Tweets, Apps, and Pods: Results of the 6-month Mobile Pounds Off Digitally (Mobile POD) randomized weight-loss intervention among adults

Evaluation of the Aspects of Digital Interventions That Successfully Support Weight Loss: Systematic Review With Component Network Meta-Analysis

BACKGROUND

Obesity is a chronic complex disease associated with increased risks of developing several serious and potentially life-threatening conditions. It is a growing global health issue. Pharmacological treatment is an option for patients living with overweight or obesity. Digital technology may be leveraged to support patients with weight loss in the community, but it is unclear which of the multiple digital options are important for success.

OBJECTIVE

This systematic review and component network meta-analysis aimed to identify components of digital support for weight loss interventions that are most likely to be effective in supporting patients to achieve weight loss goals.

METHODS

We searched MEDLINE, Embase, APA PsycInfo, and Cochrane Central Register of Controlled Trials from inception to November 2023 for randomized controlled trials using any weight loss intervention with digital components and assessing weight loss outcomes in adults with BMI ≥25 kg/m2 (≥23 kg/m2 for Asian populations). Eligible trials were prioritized for synthesis based on intervention relevance and duration, and the target population. Trial arms with substantial face-to-face elements were deprioritized. Prioritized trials were assessed for quality using the Cochrane Risk of Bias Tool v1. We conducted intervention component analysis to identify key digital intervention features and a coding framework. All prioritized trial arms were coded using this framework and were included in component network meta-analysis.

RESULTS

Searches identified 6528 reports, of which 119 were included. After prioritization, 151 trial arms from 68 trials were included in the synthesis. Nine common digital components were identified from the 151 trial arms: provision of information or education, goal setting, provision of feedback, peer support, reminders, challenges or competitions, contact with a specialist, self-monitoring, and incentives or rewards. Of these, 3 components were identified as "best bets" because they were consistently and numerically, but not usually significantly, most likely to be associated with weight loss at 6 and 12 months. These were patient information, contact with a specialist, and incentives or rewards. An exploratory model combining these 3 components was significantly associated with successful weight loss at 6 months (-2.52 kg, 95% CI -4.15 to -0.88) and 12 months (-2.11 kg, 95% CI -4.25 to 0.01). No trial arms used this specific combination of components.

CONCLUSIONS

Our findings indicate that the design of digital interventions to support weight loss should be carefully crafted around core components. On their own, no single digital component could be considered essential for success, but a combination of information, specialist contact, and incentives warrants further examination.

TRIAL REGISTRATION

PROSPERO CRD42023493254; https://tinyurl.com/ysyj8j8s.

Podcasts in Mental, Physical, or Combined Health Interventions for Adults: Scoping Review

BACKGROUND

The increasing prevalence of poor mental and physical health in adults is a global health concern. Given the scope of this problem, scalable and effective treatment interventions are needed. While podcasts (online digital audio files) are becoming more popular, the extent of podcast use in mental, physical, or combined health interventions has not been reviewed.

OBJECTIVE

This scoping review primarily aims to summarize the available evidence regarding the use of podcasts in health promotion interventions. A series of 5 research questions was designed to systematically review and accurately represent podcast use in current research interventions.

METHODS

We conducted a search of electronic databases (MEDLINE, PsycINFO, Embase, CINAHL, Scopus, and CENTRAL), gray literature articles, and relevant journals reported in the English language. Eligible studies targeted adults (aged ≥18 y), included a podcast in at least 1 intervention, and measured a mental or physical health outcome.

RESULTS

Overall, 51 articles (published studies: n=26, 51% and gray literature articles: n=25, 49%) were deemed eligible. In total, 58% (15/26) of the included peer-reviewed studies were published in the last 5 years, suggesting that the use of podcasts as an intervention approach is increasing. On average, 85.6% (n=2104) of the participants included in these research studies were women. In total, 31% (8/26) of the studies contained a female-only sample. In contrast, no research studies contained a male-only sample. Most peer-reviewed published studies (19/26, 73%) and gray literature sources (22/25, 88%) of the podcasts were used within multicomponent interventions, with most targeting physical health outcomes (peer-reviewed publications: 14/26, 54% and gray literature sources: 13/25, 52%). Results pertaining to podcast design, sources, theoretical principles, and thorough process evaluation indicators were heterogeneous.

CONCLUSIONS

The versatility that podcasts can offer as a medium for reaching and engaging with participants and end users was evident in this scoping review. While research using podcasts is growing, many (18/26, 69%) studies included in this scoping review were conducted in the United States and sampled female participants, highlighting the need to diversify the field. As expected, there was a high level of variation across the included studies in relation to how podcasts were used and designed within interventions. To address this, a standardized approach would be valuable in guiding researchers and practitioners through both the development and reporting phases of future podcast research, including aspects such as theoretical framework, a description of podcast development (eg, co-design and end-user engagement), objective podcast use, and process evaluation data.

Effectiveness and Components of Health Behavior Interventions on Increasing Physical Activity Among Healthy Young and Middle-Aged Adults: A Systematic Review with Meta-Analyses

Physical inactivity contributes to various health risks; however, approximately one-third of the global population remains insufficiently active. Many researchers have attempted to increase physical activity levels; however, the effectiveness and the specific components of these interventions remain unclear. This systematic review with meta-analyses utilized a behavior change technique taxonomy to identify and extract effective intervention components, aiming to develop more efficient programs to promote physical activity. We searched the PubMed and Ichu-shi Web databases for studies targeting healthy young and middle-aged adults with data on physical activity changes and extracted the intervention components. A random-effects model was used for the primary meta-analysis, and a meta-regression was conducted for the selected outcomes. Overall, 116 studies were included, with 102 used for the primary analysis. The interventions showed a small effect on overall physical activity promotion. Subsequent meta-regressions identified 1.5 Review behavior goal(s) as a significant positive intervention component, as well as four and three potential positive and negative components, respectively. This systematic review and meta-analysis demonstrated the effectiveness of physical activity interventions and highlighted effective and negative components. These findings may inform the design of future programs aimed at promoting physical activity.

A web-based physical activity intervention targeting affect regulation: a randomized feasibility trial

Early career professionals (ECPs) are a critical target for physical activity (PA) promotion. Affect contributes to an established PA intention-behaviour gap and is pertinent among ECPs.

OBJECTIVE

The purpose of this study was to examine the feasibility and acceptability of a web-based intervention and explore the effects on secondary outcomes (moderate-to-vigorous PA (MVPA), emotion regulation, multi-process action control constructs).

METHODS

Adults aged 25-44 who were employed at least part-time in a desk-based job and not meeting PA guidelines (<150 min MVPA) were recruited and randomized into a 6-week online intervention integrating acceptance and commitment principles and affect regulation strategies, or a control group.

RESULTS

Forty adults were recruited and randomized to the web-based intervention (n = 21) and waitlist control (n = 19). The recruitment rate was 29%, retention was 75%, engagement was 68%, and satisfaction was high in both quantitative and qualitative assessment. Participants allocated to the intervention improved MVPA (ηp2=0.30), emotion regulation (ηp2 =0.49), behavioural regulation (ηp2=0.53), affective attitude (ηp2=0.23), identity (ηp2=0.24), and constructs of mindfulness (ηp2=0.44), and valued living (ηp2=0.20).

CONCLUSIONS

Primary outcomes concerning feasibility were adequate and secondary outcomes improved, suggesting a full-scale randomized controlled trial is feasible with minor modifications. A large-scale study is warranted to establish intervention effectiveness.

Weight Loss with an AI-Powered Digital Platform for Lifestyle Intervention

BACKGROUND

Lifestyle intervention remains the cornerstone of weight loss programs in addition to pharmacological or surgical therapies. Artificial intelligence (AI) and other digital technologies can offer individualized approaches to lifestyle intervention to enable people with obesity to reach successful weight loss.

METHODS

SureMediks, a digital lifestyle intervention platform using AI, was tested by 391 participants (58% women) with a broad range of BMI (20-78 kg/m2), with the aim of losing weight over 24 weeks in a multinational field trial. SureMediks consists of a mobile app, an Internet-connected scale, and a discipline of artificial intelligence called Expert system to provide individualized guidance and weight-loss management.

RESULTS

All participants lost body weight (average 14%, range 4-22%). Almost all (98.7%) participants lost at least 5% of body weight, 75% lost at least 10%, 43% at least 15%, and 9% at least 20%, suggesting that this AI-powered lifestyle intervention was also effective in reducing the burden of obesity co-morbidities. Weight loss was partially positively correlated with female sex, accountability circle size, and participation in challenges, while it was negatively correlated with sub-goal reassignment. The latter three variables are specific features of the SureMediks weight loss program.

CONCLUSION

An AI-assisted lifestyle intervention allowed people with different body sizes to lose 14% body weight on average, with 99% of them losing more than 5%, over 24 weeks. These results show that digital technologies and AI might provide a successful means to lose weight, before, during, and after pharmacological or surgical therapies.

Use of podcasts for health education: a scoping review

OBJECTIVES

to map the scientific evidence related to the characteristics, themes, and outcomes of using health education podcasts aimed at individuals over 18 years of age in intra or extrahospital environments.

METHODS

a scoping review, based on the Joanna Briggs Institute method, conducted in 11 databases, including studies from 2004 to 2022.

RESULTS

11 studies were selected, categorized, highlighting the characteristics, evaluated outcomes, areas, and conditions of podcast application, indicating it as an effective tool for promoting behavioral change, health promotion, and social interaction, demonstrating its potential to improve well-being, quality of life, and user/client autonomy.

CONCLUSIONS

the use of podcasts proves to be an effective, innovative, and low-cost tool, with a significant social impact, being effective for behavioral change, satisfaction, and social interaction. However, the lack of comprehensive studies on podcast development methodologies represents challenges to be overcome.

