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There are many physical activity apps but only few are evidence-informed: Results of a content-analysis (Preprint)
Abstract
Background:
Regular moderate to vigorous physical activity (PA) is essential for maintaining health and preventing the onset of chronic diseases. Both, global rates of smartphone ownership and the market for PA and fitness applications (apps) have grown rapidly in recent years. The use of PA and fitness apps may assist the general population in reaching evidence-based PA recommendations. However, it is unclear whether there are evidence-informed PA (EIPA) apps and whether behavior change techniques (BCTs) previously identified as effective for PA promotion are employed in these apps.
Objective:
The study aim was to identify English and German EIPA apps and employment of BCTs in those apps.
Methods:
Apps were identified in a systematic search using 25 predefined search terms in the Google Play Store. Two reviewers independently screened descriptions of apps and screenshots applying predefined inclusion and exclusion criteria. Apps were included if a.) their description contained information about PA promotion, b.) they were in English/German, c.) PA recommendations of the World Health Organization or the American College of Sport Medicine were mentioned, and d.) any kind of objective PA measurement was included. Two researchers downloaded and tested apps matching inclusion criteria for two weeks and coded their content using the Behavioral Change Technique Taxonomy v1 (BCTTv1).
Results:
The initial screening in the Google Play Store yielded 6,018 apps, 4,108 of which were not focused on PA or neither in German nor English. Descriptions of 1,216 apps were further screened for eligibility. Duplicate apps and light versions (n=694) and those with no objective measurement of PA, requiring additional equipment or not outlining any PA guideline in their description (n=1,184) were excluded. Of the remaining 32 apps, four were no longer available at the time of the download. Hence, 28 apps were downloaded and tested. Of these 28 apps, 14 did not contain any PA guideline as an app feature, despite having mentioning it in the description, five had technical problems, and three did not provide objective PA measurement. Thus, six were included in the final analyses. Of the 93 individual BCTs of the BCTTv1, on average, nine (SD: 5) were identified in these apps. Of the 16 hierarchical clusters, on average, five (SD: 3) were addressed. Only BCTs of the two hierarchical clusters ‘goals and planning’ and ‘feedback and monitoring’ were identified in all apps.
Conclusions:
Despite the availability of several thousand PA and fitness apps for Android platforms, very few addressed evidence-based PA guidelines and provided objective PA measurement. Further, available descriptions did not accurately reflect app content and only few EIPA apps incorporated several BCTs. Future apps should address evidence-based PA guidelines and a greater scope of BCTs to further increase their potential impact for PA promotion.
... Typische Funktionen von Bewegungsund Fitness-Apps (B&F-Apps) sind u. a. die Schritte des Nutzers zu überwachen, Feedback bereitzustellen und das Erreichen einer körperlichen Aktivitätsrichtlinie (z. B. der WHO) zu unterstützen [12]. Überdies kann der Einsatz von Techniken zur Verhaltensänderung die Nutzung und Wirksamkeit von Apps verbessern [8,30]. ...
... B. [14]). Eine aktuelle Inhaltsanalyse von Apps auf dem deutschen Markt fand lediglich sechs B&F-Apps, die beide Kriterien erfüllten und evidenzinformiert waren [12]. Auch ist bisher wenig zu Präferenzen in Bezug auf eingesetzte Techniken seitens der Nutzer bekannt. ...
... B. [8]), ist ihr Einsatz in B&F-Apps noch selten (z. B. [12] ...
Background
Smartphone applications (apps) represent an opportunity to promote physical activity (PA).
Objectives
To explore (1) the extent and sociodemographic differences in utilization of PA and fitness apps, (2) associations between technology acceptance and competence and PA and fitness app use, (3) associations between PA and fitness app use, PA and subjective health status and (4) attitudes towards the use of behavior change techniques (BCTs) in apps among persons aged 50 years and older.
Material and methods
Sociodemographic information, PA and fitness app use, technology acceptance and competence, PA, subjective health status and attitudes towards employing BCTs in apps were assessed via an online survey. Associations between variables were analyzed using logistic regressions.
Results
Of the 146 survey participants (average age 59.5 years), 18.5% used PA and fitness apps. Compared to non-users, users were more likely to be male (odds ratio, OR 7.23; 95% confidence interval, CI 1.82–28.68). Medium to high technology acceptance (OR 2.66; 95% CI 0.57–12.49) was associated with a higher PA and fitness app use but not technology competence. Persons who were physically active for at least 150 min/week had a greater likelihood to use PA and fitness apps (OR 7.06; 95% CI 1.83–27.19). Users rated the following BCTs as helpful in PA and fitness apps: prompt review of behavioral goals, goal setting of behavior and instruction on how to perform the behavior.
Conclusion
The results of this exploratory study suggest that future smartphone-assisted interventions should address a wider spectrum of the general population. Because low levels of technology acceptance appear to be related to little PA and fitness app use among older adults, future interventions should address this issue and spark interest and curiosity for technology use.
... Im ersten Teil wurde eine Content-Analyse von kommerziell erhältlichen B&F-Apps auf dem deutschen Markt durchgeführt. Apps wurden schrittweise nach vorab definierten Kriterien dahingehend untersucht, ob sie wissenschaftliche Techniken zur Verhaltensänderung einsetzen, deren Anwendung mit einer höheren Wahrscheinlichkeit der Verhaltensänderung einhergeht [11]. Im zweiten Teil wurde in einem Online-Survey mit Personen ab 50 Jahren das Ausmaß und soziodemografische Unterschiede im Nutzungsverhalten und der Einfluss von verschiedenen Faktoren, wie z. ...
... Anschließend wurden hierzu Leitfragen entwickelt. Die App Health Mate wurde zur Beurteilung in den Fokusgruppeninterviews ausgewählt, da sie eine der evidenzinformierten B&F-Apps, welche in der vorangegangenen Content-Analyse identifiziert worden waren, ist [11]. Die App gibt u. a. Feedback zum erreichten Schrittziel des Nutzers, des Weiteren können Körperfunktionen (z. ...
Background
International studies have examined different aspects of attitudes toward the use of evidence-informed applications (apps) for physical activity (PA) promotion among adults aged 50 years and above. In Germany, little is known about attitudes towards such apps in this population.
Objectives
The aim of the focus groups conducted with smartphones owners aged 50 years and above was to gain insights into attitudes, acceptance and utilization preferences regarding smartphone applications (apps) for PA promotion.
Materials and methods
Focus groups were conducted with users and non-users of PA and fitness apps following an interview guide. Factors influencing use were discussed, as well as attitudes towards using a specific evidence-informed PA and fitness app (i. e., Health Mate). Interviews were transcribed and analyzed following a structured qualitative content analysis.
