Intervention use and action planning in a web-based computer-tailored weight management program for overweight adults: randomized controlled trial

Engagement, acceptability, usability and satisfaction with Active for Life, a computer-tailored web-based physical activity intervention using Fitbits in older adults

BACKGROUND

Preliminary evidence suggests that web-based physical activity interventions with tailored advice and Fitbit integration are effective and may be well suited to older adults. Therefore, this study aimed to examine the engagement, acceptability, usability, and satisfaction with 'Active for Life,' a web-based physical activity intervention providing computer-tailored physical activity advice to older adults.

METHODS

Inactive older adults (n = 243) were randomly assigned into 3 groups: 1) tailoring + Fitbit, 2) tailoring only, or 3) a wait-list control. The tailoring + Fitbit group and the tailoring-only group received 6 modules of computer-tailored physical activity advice over 12 weeks. The advice was informed by objective Fitbit data in the tailoring + Fitbit group and self-reported physical activity in the tailoring-only group. This study examined the engagement, acceptability, usability, and satisfaction of Active for Life in intervention participants (tailoring + Fitbit n = 78, tailoring only n = 96). Wait-list participants were not included. Engagement (Module completion, time on site) were objectively recorded through the intervention website. Acceptability (7-point Likert scale), usability (System Usability Scale), and satisfaction (open-ended questions) were assessed using an online survey at post intervention. ANOVA and Chi square analyses were conducted to compare outcomes between intervention groups and content analysis was used to analyse program satisfaction.

RESULTS

At post-intervention (week 12), study attrition was 28% (22/78) in the Fitbit + tailoring group and 39% (37/96) in the tailoring-only group. Engagement and acceptability were good in both groups, however there were no group differences (module completions: tailoring + Fitbit: 4.72 ± 2.04, Tailoring-only: 4.23 ± 2.25 out of 6 modules, p = .14, time on site: tailoring + Fitbit: 103.46 ± 70.63, Tailoring-only: 96.90 ± 76.37 min in total, p = .56, and acceptability of the advice: tailoring + Fitbit: 5.62 ± 0.89, Tailoring-only: 5.75 ± 0.75 out of 7, p = .41). Intervention usability was modest but significantly higher in the tailoring + Fitbit group (tailoring + Fitbit: 64.55 ± 13.59, Tailoring-only: 57.04 ± 2.58 out of 100, p = .003). Participants reported that Active for Life helped motivate them, held them accountable, improved their awareness of how active they were and helped them to become more active. Conversely, many participants felt as though they would prefer personal contact, more detailed tailoring and more survey response options.

CONCLUSIONS

This study supports web-based physical activity interventions with computer-tailored advice and Fitbit integration as engaging and acceptable in older adults.

TRIAL REGISTRATION

Australian and New Zealand Clinical Trials Registry: ACTRN12618000646246. Registered April 23 2018, https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=374901.

Behaviour change techniques that constitute effective planning interventions to improve physical activity and diet behaviour for people with chronic conditions: a systematic review

OBJECTIVES

Action planning is a brief and effective behaviour change technique (BCT) to improve physical activity (PA) and diet behaviour (DB). This study aimed to identify critical BCTs and mechanisms of action (MoAs) to interpret the effectiveness of planning interventions based on the Health Action Process Approach (HAPA) model.

DESIGN

Systematic review.

DATA SOURCES

PubMed, Web of Science, CINAHL (EBSCO), PsycINFO (EBSCO), Psychology and Behavioural Sciences Collection (EBSCO), psyARTICLES and Medline were searched for studies from January 1990 to September 2021 published in English.

ELIGIBILITY CRITERIA

Experiment involving action planning intervention to improve PA or DB in community-dwelling adult patients with chronic conditions.

DATA EXTRACTION AND SYNTHESIS

Two reviewers independently coded the planning interventions into BCT combinations and MoA assemblies. Outcome was dichotomised according to the statistical power and Cohen's d. The Cochrane risk of bias assessment tool and the Risk of Bias in Nonrandomized Studies-of Interventions assessment tool were used to assess the quality of randomised controlled trials (RCTs) and non-RCTs, respectively.