Mobile health (m-health) smartphone interventions for adolescents and adults with overweight or obesity

BACKGROUND

Obesity is considered to be a risk factor for various diseases, and its incidence has tripled worldwide since 1975. In addition to potentially being at risk for adverse health outcomes, people with overweight or obesity are often stigmatised. Behaviour change interventions are increasingly delivered as mobile health (m-health) interventions, using smartphone apps and wearables. They are believed to support healthy behaviours at the individual level in a low-threshold manner.

OBJECTIVES

To assess the effects of integrated smartphone applications for adolescents and adults with overweight or obesity.

SEARCH METHODS

We searched CENTRAL, MEDLINE, PsycINFO, CINAHL, and LILACS, as well as the trials registers ClinicalTrials.gov and World Health Organization International Clinical Trials Registry Platform on 2 October 2023 (date of last search for all databases). We placed no restrictions on the language of publication.

SELECTION CRITERIA

Participants were adolescents and adults with overweight or obesity. Eligible interventions were integrated smartphone apps using at least two behaviour change techniques. The intervention could target physical activity, cardiorespiratory fitness, weight loss, healthy diet, or self-efficacy. Comparators included no or minimal intervention (NMI), a different smartphone app, personal coaching, or usual care. Eligible studies were randomised controlled trials of any duration with a follow-up of at least three months.

DATA COLLECTION AND ANALYSIS

We used standard Cochrane methodology and the RoB 2 tool. Important outcomes were physical activity, body mass index (BMI) and weight, health-related quality of life, self-efficacy, well-being, change in dietary behaviour, and adverse events. We focused on presenting studies with medium- (6 to < 12 months) and long-term (≥ 12 months) outcomes in our summary of findings table, following recommendations in the core outcome set for behavioural weight management interventions.

MAIN RESULTS

We included 18 studies with 2703 participants. Interventions lasted from 2 to 24 months. The mean BMI in adults ranged from 27 to 50, and the median BMI z-score in adolescents ranged from 2.2 to 2.5. Smartphone app versus no or minimal intervention Thirteen studies compared a smartphone app versus NMI in adults; no studies were available for adolescents. The comparator comprised minimal health advice, handouts, food diaries, smartphone apps unrelated to weight loss, and waiting list. Measures of physical activity: at 12 months' follow-up, a smartphone app compared to NMI probably reduces moderate to vigorous physical activity (MVPA) slightly (mean difference (MD) -28.9 min/week (95% confidence interval (CI) -85.9 to 28; 1 study, 650 participants; moderate-certainty evidence)). We are very uncertain about the results of estimated energy expenditure and cardiorespiratory fitness at eight months' follow-up. A smartphone app compared with NMI probably results in little to no difference in changes in total activity time at 12 months' follow-up and leisure time physical activity at 24 months' follow-up. Anthropometric measures: a smartphone app compared with NMI may reduce BMI (MD of BMI change -2.6 kg/m2, 95% CI -6 to 0.8; 2 studies, 146 participants; very low-certainty evidence) at six to eight months' follow-up, but the evidence is very uncertain. At 12 months' follow-up, a smartphone app probably resulted in little to no difference in BMI change (MD -0.1 kg/m2, 95% CI -0.4 to 0.3; 1 study; 650 participants; moderate-certainty evidence). A smartphone app compared with NMI may result in little to no difference in body weight change (MD -2.5 kg, 95% CI -6.8 to 1.7; 3 studies, 1044 participants; low-certainty evidence) at 12 months' follow-up. At 24 months' follow-up, a smartphone app probably resulted in little to no difference in body weight change (MD 0.7 kg, 95% CI -1.2 to 2.6; 1 study, 245 participants; moderate-certainty evidence). A smartphone app compared with NMI may result in little to no difference in self-efficacy for a physical activity score at eight months' follow-up, but the results are very uncertain. A smartphone app probably results in little to no difference in quality of life and well-being at 12 months (moderate-certainty evidence) and in little to no difference in various measures used to inform dietary behaviour at 12 and 24 months' follow-up. We are very uncertain about adverse events, which were only reported narratively in two studies (very low-certainty evidence). Smartphone app versus another smartphone app Two studies compared different versions of the same app in adults, showing no or minimal differences in outcomes. One study in adults compared two different apps (calorie counting versus ketogenic diet) and suggested a slight reduction in body weight at six months in favour of the ketogenic diet app. No studies were available for adolescents. Smartphone app versus personal coaching Only one study compared a smartphone app with personal coaching in adults, presenting data at three months. Two studies compared these interventions in adolescents. A smartphone app resulted in little to no difference in BMI z-score compared to personal coaching at six months' follow-up (MD 0, 95% CI -0.2 to 0.2; 1 study; 107 participants). Smartphone app versus usual care Only one study compared an app with usual care in adults but only reported data at three months on participant satisfaction. No studies were available for adolescents. We identified 34 ongoing studies.

AUTHORS' CONCLUSIONS

The available evidence is limited and does not demonstrate a clear benefit of smartphone applications as interventions for adolescents or adults with overweight or obesity. While the number of studies is growing, the evidence remains incomplete due to the high variability of the apps' features, content and components, which complicates direct comparisons and assessment of their effectiveness. Comparisons with either no or minimal intervention or personal coaching show minor effects, which are mostly not clinically significant. Minimal data for adolescents also warrants further research. Evidence is also scarce for low- and middle-income countries as well as for people with different socio-economic and cultural backgrounds. The 34 ongoing studies suggest sustained interest in the topic, with new evidence expected to emerge within the next two years. In practice, clinicians and healthcare practitioners should carefully consider the potential benefits, limitations, and evolving research when recommending smartphone apps to adolescents and adults with overweight or obesity.

Effectiveness of Different Online Intervention Modalities for Middle-Aged Adults with Overweight and Obesity: A 20-Year Systematic Review and Meta-analysis

The main objectives of this systematic review and meta-analysis study include evaluating the methodological quality of existing randomized controlled trials (RCTs) for weight loss and features of online intervention [OI]s in each trial, examining the associations between the methodological quality, intervention features and the effectiveness of OIs, and comparing the effectiveness of OIs and other intervention modalities through systematic review and meta-analysis. Systematic searches were conducted using PubMed, Cochrane Library, CINAHL, and PsycINFO in the past two decades (2000 through 2019). Inclusion criteria includes Online intervention (intervention modality), middle-aged adults with overweight or obesity, at least six months or longer study period, an RCT, and 70% plus retention rate. Risk of Bias was assessed using Miller et al. in (Hester, Miller (eds) Handbook of alcoholism treatment approaches: Effective alternatives (3rd ed.). Allyn & Bacon, Boston, 2003)'s Methodological Quality Rating Scale (MQRS) and GRADE. MOOSE guidelines was referred for data synthesis. In total, 29 OIs were evaluated using 10 criteria for methodological quality and eight criteria for intervention features. Results revealed that the mean methodological quality score of the RCTs was 12.1 (out of 16), and the mean intervention features score was 6.6 (out of 8). RCTs with higher scores were more effective in weight loss than those with lower scores. Results of meta-regression showed that methodological quality was more important than intervention features to increase the effectiveness. Results of meta-analysis showed that OIs were significantly more effective than controls. Compared to OIs only, OIs with interactions with others and professionals were more effective. The study limitation includes assessing 'effectiveness' based on weight only due to lack of other indicators to compare between studies; some results are self-reported; and feedback from intervention participants were hard to review. Nevertheless, this study may contribute to improving the effectiveness of existing OIs for weight loss considering methodological quality and better intervention features.

The use of internet-based smartphone apps consistently improved consumers' healthy eating behaviors: a systematic review of randomized controlled trials

Introduction

Digital tools, such as mobile apps and the Internet, are being increasingly used to promote healthy eating habits. However, there has been inconsistent reporting on the effectiveness of smartphones and web-based apps in influencing dietary behaviors. Moreover, previous reviews have been limited in scope, either by focusing on a specific population group or by being outdated. Therefore, the purpose of this review is to investigate the impacts of smartphone- and web-based dietary interventions on promoting healthy eating behaviors worldwide.

Methods

A systematic literature search of randomized controlled trials was conducted using databases such as Google Scholar, PubMed, Global Health, Informit, Web of Science, and CINAHL (EBSCO). The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed to prepare the entire document. EndNote (version 20) was used for reference management. The risk of bias in the articles was assessed using the "Revised Cochrane Risk of Bias tool for randomized trials (RoB 2.0)" by the Cochrane Collaboration. Narrative synthesis, using text and tables, was used to present the results. The study was registered in PROSPERO under protocol number CRD42023464315.

Results

This review analyzed a total of 39 articles, which consisted of 25 smartphone-based apps and 14 web-based apps. The studies involved a total of 14,966 participants. Out of the 25 studies, 13 (52%) showed that offline-capable smartphone apps are successful in promoting healthier eating habits. The impact of smartphone apps on healthy adults has been inconsistently reported. However, studies have shown their effectiveness in chronically ill patients. Likewise, internet-based mobile apps, such as social media or nutrition-specific apps, have been found to effectively promote healthy eating behaviors. These findings were consistent across 14 studies, which included healthy adults, overweight or obese adults, chronically ill patients, and pregnant mothers.

Conclusion

Overall, the findings suggest that smartphone apps contribute to improving healthy eating behaviors. Both nutrition-specific and social media-based mobile apps consistently prove effective in promoting long-term healthy eating habits. Therefore, policymakers in the food system should consider harnessing the potential of internet-based mobile apps and social media platforms to foster sustainable healthy eating behaviors.

Podcasts as a tool for promoting health-related behaviours: A scoping review

Background

Podcasts are a popular medium for delivering health-related content, potentially influencing physical and mental health behaviours and outcomes. This scoping review evaluates the impact of health podcasts on such outcomes, engagement levels, and public perceptions.

