-day alertness ( Peach et al., 2016 ). Sleep hygiene consists of four domains, arousal-related behavior, sleep scheduling and timing, eating/drinking behaviors, and sleep environment ( Peach et al., 2016 ). Within the postsecondary population, sleep hygiene behaviors are strongly associated with sleep quality
Jessica Murphy, Christopher Gladney, and Philip Sullivan
Diane K. Ehlers, Jennifer Huberty, Matthew Buman, Steven Hooker, Michael Todd, and Gert-Jan de Vreede
Commercially available mobile and Internet technologies present a promising opportunity to feasibly conduct ecological momentary assessment (EMA). The purpose of this study was to describe a novel EMA protocol administered on middle-aged women’s smartphones via text messaging and mobile Internet.
Women (N = 9; mean age = 46.2 ± 8.2 y) received 35 text message prompts to a mobile survey assessing activity, self-worth, and self-efficacy over 14 days. Prompts were scheduled and surveys were administered using commercial, Internet-based programs. Prompting was tailored to each woman’s daily wake/sleep schedule. Women concurrently wore a wrist-worn accelerometer. Feasibility was assessed via survey completion, accelerometer wear, participant feedback, and researcher notes.
Of 315 prompted surveys, 287 responses were valid (91.1%). Average completion time was 1.52 ± 1.03 minutes. One participant’s activity data were excluded due to accelerometer malfunction, resulting in complete data from 8 participants (n = 252 [80.0%] valid observations). Women reported the survey was easily and quickly read/completed. However, most thought the accelerometer was inconvenient.
High completion rates and perceived usability suggest capitalizing on widely available technology and tailoring prompting schedules may optimize EMA in middle-aged women. However, researchers may need to carefully select objective monitors to maintain data validity while limiting participant burden.
Courteney L. Benjamin, William M. Adams, Ryan M. Curtis, Yasuki Sekiguchi, Gabrielle E.W. Giersch, and Douglas J. Casa
The effects of training time on sleep has been previously studied; however, the influence on sleep in female collegiate cross-country runners is unknown. The aim of this study was to investigate the influence of training time on self-reported sleep metrics. Eleven female collegiate cross-country runners (mean [M] age = 19 years, standard deviation [SD] age = 1 year; M [SD] body mass = 58.8 [9.6] kg; M [SD] height = 168.4 [7.7] cm; M [SD] VO2max = 53.6 [5.6] mL·kg−1·min−1) competing in the 2016 NCAA cross-country season were included in this study. Participants completed a sleep diary daily to assess perceived measures of sleep on days when training took place between the hours of 5:00–8:00 a.m. (AM), and when training did not take place during this time (NAM). Sleep quality questions utilized a 5-point Likert scale, in which a score of 1 is associated with the worst outcomes and a score of 5 is associated with the best outcomes. Sleep duration was significantly higher on NAM (M [SD] = 8.26 [1.43] h) compared to AM (M [SD] = 7.97 [1.09] h, p < .001). Sleep quality was significantly higher on NAM (M [SD] = 3.30 [1.01]) compared to AM (M [SD] = 3.02 [1.06], p < .001). The impairment of sleep quantity and quality the night prior to early morning training suggests that future considerations should be made to sleep schedules and/or training times to optimize perceived sleep quality.
Cédric Leduc, Julien Robineau, Jason C. Tee, Jeremy Cheradame, Ben Jones, Julien Piscione, and Mathieu Lacome
. Prior to each journey, advice regarding jet lag and travel fatigue management were provided to the players by the team’s medical staff. These encompassed a sleep schedule and appropriate time to sleep during the flight, an explanation of sleep hygiene strategies to be used, and the availability of
Carlie K. Elmer and Tamara C. Valovich McLeod
(psychoeducation, activity and sleep scheduling, relaxation training, and cognitive restructuring) in two to five sessions ranging from 45 min to 1 hr. Outcome measures (a) Symptoms were monitored using the 22-item PCSS. Activity monitoring included mental, screen time, and physical activity. (b
Anis Aloulou, Francois Duforez, Damien Léger, Quentin De Larochelambert, and Mathieu Nedelec
< .05) among young soccer players. 14 High training load days during training camp were associated with earlier sleep schedules and reduced TST (approximately −72 min; P < .05) compared with rest days, and lower SE (approximately −2.7%; P < .05) compared with low training load days in the
Robert J. Brychta, Vaka Rögnvaldsdóttir, Sigríður L. Guðmundsdóttir, Rúna Stefánsdóttir, Soffia M. Hrafnkelsdóttir, Sunna Gestsdóttir, Sigurbjörn A. Arngrímsson, Kong Y. Chen, and Erlingur Jóhannsson
different between the methods. These results suggest bias affecting self-report is not longitudinally consistent in this adolescent sample. Caution should be used when interpreting longitudinal self-reported sleep measures in populations with similar highly varied sleep schedules. Thus, along with issues of
Andressa Silva, Fernanda V. Narciso, Igor Soalheiro, Fernanda Viegas, Luísa S.N. Freitas, Adriano Lima, Bruno A. Leite, Haroldo C. Aleixo, Rob Duffield, and Marco T. de Mello
reentrainment of human sleep-wake cycle but not of plasma melatonin rhythm to 8-h phase-advanced sleep schedule . Am J Physiol Integr Comp Physiol . 2010 ; 298 ( 3 ): R681 – R691 . doi:10.1152/ajpregu.00345.2009 10.1152/ajpregu.00345.2009 27. Atkinson G , Edwards B , Reilly T , Waterhouse J
Bruce W. Bailey, Landon S. Deru, William F. Christensen, Andrew J. Stevens, Stephen Tanner Ward, Matthew L. Starr, Ciera L. Bartholomew, and Larry A. Tucker
across subjects and within a given subject’s 7 measured days. Specifically, by modeling SED and MVPA in terms of minutes per waking hour, we more easily assessed how a subject’s observed activity allocations (SED vs MVPA) were related to her sleep schedule. All SEMs were analyzed using Amos (version 25
Junxin Li, Binbin Yang, Miranda Varrasse, Xiaopeng Ji, MaoChun Wu, Manman Li, and Kun Li
-related behaviors, eating or drinking habits prior to sleep, sleep environment, and sleep schedule and timing. Each item is scored based on a scale ranging from 1 ( never ) to 6 ( always ). The total score is the composite score of the 30 items. A higher score represents worse sleep hygiene ( Lin et al., 2007