Behavior change techniques in mobile applications for sedentary behavior.
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ABSTRACT: Objective
Mobile applications (apps) are increasingly being utilized in health behavior change interventions. To determine the presence of underlying behavior change mechanisms, apps for physical activity have been coded for behavior change techniques (BCTs). However, apps for sedentary behavior have yet to be assessed for BCTs. Thus, the purpose of the present study was to review apps designed to decrease sedentary time and determine the presence of BCTs.Methods
Systematic searches of the iTunes App and Google Play stores were completed using keyword searches. Two reviewers independently coded free (n = 36) and paid (n = 14) app descriptions using a taxonomy of 93 BCTs (December 2016-January 2017). A subsample (n = 4) of free apps were trialed for one week by the reviewers and coded for the presence of BCTs (February 2017).Results
In the free and paid app descriptions, only 10 of 93 BCTs were present with a mean of 2.42 BCTs (range 0-6) per app. The BCTs coded most frequently were "prompts/cues" (n = 43), "information about health consequences" (n = 31), and "self-monitoring of behavior" (n = 17). For the four free apps that were trialed, three additional BCTs were coded that were not coded in the descriptions: "graded tasks," "focus on past successes," and "behavior substitution."Conclusions
These sedentary behavior apps have fewer BCTs compared with physical activity apps and traditional (i.e., non-app) physical activity and healthy eating interventions. The present study sheds light on the behavior change potential of sedentary behavior apps and provides practical insight about coding for BCTs in apps.
SUBMITTER: Dunn EE
PROVIDER: S-EPMC6043935 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundWhile there has been an explosion of mobile device applications (apps) promoting healthful behaviors, including physical activity and sedentary patterns, surprisingly few have been based explicitly on strategies drawn from behavioral theory and evidence.ObjectiveThis study provided an initial 8-week evaluation of three different customized physical activity-sedentary behavior apps drawn from conceptually distinct motivational frames in comparison with a commercially available control app.Study design and methodsNinety-five underactive adults ages 45 years and older with no prior smartphone experience were randomized to use an analytically framed app, a socially framed app, an affectively framed app, or a diet-tracker control app. Daily physical activity and sedentary behavior were measured using the smartphone's built-in accelerometer and daily self-report measures.ResultsMixed-effects models indicated that, over the 8-week period, the social app users showed significantly greater overall increases in weekly accelerometry-derived moderate to vigorous physical activity relative to the other three arms (P values for between-arm differences = .04-.005; Social vs. Control app: d = 1.05, CI = 0.44,1.67; Social vs. Affect app: d = 0.89, CI = 0.27,1.51; Social vs. Analytic app: d = 0.89, CI = 0.27,1.51), while more variable responses were observed among users of the other two motivationally framed apps. Social app users also had significantly lower overall amounts of accelerometry-derived sedentary behavior relative to the other three arms (P values for between-arm differences = .02-.001; Social vs. Control app: d = 1.10,CI = 0.48,1.72; Social vs. Affect app: d = 0.94, CI = 0.32,1.56; Social vs. Analytic app: d = 1.24, CI = 0.59,1.89). Additionally, Social and Affect app users reported lower overall sitting time compared to the other two arms (P values for between-arm differences < .001; Social vs. Control app: d = 1.59,CI = 0.92, 2.25; Social vs. Analytic app: d = 1.89,CI = 1.17, 2.61; Affect vs. Control app: d = 1.19,CI = 0.56, 1.81; Affect vs. Analytic app: d = 1.41,CI = 0.74, 2.07).ConclusionThe results provide initial support for the use of a smartphone-delivered social frame in the early induction of both physical activity and sedentary behavior changes. The information obtained also sets the stage for further investigation of subgroups that might particularly benefit from different motivationally framed apps in these two key health promotion areas.Trial registrationClinicalTrials.gov NCT01516411.
| S-EPMC4924838 | biostudies-literature
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.
| S-EPMC8160788 | biostudies-literature
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.
| S-EPMC9006128 | biostudies-literature
Project description:Sedentary behavior is not a new topic, but trying to examine the direct links between sedentary behavior and health outcomes, independent of time spent in moderate- and vigorous-intensity physical activity, is a relatively new addition to the relationships between physical activity and health. Defining sedentary behavior as a risk factor and target for intervention opens up novel avenues for disease prevention and health promotion. The relationship between sedentary behavior and obesity is complex and not well understood, but the increased risk of disease due to sedentary behavior may be even greater in obese patients. Objective measurement of sedentary behavior is an important link in being able to understand the real effects of being sedentary, and a few measurement devices are described. Interventions targeting sedentary behavior should reduce total sedentary time, break long bouts of sitting with intermittent activity and encourage light-intensity activity throughout the day. New technologies can both measure and deliver an intervention aimed at reducing sitting time, the most common category of sedentary behavior. An optimal activity profile will include minimal amounts of sedentary behavior, in addition to regular physical activity and healthy sleep patterns.
