Designing a theory- and evidence-based tailored eHealth rehabilitation aftercare program in Germany and the Netherlands: study protocol

Physical Activity Interventions Framed by the Health Action Process Approach for Adults with Long-Term Conditions: A Scoping Review

BACKGROUND

Interventions that use the Health Action Process Approach (HAPA) model show promise for increasing PA frequency, duration, and intensity. However, there is limited understanding of how HAPA model variables have been operationalized for PA interventions in chronic disease to promote behavior change and sustained PA or whether the phase or continuous form of the HAPA model was used. The aim of this scoping review is to describe how the HAPA model variables for PA interventions were operationalized and provide details of implementation.

METHOD

We searched five databases to identify studies published between January 1992 and March 2024. We aimed to describe (1) the characteristics of interventions including setting, delivery mode, duration, and content; (2) which HAPA variables were operationalized and the strategies used; and (3) the physical activity measures and outcome effects.

RESULTS

The search identified 23 interventions in 30 papers (12 protocols, 3 quasi-experimental studies, and 15 randomized controlled trials (RCTs)). Seven of the 15 RCTs reported significant positive effects of the HAPA model on PA behavior outcomes. Interventions operationalized between three and nine HAPA constructs showed significant variability in how the HAPA model is used in intervention research. PA measures varied from self-report to validated objective instruments.

CONCLUSION

We found a lack of clarity in decisions about which HAPA constructs were included in interventions. The wide variability in operationalized HAPA constructs made it challenging to compare interventions. Researchers should provide more detail about intervention design and implementation procedures to enhance transparency.

A WeChat Mini Program-Based Intervention for Physical Activity, Fruit and Vegetable Consumption Among Chinese Cardiovascular Patients in Home-Based Rehabilitation: A Study Protocol

Background

Cardiac rehabilitation programs aim to avoid further progression and relapse of cardiovascular diseases (CVDs). Patients with CVDs undergoing rehabilitation often experience difficulties in integrating and transferring the learned health behaviors into their daily life after returning home. This includes regular physical activity (PA) and sufficient fruit and vegetable consumption (FVC). eHealth individualized interventions have shown to be effective in increasing PA and FVC in home settings. As WeChat is the most popular social media site in China, this should be used for the intervention. The aim of this study is to develop and examine a WeChat mini program-based intervention on PA and FVC promotion among Chinese cardiovascular patients in home-based rehabilitation.

Methods

The study will adopt a randomized controlled trial (RCT), comprising a WeChat mini program-based intervention group and a waiting-list control group. The intervention content will be designed based on the Health Action Process Approach (HAPA). One hundred fifty-eight outpatients will be recruited from the cardiac rehabilitation center of a hospital in southern China and randomly assigned to one of the two groups. During the 10-week duration of the intervention, participants will be invited to access a WeChat mini program comprising two Modules. Module 1 provides weekly learning sessions addressing PA and FVC simultaneously for 10 weeks. Module 2 provides a platform, where participants can review their progress with Module 1 at any time and attend incentive activities aiming at promoting engagement and retention. The outcome variables include PA (mins/week), FVC (portion/day), healthy lifestyle (the synthesis of PA and FVC), social-cognitive predictors of behavior change (risk perception, outcome expectancies, self-efficacy, intention, planning, social support, and action control) as well as health outcomes (Body mass index, depression, and quality of life). Data collection will be implemented at pre-test, post-test and a post-test after 3-month respectively.

Discussion

The current study will be significant to understand how such a cost-effective social media mini program-based intervention enables participants to adopt and maintain a healthy lifestyle. If it is effective, it will enrich home-based cardiac rehabilitation approaches which can in turn save the lives of patients as well as much monetary, time and other investments.

Trial Registration

The study was registered at ClinicalTrials.gov (Identifier: NCT03636724; Last update posted: July 28, 2020).

An 8-Week Study on Social-Cognitive Variables for Physical Activity and Fruit and Vegetable Intake: Are there Stage Transitions?