Results
Overall, experienced users of PA and fitness apps rated the use of such apps slightly more positively than non-users. Users and non-users of PA and fitness apps stated that they would like an app which was easy to use with only few features for PA promotion. Manual entry of PA data was rather disliked and focus group participants preferred automated tracking. Feedback for self-monitoring of PA and social comparisons were considered helpful for PA promotion and for reaching personal PA goals. However, there were concerns about data safety. Features of the evidence-informed app Health Mate (e. g., feedback, badges) were appreciated by different participants to a varying degree.
Conclusions
Physical activity and fitness apps were cautiously appreciated by users and non-users aged 50 years and above. Both groups prefer easy to use apps with a limited number of features.
Physical activity is important in the self-management of long-term conditions (LTCs). However, implementing physical activity into clinical practice is challenging, due to complex barriers including access to programmes, time pressures, and transport costs, for people with comorbidities, managing multiple responsibilities. Various digital tools exist to overcome these barriers and support wide-scale implementation to help people stay physically active. We explored the experiences, needs and preferences of healthcare professionals and commissioners, regarding the use of digital tools to support people with LTCs to self-manage using physical activity. This included barriers and facilitators to implementing digital tools to support people with LTCs in NHS settings. Semi-structured interviews were conducted (April 2021 to January 2022) in Wessex, southern England, UK. Purposive sampling was used to recruit general practitioners and healthcare professionals, and convenience sampling to recruit commissioners (n = 15). Transcripts were coded to develop conceptual themes allowing comparisons between and among perspectives, with the Normalisation Process Theory (NPT)’s four constructs used to aid interpretation. Results showed that most digital tools supporting physical activity for LTCs, are not well implemented clinically. Current digital tools were seen to lack condition-specificity, usability/acceptability evidence-base, and voluntary sector involvement (i.e., NPT: coherence or ‘making sense’). Healthcare professionals and commissioners were unlikely to engage with use of digital tools unless they were integrated into health service IT systems and professional networks (i.e., NPT: cognitive participation), or adaptable to the digital literacy levels of service users and staff (i.e., NPT: collective action–needs for implementation). In practice, this meant being technically, easy to use and culturally accessible (i.e., NPT: collective action–promoting healthcare work). COVID-19 changed professional attitudes towards digital tools, in that they saw them being viable, feasible and critical options in a way they had not done before the pandemic. Implementation was also influenced by endorsement and trustworthiness enhancing the perception of them as secure and evidence-based (i.e., NPT: reflective monitoring). Our findings highlight that consideration must be given to ensuring that digital tools are accessible to both healthcare professionals and patients, have usability/acceptability, and are adaptable to specific LTCs. To promote clinical engagement, digital tools must be evidence-based, endorsed by professional networks, and integrated into existing health systems. Digital literacy of patients and professionals is also crucial for cross-service implementation.
Background
Physical activity is a critical target for health interventions, but effective interventions remain elusive. A growing body of work suggests that interventions targeting affective attitudes toward physical activity may be more effective for sustaining activity long term than those that rely on cognitive constructs alone, such as goal setting and self-monitoring. Anticipated affective response in particular is a promising target for intervention.
Objective
We will evaluate the efficacy of an SMS text messaging intervention that manipulates anticipated affective response to exercise to promote physical activity. We hypothesize that reminding users of a positive postexercise affective state before their planned exercise sessions will increase their calories burned during this exercise session. We will deploy 2 forms of affective SMS text messages to explore the design space: low-reflection messages written by participants for themselves and high-reflection prompts that require users to reflect and respond. We will also explore the effect of the intervention on affective attitudes toward exercise.
MethodsA total of 120 individuals will be enrolled in a 9-week microrandomized trial testing affective messages that remind users about feeling good after exercise (40% probability), control reminders (30% probability), or no message (30% probability). Two types of affective SMS text messages will be deployed: one requiring a response and the other in a read-only format. Participants will write the read-only messages themselves to ensure that the messages accurately reflect the participants’ anticipated postexercise affective state. Affective attitudes toward exercise and intrinsic motivation for exercise will be measured at the beginning and end of the study. The weighted and centered least squares method will be used to analyze the effect of delivering the intervention versus not on calories burned over 4 hours around the time of the planned activity, measured by the Apple Watch. Secondary analyses will include the effect of the intervention on step count and active minutes, as well as an investigation of the effects of the intervention on affective attitudes toward exercise and intrinsic motivation for exercise. Participants will be interviewed to gain qualitative insights into intervention impact and acceptability.
ResultsEnrollment began in May 2023, with 57 participants enrolled at the end of July 2023. We anticipate enrolling 120 participants.
Conclusions
This study will provide early evidence about the effect of a repeated manipulation of anticipated affective response to exercise. The use of 2 different types of messages will yield insight into optimal design strategies for improving affective attitudes toward exercise.
Trial RegistrationClinicalTrials.gov NCT05582369; https://classic.clinicaltrials.gov/ct2/show/NCT05582369
International Registered Report Identifier (IRRID)PRR1-10.2196/46560
Background:
Rheumatoid arthritis (RA) is a disabling, inflammatory joint condition affecting 0.5%-1% of the global population. Physical activity (PA) and exercise are recommended for people with RA, but uptake and adherence tend to be low. Smartphone apps could assist people with RA to achieve PA recommendations. However, it is not known whether high quality, evidence-informed PA apps that include behavior change techniques (BCTs) previously identified as effective for PA adherence are available for people with RA.
Objective:
This study aims to systematically identify apps that include goals to facilitate PA for adults with RA and assess app quality and content for the inclusion of relevant BCTs against recommendations for cardiorespiratory, resistance, flexibility, and neuromotor PA and exercise.
Methods:
A systematic search of the Apple App Store and Google Play Store in the United Kingdom was conducted to identify English language apps that promote PA for adults with RA. Two researchers independently assessed app quality (mobile app rating scale [MARS]; range 0-5) and content (BCT Taxonomy version 1, World Health Organization, the American College of Sports Medicine, and the European League against Rheumatism recommendations for PA). The completeness of reporting of PA prescription was evaluated using a modified version of the Consensus on Exercise Reporting Template (CERT; range 0-14).
Results:
A total of 14,047 apps were identified. Following deduplication, 2737 apps were screened for eligibility; 6 apps were downloaded (2 on the Apple App Store and 4 on the Google Play Store), yielding 4 unique apps. App quality varied (MARS score 2.25-4.17). Only 1 app was congruent with all aspects of the PA recommendations. All apps completely or partially recommended flexibility and resistance exercises, 3 apps completely or partially advised some form of neuromotor exercise, but only 2 offered full or partial guidance on cardiorespiratory exercise. Completeness of exercise reporting was mixed (CERT scores 7-14 points) and 3-7 BCTs were identified. Two BCTs were common to all apps (information about health consequences and instruction on how to perform behavior). Higher quality apps included a greater number of BCTs and were more closely aligned to PA guidance. No published trials evaluating the effect of the included apps were identified.
Conclusions:
This review identifies 4 PA apps of mixed quality and content for use by people with RA. Higher quality apps were more closely aligned to PA guidance and included a greater number of BCTs. One high-quality app (Rheumatoid Arthritis Information Support and Education) included 7 BCTs and was fully aligned with PA and exercise guidance. The effect of apps on PA adherence should be established before implementation.
