An evidence-based gamified mHealth intervention for overweight young adults with maladaptive eating habits: study protocol for a randomized controlled trial

Attraction and achievement as 2 attributes of gamification in healthcare: an evolutionary concept analysis

This study conducted a conceptual analysis of gamification in healthcare utilizing Rogers’ evolutionary concept analysis methodology to identify its attributes and provide a method for its applications in the healthcare field. Gamification has recently been used as a health intervention and education method, but the concept is used inconsistently and confusingly. A literature review was conducted to derive definitions, surrogate terms, antecedents, influencing factors, attributes (characteristics with dimensions and features), related concepts, consequences, implications, and hypotheses from various academic fields. A total of 56 journal articles in English and Korean, published between August 2 and August 7, 2023, were extracted from databases such as PubMed Central, the Institute of Electrical and Electronics Engineers, the Association for Computing Machinery Digital Library, the Research Information Sharing Service, and the Korean Studies Information Service System, using the keywords “gamification” and “healthcare.” These articles were then analyzed. Gamification in healthcare is defined as the application of game elements in health-related contexts to improve health outcomes. The attributes of this concept were categorized into 2 main areas: attraction and achievement. These categories encompass various strategies for synchronization, enjoyable engagement, visual rewards, and goal-reinforcing frames. Through a multidisciplinary analysis of the concept’s attributes and influencing factors, this paper provides practical strategies for implementing gamification in health interventions. When developing a gamification strategy, healthcare providers can reference this analysis to ensure the game elements are used both appropriately and effectively.

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

Background

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

Methods

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

Findings

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

Interpretation

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

Funding

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

Gamification: a Novel Approach to Mental Health Promotion

PURPOSE OF REVIEW

Gamification has emerged as a novel technique for improving mental health and enhancing treatment effectiveness. This paper provides an overview of gamification approaches to mental health intervention, identifies factors that may be related to variations in treatment effectiveness, and discusses possible strategies for tailoring gamified interventions to clients' needs.

RECENT FINDINGS

Recent research has documented the potential of gamified mental health interventions for bolstering mental wellness and mitigating psychological symptoms. However, their effectiveness may vary depending on study design-related factors and gender-specific considerations. Literature reviews have also identified yet-to-be resolved issues surrounding the possible strengths and weaknesses of the personalization versus standardization of gamification, as well as the potential benefits of gamification for increasing engagement versus the potential risks of over-engagement and behavioral addiction to gamified components. This review highlights the need for careful planning and execution of gamified mental health interventions to optimize their effectiveness and suitability for meeting clients' individual needs and preferences.

Operationalizing Engagement With an Interpretation Bias Smartphone App Intervention: Case Series

BACKGROUND

Engagement with mental health smartphone apps is an understudied but critical construct to understand in the pursuit of improved efficacy.

OBJECTIVE

This study aimed to examine engagement as a multidimensional construct for a novel app called HabitWorks. HabitWorks delivers a personalized interpretation bias intervention and includes various strategies to enhance engagement such as human support, personalization, and self-monitoring.

METHODS

We examined app use in a pilot study (n=31) and identified 5 patterns of behavioral engagement: consistently low, drop-off, adherent, high diary, and superuser.

RESULTS

We present a series of cases (5/31, 16%) from this trial to illustrate the patterns of behavioral engagement and cognitive and affective engagement for each case. With rich participant-level data, we emphasize the diverse engagement patterns and the necessity of studying engagement as a heterogeneous and multifaceted construct.

CONCLUSIONS

Our thorough idiographic exploration of engagement with HabitWorks provides an example of how to operationalize engagement for other mental health apps.

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.

Anxiety Sensitivity and Fast-Food Ordering Habits Among Black Adults

Background

Black adults experience high rates of overweight/obesity, which is linked to chronic diseases and is exacerbated by fast-food consumption. Anxiety sensitivity, a relative stable fear of anxiety-related sensations, has been linked to high caloric intake. Here, we examine whether anxiety sensitivity is associated with fast-food ordering habits within a convenience sample of Black adults.

