Paolo Pilloni

Everywhere run: a virtual personal trainer for supporting people in their running activity

In the last years many medical researches have reported an increase of health problems in developed countries, mostly related to a sedentary lifestyle (as obesity and linked pathologies like diabetes and cardiovascular diseases). As a consequence. many research efforts have been carried out for finding strategies for motivating people to exercise regularly. In this paper we present an Android-based mobile application, called Everywhere Run [1], that aims at motivating and supporting people during their running activities, behaving as a virtual personal trainer. Everywhere Run fosters the interaction between users and real personal trainers, in order to make it easy to non expert people to start working out in a healthy and safe way.

An e-coaching ecosystem: design and effectiveness analysis of the engagement of remote coaching on athletes

Nowadays, the use of mobile applications and wearable technologies to support and encourage an active lifestyle has become widespread. Several studies put in evidence that the usage of these kinds of support has to be monitored by high-qualified figures, to favor a safe and a long-term adherence to training routines. In order to investigate the impact of these professionals, this work sets out to provide an overview and an evaluation of an e-coaching ecosystem specifically designed for runners. The platform supports and guides people towards an active lifestyle by stimulating their motivation to exercise through the engagement provided by the interactions between users and human trainers. In this study, we investigate the effectiveness of the support offered by the human trainers and the engagement of the users. The results show that the support of human qualified trainers is crucial. Users tend to be more engaged to train when their trainings are developed and remotely supervised by a human coach. This has resulted in more workout sessions performed with respect to users exercising by following standard or self-made routines without direct professional supervision. Our findings show that e-coaching systems should develop their coaching protocols always taking into account the effectiveness of the support of qualified professionals over completely automated approaches.

Influence of Rating Prediction on Group Recommendation's Accuracy

Recommender systems suggest items that might be interesting to a user. To achieve this, rating prediction is the main form of information processing that these systems perform. This article tackles the problem of predicting ratings in a group recommender system by analyzing how system accuracy is influenced by the choice of prediction approach and by a solution that employs the predicted values to avoid data sparsity. The results of more than 100 experiments show that by predicting the ratings for individual users instead of predicting them for groups, and by using these predictions in a systems group detection task, accuracy increases and problems caused by data sparsity are reduced.

Using new communication technologies and social media interaction to improve the motivation of users to exercise

The number of communication technologies and devices from which users can access information has rapidly increased. Moreover, users now have the chance to interact through social media channels, in order to share what they like and what they are experiencing in their everyday life. Both these aspects influence the design and development of Human-Computer Interaction applications that aim at motivating users to exercise more. In fact, the possibility to manage the exercising activity from different types of devices and the possibility to interact with other users, can increase the motivation of a user to exercise more. This paper presents a persuasive web application for sport and health, designed to motivate people in their exercising activity. The innovative aspect of our application is the possibility to use on a web browser some features previously available only through a mobile application. Moreover, it allows a richer interaction with the Facebook social network. Preliminary results show how different types of devices and new communication networks can be integrated, in order to improve the user experience and motivate users to a more active lifestyle.

The role of social interaction on users motivation to exercise: A persuasive web framework to enhance the self-management of a healthy lifestyle

Abstract The current research guidelines of the European community suggest the importance of the development of systems that help users manage their health themselves. The increasing amount of communication technologies and devices from which users can access information, and the possibility to interact through social media channels, play an important role in this scenario. Based on these considerations, in this paper we present an innovative persuasive web application, designed both to exploit social networking sites and to cooperate with a mobile application that already operates in the e-health and motivational domains. In particular, the innovative aspects introduced by the web application are the possibility to access also from a web browser some features previously available only through a mobile application and a more direct and user-friendly integration of social network sites. Indeed, thanks to an extensive interaction with the Facebook social network, users are allowed to share their experience with the application. This generates a strong social influence effect, which inspires and motivates other users to improve their exercising activity. Experimental results put in evidence that our web application, also thanks to social interactions, is favoring an enhancement of users’ motivation to a more active lifestyle. This is mainly due to its capability to have an impact on the other users thanks to the posts generated on the Facebook social network.

Assessing the User Experience Design as a Persuasive Methodology in a Real World Sport Application

In the last years, researchers are experimenting with innovative methodologies to help people in their daily training routines. Our research activity focuses on the study of the effects of the former technologies on peoples sport habits. This work describes an experimentation conducted on Everywhere Run! (EWRun), a mobile application part of a bigger platform, that aims at helping people to stay active behaving like a virtual personal trainer. In this work we show some interesting results that arise from recent radical changes we made to the software usability and its graphical design. We observed a considerable increment of the user base and, as a consequence, of the total number of daily trainings. To statistically prove the effectiveness of the redesign, we decided to compare the two versions of the application. The results confirm its effectiveness in terms of usability and brought us to investigate how the new design is affecting user motivation by means of a custom questionnaire and a well known motivation assessment tool. The positive result observed will be the starting point of our forthcoming researches: we aim at further validating the results presented in this work over a longer period of time and over a larger number of real users.

Recommender System Lets Coaches Identify and Help Athletes Who Begin Losing Motivation

This article presents a novel approach to monitoring athletes’ behavioral changes to predict a decline in motivation. When the system detects such a decline, it refers the athlete to her coach, along with a concise explanation of the detected behavioral changes. The coach thus has all the information needed for a prompt, targeted intervention.

Influence of Rating Prediction on the Accuracy of a Group Recommender System that Detects Groups

Recommender systems suggest items that might be interesting for a user. In order to do so, rating prediction is the main form of information processing performed by them. In this paper, we tackle the problem of predicting ratings in a group recommender system, by analyzing how the accuracy of a system is influenced by the choice of a different prediction approach and by a solution that employs the predicted values to avoid data sparsity. The results of more than one hundred experiments show that, by predicting the ratings for the individual users instead of predicting them for the groups, and by using these predictions in the group detection task of a system, the accuracy increases and the problems caused by data sparsity are reduced.

Experiences from a long run with a virtual personal trainer

In this paper, we report on our two-years experience with the commercial application Everywhere Run!, a mobile app that allows people to self monitor their running sessions and stay motivated in pursuing a wellbeing life-style. We consider a time interval of two-years, taking as breakpoint the first release of the application that improved the Virtual Personal Trainer presentation. The quantitative data we report comes from a remote logging of the app usage, while the qualitative data comes from the application reviews on the Google Play Store.