Introduction In an ageing population, increasing chronic disease prevalence puts a high economic burden on society. Physical activity plays an important role in disease prevention and should therefore be promoted in the elderly. Methods In this study, a mobile health (mHealth) system was implemented in a care home setting to monitor and promote elderly peoples' daily activity. The physical activity of 20 elderly people (8 female and 12 male, aged 81 ± 9 years old) was monitored over 10 weeks using the mHealth system, consisting of a smartphone and heart rate belt. Feedback on physical activity was provided weekly. A reference performance test battery derived from the Senior Fitness Test determined the participants' physical fitness. Results Activity levels increased from week 1 onwards, peaking at week 5, and decreasing slightly until week 10. This illustrates that the use of mHealth and feedback on physical activity can motivate the elderly to become more active, but that the effect is transient without other incentives. Bio-data from the mHealth system were translated into a fitness score explaining 65% of the test battery's variance. After separating the elderly into three groups depending on physical fitness determined from the test battery, classification based on the fitness score resulted in a correct classification rate of 67.3%. Discussion This study demonstrates that an mHealth system can be implemented in a care home setting to motivate activity of the elderly, and that the bio-data can be translated in a fitness score predicting the outcome of labour-intensive tests.
Car racing at a high level is a physically and mentally intensive sport. Despite the fact that a large number of variables are measured on the car during racing, nothing is measured on the driver. It is well known that to achieve peak performance in competitive sports it is important that the athlete is at their peak both physically and mentally. The objective of this work is to monitor the mental state of the driver in real-time and provide this information to the pit crew. A number of interesting cases are presented that show the potential of realtime stress monitoring in race car driving as a means for driver performance optimisation and as a means to reduce accidents.
This work has tested heart rate to measure anxiety during a penalty shootout. Until now, anxiety is measured through questionnaires, where online monitoring is not possible. Therefore there is a need for physiological parameters to represent anxiety online. Since it is proven that the level of anxiety is a good predictor of penalty outcome, it was hypothesised that this outcome can be estimated with heart rate and activity. To test this hypothesis an experiment has been conducted with 54 participants (age= 23±4,54 years). They each performed three sessions of a penalty shootout, where heart rate and activity were measured. An adapted version of the State-Trait Anxiety Inventory was used as reference for anxiety level. The data have been analysed using a static and dynamic approach. These resulted in parameters that were used to predict the anxiety level and penalty performance of the participant with a multinomial logistic regression model. The results show that 47,11% of the participants were correctly classified into three classes of anxiety. Based on a classification into penalty performance 55,11 % of the participants were correctly classified. It can be concluded that heart rate in combination with activity shows promising results as predictor for anxiety.
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