RESULTS

From the 52 included studies, 46 BCTs were identified and linked to 21 MoAs. Long-term facilitators for planning intervention included 'self-monitoring of behaviour', 'problem solving', 'instruction on how to perform the behaviour' and 'adding objects to the environments'. The three most frequently occurring MoAs were 'intention', 'behavioural regulation', 'beliefs about capabilities'. The effective intervention groups had higher MoA scores that corresponded to the HAPA model constructs than the ineffective groups.

CONCLUSIONS

The findings from this review may inform scientific and effective planning intervention designs for community-dwelling people with chronic conditions in the future.

PROSPERO REGISTRATION NUMBER

CRD42021241227.

Making Specific Plan Improves Physical Activity and Healthy Eating for Community-Dwelling Patients With Chronic Conditions: A Systematic Review and Meta-Analysis

Background

Implementation intention formed by making a specific action plan has been proved effective in improving physical activity (PA) and dietary behavior (DB) for the general, healthy population, but there has been no meta-analysis of their effectiveness for patients with chronic conditions. This research aims to analyze several explanatory factors and overall effect of implementation intention on behavioral and health-related outcomes among community-dwelling patients.

Methods

We searched CIHNAL (EBSCO), PUBMED, Web of Science, Science Direct, SAGE Online, Springer Link, Taylor & Francis, Scopus, Wiley Online Library, CNKI, and five other databases for eligible studies. Random-effects meta-analysis was conducted to estimate effect sizes of implementation intention on outcomes, including PA, DB, weight, and body mass index. And the eligible studies were assessed by the Cochrane Collaboration's tool for risk of bias assessment. Sensitivity analysis adopted sequential algorithm and the p-curve analysis method.

Results

A total of 54 studies were identified. Significant small effect sizes of the intervention were found for PA [standard mean difference (SMD) 0.24, 95% confidence interval (CI) (0.10, 0.39)] and for the DB outcome [SMD -0.25, 95% CI (-0.34, -0.15)]. In moderation analysis, the intervention was more effective in improving PA for men (p < 0.001), older adults (p = 0.006), and obese/overweight patients with complications (p = 0.048) and when the intervention was delivered by a healthcare provider (p = 0.01).

Conclusion

Implementation intentions are effective in improving PA and DB for community dwelling patients with chronic conditions. The review provides evidence to support the future application of implementation intention intervention. Besides, the findings from this review offer different directions to enhance the effectiveness of this brief and potential intervention in improving patients' PA and DB.

Systematic Review Registration

https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=160491.

Social Cognitive Theories and Electronic Health Design: Scoping Review

Background

There are several social cognitive theories (SCTs) and models that support platform design in electronic health (eHealth) promotion trials. The rationale for this scoping review was to determine how social design features (informational aid, expressive support, gaming, and tailored content) are used to promote self-efficacy, engagement, knowledge, and behavior change.

Objective

This study aimed to review a broad spectrum of digital health interventions in the literature seeking trials that use SCTs for the design of eHealth applications.

Methods

The author conducted a systematic scoping review of 161 Web-based health interventions from published randomized clinical trials using 1 or more tools to address the social cognitive determinants in their website design from January 2006 to April 2016. An iterative approach was used in the selection of studies and data extraction. The studies were analyzed for quality and coded for type of social design features employed.

Results

Expressive interaction tools were found in 48.6% (54/111) of studies categorized as a strong recommendation by the Joanna Briggs Institute criteria. Overall, less than half of the studies addressed participant social support and motivational needs (43.8%). The vast majority of studies (100%) relied on the use of the Web for delivery of informational aid and tailored content for the individual participant (75.9%).

Conclusions

This review fills a research gap by linking social theory to Web strategy to improve the impact and sustainability of eHealth interventions. A Digital Health Intervention Model was developed to provide a framework to enhance future Web-based health intervention design and execution.

Persuasive System Design Principles and Behavior Change Techniques to Stimulate Motivation and Adherence in Electronic Health Interventions to Support Weight Loss Maintenance: Scoping Review

BACKGROUND

Maintaining weight after weight loss is a major health challenge, and eHealth (electronic health) solutions may be a way to meet this challenge. Application of behavior change techniques (BCTs) and persuasive system design (PSD) principles in eHealth development may contribute to the design of technologies that positively influence behavior and motivation to support the sustainable health behavior change needed.

