Anouk Middelweerd

Apps to promote physical activity among adults: a review and content analysis

BackgroundIn 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.MethodsThe 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.ConclusionsThe 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.

A validation study of the fitbit one in daily life using different time intervals

Purpose Accelerometer-based wearables can provide the user with real-time feedback through the devices interface and the mobile platforms. Few studies have focused on the minute-by-minute validity of wearables, which is essential for high-quality real-time feedback. This study aims to assess the validity of the Fitbit One compared with the ActiGraph GT3x + for assessing physical activity (i.e., steps, time spent in moderate, vigorous, and moderate–vigorous physical activity) in young adults using traditional time intervals (i.e., days) and smaller time intervals (i.e., minutes and hours). Methods Healthy young adults (N = 34) wore the ActiGraph GT3x+ and a Fitbit One for 1 wk. Three aggregation levels were used: minute, hour, and day. Mixed models analyses, intraclass correlation coefficients, Bland–Altman analyses, and absolute error percentage for steps per day were conducted to analyze the validity for steps and minutes spent in moderate, vigorous, and moderate–vigorous physical activity. Results As compared with ActiGraph (mean = 9 steps per minute, 509 steps per hour and 7636 steps per day), the Fitbit One systematically overestimated physical activity for all aggregation levels: on average 0.82 steps per minute, 45 steps per hour, and 677 steps per day. Strong and significant associations were found between ActiGraph and Fitbit results for steps taken (B = 0.72–0.89). Weaker but statistically significant associations were found for minutes spent in moderate, vigorous, and moderate–vigorous physical activity for all time intervals (B = 0.39–0.57). Conclusions Although the Fitbit One overestimates the step activity compared with the ActiGraph, it can be considered a valid device to assess step activity, including for real-time minute-by-minute self-monitoring. However, agreement and correlation between ActiGraph and Fitbit One regarding time spent in moderate, vigorous, and moderate–vigorous physical activity were lower.

What technological features are used in smartphone apps that promote physical activity? A review and content analysis

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.

Evaluation of a personalized coaching system for physical activity: user appreciation and adherence

Physical inactivity is an increasingly serious global health problem, which implies a strong need for effective and engaging interventions. Smartphone technology offers new possibilities to address physical activity promotion. For app-based interventions to have an impact, both the effectiveness and user appreciation of the app are important. In this paper, we explore the user appreciation of the Active2Gether intervention, which offers personalized coaching to increase physical activity levels in daily life. The results are compared to the evaluation of a simplified version of the Active2Gether app (in which no coaching messages are sent) and the Fitbit app. Overall, the results reveal that users of a physical activity app appreciate a coaching feature to be included (on top of self-monitoring functionalities), but are also critical of how it is implemented (in terms of the number and content of the messages). The results also show that it is important to find a balance in the number of messages sent: too many messages seem to be perceived as annoying, but on the other hand, such system-initiated user interaction seems to reduce dropout.

Do intrapersonal factors mediate the association of social support with physical activity in young women living in socioeconomically disadvantaged neighbourhoods? : A longitudinal mediation analysis

Background Levels of physical activity (PA) decrease when transitioning from adolescence into young adulthood. Evidence suggests that social support and intrapersonal factors (self-efficacy, outcome expectations, PA enjoyment) are associated with PA. The aim of the present study was to explore whether cross-sectional and longitudinal associations of social support from family and friends with leisure-time PA (LTPA) among young women living in disadvantaged areas were mediated by intrapersonal factors (PA enjoyment, outcome expectations, self-efficacy). Methods Survey data were collected from 18–30 year-old women living in disadvantaged suburbs of Victoria, Australia as part of the READI study in 2007–2008 (T0, N = 1197), with follow-up data collected in 2010–2011 (T1, N = 357) and 2012–2013 (T2, N = 271). A series of single-mediator models were tested using baseline (T0) and longitudinal data from all three time points with residual change scores for changes between measurements. Results Cross-sectional analyses showed that social support was associated with LTPA both directly and indirectly, mediated by intrapersonal factors. Each intrapersonal factor explained between 5.9–37.5% of the associations. None of the intrapersonal factors were significant mediators in the longitudinal analyses. Conclusions Results from the cross-sectional analyses suggest that the associations of social support from family and from friends with LTPA are mediated by intrapersonal factors (PA enjoyment, outcome expectations and self-efficacy). However, longitudinal analyses did not confirm these findings.

Development of Active2Gether: an app-based intervention combining evidence-based behavior change techniques with a model-based reasoning system to promote physical activity among young adults (Preprint)

BACKGROUND The Active2Gether intervention is an app-based intervention designed to help and encourage young adults to become and remain physically active by means of personalized, real-time activity tracking and context-specific feedback. OBJECTIVE The objective of our study was to describe the development and content of the Active2Gether intervention for physical activity promotion. METHODS A systematic and stepwise approach was used to develop the Active2Gether intervention. This included formulating objectives and a theoretical framework, selecting behavior change techniques, specifying the tailoring, pilot testing, and describing an evaluation protocol. RESULTS The development of the Active2Gether intervention comprised seven steps: analyzing the (health) problem, developing a program framework, writing (tailored) messages, developing tailoring assessments, developing the Active2Gether intervention, pilot testing, and testing and evaluating the intervention. The primary objective of the intervention was to increase the total time spent in moderate-vigorous physical activity for those who do not meet the Dutch guideline, maintain physical activity levels of those who meet the guideline, or further increase physical activity levels if they so indicated. The theoretical framework is informed by the social cognitive theory, and insights from other theories and evidence were added for specific topics. Development of the intervention content and communication channel resulted in the development of an app that provides highly tailored coaching messages that are framed in an autonomy-supportive style. These coaching messages include behavior change techniques aiming to address relevant behavioral determinants (eg, self-efficacy and outcome expectations) and are partly context specific. A model-based reasoning engine has been developed to tailor the intervention with respect to the type of support provided by the app, send relevant and context-specific messages to the user, and tailor the graphs displayed in the app. For the input of the tailoring, different instruments and sensors are used, such as an activity monitor (Fitbit One), Web-based and mobile questionnaires, and the location services on the users mobile phone. CONCLUSIONS The systematic and stepwise approach resulted in an intervention that is based on theory and input from end users. The use of a model-based reasoning system to provide context-specific coaching messages goes beyond many existing eHealth and mHealth interventions.

Active2Gether: Innovative and smart coaching strategies to promote physical activity: A research protocol

Healthy Living: The European Congress of Epidemiology, 2015 Esther Bols • Luc Smits • Matty Weijenberg Springer Science+Business Media Dordrecht 2015