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In sports feedback systems, digital systems perform tasks such as capturing, analysing and representing data. These systems not only aim to provide athletes and coaches with insights into performances but also help athletes learn new tasks and control movements, for example, to prevent injuries. However, designing mobile feedback systems requires a high level of expertise from researchers and practitioners in many areas. As a solution to this problem, we present Direct Mobile Coaching (DMC) as a design paradigm and model for mobile feedback systems. Besides components for feedback provisioning, the model consists of components for data recording, storage and management. For the evaluation of the model, its features are compared against state-of-the-art frameworks. Furthermore, the capabilities are benchmarked using a review of the literature. We conclude that DMC is capable of modelling all 39 identified systems while other identified frameworks (MobileCoach, Garmin Connect IQ SDK, RADAR) could (at best) only model parts of them. The presented design paradigm/model is applicable for a wide range of mobile feedback systems and equips researchers and practitioners with a valuable tool.

Running field tests are utilised to assess the athlete's fitness level with high specificity. However, in graded exercise tests the lack of speed control can result in an uneven pace, reducing reproducibility and accuracy. The purpose of this study was to develop feedback variants (FV) and investigate their effect on the athlete's ability to keep the pace constant. Forty-eight participants completed four trials of a Conconi test (randomised) over four paces (7.5 − 10 km•h −1 ). A smartphone app provided athletes with four FVs: Classic, Sound (verbal), Vibration, and Sound & Vibration. We found a significant effect of FV on adherence (defined as time within 0.3 km•h −1 of target speed): F(3,141) = 41.45, p < .001, η 2 = .268. Adherence in Classic (M± SD) 39.51 ± 13.51% was significantly lower (p < .001) than in Vibration (53.24 ± 14.98%), Sound (60.34 ± 11.21%) and Sound & Vibration (59.17 ± 15.17%). The novel FV provided better adherence compared to the conventional method, with Sound and Sound & Vibration showing the highest adherence. Generally, the novel FVs have the advantage of being easily implemented and decreasing the required time for setup, data collection and processing, therefore proving useful in conducting field performance tests.

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Performance feedback can be essential for cyclists to help with pacing their efforts during competitions and also during standardized performance tests. However, the choice of feedback options on modern bike computers is limited. Moreover, little research on the effectiveness of the currently used feedback methods is available. In this study, two novel feedback variants using a bar or a tacho to visualize targets and deviation from targets were compared to a classic design using only numbers. Participants (6 female and 25 male trained to well-trained athletes) completed a protocol consisting of three heart rate-based tasks and one power-based task. The displays were compared with respect to their ability to guide athletes during their trials. Results showed lower root mean square error (RMSE) of the novel variants, but no significant effect of feedback variant on RMSE was found for both tasks (p > 0.05). However, when comparing the feedback variants on a person to person basis, significant differences were found for all investigated scenarios (p < 0.001). This leads to the conclusion that novel feedback variants can improve athletes’ ability to follow heart rate-based and power-based protocols, but even better results might be achieved by individualizing the feedback.