| S-EPMC4109084 | biostudies-other
Project description:BackgroundThe use of fitness trackers as tools of self-management to promote physical activity is increasing. However, the content of fitness trackers remains unexplored.ObjectiveThe aim of this study was to use the Behavior Change Technique Taxonomy v1 (BCTTv1) to examine if swim-proof fitness trackers below Aus $150 (US$ 105) incorporate behavior change techniques (BCTs) that relate to self-management strategies to increase physical activity and reduce sedentary behavior and to determine if content of the fitness trackers correspond to physical activity guidelines.MethodsA total of two raters used the BCTTv1 to code 6 fitness trackers that met the inclusion criteria. The inclusion criteria were the ability to track activity, be swim proof, be compatible with Android and Apple operating systems, and cost below Aus $150.ResultsAll fitness trackers contained BCTs known to promote physical activity, with the most frequently used BCTs overlapping with self-management strategies, including goal setting, self-monitoring, and feedback on behavior. Fitbit Flex 2 (Fitbit Inc) contained the most BCTs at 20. Huawei Band 2 Pro (Huawei Technologies) and Misfit Shine 2 (Fossil Group) contained the least BCTs at 11.ConclusionsFitness trackers contain evidence-based BCTs that overlap with self-management strategies, which have been shown to increase physical activity and reduce sedentary behavior. Fitness trackers offer the prospect for physical activity interventions that are cost-effective and easily accessed by a wide population.
| S-EPMC6683653 | biostudies-literature
Project description:BackgroundInterventions delivered through new device technology, including mobile phone apps, appear to be an effective method to reach young adults. Previous research indicates that self-efficacy and social support for physical activity and self-regulation behavior change techniques (BCT), such as goal setting, feedback, and self-monitoring, are important for promoting physical activity; however, little is known about evaluations by the target population of BCTs applied to physical activity apps and whether these preferences are associated with individual personality characteristics.ObjectiveThis study aimed to explore young adults' opinions regarding BCTs (including self-regulation techniques) applied in mobile phone physical activity apps, and to examine associations between personality characteristics and ratings of BCTs applied in physical activity apps.MethodsWe conducted a cross-sectional online survey among healthy 18 to 30-year-old adults (N=179). Data on participants' gender, age, height, weight, current education level, living situation, mobile phone use, personality traits, exercise self-efficacy, exercise self-identity, total physical activity level, and whether participants met Dutch physical activity guidelines were collected. Items for rating BCTs applied in physical activity apps were selected from a hierarchical taxonomy for BCTs, and were clustered into three BCT categories according to factor analysis: "goal setting and goal reviewing," "feedback and self-monitoring," and "social support and social comparison."ResultsMost participants were female (n=146), highly educated (n=169), physically active, and had high levels of self-efficacy. In general, we observed high ratings of BCTs aimed to increase "goal setting and goal reviewing" and "feedback and self-monitoring," but not for BCTs addressing "social support and social comparison." Only 3 (out of 16 tested) significant associations between personality characteristics and BCTs were observed: "agreeableness" was related to more positive ratings of BCTs addressing "goal setting and goal reviewing" (OR 1.61, 95% CI 1.06-2.41), "neuroticism" was related to BCTs addressing "feedback and self-monitoring" (OR 0.76, 95% CI 0.58-1.00), and "exercise self-efficacy" was related to a high rating of BCTs addressing "feedback and self-monitoring" (OR 1.06, 95% CI 1.02-1.11). No associations were observed between personality characteristics (ie, personality, exercise self-efficacy, exercise self-identity) and participants' ratings of BCTs addressing "social support and social comparison."ConclusionsYoung Dutch physically active adults rate self-regulation techniques as most positive and techniques addressing social support as less positive among mobile phone apps that aim to promote physical activity. Such ratings of BCTs differ according to personality traits and exercise self-efficacy. Future research should focus on which behavior change techniques in app-based interventions are most effective to increase physical activity.
| S-EPMC4704888 | biostudies-literature
Project description:BackgroundDigital behavior change interventions (DBCIs) appear to reduce alcohol consumption, but greater understanding is needed of their mechanisms of action.PurposeTo describe the behavior change techniques (BCTs) used in DBCIs and examine whether individual BCTs, the inclusion of more BCTs or more Control Theory congruent BCTs is associated with increased effectiveness.MethodsForty-one randomized control trials were extracted from a Cochrane review of alcohol reduction DBCIs and coded for up to 93 BCTs using an established and reliable method. Random effects unadjusted and adjusted meta-regression models were performed to assess associations between BCTs and intervention effectiveness.ResultsInterventions used a mean of 9.1 BCTs (range 1-22), 23 different BCTs were used in four or more trials. Trials that used "Behavior substitution" (-95.112 grams per week [gpw], 95% CI: -162.90, -27.34), "Problem solving" (-45.92 gpw, 95% CI: -90.97, -0.87) and "Credible source" (-32.09 gpw, 95% CI: -60.64, -3.55) were significantly associated with greater alcohol reduction than trials without these BCTs. The "Behavior substitution" result should be treated as preliminary because it was reported in only four trials, three of which were conducted by the same research group. "Feedback" was used in 98% of trials (n = 41); other Control Theory congruent BCTs were used less frequently: for example, "Goal setting" 43% (n = 18) and "Self-monitoring" 29%, (n = 12).Conclusions"Behavior substitution," "Problem solving," and "Credible source" were associated with greater alcohol reduction. Many BCTs were used infrequently in DBCIs, including BCTs with evidence of effectiveness in other domains, such as "Self-monitoring" and "Goal setting."
