Comparative efficacy of simultaneous versus sequential multiple health behavior change interventions among adults: A systematic review of randomised trials

Exploring Regular Exercisers' Experiences with Readiness/Recovery Scores Produced by Wearable Devices: A Descriptive Qualitative Study

Meta-session autoregulation, a person-adaptive exercise programming approach, is characterized by individuals' matching exercise demands specifically to their current readiness states. Some consumer wearables provide 'recovery' or 'readiness' scores, computed primarily based on heart rate variability. Despite the growing popularity of consumer wearables and interest in person-adaptive programming, limited research exists on how exercisers interact, interpret and use these scores. This study explores individuals' experiences with wearable devices and their associated readiness or recovery scores. Seventeen regular exercisers who owned and used a Whoop™ band or Oura™ ring for at least 3 months participated in a one-on-one virtual semi-structured interview. Interviews were analyzed using reflexive thematic analysis, with themes supported by 'in-vivo' quotes. This paper focuses on three key themes for a comprehensive demonstration. Theme 1, 'It's more about how I can make adjustments to optimize my programming,' (MPR) highlights users' intended use of wearables for guiding training decisions. Theme 2, 'So many things outside of training modifications have changed,' (Misty) reveals that users also modify non-exercise behaviors to manage and optimize their scores. Theme 3, 'You can't really capture the complexities of a human on a device' (Letty) underscores users' recognition of the limitations and errors associated with these devices emphasizing self-reliance to further direct behavioral adjustments. While wearable devices provide a numeric approach to measuring readiness and recovery, users prioritize self-awareness, flexibility, and personal judgment for exercise decisions. Understanding these experiences, in addition to exploring the psycho-behavioral aspects of user interactions, can contribute to refining meta-session autoregulation.

Effectiveness of Interventions for Changing More Than One Behavior at a Time to Manage Chronic Conditions: A Systematic Review and Meta-analysis

BACKGROUND

Health behaviors play a significant role in chronic disease management. Rather than being independent of one another, health behaviors often co-occur, suggesting that targeting more than one health behavior in an intervention has the potential to be more effective in promoting better health outcomes.

PURPOSE

We aimed to conduct a systematic review and meta-analysis of randomized trials of interventions that target more than one behavior to examine the effectiveness of multiple health behavior change interventions in patients with chronic conditions.

METHODS

Five electronic databases (Web of Science, PubMed, CINAHL, EMBASE, and Cochrane) were systematically searched in November 2023, and studies included in previous reviews were also consulted. We included randomized trials of interventions aiming to change more than one health behavior in individuals with chronic conditions. Two independent reviewers screened and extracted data, and used Cochrane's Risk of Bias 2 tool. Meta-analyses were conducted to estimate the effects of interventions on change in health behaviors. Results were presented as Cohen's d for continuous data, and risk ratio for dichotomous data.

RESULTS

Sixty-one studies were included spanning a range of chronic diseases: cardiovascular (k = 25), type 2 diabetes (k = 15), hypertension (k = 10), cancer (k = 7), one or more chronic conditions (k = 3), and multiple conditions (k = 1). Most interventions aimed to change more than one behavior simultaneously (rather than in sequence) and most targeted three particular behaviors at once: "physical activity, diet and smoking" (k = 20). Meta-analysis of 43 eligible studies showed for continuous data (k = 29) a small to substantial positive effect on behavior change for all health behaviors (d = 0.081-2.003) except for smoking (d = -0.019). For dichotomous data (k = 23) all analyses showed positive effects of targeting more than one behavior on all behaviors (RR = 1.026-2.247).

CONCLUSIONS

Targeting more than one behavior at a time is effective in chronic disease management and more research should be directed into developing the science of multiple behavior change.

Potential role of hybrid weight management intervention: A scoping review

Background

Digital health has been widely used in delivering healthcare, presenting emerging opportunities to overcome barriers to effective obesity care. One strategy suggested for addressing obesity involves a hybrid weight management intervention that incorporates digital health. This scoping review aimed to map existing evidence regarding hybrid weight management intervention.

Methods

PubMed, Scopus, Cochrane Library, and the Web of Science electronic databases were searched for studies published between January 1, 2012 and May 16, 2023, with language restricted to English. The focus was on controlled trials in which a hybrid weight management intervention was used in the intervention among overweight or obese adults. The scoping review framework followed Arksey and O'Malley's guidelines and the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols (PRISM-P).

Results

Full-text article review in the screening stage resulted in a total of 10 articles being included for narrative synthesis. Almost two-third of the articles originated from the United States (60%), followed by Europe and Australia, each accounting for 20%. The most common hybrid weight management intervention type was the combination of face-to-face and telehealth (i.e. phone call/text messaging) (40%), closely followed by a combination email intervention (30%) and mHealth apps intervention (30%). Most of the face-to-face dietary interventions were delivered as a group counseling (80%), while some were conducted as individual counseling (20%). Most studies observed a positive effect of the hybrid weight management intervention on body weight (weight lost 3.9-8.2 kg), body mass index (decreased 0.58 kg/m2), waist circumference (decreased 2.25 cm), and physical activity level compared to standard care. Findings suggest a direct association between hybrid weight management interventions and weight loss. The weight loss ranged from 3.9 to 8.2 kg, with some evidence indicating a significant weight loss of 5% from baseline. There is a need to explore stakeholders' telehealth perspective to optimize the delivery of hybrid weight management interventions, thereby maximizing greatest benefits for weight management.

Clustering of smoking, alcohol consumption and weight gain in pregnancy: prevalence, care preferences and associated factors

BACKGROUND

Smoking, alcohol consumption and weight gain outside recommendations during pregnancy are preventable health risk factors associated with poorer health outcomes for mother and infant. Clustering of these risk factors further increases the risk and severity of outcomes. Limited research has explored the characteristics of pregnant women that are associated with clustering of these risks and women's preferences for receiving support. This paper aimed to determine: (i) the prevalence of clustered preventable risk factors; (ii) associations between maternal characteristics and presence of clustered risk factors; and (iii) women's preferences for receiving care for clustered risk factors.

METHODS

A cross-sectional survey was undertaken with women who had recently given birth in public maternity services in New South Wales, Australia. Descriptive statistics were used to assess prevalence of clustered risk factors and care preferences. Associations between the presence of clustered risk factors and maternal characteristics were assessed using multiple regression analyses.

RESULTS

Of the 514 women who completed the survey, 52% reported one preventable health risk factor and 10% and 2% reported two or three. For women with two or more risk factors, the most common combination was alcohol consumption and gestational weight gain outside of recommendations (50%, n = 30). One characteristic had an association with the presence of clustered risk factors. Most women (77%, n = 46) with clustered risk factors indicated they wanted support for these health risks. Preferences for support addressing some or all risk factors, and whether the support was sequential or simultaneous, were not associated with particular risk factor combinations.

CONCLUSIONS

Around one in eight women reported clustered preventable risk factors during pregnancy, most of whom would like support to address these risks. There was only one association between maternal characteristics and clustered risk factors. This suggests a need for antenatal care that is women-centred and caters for a diverse profile of clustered risks and varied preferences for care.

Moderators of a Diet and Physical Activity Intervention: who Responds Best to Sequential vs. Simultaneous Approaches

BACKGROUND

Given that low physical activity levels and poor dietary intake are co-occurring risk factors for chronic disease, there is a need for interventions that target both health behaviors, either sequentially or simultaneously. Little is known about participant characteristics that are associated with better or worse response to sequential and simultaneous interventions.

METHOD

The 12-month Counseling Advice for Lifestyle Management (CALM) randomized trial (N = 150; Mage = 55.3 years) targeted these two behaviors either via a sequential approach - dietary advice first then exercise advice added ("Diet-First") or exercise advice first then dietary advice added ("Exercise-First") - or via a simultaneous approach. The objective was to examine demographic, clinical, and psychosocial moderators of intervention effects on 12-month change in (1) moderate-to-vigorous physical activity (MVPA), (2) fruit/vegetable intake, (3) caloric intake from saturated fat, and (4) weight. Hierarchical regressions first compared Diet-First to Exercise-First, followed by comparisons of these arms combined ("sequential") to the simultaneous arm.

RESULTS

Older age, higher baseline BMI, and lower social support were associated with higher MVPA in Exercise-First vs. Diet-First, while lower tangible support was associated with higher fruit/vegetable intake in Exercise-First but not in Diet-First. Poor sleep was associated with higher levels of MVPA in the sequential arm than in the simultaneous arm. Lower vitality was associated with greater weight loss in the sequential arm than in the simultaneous arm, while the opposite was true for those who were not married.

CONCLUSION

Identifying moderators of treatment response can allow the behavioral medicine field to enhance intervention efficacy by matching participant subgroups to their best-fitting interventions.

TRIAL REGISTRATION

NCT00131105.

The prevalence and clustering of alcohol consumption, gambling, smoking, and excess weight in an English adult population

BACKGROUND

The aim of this study was to examine the prevalence and clustering of four health risks (increasing-/higher-risk drinking, current smoking, overweight/obesity, and at-risk gambling), and to examine variation across sociodemographic groups in the English adult population.

METHODS

We analysed data from the 2012, 2015, 2016, and 2018 Health Survey for England (n = 20,698). Prevalence odds ratios (POR) were calculated to examine the clustering of risks. We undertook a multinomial multilevel regression model to examine sociodemographic variation in the clustering of health risks.

RESULTS

Overall, 23.8% of the adult English population had two or more co-occurring health risks. The most prevalent was increasing-/higher-risk drinking and overweight/obesity (17.2%). Alcohol consumption and smoking were strongly clustered, particularly higher-risk drinking and smoking (POR = 2.68; 95% CI = 2.31, 3.11; prevalence = 1.7%). Higher-risk drinking and at-risk gambling were also clustered (POR = 2.66; 95% CI = 1.76, 4.01), albeit with a very low prevalence (0.2%). Prevalence of multiple risks was higher among men for all risk combinations except smoking and obesity. The odds of multiple risks were highest for men and women aged 35-64 years. Unemployed men and women with lower educational qualifications had a higher odds of multiple risks. The relationship between deprivation and multiple risks depended on the definition of multiple risks, with the clearest socioeconomic gradients seen for the highest risk health behaviours.