Background:
Technological solutions, particularly mobile health (mHealth), have been shown to be potentially viable approaches for sustaining individuals' self-management of chronic health conditions. Theory-based interventions are more successful, as evidence-based information is an essential prerequisite for appropriate self-management. However, several reviews have shown that many existing mobile apps fail to be either theoretically grounded or based on evidence. Although some authors have attempted to address these two issues by focusing on the design and development processes of apps, concrete efforts to systematically select evidence-based content are scant.
Objective:
The objective of this study was to present a procedure for the participatory identification of evidence-based content to ground the development of a self-management app.
Methods:
To illustrate the procedure, we focused on the prevention and management of pressure injuries (PIs) in individuals with spinal cord injury (SCI). The procedure involves the following three steps: (1) identification of existing evidence through review and synthesis of existing recommendations on the prevention and self-management of PIs in SCI; (2) a consensus meeting with experts from the field of SCI and individuals with SCI to select the recommendations that are relevant and applicable to community-dwelling individuals in their daily lives; and (3) consolidation of the results of the study.
Results:
In this case study, at the end of the three-step procedure, the content for an mHealth intervention was selected in the form of 98 recommendations.
Conclusions:
This study describes a procedure for the participatory identification and selection of disease-specific evidence and professional best practices to inform self-management interventions. This procedure might be especially useful in cases of complex chronic health conditions, as every recommendation in these cases needs to be evaluated and considered in light of all other self-management requirements. Hence, the agreement of experts and affected individuals is essential to ensure the selection of evidence-based content that is considered to be relevant and applicable.
Background: Meta-information provided about health apps on app stores is often the only readily available source of quality-related information before installation.
Objective: The purpose of this study was to assess whether physicians deem a predefined set of quality principles as relevant for health apps; whether they are able to identify corresponding information in a given sample of app descriptions; and whether, and how, this facilitates their informed usage decisions.
Methods: All members of the German Society for Internal Medicine were invited by email to participate in an anonymous online survey over a 6-week period. Participants were randomly assigned one app description focusing on cardiology or pulmonology. In the survey, participants were asked three times about whether the assigned description sufficed for a usage decision: they were asked (1) after giving an appraisal of the relevance of nine predefined app quality principles, (2) after determining whether the descriptions covered the quality principles, and (3) after they assessed the availability of detailed quality information by means of 25 additional key questions. Tests for significance of changes in their decisions between assessments 1 and 2, and between assessments 2 and 3, were conducted with the McNemar-Bowker test of symmetry. The effect size represents the discordant proportion ratio sum as a quotient of the test statistics of the Bowker test and the number of observation units. The significance level was set to alpha=.05 with a power of 1-beta=.95.
Results: A total of 441 of 724 participants (60.9%) who started the survey fully completed the questionnaires and were included in the evaluation. The participants predominantly rated the specified nine quality principles as important for their decision (approximately 80%-99% of ratings). However, apart from the practicality criterion, information provided in the app descriptions was lacking for both groups (approximately 51%-92%). Reassessment of the apps led to more critical assessments among both groups. After having familiarized themselves with the nine quality principles, approximately one-third of the participants (group A: 63/220, 28.6%; group B: 62/221, 28.1%) came to more critical usage decisions in a statistically significant manner (McNemar-Bowker test, groups A and B: P<.001). After a subsequent reassessment with 25 key questions, critical appraisals further increased, although not in a statistically significant manner (McNemar-Bowker, group A: P=.13; group B: P=.05).
Conclusions: Sensitizing physicians to the topic of quality principles via questions about attitudes toward established quality principles, and letting them apply these principles to app descriptions, lead to more critical appraisals of the sufficiency of the information they provided. Even working with only nine generic criteria was sufficient to bring about the majority of decision changes. This may lay the foundation for aiding physicians in their app-related decision processes, without unduly taking up their valuable time.
The overselling of health apps that may provide little benefit and even harm needs the health community's immediate attention. With little formal regulation, a light-touch approach to consumer protection is now warranted to give customers a modicum of information to help them choose from the vast array of so-called health apps. We suggest 4 guiding principles that should be adopted to provide the consumer with information that can guide their choice at the point of download. We call these the Transparency for Trust (T4T) principles, which are derived from experimental studies, systematic reviews, and reports of patient concerns. The T4T principles are (1) privacy and data security, (2) development characteristics, (3) feasibility data, and (4) benefits. All our questions are in a simple form so that all consumers can understand them. We suggest that app stores should take responsibility for providing this information and store it with any app marketed as a health app. Even the absence of information would provide consumers with some understanding and fuel their choice. This would also provide some commercial impetus for app developers to consider this requested information from the outset. ©Til Wykes, Stephen Schueller. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 02.05.2019.
Background
Physical activity apps are commonly used to increase levels of activity and health status. To date, the focus of research has been to determine the potential of apps to influence behavior, to ascertain the efficacy of a limited number of apps to change behavior, and to identify the characteristics of apps that users prefer.
Objective
The purpose of this study was to identify the mechanisms by which the use of physical activity apps may influence the users’ physical activity behavior.
Methods
This study used a cross-sectional survey of users of health-related physical activity apps during the past 6 months. An electronic survey was created in Qualtrics’ Web-based survey software and deployed on Amazon Mechanical Turk. Individuals who had used at least one physical activity app in the past 6 months were eligible to respond. The final sample comprised 207 adults living in the United States. 86.0% (178/207) of respondents were between the ages of 26 and 54 years, with 51.2% (106/207) of respondents being female. Behavior change theory informed the creation of 20 survey items relating to the mechanisms of behavior change. Respondents also reported about engagement with the apps, app likeability, and physical activity behavior.
ResultsRespondents reported that using a physical activity app in the past 6 months resulted in a change in their attitudes, beliefs, perceptions, and motivation. Engagement with the app (P
Despite the well-known health benefits of physical activity, a large proportion of the population does not meet the guidelines. Hence, effective and widely accessible interventions to increase levels of physical activity are needed. Over the recent years, the number of health and fitness apps has grown rapidly, and they might form part of the solution to the widespread physical inactivity. However, it remains unclear to which extent they make use of the possibilities of mobile technology and form real e-coaching systems. This study aims to investigate the current landscape of smartphone apps that promote physical activity for healthy adults. Therefore, we present a framework to rate the extent to which such apps incorporate technological features. And, we show that the physical activity promotion apps included in the review implemented an average of approximately eight techniques and functions. The features that were implemented most often were user input, textual/numerical overviews of the user’s behavior and progress, sharing achievements or workouts in social networks, and general advice on physical activity. The features that were present least often were adaptation, integration with external sources, and encouragement through gamification, some form of punishment or the possibility to contact an expert. Overall, the results indicate that apps can be improved substantially in terms of their utilization of the possibilities that current mobile technology offers.