OBJECTIVE

This review aimed to identify BCTs and PSD principles applied in eHealth interventions to support weight loss and weight loss maintenance, as well as techniques and principles applied to stimulate motivation and adherence for long-term weight loss maintenance.

METHODS

A systematic literature search was conducted in PsycINFO, Ovid MEDLINE (including PubMed), EMBASE, Scopus, Web of Science, and AMED, from January 1, 2007 to June 30, 2018. Arksey and O'Malley's scoping review methodology was applied. Publications on eHealth interventions were included if focusing on weight loss or weight loss maintenance, in combination with motivation or adherence and behavior change.

RESULTS

The search identified 317 publications, of which 45 met the inclusion criteria. Of the 45 publications, 11 (24%) focused on weight loss maintenance, and 34 (76%) focused on weight loss. Mobile phones were the most frequently used technology (28/45, 62%). Frequently used wearables were activity trackers (14/45, 31%), as well as other monitoring technologies such as wireless or digital scales (8/45, 18%). All included publications were anchored in behavior change theories. Feedback and monitoring and goals and planning were core behavior change technique clusters applied in the majority of included publications. Social support and associations through prompts and cues to support and maintain new habits were more frequently used in weight loss maintenance than weight loss interventions. In both types of interventions, frequently applied persuasive principles were self-monitoring, goal setting, and feedback. Tailoring, reminders, personalization, and rewards were additional principles frequently applied in weight loss maintenance interventions. Results did not reveal an ideal combination of techniques or principles to stimulate motivation, adherence, and weight loss maintenance. However, the most frequently mentioned individual techniques and principles applied to stimulate motivation were, personalization, simulation, praise, and feedback, whereas associations were frequently mentioned to stimulate adherence. eHealth interventions that found significant effects for weight loss maintenance all applied self-monitoring, feedback, goal setting, and shaping knowledge, combined with a human social support component to support healthy behaviors.

CONCLUSIONS

To our knowledge, this is the first review examining key BCTs and PSD principles applied in weight loss maintenance interventions compared with those of weight loss interventions. This review identified several techniques and principles applied to stimulate motivation and adherence. Future research should aim to examine which eHealth design combinations can be the most effective in support of long-term behavior change and weight loss maintenance.

What are the working mechanisms of a web-based workplace sitting intervention targeting psychosocial factors and action planning?

Background

Office workers demonstrate high levels of sitting on workdays. As sitting is positively associated with adverse health risks in adults, a theory-driven web-based computer-tailored intervention to influence workplace sitting, named ‘Start to Stand,’ was developed. The intervention was found to be effective in reducing self-reported workplace sitting among Flemish employees. The aim of this study was to investigate through which mechanisms the web-based computer-tailored intervention influenced self-reported workplace sitting.

Methods

Employees (n = 155) participated in a clustered randomised controlled trial and reported socio-demographics (age, gender, education), work-related (hours at work, employment duration), health-related (weight and height, workplace sitting and physical activity) and psychosocial (knowledge, attitudes, self-efficacy, social support, intention regarding (changing) sitting behaviours) variables at baseline and 1-month follow-up. The product-of-coefficients test of MacKinnon based on multiple linear regression analyses was conducted to examine the mediating role of five psychosocial factors (knowledge, attitudes, self-efficacy, social support, intention). The influence of one self-regulation skill (action planning) in the association between the intervention and self-reported workplace sitting time was investigated via moderation analyses.

Results

The intervention had a positive influence on knowledge (p = 0.040), but none of the psychosocial variables did mediate the intervention effect on self-reported workplace sitting. Action planning was found to be a significant moderator (p < 0.001) as the decrease in self-reported workplace sitting only occurred in the group completing an action plan.

Conclusions

Future interventions aimed at reducing employees’ workplace sitting are suggested to focus on self-regulatory skills and promote action planning when using web-based computer-tailored advice.

Trial registration

Clinicaltrials.gov NCT02672215; (Archived by WebCite at https://clinicaltrials.gov/ct2/show/NCT02672215).

Electronic supplementary material

The online version of this article (doi:10.1186/s12889-017-4325-5) contains supplementary material, which is available to authorized users.