CONCLUSION

An understanding of the prevalence, clustering, and risk factors for multiple health risks can help inform effective prevention and treatment approaches and may support the design and use of multiple behaviour change interventions.

Smoking Behavior and Wellness among Individuals in Substance Use Disorder Treatment

Tobacco-related morbidity and mortality disproportionately affect people with substance use disorders (SUD). Encouraging overall wellness may support tobacco use cessation. We investigated relationships between wellness (health status, physical activity, sugar-sweetened beverage (SSB) consumption), cigarette smoking, and smoking cessation among SUD treatment patients to inform clinical care. Cross-sectional surveys were conducted with 395 patients in 20 California residential SUD programs. Using multivariate regression, we examined associations between smoking status and wellness. Among smokers, we examined associations between lifetime smoking exposure, cessation behaviors and attitudes, and wellness. Compared to nonsmokers (n = 121), smokers (n = 274) reported more SSB consumption, poorer physical health, and more respiratory symptoms. Among smokers, SSB consumption and respiratory symptoms increased per ten pack-years of smoking. Smokers with respiratory symptoms reported higher motivation to quit and more use of nicotine replacement therapy (NRT). Smokers with more days of poor mental health reported lower motivation to quit. Overall, cigarette smoking was associated with other health-risk behaviors among SUD treatment patients. Respiratory symptoms may increase, and poor mental health may decrease, SUD patients' intent to quit smoking. To reduce chronic disease risk among SUD patients, treatment programs should consider promoting overall wellness concurrently with smoking cessation.

Patient motivation and preferences in changing co-occurring health risk behaviors in general hospital patients

OBJECTIVES

More than 60% of general hospital patients report ≥ 2 health risk behaviors (HRBs), i.e. tobacco smoking, at-risk alcohol use, unhealthy diet, and/or insufficient physical activity. This study investigates a) the association between numbers of HRBs and motivation to change, b) patient preferences for receiving feedback on HRBs, and c) patients' expected gain in quality of life if behavior change made.

METHODS

In 2020/2021, 256 18-64-year-old general hospital patients (72.1% of eligibles) reported on their motivation to change each of their HRBs. Associations between HRB number and motivation were assessed using multivariate linear regressions. Participants ranked HRBs concerning their interest in receiving feedback and concerning their expected gain in quality of life if behavior change occurred.

RESULTS

Higher HRB number was negatively related to motivation among at-risk alcohol users (p = 0.034); 24.6% expected gain in their quality of life from behavior change. Participants overall appeared more favorable to feedback about vegetable/fruit intake and physical activity.

CONCLUSIONS

Unhealthier lifestyle may be accompanied by decreased motivation to change in at-risk alcohol users. In case of co-occurring HRBs, asking patients for expected gain in quality of life may help guiding intervention target.

PRACTICE IMPLICATIONS

Relying on patient selection only, may often leave substance-use unaddressed.

Health4Life eHealth intervention to modify multiple lifestyle risk behaviours among adolescent students in Australia: a cluster-randomised controlled trial

BACKGROUND

Lifestyle risk behaviours are prevalent among adolescents and commonly co-occur, but current intervention approaches tend to focus on single risk behaviours. This study aimed to evaluate the efficacy of the eHealth intervention Health4Life in modifying six key lifestyle risk behaviours (ie, alcohol use, tobacco smoking, recreational screen time, physical inactivity, poor diet, and poor sleep, known as the Big 6) among adolescents.

METHODS

We conducted a cluster-randomised controlled trial in secondary schools that had a minimum of 30 year 7 students, in three Australian states. A biostatistician randomly allocated schools (1:1) to Health4Life (a six-module, web-based programme and accompanying smartphone app) or an active control group (usual health education) with the Blockrand function in R, stratified by site and school gender composition. All students aged 11-13 years who were fluent in English and attended participating schools were eligible. Teachers, students, and researchers were not masked to allocation. Primary outcomes were alcohol use, tobacco use, recreational screen time, moderate to vigorous physical activity (MVPA), sugar-sweetened beverage intake, and sleep duration at 24 months, measured by self-report surveys, and analysed in all students who were eligible at baseline. Latent growth models estimated between-group change over time. This trial is registered with the Australian New Zealand Clinical Trials Registry (ACTRN12619000431123).

FINDINGS

Between April 1, 2019, and Sept 27, 2019, we recruited 85 schools (9280 students), of which 71 schools with 6640 eligible students (36 schools [3610 students] assigned to the intervention and 35 [3030 students] to the control) completed the baseline survey. 14 schools were excluded from the final analysis or withdrew, mostly due to a lack of time. We found no between-group differences for alcohol use (odds ratio 1·24, 95% CI 0·58-2·64), smoking (1·68, 0·76-3·72), screen time (0·79, 0·59-1·06), MVPA (0·82, 0·62-1·09), sugar-sweetened beverage intake (1·02, 0·82-1·26), or sleep (0·91, 0·72-1·14) at 24 months. No adverse events were reported during this trial.

INTERPRETATION

Health4Life was not effective in modifying risk behaviours. Our results provide new knowledge about eHealth multiple health behaviour change interventions. However, further research is needed to improve efficacy.

FUNDING

Paul Ramsay Foundation, the Australian National Health and Medical Research Council, the Australian Government Department of Health and Aged Care, and the US National Institutes of Health.

Diet, Physical Activity, and Stress Among Wheelchair Users With Multiple Sclerosis: Examining Individual and Co-Occurring Behavioral Risk Factors

OBJECTIVE

This study examined individual and co-occurring behavioral risk factors (diet, exercise, and stress) in wheelchair users with multiple sclerosis (MS) and potential association with MS symptoms (ie, fatigue, depression, anxiety, pain, sleep, and health-related quality of life [HRQOL]).

DESIGN

Survey.

SETTING

General Community.

PARTICIPANTS

One hundred twenty-three wheelchair users with MS completed this study (N=123).

INTERVENTIONS

Not applicable.

MAIN OUTCOME MEASURES

Participants were mailed instructions for accessing online questionnaires (demographic and clinical characteristics, Godin Leisure-Time Exercise Questionnaire, Perceived Stress Scale, Automated Self-Administered 24-Hour Dietary Assessment Tool, and MS symptoms).

RESULTS

Standard cut-points were used to categorize behavioral risk factors and then identify the extent and distribution of these behaviors both individually and co-occurring. We then analyzed the associations between behavioral risk factors and MS symptoms using bivariate correlation analyses and Mann-Whitney U tests. The mean age of participants was 60.6±10.0 years, 76% identified as women, 82% had a progressive disease course, and the mean MS duration was 23.0±9.7 years. Seven participants were classified as having 0 negative health behaviors, 41 participants had 1 negative health behavior, 49 participants had 2 negative health behaviors, and 26 participants had 3 negative health behaviors. The number of negative health behaviors was significantly correlated with HRQOL (physical, r=.30; psychological, r=.47), sleep (r=.25), depressive symptoms (r=.36), and anxiety (r=.43). Mann-Whitney U tests indicated greater fatigue, depression, and anxiety as well as lower sleep quality and HRQOL among participants who reported 2 or 3 behavioral risk factors compared with 0 or 1 behavioral risk factor.

CONCLUSIONS

Future research should examine the design and implementation of multiple health behavior change interventions targeting co-occurring behavioral risk factors among wheelchair users with MS.

The Role of Health Education, Policies, and Services on College Students' Health Behavior

Objective: In this study, we aimed to define the role of health education, health policies, and health service as factors in the health behavior of Chinese college students. It is critical to understand that there is an important role of health behavior in the life of college students in any society because the survival of society is dependent on better health status. Methods: We used SPSS software for analysis to test our hypotheses.Results: We found an important role for health education, health policies, and health services as determinants of health behavior. Conclusion: The focus of healthcare organizations must be to focus on health education, health policies, and health services by creating awareness in college students that ensures they are provided with the appropriate health sector solutions. Additionally, we found theoretical and practical implications to improve the health behavior of Chinese college students via a moderating role of information technology, because, in modern times, the role of information technology is critical to convey information productively.

Patterns of Risky Health Behaviors and Associations With Chronic Diseases Among Young Adult Nursing Students: A Latent Class Analysis

BACKGROUND

Little is known about how health behaviors cluster to form meaningful patterns that influence health outcomes in young adult nursing students.

PURPOSE

The purpose of this study was to identify the unique health behavior patterns among young adult nursing students in China and examine the associations between health behaviors and chronic diseases.

METHODS

Using an electronic app, the achievements of an exercise target, sedentary behavior, smoking and drinking, and dietary patterns were assessed in 1,480 nursing student participants aged 18-24 years from two medical universities in Eastern China.

RESULTS

A four-class model was developed using latent class analysis that included the "failure to achieve exercise target, alcohol-drinking, and insufficient fruit and vegetable group" (Group 1, n = 187, 12.6%), the "alcohol-drinking and sedentary behavior group" (Group 2, n = 290, 19.6%), the "sedentary behavior only group" (Group 3, n = 721, 48.7%), and the "failure to achieve exercise target only group" (Group 4, n = 282, 19.1%). Logistic regressions indicated that nursing students in Group 2 (odds ratio [ OR ] = 0.42), Group 3 ( OR = 0.51), and Group 4 ( OR = 0.30) were less likely to have chronic diseases than those in Group 1 after adjusting for sociodemographic variables.

CONCLUSIONS

The health behaviors were clustered in different patterns among young adult nursing students. Tailoring interventions to specific groups is suggested to improve health outcomes.

