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      Health related quality of life in adolescents with chronic fatigue syndrome: a cross-sectional study

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          To study health related quality of life (HRQOL) and depressive symptoms in adolescents with chronic fatigue syndrome (CFS) and to investigate in which domains their HRQOL and depressive symptoms differ from those of healthy adolescents.

          Background and objective

          Several symptoms such as disabling fatigue, pain and depressive symptoms affect different life domains of adolescents with CFS. Compared to adolescents with other chronic diseases, young people with CFS are reported to be severely impaired, both physiologically and mentally. Despite this, few have investigated the HRQOL in this group.


          This is a cross-sectional study on HRQOL including 120 adolescents with CFS and 39 healthy controls (HC), between 12 and 18 years. The Pediatric Quality of Life Inventory™, 4.0 (PedsQL) was used to assess HRQOL. The Mood and Feelings Questionnaire assessed depressive symptoms. Data were collected between March 2010 and October 2012 as part of the NorCAPITAL project (Norwegian Study of Chronic Fatigue Syndrome in Adolescents: Pathophysiology and Intervention Trial). Linear and logistic regression models were used in analysis, and all tests were two-sided.


          Adolescents with CFS reported significantly lower overall HRQOL compared to HCs. When controlling for gender differences, CFS patients scored 44 points lower overall HRQOL on a scale from 0–100 compared to HCs. The domains with the largest differences were interference with physical health (B = −59, 95 % CI −54 to −65) and school functioning (B = −52, 95 % CI −45 to −58). Both depressive symptoms and being a patient were independently associated with lower levels of HRQOL


          The difference in HRQOL between CFS patients and healthy adolescents was even larger than we expected. The large sample of adolescents with CFS in our study confirms previous findings from smaller studies, and emphasizes that CFS is a seriously disabling condition that has a strong impact on their HRQOL. Even though depressive symptoms were found in the group of patients, they could not statistically explain the poor HRQOL.

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          Most cited references 30

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          Dimensional assessment of chronic fatigue syndrome.

          The absence of laboratory tests and clear criteria to identify homogeneous (sub)groups in patients presenting with unexplained fatigue, and to assess clinical status and disability in these patients, calls for further assessment methods. In the present study, a multi-dimensional approach to the assessment of chronic fatigue syndrome (CFS) is evaluated. Two-hundred and ninety-eight patients with CFS completed a set of postal questionnaires that assessed the behavioural, emotional, social, and cognitive aspects of CFS. By means of statistical analyses nine relatively independent dimensions of CFS were identified along which CFS-assessment and CFS-research can be directed. These dimensions were named: psychological well-being, functional impairment in daily life, sleep disturbances, avoidance of physical activity, neuropsychological impairment, causal attributions related to the complaints, social functioning, self-efficacy expectations, and subjective experience of the personal situation. A description of the study sample on these dimensions is presented.
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            Overweight, obesity, and health-related quality of life among adolescents: the National Longitudinal Study of Adolescent Health.

            Childhood and adolescent overweight and obesity have increased substantially in the past 2 decades, raising concerns about the physical and psychosocial consequences of childhood obesity. We investigated the association between obesity and health-related quality of life in a nationally representative sample of adolescents. A cross-sectional analysis was conducted using the 1996 National Longitudinal Study of Adolescent Health, a nationally representative sample of adolescents in grades 7 to 12 during the 1994-1995 school year, and 4743 adolescents with direct measures of height and weight. Using Centers for Disease Control and Prevention growth charts to determine percentiles, we used 5 body mass categories. Underweight was at or below the 5th percentile, normal BMI was between the 5th and 85th percentiles, at risk for overweight was between the 85th and 95th percentiles, overweight was between the 95th and 97th percentiles + 2 BMI units, and obese was at or above the 97th percentile + 2 BMI units. Four dimensions of health-related quality of life were measured: general health (self-reported general health), physical health (absence or presence of functional limitations and illness symptoms), emotional health (the Center for Epidemiologic Studies Depression Scale and Rosenberg's self-esteem scale), and a school and social functioning scale. We found a statistically significant relationship between BMI and general and physical health but not psychosocial outcomes. Adolescents who were overweight had significantly worse self-reported health (odds ratio [OR]: 2.17; 95% confidence interval [CI]: 1.34-3.51), as did obese adolescents (OR: 4.49; 95% CI: 2.87-7.03). Overweight (OR: 1.81; 95% CI: 1.22-2.68) and obese (OR: 1.91; 95% CI: 1.24-1.95) adolescents were also more likely to have a functional limitation. Only among the youngest adolescents (ages 12-14) did we find a significant deleterious impact of overweight and obesity on depression, self-esteem, and school/social functioning. Using a nationally representative sample, we found that obesity in adolescence is linked with poor physical quality of life. However, in the general population, adolescents with above normal body mass did not report poorer emotional, school, or social functioning.
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              The measurement of quality of life in children: past and future perspectives.

              Quality of life (QoL) is central to pediatric practice. Where it is possible to manage but not cure a disease, it is important to determine how far treatment and disease compromise the child's QoL. In this way, informed judgments can be made about whether or not treatment is appropriate, and, where there is a choice, which choice might be the best option for the child. In this review, we consider different approaches to measuring child QoL, report a methodological review of measures currently available, evaluate the quality of these measures, and finally consider the implications for the future development and use of QoL measures. Computer searches identified 269 potentially relevant articles, of which 137 were included in the review. Of these, 43 were primarily concerned with the development of a new measure of QoL, 79 reported subsequent development of these same measures, and 15 used a battery approach to measure QoL. All currently available measures have limitations (e.g., limited psychometric data, lack of parallel forms for children and proxy raters, and insufficient attention to children's ability to complete paper-and-pencil measures). However, recommendations are made on the basis of those considered to be most satisfactory. It is essential that attempts be made to use QoL measures in research (e.g., evaluation of clinical trials and alternative treatments) to gain experience that will guide development of a second generation of more sophisticated measures. Despite the practical difficulties identified, measurement of QoL remains of central interest to all those concerned with the well-being of children.

                Author and article information

                47 67 23 50 00/47 67 23 62 12 , anette.winger@hioa.no
                Health Qual Life Outcomes
                Health Qual Life Outcomes
                Health and Quality of Life Outcomes
                BioMed Central (London )
                3 July 2015
                3 July 2015
                : 13
                [ ]Institute of Nursing, Faculty of Health Sciences, Oslo and Akershus University College of Applied Sciences, Postboks 4 St. Olavs plass, NO-0130 Oslo, Norway
                [ ]Department of Clinical Medicine, UIT The Arctic University of Norway, Tromso, Norway
                [ ]Institute of Clinical Medicine, Medical Faculty, University of Oslo, Oslo, Norway
                [ ]Department of Pediatrics, Oslo University Hospital, Oslo, Norway
                [ ]Department of Pediatrics, Akershus University Hospital, Lørenskog, Norway
                [ ]Department of Pediatrics, Innlandet Hospital Trust, Lillehammer, Norway
                [ ]KTH, Royal Institute of Technology, School for Technology and Health, Stockholm, Sweden
                [ ]Center for Shared Decision Making and Collaborative Care, Oslo University Hospital, Oslo, Norway
                © Winger et al. 2015

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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