| S-EPMC6361280 | biostudies-literature
Project description:ObjectiveTo examine whether the self-monitoring interventions of a mobile health app reduce sedentary behavior in the short and long terms.MethodWe designed a double-blind randomized control trial. Participants were selected from among the staff of a medical institution and registrants of an online research firm. Forty-nine participants were randomly assigned to either a control group (n = 25) or an intervention group (n = 24). The control group was given only the latest information about sedentary behavior, and the intervention was provided real-time feedback for self-monitoring in addition to the information. These interventions provided for 5 weeks (to measure the short-term effect) and 13 weeks (to measure the long-term effect) via the smartphone app. Measurements were as follows: subjective total sedentary time (SST), objective total sedentary time (OST), mean sedentary bout duration (MSB), and the number of sedentary breaks (SB). Only SST was measured by self-report based on the standardized International Physical Activity Questionnaire and others were measured with the smartphone.ResultsNo significant results were observed in the short term. In the long term, while no significant results were also observed in objective sedentary behavior (OST, MSB, SB), the significant differences were observed in subjective sedentary behavior (SST, βint - βctrl between baseline and 9/13 weeks; 1.73 and 1.50 h/d, respectively).ConclusionsReal-time feedback for self-monitoring with smartphone did not significantly affect objective sedentary behavior. However, providing only information about sedentary behavior to users with smartphones may make misperception on the amount of their subjective sedentary behavior.
| S-EPMC7448798 | biostudies-literature
Project description:BackgroundBehavioral interventions to prevent pediatric obesity have shown inconsistent results across the field. Studying what happens within the "black box" of these interventions and how differences in implementation lead to different outcomes will help researchers develop more effective interventions.AimTo compare the implementation of three features of a phone-based intervention for parents (time spent discussing weight-related behaviors, behavior change techniques used in sessions, and intervention activities implemented by parents between sessions) with study outcomes.MethodsA random selection of 100 parent-child dyads in the intervention arm of a phone-based obesity prevention trial was included in this analysis. Sessions were coded for overall session length, length of time spent discussing specific weight-related behaviors, number of behavior change techniques used during the sessions, and number of intervention-recommended activities implemented by the parents between sessions (e.g., parent-reported implementation of behavioral practice/rehearsal between sessions). The primary study outcome, prevention of unhealthy increase in child body mass index (BMI) percentile, was measured at baseline and 12 months.ResultsOverall session length was associated with decreases in child BMI percentile ( b = -0.02, p = .01). There was no association between the number of behavior change techniques used in the sessions and decreases in child BMI percentile ( b = -0.29, p = .27). The number of activities the parents reported implementing between sessions was associated with decreases in child BMI percentile ( b = -1.25, p = .02).DiscussionTo improve future interventions, greater attention should be paid to the intended and delivered session length, and efforts should be made to facilitate parents' implementation of intervention-recommended activities between sessions (ClinicalTrials.gov, No. NCT01084590).
| S-EPMC6417983 | biostudies-literature
Project description:BackgroundDiabetes management apps may have positive effects on diabetes self-management. It remains unclear, however, which app features are particularly effective and encourage sustained app usage. Behavior change techniques (BCTs) and gamification are promising approaches to improve user engagement. However, little is known about the frequency BCTs and gamification techniques (GTs) are actually used. This app review aims to provide an overview of BCTs and GTs in current diabetes management apps.MethodsGoogle's Play Store was searched for applications using a broad search strategy (keyword: "diabetes"). We limited our research to freely available apps. A total of 56 apps matched the inclusion criteria and were reviewed in terms of the features they offer to support self-management. We used a taxonomy comprising 29 BCTs and 17 GTs to evaluate the applications. Two independent raters tested and evaluated each app.ResultsInterrater agreement was high (ICC = .75 for BCTs; ICC = .90 for GTs). An average of 7.4 BCTs (SD = 3.1) and an average of 1.4 out of 17 GTs (SD = 1.6) were implemented in each app. Five out of 29 BCTs accounted for 55.8% of the BCTs identified in total. The GT most often identified was "feedback" and accounted for 50% of the GTs.ConclusionsThe potential of BCTs and GTs in diabetes management apps has not been fully exploited yet. Only very restricted sets of BCTs and gamification features were implemented. Systematic research on the efficacy of specific BCTs and GTs is needed to provide further guidance for app design.
| S-EPMC6955442 | biostudies-literature