Blended Care Interventions to Promote Physical Activity: A Systematic Review of Randomized Controlled Trials

BACKGROUND

Blended care interventions combine therapeutic guidance with digital care. Current research results show the promising role of the blended care approach in clinical care. This new way of delivering health care could have the potential to effectively promote physical activity in different public health settings.

OBJECTIVE

The aim of the systematic review is to investigate the varieties of intervention characteristics of blended care interventions to promote physical activity in terms of structure, behavior change goals, behavior change techniques, and effectiveness of blended care interventions compared to a control group.

METHODS

We searched for randomized controlled trials published from 2000 to March 2021 in MEDLINE, CINAHL, Cochrane Central Register of Controlled Trials, SPORTDiscus, PsycINFO, and Web of Science according to the PRISMA guidelines. Risk of bias was assessed using the Cochrane Collaboration tool. Study characteristics, intervention characteristics, and outcome data were extracted. Furthermore, the effect size on the outcome of physical activity was examined or calculated.

RESULTS

In total, the number of reports identified from the database searches was 4828. Of these, 25 studies were included in the review, with a total of 5923 study participants. Results indicated that the characteristics of blended care interventions showed a high heterogeneity. The combinations of therapist-guided interventions and digital interventions allowed the identification of specific subgroups, but they varied in length (range 8-52 weeks, SD 16.6), intensity, and the combination of the components. The most used combination of blended care interventions to promote physical activity was the combination of one-on-one meetings via telephone and Web-based interventions. Motivational models of behavior change were used most frequently as underlying theoretical foundations. Certain behavior change techniques were used consistently across the individual components, e.g., "problem solving" in the therapist-guided component and "feedback on behavior" in the digital component. Considering the effect size of blended care interventions compared with control groups, most studies showed a small effect.

CONCLUSIONS

It can be concluded that blended care interventions have potential to promote physical activity. In the future, further high-quality studies should investigate which type of blended care intervention is effective for which target group. Additionally, insights are required on which intervention characteristics are most effective, taking into account new evidence on behavior change. Registration This systematic literature review was registered in PROSPERO ( CRD42020188556 ).

Residential Characteristics as Factors Related to Healthy Behavior Practices-Decision Tree Model Analysis Using a Community Health Survey from Korea

In this study, we sought to identify relevant factors in healthy behavior practices, including not only individual-level variables but also regional and physical environments. Data from the Korea Community Health Survey (KCHS) of Gyeongsangnam-do in 2018 were used, with data from 16,519 of the 17,947 individuals (excluding 1428 individuals who had missing values) who participated in the survey. Healthy behavior practices were defined as meeting the criteria for all three modifiable healthy behaviors (non-smoking, moderate alcohol consumption, regular walking). A decision tree analysis was performed. In men, healthy behavior practices were lower in the unemployed population, in those aged 40−50 years, living in rural residential areas, and with stress. For women who lived in areas with small populations (<100,000 population), healthy behavior practices were below-average. Men and women who had below-average healthy behavior practices reported poor access to places for exercise and fair or poor self-rated health statuses. It is necessary to implement a health behavior practice intervention that considers not only individual characteristics but also access to local exercise facilities and residential area characteristics (urban, rural). Since age is an important variable in healthy behaviors for both men and women, customized programs that consider age should be provided.

Adapting MultiPLe behavior Interventions that eFfectively Improve (AMPLIFI) cancer survivor health: program project protocols for remote lifestyle intervention and assessment in 3 inter-related randomized controlled trials among survivors of obesity-related cancers

BACKGROUND

Scalable, multiple behavior change interventions are needed to address poor diet, inactivity, and excess adiposity among the rising number of cancer survivors. Efficacy-tested diet (RENEW) and exercise (BEAT Cancer) programs were adapted for web delivery among middle-aged and older cancer survivors for the AMPLIFI study, a National Cancer Institute-funded, multi-site, program project.

METHODS

Throughout the continental U.S., survivors of several obesity-related cancers are being recruited for three interconnected randomized controlled trials (RCTs). Projects 1 and 2 test 6-month diet or exercise interventions versus a wait-list control condition. Upon completion of the 6-month study period, the intervention participants receive the next behavior change sequence (i.e., diet receives exercise, exercise receives diet) and the wait-list control arm initiates a 12-month combined diet and exercise intervention. Project 3 tests the efficacy of the sequential versus simultaneous interventions. Assessments occur at baseline and semi-annually for up to 2-years and include: body mass index, health behaviors (diet quality, accelerometry-assessed physical activity/sleep), waist circumference, D3 creatine-assessed muscle mass, physical performance, potential mediators/moderators of treatment efficacy, biomarkers of inflammation and metabolic regulation, health care utilization, cost, and overall health. Four shared resources support AMPLIFI RCTs: 1) Administrative; 2) Adaptation, Dissemination and Implementation; 3) Recruitment and Retention; and 4) Assessment and Analysis.

DISCUSSION

Representing a new generation of RCTs, AMPLIFI will exclusively use remote technologies to recruit, intervene and assess the efficacy of the newly-adapted, web-based diet and exercise interventions and determine whether sequential or combined delivery works best for at-risk (older, rural, racial minority) cancer survivors.

TRIAL REGISTRATION

ClinicalTrials.gov , NCT04000880 . Registered 27 June 2019.

The Implementation of Behavior Change Techniques in mHealth Apps for Sleep: Systematic Review

Background

Mobile health (mHealth) apps targeting health behaviors using behavior change techniques (BCTs) have been successful in promoting healthy behaviors; however, their efficacy with sleep is unclear. Some work has shown success in promoting sleep through mHealth, whereas there have been reports that sleep apps can be adverse and lead to unhealthy obsessions with achieving perfect sleep.

Objective

This study aims to report and describe the use of BCTs in mHealth apps for sleep with the following research questions: How many BCTs are used on average in sleep apps, and does this relate to their effectiveness on sleep outcomes? Are there specific BCTs used more or less often in sleep apps, and does this relate to their effectiveness on sleep outcomes? Does the effect of mHealth app interventions on sleep change when distinguishing between dimension and measurement of sleep?

Methods

We conducted a systematic review following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines to review articles on mHealth app interventions for sleep published between 2010 and 2020.

Results

A total of 12 studies met the eligibility criteria. Most studies reported positive sleep outcomes, and there were no negative effects reported. Sleep quality was the most common dimension of sleep targeted. Subjective measures of sleep were used across all apps, whereas objective measures were often assessed but rarely reported as part of results. The average number of BCTs used was 7.67 (SD 2.32; range 3-11) of 16. Of the 12 studies, the most commonly used BCTs were feedback and monitoring (n=11, 92%), shaping knowledge (n=11, 92%), goals and planning (n=10, 83%), and antecedents (n=10, 83%), whereas the least common were scheduled consequences (n=0, 0%), self-belief (n=0, 0%), and covert learning (n=0, 0%). Most apps used a similar set of BCTs that unfortunately did not allow us to distinguish which BCTs were present when studies reported more positive outcomes.

Conclusions

Our study describes the peer-reviewed literature on sleep apps and provides a foundation for further examination and optimization of BCTs used in mHealth apps for sleep. We found strong evidence that mHealth apps are effective in improving sleep, and the potential reasons for the lack of adverse sleep outcome reporting are discussed. We found evidence that the type of BCTs used in mHealth apps for sleep differed from other health outcomes, although more research is needed to understand how BCTs can be implemented effectively to improve sleep using mHealth and the mechanisms of action through which they are effective (eg, self-efficacy, social norms, and attitudes).

Clustering of unhealthy lifestyle behaviors, self-rated health and disability

The main objective was to identify sociodemographic characteristics of the population at risk for a greater clustering of unhealthy behaviors and to evaluate the association of such clustering with self-rated health status and disability. Data come from the 2017 Spanish National Health Survey with a sample of 21,947 participants of 15 years of age or older. Based on tobacco consumption, risk drinking, unbalanced diet, sedentarism, and body mass index <18.5/≥25 we created two indicators of risk factor clustering: 1) Number of unhealthy behaviors (0-5); and 2) Unhealthy lifestyle index (score: 0-15). Self-rated health was dichotomized into "optimal" and "suboptimal," and disability was classified as "no disability," "mild," and "severe" based on the Global Activity Limitation Index (GALI). We estimated prevalence ratios (PR) adjusted for covariates using generalized linear models using the clustering count variable, and dose-response curves using the unhealthy lifestyle index. Most participants (77.4%) reported 2 or more risk factors, with men, middle-age individuals, and those with low socioeconomic status being more likely to do so. Compared to those with 0-1 risk factors, the PR for suboptimal health was 1.26 (95% CI:1.18-1.34) for those reporting 2-3 factors, reaching 1.43 (95% CI:1.31-1.55) for 4-5 factors. The PR for severe activity limitation was 1.66 (95% CI:1.35-2.03) for those reporting 2-3 factors and 2.06 (95% CI:1.59-2.67) for 4-5 factors. The prevalence of both health indicators increased in a non-linear fashion as the unhealthy lifestyle index score increased, increasing rapidly up to 5 points, slowing down between 5 and 10 points, and plateauing afterwards.

The Effectiveness of Sequentially Delivered Web-Based Interventions on Promoting Physical Activity and Fruit-Vegetable Consumption Among Chinese College Students: Mixed Methods Study

Background

Web-based interventions for multiple health behavior change (MHBC) appear to be a promising approach to change unhealthy habits. Limited research has tested this assumption in promoting physical activity (PA) and fruit-vegetable consumption (FVC) among Chinese college students. Moreover, the timing of MHBC intervention delivery and the order of components need to be addressed.

Objective

This study aims to examine the effectiveness of 2 sequentially delivered 8-week web-based interventions on physical activity, FVC, and health-related outcomes (BMI, depression, and quality of life) and the differences in the intervention effects between the 2 sequential delivery patterns. The study also aims to explore participants’ experiences of participating in the health program.