BACKGROUND

Health behavior change can be modelled in terms of stages, and outcomes of transitions between stages can be categorized into progression, regression, and stagnation. Based on the Health Action Process Approach this study tested whether changes in social-cognitive variables are associated with transitions between stages regarding physical activity (PA) and fruit and vegetable intake (FVI).

METHODS

N = 132 participants (M = 50.86 years, SD = 13.17, 61.4% women) were assessed at baseline and 8 weeks later. Data were analysed using multivariate analyses of variance (MANOVA) and post-hoc comparisons.

RESULTS

Changes in motivational self-efficacy (η²  = 0.081), maintenance self-efficacy (η²  = 0.119), and recovery self-efficacy (η²  = 0.049) as well as positive outcome expectancies (η²  = 0.070), negative outcome expectancies (η²  = 0.055), and coping planning (η²  = 0.065) were associated with FVI stage progression. For PA, changes were not associated with stage progression.

CONCLUSION

To facilitate behavior change effectively, at least for FVI, it is essential to consider underlying mechanisms such as several aspects of self-efficacy in performing the desired health behaviors, outcome expectations, and planning how to overcome barriers. Additionally, the adoption of a stage approach may be a useful starting point to develop stage-matched interventions.

A web-based lifestyle intervention program for Chinese college students: study protocol and baseline characteristics of a randomized placebo-controlled trial

Background

This study aimed to describe the design and present the baseline characteristics of a web-based lifestyle intervention program, which comprises of sequentially and simultaneously delivered intervention modules targeting physical activity (PA) and fruit and vegetable consumption (FVC) in Chinese college students.

Methods

The study adopted a randomized placebo-controlled trial, using the Health Action Process Approach (HAPA) and the Compensatory Carry-Over Action Model (CCAM) as the theoretical backdrops. 556 Chinese college students participated in the 8-week web-based lifestyle intervention program. All eligible participants were randomly assigned to one of four groups: 1) the PA-first arm which received a 4-week intervention addressing PA followed by a 4-week intervention addressing FVC; 2) the FVC-first arm which received a 4-week intervention addressing FVC followed by a 4-week intervention addressing PA; 3) the PA + FVC simultaneous arm that received an 8-week intervention addressing both PA and FVC at the same time; and 4) the placebo-control arm that received 8 weeks of general health information, which is not relevant for changing actual PA and FVC behaviors. Data collection includes four time-points: at the beginning and end of the intervention, and a 3-month and 12-month follow-up after the intervention.

Results

At baseline, 41.7% of participants were male and 58.3% were female. 41.0% of the participants did not meet the standard PA-recommendations, while 69.6% did not adhere to the standard FVC-recommendations. In total, only 19.6% of participants met both PA and FVC recommendations. Baseline characteristics across the four groups had no significant differences (all P = .17–.99), indicating successful randomization.

Conclusions

The preliminary results indicate a high prevalence of unhealthy lifestyles in college students in China, which further supports the need for web-based health intervention programs. This is also the first study that examines the comparative effectiveness of simultaneously and sequentially delivered lifestyle interventions in the Chinese population. These findings may contribute to the creation of future web-based health behavior change interventions.

Trial registration

ClinicalTrails.gov: NCT03627949, 14 August, 2018.

Electronic supplementary material

The online version of this article (10.1186/s12889-019-7438-1) contains supplementary material, which is available to authorized users.

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

User Models for Personalized Physical Activity Interventions: Scoping Review

Background

Fitness devices have spurred the development of apps that aim to motivate users, through interventions, to increase their physical activity (PA). Personalization in the interventions is essential as the target users are diverse with respect to their activity levels, requirements, preferences, and behavior.

Objective

This review aimed to (1) identify different kinds of personalization in interventions for promoting PA among any type of user group, (2) identify user models used for providing personalization, and (3) identify gaps in the current literature and suggest future research directions.

