Combined interventions for physical activity, sleep, and diet using smartphone apps: A scoping literature review

Effectiveness of holistic mobile health interventions on diet, and physical, and mental health outcomes: a systematic review and meta-analysis

Background

Good physical and mental health are essential for healthy ageing. Holistic mobile health (mHealth) interventions-including at least three components: physical activity, diet, and mental health-could support both physical and mental health and be scaled to the population level. This review aims to describe the characteristics of holistic mHealth interventions and their effects on related behavioural and health outcomes among adults from the general population.

Methods

In this systematic review and meta-analysis, we searched MEDLINE, Embase, Cochrane Central Register of Controlled Trials, PsycINFO, Scopus, China National Knowledge Infrastructure, and Google Scholar (first 200 records). The initial search covered January 1, 2011, to April 13, 2022, and an updated search extended from April 13, 2022 to August 30, 2023. Randomised controlled trials (RCTs) and non-randomised studies of interventions (NRSIs) were included if they (i) were delivered via mHealth technologies, (ii) included content on physical activity, diet, and mental health, and (iii) targeted adults (≥18 years old) from the general population or those at risk of non-communicable diseases (NCDs) or mental disorders. Studies were excluded if they targeted pregnant women (due to distinct physiological responses), individuals with pre-existing NCDs or mental disorders (to emphasise prevention), or primarily utilised web, email, or structured phone support (to focus on mobile technologies without exclusive human support). Data (summary data from published reports) extraction and risk-of-bias assessment were completed by two reviewers using a standard template and Cochrane risk-of-bias tools, respectively. Narrative syntheses were conducted for all studies, and random-effects models were used in the meta-analyses to estimate the pooled effect of interventions for outcomes with comparable data in the RCTs. The study was registered in PROSPERO, CRD42022315166.

Findings

After screening 5488 identified records, 34 studies (25 RCTs and 9 pre-post NRSIs) reported in 43 articles with 5691 participants (mean age 39 years, SD 12.5) were included. Most (91.2%, n = 31/34) were conducted in high-income countries. The median intervention duration was 3 months, and only 23.5% (n = 8/34) of studies reported follow-up data. Mobile applications, short-message services, and mobile device-compatible websites were the most common mHealth delivery modes; 47.1% (n = 16/34) studies used multiple mHealth delivery modes. Of 15 studies reporting on weight change, 9 showed significant reductions (6 targeted on individuals with overweight or obesity), and in 10 studies reporting perceived stress levels, 4 found significant reductions (all targeted on general adults). In the meta-analysis, holistic mHealth interventions were associated with significant weight loss (9 RCTs; mean difference -1.70 kg, 95% CI -2.45 to -0.95; I2 = 89.00%) and a significant reduction in perceived stress levels (6 RCTs; standardised mean difference [SMD] -0.32; 95% CI -0.52 to -0.12; I2 = 14.52%). There were no significant intervention effects on self-reported moderate-to-vigorous physical activity (5 RCTs; SMD 0.21; 95%CI -0.25 to 0.67; I2 = 74.28%) or diet quality scores (5 RCTs; SMD 0.21; 95%CI -0.47 to 0.65; I2 = 62.27%). All NRSIs were labelled as having a serious risk of bias overall; 56% (n = 14/25) of RCTs were classified as having some concerns, and the others as having a high risk of bias.

Interpretation

Findings from identified studies suggest that holistic mHealth interventions may aid reductions in weight and in perceived stress levels, with small to medium effect sizes. The observed effects on diet quality scores and self-reported moderate-to-vigorous physical activity were less clear and require more research. High-quality RCTs with longer follow-up durations are needed to provide more robust evidence. To promote population health, future research should focus on vulnerable populations and those in middle- and low-income countries. Optimal combinations of delivery modes and components to improve efficacy and sustain long-term effects should also be explored.

Funding

National Research Foundation, Prime Minister's Office, Singapore, under its Campus for Research Excellence and Technological Enterprise (CREATE) Programme and Physical Activity and Nutrition Determinants in Asia (PANDA) Research Programme.

Developing a Healthy Lifestyle Program for Recent Kidney Transplant Recipients

INTRODUCTION

Many kidney transplant recipients experience weight gain in the first year after transplantation.