Methods

We conducted a randomized controlled trial, in which 552 eligible college students (mean 19.99, SD 1.04 years, 322/552, 58.3% female) were randomly assigned to 1 of 3 groups: PA-first group (4 weeks of PA followed by 4 weeks of FVC intervention), FVC-first group (4 weeks of FVC followed by 4 weeks of PA intervention), and a control group (8 weeks of placebo treatment unrelated to PA and FVC). The treatment content of two intervention groups was designed based on the Health Action Process Approach (HAPA) framework. A total of four web-based assessments were conducted: at baseline (T1, n=565), after 4 weeks (T2, after the first behavior intervention, n=486), after 8 weeks (T3, after the second behavior intervention, n=420), and after 12 weeks (T4, 1-month postintervention follow-up, n=348). In addition, after the completion of the entire 8-week intervention, 18 participants (mean 19.56, SD 1.04 years, 10/18, 56% female) who completed the whole program were immediately invited to attend one-to-one and face-to-face semistructured interviews. The entire study was conducted during the fall semester of 2017.

Results

The quantitative data supported superior effects on physical activity, FVC, and BMI in the 2 sequential intervention groups compared with the control group. There were no significant differences in physical activity, FVC, and health-related outcomes between the 2 intervention groups after 8 weeks. The FVC-first group contributed to more maintenance of FVC compared with the PA-first group after 12 weeks. Four major themes with several subthemes were identified in the qualitative thematic analysis: PA and FVC behavior, health-related outcomes, correlates of behavior change, and contamination detection.

Conclusions

This study provides empirical evidence for the effectiveness of sequentially delivered, web-based MHBC interventions on PA and FVC among Chinese college students. The timing issue of MHBC intervention delivery was preliminarily addressed. Qualitative findings provide an in-depth understanding and supplement the quantitative findings. Overall, this study may contribute considerably to future web-based MHBC interventions.

Trial Registration

ClinicalTrials.gov NCT03627949; https://clinicaltrials.gov/ct2/show/NCT03627949

International Registered Report Identifier (IRRID)

RR2-10.1186/s12889-019-7438-1

Tobacco cessation among smokers under substance use treatment for alcohol and/or cannabis: study protocol and pilot study

BACKGROUND

Approximately 80% of people with a substance use disorder (SUD) are smokers. Starting SUD treatment offers the opportunity to also quit smoking. The ACT-ATAC project aims to identify the predictors associated with smoking cessation among persons treated for alcohol and/or cannabis use disorder in Barcelona. This manuscript reports its methodology and the experience of carrying it out during the COVID-19 pandemic.

METHODS

Mixed methods project with three substudies. Substudy 1 (S1) comprises heterogeneous discussion groups among clinicians. S2 has two prospective cohorts composed of smokers under treatment for alcohol and/or cannabis use disorder and the clinicians in charge of these patients. Participating smokers will be followed for 12 months and interviewed about their substance use and the tobacco cessation services received using the Spanish version of the users' Knowledge, Attitudes, and Services (S-KAS) scale. The clinicians will be asked about their self-reported practices in smoking cessation using the Knowledge, Attitudes, and Practices (S-KAP) scale. S3 comprises heterogeneous discussion groups with smokers. Data will be triangulated using qualitative and quantitative analyses. To facilitate the recruitment process, the researchers have introduced several strategies (design clear protocols, set monthly online meetings, extend the project, provide gift cards, etc.).

DISCUSSION

The results of S1 were used to develop the questionnaires. S2 required some adjustments due to the COVID-19 pandemic, particularly the follow-up interviews being conducted by phone instead of face-to-face, and the recruitment rhythm was lower than expected. Recruitment will last until reaching at least 200-250 users. The fieldwork could not have been possible without the collaboration of the ACT-ATAC team and the introduction of several strategies. Trial registration The ACT-ATAC project has been successfully registered at Clinicaltrials.gov [NCT04841655].

The Long-Term Effectiveness of Internet-Based Interventions on Multiple Health Risk Behaviors: Systematic Review and Robust Variance Estimation Meta-analysis

BACKGROUND

Smoking tobacco, poor nutrition, risky alcohol use, and physical inactivity (SNAP) behaviors tend to cluster together. Health benefits may be maximized if interventions targeted multiple health risk behaviors together rather than addressing single behaviors. The internet has wide reach and is a sustainable mode for delivery of interventions for multiple health behaviors. However, no systematic reviews have examined the long-term effectiveness of internet-based interventions on any combination of or all SNAP behaviors in adults aged 18 years or older.

OBJECTIVE

This systematic review examined, among adults (aged ≥18 years), the effectiveness of internet-based interventions on SNAP behaviors collectively in the long term compared with a control condition.

METHODS

The electronic databases Medline, PsycINFO, Embase, CINAHL, and Scopus were searched to retrieve studies describing the effectiveness of internet-based interventions on ≥2 SNAP behaviors published by November 18, 2019. The reference lists of retrieved articles were also checked to identify eligible publications. The inclusion criteria were randomized controlled trials or cluster randomized controlled trials with adults examining an internet-based intervention measuring the effect on ≥2 SNAP behaviors at least 6 months postrecruitment and published in English in a peer-reviewed journal. Two reviewers independently extracted data from included studies and assessed methodological quality using the Quality Assessment Tool for Quantitative Studies. A robust variance estimation meta-analysis was performed to examine the long-term effectiveness of internet-based interventions on all 4 SNAP risk behavior outcomes. All SNAP outcomes were coded so they were in the same direction, with higher scores equating to worse health risk behaviors.

RESULTS

The inclusion criteria were met by 11 studies: 7 studies measured the effect of an internet-based intervention on nutrition and physical activity; 1 study measured the effect on smoking, nutrition, and physical activity; and 3 studies measured the effect on all SNAP behaviors. Compared with the control group, internet-based interventions achieved an overall significant improvement across all SNAP behaviors in the long term (standardized mean difference -0.12 [improvement as higher scores = worse health risk outcomes], 95% CI -0.19 to -0.05; I2=1.5%, P=.01). The global methodological quality rating was "moderate" for 1 study, while the remaining 10 studies were rated as "weak."

CONCLUSIONS

Internet-based interventions were found to produce an overall significant improvement across all SNAP behaviors collectively in the long term. Internet-based interventions targeting multiple SNAP behaviors have the potential to maximize long-term improvements to preventive health outcomes.

Identifying active ingredients, working mechanisms and fidelity characteristics reported in smoking cessation interventions in Dutch primary care: a systematic review

Background

Evidence-based smoking cessation interventions provided by healthcare professionals can be successful in helping citizens to quit smoking. Yet, evidence is needed about the active ingredients of these interventions, how these ingredients work and how they are implemented in practice. Such knowledge is required to effectively support healthcare professionals to optimally put evidence-based smoking cessation interventions to (inter)national practice.

Objective

To identify active ingredients (including behavior change techniques), mechanisms of action and implementation fidelity reported in smoking cessation interventions in Dutch primary care settings and to relate these to intervention effectiveness.

Methods

A systematic review was conducted by searching nine national intervention or funding databases, five international scientific databases and consulting 17 national smoking cessation experts. Out of 1066 identified manuscripts, 40 interventions were eligible for this review. Based on published protocols, information regarding behavior change techniques and mechanisms of action was systematically abstracted. Additionally, information regarding study characteristics and other active ingredients, effects on smoking behavior and implementation fidelity was abstracted. Comparative effectiveness concerning abstracted intervention characteristics was qualitatively explored.

Results

Active ingredients, mechanisms of action and implementation fidelity were moderately to poorly reported. Interventions applying behavior change techniques and interventions with a single behavioral target (i.e. smoking-only versus multiple behaviors) seemed to provide stronger evidence for successfully changing smoking behavior.

Conclusion

Attention to and reporting on interventions’ active ingredients (e.g. behavior change techniques), mechanisms of action and implementation fidelity are prerequisites for developing more effective evidence-based smoking cessation interventions to be successfully implemented in primary healthcare.

Implications

This systematic review provides an overview of smoking cessation interventions in Dutch primary care settings, identified since the year 2000. Smoking cessation support is offered in various forms, but our qualitative findings show that interventions including more behavior change techniques and interventions targeting only smoking cessation (compared to multiple behaviors) might be more effective. Results also show that—based on available intervention reports—it is difficult to distinguish patterns of active ingredients (such as behavior change techniques), mechanisms of action and fidelity of implementation in relation to interventions’ effectiveness. This means (quality of) reporting on these intervention characteristics should improve.

Dose and engagement during an extended contact physical activity and dietary behavior change intervention delivered via tailored text messaging: exploring relationships with behavioral outcomes

Background

Extended contact interventions delivered via text messaging are a low-cost option for promoting the long-term continuation of behavior change. This secondary analysis of a text message–delivered extended contact intervention (‘Get Healthy, Stay Healthy’ (GHSH)) explores the extent to which changes in physical activity, dietary behaviors and body weight were associated with the frequency of text messages (dose) and contact between the health coach and participant (engagement).

Methods

Following a telephone coaching program, participants were randomised to receive extended contact via tailored text messages (GHSH, n = 114) or no additional contact (n = 114) over a 6-month period. Message dose, timing, and content were based on participant preferences, ascertained during two tailoring telephone calls. All incoming and outgoing messages were recorded. At baseline and 6 months, participants self-reported body weight and dietary behaviors (fruit and vegetable servings/day). Moderate-vigorous physical activity (MVPA) was assessed via accelerometry.

