimated glomerular filtration rate was 55.0±22.0mL/min/1.73m². During the intervention period, sodium excretion decreased in the intervention group from 188±8 (SE) to 148±8mmol/d (P<0.001), but did not change significantly in the control group. At 3 months, mean sodium excretion was 24.8 (95% CI, 0.1-49.6) mmol/d lower in the intervention group (P=0.049). At 3 months, systolic blood pressure (SBP) decreased in the intervention group from 140±3 to 132±3mm Hg (P<0.001), but was unchanged in the control group. Mean difference in SBP across groups was-4.7 (95% CI, -10.7 to 1.3) mm Hg (P=0.1). During the maintenance phase, sodium excretion increased in the intervention group, but remained lower than at baseline at 160±8mmol/d (P=0.01), while it decreased in the control group from 174±9 at the end of the intervention period to 154±9mmol/d (P=0.001). Consequently, no difference in sodium excretion between groups was observed after the maintenance phase. There was no difference in SBP between groups after the maintenance phase.

LIMITATIONS

Limited power, postrandomization loss to follow-up, Hawthorne effect, lack of dietary data, short-term follow-up.

CONCLUSIONS

A coaching intervention reduced sodium intake at 3 months. Efficacy during the maintenance phase was diminished, possibly due to inadvertent adoption of the intervention by the control group.

FUNDING

Grant funding from the Netherlands Organization for Health Research and Development and the Dutch Kidney Foundation.

TRIAL REGISTRATION

Registered at ClinicalTrials.gov with study number NCT02132013.

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

Who Uses Action Planning in a Web-Based Computer-Tailored Intervention to Reduce Workplace Sitting and What do Action Plans Look Like? Analyses of the Start to stand Intervention among Flemish Employees

BACKGROUND

Action planning plays an important role in many web-based behaviour change interventions. As such, it is important to identify who engages in action planning and what action plans look like. This study examines (1) attributes of users creating an action plan and (2) the content of action plans made during a web-based intervention to reduce workplace sitting.

METHODS

Users answered "what" (short standing breaks and/or longer periods of standing), "when" (working hours, work breaks, commuting), "where" (workplace, transport mode), and "how" (frequency, duration, implementation intentions) questions. MANOVA and chi-squared tests were conducted to compare those creating an action plan with those who did not. Descriptive statistics were used to examine the content of the action plans.

RESULTS

Those creating an action plan (n = 236/1,701) were significantly older, more sedentary at work, and more aware of health risks related to excessive sitting compared to those not planning (n = 1,465). The majority planned standing breaks (n = 212) every 30 minutes, and periods of standing (n = 173) for 1 to 2 hours.

CONCLUSIONS

Future interventions should promote action planning more, especially among younger employees and those with less health-related knowledge about sitting. Action plans were compatible with current sitting messages used in health promotion.

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.

Supporting Engagement, Adherence, and Behavior Change in Online Dietary Interventions

INTRODUCTION

Poor diet is a leading cause of death and disease globally. This epidemic requires effective and accessible interventions to stop the increasing number of diet-related deaths and the health and economic impacts of diet-related disease. Online interventions provide flexibility and accessibility. With the ubiquitous use of smartphones, they can be intertwined with daily activities such as shopping and eating. The aim of this review is to determine what features and behavior change techniques employed in online dietary interventions for adult populations promoting dietary behavior change.

METHODS

The researchers conducted a systematic search of Cumulative Index of Nursing and Allied Health, Cochrane Library, Global Health, MEDLINE, PsychINFO, and psychological and behavioral sciences electronic bibliography databases, and specialist electronic health (e-health) journals from database inception to January, 2018. Studies were included if they were randomized controlled trials of online dietary interventions with active comparator conditions in adult populations, and with reported dietary change measures. A quality score was applied to each study calculated by a developed scoring system. The review analyzed intervention dietary change measures, attrition (nonuse and dropout), engagement (metrics and intensity of use), adherence (defined as compliance to the treatment protocol), behavior change techniques employed to achieve dietary change, and techniques employed in successful (those who achieved significant results in the targeted dietary behavior) vs unsuccessful interventions as reported by the studies.

RESULTS

A total of 21 studies composed of a total of 7,455 adults and reporting on 19 different e-health interventions were included from 1,237 records. These studies targeted dietary change as measured by reduced energy intake (5) or changes in specific dietary components (15) and overall diet quality (4). Dietary change was a behavior target in general healthy populations (12) and for managing diseases such as obesity and cardiovascular disease (7), or for improving quality of life for those with chronic conditions (1). Improvements in dietary behavior were seen in 14 of the 19 interventions reported.

DISCUSSION

The results suggest that online interventions can be successful in achieving dietary behavior change across a range of defined populations. However, disparate reporting of engagement and limited reporting of nonuse attrition rates limited the analysis of which behavior change techniques were most effective in achieving this change.