RESEARCH QUESTION

The objective of this research study was to assess the desires of recent kidney transplant patients about the design features of a healthy lifestyle program to counter unnecessary weight gain.

DESIGN

In this descriptive study, recent recipients at 2 transplant centers were invited to participate in an online survey. Survey items included sociodemographic information, current medications, health conditions, weight change posttransplant, diet behaviors, physical activity participation, and desired features of a lifestyle program.

RESULTS

Fifty-three participants, mean age 60.5 (11.2) years, primarily males, completed surveys. Forty percent gained weight posttransplantation with many indicating struggling with their diet. Physical activity levels stayed the same (17%) or decreased (40%) posttransplantation. Eighty-seven percent of participants indicated they would participate in an online lifestyle program and 76% wanted online physical activity and nutrition sessions to meet at least once weekly. Suggestions about the type of information and activities, included eating strategies (eg, how to eat healthfully at restaurants, grocery shopping tips, and recipes), resources for at-home physical activities, access to cooking classes, and apps to track both activity and food intake.

CONCLUSION

Recent kidney transplant recipients would benefit from and desired to join a lifestyle program featuring tailored nutrition education and physical activity coaching. Gathered information will be used to inform and tailor a lifestyle program. Identifying features for the prevention of unnecessary weight gain with patients' input is essential for promoting and sustaining healthy behaviors.

Holistic mHealth interventions for the promotion of healthy ageing: protocol for a systematic review

INTRODUCTION

Maintaining physical and mental health is essential for healthy ageing. It can be supported by modifying lifestyle factors such as physical activity and diet. Poor mental health, in turn, contributes to the opposing effect. The promotion of healthy ageing may therefore benefit from holistic interventions integrating physical activity, diet and mental health. These interventions can be scaled up to the population level by using mobile technologies. However, systematic evidence regarding the characteristics and effectiveness of such holistic mHealth interventions remains limited. This paper presents a protocol for a systematic review that aims to provide an overview of the current state of the evidence for holistic mHealth interventions, including their characteristics and effects on behavioural and health outcomes in general adult populations .

METHODS AND ANALYSIS

We will conduct a comprehensive search for randomised controlled trials and non-randomised studies of interventions published between January 2011 and April 2022 in MEDLINE, Embase, Cochrane Central Register of Controlled Trials, PsycINFO, Scopus, China National Knowledge Infrastructure and Google Scholar (first 200 records). Eligible studies will be mHealth interventions targeting general adult populations with content on physical activity, diet and mental health. We will extract information on all relevant behavioural and health outcomes, as well as those related to intervention feasibility. Screening and data extraction processes will be carried out independently by two reviewers. Cochrane risk-of-bias tools will be used to assess risk of bias. We will provide a narrative overview of the findings from eligible studies. With sufficient data, a meta-analysis will be conducted.

ETHICS AND DISSEMINATION

Ethical approval is not required because this study is a systematic review based on published data. We intend to publish our findings in a peer-reviewed journal and present the study at international conferences.

PROSPERO REGISTRATION NUMBER

CRD42022315166.

A Contactless App-Based Intervention to Improve Health Behaviors in Airline Pilots: A Randomized Trial

INTRODUCTION

There is a need for enhanced preventive health care among airline pilots to mitigate the prevalence of cardiometabolic health risk factors.

DESIGN

A randomized, waitlist-controlled trial was utilized to evaluate the effectiveness of a smartphone-based app intervention for improving health behaviors and cardiometabolic health parameters.

SETTING/PARTICIPANTS

A total of 186 airline pilots (aged 43.2±9.1 years; male, 64%) were recruited and participated in the trial during 2022.

INTERVENTION

This intervention was a personalized, 16-week smartphone-based app multicomponent physical activity, healthy eating, and sleep hygiene intervention.

MAIN OUTCOME MEASURES

Outcome measures of objective health (Cooper's 12-minute exercise test, resting heart rate, push ups, plank isometric hold, body mass), subjective health (self-rated health, perceived psychological stress and fatigue), and health behaviors (weekly physical activity, sleep quality and duration, fruit and vegetable intake) were collected at baseline and after intervention. The waitlist control completed the same measures.