Results

Median dose (25th, 75th percentile) was 53 (33, 72) text messages in total across six months. Mean fortnightly dose in weeks 1–2 was 5.5 (95 % CI: 4.3, 6.6) text messages, and remained stable (with the exception of planned decreases in weeks involving additional intervention contacts). Offset against the average fortnightly dose of goal checks (1.6, 95 % CI: 1.3, 2.0 and 1.5, 95 % CI: 1.2, 1.8, for physical activity and diet respectively), mean replies to goal checks were highest in weeks 1–2 (1.4, 95 % CI: 1.4, 1.5 and 1.3, 95 % CI: 1.2, 1.4, respectively) and tended to become lower in most weeks thereafter. Greater weight loss was positively associated with text message dose (P = 0.022), with a difference of 1.9 kg between participants receiving the most and fewest texts. There was no association between engagement and changes in outcome measures.

Conclusions

A fixed dose of texts does not seem suitable to meet participants’ individual preferences. Higher self-selected text doses predicted better weight outcomes. However, greater participant engagement through text replies does not predict more favourable outcomes, despite being a suggested facilitator of successful behavior change maintenance.

Trial registration

Australian New Zealand Clinical Trials Registry number: ACTRN12613000949785. Date registered: 27 August 2013. Retrospectively registered. http://www.anzctr.org.au/.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12966-021-01179-8.

Smoking cessation, metabolic risk behaviors, and stress management over time in a sample of young adult smokers

Smoking cessation may support changes in metabolic risk behaviors (e.g., high-fat diet, physical inactivity, poor sleep, low fruit and vegetable consumption [FVC]). We examined the association between smoking cessation and metabolic risk behavior profiles, mediated by readiness to change risk behaviors and moderated by stress management. Participants were young adult smokers in a randomized controlled trial of a Facebook smoking cessation intervention. Measures included stage of change for five metabolic risk behaviors: FVC, diet, physical activity, sleep hygiene, and stress management. Moderated mediation was used to examine relationships between smoking cessation at T1 (predictor), readiness to change metabolic risk behaviors at T2 (mediators), stress management at T3 (moderator), and metabolic risk behavior profile at T3 (outcome) over 9 months. T1 smoking abstinence was associated with greater readiness to increase FVC at T2, which predicted lower likelihood of T3 metabolic risk (β = -0.22, 95% confidence interval [CI] [-0.53, -0.03]). This indirect effect was moderated by stress management such that greater readiness to increase FVC at T2 was associated with lower T3 metabolic risk for participants with unmanaged stress (β = -0.90, 95% CI [-1.32, -0.49], p < .001), but not for participants with well-managed stress (β = -.22, 95% CI [-0.48, 0.04], p = .096). Young adults who quit smoking subsequently had lower metabolic risk behaviors. Among participants with unmanaged stress, those who quit smoking had greater readiness to increase FVC and lower likelihood of subsequent metabolic risk. Smoking cessation interventions could aim to teach broadly applicable behavior change skills and build confidence for decreasing metabolic risk.

Efficacy of an m-Health Physical Activity and Sleep Intervention to Improve Sleep Quality in Middle-Aged Adults: The Refresh Study Randomized Controlled Trial

BACKGROUND

Poor sleep health is highly prevalent. Physical activity is known to improve sleep quality but not specifically targeted in sleep interventions.

PURPOSE

To compare the efficacy of a combined physical activity and sleep intervention with a sleep-only intervention and a wait-list control, for improving sleep quality in middle-aged adults without a diagnosed sleep disorder.

METHODS

Three-arm randomized controlled trial (Physical Activity and Sleep Health (PAS), Sleep Health Only (SO), Wait-list Control (CON) groups; 3-month primary time-point, 6-month follow-up) of 275 (PAS = 110, SO = 110, CON = 55) inactive adults (40-65 years) reporting poor sleep quality. The main intervention component was a smartphone/tablet "app" to aid goal setting and self-monitoring physical activity and/or sleep hygiene behaviors (including stress management), and a pedometer for PAS group. Primary outcome was Pittsburgh Sleep Quality Index (PSQI) global score. Secondary outcomes included several self-reported physical activity measures and PSQI subcomponents. Group differences were examined stepwise, first between pooled intervention (PI = PAS + SO) and CON groups, then between PAS and SO groups.

RESULTS

Compared with CON, PI groups significantly improved PSQI global and subcomponents scores at 3 and 6 months. There were no differences in sleep quality between PAS and SO groups. The PAS group reported significantly less daily sitting time at 3 months and was significantly more likely to report ≥2 days/week resistance training and meeting physical activity guidelines at 6 months than the SO group.

CONCLUSIONS

PIs had statistically significantly improved sleep quality among middle-aged adults with poor sleep quality without a diagnosed sleep disorder. The adjunctive physical activity intervention did not additionally improve sleep quality.

CLINICAL TRIAL INFORMATION

Australian New Zealand Clinical Trial Registry: ACTRN12617000680369; Universal Trial number: U1111-1194-2680; Human Research Ethics Committee, Blinded by request of journal: H-2016-0267.

Moms in motion: weight loss intervention for postpartum mothers after gestational diabetes: a randomized controlled trial

Background

Up to 50 % of women with gestational diabetes mellitus (GDM) will receive a diagnosis of type 2 diabetes mellitus (T2DM) within a decade after pregnancy. While excess postpartum weight retention exacerbates T2DM risk, lifestyle changes and behavior modifications can promote healthy postpartum weight loss and contribute to T2DM prevention efforts. However, some women have difficulty prioritizing self-care during this life stage. Efficacious interventions that women can balance with motherhood to reduce T2DM risk remain a goal. The objective of the Moms in Motion study is to evaluate the efficacy of a simple, novel, activity-boosting intervention using ankle weights worn with daily activities during a 6-month postpartum intervention among women with GDM. We hypothesize that women randomized to the 6-month intensity-modifying intervention will (1) demonstrate greater weight loss and (2) greater improvement in body composition and biomarker profile versus controls.

Methods

This study will be a parallel two-arm randomized controlled trial (n = 160). Women will be allocated 1:1 to an ankle weight intervention group or a standard-of-care control group. The intervention uses ankle weights (1.1 kg) worn on each ankle during routine daily activities (e.g., cleaning, childcare). Primary outcomes include pre- and post-assessments of weight from Visit 2 to Visit 3. Secondary outcomes include body composition, glycemia (2-h, 75 g oral glucose tolerance test), and fasting insulin. Exploratory outcomes include energy expenditure, diet, and psychosocial well-being.

Discussion

Beyond the expected significance of this study in its direct health impacts from weight loss, it will contribute to exploring (1) the mechanism(s) by which the intervention is successful (mediating effects of energy expenditure and diet on weight loss) and (2) the effects of the intervention on body composition and biomarkers associated with insulin resistance and metabolic health. Additionally, we expect the findings to be meaningful regarding the intervention’s effectiveness on engaging women with GDM in the postpartum period to reduce T2DM risk.

Trial registration

The ClinicalTrials.gov Identifier, is NCT03664089. The trial registration date is September 10, 2018. The trial sponsor is Dr. Sarah A. Keim.

Effects of eHealth-Based Multiple Health Behavior Change Interventions on Physical Activity, Healthy Diet, and Weight in People With Noncommunicable Diseases: Systematic Review and Meta-analysis

Background

Noncommunicable diseases (NCDs) are associated with the burden of premature deaths and huge medical costs globally. There is an increasing number of studies combining a multiple health behavior change (MHBC) intervention paradigm with eHealth approaches to jointly promote weight-related health behaviors among people with NCD; yet, a comprehensive summary of these studies is lacking.

Objective

This review aims to meta-analyze the effectiveness and systematically summarize the characteristics of the relevant intervention studies for improving the outcomes of physical activity, healthy diet, and weight among people with NCD.

Methods

Following PRISMA guidelines, 4 electronic databases (PsycINFO, PubMed, Scopus, SPORTDiscus) were systematically searched to identify eligible articles based on a series of inclusion and exclusion criteria. Article selection, quality assessment, and data extraction were independently performed by 2 authors. The standardized mean difference (SMD) was calculated to evaluate the effectiveness of interventions for 3 intervention outcomes (physical activity, healthy diet, and weight), and subsequent subgroup analyses were performed for gender, age, intervention duration, channel, and theory. Calculations were conducted, and figures were produced in SPSS 22 and Review Manager 5.3.

Results

Of the 664 original hits generated by the systematic searches, 15 eligible studies with moderate to high quality were included. No potential publication bias was detected using statistical analyses. Studies varied in intervention channel, intensity, and content. The meta-analysis revealed that the eHealth MHBC interventions significantly promoted physical activity (SMD 0.85, 95% CI 0.23 to 1.47, P=.008) and healthy diet (SMD 0.78, 95% CI 0.13 to 1.43, P=.02), but did not contribute to a healthy weight status (SMD –0.13, 95% CI= –0.47 to 0.20, P=.43) among people with NCDs, compared to the control conditions. Results from subgroup analysis indicated that theory-based interventions achieved greater effect than nontheory-based interventions in promoting physical activity, and interventions with traditional approaches (SMS, telephone) were more effective than those with modern internet-based approaches in promoting healthy diet.

Conclusions

The results of this review indicates that eHealth MHBC interventions achieve preliminary success in promoting physical activity and healthy diet behaviors among people with NCD. Future studies could improve the intervention design to achieve better intervention effectiveness.

Trial Registration

PROSPERO International Prospective Register of Systematic Reviews CRD42019118629; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=118629

What do cancer survivors and their health care providers want from a healthy living program? Results from the first round of a co-design project

PURPOSE

Healthy lifestyles are an important part of cancer survivorship, though survivors often do not adhere to recommended guidelines. As part of the co-design of a new online healthy living intervention, this study aimed to understand cancer survivors', oncology healthcare professionals' (HCP) and cancer non-government organisation (NGO) representatives' preferences regarding intervention content and format.

METHODS

Survivors, HCP and NGO representatives participated in focus groups and interviews exploring what healthy living means to survivors, their experience with past healthy living programs and their recommendations for future program content and delivery. Sessions were audio recorded, transcribed verbatim and analysed thematically.

