Julia S. Mollee

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.

Effect of Changes in the Structure of a Social Network on Emotion Contagion

Agent-based support systems are used to help people with developing and maintaining a healthy lifestyle. Interventions on the social network of an individual could play a role in achieving behaviour change. In this paper, a method for finding effective network interventions to influence specific individuals is proposed. The effect of these interventions was analysed by simulating the diffusion of emotional values about intentions and goals in a social network. Experiments showed that changing connections closer to the target have a larger influence than changing connections further from the target node. A comparison of the effect of the proposed interventions with all possible interventions showed that they are among the most optimal possible interventions. Finally, it was shown that nodes with fewer connections are easier to influence. The proposed interventions could form the basis for a support system that focus on affecting the social interaction between people in an online social network.

A Computational Agent Model of Influences on Physical Activity Based on the Social Cognitive Theory

A computational agent model of social and cognitive influences on physical activity based on Bandura’s Social Cognitive Theory is proposed. The utility of this model is twofold. First, it is used to run simulations of many different scenarios, that cannot be manipulated easily in reality, and that can possibly lead to new hypotheses about how social and cognitive factors influence physical activity. Second, as a next step, this computational model will be deployed in a real world coaching agent. The coach will use the current model to reason about the social and cognitive influences on the user’s physical activity and derive which coaching strategy fits the user best.

Analysis and Evaluation of Social Contagion of Physical Activity in a Group of Young Adults

It is known that opinions, attitudes and emotions spread through social networks. Several of these cognitions influence behavioral choices. Therefore, it is assumed that the level of physical activity of a person is influenced by the activity levels of the people in its social network. We have performed an experiment with 20 participants between 19 and 28 years old, measuring their physical activity levels for 30 days, in order to observe if there is a contagion effect due to the relationships in the social network. Using our social contagion model, we investigated if people will become more or less active according to the contacts with their peers within the network. Our model correctly predicts the direction of the change (increasing or decreasing) in 80% up to 87% of the cases investigated.

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.

Active2Gether: A Personalized m-Health Intervention to Encourage Physical Activity

Lack of physical activity is an increasingly important health risk. Modern mobile technology, such as smartphones and digital measurement devices, provides new opportunities to tackle physical inactivity. This paper describes the design of a system that aims to encourage young adults to be more physically active. The system monitors the user’s behavior, uses social comparison and provides tailored and personalized feedback based on intelligent reasoning mechanisms. As the name suggests, social processes play an important role in the Active2Gether system. The design choices and functioning of the system are described in detail. Based on the experiences with the development and deployment of the system, a number of lessons learnt are provided and suggestions are proposed for improvements in future developments.

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.

Exploring Parameter Tuning for Analysis and Optimization of a Computational Model

Computational models of human processes are used for many different purposes and in many different types of applications. A common challenge in using such models is to find suitable parameter values. In many cases, the ideal parameter values are those that yield the most realistic simulation results. However, there are situations in which the goodness of fit is not the main or only criterion to evaluate the appropriateness of a model, but where other aspects of the model behavior are also relevant. This is often the case when computational models are employed in real-life applications, such as mHealth systems. In this paper, we explore how parameter tuning techniques can be used to analyze the behavior of computational models systematically and to investigate the reasons behind the observed behavior. We study a computational model of psychosocial influences on physical activity behavior as an in-depth use case. In this particular case, an important measure of the feasibility of the model is the diversity in the simulation outcomes. This novel application of parameter tuning techniques for analysis and understanding of model behavior is transferable to other cases, and is therefore a valuable new approach in the toolset of computational modelers.

The Effectiveness of Upward and Downward Social Comparison of Physical Activity in an Online Intervention

It has been established that social processes play an important role in achieving and maintaining a healthy lifestyle, but there are still gaps in the knowledge on how to apply such processes in behavior change interventions. One of these mechanisms is social comparison, i.e. the tendency to self-evaluate by comparing oneself to others. Social comparison can be either downward or upward, depending on whether individuals compare themselves to a target that performs worse or better. Depending on personal preferences, the variants can have beneficial or adverse effects. In this paper, we present the results of an experiment where participants (who indicated to prefer either upward comparison or downward comparison) were sequentially shown both directions of social comparison, in order to influence their physical activity levels. The results show that presenting users with the type of social comparison they do not prefer may indeed be counter-effective. Therefore, it is important to take this risk into account when designing physical activity promotion programs with social comparison features.

Online Sharing of Physical Activity: Does It Accelerate the Impact of a Health Promotion Program?

Influence on health behavior from peers is well known and it has been shown that participants in an online physical activity promotion program are generally more successful when they share their achievements through an online community. However, more detailed insights are needed into the mechanisms that explain the influence of a community on physical activity level (PAL). This paper discusses a detailed analysis of a data set of participants in an online physical activity promotion program. The analysis focuses on a method to compare community members with non-community members that eliminates to a large extent factors that dilute the community effect. We create statistical models that describe the PAL increase at the end of the program. A comparison of these models shows that community members not only have a higher PAL at the start of the program, but also that the PAL increase is significantly greater compared to non-community members. The results further support the hypothesis that stimulating participants to share their achievements with peers makes physical activity programs more successful to help people achieve a healthy activity level.