I consider my PhD as having 2 distinctive parts: A) to ascertain the quality of physical activity (PA) applications (apps) on the market in terms of safety, effectiveness, and user experience (Studies 1, 2); B) to assessthe efficacy of selected PA apps with potential, physically inactive, users (Studies 3, 4). I am finalising part A of the PhD and I am writing the protocols for part B. It would be valuable to gain the views of experts to make sure I am considering the topic from both behaviour change discipline and user experience research.
Background
Chronic conditions are an increasing challenge for individuals and the health care system. Smartphones and health apps are potentially promising tools to change health-related behaviors and manage chronic conditions.
Objective
The aim of this study was to explore (1) the extent of smartphone and health app use, (2) sociodemographic, medical, and behavioral correlates of smartphone and health app use, and (3) associations of the use of apps and app characteristics with actual health behaviors.
Methods
A population-based survey (N=4144) among Germans, aged 35 years and older, was conducted. Sociodemographics, presence of chronic conditions, health behaviors, quality of life, and health literacy, as well as the use of the Internet, smartphone, and health apps were assessed by questionnaire at home visit. Binary logistic regression models were applied.
Results
It was found that 61.25% (2538/4144) of participants used a smartphone. Compared with nonusers, smartphone users were younger, did more research on the Internet, were more likely to work full-time and more likely to have a university degree, engaged more in physical activity, and less in low fat diet, and had a higher health-related quality of life and health literacy. Among smartphone users, 20.53% (521/2538) used health apps. App users were younger, less likely to be native German speakers, did more research on the Internet, were more likely to report chronic conditions, engaged more in physical activity, and low fat diet, and were more health literate compared with nonusers who had a smartphone. Health apps focused on smoking cessation (232/521, 44.5%), healthy diet (201/521, 38.6%), and weight loss (121/521, 23.2%). The most common app characteristics were planning (264/521, 50.7%), reminding (188/521, 36.1%), prompting motivation (179/521 34.4%), and the provision of information (175/521, 33.6%). Significant associations were found between planning and the health behavior physical activity, between feedback or monitoring and physical activity, and between feedback or monitoring and adherence to doctor’s advice.
Conclusions
Although there were many smartphone and health app users, a substantial proportion of the population was not engaged. Findings suggest age-related, socioeconomic-related, literacy-related, and health-related disparities in the use of mobile technologies. Health app use may reflect a user’s motivation to change or maintain health behaviors. App developers and researchers should take account of the needs of older people, people with low health literacy, and chronic conditions.
PurposeThis systematic review aims to explain the heterogeneity in results of interventions to promote physical activity and healthy eating for overweight and obese adults, by exploring the differential effects of behaviour change techniques (BCTs) and other intervention characteristics. Methods
The inclusion criteria specified RCTs with ? 12 weeks? duration, from January 2007 to October 2014, for adults (mean age ? 40 years, mean BMI ? 30). Primary outcomes were measures of healthy diet or physical activity. Two reviewers rated study quality, coded the BCTs, and collected outcome results at short (?6 months) and long term (?12 months). Meta-analyses and meta-regressions were used to estimate effect sizes (ES), heterogeneity indices (I2) and regression coefficients. ResultsWe included 48 studies containing a total of 82 outcome reports. The 32 long term reports had an overall ES = 0.24 with 95% confidence interval (CI): 0.15 to 0.33 and I2 = 59.4%. The 50 short term reports had an ES = 0.37 with 95% CI: 0.26 to 0.48, and I2 = 71.3%. The number of BCTs unique to the intervention group, and the BCTs goal setting and self-monitoring of behaviour predicted the effect at short and long term. The total number of BCTs in both intervention arms and using the BCTs goal setting of outcome, feedback on outcome of behaviour, implementing graded tasks, and adding objects to the environment, e.g. using a step counter, significantly predicted the effect at long term. Setting a goal for change; and the presence of reporting bias independently explained 58.8% of inter-study variation at short term. Autonomy supportive and person-centred methods as in Motivational Interviewing, the BCTs goal setting of behaviour, and receiving feedback on the outcome of behaviour, explained all of the between study variations in effects at long term. Conclusion
There are similarities, but also differences in effective BCTs promoting change in healthy eating and physical activity and BCTs supporting maintenance of change. The results support the use of goal setting and self-monitoring of behaviour when counselling overweight and obese adults. Several other BCTs as well as the use of a person-centred and autonomy supportive counselling approach seem important in order to maintain behaviour over time. Trial RegistrationPROSPERO CRD42015020624
Background
Today, runners use wearable technology such as global positioning system (GPS)–enabled sport watches to track and optimize their training activities, for example, when participating in a road race event. For this purpose, an increasing amount of low-priced, consumer-oriented wearable devices are available. However, the variety of such devices is overwhelming. It is unclear which devices are used by active, healthy citizens and whether they can provide accurate tracking results in a diverse study population. No published literature has yet assessed the dissemination of wearable technology in such a cohort and related influencing factors.
Objective
The aim of this study was 2-fold: (1) to determine the adoption of wearable technology by runners, especially “smart” devices and (2) to investigate on the accuracy of tracked distances as recorded by such devices.
MethodsA pre-race survey was applied to assess which wearable technology was predominantly used by runners of different age, sex, and fitness level. A post-race survey was conducted to determine the accuracy of the devices that tracked the running course. Logistic regression analysis was used to investigate whether age, sex, fitness level, or track distance were influencing factors. Recorded distances of different device categories were tested with a 2-sample t test against each other.
ResultsA total of 898 pre-race and 262 post-race surveys were completed. Most of the participants (approximately 75%) used wearable technology for training optimization and distance recording. Females (P=.02) and runners in higher age groups (50-59 years: P=.03; 60-69 years: P
Background:
Smartphones are ideal for promoting physical activity in those with little intrinsic motivation for exercise. This study tested three hypotheses: H1 - receipt of social feedback generates higher step-counts than receipt of no feedback; H2 - receipt of social feedback generates higher step-counts than only receiving feedback on one's own walking; H3 - receipt of feedback on one's own walking generates higher step-counts than no feedback (H3).
Methods:
A parallel group randomised controlled trial measured the impact of feedback on steps-counts. Healthy male participants (n = 165) aged 18-40 were given phones pre-installed with an app that recorded steps continuously, without the need for user activation. Participants carried these with them as their main phones for a two-week run-in and six-week trial. Randomisation was to three groups: no feedback (control); personal feedback on step-counts; group feedback comparing step-counts against those taken by others in their group. The primary outcome measure, steps per day, was assessed using longitudinal multilevel regression analysis. Control variables included attitude to physical activity and perceived barriers to physical activity.