Evaluation of a Serious Self-Regulation Game Intervention for Overweight-Related Behaviors ("Balance It"): A Pilot Study

Background

Serious games have the potential to promote health behavior. Because overweight is still a major issue among secondary vocational education students in the Netherlands, this study piloted the effects of “Balance It,” a serious self-regulation game intervention targeting students’ overweight-related behaviors: dietary intake and physical activity (PA).

Objective

We aimed to pilot the effects of Balance It on secondary vocational education students’ dietary intake and PA.

Methods

In total, 501 secondary vocational education students participated at baseline (intervention: n=250; control: n=251) in this pre-post cluster randomized trial. After 4 weeks, at immediate posttest, 231 students filled in the posttest questionnaire (intervention: n=105; control: n=126). The sample had a mean age of 17.28 (SD 1.26, range 15-21) years, 62.8% (145/231) were female, and 26.8% (62/231) had a non-Dutch background. Body mass index (BMI kg/m²) ranged from 14.4 to 31.1 (mean 21.1, SD 3.3). The intervention and control groups were compared on the primary (behavioral) outcomes of dietary intake (fruit and vegetable consumption, snack consumption, and soft drink consumption) and PA (moderate and vigorous). Additionally, we explored (1) differences between the intervention and control groups in determinants of dietary intake and PA, including attitude, self-efficacy, intention, barrier identification, action planning, and action control, and (2) differences between active (intervention) users and the control group in dietary intake, PA, and associated determinants.

Results

After corrections for multiple testing, we did not find significant differences between the intervention group and control group in terms of dietary intake, PA, and determinants of dietary intake and PA. Exploratory research indicated that only 27.6% (29/105) of the intervention group reported actual intervention use (ie, active users). For exploratory reasons, we compared the active users (n=29) with the control group (n=124) and corrected for multiple testing. Results showed that active users’ snack consumption decreased more strongly (active users: mean change=–0.20; control group: mean change=–0.08; beta=–0.36, P=.01, R² change=.05), and their use of active transport had a stronger increase (active users: mean change=0.92; control group=–0.12; beta=1.58, P=.02, R² change=.03) than the control group. Results also revealed significant differences in action planning (active users: mean change=0.42; control group: mean change=0.07; beta=0.91, P=.01, R² change=.04) and action control (active users: mean change=0.63; control group: mean change=–0.05; beta=1.25, P=.001, R² change=.08) in terms of unhealthy eating.

Conclusions

The Balance It intervention did not show favorable effects on dietary intake and PA compared to the control condition. However, only a small number of people in the intervention condition actually used Balance It (27.6%). Exploratory analyses did suggest that, if used as planned, Balance It could contribute to changing dietary intake and PA behaviors, albeit it remains debatable whether this would be sufficient to prevent overweight.

The Effect of Tailored Web-Based Feedback and Optional Telephone Coaching on Health Improvements: A Randomized Intervention Among Employees in the Transport Service Industry

Background

Lifestyle-related health problems are an important health concern in the transport service industry. Web- and telephone-based interventions could be suitable for this target group requiring tailored approaches.

Objective

To evaluate the effect of tailored Web-based health feedback and optional telephone coaching to improve lifestyle factors (body mass index—BMI, dietary intake, physical activity, stress, sleep, tobacco and alcohol consumption, disease history, self-perceived health, and motivation to change health habits), in comparison to no health feedback or telephone coaching.

Methods

Overall, 3,876 employees in the Swedish transport services were emailed a Web-based questionnaire. They were randomized into: control group (group A, 498 of 1238 answered, 40.23%), or intervention Web (group B, 482 of 1305 answered, 36.93%), or intervention Web + telephone (group C, 493 of 1333 answered, 36.98%). All groups received an identical questionnaire, only the interventions differed. Group B received tailored Web-based health feedback, and group C received tailored Web-based health feedback + optional telephone coaching if the participants’ reported health habits did not meet the national guidelines, or if they expressed motivation to change health habits. The Web-based feedback was fully automated. Telephone coaching was performed by trained health counselors. Nine months later, all participants received a follow-up questionnaire and intervention Web + telephone. Descriptive statistics, the chi-square test, analysis of variance, and generalized estimating equation (GEE) models were used.