Methods

This scoping review followed the Arksey and O'Malley framework and PRISMA-ScR guidelines. Qualitative or quantitative articles published from 2004 onward were included. Eligibility criteria were developed using the Population, Exposure and Outcome framework. Included articles examined the use, engagement with, or perspectives towards health-related podcasts. Data were synthesised narratively.

Results

Fifty articles encompassing 38 studies were analysed. Significant improvements (p < 0.05) were observed in health monitoring, knowledge, behaviours, attitudes, chronic disease management, maternal health and behavioural improvements. Results were mixed for physical activity (n = 2 significantly improved, n = 2 no change), fruit and vegetable consumption (n = 1 significantly improved, n = 1 no change), and weight outcomes (n = 2 significantly improved, n = 2 no change). No significant changes were observed in depression and anxiety. Podcast engagement studies (n = 7) reported participation rates between 62% and 83% and an average weekly listening duration of 103-124 minutes. There was high satisfaction, trust and appreciation for podcasts that effectively blended personal anecdotes with reliable medical information. Sample sizes ranged from 7 to 722, with sample age ranging from under 18 to 73.2 ± 6.2 years. Studies included both male (n = 1), female (n = 6) and mixed samples (n = 24). Podcasts were used individually (n = 19), alongside other supportive technology such as apps and online material (n = 11) and retrospective podcast use was analysed in cross-sectional (n = 3) and a single (n = 1) audit. Duration of podcasts ranged from 30 seconds to 24 minutes.

Conclusion

Podcasts show promise as effective tools for health promotion, achieving strong engagement and effects on knowledge and behaviours. Future research should explore content innovation and integration into health interventions, and long-term effectiveness.

Social media-based interventions for adults with obesity and overweight: a meta-analysis and meta-regression

Social isolation and loneliness are growing public health concerns in adults with obesity and overweight. Social media-based interventions may be a promising approach. This systematic review aims to (1) evaluate the effectiveness of social media-based interventions on weight, body mass index, waist circumference, fat, energy intake and physical activity among adults with obesity and overweight and (2) explore potential covariates on treatment effect. Eight databases, namely, PubMed, Cochrane Library, Embase, CINAHL, Web of Science, Scopus PsycINFO and ProQuest, were searched from inception until December 31, 2021. The Cochrane Collaboration Risk of Bias Tool and Grading of Recommendations, Assessment, Development and Evaluation criteria evaluated the evidence quality. Twenty-eight randomised controlled trials were identified. Meta-analyses found that social media-based interventions had small-to-medium significant effects on weight, BMI, waist circumference, body fat mass and daily steps. Subgroup analysis found greater effect in interventions without published protocol or not registered in trial registries than their counterparts. Meta-regression analysis showed that duration of intervention was a significant covariate. The certainty of evidence quality of all outcomes was very low or low. Social media-based interventions can be considered an adjunct intervention for weight management. Future trials with large sample sizes and follow-up assessment are needed.

The Impact of Social Media Interventions on Weight Reduction and Physical Activity Improvement Among Healthy Adults: Systematic Review

BACKGROUND

A sedentary lifestyle and being overweight or obese are well-established cardiovascular risk factors and contribute substantially to the global burden of disease. Changing such behavior is complex and requires support. Social media interventions show promise in supporting health behavior change, but their impact is unclear. Moreover, previous reviews have reported contradictory evidence regarding the relationship between engagement with social media interventions and the efficacy of these interventions.

OBJECTIVE

This review aimed to critically synthesize available evidence regarding the impact of social media interventions on physical activity and weight among healthy adults. In addition, this review examined the effect of engagement with social media interventions on their efficacy.

METHODS

CINAHL and MEDLINE were searched for relevant randomized trials that were conducted to investigate the impact of social media interventions on weight and physical activity and were published between 2011 and 2021 in the English language. Studies were included if the intervention used social media tools that provided explicit interactions between the participants. Studies were excluded if the intervention was passively delivered through an app website or if the participants had a known chronic disease. Eligible studies were appraised for quality and synthesized using narrative synthesis.

RESULTS

A total of 17 papers reporting 16 studies from 4 countries, with 7372 participants, were identified. Overall, 56% (9/16) of studies explored the effect of social media interventions on physical activity; 38% (6/16) of studies investigated weight reduction; and 6% (1/16) of studies assessed the effect on both physical activity and weight reduction. Evidence of the effects of social media interventions on physical activity and weight loss was mixed across the included studies. There were no standard metrics for measuring engagement with social media, and the relationship between participant engagement with the intervention and subsequent behavior change was also mixed. Although 35% (6/16) of studies reported that engagement was not a predictor of behavior change, engagement with social media interventions was found to be related to behavior change in 29% (5/16) of studies.

CONCLUSIONS

Despite the promise of social media interventions, evidence regarding their effectiveness is mixed. Further robust studies are needed to elucidate the components of social media interventions that lead to successful behavior change. Furthermore, the effect of engagement with social media interventions on behavior change needs to be clearly understood.

TRIAL REGISTRATION

PROSPERO International Prospective Register of Systematic Reviews CRD42022311430; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=311430.

Exercise in Obesity-the Role of Technology in Health Services: Can This Approach Work?

PURPOSE OF REVIEW

Physical activity (PA) is an important strategy to prevent and treat obesity. Electronic health (eHealth) interventions, such as wearable activity monitors and smartphone apps, may promote adherence to regular PA and successful weight loss. This review highlights the evidence for eHealth interventions in promoting PA and reducing weight.

RECENT FINDINGS

Wearables can increase PA and are associated with moderate weight loss in middle/older-aged individuals, with less convincing effects long-term (> 1 year) and in younger people. Data for interventions such as mobile phone applications, SMS, and exergaming are less robust. Investigations of all eHealth interventions are often limited by complex, multi-modality study designs, involving concomitant dietary modification, making the independent contribution of each eHealth intervention on body weight challenging to assess. eHealth interventions may promote PA, thereby contributing to weight loss/weight maintenance; however, further evaluation is required for this approach to be adopted into routine clinical practice.

Investigating psychological variables for technologies promoting physical activity

Background

Many technological interventions designed to promote physical activity (PA) have limited efficacy and appear to lack important factors that could increase engagement. This may be due to a discrepancy between research conducted in this space, and software designers' and developers' use of this research to inform new digital applications.

Objectives

This study aimed to identify (1) what are the variables that act as barriers and facilitators to PA and (2) which PA variables are currently considered in the design of technologies promoting PA including psychological, physical, and personal/contextual ones which are critical in promoting PA. We emphasize psychological variables in this work because of their sparse and often simplistic integration in digital applications for PA.

Methods

We conducted two systematized reviews on PA variables, using PsycInfo and Association for Computing Machinery Digital Libraries for objectives 1 and 2.

Results

We identified 38 PA variables (mostly psychological ones) including barriers/facilitators in the literature. 17 of those variables were considered when developing digital applications for PA. Only few studies evaluate PA levels in relation to these variables. The same barriers are reported for all weight groups, though some barriers are stronger in people with obesity.

Conclusions

We identify PA variables and illustrate the lack of consideration of these in the design of PA technologies. Digital applications to promote PA may have limited efficacy if they do not address variables acting as facilitators or barriers to participation in PA, and that are important to people representing a range of body weight characteristics.

Predicting Participant Engagement in a Social Media-Delivered Lifestyle Intervention Using Microlevel Conversational Data: Secondary Analysis of Data From a Pilot Randomized Controlled Trial

BACKGROUND

Social media-delivered lifestyle interventions have shown promising outcomes, often generating modest but significant weight loss. Participant engagement appears to be an important predictor of weight loss outcomes; however, engagement generally declines over time and is highly variable both within and across studies. Research on factors that influence participant engagement remains scant in the context of social media-delivered lifestyle interventions.

OBJECTIVE

This study aimed to identify predictors of participant engagement from the content generated during a social media-delivered lifestyle intervention, including characteristics of the posts, the conversation that followed the post, and participants' previous engagement patterns.

METHODS

We performed secondary analyses using data from a pilot randomized trial that delivered 2 lifestyle interventions via Facebook. We analyzed 80 participants' engagement data over a 16-week intervention period and linked them to predictors, including characteristics of the posts, conversations that followed the post, and participants' previous engagement, using a mixed-effects model. We also performed machine learning-based classification to confirm the importance of the significant predictors previously identified and explore how well these measures can predict whether participants will engage with a specific post.

RESULTS

The probability of participants' engagement with each post decreased by 0.28% each week (P<.001; 95% CI 0.16%-0.4%). The probability of participants engaging with posts generated by interventionists was 6.3% (P<.001; 95% CI 5.1%-7.5%) higher than posts generated by other participants. Participants also had a 6.5% (P<.001; 95% CI 4.9%-8.1%) and 6.1% (P<.001; 95% CI 4.1%-8.1%) higher probability of engaging with posts that directly mentioned weight and goals, respectively, than other types of posts. Participants were 44.8% (P<.001; 95% CI 42.8%-46.9%) and 46% (P<.001; 95% CI 44.1%-48.0%) more likely to engage with a post when they were replied to by other participants and by interventionists, respectively. A 1 SD decrease in the sentiment of the conversation on a specific post was associated with a 5.4% (P<.001; 95% CI 4.9%-5.9%) increase in the probability of participants' subsequent engagement with the post. Participants' engagement in previous posts was also a predictor of engagement in subsequent posts (P<.001; 95% CI 0.74%-0.79%). Moreover, using a machine learning approach, we confirmed the importance of the predictors previously identified and achieved an accuracy of 90.9% in terms of predicting participants' engagement using a balanced testing sample with 1600 observations.

CONCLUSIONS

Findings revealed several predictors of engagement derived from the content generated by interventionists and other participants. Results have implications for increasing engagement in asynchronous, remotely delivered lifestyle interventions, which could improve outcomes. Our results also point to the potential of data science and natural language processing to analyze microlevel conversational data and identify factors influencing participant engagement. Future studies should validate these results in larger trials.