RESULTS

Six focus groups and eight interviews were conducted including a total of 38 participants (21 survivors, 12 HCP, 5 NGO representatives). Two overarching messages emerged: (1) healthy living goes beyond physical health to include mental health and adjustment to a new normal and (2) healthy living programs should incorporate mental health strategies and peer support and offer direction in a flexible format with long-term accessibility. There was a high degree of consensus between participant groups across themes.

CONCLUSIONS

These findings highlight the need for integration of physical and mental health interventions with flexibility in delivery. Future healthy living programs should investigate the potential for increased program adherence if mental health interventions and a hybrid of delivery options were included.

Latent Class Analysis of Multiple Health Risk Behaviors among Australian University Students and Associations with Psychological Distress

University students have high rates of health risk behaviors and psychological distress. This study explores patterns of health behaviors among a sample of Australian university students, and determines whether patterns of health behaviors are associated with psychological distress and demographic characteristics. Cross-sectional data from the University of Newcastle Student Healthy Lifestyle Survey 2019 were analyzed. Fruit and vegetable intake, sugar-sweetened beverage intake, physical activity, sitting time, smoking, alcohol intake, drug use, sleep and psychological distress were assessed. Latent class analysis (LCA) was used to identify patterns of health risk behaviors, and latent class regression to explore associations between psychological distress and demographic characteristics with health behavior classes. Analysis included 1965 students (mean age 25.8 ± 8.6 years, 70.7% female). Three patterns of health behaviors were identified: healthier (48.6%), moderate (40.2%) and unhealthy (11.2%) lifestyle classes. Students in the moderate and unhealthy lifestyle classes had higher odds of moderate (OR 1.43 and 2.37) and high/very high psychological distress risk (OR 2.71 and 11.69). Students in the unhealthy and moderate lifestyle classes had a higher odds of being male, younger, enrolled in transition to university and English language courses, Aboriginal or Torres Strait Islander descent and to report some financial difficulty. Study findings may be used to inform the design of mental health interventions for university students that target key health risk behaviors.

Associations between multiple positive health behaviors and cardiometabolic risk using 3 alternative measures of physical activity: NHANES 2005-2006

The study aimed to investigate the association between clustered cardiometabolic risk (CCMR) and health-behavior indices comprising 3 different measures of physical activity, screen time, diet and sleep in NHANES 2005-2006. CCMR was calculated by standardizing and summarizing measures of blood pressure, fasting glucose, triglycerides, insulin, high-density lipoprotein and waist circumference to create a z score. Three health behavior indices were constructed with a single point allocated to each of the following lower risk behaviors: muscle strengthening activity, healthy eating score, sleep disorder/disruption, sleep duration, screen time and physical activity (self-reported moderate-to-vigorous physical activity [MVPA] (Index Score-SR), accelerometer-measured MVPA (Index Score-MVPA) or accelerometer-measured steps Index Score-Steps). Linear regression models explored associations between index scores and CCMR. In the sample (n = 1537, 52% male, aged 45.5 [SE: 0.9] years), reporting 0-5 vs. 6 health behaviors using Index Score-SR and Index Score-MVPA, and 0-4 vs. 6 health behaviors using Index Score-Steps, were associated with a significantly higher CCMR. The beta (β [95% CI]) for zero vs. 6 behaviors were Index Score-SR (2.86 [2.02, 3.69], Index Score-MVPA (2.41 [1.49, 3.33] and Index Score-Steps (2.41 [1.68, 3.15]). Irrespective of the measure of physical activity, engaging in fewer positive health behaviors was associated with greater CCMR. Novelty: Physical activity, screen time, diet and sleep may exert synergistic/cumulative effects on clustered cardiometabolic risk. A greater number of positive health behaviors was associated with a lower clustered cardiometabolic risk factor score. The reduction in cardiometabolic risk was similar irrespective of which physical activity measure was used.

Efficacy of a Multi-component m-Health Weight-loss Intervention in Overweight and Obese Adults: A Randomised Controlled Trial

BACKGROUND

This study compared the efficacy of two multi-component m-health interventions with a wait-list control group on body weight (primary outcome), and secondary outcomes of cardiovascular risk factors, lifestyle behaviours, and mental health.

METHODS

Three-arm randomised controlled trial (Enhanced: physical activity, diet, sleep, Traditional: physical activity, diet, Control) with assessments conducted at baseline, 6 and 12 months. Participants (n = 116) were overweight or obese adults aged 19-65 (M = 44.5 [SD = 10.5]). The 6-month intervention was delivered via a smartphone app providing educational materials, goal-setting, self-monitoring and feedback, and also included one face-to-face dietary consultation, a Fitbit and scales. The trial was prospectively registered and conducted between May 2017 and September 2018. Group differences on primary and secondary outcomes were examined between the Pooled Intervention groups (Pooled Intervention = Enhanced and Traditional) and Control groups, and then between Enhanced and Traditional groups.

RESULTS

Nineteen participants (16.4%) formally withdrew from the trial. Compared with the Control group, average body weight of the Pooled Intervention group did not differ at 6 (between-group difference = -0.92, (95% CI -3.33, 1.48)) or 12 months (0.00, (95% CI -2.62, 2.62)). Compared with the Control group, the Pooled Intervention group significantly increased resistance training (OR = 7.83, (95% CI 1.08, 56.63)) and reduced energy intake at 6 months (-1037.03, (-2028.84, -45.22)), and improved insomnia symptoms at 12 months (-2.59, (-4.79, -0.39)). Compared with the Traditional group, the Enhanced group had increased waist circumferences (2.69, (0.20, 5.18)) and sedentary time at 6 months (105.66, (30.83, 180.48)), and improved bed time variability at 12 months (-1.08, (-1.86, -0.29)). No other significant differences were observed between groups.

CONCLUSIONS

Relative to Controls, the Pooled Intervention groups did not differ on body weight but improved resistance training, and reduced energy intake and insomnia symptom severity. No additional weight loss was apparent when targeting improvements in physical activity, diet and sleep in combination compared with physical activity and diet.

Examining mediators of intervention efficacy in a randomised controlled m-health trial to improve physical activity and sleep health in adults

Objectives: Examining mediators of intervention efficacy in an m-health intervention targeting physical activity and sleep in 160 Australian adults.Design: Nationwide randomised controlled trial.Main outcome measures: Moderate- and vigorous-intensity physical activity (MVPA), assessed using the Active Australia Questionnaire; sleep quality (Pittsburgh Sleep Quality Index); and sleep hygiene practices (Sleep Hygiene Index). Hypothesised psychosocial (e.g. self-efficacy) and behavioural (i.e. MVPA, sleep quality, sleep hygiene) mediators were tested on primary endpoint data at 3 months using bias-corrected bootstrapping (PROCESS 2 for SPSS). All outcomes and mediators were assessed using self-report.Results: At three months, the intervention had significantly improved sleep quality (d = 0.48, 95% CI: -2.26, -0.33, p = 0.009) and sleep hygiene (d = 0.40, 95% CI: -3.10, -0.19, p = 0.027). Differences in MVPA were not significant (d = 0.24, 95% CI: -35.53, 254.67, p = 0.139). Changes in MVPA were mediated by self-efficacy, perceived capability, environment, social support, intentions and planning, some of which showed inconsistent mediation (suppression). None of the hypothesised psychosocial factors mediated sleep outcomes. Changes in sleep hygiene mediated changes in sleep quality.Conclusions: Several psychosocial factors mediated changes in physical activity but not in sleep outcomes. Mediation effects of sleep hygiene on sleep quality highlight the importance of providing evidence-based strategies to improve sleep quality.

Social-Ecological Correlates of Regular Leisure-Time Physical Activity Practice among Adults

This study calculated the exposure-response rates of social-ecological correlates of practicing regular (>150 min/week) leisure-time physical activity (PA) in 393,648 adults from the 27 Brazilian state capitals who participated in a national survey between 2006 and 2016. Regular PA encouraging factors were inputted into an exposure-response model. Growth rates for the odds ratio and prevalence of regular PA were calculated for each increase of one encouraging factor. Regular PA was reported by 22% of the participants (25% of men and 20% of women). More than 40% of men and 30% of women with higher intra-personal encouraging conditions reported practicing regular PA. There was a 3% (ages 18–32 years) to 5% (ages 46–60 years) increase in regular PA practice in men for each increase in an encouraging climate factor (temperature from 21 °C to 31 °C, humidity from 65% to 85%, 2430 to 3250 h of sun/year, and from 1560 to 1910 mm of rain/year). Encouraging intra-personal factors and favorable climate conditions had larger effects on regular PA practice than the built environment and socio-political conditions; the latter two had independent effects, but did not have a cumulative effect on PA.

González-Gil E, Tamás H, JANCSÓ Z, Liatis S, Makrilakis K, Manios Y. Effective strategies for childhood obesity prevention via school based, family involved interventions: a critical review for the development of the Feel4Diabetes-study school based component

BACKGROUND

Although there are many interventions targeting childhood obesity prevention, only few have demonstrated positive results. The current review aimed to gather and evaluate available school-based intervention studies with family involvement targeting dietary, physical activity and sedentary behaviors among primary schoolchildren and their families, in order to identify the most effective strategies.

METHODS

Studies published between 2000 and January 2015 were retrieved from scientific electronic databases and grey literature. The databases used included MEDLINE/PubMed, Web-of-Science, CINAHL and Scopus. Included studies had to be experimental controlled studies and had duration over 1 school year, had family involvement, combined PA and dietary behaviors and were implemented in school setting. A complementary search was executed to update the review to cover the period from February 2015 to January 2019.

RESULTS

From the studies examined (n = 425), 27 intervention programs (33 publications) fulfilled the inclusion criteria. Among these, 15 presented significant effect on weight status and/ or overweight/ obesity or clinical indices, 3 presented significant effect on most energy balance-related behaviors (EBRBs) while 9 presented significant effect on some/few EBRBs or determinants. Strategies implemented in effective interventions were: teachers acting as role-models and being actively involved in the delivery of the intervention, school policies supporting the availability of healthy food and beverage choices and limiting unhealthy snacks, changes in the schoolyard, in the recess rules and in the physical education classes to increase physical activity, and involving parents in the intervention via assignments, meetings, informative material and encouraging them to improve the home environment. Use of incentives for children, social marketing techniques, collaboration with local stakeholders were found to increase effectiveness. Programs that focused only on educational sessions and material for parents, without promoting relevant environmental and policy changes, were found to be less effective. Cultural adaptations have been suggested to increase the intervention's acceptance in specific or vulnerable population groups.

CONCLUSIONS

Several effective strategies were identified in the reviewed programs. Outcomes of the current review were taken into account in developing the Feel4Diabetes-intervention and summed up as recommendations in the current work in order to facilitate other researchers designing similar childhood obesity prevention initiatives.

Exercise interventions for smoking cessation

BACKGROUND

Taking regular exercise, whether cardiovascular-type exercise or resistance exercise, may help people to give up smoking, particularly by reducing cigarette withdrawal symptoms and cravings, and by helping to manage weight gain.

OBJECTIVES

To determine the effectiveness of exercise-based interventions alone, or combined with a smoking cessation programme, for achieving long-term smoking cessation, compared with a smoking cessation intervention alone or other non-exercise intervention.

SEARCH METHODS

We searched the Cochrane Tobacco Addiction Group Specialised Register for studies, using the term 'exercise' or 'physical activity' in the title, abstract or keywords. The date of the most recent search was May 2019.

SELECTION CRITERIA

We included randomised controlled trials that compared an exercise programme alone, or an exercise programme as an adjunct to a cessation programme, with a cessation programme alone or another non-exercise control group. Trials were required to recruit smokers wishing to quit or recent quitters, to assess abstinence as an outcome and have follow-up of at least six months.

DATA COLLECTION AND ANALYSIS

We followed standard Cochrane methods. Smoking cessation was measured after at least six months, using the most rigorous definition available, on an intention-to-treat basis. We calculated risk ratios (RRs) and 95% confidence intervals (CIs) for smoking cessation for each study, where possible. We grouped eligible studies according to the type of comparison, as either smoking cessation or relapse prevention. We carried out meta-analyses where appropriate, using Mantel-Haenszel random-effects models.

MAIN RESULTS

We identified 24 eligible trials with a total of 7279 adult participants randomised. Two studies focused on relapse prevention among smokers who had recently stopped smoking, and the remaining 22 studies were concerned with smoking cessation for smokers who wished to quit. Eleven studies were with women only and one with men only. Most studies recruited fairly inactive people. Most of the trials employed supervised, group-based cardiovascular-type exercise supplemented by a home-based exercise programme and combined with a multi-session cognitive behavioural smoking cessation programme. The comparator in most cases was a multi-session cognitive behavioural smoking cessation programme alone. Overall, we judged two studies to be at low risk of bias, 11 at high risk of bias, and 11 at unclear risk of bias. Among the 21 studies analysed, we found low-certainty evidence, limited by potential publication bias and by imprecision, comparing the effect of exercise plus smoking cessation support with smoking cessation support alone on smoking cessation outcomes (RR 1.08, 95% CI 0.96 to 1.22; I² = 0%; 6607 participants). We excluded one study from this analysis as smoking abstinence rates for the study groups were not reported. There was no evidence of subgroup differences according to the type of exercise promoted; the subgroups considered were: cardiovascular-type exercise alone (17 studies), resistance training alone (one study), combined cardiovascular-type and resistance exercise (one study) and type of exercise not specified (two studies). The results were not significantly altered when we excluded trials with high risk of bias, or those with special populations, or those where smoking cessation intervention support was not matched between the intervention and control arms. Among the two relapse prevention studies, we found very low-certainty evidence, limited by risk of bias and imprecision, that adding exercise to relapse prevention did not improve long-term abstinence compared with relapse prevention alone (RR 0.98, 95% CI 0.65 to 1.47; I² = 0%; 453 participants).

AUTHORS' CONCLUSIONS

There is no evidence that adding exercise to smoking cessation support improves abstinence compared with support alone, but the evidence is insufficient to assess whether there is a modest benefit. Estimates of treatment effect were of low or very low certainty, because of concerns about bias in the trials, imprecision and publication bias. Consequently, future trials may change these conclusions.

Efficacy of an m-Health Physical Activity and Sleep Health Intervention for Adults: A Randomized Waitlist-Controlled Trial

INTRODUCTION

Interventions that improve both physical activity and sleep quality may be more effective in improving overall health. The purpose of the Synergy Study is to test the efficacy of a mobile health combined behavior intervention targeting physical activity and sleep quality.

STUDY DESIGN

Randomized, waitlist-controlled trial.

SETTING/PARTICIPANTS

This study had an app-based delivery mode, Australia-wide. The participants were 160 adults who reported insufficient physical activity and poor sleep quality in an eligibility survey.

INTERVENTION

The intervention was a mobile app providing educational resources, goal setting, self-monitoring, and feedback strategies. It included 12 weeks of personalized support including weekly reports, tool sheets, and prompts.

MAIN OUTCOME MEASURES

Outcomes were assessed at baseline, 3 months (primary), and 6 months (secondary endpoint). Self-reported minutes of moderate-to-vigorous intensity physical activity and sleep quality were co-primary outcomes. Resistance training; sitting time; sleep hygiene; sleep timing variability; insomnia severity; daytime sleepiness; quality of life; and depression, anxiety, and stress symptoms were secondary outcomes. Data were collected between June 2017 and February 2018 and analyzed in August 2018.

RESULTS

At 3 months, between-group differences in moderate-to-vigorous intensity physical activity were not statistically significant (p=0.139). Significantly more participants in the intervention group engaged in ≥2 days/week (p=0.004) of resistance training. The intervention group reported better overall sleep quality (p=0.009), subjective sleep quality (p=0.017), sleep onset latency (p=0.013), waketime variability (p=0.018), sleep hygiene (p=0.027), insomnia severity (p=0.002), and lower stress symptoms (p=0.032) relative to waitlist controls. At 6 months, group differences were maintained for sleep hygiene (p=0.048), insomnia severity (p=0.002), and stress symptoms (p=0.006). Differences were observed for bedtime variability (p=0.023), sleepiness (p<0.001), daytime dysfunction (p=0.039), and anxiety symptoms (p=0.003) at 6 months, but not 3 months.

CONCLUSIONS

This remotely delivered intervention did not produce statistically significant between-group differences in minutes of moderate-to-vigorous intensity physical activity. Significant short-term differences in resistance training and short- and medium-term differences in sleep health in favor of the intervention were observed.

TRIAL REGISTRATION

This study is registered at anzctr.org.au ACTRN12617000376347.

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.

Boosting healthy food choices by meal colour variety: results from two experiments and a just-in-time Ecological Momentary Intervention

BACKGROUND

Dietary guidelines typically specify rather complex goals and indicators for healthy food choices, such as nutrient and energy content patterns. However, translating these complex goals into practice in real life is often a major obstacle for many people. The present studies propose an intervention strategy for boosting healthy food choices by prompting consumers at a meaningful moment with a simple behavioural trigger, that is to eat a colourful lunch. Effectivity and feasibility of this intervention strategy were tested in two laboratory experiments and one real-life, smartphone-based Ecological Momentary Intervention.

METHODS

In Studies 1 and 2, 83 / 42 participants self-served four meals (colourful, typical, healthy, and low-calorie) / three meals (colourful, typical, and varied) from a Fake Food Buffet. In Study 3, 80 participants recorded images of 1,210 lunch meals over a period of 3 weeks using mobile visual food recording. In the second week, participants additionally received a daily smartphone prompt to eat a colourful lunch. In all studies, participants were asked to rate the prompts' feasibility.

RESULTS

Prompting participants to eat a colourful meal increased the proportion of healthy foods consumed compared to typical meals in all three studies. In Studies 1 and 2, colourful meals contained more fruit and vegetables, while in Study 3 the prompt increased vegetable consumption. Furthermore, participants evaluated colourful meals to be the tastiest (Study 1) and most pleasant, and reported that the prompt was easy to follow and act upon.

CONCLUSIONS

Results suggest that prompting individuals to eat colourful meals is a promising strategy to facilitate healthy food choices in daily life.

TRIAL REGISTRATION

German Clinical Trials Register, DRKS00017552 (Study 3; retrospectively registered on 24th June 2019).

Patterns of Diet, Physical Activity, Sitting and Sleep Are Associated with Socio-Demographic, Behavioural, and Health-Risk Indicators in Adults

Our understanding of how multiple health-behaviours co-occur is in its infancy. This study aimed to: (1) identify patterns of physical activity, diet, sitting, and sleep; and (2) examine the association between sociodemographic and health-risk indicators. Pooled data from annual cross-sectional telephone surveys of Australian adults (2015–2017, n = 3374, 51.4% women) were used. Participants self-reported physical activity, diet, sitting-time, sleep/rest insufficiency, sociodemographic characteristics, smoking, alcohol use, height and weight to calculate body mass index (BMI), and mental distress frequency. Latent class analysis identified health-behaviour classes. Latent class regression determined the associations between health-behaviour patterns, sociodemographic, and health-risk indicators. Three latent classes were identified. Relative to a ‘moderate lifestyle’ pattern (men: 43.2%, women: 38.1%), a ‘poor lifestyle’ pattern (men: 19.9%, women: 30.5%) was associated with increased odds of a younger age, smoking, BMI ≥ 30.0 kg/m2, frequent mental distress (men and women), non-partnered status (men only), a lower Socioeconomic Index for Areas centile, primary/secondary education only, and BMI = 25.0–29.9 kg/m² (women only). An ‘active poor sleeper’ pattern (men: 37.0%, women: 31.4%) was associated with increased odds of a younger age (men and women), working and frequent mental distress (women only), relative to a ‘moderate lifestyle’ pattern. Better understanding of how health-behaviour patterns influence future health status is needed. Targeted interventions jointly addressing these behaviours are a public health priority.