IMPLICATIONS FOR RESEARCH AND PRACTICE

The results of this review support the potential of online and smartphone dietary interventions as a method to achieve change in diet in defined populations. However, further work needs to be done in examining how users engage with interventions, and thus which behavior change techniques are most effective.

Exploring Associations Between the Self-Reported Values, Well-Being, and Health Behaviors of Finnish Citizens: Cross-Sectional Analysis of More Than 100,000 Web-Survey Responses

BACKGROUND

Understanding the relationship between personal values, well-being, and health-related behavior could facilitate the development of engaging, effective digital interventions for promoting well-being and the healthy lifestyles of citizens. Although the associations between well-being and values have been quite extensively studied, the knowledge about the relationship between health behaviors and values is less comprehensive.

OBJECTIVE

The aim of this study was to assess retrospectively the associations between self-reported values and commitment to values combined with self-reported well-being and health behaviors from a large cross-sectional dataset.

METHODS

We analyzed 101,130 anonymous responses (mean age 44.78 years [SD 13.82]; 78.88%, 79,770/101,130 women) to a Finnish Web survey, which were collected as part of a national health promotion campaign. The data regarding personal values were unstructured, and the self-reported value items were classified into value types based on the Schwartz value theory and by applying principal component analysis. Logistic and multiple linear regression were used to explore the associations of value types and commitment to values with well-being factors (happiness, communal social activity, work, and family-related distress) and health behaviors (exercise, eating, smoking, alcohol consumption, and sleep).

RESULTS

Commitment to personal values was positively related to happiness (part r²=0.28), communal social activity (part r²=0.09), and regular exercise (part r²=0.06; P<.001 for all). Health, Power (social status and dominance), and Mental balance (self-acceptance) values had the most extensive associations with health behaviors. Regular exercise, healthy eating, and nonsmoking increased the odds of valuing Health by 71.7%, 26.8%, and 40.0%, respectively (P<.001 for all). Smoking, unhealthy eating, irregular exercise, and increased alcohol consumption increased the odds of reporting Power values by 27.80%, 27.78%, 24.66%, and 17.35%, respectively (P<.001 for all). Smoking, unhealthy eating, and irregular exercise increased the odds of reporting Mental balance values by 20.79%, 16.67%, and 15.37%, respectively (P<.001 for all). In addition, lower happiness levels increased the odds of reporting Mental balance and Power values by 24.12% and 20.69%, respectively (P<.001 for all).

CONCLUSIONS

The findings suggest that commitment to values is positively associated with happiness and highlight various, also previously unexplored, associations between values and health behaviors.

Predicting Outcomes from Engagement With Specific Components of an Internet-Based Physical Activity Intervention With Financial Incentives: Process Analysis of a Cluster Randomized Controlled Trial

Background

Investigating participant engagement and nonusage attrition can help identify the likely active ingredients of electronic health interventions. Research on engagement can identify which intervention components predict health outcomes. Research on nonusage attrition is important to make recommendations for retaining participants in future studies.

Objective

This study aimed to investigate engagement and nonusage attrition in the Physical Activity Loyalty (PAL) scheme, a 6-month complex physical activity intervention in workplaces in Northern Ireland. The intervention included financial incentives with reward redemption and self-regulation techniques. Specific objectives were (1) to determine whether engagement in specific intervention components predicted physical activity at 6 months, (2) to determine whether engagement in specific intervention components predicted targeted mediators at 6 months, and (3) to investigate predictors of nonusage attrition for participants recording daily activity via the PAL scheme physical activity monitoring system and logging onto the website.

Methods

Physical activity was assessed at baseline and 6 months using pedometers (Yamax Digiwalker CW-701, Japan). Markers of engagement and website use, monitoring system use, and reward redemption were collected throughout the scheme. Random-effects generalized least-squares regressions determined whether engagement with specific intervention components predicted 6-month physical activity and mediators. Cox proportional hazards regressions were used to investigate predictors of nonusage attrition (days until first 2-week lapse).

Results

A multivariable generalized least-squares regression model (n=230) showed that the frequency of hits on the website’s monitoring and feedback component (regression coefficient [b]=50.2; SE=24.5; P=.04) and the percentage of earned points redeemed for financial incentives (b=9.1; SE=3.3; P=.005) were positively related to 6-month pedometer steps per day. The frequency of hits on the discussion forum (b=−69.3; SE=26.6; P=.009) was negatively related to 6-month pedometer steps per day. Reward redemption was not related to levels of more internal forms of motivation. Multivariable Cox proportional hazards regression models identified several baseline predictors associated with nonusage attrition. These included identified regulation (hazard ratio [HR] 0.88, 95% CI 0.81-0.97), recovery self-efficacy (HR 0.88, 95% CI 0.80-0.98), and perceived workplace environment safety (HR 1.07, 95% CI 1.02-1.11) for using the physical activity monitoring system. The EuroQoL health index (HR 0.33, 95% CI 0.12-0.91), financial motivation (HR 0.93, 95% CI 0.87-0.99), and perceived availability of physical activity opportunities in the workplace environment (HR 0.96, 95% CI 0.93-0.99) were associated with website nonusage attrition.

