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Use of Evidence-Based Best Practices and Behavior Change Techniques in Breast Cancer Apps: Systematic Analysis.

ABSTRACT: BACKGROUND:Theoretically designed mobile health (mHealth) breast cancer interventions are essential for achieving positive behavior change. In the case of breast cancer, they can improve the health outcomes of millions of women by increasing prevention and care efforts. However, little is known about the theoretical underpinnings of breast cancer apps available to the general public. OBJECTIVE:Given that theories may strengthen mHealth interventions, this study aimed to identify breast cancer apps designed to support behavior change, to assess the extent to which they address content along the cancer care continuum and contain behavior change techniques, and to assess the degree to which star rating is related to theory-based design. METHODS:Using a criteria-based screening process, we searched 2 major app stores for breast cancer apps designed to promote behavior change. Apps were coded for content along the cancer care continuum and analyzed for behavior change techniques. The Mann-Whitney U test was used to examine the relationship between star ratings and the use of behavior change techniques in apps with star ratings compared to those without ratings. RESULTS:The search resulted in a total of 302 apps, of which 133 were identified as containing breast cancer content. Only 9.9% (30/302) of apps supported behavior change and were further analyzed. These apps were disproportionally focused on behaviors to enhance early detection, whereas only a few apps supported care management, treatment, and posttreatment behaviors. Regarding theories, 63% (19/30) of apps customized content to users, 70% (21/30) established a health-behavior link, and 80% (24/30) provided behavior change instructions. Of the 30 apps, 15 (50%) prompted intention formation whereas less than half of the apps included goal setting (9/30, 30%) and goal reviewing (7/30, 23%). Most apps did not provide information on peer behavior (7/30, 23%) or allow for social comparison (6/30, 20%). None of the apps mobilized social norms. Only half of the apps (15/30, 50%) were user rated. The results of the Mann-Whitney U test showed that apps with star ratings contained significantly more behavior change techniques (median 6.00) than apps without ratings. The analysis of behavior change techniques used in apps revealed their shortcomings in the use of goal setting and social influence features. CONCLUSIONS:Our findings indicate that commercially available breast cancer apps have not yet fully realized their potential to promote behavior change, with only a minority of apps focusing on behavior change, and even fewer including theoretical design to support behavior change along the cancer care continuum. These shortcomings are likely limiting the effectiveness of apps and their ability to improve public health. More attention needs to be paid to the involvement of professionals in app development and adherence to theories and best practices in app design to support individuals along the cancer care continuum.

SUBMITTER: Kalke K

PROVIDER: S-EPMC7007595 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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Use of Evidence-Based Best Practices and Behavior Change Techniques in Breast Cancer Apps: Systematic Analysis.

Kalke Kerstin K Ginossar Tamar T Bentley Joshua M JM Carver Hannah H Shah Sayyed Fawad Ali SFA Kinney Anita Y AY

JMIR mHealth and uHealth 20200124 1

<h4>Background</h4>Theoretically designed mobile health (mHealth) breast cancer interventions are essential for achieving positive behavior change. In the case of breast cancer, they can improve the health outcomes of millions of women by increasing prevention and care efforts. However, little is known about the theoretical underpinnings of breast cancer apps available to the general public.<h4>Objective</h4>Given that theories may strengthen mHealth interventions, this study aimed to identify b ...[more]

