Need for recovery from work and sleep-related complaints among nursing professionals

The concept of need for recovery from work (NFR) was deduced from the effort recuperation model. In this model work produces costs in terms of effort during the working day. When there is enough time and possibilities to recuperate, a worker will arrive at the next working day with no residual symptoms of previous effort. NFR evaluates work characteristics such as psychosocial demands, professional work hours or schedules. However, sleep may be an important part of the recovery process. The aim of the study was to test the association between sleep-related complaints and NFR. A cross-sectional study was carried out at three hospitals. All females nursing professionals engaged in assistance to patients were invited to participate (N=1,307). Participants answered a questionnaire that included four sleep-related complaints (insomnia, unsatisfactory sleep, sleepiness during work hours and insufficient sleep), work characteristics and NRF scale. Binomial logistic regression analysis showed that all sleep-related complaints are associated with a high need for recovery from work. Those who reported insufficient sleep showed a greater chance of high need for recovery; OR=2.730 (CI 95% 2.074-3.593). These results corroborate the hypothesis that sleep is an important aspect of the recovery process and, therefore, should be thoroughly investigated.

Design of alternative energy storage systems for hybrid vehicles based on statistical processing of driving cycles information

Hybrid vehicles represent the future for automakers, since they allow to improve the fuel economy and to reduce the pollutant emissions. A key component of the hybrid powertrain is the Energy Storage System, that determines the ability of the vehicle to store and reuse energy. Though electrified Energy Storage Systems (ESS), based on batteries and ultracapacitors, are a proven technology, Alternative Energy Storage Systems (AESS), based on mechanical, hydraulic and pneumatic devices, are gaining interest because they give the possibility of realizing low-cost mild-hybrid vehicles. Currently, most literature of design methodologies focuses on electric ESS, which are not suitable for AESS design. In this contest, The Ohio State University has developed an Alternative Energy Storage System design methodology. This work focuses on the development of driving cycle analysis methodology that is a key component of Alternative Energy Storage System design procedure. The proposed methodology is based on a statistical approach to analyzing driving schedules that represent the vehicle typical use. Driving data are broken up into power events sequence, namely traction and braking events, and for each of them, energy-related and dynamic metrics are calculated. By means of a clustering process and statistical synthesis methods, statistically-relevant metrics are determined. These metrics define cycle representative braking events. By using these events as inputs for the Alternative Energy Storage System design methodology, different system designs are obtained. Each of them is characterized by attributes, namely system volume and weight. In the last part the work, the designs are evaluated in simulation by introducing and calculating a metric related to the energy conversion efficiency. Finally, the designs are compared accounting for attributes and efficiency values. In order to automate the driving data extraction and synthesis process, a specific script Matlab based has been developed. Results show that the driving cycle analysis methodology, based on the statistical approach, allows to extract and synthesize cycle representative data. The designs based on cycle statistically-relevant metrics are properly sized and have satisfying efficiency values with respect to the expectations. An exception is the design based on the cycle worst-case scenario, corresponding to same approach adopted by the conventional electric ESS design methodologies. In this case, a heavy system with poor efficiency is produced. The proposed new methodology seems to be a valid and consistent support for Alternative Energy Storage System design.

Manipulations to reduce simulator-related transient adverse health effects during simulated driving

User comfort during simulated driving is of key importance, since reduced comfort can confound the experiment and increase dropout rates. A common comfort-affecting factor is simulator-related transient adverse health effect (SHE). In this study, we propose and evaluate methods to adapt a virtual driving scene to reduce SHEs. In contrast to the manufacturer-provided high-sensory conflict scene (high-SCS), we developed a low-sensory conflict scene (low-SCS). Twenty young, healthy participants drove in both the high-SCS and the low-SCS scene for 10 min on two different days (same time of day, randomized order). Before and after driving, participants rated SHEs by completing the Simulator Sickness Questionnaire (SSQ). During driving, several physiological parameters were recorded. After driving in the high-SCS, the SSQ score increased in average by 129.4 (122.9 %, p = 0.002) compared to an increase of 5.0 (3.4 %, p = 0.878) after driving in the low-SCS. In the low-SCS, skin conductance decreased by 13.8 % (p < 0.01) and saccade amplitudes increased by 16.1 % (p < 0.01). Results show that the investigated methods reduce SHEs in a younger population, and the low-SCS is well accepted by the users. We expect that these measures will improve user comfort.