Methods

A scoping review was undertaken by searching the databases PsycINFO, PubMed, Scopus, and Web of Science. The main inclusion criteria were (1) studies that aimed to promote PA; (2) studies that had personalization, with the intention of promoting PA through technology-based interventions; and (3) studies that described user models for personalization.

Results

The literature search resulted in 49 eligible studies. Of these, 67% (33/49) studies focused solely on increasing PA, whereas the remaining studies had other objectives, such as maintaining healthy lifestyle (8 studies), weight loss management (6 studies), and rehabilitation (2 studies). The reviewed studies provide personalization in 6 categories: goal recommendation, activity recommendation, fitness partner recommendation, educational content, motivational content, and intervention timing. With respect to the mode of generation, interventions were found to be semiautomated or automatic. Of these, the automatic interventions were either knowledge-based or data-driven or both. User models in the studies were constructed with parameters from 5 categories: PA profile, demographics, medical data, behavior change technique (BCT) parameters, and contextual information. Only 27 of the eligible studies evaluated the interventions for improvement in PA, and 16 of these concluded that the interventions to increase PA are more effective when they are personalized.

Conclusions

This review investigates personalization in the form of recommendations or feedback for increasing PA. On the basis of the review and gaps identified, research directions for improving the efficacy of personalized interventions are proposed. First, data-driven prediction techniques can facilitate effective personalization. Second, use of BCTs in automated interventions, and in combination with PA guidelines, are yet to be explored, and preliminary studies in this direction are promising. Third, systems with automated interventions also need to be suitably adapted to serve specific needs of patients with clinical conditions. Fourth, previous user models focus on single metric evaluations of PA instead of a potentially more effective, holistic, and multidimensional view. Fifth, with the widespread adoption of activity monitoring devices and mobile phones, personalized and dynamic user models can be created using available user data, including users’ social profile. Finally, the long-term effects of such interventions as well as the technology medium used for the interventions need to be evaluated rigorously.

The Mediating Role of Perceived Social Support Between Physical Activity Habit Strength and Depressive Symptoms in People Seeking to Decrease Their Cardiovascular Risk: Cross-Sectional Study

BACKGROUND

Regular physical activity treatment has been advocated for the prevention and rehabilitation of patients at risk of cardiovascular diseases and depressive symptoms. How physical activity is related to depressive symptoms is widely discussed.

OBJECTIVE

The aim of this internet-based study was to investigate the role of perceived social support in the relationship between physical activity habit strength and depressive symptoms.

METHODS

In total, 790 participants (mean 50.9 years, SD 12.2, range 20-84 years) who were interested in reducing their cardiovascular risk were recruited in Germany and the Netherlands. Data collection was conducted via an internet-based questionnaire addressing physical activity habit strength, depressive symptoms, and perceived social support. Cross-sectional data analysis was done with SPSS version 24 using the Macro PROCESS version 2 16.3 by Hayes with bootstrapping (10,000 samples), providing 95% CIs.

RESULTS

Physical activity habit strength was negatively related to depressive symptoms (r=-.13, P=.006), but this interrelation disappeared when controlling for perceived social support (beta=-.14, SE 0.09, P=.11). However, there was an indirect relationship between physical activity habit strength and depressive symptoms, which was mediated via perceived social support (beta=-.13; SE 0.04, 95% CI -0.21 to 0.06). The negative relationship between physical activity habit strength and depressive symptoms was fully mediated by perceived social support.

CONCLUSIONS

We suggest that physical activity treatment in people interested in reducing their cardiovascular risk should also embed social support to target depressive symptoms. Internet-based interventions and electronic health may provide a good option for doing so.

TRIAL REGISTRATION

ClinicalTrials.gov NCT01909349; https://clinicaltrials.gov/ct2/show/NCT01909349 (Archived by WebCite at http://www.webcitation.org/73Y9RfdiY).