TRIAL REGISTRATION

ClinicalTrials.gov NCT02656680; https://clinicaltrials.gov/ct2/show/NCT02656680.

A Facebook-Delivered Weight Loss Intervention Using Open Enrollment: Randomized Pilot Feasibility Trial

BACKGROUND

Behavioral weight loss programs typically enroll 12-40 people into groups that then suffer from declining engagement over time. Web-based patient communities, on the other hand, typically offer no limits on capacity and membership is fluid. This model may be useful for boosting engagement in behavioral weight loss interventions, which could lead to better outcomes.

OBJECTIVE

In this study, we aimed to examine the feasibility and acceptability of continuously enrolling participants into a Facebook-delivered weight loss intervention for the first 8 of 16 weeks relative to the same intervention where no new participants were enrolled after randomization.

METHODS

We conducted a randomized pilot trial to compare a Facebook weight loss group that used open enrollment with a group that used closed enrollment on feasibility and acceptability in adults with BMI 27-45 kg/m2. The feasibility outcomes included retention, engagement, and diet tracking adherence. We described the percentage loss of ≥5% weight in both groups as an exploratory outcome. We also explored the relationship between total volume of activity in the group and weight loss. The participants provided feedback via web-based surveys and focus groups.

RESULTS

Randomized participants (68/80, 85% women) were on average, aged 40.2 (SD 11.2) years with a mean BMI of 34.4 (SD 4.98) kg/m2. We enrolled an additional 54 participants (50/54, 93% female) in the open enrollment condition between weeks 1 and 8, resulting in a total group size of 94. Retention was 88% and 98% under the open and closed conditions, respectively. Randomized participants across conditions did not differ in engagement (P=.72), or diet tracking adherence (P=.42). Participant feedback in both conditions revealed that sense of community was what they liked most about the program and not enough individualized feedback was what they liked the least. Weight loss of ≥5% was achieved by 30% (12/40) of the participants randomized to the open enrollment condition and 18% (7/40) of the participants in the closed enrollment condition. Exploratory analyses revealed that the open condition (median 385, IQR 228-536.5) had a greater volume of engagement than the closed condition (median 215, IQR 145.5-292; P=.007). Furthermore, an increase of 100 in the total volume of engagement in the Facebook group each week was associated with an additional 0.1% weekly weight loss among the randomized participants (P=.02), which was independent of time, individual participant engagement, and sociodemographic characteristics.

CONCLUSIONS

Open enrollment was as feasible and acceptable as closed enrollment. A greater volume of engagement in the Facebook group was associated with weight loss, suggesting that larger groups that produce more engagement overall may be beneficial. Future research should examine the efficacy of the open enrollment approach for weight loss in a fully powered randomized trial.

TRIAL REGISTRATION

ClinicalTrials.gov NCT02656680; https://clinicaltrials.gov/ct2/show/NCT02656680.

The Effectiveness of Combining Nonmobile Interventions With the Use of Smartphone Apps With Various Features for Weight Loss: Systematic Review and Meta-analysis

Background

The effectiveness of smartphone apps for weight loss is limited by the diversity of interventions that accompany such apps. This research extends the scope of previous systematic reviews by including 2 subgroup analyses based on nonmobile interventions that accompanied smartphone use and human-based versus passive behavioral interventions.

Objective

The primary objective of this study is to systematically review and perform a meta-analysis of studies that evaluated the effectiveness of smartphone apps on weight loss in the context of other interventions combined with app use. The secondary objective is to measure the impact of different mobile app features on weight loss and mobile app adherence.

Methods

We conducted a systematic review and meta-analysis of relevant studies after an extensive search of the PubMed, MEDLINE, and EBSCO databases from inception to January 31, 2022. Gray literature, such as abstracts and conference proceedings, was included. Working independently, 2 investigators extracted the data from the articles, resolving disagreements by consensus. All randomized controlled trials that used smartphone apps in at least 1 arm for weight loss were included. The weight loss outcome was the change in weight from baseline to the 3- and 6-month periods for each arm. Net change estimates were pooled across the studies using random-effects models to compare the intervention group with the control group. The risk of bias was assessed independently by 2 authors using the Cochrane Collaboration tool for assessing the risk of bias in randomized trials.

Results

Overall, 34 studies were included that evaluated the use of a smartphone app in at least 1 arm. Compared with controls, the use of a smartphone app–based intervention showed a significant weight loss of –1.99 kg (95% CI –2.19 to –1.79 kg; I²=81%) at 3 months and –2.80 kg (95% CI –3.03 to –2.56 kg; I²=91%) at 6 months. In the subgroup analysis, based on the various intervention components that were added to the mobile app, the combination of the mobile app, tracker, and behavioral interventions showed a statistically significant weight loss of –2.09 kg (95% CI –2.32 to –1.86 kg; I²=91%) and –3.77 kg (95% CI –4.05 to –3.49 kg; I²=90%) at 3 and 6 months, respectively. When a behavioral intervention was present, only the combination of the mobile app with intensive behavior coaching or feedback by a human coach showed a statistically significant weight loss of –2.03 kg (95% CI –2.80 to –1.26 kg; I²=83%) and –2.63 kg (95% CI –2.97 to –2.29 kg; I²=91%) at 3 and 6 months, respectively. Neither the type nor the number of mobile app features was associated with weight loss.

Conclusions

Smartphone apps have a role in weight loss management. Nevertheless, the human-based behavioral component remained key to higher weight loss results.

The Effect of Tailored, Daily Smartphone Feedback to Lifestyle Self-Monitoring on Weight Loss at 12 Months: The SMARTER Randomized Clinical Trial (Preprint)

Background

Self-monitoring (SM) is the centerpiece of behavioral weight loss treatment, but the efficacy of smartphone-delivered SM feedback (FB) has not been tested in large, long-term, randomized trials.

Objective

The aim of this study was to establish the efficacy of providing remote FB to diet, physical activity (PA), and weight SM on improving weight loss outcomes when comparing the SM plus FB (SM+FB) condition to the SM-only condition in a 12-month randomized controlled trial. The study was a single-site, population-based trial that took place in southwestern Pennsylvania, USA, conducted between 2018 and 2021. Participants were smartphone users age ≥18 years, able to engage in moderate PA, with a mean BMI between 27 and 43 kg/m².

Methods

All participants received a 90-minute, one-to-one, in-person behavioral weight loss counseling session addressing behavioral strategies, establishing participants’ dietary and PA goals, and instructing on use of the PA tracker (Fitbit Charge 2), smart scale, and diet SM app. Only SM+FB participants had access to an investigator-developed smartphone app that read SM data, in which an algorithm selected tailored messages sent to the smartphone up to 3 times daily. The SM-only participants did not receive any tailored FB based on SM data. The primary outcome was percent weight change from baseline to 12 months. Secondary outcomes included engagement with digital tools (eg, monthly percentage of FB messages opened and monthly percentage of days adherent to the calorie goal).

Results

Participants (N=502) were on average 45.0 (SD 14.4) years old with a mean BMI of 33.7 (SD 4.0) kg/m². The sample was 79.5% female (n=399/502) and 82.5% White (n=414/502). At 12 months, retention was 78.5% (n=394/502) and similar by group (SM+FB: 202/251, 80.5%; SM: 192/251, 76.5%; P=.28). There was significant percent weight loss from baseline in both groups (SM+FB: –2.12%, 95% CI –3.04% to –1.21%, P<.001; SM: –2.39%, 95% CI –3.32% to –1.47%; P<.001), but no difference between the groups (–0.27%; 95% CI –1.57% to 1.03%; t =–0.41; P=.68). Similarly, 26.3% (66/251) of the SM+FB group and 29.1% (73/251) of the SM group achieved ≥5% weight loss (chi-square value=0.49; P=.49). A 1% increase in FB messages opened was associated with a 0.10 greater percent weight loss at 12 months (b=–0.10; 95% CI –0.13 to –0.07; t =–5.90; P<.001). A 1% increase in FB messages opened was associated with 0.12 greater percentage of days adherent to the calorie goal per month (b=0.12; 95% CI 0.07-0.17; F=22.19; P<.001).

Conclusions

There were no significant between-group differences in weight loss; however, the findings suggested that the use of commercially available digital SM tools with or without FB resulted in a clinically significant weight loss in over 25% of participants. Future studies need to test additional strategies that will promote greater engagement with digital tools.

Trial Registration

Clinicaltrials.gov NCT03367936; https://clinicaltrials.gov/ct2/show/NCT03367936

Weight Reduction Interventions Using Digital Health for Employees with Obesity: A Systematic Review

PURPOSE

This study aimed to systematically review the literature on randomized controlled trials on weight reduction interventions using digital health for employees with obesity.

METHODS

All relevant articles published until September 2021 were systematically identified from six electronic databases: MEDLINE, EMBASE, CINAHL, PsycINFO, RISS, and KISS. Data selection and extraction were independently performed by three researchers. Methodological quality was assessed using the JBI Critical Appraisal Checklist for Randomized Controlled Trials. The results were narratively synthesized.

RESULTS

Eleven studies were included in the systematic review. All studies had a low risk of bias. The settings and sample sizes of the included studies were different. The contents of the interventions included nutrition, physical activity, behavioral change, incentives, and motivation. Four studies were based on social cognitive theory. A total of ten studies delivered web-based intervention, while the other used tele-monitoring device. A wide range of intervention strategies was used including providing online resources, tele-counseling, and patient-tailored advice. As a result of the intervention, a total of seven studies showed a significant weight reduction in both the intervention and comparison groups, with significant differences between groups.