Methods

Of 124 adults (79.4% women; M_age=49.3±11.6; 84.8% overweight/obese), 107 (86.3%) reported eating from a fast-food restaurant in the last month. Participants completed the Anxiety Sensitivity-Index 3, which has a total score and physical, cognitive, and social concerns subscales. Investigator-generated items queried frequency of ordering "supersized" quantities of fast-food (e.g., cheeseburgers, fries), and healthy items (e.g., salads, oatmeal, yogurt), respectively, from "never" to "always." Covariate-adjusted ordinal logistic regression models were used to assess relations between measures of interest.

Results

Anxiety sensitivity (total and physical concerns) was associated with greater odds of more frequently ordering supersized unhealthy fast-food; and anxiety sensitivity (total and cognitive concerns) was associated with lower odds of more frequently ordering healthy items from fast-food restaurants.

Conclusions

Results suggest that adults with greater anxiety sensitivity may engage in fast-food ordering habits that can contribute to the overweight/obesity epidemic. Future studies should replicate results and determine the potential for anxiety sensitivity-reduction interventions to affect dietary choices that contribute to overweight/obesity.

Identification of the minimum data set to design a mobile-based application on overweight and obesity management for children and adolescents

PURPOSE

Designing mobile-based applications is one of the tools to raise the awareness of patients and the care team. Aim of this study is to identify the data elements of a mobile-based application to overweight and obesity management for children and adolescents from the experts' point of view.

METHODS

In this descriptive-analytical article, data collection was conducted through library and Internet research. The research population comprised 30 nutritionists selected via simple sampling method. The research instrument was a questionnaire developed by the researcher in four sections: demographic data, assessment data, therapeutic recommendations and application capabilities. Validity and reliability were confirmed by Content Validity Ratio (CVR) and Delphi method respectively.

RESULTS

The Minimum Data Set (MDS) required for overweight and obesity management in children and adolescents was designed based on the data from the guidelines of the United States, Canada, Australia, Britain, Iran, and experts' opinions. The importance of this MDS suggested was calculated based on the percentage points given by experts for the demographic data of 100%, the assessment data of 88.33%, the therapeutic recommendations of 97.67%, and the application capabilities of 88.94%.

CONCLUSION

Identifying prevention and control minimum data set of overweight and obesity in children and adolescents from the point of view of experts will be effective in improving the applications in this field. This MDS has two parts of data elements: the first for recognition of the framework of evaluating and applying therapeutic methods that can empower parents to manage the child's body mass and the second as a patient's personal record for storage a set of data that can be used by nutritionists in visits to healthcare centers.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s40200-021-00807-1.

Does gamification improve fruit and vegetable intake in adolescents? a systematic review

BACKGROUND

Nutrition and diet-related non-communicable diseases are a major cause of death worldwide. Food preferences and eating behaviours are likely to be established during adolescence, making it an important period to promote healthy behaviours.

AIM

To review the effectiveness of gamification to improve fruit and vegetable intake in adolescents.

METHODS

A systematic search was conducted using eight databases and grey literature sources for articles published to date on the effectiveness of gamification on fruit and vegetable intake in adolescents. Search criteria included articles that were complete and peer reviewed, conducted empirical research, described gamified elements used, focused on individuals between 10 and 24 years, and were available in English.

RESULTS

Out of 402 studies identified by the search, 7 were included in the review. Overall, short-term gamified interventions showed promise in improving fruit and vegetable intake in those aged 10 to 24 years old. Gamification was primarily facilitated through extrinsic motivation (i.e. points, badges, vouchers, leaderboard, narration, avatars, challenges) rather than intrinsic motivation (i.e. team-based competition). Studies were moderate in quality and key methodological issues related to non-randomized study design, lack of comparison group, inadequate control for confounding, and small sample size.

CONCLUSIONS

Gamification can be an effective tool in changing nutrition-related behaviour in adolescents over the short term. Future research should consider gamified interventions that are of longer duration, incorporate additional intrinsic gamified elements, tailor game elements for population subgroups, and address methodological issues.

