Biomedical subjects

Jens Nilsson

Publications and source records attributed to Jens Nilsson.

3 recordsLinked to original sources

PubMed2006

Disturbed sleep and fatigue in occupational burnout.

OBJECTIVES: The purpose of this study was to investigate sleep with polysomnography and self-ratings and the diurnal pattern of sleepiness and fatigue in a group suffering from severe occupational burnout. METHOD: Twelve white-collar workers on long-term sick leave (>3 months) and 12 healthy controls with high and low scores on the Shirom Melamed Burnout Questionnaire (SMBQ) were included. A 1-night polysomnographic recording (after habituation) was carried out at home, and sleepiness and mental fatigue were rated at different times of the day for weekdays and the weekend. Precipitating factors at the time of the illness at work and real life were considered, and different dimensions of occupational fatigue were described. A repeated-measures analysis of variance using two or three within group factors was used to analyze the data. RESULTS: The main polysomnographic findings were more arousals and sleep fragmentation, more wake time and stage-1 sleep, lower sleep efficiency, less slow wave sleep and rapid eye movement sleep, and a lower delta power density in non-rapid eye movement sleep in the burnout group. The burnout patients showed pronounced sleepiness and mental fatigue at most times of the day for weekdays without reduction during weekends. The precipitating factor was occupational stress (psychiatric interview), and work stress indicators were increased. CONCLUSIONS: Occupational burnout is characterized by impaired sleep. It is suggested that impaired sleep may play a role in the development of fatigue or exhaustion in burnout.

Burnout, Professional↗

PubMed2004

Sleep and sleepiness in young individuals with high burnout scores.

STUDY OBJECTIVES: Burnout is a growing health problem in Western society. This study aimed to investigate sleep in subjects scoring high on burnout but still at work. The purpose was also to study the diurnal pattern of sleepiness, as well as ratings of work stress and mood in groups with different burnout scores. DESIGN: Sleep was recorded in 2 groups (high vs low on burnout) during 2 nights; 1 before a workday and 1 before a day off, in a balanced order. Sleepiness ratings as well as daytime diary ratings were analyzed for the workday and the day off after the sleep recordings. SETTING: The polysomnographic recordings were made in the subjects' home. PARTICIPANTS: Twenty-four healthy individuals (14 women and 10 men) between the ages of 24 and 43 years participated. INTERVENTIONS: N/A. MEASUREMENTS AND RESULTS: A higher frequency of arousals during sleep (Workday: high burnout = 12+/-1 per hour, low burnout = 8+/-1 per hour; Day off: high burnout = 12+/-2 per hour, low burnout =8+/-1 per hour), and more subjective awakening problems were found in the high-burnout group. The diurnal pattern of sleepiness indicated that the high-burnout group did not recover in the same way as did the low-burnout group on the day off. Indicators of impaired recovery were also seen within the high-burnout group as a higher degree of bringing work home and working on weekends, as well as more complaints of work interfering with leisure time. CONCLUSIONS: Young subjects with high burnout scores, but who are still working, show more arousals during sleep and an absence of reduced sleepiness during days off.

Adult↗

PubMed2004

Approximate geodesic distances reveal biologically relevant structures in microarray data.

MOTIVATION: Genome-wide gene expression measurements, as currently determined by the microarray technology, can be represented mathematically as points in a high-dimensional gene expression space. Genes interact with each other in regulatory networks, restricting the cellular gene expression profiles to a certain manifold, or surface, in gene expression space. To obtain knowledge about this manifold, various dimensionality reduction methods and distance metrics are used. For data points distributed on curved manifolds, a sensible distance measure would be the geodesic distance along the manifold. In this work, we examine whether an approximate geodesic distance measure captures biological similarities better than the traditionally used Euclidean distance. RESULTS: We computed approximate geodesic distances, determined by the Isomap algorithm, for one set of lymphoma and one set of lung cancer microarray samples. Compared with the ordinary Euclidean distance metric, this distance measure produced more instructive, biologically relevant, visualizations when applying multidimensional scaling. This suggests the Isomap algorithm as a promising tool for the interpretation of microarray data. Furthermore, the results demonstrate the benefit and importance of taking nonlinearities in gene expression data into account.

Algorithms↗