CONCLUSION

Until now, use of digital health in weight reduction interventions for employees with obesity has been conducted on a web-based. Various contents such as nutrition, physical activity and theories were explored. Further study is required using more diverse delivery methods such as mobile application, use of wearable devices.

Nutrition Concepts for the Treatment of Obesity in Adults

Obesity caused by a positive energy balance is a serious health burden. Studies have shown that obesity is the major risk factor for many diseases like type 2 diabetes mellitus, coronary heart diseases, or various types of cancer. Therefore, the prevention and treatment of increased body weight are key. Different evidence-based treatment approaches considering weight history, body mass index (BMI) category, and co-morbidities are available: lifestyle intervention, formula diet, drugs, and bariatric surgery. For all treatment approaches, behaviour change techniques, reduction in energy intake, and increasing energy expenditure are required. Self-monitoring of diet and physical activity provides an effective behaviour change technique for weight management. Digital tools increase engagement rates for self-monitoring and have the potential to improve weight management. The objective of this narrative review is to summarize current available treatment approaches for obesity, to provide a selective overview of nutrition trends, and to give a scientific viewpoint for various nutrition concepts for weight loss.

A randomized controlled trial of an innovative, user-friendly, interactive smartphone app-based lifestyle intervention for weight loss

OBJECTIVES

Most electronically delivered lifestyle interventions are labor intensive, requiring logging onto websites and manually recording activity and diet. Cumbersome technology and lack of a human coach may have contributed to the limitations of prior interventions. In response, the current program of research created a comprehensive electronically delivered lifestyle intervention using a user-friendly, interactive, smartphone app-based model, and evaluated it in a randomized controlled trial.

METHODS

Twenty-eight adults, body mass index 25-42 kg/m2, with smartphones and sedentary jobs, were randomized to the intervention, along with conventional outpatient weight-management visits every 3 months, or to a wait-listed control group that received only weight-management visits. The intervention included wearable activity trackers, smartscales, food photography logs, physician-driven app-based behavioral coaching, and peer support via the app. The prespecified primary outcome was a comparison of change in weight in kilograms, in the intervention versus control group at 6 months.

RESULTS

At 6 months, the intervention group experienced a statistically significant weight change of -7.16 ± 1.78 kg (mean ± SE, 95% CI -11.05 to -3.26, p < 0.01), which differed from the weight change in controls by -4.16 ± 2.01 kg (95% CI -8.29 to -0.02, p < 0.05, prespecified primary outcome). Weight change in the control group was -3.00 ± 1.05 kg (95% CI -5.27 to -0.73, p < 0.05). Waist circumference and hemoglobin A1c significantly improved (intervention vs. control: p < 0.01, p < 0.05, respectively, prespecified secondary outcomes). Weight change in the intervention group correlated with numbers of food photographs participants shared (rho = -0.86, p < 0.01), numbers of their text messages (rho = -0.80, p < 0.01), number of times and days each participant stepped on the smartscale (rho = -0.73, p < 0.01; rho = -0.608, p < 0.05, respectively), and mean daily step counts (rho = -0.55, p < 0.05).

CONCLUSION

This app-based electronically delivered lifestyle intervention produced statistically significant, clinically meaningful weight loss and improved metabolic health. Engagement with the intervention correlated strongly with weight loss. Given the limited sample size, larger and longer studies of this intervention are needed.

A systematic review of the use of dietary self-monitoring in behavioural weight loss interventions: delivery, intensity and effectiveness

OBJECTIVE

To identify dietary self-monitoring implementation strategies in behavioural weight loss interventions.

DESIGN

We conducted a systematic review of eight databases and examined fifty-nine weight loss intervention studies targeting adults with overweight/obesity that used dietary self-monitoring.

SETTING

NA.

PARTICIPANTS

NA.

RESULTS

We identified self-monitoring implementation characteristics, effectiveness of interventions in supporting weight loss and examined weight loss outcomes among higher and lower intensity dietary self-monitoring protocols. Included studies utilised diverse self-monitoring formats (paper, website, mobile app, phone) and intensity levels (recording all intake or only certain aspects of diet). We found the majority of studies using high- and low-intensity self-monitoring strategies demonstrated statistically significant weight loss in intervention groups compared with control groups.

CONCLUSIONS

Based on our findings, lower and higher intensity dietary self-monitoring may support weight loss, but variability in adherence measures and limited analysis of weight loss relative to self-monitoring usage limits our understanding of how these methods compare with each other.

Research on Diagnostic Information of Smart Medical Care Based on Big Data

The medical field has gradually become intelligent, and information and the research of intelligent medical diagnosis information have received extensive attention in the medical field. In response to this situation, this paper proposes a Hadoop-based medical big data processing system. The system first realized the ETL module based on Sqoop and the transmission function of unstructured data and then realized the distributed storage management function based on HDFS. Finally, a MapReduce algorithm with variable key values is proposed in the data statistical analysis module. Through simulation experiments on the system modules and each algorithm, the results show that because the traditional nondistributed big data acquisition module transmits the same scale of data, it consumes more than 3200 s and the transmission time exceeds 3000 s. The time consumption of smart medical care under the 6G protocol is 150 s, the transmission time is 146 s, and the time is reduced to 1/10 of the original. The research of intelligent medical diagnosis information based on big data has good rationality and feasibility.

Behavioural interventions delivered through interactive social media for health behaviour change, health outcomes, and health equity in the adult population

BACKGROUND

Social networking platforms offer a wide reach for public health interventions allowing communication with broad audiences using tools that are generally free and straightforward to use and may be combined with other components, such as public health policies. We define interactive social media as activities, practices, or behaviours among communities of people who have gathered online to interactively share information, knowledge, and opinions.

OBJECTIVES

We aimed to assess the effectiveness of interactive social media interventions, in which adults are able to communicate directly with each other, on changing health behaviours, body functions, psychological health, well-being, and adverse effects. Our secondary objective was to assess the effects of these interventions on the health of populations who experience health inequity as defined by PROGRESS-Plus. We assessed whether there is evidence about PROGRESS-Plus populations being included in studies and whether results are analysed across any of these characteristics.

SEARCH METHODS

We searched CENTRAL, CINAHL, Embase, MEDLINE (including trial registries) and PsycINFO. We used Google, Web of Science, and relevant web sites to identify additional studies and searched reference lists of included studies. We searched for published and unpublished studies from 2001 until June 1, 2020. We did not limit results by language.

SELECTION CRITERIA

We included randomised controlled trials (RCTs), controlled before-and-after (CBAs) and interrupted time series studies (ITSs). We included studies in which the intervention website, app, or social media platform described a goal of changing a health behaviour, or included a behaviour change technique. The social media intervention had to be delivered to adults via a commonly-used social media platform or one that mimicked a commonly-used platform. We included studies comparing an interactive social media intervention alone or as a component of a multi-component intervention with either a non-interactive social media control or an active but less-interactive social media comparator (e.g. a moderated versus an unmoderated discussion group). Our main outcomes were health behaviours (e.g. physical activity), body function outcomes (e.g. blood glucose), psychological health outcomes (e.g. depression), well-being, and adverse events. Our secondary outcomes were process outcomes important for behaviour change and included knowledge, attitudes, intention and motivation, perceived susceptibility, self-efficacy, and social support.

DATA COLLECTION AND ANALYSIS

We used a pre-tested data extraction form and collected data independently, in duplicate. Because we aimed to assess broad outcomes, we extracted only one outcome per main and secondary outcome categories prioritised by those that were the primary outcome as reported by the study authors, used in a sample size calculation, and patient-important.

MAIN RESULTS

We included 88 studies (871,378 participants), of which 84 were RCTs, three were CBAs and one was an ITS. The majority of the studies were conducted in the USA (54%). In total, 86% were conducted in high-income countries and the remaining 14% in upper middle-income countries. The most commonly used social media platform was Facebook (39%) with few studies utilising other platforms such as WeChat, Twitter, WhatsApp, and Google Hangouts. Many studies (48%) used web-based communities or apps that mimic functions of these well-known social media platforms. We compared studies assessing interactive social media interventions with non-interactive social media interventions, which included paper-based or in-person interventions or no intervention. We only reported the RCT results in our 'Summary of findings' table. We found a range of effects on health behaviours, such as breastfeeding, condom use, diet quality, medication adherence, medical screening and testing, physical activity, tobacco use, and vaccination. For example, these interventions may increase physical activity and medical screening tests but there was little to no effect for other health behaviours, such as improved diet or reduced tobacco use (20,139 participants in 54 RCTs). For body function outcomes, interactive social media interventions may result in small but important positive effects, such as a small but important positive effect on weight loss and a small but important reduction in resting heart rate (4521 participants in 30 RCTs). Interactive social media may improve overall well-being (standardised mean difference (SMD) 0.46, 95% confidence interval (CI) 0.14 to 0.79, moderate effect, low-certainty evidence) demonstrated by an increase of 3.77 points on a general well-being scale (from 1.15 to 6.48 points higher) where scores range from 14 to 70 (3792 participants in 16 studies). We found no difference in effect on psychological outcomes (depression and distress) representing a difference of 0.1 points on a standard scale in which scores range from 0 to 63 points (SMD -0.01, 95% CI -0.14 to 0.12, low-certainty evidence, 2070 participants in 12 RCTs). We also compared studies assessing interactive social media interventions with those with an active but less interactive social media control (11 studies). Four RCTs (1523 participants) that reported on physical activity found an improvement demonstrated by an increase of 28 minutes of moderate-to-vigorous physical activity per week (from 10 to 47 minutes more, SMD 0.35, 95% CI 0.12 to 0.59, small effect, very low-certainty evidence). Two studies found little to no difference in well-being for those in the intervention and control groups (SMD 0.02, 95% CI -0.08 to 0.13, small effect, low-certainty evidence), demonstrated by a mean change of 0.4 points on a scale with a range of 0 to 100. Adverse events related to the social media component of the interventions, such as privacy issues, were not reported in any of our included studies. We were unable to conduct planned subgroup analyses related to health equity as only four studies reported relevant data.

