Living a sedentary lifestyle is one of the major causes of numerous health problems. To encourage employees to lead a less sedentary life, the Hanze University started a health promotion program. One of the interventions in the program was the use of an activity tracker to record participants' daily step count. The daily step count served as input for a fortnightly coaching session. In this paper, we investigate the possibility of automating part of the coaching procedure on physical activity by providing personalized feedback throughout the day on a participant’s progress in achieving a personal step goal. The gathered step count data was used to train eight different machine learning algorithms to make hourly estimations of the probability of achieving a personalized, daily steps threshold. In 80% of the individual cases, the Random Forest algorithm was the best performing algorithm (mean accuracy = 0.93, range = 0.88–0.99, and mean F1-score = 0.90, range = 0.87–0.94). To demonstrate the practical usefulness of these models, we developed a proof-of-concept Web application that provides personalized feedback about whether a participant is expected to reach his or her daily threshold. We argue that the use of machine learning could become an invaluable asset in the process of automated personalized coaching. The individualized algorithms allow for predicting physical activity during the day and provides the possibility to intervene in time.

Injuries of runners reduce the ability to train and hinder competing. Literature shows that the relation between potential risk factors and injuries are not definitive, limited, and inconsistent. In team sports, workload derivatives were identified as risk factors. However, there is an absence of literature in running on workload derivatives. This study used the workload derivatives acute workload, chronic workload, and acute: chronic workload ratios to investigate the relation between workload and injury risk in running. Twenty-three competitive runners kept a daily training log for 24 months. The runners reported training duration, training intensity and injuries. One-week (acute) and 4-week (chronic) workloads were calculated as the average of training duration multiplied by training intensity. The acute:chronic workload ratio was determined dividing the acute and chronic workloads. Results show that a fortnightly low increase of the acute:chronic workload ratio (0.10–0.78) led to an increased risk of sustaining an injury (p<0.001). Besides, a low increase of the acute:chronic workload ratio (0.05–0.62) between the second week and third week before an injury showed an association with increased injury risk (p=0.013). These findings demonstrate that the acute:chronic workload ratio relates to injury risk.