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Try out PMC Labs and tell us what you think. Learn More. Norm scores were obtained by calculating the mean PedsQL scale scores by gender, age and health status. Differences in scale scores were analyzed for gender, age and health status construct validity using two-sample t-tests and effect sizes were calculated.

Construct validity was determined by testing differences in PedsQL scores between healthy young adults and young adults with chronic health conditions. No age differences were found. Effect sizes varied from medium to large. The importance of these aspects has led to the development of instruments deed to measure HRQOL [ 167 ]. In pediatrics, the use of a generic HRQOL instrument provides the possibility to compare children across different chronic health conditions and to compare them to the healthy population [ 8 - 10 ].

So far, several age-appropriate measures are available to measure HRQOL for children up to Adult want sex Perrine years old. The PedsQL is considered one of the most frequently used generic HRQOL instruments for children and adolescents, and has the option of adding disease-specific modules to the generic core scales [ 16 ]. Research on children with chronic health conditions has shown that these children are at risk for HRQOL problems [ 17 ].

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Due to advances in medicine, more children with chronic health conditions are now able to grow up as adults, which can lead to subsistent HRQOL problems in adulthood. Research on young adults Adult want sex Perrine with chronic health conditions has shown that these YAs report lower HRQOL and achieve fewer milestones in independence, psychosexual and social development [ 318 ].

Moreover, it has been found that HRQOL of adult patients with chronic health conditions is frequently impaired [ 19 ]. Routine HRQOL assessment can facilitate detection and discussion of psychosocial issues related to chronic health conditions. Moreover, it can provide a chance to refer patients with ificant risks of an unfavorable psychosocial outcome for interventions and to help these patients to achieve optimal development [ 2021 ]. To be able to monitor HRQOL over time in children moving from adolescence into adulthood, appropriate instruments and normative data are needed [ 622 ].

It is strongly recommended by the literature to use the same mode of administration when comparing groups or changes over time [ 23 ]. With the increase of research on young adults with a chronic health condition and for use in clinical practice, norm data for this age group have become indispensable. Age and gender differences are examined explorative. With the objective of obtaining at least respondents, a stratified sample of young adults in the age of 18—30 years was drawn from the panel.

In order to meet the participation criteria for this study, the young adults had to be fluent in Dutch.

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The sample was stratified based on Dutch population figures regarding key demographics age, sex, marital status and education. A stratified random sampling technique was used to minimize sample variance and to increase precision. Prior to the data collection, informed consent was obtained from all participants. Responders were able to answer the questions online, on their own computer at home.

Participants were told that the study was anonymous. The security of the website was guaranteed.

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To assess the socio-demographics of the participants, questions from the Course of Life Questionnaire CoLQ [ 26 ] were used regarding age, gender, ethnicity, education, employment and marital status. Education was divided into three according to the classification of Statistics Netherlands; low primary education, lower vocational education, lower and middle general secondary educationintermediate middle vocational education, higher secondary education, pre-university educationhigh higher vocational education, university.

In addition, the respondents were asked about the presence and type of chronic health condition. The answers to this question were checked according to the definition by Mokkink et al. A psychosocial health scale score and a total scale score can be computed. The total scale score was computed as the sum of all items divided by the of items answered on all scales.

First, descriptive analyses were performed to describe the sample. To compare demographics of this sample to the stratified sample, we performed one sample t-tests age and binomial tests gender and education as dichotomous variable. The PedsQL data were normally distributed, so parametric tests were performed. Scales with reliabilities of. In order to provide precise norm data, we split the sample into two age groups: 18—25 years and 26—30 years. We chose this division, based on the age group studied by Varni et al. Differences in PedsQL scores between the age groups and between men and women were analyzed using two-sample t-tests.

To get insight into the extent of these differences, pooled effect sizes were calculated by dividing the difference in mean scores 18—25 versus 26—30 and women versus men by the pooled standard deviation. Finally, construct validity was determined by testing differences in the PedsQL scores between the healthy sample and young adults with a chronic health condition also using two-sample t-tests.

In this case, effect sizes were calculated by dividing the difference in mean scores of healthy young adults and young adults with a chronic health condition by the standard deviation of the healthy sample. All effect sizes of about 0. The key demographics age, sex, marital status and education of the respondents are comparable to the key demographics of the total stratified sample drawn by TNS NIPO.

Table 1 represents the socio-demographics of the sample. The average age of the women Table 1 also shows socio-demographics of the sample split into young adults with a chronic health condition and the healthy sample. The sample included No differences were found in key demographics, except for gender. Table 3 contains the PedsQL scale scores of the total sample and by gender, age and health status. The mean total PedsQL score was The mean of the total PedsQL Adult want sex Perrine by gender not split by age and health statuswas The mean of the total PedsQL score by age not split by gender and health Adult want sex Perrine was When comparing the two age groups, no differences were found in any of the PedsQL scale scores.

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The mean of the total PedsQL score for the healthy sample was No differences were found on the scales physical health and emotional functioning. In men aged 26—30 years, no differences in HRQOL scores were found between the healthy sample and the men with a chronic health condition. Moreover, the total scale scores exceed an alpha of. The Dutch sample shows similar in reliability across scales to the US sample [ 6 ], with slightly higher alphas. As far as we know, the PedsQL Generic Core scales is the only generic quality of life instrument to span ages 5—30 years in the Netherlands for self-report while maintaining scale construct consistency [ 6 ].

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In accordance with studies [ 615 ], young adults with a chronic health condition, report lower functioning in all HRQOL domains than their healthy peers. The lower emotional and social functioning of YAs with chronic health conditions may reflect the reduced social participation and delayed achievement of psychosocial developmental milestones in these YAs, compared to their healthy peers [ 1833 ]. It has been found that YAs with chronic health conditions are less able to work than their healthy peers and have paid jobs less often [ 3536 ].

Surprisingly, the differences described above seem not to apply for men ages 26— This finding might be explained by the small of respondents in the subgroup of young men ages 26—30 with a chronic health condition. The present findings of young adult men reporting better HRQOL than young adult women are in line with literature [ 2623 ]. Unfortunately, limited research has been done on comparing HRQOL in young adults up to 25 years old and older adults.

In our study no differences were found in HRQOL scores between the two age groups 18—25 and 26—30 years. Studies about young adults with a history of cancer suggest that young adults ages 18—25 years report better HRQOL than older young adults [ 3738 ]. So, further research is needed to study the differences in HRQOL between these specific aged groups 18—25 and 26—30 from the general population. A strong point of our study is that the PedsQL norm data collected in this study are an adequate representation of the general Dutch population due to the stratified sampling.

Moreover, we have a large sample size without any missing data. The online method of data collection explains this, with missing values not being allowed. It has been shown that online data collection increases response rates Adult want sex Perrine data quality [ 39 ].

Despite the strengths mentioned above, the present study has several limitations that need to be taken into. First, the reliability of the assessment of the health condition status was based on self-report rather than on physician-report. However, according to literature, self-reports of health status are consistent with proxy-reports of patient health status, including physician diagnoses of chronic health conditions [ 40 ].

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