Results

Overall, 981 of 1473 (66.60%) employees participated at baseline (men: 66.7%, mean age: 44 years, mean BMI: 26.4 kg/m²) and follow-up. No significant differences were found in reported health habits between the 3 groups over time. However, significant changes were found in motivation to change. The intervention groups reported higher motivation to improve dietary habits (144 of 301 participants, 47.8%, and 165 of 324 participants, 50.9%, for groups B and C, respectively) and physical activity habits (181 of 301 participants, 60.1%, and 207 of 324 participants, 63.9%, for B and C, respectively) compared with the control group A (122 of 356 participants, 34.3%, for diet and 177 of 356 participants, 49.7%, for physical activity). At follow-up, the intervention groups had significantly decreased motivation (group B: P<.001 for change in diet; P<.001 for change in physical activity; group C: P=.007 for change in diet; P<.001 for change in physical activity), whereas the control group reported significantly increased motivation to change diet and physical activity (P<.001 for change in diet; P<.001 for change in physical activity).

Conclusion

Tailored Web-based health feedback and the offering of optional telephone coaching did not have a positive health effect on employees in the transport services. However, our findings suggest an increased short-term motivation to change health behaviors related to diet and physical activity among those receiving tailored Web-based health feedback.

Generating and predicting high quality action plans to facilitate physical activity and fruit and vegetable consumption: results from an experimental arm of a randomised controlled trial

Background

In order to improve the transition from an intention to a change in health behaviour, action planning is a frequently used behavioural change method. The quality of action plans in terms of instrumentality and specificity is important in terms of supporting a successful change in health behaviour. Until now, little has been known about the predictors of action plan generation and the predictors of high quality action plans and, therefore, the current study investigates these predictors.

Method

A randomised controlled trial was conducted to improve physical activity (PA) and fruit and vegetable (FV) consumption using a web-based computer tailored intervention. During the 8-week intervention period, participants in the intervention arm (n = 346) were guided (step-by-step) to generate their own action plans to improve their health behaviours. Demographic characteristics, social cognitions, and health behaviour were assessed at baseline by means of self-reporting. Whether participants generated action plans was tracked by means of server registrations within two modules of the intervention.

Results

The action planning component of the intervention regarding physical activity and fruit and vegetable consumption was used by 40.9 and 20.7 % of the participants, respectively. We found that participants who were physically active at baseline were less likely to generate action plans concerning physical activity. With regards to generating fruit and vegetable action plans, participants with a high risk perception and a strong intention to eat fruit and vegetables on a daily basis made more use of the action planning component for this behaviour. Finally, the large majority of the action plans for physical activity (96.6 %) and fruit and vegetable consumption (100 %) were instrumental and about half of the action plans were found to be highly specific (PA = 69.6 %/FV = 59.7 %). The specificity of the action plans is associated with having a relationship and low levels of negative outcome expectancies.

Conclusion

Risk perception and intention are predictors of using the application of action planning. Increasing the motivation to change behaviour should be prioritised in interventions concerning changes in health behaviour before participants are asked to generate action plans. This would also make the intervention suitable for unmotivated people. For those participants who already perform the desired health behaviour prior to the intervention, action plans might be less relevant. Nevertheless, using a guided step-by-step approach to generate action plans resulted in highly instrumental and specific action plans and might be integrated into other interventions concerning changes in health behaviour.

Trial Registration

Netherlands Trial Register: NTR 3706, ClinicalTrials.gov: NCT01909349.

Long-term efficacy of a Web-based computer-tailored nutrition education intervention for adults including cognitive and environmental feedback: a randomized controlled trial

Background

Unhealthy diets are prevalent in Western countries, especially among low-educated people. To have an effect on health, it is important that dietary changes are sustained over time. This study examines long-term effects of a cognitive and environmental-feedback version of a Web-based computer-tailored (CT) nutrition education intervention targeting fruit, vegetables, high-energy snacks and saturated fat.

Methods

A randomized controlled trial was conducted with a basic (tailored intervention targeting individual cognitions and self-regulation processes; n = 456), plus (additionally targeting environmental-level factors; n = 459) and control group (generic nutrition information; n = 434). Participants were recruited from the general population and randomly assigned to a study group. Online self-reported questionnaires assessed fruit, vegetable, high-energy snack and saturated fat intake, self-regulation, self-control, and Body Mass Index (BMI) at baseline and nine-months post-intervention. Linear mixed model analyses examined group differences in change over time. Educational differences were examined by ‘group X time X education’ interaction terms. Effects were examined in the total sample and among participants who did not comply with dietary- or BMI guidelines.

