- Email: [email protected]
acquired immunodeficiency syndrome
S148
Abstracts/Sleep Medicine 16 (2015) S2–S199
each severity group at each age were compared using chi-square analysis. Results: The median BMI z-score was 0.66 (range −2.45 to 4.41). BMI z-score was similar between severity groups in preschool, school-age and adolescent cohorts. In preschool children, OAHI was not significantly correlated with NC, WC, HC or any indices of body fat distribution. Preschool children with mild OSA had a significantly higher WC and HC than control or PS children (p < 0.05 for both). NC was significantly smaller in preschool children with PS than mild or MS OSA groups (p < 0.05 for both). There were no preschool group differences in indices of body fat distribution. School-age and adolescent cohorts showed no severity group differences in NC, WC or HC. However, school-age children with mild OSA had a significantly higher WHR than controls (p < 0.01). In school age children, OAHI correlated with NC, WC, HC and WHtR (p < 0.01 for all, r = 0.42, r = 0.3, r = 0.3, r = 0.36 respectively). Adolescents with MS OSA had a significantly higher WHR, NWR and WHtR than controls (p < 0.05 for all). In adolescents, OAHI correlated with NC, WC, HC, WHtR, WHR and NWR (p < 0.05 for all, r = 0.29, r = 0.4, r = 0.3, r = 0.41, r = 0.39, r = −0.32 respectively. The proportion of preschool children with NC >90th percentile in each severity group was significantly different (p<0.05), however proportions in each severity group were similar at the school-age and adolescent age. There were no severity group differences in proportions of subjects with WC > 90th percentile at any age. Conclusion: In a heterogenous population of healthy, overweight and obese children, indices of body fat distribution relate to OSA severity in school-aged and adolescent years, however less so at the preschool age. These findings are largely underpinned by the contrasting causes of OSA in each age group. The use of NC and WC as a screening tool for OSA is likely to be age dependent.
scale. Multivariate regression was used to examine associations between cognition and TST, WASO%, subjective sleep quality and fatigue, with adjustment for age, gender, race, education, CD4+ T-cell count, and HIV viral load log. Results: The mean TST (hours) was 6.21 ± 1.64 (SD) and mean WASO (%) was 20.46 ± 14.69 (SD). The mean Pittsburgh Sleep Quality Index score was 7.25 ± 3.35 (SD) and 63% of adults with HIV/AIDS had poor sleep quality using a cutoff point of 5. The mean score for the Fatigue Severity Scale was 4.78 ± 2.03 (SD). The mean Medical Outcome Study Cognitive Functioning Scale score was 27.23 ± 7.22 and 19% of the adults with HIV/AIDS had poor cognitive function. Poorer subjective sleep quality was correlated with greater fatigue (r = 0.43 p < 0.001), but the relationships between subjective sleep quality scores and actigraphy values for TST (r = −0.115, p = 0.059) and WASO (r = −0.118, p = 0.053) were weak. Poorer cognitive function was associated with being female or transgender (vs. male, p = 0.025), higher CD4+ T-cell count (p < 0.001) and higher HIV viral load (p = 0.013). Poorer subjective sleep quality (p < 0.001) and greater fatigue (p < 0.001) were associated with poorer cognitive function, even after controlling for socio-demographic and clinical variables. Conclusion: Adults with HIV/AIDS who experience poorer sleep quality and greater fatigue are more likely to have poorer cognitive function, regardless of CD4+ cell count and viral load. Assessing and treating both sleep problems and fatigue may have greater impact on improving cognitive function, and suggests potential areas for intervention. Acknowledgements: This study was supported by a grant from the National Institutes of Health/National Institute of Mental Health (R01MH074358). Dr. Eeeseung Byun is currently supported by a training grant from the National Institutes of Health/National Institute of Nursing Research (T32NR007088).
http://dx.doi.org/10.1016/j.sleep.2015.02.1577
http://dx.doi.org/10.1016/j.sleep.2015.02.1498
Sleep quality and fatigue are associated with cognitive function in adults living with human immunodeficiency virus/acquired immunodeficiency syndrome E. Byun, C. Gay, K. Lee Department of Family Health Care Nursing, University of California at San Francisco, USA
Promoting sleep among undergraduate nursing students in Hong Kong J. Chan, S. Lam, M. Cheung, K. Lee, J. Lee The Chinese University of Hong Kong, Hong Kong
Introduction: Up to 50% of persons with human immunodeficiency virus/acquired immunodeficiency syndrome (HIV/AIDS) have some neurocognitive impairment. The purpose of this study was to examine associations of objectively and subjectively measured sleep and fatigue with cognitive function in adults living with HIV/AIDS. Materials and methods: A cross sectional descriptive study was conducted with a convenience sample of 268 adults (179 men, 67 women, and 22 transgender, mean age 45 ± 8.5) living with HIV/ AIDS recruited from HIV clinics and community sites in the San Francisco Bay Area. A wrist actigraph was worn for 72 hours to assess total sleep time (TST) and percent of TST spent awake after sleep onset (WASO%). The Pittsburgh Sleep Quality Index was used to measure subjective sleep quality (range: 0–21, higher scores = poorer sleep quality). The Fatigue Severity Scale was used to measure fatigue (range: 1–7, higher scores = greater fatigue). Cognitive function was assessed by the Medical Outcome Study Cognitive Functioning Scale (range: 6–36, higher scores = better cognition) that includes six cognitive aspects (reasoning, concentration and thinking, confusion, memory, attention and psychomotor). Poor cognitive function was defined as scoring >1 standard deviation below the mean on the
Introduction: There is little research on the sleep knowledge and quality of nursing students. This study aims to compare the sleep knowledge, sleep quality and sleep parameters prior to, and after, a sleep education programme for nursing students. Materials and methods: A single group pre-test and post-test quasi-experimental design is used in this study. We aim to recruit 57 undergraduate nursing students in Hong Kong. The program consists of about 30-minute oral presentation with utilization of sleep education PowerPoint and sleep education handout. The educational contents include importance of good sleep, impact of shift work on sleep, negative consequences of poor sleep, sleep hygiene and practice guidelines, stimulus control instructions and information about substances with caffeine. Pittsburgh Sleep Quality Index (PSQI), Sleep Beliefs Scale (SBS) and client satisfaction survey are used as instruments to measure the outcomes which include students’ sleep quality, sleep parameters, sleep knowledge and their satisfaction toward the program respectively. The participants will be assessed over three time points which are pre-program, postprogram and 6-week follow up. Prior to the program, students will answer questions about demographics, PSQI and SBS; right after the program, they will complete SBS and client satisfaction questionnaire. Six weeks after the program, they will complete PSQI. An email with the PSQI will be sent to them and they will have to return the
Abstracts/Sleep Medicine 16 (2015) S2–S199
each severity group at each age were compared using chi-square analysis. Results: The median BMI z-score was 0.66 (range −2.45 to 4.41). BMI z-score was similar between severity groups in preschool, school-age and adolescent cohorts. In preschool children, OAHI was not significantly correlated with NC, WC, HC or any indices of body fat distribution. Preschool children with mild OSA had a significantly higher WC and HC than control or PS children (p < 0.05 for both). NC was significantly smaller in preschool children with PS than mild or MS OSA groups (p < 0.05 for both). There were no preschool group differences in indices of body fat distribution. School-age and adolescent cohorts showed no severity group differences in NC, WC or HC. However, school-age children with mild OSA had a significantly higher WHR than controls (p < 0.01). In school age children, OAHI correlated with NC, WC, HC and WHtR (p < 0.01 for all, r = 0.42, r = 0.3, r = 0.3, r = 0.36 respectively). Adolescents with MS OSA had a significantly higher WHR, NWR and WHtR than controls (p < 0.05 for all). In adolescents, OAHI correlated with NC, WC, HC, WHtR, WHR and NWR (p < 0.05 for all, r = 0.29, r = 0.4, r = 0.3, r = 0.41, r = 0.39, r = −0.32 respectively. The proportion of preschool children with NC >90th percentile in each severity group was significantly different (p<0.05), however proportions in each severity group were similar at the school-age and adolescent age. There were no severity group differences in proportions of subjects with WC > 90th percentile at any age. Conclusion: In a heterogenous population of healthy, overweight and obese children, indices of body fat distribution relate to OSA severity in school-aged and adolescent years, however less so at the preschool age. These findings are largely underpinned by the contrasting causes of OSA in each age group. The use of NC and WC as a screening tool for OSA is likely to be age dependent.
