Marie-Pierre St-Onge1,2,3, Ayanna Campbell1, Ismel Salazar1, Theresa Pizinger1, Ming Liao4, Brooke Aggarwal2,4. 1. Division of Endocrinology, New York Obesity Nutrition Research Center, Department of Medicine, Columbia University Irving Medical Center, New York, New York. 2. Sleep Center of Excellence, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, New York. 3. Institute of Human Nutrition, College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, New York. 4. Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center.
Abstract
STUDY OBJECTIVES: To investigate the correlation between self-reported and objective measures of total sleep time (TST) in adults and examine whether sex, race/ethnicity, and weight status influence this association. METHODS: Participants were individuals who screened for sleep intervention studies, providing > 7 days of wrist actigraphy sleep data (TSTobj) and reporting sleep duration (TSTPSQI) using the Pittsburgh Sleep Quality Index (PSQI) questionnaire (n = 53 men, 60 women; 59% minority/Hispanics; age 30.9 ± 9.7 years; body mass index 26.2 ± 3.2 kg/m2). In addition, TSTbed/wake was calculated as the difference between bedtime and waketime, minus the time to fall asleep, from the PSQI. Univariate regression analyses were performed to assess the relation between TSTobj and TSTPSQI and TSTbed/wake and compare the relation by sex, race, and ethnicity. Bland-Altman tests were done to assess bias by sex, race/ethnicity, weight status, and sleep quality. RESULTS: TSTbed/wake correlated with TSTobj (r = .57, P < .0001). TSTPSQI and TSTbed/wake were greater than TSTobj (0.63 ± 0.99 hours and 0.79 ± 0.76 hours, respectively, both P < .0001). The difference between TSTPSQI and TSTobj did not vary by sex (β = .12, P = .52), race/ethnicity (β = .15, P = .48), age (β = -.01, P = .27), or body mass index (β = .04, P = .13) whereas that between TSTbed/wake and TSTobj varied by age (β = -.020, P = .0051), BMI (β = .054, P = .0021), race/ethnicity (β = .36, P = .021), and sleep efficiency (β = -.089, P < .0001). CONCLUSIONS: Calculating TST using self-reported bedtimes and wake times provided TST estimate that correlated with TSTobj but greater over-reporting occurred in younger and heavier individuals, non-whites or Hispanics, and those with low sleep efficiency. In clinical and research settings, asking individuals to report bedtimes, wake times, and length of time to fall asleep may more accurately estimate TST than asking about sleep duration alone.
STUDY OBJECTIVES: To investigate the correlation between self-reported and objective measures of total sleep time (TST) in adults and examine whether sex, race/ethnicity, and weight status influence this association. METHODS:Participants were individuals who screened for sleep intervention studies, providing > 7 days of wrist actigraphy sleep data (TSTobj) and reporting sleep duration (TSTPSQI) using the Pittsburgh Sleep Quality Index (PSQI) questionnaire (n = 53 men, 60 women; 59% minority/Hispanics; age 30.9 ± 9.7 years; body mass index 26.2 ± 3.2 kg/m2). In addition, TSTbed/wake was calculated as the difference between bedtime and waketime, minus the time to fall asleep, from the PSQI. Univariate regression analyses were performed to assess the relation between TSTobj and TSTPSQI and TSTbed/wake and compare the relation by sex, race, and ethnicity. Bland-Altman tests were done to assess bias by sex, race/ethnicity, weight status, and sleep quality. RESULTS: TSTbed/wake correlated with TSTobj (r = .57, P < .0001). TSTPSQI and TSTbed/wake were greater than TSTobj (0.63 ± 0.99 hours and 0.79 ± 0.76 hours, respectively, both P < .0001). The difference between TSTPSQI and TSTobj did not vary by sex (β = .12, P = .52), race/ethnicity (β = .15, P = .48), age (β = -.01, P = .27), or body mass index (β = .04, P = .13) whereas that between TSTbed/wake and TSTobj varied by age (β = -.020, P = .0051), BMI (β = .054, P = .0021), race/ethnicity (β = .36, P = .021), and sleep efficiency (β = -.089, P < .0001). CONCLUSIONS: Calculating TST using self-reported bedtimes and wake times provided TST estimate that correlated with TSTobj but greater over-reporting occurred in younger and heavier individuals, non-whites or Hispanics, and those with low sleep efficiency. In clinical and research settings, asking individuals to report bedtimes, wake times, and length of time to fall asleep may more accurately estimate TST than asking about sleep duration alone.
Authors: Elizabeth M Cespedes; Frank B Hu; Susan Redline; Bernard Rosner; Carmela Alcantara; Jianwen Cai; Martica H Hall; Jose S Loredo; Yasmin Mossavar-Rahmani; Alberto R Ramos; Kathryn J Reid; Neomi A Shah; Daniela Sotres-Alvarez; Phyllis C Zee; Rui Wang; Sanjay R Patel Journal: Am J Epidemiol Date: 2016-03-02 Impact factor: 4.897
Authors: Terri Blackwell; Susan Redline; Sonia Ancoli-Israel; Jennifer L Schneider; Susan Surovec; Nathan L Johnson; Jane A Cauley; Katie L Stone Journal: Sleep Date: 2008-02 Impact factor: 5.849