Results:
Fifty-five participants were allocated to each group; 152 completed the study and were included in the analysis: n = 49, no feedback; n = 53, individual feedback; n = 50, individual and social feedback. The study provided support for H1 and H3 but not H2. Receipt of either form of feedback explained 7.7 % of between-subject variability in step-count (F = 6.626, p < 0.0005). Compared to the control, the expected step-count for the individual feedback group was 60 % higher (effect on log step-count = 0.474, 95 % CI = 0.166-0.782) and that for the social feedback group, 69 % higher (effect on log step-count = 0.526, 95 % CI = 0.212-0.840). The difference between the two feedback groups (individual vs social feedback) was not statistically significant.
Conclusions:
Always-on smartphone apps that provide step-counts can increase physical activity in young to early-middle-aged men but the provision of social feedback has no apparent incremental impact. This approach may be particularly suitable for inactive people with low levels of physical activity; it should now be tested with this population.
Background: There are thousands of apps promoting dietary improvement, increased physical activity (PA) and weight management. Despite a growing number of reviews in this area, popular apps have not been comprehensively analysed in terms of features related to engagement, functionality, aesthetics, information quality, and content, including the types of change techniques employed.
Methods: The databases containing information about all Health and Fitness apps on GP and iTunes (7,954 and 25,491 apps) were downloaded in April 2015. Database filters were applied to select the most popular apps available in both stores. Two researchers screened the descriptions selecting only weight management apps. Features, app quality and content were independently assessed using the Mobile App Rating Scale (MARS) and previously-defined categories of techniques relevant to behaviour change. Inter-coder reliabilities were calculated, and correlations between features explored.
Results: Of the 23 popular apps included in the review 16 were free (70 %), 15 (65 %) addressed weight control, diet and PA combined; 19 (83 %) allowed behavioural tracking. On 5-point MARS scales, apps were of average quality (Md = 3.2, IQR = 1.4); “functionality” (Md = 4.0, IQR = 1.1) was the highest and “information quality” (Md = 2.0, IQR = 1.1) was the lowest domain. On average, 10 techniques were identified per app (range: 1–17) and of the 34 categories applied, goal setting and self-monitoring techniques were most frequently identified. App quality was positively correlated with number of techniques included (rho = .58, p < .01) and number of “technical” features (rho = .48, p < .05), which was also associated with the number of techniques included (rho = .61, p < .01). Apps that provided tracking used significantly more techniques than those that did not. Apps with automated tracking scored significantly higher in engagement, aesthetics, and overall MARS scores. Those that used change techniques previously associated with effectiveness (i.e., goal setting, self-monitoring and feedback) also had better “information quality”.
Conclusions: Popular apps assessed have overall moderate quality and include behavioural tracking features and a range of change techniques associated with behaviour change. These apps may influence behaviour, although more attention to information quality and evidence-based content are warranted to improve their quality.
Meeting global health challenges requires effective behaviour change interventions (BCIs). This depends on advancing the science of behaviour change which, in turn, depends on accurate intervention reporting. Current reporting often lacks detail, preventing accurate replication and implementation. Recent developments have specified intervention content into behaviour change techniques (BCTs) – the ‘active ingredients’, for example goal-setting, self-monitoring of behaviour. BCTs are ‘the smallest components compatible with retaining the postulated active ingredients, i.e. the proposed mechanisms of change. They can be used alone or in combination with other BCTs’ (Michie S, Johnston M. Theories and techniques of behaviour change: developing a cumulative science of behaviour change.
Health Psychol Rev
2012;
6
:1–6). Domain-specific taxonomies of BCTs have been developed, for example healthy eating and physical activity, smoking cessation and alcohol consumption. We need to build on these to develop an internationally shared language for specifying and developing interventions. This technology can be used for synthesising evidence, implementing effective interventions and testing theory. It has enormous potential added value for science and global health.
Objective
(1) To develop a method of specifying content of BCIs in terms of component BCTs; (2) to lay a foundation for a comprehensive methodology applicable to different types of complex interventions; (3) to develop resources to support application of the taxonomy; and (4) to achieve multidisciplinary and international acceptance for future development.
Design and participants
Four hundred participants (systematic reviewers, researchers, practitioners, policy-makers) from 12 countries engaged in investigating, designing and/or delivering BCIs.
Development of the taxonomy
involved a Delphi procedure, an iterative process of revisions and consultation with 41 international experts;
hierarchical structure
of the list was developed using inductive ‘bottom-up’ and theory-driven ‘top-down’ open-sort procedures (
n
= 36);
training
in use of the taxonomy (1-day workshops and distance group tutorials) (
n
= 161) was
evaluated
by changes in intercoder reliability and validity (agreement with expert consensus);
evaluating
the taxonomy for coding interventions was assessed by reliability (intercoder; test–retest) and validity (
n
= 40 trained coders); and
evaluating
the taxonomy for writing descriptions was assessed by reliability (intercoder; test–retest) and by experimentally testing its value (
n
= 190).
Results
Ninety-three distinct, non-overlapping BCTs with clear labels and definitions formed Behaviour Change Technique Taxonomy version 1 (BCTTv1). BCTs clustered into 16 groupings using a ‘bottom-up’ open-sort procedure; there was overlap between these and groupings produced by a theory-driven, ‘top-down’ procedure. Both training methods improved validity (both
p
Introduction:
There are a vast number of smartphone applications (apps) aimed at promoting medication adherence on the market; however, the theory and evidence base in terms of applying established health behavior change techniques underpinning these apps remains unclear. This study aimed to code these apps using the Behavior Change Technique Taxonomy (v1) for the presence or absence of established behavior change techniques.
Methods:
The sample of apps was identified through systematic searches in both the Google Play Store and Apple App Store in February 2015. All apps that fell into the search categories were downloaded for analysis. The downloaded apps were screened with exclusion criteria, and suitable apps were reviewed and coded for behavior change techniques in March 2015. Two researchers performed coding independently.
Results:
In total, 166 medication adherence apps were identified and coded. The number of behavior change techniques contained in an app ranged from zero to seven (mean=2.77). A total of 12 of a possible 96 behavior change techniques were found to be present across apps. The most commonly included behavior change techniques were "action planning" and "prompt/cues," which were included in 96% of apps, followed by "self-monitoring" (37%) and "feedback on behavior" (36%).
Conclusions:
The current extent to which established behavior change techniques are used in medication adherence apps is limited. The development of medication adherence apps may not have benefited from advances in the theory and practice of health behavior change.
Background:
Physical activity games developed for a mobile phone platform are becoming increasingly popular, yet little is known about their content or inclusion of health behavior theory (HBT).
Objective:
The objective of our study was to quantify elements of HBT in physical activity games developed for mobile phones and to assess the relationship between theoretical constructs and various app features.
Methods:
We conducted an analysis of exercise and physical activity game apps in the Apple App Store in the fall of 2014. A total of 52 apps were identified and rated for inclusion of health behavior theoretical constructs using an established theory-based rubric. Each app was coded for 100 theoretical items, containing 5 questions for 20 different constructs. Possible total theory scores ranged from 0 to 100. Descriptive statistics and Spearman correlations were used to describe the HBT score and association with selected app features, respectively.