RESULTS

Significant interactions for time Χ group from baseline to 16 weeks were found for all outcome measures (p<0.001). Significant between-group differences for positive health changes in favor of the intervention group were found after intervention for all outcome measures (p<0.05, d=0.4-1.0) except for self-rated health, body mass, and Pittsburgh Sleep Quality Index score.

CONCLUSIONS

Study findings show that an app-based health behavior intervention can elicit positive cardiometabolic health changes among airline pilots over 16 weeks, associated with trivial to large effect sizes.

TRIAL REGISTRATION

The trial protocol was prospectively registered at The Australian New Zealand Clinical Trials Registry (ACTRN12622000288729).

Health Recommender Systems Development, Usage, and Evaluation from 2010 to 2022: A Scoping Review

A health recommender system (HRS) provides a user with personalized medical information based on the user's health profile. This scoping review aims to identify and summarize the HRS development in the most recent decade by focusing on five key aspects: health domain, user, recommended item, recommendation technology, and system evaluation. We searched PubMed, ACM Digital Library, IEEE Xplore, Web of Science, and Scopus databases for English literature published between 2010 and 2022. Our study selection and data extraction followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews. The following are the primary results: sixty-three studies met the eligibility criteria and were included in the data analysis. These studies involved twenty-four health domains, with both patients and the general public as target users and ten major recommended items. The most adopted algorithm of recommendation technologies was the knowledge-based approach. In addition, fifty-nine studies reported system evaluations, in which two types of evaluation methods and three categories of metrics were applied. However, despite existing research progress on HRSs, the health domains, recommended items, and sample size of system evaluation have been limited. In the future, HRS research shall focus on dynamic user modelling, utilizing open-source knowledge bases, and evaluating the efficacy of HRSs using a large sample size. In conclusion, this study summarized the research activities and evidence pertinent to HRSs in the most recent ten years and identified gaps in the existing research landscape. Further work shall address the gaps and continue improving the performance of HRSs to empower users in terms of healthcare decision making and self-management.

Automated Cognitive Health Assessment Using Partially Complete Time Series Sensor Data

BACKGROUND

Behavior and health are inextricably linked. As a result, continuous wearable sensor data offer the potential to predict clinical measures. However, interruptions in the data collection occur, which create a need for strategic data imputation.

OBJECTIVE

The objective of this work is to adapt a data generation algorithm to impute multivariate time series data. This will allow us to create digital behavior markers that can predict clinical health measures.

METHODS

We created a bidirectional time series generative adversarial network to impute missing sensor readings. Values are imputed based on relationships between multiple fields and multiple points in time, for single time points or larger time gaps. From the complete data, digital behavior markers are extracted and are mapped to predicted clinical measures.

RESULTS

We validate our approach using continuous smartwatch data for n = 14 participants. When reconstructing omitted data, we observe an average normalized mean absolute error of 0.0197. We then create machine learning models to predict clinical measures from the reconstructed, complete data with correlations ranging from r = 0.1230 to r = 0.7623. This work indicates that wearable sensor data collected in the wild can be used to offer insights on a person's health in natural settings.

My Wellness Coach: evaluation of a mobile app designed to promote integrative health among underserved populations

Underserved populations, including those from racial and ethnic groups and with low socioeconomic status, often lack access to mobile apps aimed at reducing health risk factors. This study evaluated the feasibility, acceptability, and preliminary effectiveness of the mobile app, My Wellness Coach (MWC), designed to promote behavior change in seven core areas of integrative health among underserved populations. Patients and staff were recruited from clinic and other settings. Some participants used MWC in a weekly group setting (n = 5); others on their own with support from a coordinator (n = 36). Health outcomes were assessed at baseline and 3 months. Mobile app ratings were collected at 5 weeks and 3 months. Goal setting data were analyzed at 3 months. Most participants (76%) set at least one goal, 71% created action steps for goals, and 29% completed a goal. Patients in the group setting had the highest rate of goal completion (60%) compared to patients (20%) and staff (27%) using the app on their own. Significant (p < .05) changes in pre- and post-test scores were documented for overall wellbeing, global physical health, BMI, vigorous physical activity, and eHealth literacy. Most participants (75%-91%) gave MWC high ratings for impact on behavior change, help seeking, intent to change, attitudes, knowledge, and awareness. This study documented preliminary evidence of the potential benefits of MWC among underserved communities. Future evaluations of Spanish and Android versions and comparisons between group and individual administration will inform implementation strategies for scaling MWC-based interventions to reach underserved communities nationally.