Persuasive system features in computer-mediated lifestyle modification interventions for physical activity

Objective: Increasing physical activity has been identified as one of the most important factors in lifestyle modification. Previous studies have reported the effectiveness of using the Internet in motivating behavioral modifications of physical activities. The aim of this study is to identify the persuasive system features most frequently used in computer-mediated physical activities in the current literature.Materials and Methods: In this review, intervention studies were identified through a structured computerized search of PubMed, PsychInfo, and Web of Science. The results of the search were analyzed using the persuasive systems design (PSD) features identified by Oinas-Kukkonen and Harjumaa (2009).Results: Thirty-eight articles were reviewed, and the features of the physical activity interventions described were mapped to the identified facets of PSD. The PSD features used most often by researchers in the studies considered in this research included tailoring, tunneling, reminders, trustworthiness, and expertise. The effectiveness of the interventions described in the studies was also compared. The stage of change theory was applied in several intervention studies, and the importance of stage of change has been identified in effectiveness of persuasion toward physical activity.

Latent user groups of an eHealth physical activity behaviour change intervention for people interested in reducing their cardiovascular risk

EHealth behaviour change interventions that help participants to adhere to professional physical activity recommendations can help to prevent future events of cardiovascular diseases (CVD). Therefore, identifying user groups of such interventions based on stages of health behaviour change is of great importance to provide tailored content to users instead of one-size-fits-all approaches. Our study used Latent Class Analysis (LCA) to identify underlying classes of users of an eHealth behaviour change intervention based on stages of change and associated variables. We compared participants' self-allocated stage with their latent class stage membership to display the correlation and mean differences between the two approaches. This was done by analysing baseline data of N = 310 people interested in reducing their CVD risk. LCA identified a three-class solution: (non-)intenders (19.4%), non-habituated actors (43.2%) and habituated actors (37.4%). The interrelation between self-allocated and latent class stage membership was moderate (ρ(308) = .49, p < .001). Significant mean differences for (non-)intenders and non-habituated actors were found in social-cognitive variables. Results showed that self-allocated stage outcomes represent a pseudo stage model - linear trends can be reported for stage-associated social-cognitive variables. The study provides information on the validity of stage measures, which can inform future interventions.

Understanding the Positive Associations of Sleep, Physical Activity, Fruit and Vegetable Intake as Predictors of Quality of Life and Subjective Health Across Age Groups: A Theory Based, Cross-Sectional Web-Based Study

Background: Due to the increase in unhealthy lifestyles and associated health risks, the promotion of healthy lifestyles to improve the prevention of non-communicable diseases is imperative. Thus, research aiming to identify strategies to modify health behaviors has been encouraged. Little is known about addressing multiple health behaviors across age groups (i.e., young, middle-aged, and older adults) and the underlying mechanisms. The theoretical framework of this study is Compensatory Carry-Over Action Model which postulates that different health behaviors (i.e., physical activity and fruit and vegetable intake) are interrelated, and they are driven by underlying mechanisms (more details in the main text). Additionally, restful sleep as one of the main indicators of good sleep quality has been suggested as a mechanism that relates to other health behaviors and well-being, and should therefore also be investigated within this study. The present study aims to identify the interrelations of restful sleep, physical activity, fruit and vegetable intake, and their associations with sleep quality as well as overall quality of life and subjective health in different age groups.

Methods: A web-based cross-sectional study was conducted in Germany and the Netherlands. 790 participants aged 20–85 years filled in the web-based baseline questionnaire about their restful sleep, physical activity, fruit and vegetable intake, sleep quality, quality of life, and subjective health. Descriptive analysis, multivariate analysis of covariance, path analysis, and multi-group analysis were conducted.

Results: Restful sleep, physical activity, and fruit and vegetable intake were associated with increased sleep quality, which in turn was associated with increased overall quality of life and subjective health. The path analysis model fitted the data well, and there were age-group differences regarding multiple health behaviors and sleep quality, quality of life, and subjective health. Compared to young and older adults, middle-aged adults showed poorest sleep quality and overall quality of life and subjective health, which were associated with less engagement in multiple health behaviors.