Surveying the Role of Analytics in Evaluating Digital Mental Health Interventions for Transition-Aged Youth: Scoping Review

BACKGROUND

Consumer-facing digital health interventions provide a promising avenue to bridge gaps in mental health care delivery. To evaluate these interventions, understanding how the target population uses a solution is critical to the overall validity and reliability of the evaluation. As a result, usage data (analytics) can provide a proxy for evaluating the engagement of a solution. However, there is paucity of guidance on how usage data or analytics should be used to assess and evaluate digital mental health interventions.

OBJECTIVE

This review aimed to examine how usage data are collected and analyzed in evaluations of mental health mobile apps for transition-aged youth (15-29 years).

METHODS

A scoping review was conducted using the Arksey and O'Malley framework. A systematic search was conducted on 5 journal databases using keywords related to usage and engagement, mental health apps, and evaluation. A total of 1784 papers from 2008 to 2019 were identified and screened to ensure that they included analytics and evaluated a mental health app for transition-aged youth. After full-text screening, 49 papers were included in the analysis.

RESULTS

Of the 49 papers included in the analysis, 40 unique digital mental health innovations were evaluated, and about 80% (39/49) of the papers were published over the past 6 years. About 80% involved a randomized controlled trial and evaluated apps with information delivery features. There were heterogeneous findings in the concept that analytics was ascribed to, with the top 3 being engagement, adherence, and acceptability. There was also a significant spread in the number of metrics collected by each study, with 35% (17/49) of the papers collecting only 1 metric and 29% (14/49) collecting 4 or more analytic metrics. The number of modules completed, the session duration, and the number of log ins were the most common usage metrics collected.

CONCLUSIONS

This review of current literature identified significant variability and heterogeneity in using analytics to evaluate digital mental health interventions for transition-aged youth. The large proportion of publications from the last 6 years suggests that user analytics is increasingly being integrated into the evaluation of these apps. Numerous gaps related to selecting appropriate and relevant metrics and defining successful or high levels of engagement have been identified for future exploration. Although long-term use or adoption is an important precursor to realizing the expected benefits of an app, few studies have examined this issue. Researchers would benefit from clarification and guidance on how to measure and analyze app usage in terms of evaluating digital mental health interventions for transition-aged youth. Given the established role of adoption in the success of health information technologies, understanding how to abstract and analyze user adoption for consumer digital mental health apps is also an emerging priority.

Usability of Food Size Aids in Mobile Dietary Reporting Apps for Young Adults: Randomized Controlled Trial

Background

Young adults are more likely to use self-managed dietary reporting apps. However, there is scant research examining the user experience of different measurement approaches for mobile dietary reporting apps when dealing with a wide variety of food shapes and container sizes.

Objective

Field user experience testing was conducted under actual meal conditions to assess the accuracy, efficiency, and subjective reaction of three food portion measurement methods embedded in a developed mobile app. Key-in–based aid (KBA), commonly used in many current apps, relies on the user’s ability to key in volumes or weights. Photo-based aid (PBA) extends traditional assessment methods, allowing users to scroll, observe, and select a reduced-size image from a set of options. Gesture-based aid (GBA) is a new experimental approach in which the user makes finger movements on the screen to roughly describe food portion boundaries accompanied by a background reference.

Methods

A group of 124 young adults aged 19 to 26 years was recruited for a head-to-head randomized comparison and divided into 3 groups: a KBA (n=42) control group and PBA (n=41) and GBA (n=41) experimental groups. In total, 3 meals (ie, breakfast, lunch, and dinner) were served in a university cafeteria. Participants were provided with 25 dishes and beverages for selection, with a variety of food shapes and containers that reflect everyday life conditions. The accuracy of and time spent on realistic interaction during food portion estimation and the subjective reaction of each aid were recorded and analyzed.

Results

Participants in the KBA group provided the highest accuracy in terms of hash brown weight (P=.004) and outperformed PBA or GBA for many soft drinks in cups. PBA had the best results for a cylindrical hot dog (P<.001), irregularly shaped pork chop (P<.001), and green tea beverage (660 mL; P<.001). GBA outperformed PBA for most drinks, and GBA outperformed KBA for some vegetables. The GBA group spent significantly more time assessing food items than the KBA and PBA groups. For each aid, the overall subjective reaction based on the score of the System Usability Scale was not significantly different.

