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Exploring the Relationship Between Sleep Quality, Sleep Hygiene, and Psychological Distress in a Sample of Canadian Varsity Athletes

Jessica Murphy, Christopher Gladney, and Philip Sullivan

-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

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A Novel Inexpensive Use of Smartphone Technology for Ecological Momentary Assessment in Middle-Aged Women

Diane K. Ehlers, Jennifer Huberty, Matthew Buman, Steven Hooker, Michael Todd, and Gert-Jan de Vreede

Background:

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.

Methods:

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.

Results:

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.

Conclusions:

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.

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Early Morning Training Impacts Previous Night’s Sleep in NCAA Division I Cross Country Runners

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.

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The Effect of Rugby Union Match Play on Sleep Patterns and Subsequent Impact on Postmatch Fatigue Responses

Cedric Leduc, Dan Weaving, Cameron Owen, Mathieu Lacome, Carlos Ramirez-Lopez, Maj Skok, Jason C. Tee, and Ben Jones

Descriptive match and sleep parameters for baseline, MD night, and MD + 1 night with associated comparisons are displayed in Table  3 and Figure  2 (more details see Supplementary Table S1 [available online]). Regarding sleep schedule, very likely to most likely meaningful changes in sleep schedule

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The Travel Demands of an Elite Rugby Sevens Team: Effects on Objective and Subjective Sleep Parameters

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

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Addressing Circadian Disruptions in Visually Impaired Paralympic Athletes

Travis Anderson, William M. Adams, Geoffrey T. Burns, Eric G. Post, Sally Baumann, Emily Clark, Karen Cogan, and Jonathan T. Finnoff

adjusting sleep schedules accordingly, it is believed that the disruption caused by jet lag can be minimized. 36 , 37 However, like other nonpharmacological interventions, current evidence on sleep interventions is extremely limited within athlete populations. 15 Finally, research on light exposure as an

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Changes in 24-Hour Domain-Specific Movement Behaviors and Their Associations With Children’s Psychosocial Health During the Transition From Primary to Secondary School: A Compositional Data Analysis

Kar Hau Chong, Dorothea Dumuid, Dylan P. Cliff, Anne-Maree Parrish, and Anthony D. Okely

-ended, paper-based time-use diary with instructions to record their sleep schedule (ie, wake-up time and go-to-bed/sleep time) and the start times of all activities they engaged in during the waking period on the day prior to their scheduled home interview. 32 It was assumed that each activity ended at the

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Do Cognitive Behavioral Therapy Techniques Reduce Symptom Duration in Children and Adolescents Who Have Sustained a Mild Traumatic Brain Injury?

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

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The Relationships Between Training Load, Type of Sport, and Sleep Among High-Level Adolescent Athletes

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

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Longitudinal Change in Adolescent Bedtimes Measured by Self-Report and Actigraphy

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