The Behavioral Medicine Research Council: Its origins, mission, and methods

The Behavioral Medicine Research Council (BMRC) is a new, autonomous joint committee of 4 of the leading behavioral medicine research organizations, including the Academy of Behavioral Medicine Research, the American Psychosomatic Society, the Society for Health Psychology, and the Society of Behavioral Medicine. The BMRC's work has important implications for the science and practice of behavioral medicine. The distinguished senior scientists who comprise this new committee will identify a series of strategic research goals for behavioral medicine and promote systematic, interdisciplinary efforts to achieve them. This special report discusses the developments that led to the formation of the BMRC, describes the BMRC's mission, and explains the methods that its members will use. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Compliance with Multiple Health Behaviour Recommendations: A Cross-Sectional Comparison between Female Cancer Survivors and Those with no Cancer History

Lifestyle behaviours have an important role in preventing cancer, reducing treatment side effects, and improving survival and quality of life for cancer survivors. This study investigated adherence to multiple lifestyle behaviours among women with and without a cancer history. From the Australian Longitudinal Study on Women’s Health (ALSWH) surveys, 2407 cancer survivors and 3896 controls (cancer free population) were identified. Based on the World Cancer Research Fund/American Institute for Cancer Research (WCRF/AICR) recommendations, adherence to six health behaviours (smoking, physical activity, fruit and vegetable intake, alcohol consumption, sugary drink intake, and Body Mass Index [BMI]) were assessed. Overall adherence was low, and there were no differences between survivors and controls on adherence to any of the six individual health behaviours. However, both recent and long-term cancer survivors were more likely than controls to adhere to multiple health behaviours (p < 0.05). When participants with melanoma or non-melanoma skin cancer were excluded, adherence was less likely (but not significant) in the cancer group than controls. Higher education (p < 0.01), being married (p < 0.01), and lower comorbidity of chronic illnesses (p < 0.01) were significantly associated with adherence to multiple lifestyle behaviours. Overall, the findings suggest that a cancer diagnosis may result in increased compliance with multiple health behaviour guidelines.

Can High Schools Be an Effective Setting to Promote Healthy Lifestyles? Effects of a Multiple Behavior Change Intervention in Adolescents

PURPOSE

The main aim was to examine the effects of a school-based intervention on multiple health behaviors in adolescents, such as 24-hour movement behaviors (i.e., physical activity, sedentary time, and sleep duration), diet, and substance consumption (i.e., alcohol and tobacco). Another aim was to examine intragender differences in the effectiveness of the programme.

METHODS

A quasi-experimental design was carried out in two secondary schools throughout one academic year. A final sample of 210 students participated: 105 in the control (Mage = 13.07 ± 0.63) and 105 in the experimental school (Mage = 13.05 ± 0.59). A multicomponent school-based intervention was conducted by teachers in the experimental school via curricular (i.e., tutorial action plan, interdisciplinary project, and school break) and extracurricular (i.e., family involvement, institutional and noncurricular activities, and dissemination of health information and events) actions to promote adolescents' healthy lifestyles. Physical activity and sedentary time were measured by accelerometers, and the rest of health behaviors by using self-reported scales.

RESULTS

Experimental school students showed a significant improvement in meeting specific and general combinations of 24-hour movement guidelines, sedentary screen time levels, nap duration, (un)healthy diet scores, breakfast intake, and soft drink consumption compared to both control school students and their baseline values (p >.05). Active commuting and substance consumption rates only showed a significant improvement compared to control school students (p >.05). Although the intervention programme was effective in improving health-related behaviors in both genders, a large effect size was observed in boys.

CONCLUSIONS

Conducting multiple health behavior interventions becomes essential to improve adolescents' healthy lifestyles.

Comparing Self-Monitoring Strategies for Weight Loss in a Smartphone App: Randomized Controlled Trial

BACKGROUND

Self-monitoring of dietary intake is a valuable component of behavioral weight loss treatment; however, it declines quickly, thereby resulting in suboptimal treatment outcomes.

OBJECTIVE

This study aimed to examine a novel behavioral weight loss intervention that aims to attenuate the decline in dietary self-monitoring engagement.

METHODS

GoalTracker was an automated randomized controlled trial. Participants were adults with overweight or obesity (n=105; aged 21-65 years; body mass index, BMI, 25-45 kg/m²) and were randomized to a 12-week stand-alone weight loss intervention using the MyFitnessPal smartphone app for daily self-monitoring of either (1) both weight and diet, with weekly lessons, action plans, and feedback (Simultaneous); (2) weight through week 4, then added diet, with the same behavioral components (Sequential); or (3) only diet (App-Only). All groups received a goal to lose 5% of initial weight by 12 weeks, a tailored calorie goal, and automated in-app reminders. Participants were recruited via online and offline methods. Weight was collected in-person at baseline, 1 month, and 3 months using calibrated scales and via self-report at 6 months. We retrieved objective self-monitoring engagement data from MyFitnessPal using an application programming interface. Engagement was defined as the number of days per week in which tracking occurred, with diet entries counted if ≥800 kcal per day. Other assessment data were collected in-person via online self-report questionnaires.

RESULTS

At baseline, participants (84/100 female) had a mean age (SD) of 42.7 (11.7) years and a BMI of 31.9 (SD 4.5) kg/m². One-third (33/100) were from racial or ethnic minority groups. During the trial, 5 participants became ineligible. Of the remaining 100 participants, 84% (84/100) and 76% (76/100) completed the 1-month and 3-month visits, respectively. In intent-to-treat analyses, there was no difference in weight change at 3 months between the Sequential arm (mean -2.7 kg, 95% CI -3.9 to -1.5) and either the App-Only arm (-2.4 kg, -3.7 to -1.2; P=.78) or the Simultaneous arm (-2.8 kg, -4.0 to -1.5; P=.72). The median number of days of self-monitoring diet per week was 1.9 (interquartile range [IQR] 0.3-5.5) in Sequential (once began), 5.3 (IQR 1.8-6.7) in Simultaneous, and 2.9 (IQR 1.2-5.2) in App-Only. Weight was tracked 4.8 (IQR 1.9-6.3) days per week in Sequential and 5.1 (IQR 1.8-6.3) days per week in Simultaneous. Engagement in neither diet nor weight tracking differed between arms.

CONCLUSIONS

Regardless of the order in which diet is tracked, using tailored goals and a commercial mobile app can produce clinically significant weight loss. Stand-alone digital health treatments may be a viable option for those looking for a lower intensity approach.

TRIAL REGISTRATION

ClinicalTrials.gov NCT03254953; https://clinicaltrials.gov/ct2/show/NCT03254953 (Archived by WebCite at http://www.webcitation.org/72PyQrFjn).

Lifestyle risk indices in adolescence and their relationships to adolescent disease burden: findings from an Australian national survey

BACKGROUND

The current study investigates the extent to which an adolescent-specific lifestyle risk factor index predicts indicators of the leading causes of adolescent morbidity and mortality.

METHODS

Data came from 13 to 17 year-old respondents from the 2013-2014 nationally representative Australian Child and Adolescent Survey of Mental Health and Wellbeing (n = 2314). Indicators of adolescent disease burden included Major Depressive Disorder, psychological distress, self-harm and suicide attempt. Risk factors included risky alcohol use, drug use, unprotected sex, smoking, BMI and sleep duration. The extent to which these risk factors co-occurred were investigated using tetrachoric correlations. Several risk indices were then constructed based on these risk factors. Receiver Operating Characteristic curves determined the precision with which these indices predicted the leading causes of adolescent disease burden.

RESULTS

Risky alcohol use, drug use, smoking, unprotected sex, and sleep were all highly clustered lifestyle risk factors, whereas BMI was not. A risk index comprising risky alcohol use, drug use, unprotected sex and sleep duration predicted the disease burden outcomes with the greatest precision. 31.9% of the sample reported one or more of these behaviours.

CONCLUSIONS

This lifestyle risk factor index represents a useful summary metric in the context of adolescent health promotion and non-communicable disease prevention. Lifestyle risk factors were found to cluster in adolescence, supporting the implementation of multiple health behaviour change interventions.

Associations of health-behavior patterns, mental health and self-rated health

Diet quality, physical activity, alcohol use, smoking, sleep and sitting-time are behaviors known to influence health. The aims of this study were to identify how these behaviors co-occur to form distinct health-behavior patterns, and to investigate the relationship between these patterns, and mental and self-rated health. Members of the Australian 10,000 Steps project were invited to participate in an online survey in November-December 2011. The participants self-reported demographic and behavioral characteristics (fruit and vegetable intake, fast food, soft drink and alcohol consumption, smoking, physical activity, sitting-time and sleep), frequency of mental distress and self-rated health. Latent Class Analysis was used to identify health-behavior patterns. Latent class regression was used to examine relationships between behavior patterns, mental and self-rated health, and socio-demographic and economic factors. Data were analyzed in October 2017. Complete datasets were obtained from 10,638 participants. Four latent classes were identified, characterized by 'Low-Risk Behavior', 'Poor Sleep, Low-Risk Daytime Behavior', 'Sound Sleep, High-Risk Daytime Behavior' and 'High-Risk Behavior'. The latter two classes, both characterized by high-risk daytime behaviors, were associated with poor self-rated health. Participants in classes with high-risk daytime behaviors were more likely to be younger, non-partnered, non-university educated, from lower income households and work longer hours. Classes characterized by poor sleep quality were associated with higher frequency of mental distress. Findings suggest that experiencing poor sleep is partly independent of daytime behaviors, demographic and socioeconomic factors, but has a strong association with mental health.