Deborah Piette

Damaging behaviors, like feather pecking (FP), have large economic and welfare consequences in the commercial laying hen industry. Selective breeding can be used to obtain animals that are less likely to perform damaging behavior on their pen-mates. However, with the growing tendency to keep birds in large groups, identifying specific birds that are performing or receiving FP is difficult. With current developments in sensor technologies, it may now be possible to identify laying hens in large groups that show less FP behavior and select them for breeding. We propose using a combination of sensor technology and genomic methods to identify feather peckers and victims in groups. In this review, we will describe the use of “-omics” approaches to understand FP and give an overview of sensor technologies that can be used for animal monitoring, such as ultra-wideband, radio frequency identification, and computer vision. We will then discuss the identification of indicator traits from both sensor technologies and genomics approaches that can be used to select animals for breeding against damaging behavior.

The interaction of physical and mental vulnerability and environmental constraints is thought to foster the development of psychiatric disorders such as major depressive disorder (MDD). A central factor in the development of psychopathology is mental stress. Despite some evidence for parasympathetic withdrawal and sympathetic overactivity in MDD, the psychophysiological response to stress in depression is not clear-cut. Given the growing interest in heart rate and heart rate variability as indicators for remote monitoring of patients, it is important to understand how patients with MDD react to stress in a laboratory-controlled environment. We conducted a systematic review of studies using electrocardiography to derive heart rate and heart rate variability during stress in patients with clinical depression. We focused on well-validated stress tasks- the mental arithmetic stress task, the Trier social stress task and public speaking task- to minimize confounding effects due to the nature of the stressor. The majority of studies found hypo-reactivity during stress as a hallmark of depression as evidenced by lower fluctuation in heart rate and heart rate variability in the high-frequency band. We address the potential underlying biological mechanisms, the influence of covariates on these measures and briefly discuss the specificity and potential for remote monitoring by using these variables.

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.