AUTHORS' CONCLUSIONS

This review combined data for a variety of outcomes and found that social media interventions that aim to increase physical activity may be effective and social media interventions may improve well-being. While we assessed many other outcomes, there were too few studies to compare or, where there were studies, the evidence was uncertain. None of our included studies reported adverse effects related to the social media component of the intervention. Future studies should assess adverse events related to the interactive social media component and should report on population characteristics to increase our understanding of the potential effect of these interventions on reducing health inequities.

Managing osteoarthritis pain with smart technology: a narrative review

Osteoarthritis (OA) is a highly prevalent musculoskeletal condition worldwide. More than 300 million individuals are affected by OA, and pain is the most common and challenging symptom to manage. Although many new advances have led to improved OA-related pain management, smart technology offers additional opportunities to enhance symptom management. This narrative review identifies and describes the current literature focused on smart technology for pain management in individuals with OA. In collaboration with a health sciences librarian, an interdisciplinary team of clinician-scientists searched multiple databases (e.g. PubMed, CINAHL and Embase), which generated 394 citations for review. After inclusion criteria were met, data were extracted from eight studies reporting on varied smart technologies, including mobile health, wearables and eHealth tools to measure or manage pain. Our review highlights the dearth of research in this crucial area, the implications for clinical practice and technology development, and future research needs.

Use of Technology-Based Interventions in the Treatment of Patients with Overweight and Obesity: A Systematic Review

Introduction: Obesity is one of the most important health problems worldwide. The prevalence of obesity has increased dramatically in the last decades and is now recognized as a global epidemic. Given the dramatic consequences of obesity, new intervention approaches based on the potential of technologies have been developed. Methods: We conducted a systematic review of studies using PubMed, ScienceDirect, Cochrane Library, and MedLine databases to assess how different types of technologies may play an important role on weight loss in obese patients. Results: Forty-seven studies using different types of technologies including smartphones, app, websites, virtual reality and personal digital assistant were included in the review. About half of interventions (47%) found a significant effect of the technology-based interventions for weight lost in obese patients. The provision of feedback could also be effective as a complement to interventions carried out using technology to promote weight loss. Conclusions: The use of technologies can be effective to increase weight loss in patients with obesity improving treatment adherence through self-monitoring.

A Systematic Review of Randomized Controlled Trials of Telehealth and Digital Technology Use by Community Pharmacists to Improve Public Health

Community pharmacists (CPs) continue to have an important role in improving public health, however, advances in telehealth and digital technology mean that the methods by which they support their customers and patients are changing. The primary aim of this study was to identify which telehealth and digital technology tools are used by CPs for public health purposes and determine if these have a positive impact on public health outcomes. A systematic review was carried out using databases including PubMed and ScienceDirect, covering a time period from April 2005 until April 2020. The search criteria were the following: randomized controlled trials, published in English, investigating the delivery of public health services by community pharmacists using a telehealth or digital tool. Thirteen studies were included out of 719 initially identified. Nine studies detailed the use of telephone prompts or calls, one study detailed the use of a mobile health application, two studies detailed the use of a remote monitoring device, and one study detailed the use of photo-aging software. Public health topics that were addressed included vaccination uptake (n = 2), smoking cessation (n = 1), hypertension management (n = 2), and medication adherence and counseling (n = 8). More studies are needed to demonstrate whether or not the use of novel technology by CPs can improve public health.

Use of Mobile Phone App Interventions to Promote Weight Loss: Meta-Analysis

BACKGROUND

Obesity and lack of physical activity are major health risk factors for many life-threatening diseases, such as cardiovascular diseases, type 2 diabetes, and cancer. The use of mobile app interventions to promote weight loss and boost physical activity among children and adults is fascinating owing to the demand for cutting-edge and more efficient interventions. Previously published studies have examined different types of technology-based interventions and their impact on weight loss and increase in physical activity, but evidence regarding the impact of only a mobile phone app on weight loss and increase in physical activity is still lacking.

OBJECTIVE

The main objective of this study was to assess the efficacy of a mobile phone app intervention for reducing body weight and increasing physical activity among children and adults.

METHODS

PubMed, Google Scholar, Scopus, EMBASE, and the Web of Science electronic databases were searched for studies published between January 1, 2000, and April 30, 2019, without language restrictions. Two experts independently screened all the titles and abstracts to find the most appropriate studies. To be included, studies had to be either a randomized controlled trial or a case-control study that assessed a mobile phone app intervention with body weight loss and physical activity outcomes. The Cochrane Collaboration Risk of Bias tool was used to examine the risk of publication bias.

RESULTS

A total of 12 studies involving a mobile phone app intervention were included in this meta-analysis. Compared with the control group, the use of a mobile phone app was associated with significant changes in body weight (-1.07 kg, 95% CI -1.92 to -0.21, P=.01) and body mass index (-0.45 kg/m2, 95% CI -0.78 to -0.12, P=.008). Moreover, a nonsignificant increase in physical activity was observed (0.17, 95% CI -2.21 to 2.55, P=.88).

CONCLUSIONS

The findings of this study demonstrate the promising and emerging efficacy of using mobile phone app interventions for weight loss. Future studies are needed to explore the long-term efficacy of mobile app interventions in larger samples.

Use of Mobile Phone App Interventions to Promote Weight Loss: Meta-Analysis

Background

Obesity and lack of physical activity are major health risk factors for many life-threatening diseases, such as cardiovascular diseases, type 2 diabetes, and cancer. The use of mobile app interventions to promote weight loss and boost physical activity among children and adults is fascinating owing to the demand for cutting-edge and more efficient interventions. Previously published studies have examined different types of technology-based interventions and their impact on weight loss and increase in physical activity, but evidence regarding the impact of only a mobile phone app on weight loss and increase in physical activity is still lacking.

Objective

The main objective of this study was to assess the efficacy of a mobile phone app intervention for reducing body weight and increasing physical activity among children and adults.

Methods

PubMed, Google Scholar, Scopus, EMBASE, and the Web of Science electronic databases were searched for studies published between January 1, 2000, and April 30, 2019, without language restrictions. Two experts independently screened all the titles and abstracts to find the most appropriate studies. To be included, studies had to be either a randomized controlled trial or a case-control study that assessed a mobile phone app intervention with body weight loss and physical activity outcomes. The Cochrane Collaboration Risk of Bias tool was used to examine the risk of publication bias.

Results

A total of 12 studies involving a mobile phone app intervention were included in this meta-analysis. Compared with the control group, the use of a mobile phone app was associated with significant changes in body weight (−1.07 kg, 95% CI −1.92 to −0.21, P=.01) and body mass index (−0.45 kg/m2, 95% CI −0.78 to −0.12, P=.008). Moreover, a nonsignificant increase in physical activity was observed (0.17, 95% CI −2.21 to 2.55, P=.88).

Conclusions

The findings of this study demonstrate the promising and emerging efficacy of using mobile phone app interventions for weight loss. Future studies are needed to explore the long-term efficacy of mobile app interventions in larger samples.

Behavior Change Techniques Included in Reports of Social Media Interventions for Promoting Health Behaviors in Adults: Content Analysis Within a Systematic Review

BACKGROUND

Social media are an increasingly commonly used platform for delivering health promotion interventions. Although recent research has focused on the effectiveness of social media interventions for health promotion, very little is known about the optimal content within such interventions, and the active ingredients to promote health behavior change using social media are not clear. Identifying which behavior change techniques (BCTs) are reported may help to clarify the content of interventions using a generalizable terminology that may facilitate future intervention development.

OBJECTIVE

This study aimed to identify which BCTs are reported in social media interventions for promoting health behavior change in adults.

METHODS

We included 71 studies conducted with adult participants (aged ≥18 years) and for which social media intervention was considered interactive in a Cochrane review of the effectiveness of such interventions. We developed a coding manual informed by the Behavior Change Technique Taxonomy version 1 (BCTTv1) to identify BCTs in the included studies. We identified BCTs in all study arms (including control) and described BCTs in the group and self-directed components of studies. We characterized the dose of delivery for each BCT by low and high intensity. We used descriptive analyses to characterize the reported BCTs.

RESULTS

Our data consisted of 71 studies published from 2001 to 2017, mainly conducted in high-income countries (n=65). Most studies (n=31) used tailored, interactive websites to deliver the intervention; Facebook was the most used mainstream platform. In developing our coding manual, we adapted some BCTTv1 instructions to better capture unique nuances of how BCTs were operationalized in social media with respect to likes, retweets, smiles, congratulations, and badges. Social support (unspecified), instruction on how to perform the behavior, and credible source were most frequently identified BCTs in intervention arms of studies and group-delivery settings, whereas instruction on how to perform the behavior was most commonly applied in self-directed components of studies, control arms, and individual participant settings. Instruction on how to perform the behavior was also the most frequently reported BCT in both intervention and control arms simultaneously. Instruction on how to perform the behavior, social support (unspecified), self-monitoring of behavior, information about health consequences, and credible source were identified in the top 5 BCTs delivered with the highest intensity.

CONCLUSIONS

This study within a review provides a detailed description of the BCTs and their dose to promote behavior change in web-based, interactive social media interventions. Clarifying active ingredients in social media interventions and the intensity of their delivery may help to develop future interventions that can more clearly build upon the existing evidence.

A Clinical Trial to Increase Self-Monitoring of Physical Activity and Eating Behaviors Among Adolescents: Protocol for the ImPACT Feasibility Study

Background

Severe obesity among youths (BMI for age≥120th percentile) has been steadily increasing. The home environment and parental behavioral modeling are two of the strongest predictors of child weight loss during weight loss interventions, which highlights that a family-based treatment approach is warranted. This strategy has been successful in our existing evidence-based pediatric weight management program, Brenner Families in Training (Brenner FIT). However, this program relies on face-to-face encounters, which are limited by the time constraints of the families enrolled in treatment.