Conclusions

Our results provide evidence opposing one of the main hypotheses of self-determination theory by showing that financial rewards are not necessarily associated with decreases in more internal forms of motivation when offered as part of a complex multicomponent intervention. Identifying baseline predictors of nonusage attrition can help researchers to develop strategies to ensure maximum intervention adherence.

Trial Registration

ISRCTN Registry ISRCTN17975376; http://www.isrctn.com/ISRCTN17975376 (Archived by WebCite at http://www.webcitation.org/76VGZsZug)

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

Efficacy and Moderation of Mobile App-Based Programs for Mindfulness-Based Training, Self-Compassion Training, and Cognitive Behavioral Psychoeducation on Mental Health: Randomized Controlled Noninferiority Trial

BACKGROUND

Mindfulness-based interventions, self-compassion training, and cognitive behavioral therapy have garnered much evidence in its salutary effects on mental health. With increasing application of smartphone and mobile technology on health promotion, this study investigated the efficacy and possible moderators of mindfulness, self-compassion, and cognitive behavioral psychoeducation training mobile apps in the improvement of mental health.

OBJECTIVE

The aim of this study was to examine the efficacy of 3 mobile app-based programs: mindfulness-based program, self-compassion program, and cognitive behavioral psychoeducation program in improving mental well-being and reducing psychological distress. Changes in mindful awareness and self-compassion were also assessed. To further delineate the suitability of each program for different types of individuals, individual difference variables (ie, discomfort with emotions and tolerance for ambiguity) were explored for potential moderation.

METHODS

This study was a 3-arm, randomized, controlled, noninferiority trial examining the efficacy of mindfulness-based program, self-compassion program, and cognitive behavioral psychoeducation. Participants were randomized into either 1 of the 3 conditions. Throughout the 4-week, 28-session program, participants spent 10-15 min daily reviewing the course content and practicing various related exercises. At preprogram, postprogram, and 3-month follow-up, participants also completed Web-based measures of mental well-being, psychological distress, mindful-awareness, and self-compassion as well as the proposed moderators.

RESULTS

Among the 2161 study participants, 508 and 349 completed the post- and 3-month follow-up assessment, respectively. All 3 conditions (mindfulness-based program: N=703; cognitive behavioral psychoeducation: N=753; self-compassion program: N=705) were found to be efficacious in improving mental well-being and reducing psychological distress. All conditions enhanced mindful awareness at postprogram. Significant interaction effect was found on self-compassion; cognitive behavioral psychoeducation and self-compassion program, but not mindfulness-based program, significantly enhanced self-compassion at postprogram. No significant differences regarding usage and users' satisfaction were found among the 3 conditions. None of the proposed moderators were found to be significant.

CONCLUSIONS

Mindfulness-based, self-compassion, and cognitive behavioral psychoeducation mobile apps were efficacious in improving mental well-being and reducing psychological distress among adults at postprogram and 3-month follow-up. Future app-based psychological training programs should consider gamification and personalization of content or feedback to enhance engagement and mitigate the high attrition rates that are common in app-based health promotion programs.

TRIAL REGISTRATION

Chinese Clinical Trial Registry (ChiCTR) ChiCTR-TRC-13003468; http://www.chictr.org.cn/hvshowproject.aspx?id=6220 (Archived by WebCite at http://www.webcitation.org/734PlOz50).

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.

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.

From Evidence-Based Research to Practice-Based Evidence: Disseminating a Web-Based Computer-Tailored Workplace Sitting Intervention through a Health Promotion Organisation

Prolonged sitting has been linked to adverse health outcomes; therefore, we developed and examined a web-based, computer-tailored workplace sitting intervention. As we had previously shown good effectiveness, the next stage was to conduct a dissemination study. This study reports on the dissemination efforts of a health promotion organisation, associated costs, reach achieved, and attributes of the website users. The organisation systematically registered all the time and resources invested to promote the intervention. Website usage statistics (reach) and descriptive statistics (website users’ attributes) were also assessed. Online strategies (promotion on their homepage; sending e-mails, newsletters, Twitter, Facebook and LinkedIn posts to professional partners) were the main dissemination methods. The total time investment was 25.6 h, which cost approximately 845 EUR in salaries. After sixteen months, 1599 adults had visited the website and 1500 (93.8%) completed the survey to receive personalized sitting advice. This sample was 38.3 ± 11.0 years, mainly female (76.9%), college/university educated (89.0%), highly sedentary (88.5% sat >8 h/day) and intending to change (93.0%) their sitting. Given the small time and money investment, these outcomes are positive and indicate the potential for wide-scale dissemination. However, more efforts are needed to reach men, non-college/university educated employees, and those not intending behavioural change.