Conclusions

Experimental results show that each aid had some distinguishing advantages. In terms of user acceptance, participants considered all 3 aids to be usable. Furthermore, users’ subjective opinions regarding measurement accuracy contradicted the empirical findings. Future work will consider the use of each aid based on food or container shape and integrate the various advantages of the 3 different aids for better results. Our findings on the use of portion size aids are based on realistic and diverse food items, providing a useful reference for future app improvement of an effective, evidence-based, and acceptable feature.

Trial Registration

International Standard Randomized Controlled Trial Registry ISRCTN36710750; http://www.controlled-trials.com/ISRCTN36710750.

Editor's choice: Optimizing healthy food preferences by serious gaming

Objective: Serious gaming is an upcoming and promising tool in prevention and health promotion. The aim of this experimental study was to examine whether health-related serious gaming could optimize food-related outcomes and physical activity.Design: Eighty-one healthy participants (80% female) were randomly allocated to an experimental condition, in which participants played serious games based on transferring information, priming and evaluative conditioning, for half an hour, or a control condition, in which participants played non-health-related computer games.Main outcome measures: The primary study outcome was self-reported food preference and self-reported food choice, assessed by the Food Choice Task with food pairs differing in healthiness, or in both healthiness and attractiveness. Secondary outcomes were actual food choice and physical activity.Results: A significantly healthier food preference for pairs differing in healthiness was found on the Food Choice Task in the experimental compared to the control condition. No significant differences were found on the other outcomes.Conclusions: This study provides preliminary support for the effects of serious gaming based on optimizing food preferences. More research is needed to confirm the present findings and to further elucidate and optimize the effects of serious gaming on health behaviours.

Gamification in Apps and Technologies for Improving Mental Health and Well-Being: Systematic Review

BACKGROUND

There is little research on the application of gamification to mental health and well-being. Furthermore, usage of gamification-related terminology is inconsistent. Current applications of gamification for health and well-being have also been critiqued for adopting a behaviorist approach that relies on positive reinforcement and extrinsic motivators.

OBJECTIVE

This study aimed to analyze current applications of gamification for mental health and well-being by answering 3 research questions (RQs). RQ1: which gamification elements are most commonly applied to apps and technologies for improving mental health and well-being? RQ2: which mental health and well-being domains are most commonly targeted by these gamified apps and technologies? RQ3: what reasons do researchers give for applying gamification to these apps and technologies? A systematic review of the literature was conducted to answer these questions.

METHODS

We searched ACM Digital Library, CINAHL, Cochrane Library, EMBASE, IEEE Explore, JMIR, MEDLINE, PsycINFO, PubMed, ScienceDirect, Scopus, and Web of Science for qualifying papers published between the years 2013 and 2018. To answer RQ1 and RQ2, papers were coded for gamification elements and mental health and well-being domains according to existing taxonomies in the game studies and medical literature. During the coding process, it was necessary to adapt our coding frame and revise these taxonomies. Thematic analysis was conducted to answer RQ3.

RESULTS

The search and screening process identified 70 qualifying papers that collectively reported on 50 apps and technologies. The most commonly observed gamification elements were levels or progress feedback, points or scoring, rewards or prizes, narrative or theme, personalization, and customization; the least commonly observed elements were artificial assistance, unlockable content, social cooperation, exploratory or open-world approach, artificial challenge, and randomness. The most commonly observed mental health and well-being domains were anxiety disorders and well-being, whereas the least commonly observed domains were conduct disorder and bipolar disorders. Researchers' justification for applying gamification to improving mental health and well-being was coded in 59% (41/70) of the papers and was broadly divided into 2 themes: (1) promoting engagement and (2) enhancing an intervention's intended effects.

CONCLUSIONS

Our findings suggest that the current application of gamification to apps and technologies for improving mental health and well-being does not align with the trend of positive reinforcement critiqued in the greater health and well-being literature. We also observed overlap between the most commonly used gamification techniques and existing behavior change frameworks. Results also suggest that the application of gamification is not driven by health behavior change theory, and that many researchers may treat gamification as a black box without consideration for its underlying mechanisms. We call for the inclusion of more comprehensive and explicit descriptions of how gamification is applied and the standardization of applied games terminology within and across fields.

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

BACKGROUND

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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