scale. Multivariate regression was used to examine associations between cognition and TST, WASO%, subjective sleep quality and fatigue, with adjustment for age, gender, race, education, CD4+ T-cell count, and HIV viral load log. Results: The mean TST (hours) was 6.21 ± 1.64 (SD) and mean WASO (%) was 20.46 ± 14.69 (SD). The mean Pittsburgh Sleep Quality Index score was 7.25 ± 3.35 (SD) and 63% of adults with HIV/AIDS had poor sleep quality using a cutoff point of 5. The mean score for the Fatigue Severity Scale was 4.78 ± 2.03 (SD). The mean Medical Outcome Study Cognitive Functioning Scale score was 27.23 ± 7.22 and 19% of the adults with HIV/AIDS had poor cognitive function. Poorer subjective sleep quality was correlated with greater fatigue (r = 0.43 p < 0.001), but the relationships between subjective sleep quality scores and actigraphy values for TST (r = −0.115, p = 0.059) and WASO (r = −0.118, p = 0.053) were weak. Poorer cognitive function was associated with being female or transgender (vs. male, p = 0.025), higher CD4+ T-cell count (p < 0.001) and higher HIV viral load (p = 0.013). Poorer subjective sleep quality (p < 0.001) and greater fatigue (p < 0.001) were associated with poorer cognitive function, even after controlling for socio-demographic and clinical variables. Conclusion: Adults with HIV/AIDS who experience poorer sleep quality and greater fatigue are more likely to have poorer cognitive function, regardless of CD4+ cell count and viral load. Assessing and treating both sleep problems and fatigue may have greater impact on improving cognitive function, and suggests potential areas for intervention. Acknowledgements: This study was supported by a grant from the National Institutes of Health/National Institute of Mental Health (R01MH074358). Dr. Eeeseung Byun is currently supported by a training grant from the National Institutes of Health/National Institute of Nursing Research (T32NR007088).
http://dx.doi.org/10.1016/j.sleep.2015.02.1577
http://dx.doi.org/10.1016/j.sleep.2015.02.1498
Sleep quality and fatigue are associated with cognitive function in adults living with human immunodeficiency virus/acquired immunodeficiency syndrome E. Byun, C. Gay, K. Lee Department of Family Health Care Nursing, University of California at San Francisco, USA
Promoting sleep among undergraduate nursing students in Hong Kong J. Chan, S. Lam, M. Cheung, K. Lee, J. Lee The Chinese University of Hong Kong, Hong Kong
Introduction: Up to 50% of persons with human immunodeficiency virus/acquired immunodeficiency syndrome (HIV/AIDS) have some neurocognitive impairment. The purpose of this study was to examine associations of objectively and subjectively measured sleep and fatigue with cognitive function in adults living with HIV/AIDS. Materials and methods: A cross sectional descriptive study was conducted with a convenience sample of 268 adults (179 men, 67 women, and 22 transgender, mean age 45 ± 8.5) living with HIV/ AIDS recruited from HIV clinics and community sites in the San Francisco Bay Area. A wrist actigraph was worn for 72 hours to assess total sleep time (TST) and percent of TST spent awake after sleep onset (WASO%). The Pittsburgh Sleep Quality Index was used to measure subjective sleep quality (range: 0–21, higher scores = poorer sleep quality). The Fatigue Severity Scale was used to measure fatigue (range: 1–7, higher scores = greater fatigue). Cognitive function was assessed by the Medical Outcome Study Cognitive Functioning Scale (range: 6–36, higher scores = better cognition) that includes six cognitive aspects (reasoning, concentration and thinking, confusion, memory, attention and psychomotor). Poor cognitive function was defined as scoring >1 standard deviation below the mean on the
Introduction: There is little research on the sleep knowledge and quality of nursing students. This study aims to compare the sleep knowledge, sleep quality and sleep parameters prior to, and after, a sleep education programme for nursing students. Materials and methods: A single group pre-test and post-test quasi-experimental design is used in this study. We aim to recruit 57 undergraduate nursing students in Hong Kong. The program consists of about 30-minute oral presentation with utilization of sleep education PowerPoint and sleep education handout. The educational contents include importance of good sleep, impact of shift work on sleep, negative consequences of poor sleep, sleep hygiene and practice guidelines, stimulus control instructions and information about substances with caffeine. Pittsburgh Sleep Quality Index (PSQI), Sleep Beliefs Scale (SBS) and client satisfaction survey are used as instruments to measure the outcomes which include students’ sleep quality, sleep parameters, sleep knowledge and their satisfaction toward the program respectively. The participants will be assessed over three time points which are pre-program, postprogram and 6-week follow up. Prior to the program, students will answer questions about demographics, PSQI and SBS; right after the program, they will complete SBS and client satisfaction questionnaire. Six weeks after the program, they will complete PSQI. An email with the PSQI will be sent to them and they will have to return the