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.

Evaluation of an mHealth App (DeStressify) on University Students' Mental Health: Pilot Trial

BACKGROUND

One in five Canadians experience mental health issues with those in the age range of 15 to 24 years being most at risk of a mood disorder. University students have shown significantly higher rates of mental health problems than the general public. Current university support services are limited by factors such as available staff and finances, and social stigma has frequently been identified as an additional barrier that prevents students from accessing these resources. Mobile health (mHealth) apps are one form of alternative health support that is discrete and accessible to students, and although they are recognized as a promising alternative, there is limited research demonstrating their efficacy.

OBJECTIVE

The aim of this study was to evaluate a mindfulness-based app's ("DeStressify") efficacy on stress, anxiety, depressive symptomology, sleep behavior, work or class absenteeism, work or school productivity, and quality of life (QoL) among university students.

METHODS

Full-time undergraduate students at a Canadian university with smartphones and Internet access were recruited through in-class announcements and on-campus posters. Participants randomized into an experimental condition were given and instructed to use the DeStressify app 5 days a week for 4 weeks. Control condition participants were wait-listed. All participants completed pre- and postintervention Web-based surveys to self-assess stress, anxiety, depressive symptomatology, sleep quality, and health-related QoL.

RESULTS

A total of 206 responses were collected at baseline, with 163 participants completing the study (86 control, 77 experimental). Using DeStressify was shown to reduce trait anxiety (P=.01) and improve general health (P=.001), energy (P=.01), and emotional well-being (P=.01) in university students, and more participants in the experimental condition believed their productivity improved between baseline and postintervention measurements than the number of participants expected to believe so randomly by chance (P=.01). The app did not significantly improve stress, state anxiety, physical and social functioning, and role limitations because of physical or emotional health problems or pain (P>.05).

CONCLUSIONS

Mindfulness-based apps may provide an effective alternative support for university students' mental health. Universities and other institutions may benefit from promoting the use of DeStressify or other mindfulness-based mHealth apps among students who are interested in methods of anxiety management or mindfulness-based self-driven health support. Future steps include examining DeStressify and similar mHealth apps over a longer period and in university staff and faculty.

Characteristics of Adults Seeking Health Care Provider Support Facilitated by Mobile Technology: Secondary Data Analysis

Background

Mobile health technology is rapidly evolving with the potential to transform health care. Self-management of health facilitated by mobile technology can maximize long-term health trajectories of adults. Little is known about the characteristics of adults seeking Web-based support from health care providers facilitated by mobile technology.

Objective

This study aimed to examine the following: (1) the characteristics of adults who seek human support from health care providers for health concerns using mobile technology rather than from family members and friends or others with similar health conditions and (2) the use of mobile health technology among adults with chronic health conditions. Findings of this study were interpreted in the context of the Efficiency Model of Support.

Methods

We first described characteristics of adults seeking Web-based support from health care providers. Using chi-square tests for categorical variables and t test for the continuous variable of age, we compared adults seeking Web-based and conventional support by demographics. The primary aim was analyzed using multivariate logistic regression to examine whether chronic health conditions and demographic factors (eg, sex, income, employment status, race, ethnicity, education, and age) were associated with seeking Web-based support from health care providers.

Results

The sample included adults (N=1453), the majority of whom were female 57.60% (837/1453), white 75.02% (1090/1453), and non-Hispanic 89.13% (1295/1453). The age of the participants ranged from 18 to 92 years (mean 48.6, standard deviation [SD] 16.8). The majority 76.05% (1105/1453) of participants reported college or higher level of education. A disparity was found in access to health care providers via mobile technology based on socioeconomic status. Adults with annual income of US $30,000 to US $100,000 were 1.72 times more likely to use Web-based methods to contact a health care provider, and adults with an annual income above US $100,000 were 2.41 to 2.46 times more likely to access health care provider support on the Web, compared with those with an annual income below US $30,000. After adjusting for other demographic covariates and chronic conditions, age was not a significant factor in Web-based support seeking.

Conclusions

In this study, the likelihood of seeking Web-based support increased when adults had any or multiple chronic health conditions. A higher level of income and education than the general population was found to be related to the use of mobile health technology among adults in this survey. Future study is needed to better understand the disparity in Web-based support seeking for health issues and the clinicians’ role in promoting access to and use of mobile health technology.