Results:
The average HBT score in the sample was 14.98 out of 100. One outlier, SuperBetter, scored higher than the other apps with a score of 76. Goal setting, self-monitoring, and self-reward were the most-reported constructs found in the sample. There was no association between either app price and theory score (P=.5074), or number of gamification elements and theory score (P=.5010). However, Superbetter, with the highest HBT score, was also the most expensive app.
Conclusions:
There are few content analyses of serious games for health, but a comparison between these findings and previous content analyses of non-game health apps indicates that physical activity mobile phone games demonstrate higher levels of behavior theory. The most common theoretical constructs found in this sample are known to be efficacious elements in physical activity interventions. It is unclear, however, whether app designers consciously design physical activity mobile phone games with specific constructs in mind; it may be that games lend themselves well to inclusion of theory and any constructs found in significant levels are coincidental. Health games developed for mobile phones could be potentially used in health interventions, but collaboration between app designers and behavioral specialists is crucial. Additionally, further research is needed to better characterize mobile phone health games and the relative importance of educational elements versus gamification elements in long-term behavior change.
Low physical activity level is a significant contributor to chronic disease, weight dysregulation, and mortality. Nearly 70% of the American population is overweight, and 35% is obese. Obesity costs an estimated US$ 147 billion annually in health care, and as many as 95 million years of life. Although poor nutritional habits remain the major culprit, lack of physical activity significantly contributes to the obesity epidemic and related lifestyle diseases.
Over the past 10 years, mobile devices have become ubiquitous, and there is an ever-increasing number of mobile apps that are being developed to facilitate physical activity, particularly for active people. However, no systematic assessment has been performed about their quality with respect to following the parameters of sound fitness principles and scientific evidence, or suitability for a variety of fitness levels. The aim of this paper is to fill this gap and assess the quality of mobile coaching apps on iOS mobile devices.
A set of 30 popular mobile apps pertaining to physical activity programming was identified and reviewed on an iPhone device. These apps met the inclusion criteria and provided specific prescriptive fitness and exercise programming content. The content of these apps was compared against the current guidelines and fitness principles established by the American College of Sports Medicine (ACSM). A weighted scoring method based on the recommendations of the ACSM was developed to generate subscores for quality of programming content for aerobic (0-6 scale), resistance (0-6 scale), and flexibility (0-2 scale) components using the frequency, intensity, time, and type (FITT) principle. An overall score (0-14 scale) was generated from the subscores to represent the overall quality of a fitness coaching app.
Only 3 apps scored above 50% on the aerobic component (mean 0.7514, SD 1.2150, maximum 4.1636), 4 scored above 50% on the resistance/strength component (mean 1.4525, SD 1.2101, maximum 4.1094), and no app scored above 50% on the flexibility component (mean 0.1118, SD 0.2679, maximum 0.9816). Finally, only 1 app had an overall score (64.3%) above 50% (mean 2.3158, SD 1.911, maximum 9.0072).
There are over 100,000 health-related apps. When looking at popular free apps related to physical activity, we observe that very few of them are evidence based, and respect the guidelines for aerobic activity, strength/resistance training, and flexibility, set forth by the ACSM. Users should exercise caution when adopting a new app for physical activity purposes. This study also clearly identifies a gap in evidence-based apps that can be used safely and effectively to start a physical routine program, develop fitness, and lose weight. App developers have an exciting opportunity to improve mobile coaching app quality by addressing these gaps.
Physical activity participation is an important behavior for modifying lifestyle-related disease risk. Mobile health apps for chronic disease management and prevention are being developed at a rapid rate. However, it is unclear whether these apps are evidence-based. Current public health recommendations for physical activity participation for adults highlight the importance of engaging in 150 minutes weekly of purposeful exercise, and muscle strengthening activities on at least 2 days of the week.
The aims of the present review were to (1) identify available evidence-based physical activity apps, and (2) identify technological features that could be leveraged to improve health outcomes.
iTunes and Google Play mobile app stores were searched using keyword and category searching during a single day (February 18, 2014) for physical activity apps available in English. The description pages of eligible apps were reviewed by 4 independent reviewers for evidence-based content, technological, and descriptive features. An a priori subset of apps was downloaded for further review (n=6 affiliated with a non-commercial agency; n=10 top rated; n=10 random selection), and developers were contacted for information regarding evidence-informed content.
The initial search yielded 2400 apps, of which 379 apps (n=206 iTunes; n=173 Google Play) were eligible. Primary results demonstrated no apps (n=0) adhering to evidence-based guidelines for aerobic physical activity, and 7 out of 379 implementing evidence-based guidelines for resistance training physical activity. Technological features of apps included social networking (n=207), pairing with a peripheral health device (n=61), and measuring additional health parameters (n=139). Secondary results revealed 1 app that referenced physical activity guidelines (150 minutes/weekly of exercise), and demonstrated that apps were based on various physical activity reports (n=4) or personal expertise (n=2).
The present study demonstrated a shortage of evidence-based physical activity apps. This gap underscores the need for development of evidence-informed mobile apps. Results highlight the opportunity to develop evidence-informed mobile apps that can be used clinically to enhance health outcomes.
The use of mobile apps for health and well being promotion has grown exponentially in recent years. Yet, there is currently no app-quality assessment tool beyond "star"-ratings.
The objective of this study was to develop a reliable, multidimensional measure for trialling, classifying, and rating the quality of mobile health apps.
A literature search was conducted to identify articles containing explicit Web or app quality rating criteria published between January 2000 and January 2013. Existing criteria for the assessment of app quality were categorized by an expert panel to develop the new Mobile App Rating Scale (MARS) subscales, items, descriptors, and anchors. There were sixty well being apps that were randomly selected using an iTunes search for MARS rating. There were ten that were used to pilot the rating procedure, and the remaining 50 provided data on interrater reliability.
There were 372 explicit criteria for assessing Web or app quality that were extracted from 25 published papers, conference proceedings, and Internet resources. There were five broad categories of criteria that were identified including four objective quality scales: engagement, functionality, aesthetics, and information quality; and one subjective quality scale; which were refined into the 23-item MARS. The MARS demonstrated excellent internal consistency (alpha = .90) and interrater reliability intraclass correlation coefficient (ICC = .79).
The MARS is a simple, objective, and reliable tool for classifying and assessing the quality of mobile health apps. It can also be used to provide a checklist for the design and development of new high quality health apps.
Introduction:
There are large numbers of health-related applications (apps) available in various app stores for many smartphone devices. Consequently, increasing numbers of articles are attempting to evaluate the content and the quality of health-related smartphone apps for specific health conditions. The aim of this article is to describe and summarize the methodologies used to determine the quality of health-related apps targeting health consumers and to propose a set of criteria for evaluating the quality of smartphone health-related apps.
Materials and methods:
In 2013, literature searches were performed using Medline and CINAHL, and we included all articles that had the aim of assessing the quality of health-related smartphone apps. The assessment method used in these studies was summarized and scored using a set of quality criteria developed for this study.