Results

The effects on vegetable intake in the total sample differed according to educational level (p = 02). Among low/moderate-educated participants, the basic version was significantly more effective in increasing vegetable intake than the control program (effect size (ES) = 0.32) and plus version (ES = 0.22). No effects were found for high-educated participants. Self-regulation change was significantly larger in the control group than in the basic (ES = 0.18) and plus (ES = 0.16) group.

Conclusions

In general, both intervention versions did not result in long-term intervention effects. The exception was an effect of the basic version on self-reported vegetable intake among low/moderate-educated adults in the total sample. More research is needed on how targeting self-regulation processes and environmental-level factors in Web-based CT nutrition education interventions can increase long-term efficacy.

Trial registration

Netherlands Trial Registry NTR3396.

Electronic supplementary material

The online version of this article (doi:10.1186/s12889-015-1707-4) contains supplementary material, which is available to authorized users.

Short- and medium-term efficacy of a Web-based computer-tailored nutrition education intervention for adults including cognitive and environmental feedback: randomized controlled trial

BACKGROUND

Web-based, computer-tailored nutrition education interventions can be effective in modifying self-reported dietary behaviors. Traditional computer-tailored programs primarily targeted individual cognitions (knowledge, awareness, attitude, self-efficacy). Tailoring on additional variables such as self-regulation processes and environmental-level factors (the home food environment arrangement and perception of availability and prices of healthy food products in supermarkets) may improve efficacy and effect sizes (ES) of Web-based computer-tailored nutrition education interventions.

OBJECTIVE

This study evaluated the short- and medium-term efficacy and educational differences in efficacy of a cognitive and environmental feedback version of a Web-based computer-tailored nutrition education intervention on self-reported fruit, vegetable, high-energy snack, and saturated fat intake compared to generic nutrition information in the total sample and among participants who did not comply with dietary guidelines (the risk groups).

METHODS

A randomized controlled trial was conducted with a basic (tailored intervention targeting individual cognition and self-regulation processes; n=456), plus (basic intervention additionally targeting environmental-level factors; n=459), and control (generic nutrition information; n=434) group. Participants were recruited from the general population and randomly assigned to a study group. Self-reported fruit, vegetable, high-energy snack, and saturated fat intake were assessed at baseline and at 1- (T1) and 4-months (T2) postintervention using online questionnaires. Linear mixed model analyses examined group differences in change over time. Educational differences were examined with group×time×education interaction terms.

RESULTS

In the total sample, the basic (T1: ES=-0.30; T2: ES=-0.18) and plus intervention groups (T1: ES=-0.29; T2: ES=-0.27) had larger decreases in high-energy snack intake than the control group. The basic version resulted in a larger decrease in saturated fat intake than the control intervention (T1: ES=-0.19; T2: ES=-0.17). In the risk groups, the basic version caused larger decreases in fat (T1: ES=-0.28; T2: ES=-0.28) and high-energy snack intake (T1: ES=-0.34; T2: ES=-0.20) than the control intervention. The plus version resulted in a larger increase in fruit (T1: ES=0.25; T2: ES=0.37) and a larger decrease in high-energy snack intake (T1: ES=-0.38; T2: ES=-0.32) than the control intervention. For high-energy snack intake, educational differences were found. Stratified analyses showed that the plus version was most effective for high-educated participants.

CONCLUSIONS

Both intervention versions were more effective in improving some of the self-reported dietary behaviors than generic nutrition information, especially in the risk groups, among both higher- and lower-educated participants. For fruit intake, only the plus version was more effective than providing generic nutrition information. Although feasible, incorporating environmental-level information is time-consuming. Therefore, the basic version may be more feasible for further implementation, although inclusion of feedback on the arrangement of the home food environment and on availability and prices may be considered for fruit and, for high-educated people, for high-energy snack intake.

TRIAL REGISTRATION

Netherlands Trial Registry NTR3396; http://www.trialregister.nl/trialreg/admin/rctview.asp?TC=3396 (Archived by WebCite at http://www.webcitation.org/6VNZbdL6w).