PMID: 32012084

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Project description:BackgroundThe popularization of mobile health (mHealth) apps for public health or medical care purposes has transformed human life substantially, improving lifestyle behaviors and chronic condition management.ObjectiveThis review aimed to identify behavior change techniques (BCTs) commonly used in mHealth, assess their effectiveness based on the evidence reported in interventions and reviews to highlight the most appropriate techniques to design an optimal strategy to improve adherence to data reporting, and provide recommendations for future interventions and research.MethodsWe performed a systematic review of studies published between 2010 and 2021 in relevant scientific databases to identify and analyze mHealth interventions using BCTs that evaluated their effectiveness in terms of user adherence. Search terms included a mix of general (eg, data, information, and adherence), computer science (eg, mHealth and BCTs), and medicine (eg, personalized medicine) terms.ResultsThis systematic review included 24 studies and revealed that the most frequently used BCTs in the studies were feedback and monitoring (n=20), goals and planning (n=14), associations (n=14), shaping knowledge (n=12), and personalization (n=7). However, we found mixed effectiveness of the techniques in mHealth outcomes, having more effective than ineffective outcomes in the evaluation of apps implementing techniques from the feedback and monitoring, goals and planning, associations, and personalization categories, but we could not infer causality with the results and suggest that there is still a need to improve the use of these and many common BCTs for better outcomes.ConclusionsPersonalization, associations, and goals and planning techniques were the most used BCTs in effective trials regarding adherence to mHealth apps. However, they are not necessarily the most effective since there are studies that use these techniques and do not report significant results in the proposed objectives; there is a notable overlap of BCTs within implemented app components, suggesting a need to better understand best practices for applying (a combination of) such techniques and to obtain details on the specific BCTs used in mHealth interventions. Future research should focus on studies with longer follow-up periods to determine the effectiveness of mHealth interventions on behavior change to overcome the limited evidence in the current literature, which has mostly small-sized and single-arm experiments with a short follow-up period.

Project description:BackgroundMobile health (mHealth) apps targeting health behaviors using behavior change techniques (BCTs) have been successful in promoting healthy behaviors; however, their efficacy with sleep is unclear. Some work has shown success in promoting sleep through mHealth, whereas there have been reports that sleep apps can be adverse and lead to unhealthy obsessions with achieving perfect sleep.ObjectiveThis study aims to report and describe the use of BCTs in mHealth apps for sleep with the following research questions: How many BCTs are used on average in sleep apps, and does this relate to their effectiveness on sleep outcomes? Are there specific BCTs used more or less often in sleep apps, and does this relate to their effectiveness on sleep outcomes? Does the effect of mHealth app interventions on sleep change when distinguishing between dimension and measurement of sleep?MethodsWe conducted a systematic review following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines to review articles on mHealth app interventions for sleep published between 2010 and 2020.ResultsA total of 12 studies met the eligibility criteria. Most studies reported positive sleep outcomes, and there were no negative effects reported. Sleep quality was the most common dimension of sleep targeted. Subjective measures of sleep were used across all apps, whereas objective measures were often assessed but rarely reported as part of results. The average number of BCTs used was 7.67 (SD 2.32; range 3-11) of 16. Of the 12 studies, the most commonly used BCTs were feedback and monitoring (n=11, 92%), shaping knowledge (n=11, 92%), goals and planning (n=10, 83%), and antecedents (n=10, 83%), whereas the least common were scheduled consequences (n=0, 0%), self-belief (n=0, 0%), and covert learning (n=0, 0%). Most apps used a similar set of BCTs that unfortunately did not allow us to distinguish which BCTs were present when studies reported more positive outcomes.ConclusionsOur study describes the peer-reviewed literature on sleep apps and provides a foundation for further examination and optimization of BCTs used in mHealth apps for sleep. We found strong evidence that mHealth apps are effective in improving sleep, and the potential reasons for the lack of adverse sleep outcome reporting are discussed. We found evidence that the type of BCTs used in mHealth apps for sleep differed from other health outcomes, although more research is needed to understand how BCTs can be implemented effectively to improve sleep using mHealth and the mechanisms of action through which they are effective (eg, self-efficacy, social norms, and attitudes).