Results:
From 606 articles generated by the search, only 10 met the inclusion criteria. Based on our quality criteria, the mean score was 5.05 out of 8 (range, 2-7). Eighty percent of the studies did not identify the app store country in which the apps were found. Forty percent of the studies did not clearly mention whether they only had assessed the app description or had downloaded the app content for evaluation. Sixty percent of the studies did not provide a list of the apps they had evaluated. Overall, we identified six evaluation methodologies used to assess the quality of health-related apps described in RESULTS.
Conclusions:
This article provides a summary of currently used methods for assessing the quality of smartphone health-related apps and proposes a set of criteria to enable future studies to consistently review health-related app quality in a standardized manner.
Background
In May 2013, the iTunes and Google Play stores contained 23,490 and 17,756 smartphone applications (apps) categorized as Health and Fitness, respectively. The quality of these apps, in terms of applying established health behavior change techniques, remains unclear.Methods
The study sample was identified through systematic searches in iTunes and Google Play. Search terms were based on Boolean logic and included AND combinations for physical activity, healthy lifestyle, exercise, fitness, coach, assistant, motivation, and support. Sixty-four apps were downloaded, reviewed, and rated based on the taxonomy of behavior change techniques used in the interventions. Mean and ranges were calculated for the number of observed behavior change techniques. Using nonparametric tests, we compared the number of techniques observed in free and paid apps and in iTunes and Google Play.ResultsOn average, the reviewed apps included 5 behavior change techniques (range 2¿8). Techniques such as self-monitoring, providing feedback on performance, and goal-setting were used most frequently, whereas some techniques such as motivational interviewing, stress management, relapse prevention, self-talk, role models, and prompted barrier identification were not. No differences in the number of behavior change techniques between free and paid apps, or between the app stores were found.Conclusions
The present study demonstrated that apps promoting physical activity applied an average of 5 out of 23 possible behavior change techniques. This number was not different for paid and free apps or between app stores. The most frequently used behavior change techniques in apps were similar to those most frequently used in other types of physical activity promotion interventions.
Background
There has been a recent proliferation in the development of smartphone applications (apps) aimed at modifying various health behaviours. While interventions that incorporate behaviour change techniques (BCTs) have been associated with greater effectiveness, it is not clear to what extent smartphone apps incorporate such techniques. The purpose of this study was to investigate the presence of BCTs in physical activity and dietary apps and determine how reliably the taxonomy checklist can be used to identify BCTs in smartphone apps.
Methods
The top-20 paid and top-20 free physical activity and/or dietary behaviour apps from the New Zealand Apple App Store Health & Fitness category were downloaded to an iPhone. Four independent raters user-tested and coded each app for the presence/absence of BCTs using the taxonomy of behaviour change techniques (26 BCTs in total). The number of BCTs included in the 40 apps was calculated. Krippendorff’s alpha was used to evaluate interrater reliability for each of the 26 BCTs.
Results
Apps included an average of 8.1 (range 2-18) techniques, the number being slightly higher for paid (M = 9.7, range 2-18) than free apps (M = 6.6, range 3-14). The most frequently included BCTs were “provide instruction” (83% of the apps), “set graded tasks” (70%), and “prompt self-monitoring” (60%). Techniques such as “teach to use prompts/cues”, “agree on behavioural contract”, “relapse prevention” and “time management” were not present in the apps reviewed. Interrater reliability coefficients ranged from 0.1 to 0.9 (Mean 0.6, SD = 0.2).
Conclusions
Presence of BCTs varied by app type and price; however, BCTs associated with increased intervention effectiveness were in general more common in paid apps. The taxonomy checklist can be used by independent raters to reliably identify BCTs in physical activity and dietary behaviour smartphone apps.
Little is known about how much smartphone apps for weight control adhere to evidence-informed practices. The aim of this study was to review and summarize the content of available weight control apps. Information on content, user rating, and price was extracted from iTunes on September 25, 2009. Apps (n = 204) were coded for adherence to 13 evidence-informed practices for weight control. Latent class analysis was used to identify subgroups of apps based on endorsement practices. Only a small percentage of apps had five or more of the 13 practices (15%). Latent class analysis revealed three main types of apps: diet, physical activity, and weight journals (19%); dietary advice and journals (34%); and weight trackers (46%). User ratings were not associated with apps from these three classes. Many apps have insufficient evidence-informed content. Research is needed that seeks to develop, improve, and evaluate these apps.
Background
Physical activity (PA) in older people is critically important in the prevention of disease, maintenance of independence and improvement of quality of life. Little is known about the physical activity of the older adults or their compliance with current physical activity guidelines.
Methods
A systematic literature search of the published literature was conducted. Included were published reports of original research that independently reported: the PA level of non-institutional older adults (aged 60 years and over); and the proportion of older adults in the different samples who met PA recommendations or guidelines. The review was restricted to studies published since 2000 to provide a current picture of older adults’ PA levels.
Results
Fifty three papers were included in the review. The percentage of older adults meeting recommended physical activity ranged from 2.4 – 83.0% across the studies. Definitions of “recommended” physical activity in older adults varied across the studies as did approaches to measurement which posed methodological challenges to data analysis. Older age groups were less likely than the reference group to be regularly active, and women were less likely than men to achieve regular physical activity, especially leisure time physical activity, when measured by both subjective and objective criteria.
Conclusion
The review highlights the need for studies which recruit representative random samples of community based older people and employ validated measurement methods consistently to enable comparison of PA levels over time and between countries.
Background:
CONSORT guidelines call for precise reporting of behavior change interventions: we need rigorous methods of characterizing active content of interventions with precision and specificity.
Objectives:
The objective of this study is to develop an extensive, consensually agreed hierarchically structured taxonomy of techniques [behavior change techniques (BCTs)] used in behavior change interventions.
Methods:
In a Delphi-type exercise, 14 experts rated labels and definitions of 124 BCTs from six published classification systems. Another 18 experts grouped BCTs according to similarity of active ingredients in an open-sort task. Inter-rater agreement amongst six researchers coding 85 intervention descriptions by BCTs was assessed.
Results:
This resulted in 93 BCTs clustered into 16 groups. Of the 26 BCTs occurring at least five times, 23 had adjusted kappas of 0.60 or above.
Conclusions:
"BCT taxonomy v1," an extensive taxonomy of 93 consensually agreed, distinct BCTs, offers a step change as a method for specifying interventions, but we anticipate further development and evaluation based on international, interdisciplinary consensus.
Objective:
To quantify the presence of health behavior theory constructs in iPhone apps targeting physical activity.
Methods:
This study used a content analysis of 127 apps from Apple's (App Store) Health & Fitness category. Coders downloaded the apps and then used an established theory-based instrument to rate each app's inclusion of theoretical constructs from prominent behavior change theories. Five common items were used to measure 20 theoretical constructs, for a total of 100 items. A theory score was calculated for each app. Multiple regression analysis was used to identify factors associated with higher theory scores.