Detecting Smartwatch-based Behavior Change in Response to a Multi-domain Brain Health Intervention

In this study, we introduce and validate a computational method to detect lifestyle change that occurs in response to a multi-domain healthy brain aging intervention. To detect behavior change, digital behavior markers (DM) are extracted from smartwatch sensor data and a Permutation-based Change Detection (PCD) algorithm quantifies the change in marker-based behavior from a pre-intervention, one-week baseline. To validate the method, we verify that changes are successfully detected from synthetic data with known pattern differences. Next, we employ this method to detect overall behavior change for n=28 BHI subjects and n=17 age-matched control subjects. For these individuals, we observe a monotonic increase in behavior change from the baseline week with a slope of 0.7460 for the intervention group and a slope of 0.0230 for the control group. Finally, we utilize a random forest algorithm to perform leave-one-subject-out prediction of intervention versus control subjects based on digital marker delta values. The random forest predicts whether the subject is in the intervention or control group with an accuracy of 0.87. This work has implications for capturing objective, continuous data to inform our understanding of intervention adoption and impact.

Multiple lifestyle behaviour mHealth intervention targeting Swedish college and university students: protocol for the Buddy randomised factorial trial

Introduction

The time during which many attend college or university is an important period for developing health behaviours, with potentially major implications for future health. Therefore, it is concerning that many Swedish students excessively consume alcohol, have unhealthy diets, are not physical active and smoke. The potential of digital interventions which integrate support for change of all of these behaviours is largely unexplored, as are the dismantled effects of the individual components that make up digital lifestyle behaviour interventions.

Methods and analysis

A factorial randomised trial (six factors with two levels each) will be employed to estimate the effects of the components of a novel mHealth multiple lifestyle intervention on alcohol consumption, diet, physical activity and smoking among Swedish college and university students. A Bayesian group sequential design will be employed to periodically make decisions to continue or stop recruitment, with simulations suggesting that between 1500 and 2500 participants will be required. Multilevel regression models will be used to analyse behavioural outcomes collected at 2 and 4 months postrandomisation.

Ethics and dissemination

The study was approved by the Swedish Ethical Review Authority on 2020-12-15 (Dnr 2020-05496). The main concern is the opportunity cost if the intervention is found to only have small effects. However, considering the lack of a generally available evidence-based multiple lifestyle behaviour support to university and college students, this risk was deemed acceptable given the potential benefits from the study.

Recruitment will begin in March 2021, and it is expected that recruitment will last no more than 24 months. A final data set will, therefore, be available in July 2023, and findings will be reported no later than December 2023.

Trial registration number

ISRCTN23310640; Pre-results.

eHealth interventions targeting nutrition, physical activity, sedentary behavior, or obesity in adults: A scoping review of systematic reviews

A vast body of evidence regarding eHealth interventions for nutrition, physical activity, sedentary behavior, and obesity exists. This scoping review of systematic reviews aimed to evaluate the current level of evidence in this growing field. Seven electronic databases were searched for systematic reviews published until October 27, 2019. The systematic reviews must have included adult participants only and have evaluated eHealth behavioral interventions with the primary aim of changing nutrition, physical activity, and sedentary behavior or treating or preventing overweight and obesity. One hundred and six systematic reviews, published from 2006 to 2019, were included. Almost all (n = 98) reviews evaluated the efficacy of interventions. Over half (n = 61) included interventions focused on physical activity, followed by treatment of obesity (n = 28), nutrition (n = 22), prevention of obesity (n = 18), and sedentary behavior (n = 6). Many reviews (n = 46) evaluated one type of eHealth intervention only, while 60 included two or more types. Most reviews (n = 67) were rated as being of critically low methodological quality. This scoping review identified an increasing volume of systematic reviews evaluating eHealth interventions. It highlights several evidence gaps (e.g., evaluation of other outcomes, such as reach, engagement, or cost effectiveness), guiding future research efforts in this area.

The prevalence of multimorbidity and its association with physical activity and sleep duration in middle aged and elderly adults: a longitudinal analysis from China

Background

Preventing chronic disease is important in health policy in countries with significantly ageing populations. This study aims to examine the prevalence of chronic disease multimorbidity and its association with physical activity and sleep duration; and to understand whether physical activity modifies associations between sleep duration and multimorbidity.

Methods

We utilized longitudinal data of a nationally-representative sample from the China Health and Retirement Longitudinal Study (in year 2011 and 2015; N = 5321; 54.7% female; age ≥ 45 years old). Fourteen chronic diseases were used to measure multimorbidity (ten self-reported, and four by blood test). Participants were grouped into high, moderate, and low level based on self-reported frequencies and durations of physical activity with different intensities for at least 10 min at a time in a usual week. Poor and good sleepers were categorized according to average hours of actual sleep at each night during the past month. Panel data method of random-effects logistic regression model was applied to estimate the association of physical activity and sleep with multimorbidity, adjusting for social-demographic and behavioural confounders.

Results

From 2011 to 2015, the prevalence of multimorbidity increased from 52.2 to 62.8%. In 2015, the proportion of participants engaging in high, moderate, and low level of physical activity was 30.3, 24.4 and 45.3%, respectively, and 63.6% of adults had good sleep. For both genders, compared with good sleep, poor sleep was associated with higher odds of multimorbidity (OR = 1.527, 95% CI: 1.277, 1.825). Compared to the high-level group, participants with a low level of physical activity were significantly more likely to have multimorbidity (OR = 1.457, 95% CI: 1.277, 1.825), but associations were stronger among women. The relative excess risk due to interaction between poor sleep and moderate or low physical activity was positive but non-significant on multimorbidity.

Conclusions

The burden of multimorbidity was high in China. Low physical activity and poor sleep was independently and significantly associated with a higher likelihood of multimorbidity in women and both genders, separately. Physical activity could modify the association between sleep and multimorbidity.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12966-021-01150-7.

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.

Factors influencing user's adherence to physical activity applications: A scoping literature review and future directions

BACKGROUND

Although mobile app-delivered physical activity (PA) interventions have the potential to promote exercise, poor adherence to these apps is a common issue impeding their effectiveness. Gaining insights into the factors that influence PA app adherence is an important priority for app developers and intervention designers.

OBJECTIVE

The objective of this study is to perform a literature review to synthesize the factors influencing PA app adherence and to identify directions for future research in this area.

METHODS

A scoping review of prior research was conducted to uncover the factors influencing PA app adherence. Seven online journal databases were searched for relevant articles published from January 1, 2014, to December 31, 2018. The initial search identified 5,572 articles. After a screening and eligibility check based on inclusion criteria, 24 articles were finally selected. The definition of PA app adherence in this review could be categorized along four dimensions derived from previous studies: i.e., frequency of PA app usage, intention/motivation to sustain use of the PA app, degree of function use within the PA app, and the duration of PA app usage.

RESULTS

Of the 24 included studies (both qualitative and quantitative), 12 studies were conducted in the U.S. The methods and study designs varied considerably, with the study durations ranging from 2 weeks to 24 months. The synthesized evidence indicates that 89 distinct factors influenced PA app adherence, and these could be classified into three categories: Personal Factors (n = 28), Technology Features (n = 53), and Contextual Factors (n = 8). Nine more detailed sub-categories were also compiled. Factors in sub-categories, such as psychological factors, health-related factors and predefined goals, are essential for physical activity behavior change experts to implement interventions. Factors in technology features, including PA tracking, PA goal setting and customization of exercise, are specifically useful for PA app developers or PA intervention designers. Overall, evidence of causal factors was limited. Only 5 of the 24 articles explored causal factors that affect PA app adherence. Furthermore, longitudinal studies with long durations were also limited.

CONCLUSIONS

Uncovering the factors influencing PA app adherence is critical as it can expand our current knowledge and provide guidance for app-delivered PA interventions, as well as the design of PA apps. This scoping review identified and categorized factors that influence PA app adherence in prior studies. Based on the evidence synthesized, users are paying more attention to the "playfulness" and personalized features of PA apps, in addition to basic functional requirements. Also, app glitches are the most common factors found to negatively influence app adherence. Several important directions for future research are highlighted in this review, especially the design of studies to explore causality.