Conclusion: A better understanding of age-group differences in clustering of health behaviors may set the stage for designing effective customized age-specific interventions to improve health and well-being in general and clinical settings.

Trial Registration: A clinical trial registration was conducted with ClinicalTrials.gov (NCT01909349) https://clinicaltrials.gov/ct2/show/NCT01909349.

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

BACKGROUND

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Web-Based Intervention for Physical Activity and Fruit and Vegetable Intake Among Chinese University Students: A Randomized Controlled Trial

Background

Ample evidence demonstrates that university students are at high risk for sedentary behaviors and inadequate fruit and vegetable intake (FVI). Internet-based interventions for multiple health behavior appear to be promising in changing such unhealthy habits. Limited randomized controlled trials have tested this assumption among Chinese university students.

Objective

Our objective was to test the efficacy of an 8-week Web-based intervention compared with a control group condition to improve physical activity (PA) and FVI in Chinese university students. The intervention content was based on the health action process approach, and developed on the basis of previous evidence from the Western hemisphere. We evaluated self-reported data including PA and FVI, stages of change for PA and FVI, and motivational (risk perception, outcome expectancies, self-efficacy), volitional (action planning, coping planning, social support), and distal (intention, habit) indicators for PA and FVI, as well as perceived mental health outcomes (quality of life, depression).

Methods

In a randomized controlled trial, we recruited 566 university students from one university in the central region of China during their general physical education class. After random allocation and exclusion of unsuitable participants, we assigned 493 students to 1 of 2 groups: (1) intervention group: first 4 weeks on PA and subsequent 4 weeks on FVI, (2) control group. We conducted 3 Web-based assessments: at the beginning of the intervention (T1, n=493), at the end of the 8-week intervention (T2, n=337), and at a 1-month follow-up after the intervention (T3, n=142). The entire study was conducted throughout the fall semester of 2015.

Results

Significant time ⨯ group interactions revealed superior intervention effects on FVI; motivational, volitional, and distal indicators of FVI; and PA behavior changes, with an effect size (η²) ranging from .08 to .20. In addition, the overall intervention effects were significant for stage progression to the action group from T1 to T2 in PA (χ²₁=11.75, P=.001) and FVI (χ²₁=15.64, P=.03). Furthermore, the intervention effect was seen in the improvement of quality of life (F_3,492=1.23, η²=.03, P=.02).

Conclusions

This study provides evidence for the efficacy of a Web-based multiple health behavior intervention among Chinese university students tested with different outcome variables. Future research should address the high dropout rate and optimize the most effective components of this intervention.

Trial Registration

Clinicaltrials.gov NCT01909349; https://clinicaltrials.gov/ct2/show/NCT01909349 (Archived by WebCite at http://www.webcitation.org/6pHV1A0G1)

Using Visual Analogue Scales in eHealth: Non-Response Effects in a Lifestyle Intervention

Background

Visual analogue scales (VASs) have been shown to be valid measurement instruments and a better alternative to Likert-type scales in Internet-based research, both empirically and theoretically [1,2]. Upsides include more differentiated responses, better measurement level, and less error. Their feasibility and properties in the context of eHealth, however, have not been examined so far.

Objective

The present study examined VASs in the context of a lifestyle study conducted online, measuring the impact of VASs on distributional properties and non-response.

Method

A sample of 446 participants with a mean age of 52.4 years (standard deviation (SD) = 12.1) took part in the study. The study was carried out as a randomized controlled trial, aimed at supporting participants over 8 weeks with an additional follow-up measurement. In addition to the randomized questionnaire, participants were further randomly assigned to either a Likert-type or VAS response scale version of the measures.

Results

Results showed that SDs were lower for items answered via VASs, 2P (Y ≥ 47 | n=55, P=.5) < .001. Means did not differ across versions. Participants in the VAS version showed lower dropout rates than participants in the Likert version, odds ratio = 0.75, 90% CI (0.58-0.98), P=.04. Number of missing values did not differ between questionnaire versions.