Results:
Apps were generally observed to be lacking in theoretical content. Theory scores ranged from 1 to 28 on a 100-point scale. The health belief model was the most prevalent theory, accounting for 32% of all constructs. Regression analyses indicated that higher priced apps and apps that addressed a broader activity spectrum were associated with higher total theory scores.
Conclusion:
It is not unexpected that apps contained only minimal theoretical content, given that app developers come from a variety of backgrounds and many are not trained in the application of health behavior theory. The relationship between price and theory score corroborates research indicating that higher quality apps are more expensive. There is an opportunity for health and behavior change experts to partner with app developers to incorporate behavior change theories into the development of apps. These future collaborations between health behavior change experts and app developers could foster apps superior in both theory and programming possibly resulting in better health outcomes.
The introduction of Apple's iPhone provided a platform for developers to design third-party apps, which greatly expanded the functionality and utility of mobile devices for public health.
This study provides an overview of the developers' written descriptions of health and fitness apps and appraises each app's potential for influencing behavior change.
Data for this study came from a content analysis of health and fitness app descriptions available on iTunes during February 2011. The Health Education Curriculum Analysis Tool (HECAT) and the Precede-Proceed Model (PPM) were used as frameworks to guide the coding of 3336 paid apps.
Compared to apps with a cost less than US 0.99 were more likely to be scored as intending to promote health or prevent disease (92.55%, 1925/3336 vs 83.59%, 1411/3336; P<.001), to be credible or trustworthy (91.11%, 1895/3336 vs 86.14%, 1454/3349; P<.001), and more likely to be used personally or recommended to a health care client (72.93%, 1517/2644 vs 66.77%, 1127/2644; P<.001). Apps related to healthy eating, physical activity, and personal health and wellness were more common than apps for substance abuse, mental and emotional health, violence prevention and safety, and sexual and reproductive health. Reinforcing apps were less common than predisposing and enabling apps. Only 1.86% (62/3336) of apps included all 3 factors (ie, predisposing, enabling, and reinforcing).
Development efforts could target public health behaviors for which few apps currently exist. Furthermore, practitioners should be cautious when promoting the use of apps as it appears most provide health-related information (predisposing) or make attempts at enabling behavior, with almost none including all theoretical factors recommended for behavior change.
Current reporting of intervention content in published research articles and protocols is generally poor, with great diversity of terminology, resulting in low replicability. This study aimed to extend the scope and improve the reliability of a 26-item taxonomy of behaviour change techniques developed by Abraham and Michie [Abraham, C. and Michie, S. (2008). A taxonomy of behaviour change techniques used in interventions. Health Psychology, 27(3), 379-387.] in order to optimise the reporting and scientific study of behaviour change interventions. Methods: Three UK study centres collaborated in applying this existing taxonomy to two systematic reviews of interventions to increase physical activity and healthy eating. The taxonomy was refined in iterative steps of (1) coding intervention descriptions, and assessing inter-rater reliability, (2) identifying gaps and problems across study centres and (3) refining the labels and definitions based on consensus discussions.
Labels and definitions were improved for all techniques, conceptual overlap between categories was resolved, some categories were split and 14 techniques were added, resulting in a 40-item taxonomy. Inter-rater reliability, assessed on 50 published intervention descriptions, was good (kappa = 0.79).
This taxonomy can be used to improve the specification of interventions in published reports, thus improving replication, implementation and evidence syntheses. This will strengthen the scientific study of behaviour change and intervention development.
Meta-analyses of behavior change (BC) interventions typically find large heterogeneity in effectiveness and small effects. This study aimed to assess the effectiveness of active BC interventions designed to promote physical activity and healthy eating and investigate whether theoretically specified BC techniques improve outcome.
Interventions, evaluated in experimental or quasi-experimental studies, using behavioral and/or cognitive techniques to increase physical activity and healthy eating in adults, were systematically reviewed. Intervention content was reliably classified into 26 BC techniques and the effects of individual techniques, and of a theoretically derived combination of self-regulation techniques, were assessed using meta-regression.
Valid outcomes of physical activity and healthy eating.
The 122 evaluations (N = 44,747) produced an overall pooled effect size of 0.31 (95% confidence interval = 0.26 to 0.36, I(2) = 69%). The technique, "self-monitoring," explained the greatest amount of among-study heterogeneity (13%). Interventions that combined self-monitoring with at least one other technique derived from control theory were significantly more effective than the other interventions (0.42 vs. 0.26).
Classifying interventions according to component techniques and theoretically derived technique combinations and conducting meta-regression enabled identification of effective components of interventions designed to increase physical activity and healthy eating.
Creative use of new mobile and wearable health information and sensing technologies (mHealth) has the potential to reduce the cost of health care and improve well-being in numerous ways. These applications are being developed in a variety of domains, but rigorous research is needed to examine the potential, as well as the challenges, of utilizing mobile technologies to improve health outcomes. Currently, evidence is sparse for the efficacy of mHealth. Although these technologies may be appealing and seemingly innocuous, research is needed to assess when, where, and for whom mHealth devices, apps, and systems are efficacious. In order to outline an approach to evidence generation in the field of mHealth that would ensure research is conducted on a rigorous empirical and theoretic foundation, on August 16, 2011, researchers gathered for the mHealth Evidence Workshop at NIH. The current paper presents the results of the workshop. Although the discussions at the meeting were cross-cutting, the areas covered can be categorized broadly into three areas: (1) evaluating assessments; (2) evaluating interventions; and (3) reshaping evidence generation using mHealth. This paper brings these concepts together to describe current evaluation standards, discuss future possibilities, and set a grand goal for the emerging field of mHealth research.
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Behavior change technique BCTTv1: Behavioral Change Technique Taxonomy v1 eHealth: electronic health EIPA: Evidence-informed physical activity mHealth: mobile health PA: physical activity WHO: World Health Organization Edited by G Eysenbach
- Acsm Abbreviations
Abbreviations
ACSM: American College of Sports Medicine
BCT: Behavior change technique
BCTTv1: Behavioral Change Technique Taxonomy v1
eHealth: electronic health
EIPA: Evidence-informed physical activity
mHealth: mobile health
PA: physical activity
WHO: World Health Organization
Edited by G Eysenbach; submitted 14.03.18; peer-reviewed by D Regan, C Delisle, RM Payo; comments to author 17.08.18; revised
version received 30.08.18; accepted 14.09.18; published 01.12.18
- M Kebede
- B Steenbock
- S M Helmer
- J Sill
- T Möllers
Please cite as:
Kebede M, Steenbock B, Helmer SM, Sill J, Möllers T, Pischke CR
Identifying Evidence-Informed Physical Activity Apps: Content Analysis
JMIR Mhealth Uhealth 2018;6(12):e10314
URL: https://mhealth.jmir.org/2018/12/e10314/
doi:10.2196/10314
PMID: