Using Loss Aversion to Motivate Exercise; a Longitudinal Research

The modern lifestyles have resulted in physical inactivity and sedentary behavior which cause a lot of chronic illnesses. The benefits of exercising are becoming widespread knowledge but it is still challenging to change our behavior to a healthier one. The prior works in the literature in the field of behavior change are divided into two major groups: positive and negative reinforcement. Positive reinforcements’ work are more popular in encouraging exercise but negative reinforcements are more focused on other behavior change fields such as quit smoking therapy and children’s behavior modification. Our study aims to apply negative reinforcements for promoting exercise habit. In our experiments, we observed the behavior of 10 participants to perform physical activities such as running, jogging, and cycling, under the negative reinforcements’ influence. We collected an initial deposit from the treatment groups and a portion of the deposit  was  returned per exercise only when they meet our goal for the week.  The experiment lasted for 3 weeks with increasing goals every week. We found that the treatment groups performed better in attaining the weekly goals relative to the control group with the negative intervention having a significant effect on motivating users to exercise

MODEL, FRAMEWORK, AND PLATFORM OF HEALTH PERSUASIVE SOCIAL NETWORK

Persuasive technology (PT) has the potential to support individuals to perform self-management and social support as a part of health behavior change. This has led a few researchers in the intersection of the areas of health behavior change and software engineering to apply behavior change and persuasion theories to software development practices, enabling them to create innovative design principles and development-evaluation frameworks. Unfortunately these are too general for designing and evaluating health PT. Therefore, this dissertation proposes a model, framework, and platform of PT specifically designed for health intervention. The model and framework inform what, why, and how conceptually the suggested and required health behavior change strategies should be transformed into system features; and the platform explains how technically the transformation should be done. The platform includes functional requirements and provides most of the basic and standard computer code to develop the system features of such PT. The model, framework, and platform were designed to work with various health behavior change programs. Nevertheless, in this dissertation, they support health behavior change for physical activity. As an implementation of and tool to evaluate the model, framework, and platform, a technology called Persuasive Social Network for Physical Activity (PersonA) is introduced. PersonA is a combination of automatic input of physical activity data, a smart phone, and social networking. Two systems (SocioPedometer and PAMS) as leverages of PersonA have been developed and evaluated. The model, framework, and platform were evaluated based on the results of SocioPedometer’s usability testing and 4-week trials (n=14) and on PAMS’s usability testing (n=5). The results suggest that the systems were usable and accessible and that users were satisfied and enjoyed using it. Additional evaluations to the model and framework were conducted with the main purpose of eliciting users’ preferences with respect to the characteristics and system features proposed in the model and framework. They rated most of the characteristics as extremely important (average 4.27 of a 5.00 maximum) and most of the system features as very important (average of 4.09). The platform allowed the two systems to be easily developed by customizing the data input and information presented

Predicting early user churn in a public digital weight loss intervention

Digital health interventions (DHIs) offer promising solutions to the rising global challenges of noncommunicable diseases by promoting behavior change, improving health outcomes, and reducing healthcare costs. However, high churn rates are a concern with DHIs, with many users disengaging before achieving desired outcomes. Churn prediction can help DHI providers identify and retain at-risk users, enhancing the efficacy of DHIs. We analyzed churn prediction models for a weight loss app using various machine learning algorithms on data from 1,283 users and 310,845 event logs. The best-performing model, a random forest model that only used daily login counts, achieved an F1 score of 0.87 on day 7 and identified an average of 93% of churned users during the week-long trial. Notably, higher-dimensional models performed better at low false positive rate thresholds. Our findings suggest that user churn can be forecasted using engagement data, aiding in timely personalized strategies and better health results

Predicting and breaking sedentary behaviour at work

This thesis aimed at designing and developing a system that can a) infer individuals’ need for a break from sedentary behaviour in the workplace, and b) persuade them to take a break through the use of different techniques from persuasive psychology. We postulated three variables, namely, individuals’ posture, stress levels and involvement in their computer mediated activity. We developed and field-studied a system that could infer these using a web camera and a key presses and mouse clicks log. We found that the system could predict posture from viewing depth and stress from the movement detected. We then created a general formula that predicts individuals’ need for a break using only the posture and stress predictors. Once the first objective was set, we built and field-studied a system that used three ways to communicate a recommendation for a break to a user: implicit, just-in time and ambient feedback. The implicit feedback was operationalized through changes in the users’ computer wallpaper that provided subtle visual cues. The just-in time feedback employed prompting at the bottom right side of the user’s screen. In addition, we implemented an intuitive behind-screen interaction technique where people can snooze a notification using simple gestures. The ambient feedback mechanism employed an origami sculpture sitting on the user’s desk. This prototype was continuously reflecting the user’s posture and performed rhythmic movements when to recommend breaks. A field study demonstrated the overall success of the system, with 69% of the break recommendations received by users were accepted. The study further revealed the strengths and weaknesses of the three persuasive mechanisms

Practical, appropriate, empirically-validated guidelines for designing educational games

There has recently been a great deal of interest in the potential of computer games to function as innovative educational tools. However, there is very little evidence of games fulfilling that potential. Indeed, the process of merging the disparate goals of education and games design appears problematic, and there are currently no practical guidelines for how to do so in a coherent manner. In this paper, we describe the successful, empirically validated teaching methods developed by behavioural psychologists and point out how they are uniquely suited to take advantage of the benefits that games offer to education. We conclude by proposing some practical steps for designing educational games, based on the techniques of Applied Behaviour Analysis. It is intended that this paper can both focus educational games designers on the features of games that are genuinely useful for education, and also introduce a successful form of teaching that this audience may not yet be familiar with