Project description:BACKGROUND:Employees remain at risk of developing physical and mental health problems. To improve the lifestyle, health, and productivity many workplace interventions have been developed. However, not all of these interventions are effective. Mobile and wireless technology to support health behavior change (mobile health [mHealth] apps) is a promising, but relatively new domain for the occupational setting. Research on mHealth apps for the mental and physical health of employees is scarce. Interventions are more likely to be useful if they are rooted in health behavior change theory. Evaluating the presence of specific combinations of behavior change techniques (BCTs) in mHealth apps might be used as an indicator of potential quality and effectiveness. OBJECTIVE:The aim of this study was to assess whether mHealth apps for the mental and physical health of employees incorporate BCTs and, if so, which BCTs can be identified and which combinations of BCTs are present. METHODS:An assessment was made of apps aiming to reduce the risk of physical and psychosocial work demands and to promote a healthy lifestyle for employees. A systematic search was performed in iTunes and Google Play. Forty-five apps were screened and downloaded. BCTs were identified using a taxonomy applied in similar reviews. The mean and ranges were calculated. RESULTS:On average, the apps included 7 of the 26 BCTs (range 2-18). Techniques such as "provide feedback on performance," "provide information about behavior-health link," and "provide instruction" were used most frequently. Techniques that were used least were "relapse prevention," "prompt self-talk," "use follow-up prompts," and "provide information about others' approval." "Stress management," "prompt identification as a role model," and "agree on behavioral contract" were not used by any of the apps. The combination "provide information about behavior-health link" with "prompt intention formation" was found in 7/45 (16%) apps. The combination "provide information about behavior-health link" with "provide information on consequences," and "use follow-up prompts" was found in 2 (4%) apps. These combinations indicated potential effectiveness. The least potentially effective combination "provide feedback on performance" without "provide instruction" was found in 13 (29%) apps. CONCLUSIONS:Apps for the occupational setting might be substantially improved to increase potential since results showed a limited presence of BCTs in general, limited use of potentially successful combinations of BCTs in apps, and use of potentially unsuccessful combinations of BCTs. Increasing knowledge on the effectiveness of BCTs in apps might be used to develop guidelines for app developers and selection criteria for companies and individuals. Also, this might contribute to decreasing the burden of work-related diseases. To achieve this, app developers, health behavior change professionals, experts on physical and mental health, and end-users should collaborate when developing apps for the working context.

Project description:BackgroundWomen consult information in mobile apps (apps) during pregnancy, and even obstetrics specialists highlight that pregnancy is the ideal moment for the use of apps as consultation sources. However, the high number of apps designed for pregnancy requires a careful assessment to determine their suitability before recommendation.ObjectiveThe aim of this study is to identify the apps available in Spanish that can be recommended based on their content, behavior change techniques (BCTs), and quality as a complementary tool during pregnancy.MethodsA systematic search on app stores to identify apps was performed in the Apple App Store and Google Play with the subject term "pregnancy." The apps meeting the following criteria were chosen: pregnancy-related content, free, and available in Spanish. An app was excluded if it was classified as a game or entertainment and thus lacking an educational or health aim and if it did not target the population under study. The selected apps were downloaded, and their quality was assessed using the Mobile Application Rating Scale (MARS), with the BCTs included evaluated using the BCT taxonomy version 1 and its content.ResultsA total of 457 apps were identified, 25 of which were downloaded for assessment (5.6%). The median for objective and subjective quality was 2.94 (IQR 2.71-3.46) and 1.75 (IQR 1.25-2.25), respectively. Regarding content, the median of topics included in the apps was 23 (IQR 16-23), with weight gain, nutrition, fetal development, and physical activity being the most common. The median number of BCTs was 12 (IQR 0.5-3.5). The most frequently identified BCTs in the apps were "Self-Monitoring of Outcomes," followed by "Goal Behavior" and "Instructions." Statistically significant correlations were observed between objective quality and content (ρ=0.624; P=.001), subjective quality and content (ρ=0.638; P=.001), objective quality and BCTs (ρ=0.672; P<.001), subjective quality and BCTs (ρ=0.623; P<.001), and BCTs and content (ρ=0.580; P=.002).ConclusionsThe results of this study suggest that only a small percentage of free pregnancy apps available in Spanish should be recommended. The apps with the best MARS scores were those that addressed a higher number of topics and included a higher number of BCTs. Those with the best content and quality, and a higher number of BCTs included could be recommended by health professionals.

Project description:BackgroundDigitally enabled care along with an emphasis on self-management of health is steadily growing. Mobile health apps provide a promising means of supporting health behavior change; however, engagement with them is often poor and evidence of their impact on health outcomes is lacking. As engagement is a key prerequisite to health behavior change, it is essential to understand how engagement with mobile health apps and their target health behaviors can be better supported. Although the importance of engagement is emphasized strongly in the literature, the understanding of how different components of engagement are associated with specific techniques that aim to change behaviors is lacking.ObjectiveThe purpose of this systematic review protocol is to provide a synthesis of the associations between various behavior change techniques (BCTs) and the different components and measures of engagement with mobile health apps.MethodsThe review protocol was structured using the PRISMA-P (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols) and the PICOS (Population, Intervention, Comparator, Outcome, and Study type) frameworks. The following seven databases will be systematically searched: PubMed, Embase, Cumulative Index to Nursing and Allied Health Literature, APA PsycInfo, ScienceDirect, Cochrane Library, and Web of Science. Title and abstract screening, full-text review, and data extraction will be conducted by 2 independent reviewers. Data will be extracted into a predetermined form, any disagreements in screening or data extraction will be discussed, and a third reviewer will be consulted if consensus cannot be reached. Risk of bias will be assessed using the Cochrane Collaboration Risk of Bias 2 and the Risk Of Bias In Non-Randomized Studies - of Interventions (ROBINS-I) tools; descriptive and thematic analyses will be conducted to summarize the relationships between BCTs and the different components of engagement.ResultsThe systematic review has not yet started. It is expected to be completed and submitted for publication by May 2022.ConclusionsThis systematic review will summarize the associations between different BCTs and various components and measures of engagement with mobile health apps. This will help identify areas where further research is needed to examine BCTs that could potentially support effective engagement and help inform the design and evaluation of future mobile health apps.Trial registrationPROSPERO CRD42022312596; https://tinyurl.com/nhzp8223.International registered report identifier (irrid)PRR1-10.2196/35172.

Project description:BackgroundThe mobile app market differs from country to country, and to date, no previous review of the content quality of smoking cessation apps has been conducted in France.ObjectiveThis study aimed to examine the general quality of the most popular smoking cessation apps in France and also determine the degree to which apps adhere to established behavioral and cognitive techniques (BCTs) proven effective in clinical practice.MethodsA systematic research of smoking cessation apps was conducted in both the Google Play Store and Apple Store in the French market. The general quality of popular apps was rated with the Mobile App Rating Scale (MARS), and the therapeutic quality was assessed with the ratio of adherence of the behavior change technique taxonomy for smoking cessation treatment.ResultsA total of 14 mobile apps met all the inclusion criteria of the content analysis. The interrater reliability varied from "substantial" (0.79) to "almost perfect" (0.9) for the two measures. The mean MARS score was 3.5 out of 5 (median 3.6, IQR 0.6 [3.2-3.8]). The findings suggest that popular apps focus primarily on the functionality dimension of the MARS scale (4.2/5). The mean number of BCTs was 22, with a large difference between apps (minimum 4, maximum 38). At least half of the apps addressed motivation (8.8/14, 63%) and advised on using behavioral skills in order to quit smoking or stay a nonsmoker (8.7/14, 62%). However, only a handful of apps gathered important information (5.9/14, 42%) in order to deliver proper advice regarding the use of approved medication or the implementation of behavioral techniques (4.3/14, 31%). The mean MARS score was positively correlated with the price (r=0.70, P=.007) and the number of BCTs used (r=0.67, P=.01). User rating was not correlated with any quality scale (P=.67).ConclusionsThe content quality of popular smoking cessation apps in France varied by app type and price. Most popular apps propose in general good quality content but lack implementation of evidence-based BCTs associated with effectiveness on smoking cessation treatment. Further research is needed to evaluate the improvement in the content quality of smoking cessation apps in France.

Project description:BackgroundGlobally, mobile phones have achieved wide reach at an unprecedented rate, and mobile phone apps have become increasingly prevalent among users. The number of health-related apps that were published on the two leading platforms (iOS and Android) reached more than 100,000 in 2014. However, there is a lack of synthesized evidence regarding the effectiveness of mobile phone apps in changing people's health-related behaviors.ObjectiveThe aim was to examine the effectiveness of mobile phone apps in achieving health-related behavior change in a broader range of interventions and the quality of the reported studies.MethodsWe conducted a comprehensive bibliographic search of articles on health behavior change using mobile phone apps in peer-reviewed journals published between January 1, 2010 and June 1, 2015. Databases searched included Medline, PreMedline, PsycINFO, Embase, Health Technology Assessment, Education Resource Information Center (ERIC), and Cumulative Index to Nursing and Allied Health Literature (CINAHL). Articles published in the Journal of Medical Internet Research during that same period were hand-searched on the journal's website. Behavior change mechanisms were coded and analyzed. The quality of each included study was assessed by the Cochrane Risk of Bias Assessment Tool.ResultsA total of 23 articles met the inclusion criteria, arranged under 11 themes according to their target behaviors. All studies were conducted in high-income countries. Of these, 17 studies reported statistically significant effects in the direction of targeted behavior change; 19 studies included in this analysis had a 65% or greater retention rate in the intervention group (range 60%-100%); 6 studies reported using behavior change theories with the theory of planned behavior being the most commonly used (in 3 studies). Self-monitoring was the most common behavior change technique applied (in 12 studies). The studies suggest that some features improve the effectiveness of apps, such as less time consumption, user-friendly design, real-time feedback, individualized elements, detailed information, and health professional involvement. All studies were assessed as having some risk of bias.ConclusionsOur results provide a snapshot of the current evidence of effectiveness for a range of health-related apps. Large sample, high-quality, adequately powered, randomized controlled trials are required. In light of the bias evident in the included studies, better reporting of health-related app interventions is also required. The widespread adoption of mobile phones highlights a significant opportunity to impact health behaviors globally, particularly in low- and middle-income countries.

Project description:BackgroundSmartphones have allowed for the development and use of apps. There is now a proliferation of mobile health interventions for physical activity, healthy eating, smoking and alcohol cessation or reduction, and improved mental well-being. However, the strength or potential of these apps to lead to behavior change remains uncertain.ObjectiveThe aim of this study was to review a large sample of healthy lifestyle apps at a single point in time (June to July 2018) to determine their potential for promoting health-related behavior change with a view to sharing this information with the public. In addition, the study sought to test a wide range of apps using a new scale, the App Behavior Change Scale (ABACUS).MethodsApps focusing on 5 major modifiable lifestyle behaviors were identified using a priori key search terms across the Australian Apple iTunes and Google Play stores. Lifestyle behavior categories were selected for their impact on health and included smoking, alcohol use, physical activity, nutrition, and mental well-being. Apps were included if they had an average user rating between 3 and 5, if they were updated in the last 18 months, if the description of the app included 2 of 4 behavior change features, and if they were in English. The selected behavior change apps were rated in 2 ways using previously developed rating scales: the Mobile App Rating Scale (MARS) for functionality and the ABACUS for potential to encourage behavior change.ResultsThe initial search identified 212,352 apps. After applying the filtering criteria, 5018 apps remained. Of these, 344 were classified as behavior change apps and were reviewed and rated. Apps were given an average MARS score of 2.93 out of 5 (SD 0.58, range 1.42-4.16), indicating low-to-moderate functionality. Scores for the ABACUS ranged from 1 to 17, out of 21, with an average score of 7.8 (SD 2.8), indicating a low-to-moderate number of behavior change techniques included in apps. The ability of an app to encourage practice or rehearsal, in addition to daily activities, was the most commonly identified feature across all apps (310/344, 90.1%), whereas the second most common feature was the ability of the user to easily self-monitor behavior (289/344, 84.0%).ConclusionsThe wide variety of apps included in this 2018 study and the limited number of behavior change techniques found in many apps suggest an opportunity for improvement in app design that will promote sustained and significant lifestyle behavior change and, therefore, better health. The use of the 2 scales for the review and rating of the apps was successful and provided a method that could be replicated and tested in other behavior change areas.

Dataset Information

Use of Evidence-Based Best Practices and Behavior Change Techniques in Breast Cancer Apps: Systematic Analysis.

Publications

Use of Evidence-Based Best Practices and Behavior Change Techniques in Breast Cancer Apps: Systematic Analysis.

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