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

Abstract

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 toward 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.

Your Personal Motivator is with You: A Systematic Review of Mobile Phone Applications Aiming at Increasing Physical Activity

“…First, by applying meta-synthesis and vote counting approaches, we provide an integrative account of supporting and non-supporting evidence of app techniques' ability to foster PA for five techniques and their sub-forms [16,19,56,59,89,115,126], as far as they have been examined in the literature to date. The app techniques include feedback, goal setting (sub-forms: app vs. user vs. mixed-set goals; high vs. low goal challenge levels), reward, social sharing (sub-forms: with familiar users in either segregated or social network groups or with strangers in either segregated or social network groups), and competition.…”

“…Distinguishing between app-set, user-set, and mixedset goals did not allow us to discern meaningful differences in findings, but distinguishing between goal challenge levels proved more substantial. For users with "high" goals, most findings indicated a positive effect on PA. For instance, Boratto et al [16] found a significant increase in PA in a randomized controlled group setting, and Fukuoka et al [94] provided descriptive interview results supporting these findings. Despite increased PA, high goals were often perceived by users as too tough and challenging, which might impede positive effects [18,56,98,119].…”

“…However, it has produced mixed results. Some studies report apps can motivate users, enhance their self-efficacy, and foster PA [16][17][18], but others have failed to find significant effects [19,20]. There can be many variables determining the effectiveness of apps; one reason for the mixed results may be the different behavior-change techniques (BCTs) used in these apps.…”

“…Goals can be (1) app-set, based on users' PA baseline; (2) user-set, with goals set either independently by users or by following researchers' recommendations; or (3) a mix of app-and user-set, where users choose among app-provided predefined goals. Levels of goal challenges are either higher or lower: (1) higher levels mean more challenging goals are formulated, typically in absolute numbers (e.g., taking 10,000 steps/day), or as challenging increases in PA (e.g., 20% weekly increase in steps/day), or as workout sessions planned by a (virtual) trainer (e.g., running activities, 1 km/session) [16,18,56,94,98,119,128]; (2) lower goals are less challenging and are also formulated as an absolute increase in PA (e.g., 10% weekly increase in steps/day) or as a gradual increase [115,117,118,122,123,126,136,137].…”

“…Based on vote counting, for high goals there were two quantitative studies, one of which provided non-significant effects [128] and another provided supportive results [16]. Four other qualitative studies (of a total of five qualitative studies) [18,56,94,119] provided supportive results, and another qualitative study [98] provided mixed results on PA. For low goals, there were four qualitative and four quantitative studies.…”