Objective

This study aims to refine and test a tailored suite of mobile health (mHealth) components to augment an existing evidence-based pediatric weight management program.

Methods

Study outcomes will include acceptability from a patient and clinical staff perspective, feasibility, and economic costs relative to the established weight management protocol alone (ie, Brenner FIT vs Brenner FIT + mHealth [Brenner mFIT]). The Brenner mFIT intervention will consist of 6 mHealth components designed to increase patient and caregiver exposure to Brenner FIT programmatic content including the following: (1) a mobile-enabled website, (2) dietary and physical activity tracking, (3) caregiver podcasts (n=12), (4) animated videos (n=6) for adolescent patients, (5) interactive messaging, and (6) in-person tailored clinical feedback provided based on a web-based dashboard. For the study, 80 youths with obesity (aged 13-18 years) and caregiver dyads will be randomized to Brenner FIT or Brenner mFIT. All participants will complete baseline measures before randomization and at 3- and 6-month follow-up points.

Results

This study was approved by the Institutional Review Board in July 2019, funded in August 2019, and will commence enrollment in April 2020. The results of the study are expected to be published in the fall/winter of 2021.

Conclusions

The results of this study will be used to inform a large-scale implementation-effectiveness clinical trial.

International Registered Report Identifier (IRRID)

PRR1-10.2196/18098

Extensive Review of Persuasive System Design Categories and Principles: Behavioral Obesity Interventions

In this extensive review of behavioral digital obesity interventions, we reviewed randomized control trials aimed at weight loss or maintaining weight loss and identifying persuasive categories and principles that drive these interventions. The following databases were searched for long-term obesity interventions: Medline, PsycINFO, Academic Search Complete, CINAHL and Scopus. The inclusion criteria included the following search terms: obesity, overweight, weight reduction, weight loss, obesity management, and diet control. Additional criteria included randomized control trial, ≥ 6 months intervention, ≥ 100 participants and must include persuasive technology. Forty-six publications were in the final review. Primary task support was the most frequently utilized persuasive system design (PSD) category and self-monitoring was the most utilized PSD principle. Behavioral obesity interventions that utilized PSD with a behavior change theory more frequently produced statistically significant weight loss findings. Persuasive technology and PSD in digital health play a significant role in the management and improvement of obesity especially when aligned with behavior change theories. Understanding which PSD categories and principles work best for behavioral obesity interventions is critical and future interventions might be more effective if they were based on these specific PSD categories and principles.

Multidimensional Cognitive Behavioral Therapy for Obesity Applied by Psychologists Using a Digital Platform: Open-Label Randomized Controlled Trial

Background

Developing effective, widely useful, weight management programs is a priority in health care because obesity is a major health problem.

Objective

This study developed and investigated a new, comprehensive, multifactorial, daily, intensive, psychologist coaching program based on cognitive behavioral therapy (CBT) modules. The program was delivered via the digital health care mobile services Noom Coach and InBody.

Methods

This was an open-label, active-comparator, randomized controlled trial. A total of 70 female participants with BMI scores above 24 kg/m² and no clinical problems besides obesity were randomized into experimental and control groups. The experimental (ie, digital CBT) group (n=45) was connected with a therapist intervention using a digital health care service that provided daily feedback and assignments for 8 weeks. The control group (n=25) also used the digital health care service, but practiced self-care without therapist intervention. The main outcomes of this study were measured objectively at baseline, 8 weeks, and 24 weeks and included weight (kg) as well as other body compositions. Differences between groups were evaluated using independent t tests and a per-protocol framework.

Results

Mean weight loss at 8 weeks in the digital CBT group was significantly higher than in the control group (–3.1%, SD 4.5, vs –0.7%, SD 3.4, P=.04). Additionally, the proportion of subjects who attained conventional 5% weight loss from baseline in the digital CBT group was significantly higher than in the control group at 8 weeks (32% [12/38] vs 4% [1/21], P=.02) but not at 24 weeks. Mean fat mass reduction in the digital CBT group at 8 weeks was also significantly greater than in the control group (–6.3%, SD 8.8, vs –0.8%, SD 8.1, P=.02). Mean leptin and insulin resistance in the digital CBT group at 8 weeks was significantly reduced compared to the control group (–15.8%, SD 29.9, vs 7.2%, SD 35.9, P=.01; and –7.1%, SD 35.1, vs 14.4%, SD 41.2, P=.04). Emotional eating behavior (ie, mean score) measured by questionnaire (ie, the Dutch Eating Behavior Questionnaire) at 8 weeks was significantly improved compared to the control group (–2.8%, SD 34.4, vs 21.6%, SD 56.9, P=.048). Mean snack calorie intake in the digital CBT group during the intervention period was significantly lower than in the control group (135.9 kcal, SD 86.4, vs 208.2 kcal, SD 166.3, P=.02). Lastly, baseline depression, anxiety, and self-esteem levels significantly predicted long-term clinical outcomes (24 weeks), while baseline motivation significantly predicted both short-term (8 weeks) and long-term clinical outcomes.

Conclusions

These findings confirm that technology-based interventions should be multidimensional and are most effective with human feedback and support. This study is innovative in successfully developing and verifying the effects of a new CBT approach with a multidisciplinary team based on digital technologies rather than standalone technology-based interventions.

Trial Registration

ClinicalTrials.gov NCT03465306; https://clinicaltrials.gov/ct2/show/NCT03465306

Behavioral Interventions Using Consumer Information Technology as Tools to Advance Health Equity

The digital divide related to consumer information technologies (CITs) has diminished, thus increasing the potential to use CITs to overcome barriers of access to health interventions as well as to deliver interventions situated in the context of daily lives. However, the evidence base regarding the use and impact of CIT-enabled interventions in health disparity populations lags behind that for the general population. Literature and case examples are summarized to demonstrate the use of mHealth, telehealth, and social media as behavioral intervention platforms in health disparity populations, identify challenges to achieving their use, describe strategies for overcoming the challenges, and recommend future directions. The evidence base is emerging. However, challenges in design, implementation, and evaluation must be addressed for the promise to be fulfilled. Future directions include (1) improved design methods, (2) enhanced research reporting, (3) advancement of multilevel interventions, (4) rigorous evaluation, (5) efforts to address privacy concerns, and (6) inclusive design and implementation decisions.

Angiotensin Converting Enzyme (ACE): A Marker for Personalized Feedback on Dieting

Angiotensin Converting Enzyme (ACE) expression and activity is associated with obesity. ACE is a circulating factor that predicts sustained weight loss over a time frame of months. Here, we evaluate whether ACE might also be an early marker (over a 24-hour period) for weight loss. 32 participants (78% females; BMI 28.47 ± 4.87kg/m2) followed a 1200KCal diet with an optional daily (<250KCal) snack and were asked to use an in-house generated health platform to provide recordings of food intake, physical activity and urine collection time and volume. Following a day of dieting, ACE levels in urine negatively correlated with weight loss (p = 0.015 ). This reduction in ACE levels was significantly more robust in individuals with a BMI > 25 (p = 0.0025 ). This study demonstrated that ACE levels correlate with BMI and weight loss as early as after 1 day of dieting, and thus ACE could be a potential early "biofeedback" marker for weight loss and diet efficiency.

Push Notifications From a Mobile App to Improve the Body Composition of Overweight or Obese Women: Randomized Controlled Trial

Background

Technology—in particular, access to the Internet from a mobile device—has forever changed the way we relate to others and how we behave in our daily life settings. In recent years, studies have been carried out to analyze the effectiveness of different actions via mobile phone in the field of health: telephone calls, short message service (SMS), telemedicine, and, more recently, the use of push notifications. We have continued to explore ways to increase user interaction with mobile apps, one of the pending subjects in the area of mHealth. By analyzing the data produced by subjects during a clinical trial, we were able to extract behavior patterns and, according to them, design effective protocols in weight loss programs.

Objective

A clinical trial was proposed to (1) evaluate the efficacy of push notifications in an intervention aimed at improving the body composition of adult women who are overweight or obese, through a dietary procedure, and (2) analyze the evolution of body composition based on push notifications and prescribed physical activity (PA).

Methods

A two-arm randomized controlled trial was carried out. A sample size of 117 adult obese women attended a face-to-face, 30-minute consultation once a week for 6 months. All patients were supplied with an app designed for this study and a pedometer. The control group did not have access to functionalities related to the self-monitoring of weight at home, gamification, or prescription of PA. The intervention group members were assigned objectives to achieve a degree of compliance with diet and PA through exclusive access to specific functionalities of the app and push notifications. The same diet was prescribed for all patients. Three possible PA scenarios were studied for both the control and intervention groups: light physical activity (LPA), moderate physical activity (MPA), and intense physical activity (IPA). For the analysis of three or more means, the analysis of variance (ANOVA) of repeated means was performed to evaluate the effects of the intervention at baseline and at 3 and 6 months.

Results

Receiving notifications during the intervention increased body fat loss (mean -12.9% [SD 6.7] in the intervention group vs mean -7.0% [SD 5.7] in the control group; P<.001) and helped to maintain muscle mass (mean -0.8% [SD 4.5] in the intervention group vs mean -3.2% [SD 2.8] in the control group; P<.018). These variations between groups led to a nonsignificant difference in weight loss (mean -7.9 kg [SD 3.9] in the intervention group vs mean -7.1 kg [SD 3.4] in the control group; P>.05).

Conclusions

Push notifications have proven effective in the proposed weight loss program, leading women who received them to achieve greater loss of fat mass and a maintenance or increase of muscle mass, specifically among those who followed a program of IPA. Future interventions should include a longer evaluation period; the impact of different message contents, as well as message delivery times and frequency, should also be researched.

Trial Registration

ClinicalTrials.gov NCT03911583; https://www.clinicaltrials.gov/ct2/show/NCT03911583

Refining an algorithm-powered just-in-time adaptive weight control intervention: A randomized controlled trial evaluating model performance and behavioral outcomes

Suboptimal weight losses are partially attributable to lapses from a prescribed diet. We developed an app (OnTrack) that uses ecological momentary assessment to measure dietary lapses and relevant lapse triggers and provides personalized intervention using machine learning. Initially, tension between user burden and complete data was resolved by presenting a subset of lapse trigger questions per ecological momentary assessment survey. However, this produced substantial missing data, which could reduce algorithm performance. We examined the effect of more questions per ecological momentary assessment survey on algorithm performance, app utilization, and behavioral outcomes. Participants with overweight/obesity (n = 121) used a 10-week mobile weight loss program and were randomized to OnTrack-short (i.e. 8 questions/survey) or OnTrack-long (i.e. 17 questions/survey). Additional questions reduced ecological momentary assessment adherence; however, increased data completeness improved algorithm performance. There were no differences in perceived effectiveness, app utilization, or behavioral outcomes. Minimal differences in utilization and perceived effectiveness likely contributed to similar behavioral outcomes across various conditions.

Impact of a 3-Month Anti-inflammatory Dietary Intervention Focusing on Watermelon on Body Habitus, Inflammation, and Metabolic Markers: A Pilot Study

An anti-inflammatory dietary intervention called the Inflammation Management Intervention (IMAGINE) was adapted to emphasize watermelon due to its anti-inflammatory properties. This pilot study (n = 23) tested the effect of a watermelon-enhanced IMAGINE intervention (n = 15) on body habitus and markers of inflammation and metabolism. This 3-month self-selection trial, consisting of weekly in-person classes and online education for 12 weeks, focused on incorporating watermelon into an already anti-inflammatory diet. Controls (n = 8) received basic health education via email and blogs. Measurements, including diet, anthropometrics, actigraphy, and a blood draw, were made at baseline and immediately postintervention. Linear regression analyses were conducted using intervention status as the main exposure. Post hoc analyses then ignored intervention assignment and grouped participants based on their change in their energy-adjusted Dietary Inflammatory Index (E-DIITM) score. There were no group-by-time interactions for any of the studied outcomes. However, some intervention participants' diets became more proinflammatory, and several control participants' diets became more anti-inflammatory. Those participants below the median of E-DII change (ie, more anti-inflammatory changes) showed reductions in body fat percent (-1.27% vs +0.90%, respectively, P = .01), body mass index (-0.66 vs +0.38 kg/m2, respectively, P = .06) and body weight (-0.99 vs +0.54 kg, respectively, P = .08) compared to those above the median of E-DII change. This study demonstrates that individuals who adopt a more anti-inflammatory diet containing watermelon will have improvements in body anthropometrics. Future studies should focus on increasing adherence and compliance to intervention prescriptions, exploring options to extend interventions to evaluate long-term changes, and further examining changes in inflammatory biomarkers. Clinical Trials Registration: NCT03158740.

Weight gain prevention content delivered to mothers: Lessons from an intervention embedded in a community organization

BACKGROUND

Transparent reporting of the intervention content study participants receive is particularly important for dissemination and implementation of evidence-based interventions to address obesity. This study explores intervention delivery and perceptions of content when an intervention is embedded within an organization's usual practice and associations with weight outcomes.

METHODS

This is a secondary analysis from the Healthy Eating and Active Living Taught at Home (HEALTH) randomized trial. HEALTH is a weight gain prevention intervention embedded within a national home visiting programme (Parents as Teachers, PAT). To be consistent with PAT practice, HEALTH has a suggested but also flexible home visit structure. Therefore, the number and frequency of home visits are determined by the family's needs and preferences based on the parent educator's professional judgement. The proportion of participants who received each curriculum core lesson was explored among the 105 families randomized to the HEALTH intervention group, as were mean parent educator perception ratings of the visits (5-point Likert-type scale). A chi-square test was used to examine the association between utilization (ie, low: 1-6, middle: 7-12, or high: 13-18 visits) category and weight maintenance.

RESULTS

Mothers received on average 13.3 (standard deviation 6.2) core visits. Mean parent educator perception of the mothers' response (out of 5) ranged from 3.99 to 4.27; educators' perception of their own experience with the lesson ranged from 4.13 to 4.34. Among mothers who maintained their weight, 13% were low, 22% were middle, and 65% were high utilizers, while among mothers who gained weight, 3%, 31%, and 67% were low, middle, and high utilizers, respectively; this difference was not statistically significant.

CONCLUSIONS

This study identified wide variation in the visits families received from the curriculum but minimal variation in visit perceptions. Future studies could explore whether there are optimal patterns for visit content, associated with successful outcomes.

Assessment of nutrition-focused mobile apps' influence on consumers' healthy food behaviour and nutrition knowledge

The research explored if a nutrition-information app influences consumers' healthy food behavior and whether consumers improve their knowledge towards healthy food. Diet and nutrition apps are among the most popular health and fitness apps used by an increasing number of mobile device users. The analyzed app reads the product labels. Then it assesses the quality of ingredients and nutritional values based on user's personal data, such as age and physical activity level, and recommends healthier food alternatives. Scientific evidence of the effectiveness of nutrition-information apps for promoting consumers' healthy food behavior is still limited. The theoretical framework of the study is grounded in constructs from Health Belief Model (HBM) and Trans-theoretical Model (TTM) theories. Data were collected from consumers that spontaneously downloaded an existing nutrition-information app. Out of the 7000 consumers contacted, 143 respondents filled in both the baseline and follow-up questionnaires. The questionnaires included items deriving from the HBM and TTM theoretical constructs adopted, that is self-reported stage of change, susceptibility, severity, benefits, barriers, self-efficacy, cues to action, perceived and objective healthy food knowledge. The average age of respondents is 38 year-old and the sample of respondents is well distributed in terms of level of education, gender, income, working status, and geographical distribution. Findings of the study showed that nutrition-information apps can be effective in overcoming what consumers perceive as personal limitations in approaching healthy food. This is particularly evident among consumers that are building their motivation and concretely planning actions in favor of healthy eating. In particular, using a nutrition-information app decreases the perception of the barriers to healthy food eating. Users have a higher perceived personal strength and self-confidence in approaching healthy food. App users improved their objective and subjective knowledge of healthy food. The results confirmed the effectiveness of the theoretical framework. The results support that family members and friends play a specific role in healthy food behavior inclination. This suggests the inclusion of an additional theoretical construct, the social and family influence construct, when assessing the effectiveness of nutrition-information apps. To improve nutrition-information app effectiveness, the recommendation is that consumer behavior scientists, marketing researchers, nutritionists, and app developers cooperate in the apps design.

Dietary Application for the Management of Patients with Hemodialysis: A Formative Development Study

Objectives

To describe the step-by-step person-centered, theory-based development of the KELA.AE app for Arabic speaking hemodialysis patients.

Methods

A step-by-step person-driven theory-based approach was conducted to develop a self-monitoring and educational dietary app for hemodialysis patients. The development follows the Integration, Design, Assessment, and Sharing (IDEAS) framework. Qualitative, semi-structured interviews with 6 hemodialysis patients and 6 healthcare practitioners (dietitians and nephrologists) were performed to assess the need for an app, the willingness to use an app, and features desired in an app.

Results

The KELA.AE app, which includes a self-monitoring feature, CKD-friendly recipes, and a theory-based, evidence-based educational feature was developed. Qualitative analysis of interviews revealed two predominant themes from patient interviews ‘Experience with the diet’, ‘App evaluation’, and one theme from interviews with healthcare practitioners ‘App evaluation’. Patients expressed frustration with current accessibility of dietary information along with the need for educational materials in the app. The review of the KELA.AE prototype was positive overall, and patients reported a willingness to use the app. Healthcare practitioners considered the app accurate, simple, and culturally sensitive but expressed concerns about app misuse and the replacement of healthcare practitioners.

Conclusions

The KELA.AE app was found to be satisfactory and supportive of the participants' needs. Changes were made to the app as suggested during the interviews.

The effectiveness of app-based mobile interventions on nutrition behaviours and nutrition-related health outcomes: A systematic review and meta-analysis

A systematic review and meta-analysis were conducted to assess the effectiveness of app-based mobile interventions for improving nutrition behaviours and nutrition-related health outcomes, including obesity indices (eg, body mass index [BMI]) and clinical parameters (eg, blood lipids). Seven databases were searched for studies published between 2006 and 2017. Forty-one of 10 132 identified records were included, comprising 6348 participants and 373 outcomes with sample sizes ranging from 10 to 833, including 27 randomized controlled trials (RCTs). A beneficial effect of app-based mobile interventions was identified for improving nutrition behaviours (g = 0.19; CI, 0.06-0.32, P = .004) and nutrition-related health outcomes (g = 0.23; CI, 0.11-0.36, P < .001), including positive effects on obesity indices (g = 0.30; CI, 0.15-0.45, P < .001), blood pressure (g = 0.21; CI, 0.01-0.42, P = .043), and blood lipids (g = 0.15; CI, 0.03-0.28, P = .018). Most interventions were composed of four behaviour change technique (BCT) clusters, namely, "goals/planning," "feedback/monitoring," "shaping knowledge," and "social support." Moderating effects including study design, type of app (commercial/research app), sample characteristics (clinical/non-clinical sample), and intervention characteristics were not statistically significant. The inclusion of additional treatment components besides the app or the number or type of BCTs implemented did not moderate the observed effectiveness, which underscores the potential of app-based mobile interventions for implementing effective and feasible interventions operating at scale for fighting the obesity epidemic in a broad spectrum of the population.