Conclusions

The VAS is shown to be a valid instrument in the eHealth context, offering advantages over Likert-type scales. The results of the study provide further support for the use of VASs in Internet-based research, extending the scope to senior samples in the health context.

Trial Registration

Clinicaltrials.gov NCT01909349; https://clinicaltrials.gov/ct2/show/NCT01909349 (Archived by WebCite at http://www.webcitation.org/6h88sLw2Y)

Use of the sit-to-stand task to evaluate motor function of older adults using telemetry

Background

Physical exercises are widely used in community programs, but not all older adults are willing to participate. Information and communication technology may solve this problem by allowing older people to participate in fitness programs at home. Use of remote instruction will facilitate physical exercise classes without requiring that participants gather at one place. The aim of this study was to examine use of a sit-to-stand task in evaluating motor function using conventional video communication in a telemetry system to enable real-time monitoring, and evaluation in physical performance of older adults at home.

Methods

The participants were 59 older individuals and 81 university students. Three physical exercise batteries were used: arm curl, figure-of-eight walk test, and functional reach. The knee extension maximum angular velocity (KEMAV) and the iliac elevation maximum velocity (IEMV) during standing up from a chair and the heel rise frequency were used in the motion-capture measurements. The results were assessed using multi-group structural equation modeling (SEM) for the young and older groups.

Results

Young participants consistently performed better than their older counterparts on all items. Analyses with multi-group SEM based on correlations between items yielded a good model-fit for the data. Among all path diagrams for IEMV and KEMAV in the older and young groups, paths from muscular strength to skillfulness showed significant effects. The path from the IEMV to muscular strength was also significant in the older group.

Conclusions

Multi-group SEM suggested that video-based measurements of IEMV during sit-to-stand motion can estimate muscular strength, which suggests that remote monitoring of physical performance can support wellness of community-dwelling older adults.

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.

Effectiveness of a Web-Based Computer-Tailored Multiple-Lifestyle Intervention for People Interested in Reducing their Cardiovascular Risk: A Randomized Controlled Trial

Background

Web-based computer-tailored interventions for multiple health behaviors can improve the strength of behavior habits in people who want to reduce their cardiovascular risk. Nonetheless, few randomized controlled trials have tested this assumption to date.

Objective

The study aim was to test an 8-week Web-based computer-tailored intervention designed to improve habit strength for physical activity and fruit and vegetable consumption among people who want to reduce their cardiovascular risk. In a randomized controlled design, self-reported changes in perceived habit strength, self-efficacy, and planning across different domains of physical activity as well as fruit and vegetable consumption were evaluated.

Methods

This study was a randomized controlled trial involving an intervention group (n=403) and a waiting control group (n=387). Web-based data collection was performed in Germany and the Netherlands during 2013-2015. The intervention content was based on the Health Action Process Approach and involved personalized feedback on lifestyle behaviors, which indicated whether participants complied with behavioral guidelines for physical activity and fruit and vegetable consumption. There were three Web-based assessments: baseline (T0, N=790), a posttest 8 weeks after the baseline (T1, n=206), and a follow-up 3 months after the baseline (T2, n=121). Data analysis was conducted by analyzing variances and structural equation analysis.

Results

Significant group by time interactions revealed superior treatment effects for the intervention group, with substantially higher increases in self-reported habit strength for physical activity (F1,199=7.71, P=.006, Cohen’s d=0.37) and fruit and vegetable consumption (F1,199=7.71, P=.006, Cohen’s d=0.30) at posttest T1 for the intervention group. Mediation analyses yielded behavior-specific sequential mediator effects for T1 planning and T1 self-efficacy between the intervention and habit strength at follow-up T2 (fruit and vegetable consumption: beta=0.12, 95% CI 0.09-0.16, P<.001; physical activity: beta=0.04, 95% CI 0.02-0.06, P<.001).

Conclusions

Our findings indicate the general effectiveness and practicality of Web-based computer-tailored interventions in terms of increasing self-reported habit strength for physical activity and fruit and vegetable consumption. Self-efficacy and planning may play major roles in the mechanisms that facilitate the habit strength of these behaviors; therefore, they should be actively promoted in Web-based interventions. Although the results need to take into account the high dropout rates and medium effect sizes, a large number of people were reached and changes in habit strength were achieved after 3 months.

Trial Registration

Clinicaltrials.gov NCT01909349; https://clinicaltrials.gov/ct2/show/NCT01909349 (Archived by WebCite at http://www.webcitation.org/6g5F0qoft) and Nederlands Trial Register NTR3706 http://www.trialregister.nl/ trialreg/admin/rctview.asp?TC=3706 (Archived by WebCite at http://www.webcitation.org/6g5F5HMLX)

Brief report: Compensatory health beliefs are negatively associated with intentions for regular fruit and vegetable consumption when self-efficacy is low

Compensatory health beliefs (the beliefs that an unhealthy behaviour can be compensated by a healthy behaviour) can interfere with adherence to fruit and vegetable consumption recommendations. Fruit and vegetable consumption, social cognitive variables and compensatory health beliefs were investigated via self-report at baseline (T0) and 8-week follow-up (T1) in N = 790 participants. Self-efficacy predicted fruit and vegetable consumption intentions. Planning mediated between intentions and T1 fruit and vegetable consumption. Compensatory health beliefs negatively predicted intentions at low self-efficacy levels only. The results propose the use of self-efficacy interventions to diminish the negative effects of compensatory health beliefs when forming fruit and vegetable consumption intentions and foster planning to translate intentions into behaviour.

Internet-based interventions for the secondary prevention of coronary heart disease

BACKGROUND

The Internet could provide a means of delivering secondary prevention programmes to people with coronary heart disease (CHD).

OBJECTIVES

To determine the effectiveness of Internet-based interventions targeting lifestyle changes and medicines management for the secondary prevention of CHD.

SEARCH METHODS

We searched the Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, EMBASE, in December 2014. We also searched six other databases in October 2014, and three trials registers in January 2015 together with reference checking and handsearching to identify additional studies.

SELECTION CRITERIA

Randomised controlled trials (RCTs) evaluating Internet-delivered secondary prevention interventions aimed at people with CHD.

DATA COLLECTION AND ANALYSIS

Two review authors independently assessed risk of bias and extracted data according to the Cochrane Handbook for Systematic Reviews of Interventions. We assessed evidence quality using the GRADE approach and presented this in a 'Summary of findings' table.

MAIN RESULTS

Eighteen trials met our inclusion criteria. Eleven studies are complete (1392 participants), and seven are ongoing. Of the completed studies, seven interventions are broad, targeting the lifestyle management of CHD, and four focused on physical activity promotion. The comparison group in trials was usual care (n = 6), minimal intervention (n = 3), or traditional cardiac rehabilitation (n = 2).We found no effects of Internet-based interventions for all-cause mortality (odds ratio (OR) 0.27, 95% confidence interval (CI) 0.04 to 1.63; participants = 895; studies = 6; low-quality evidence). There was only one case of cardiovascular mortality in a control group (participants = 895; studies = 6). No incidences of non-fatal re-infarction were reported across any of the studies. We found no effects for revascularisation (OR 0.69, 95% CI 0.37 to 1.27; participants = 895; studies = 6; low-quality evidence).We found no effects for total cholesterol (mean difference (MD) 0.00, 95% CI -0.27 to 0.28; participants = 439; studies = 4; low-quality evidence), high-density lipoprotein (HDL) cholesterol (MD 0.01, 95% CI -0.06 to 0.07; participants = 437; studies = 4; low-quality evidence), or triglycerides (MD 0.01, 95% CI -0.17 to 0.19; participants = 439; studies = 4; low-quality evidence). We did not pool the data for low-density lipoprotein (LDL) cholesterol due to considerable heterogeneity. Two out of six trials measuring LDL cholesterol detected favourable intervention effects, and four trials reported no effects. Seven studies measured systolic and diastolic blood pressure; we did not pool the data due to substantial heterogeneity. For systolic blood pressure, two studies showed a reduction with the intervention, but the remaining studies showed no effect. For diastolic blood pressure, two studies showed a reduction with the intervention, one study showed an increase with the intervention, and the remaining four studies showed no effect.Five trials measured health-related quality of life (HRQOL). We could draw no conclusions from one study due to incomplete reporting; one trial reported no effect; two studies reported a short- and medium-term effect respectively; and one study reported both short- and medium-term effects.Five trials assessed dietary outcomes: two reported favourable effects, and three reported no effects. Eight studies assessed physical activity: five of these trials reported no physical activity effects, and three reported effectiveness. Trials are yet to measure the impact of these interventions on compliance with medication.Two studies measured healthcare utilisation: one reported no effects, and the other reported increased usage of healthcare services compared to a control group in the intervention group at nine months' follow-up. Two trials collected cost data: both reported that Internet-delivered interventions are likely to be cost-effective.In terms of the risk of bias, the majority of studies reported appropriate randomisation and appropriate concealment of randomisation processes. A lack of blinding resulted in a risk of performance bias in seven studies, and a risk of detection bias in five trials. Two trials were at risk of attrition bias, and five were at risk for reporting bias.

AUTHORS' CONCLUSIONS

In general, evidence was of low quality due to lack of blinding, loss to follow-up, and uncertainty around the effect size. Few studies measured clinical events, and of those that did, a very small number of events were reported, and therefore no firm conclusions can be made. Similarly, there was no clear evidence of effect for cardiovascular risk factors, although again the number of studies reporting these was small. There was some evidence for beneficial effects on HRQOL, dietary outcomes, and physical activity, although firm conclusions cannot yet be made. The effects on healthcare utilisation and cost-effectiveness are also inconclusive, and trials are yet to measure the impact of Internet interventions on compliance with medication. The comparison groups differed across trials, and there were insufficient studies with usable data for subgroup analyses. We intend to study the intensity of comparison groups in future updates of this review when more evidence is available. The completion of the ongoing trials will add to the evidence base.

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

Background

Chronic diseases are the principal cause of morbidity and mortality worldwide. An increased consumption of vegetables and fruit reduces the risk of hypertension, coronary heart disease, stroke, and cancer. An increased fruit and vegetable (FV) intake may also prevent body weight gain, and therefore indirectly affect type 2 diabetes mellitus. Insufficient physical activity (PA) has been identified as the fourth leading risk factor for global mortality. Consequently, effective interventions that promote PA and FV intake in a large number of people are required.

Objective

To describe the systematic development of an eHealth intervention, MyPlan 1.0, for increasing FV intake and PA.

Methods

The intervention was developed following the six steps of the intervention mapping (IM) protocol. Decisions during steps were based upon available literature, focus group interviews, and pilot studies.

Results

Based on needs assessment (Step 1), it was decided to focus on fruit and vegetable intake and physical activity levels of adults. Based on self-regulation and the health action process approach model, motivational (eg, risk awareness) and volitional (eg, action planning) determinants were selected and crossed with performance objectives into a matrix with change objectives (Step 2). Behavioral change strategies (eg, goal setting, problem solving, and implementation intentions) were selected (Step 3). Tablet computers were chosen for delivery of the eHealth program in general practice (Step 4). To facilitate implementation of the intervention in general practice, GPs were involved in focus group interviews (Step 5). Finally, the planning of the evaluation of the intervention (Step 6) is briefly described.

Conclusions

Using the IM protocol ensures that a theory- and evidence-based intervention protocol is developed. If the intervention is found to be effective, a dynamic eHealth program for the promotion of healthy lifestyles could be available for use in general practice.