Predicting Youth Athlete Sleep Quality and the Development of a Translational Tool to Inform Practitioner Decision Making

Abstract

Background: Identifying key variables that predict sleep quality in youth athletes allows practitioners to monitor the most parsimonious set of variables that can improve athlete buy-in and compliance for athlete self-report measurement. Translating these findings into a decision-making tool could facilitate practitioner willingness to monitor sleep in athletes. Hypothesis: Key predictor variables, identified by feature reduction techniques, will lead to higher predictive accuracy in determining youth athletes with poor sleep quality. Study Design: Cross-sectional study. Level of Evidence: Level 3. Methods: A group (N = 115) of elite youth athletes completed questionnaires consisting of the Pittsburgh Sleep Quality Index and questions on sport participation, training, sleep environment, and sleep hygiene habits. A least absolute shrinkage and selection operator regression model was used for feature reduction and to select factors to train a feature-reduced sleep quality classification model. These were compared with a classification model utilizing the full feature set. Results: Sport type, training before 8 am, training hours per week, presleep computer usage, presleep texting or calling, prebedtime reading, and during-sleep time checks on digital devices were identified as variables of greatest influence on sleep quality and used for the reduced feature set modeling. The reduced feature set model performed better (area under the curve, 0.80; sensitivity, 0.57; specificity, 0.80) than the full feature set models in classifying youth athlete sleep quality. Conclusion: The findings of our study highlight that sleep quality of elite youth athletes is best predicted by specific sport participation, training, and sleep hygiene habits. Clinical Relevance: Education and interventions around the training and sleep hygiene factors that were identified to most influence the sleep quality of youth athletes could be prioritized to optimize their sleep characteristics. The developed sleep quality nomogram may be useful as a decision-making tool to improve sleep monitoring practice among practitioners.

Reliability and validity of the Chinese version of the Athens insomnia scale for non-clinical application in Chinese athletes

“…Scales have long been a popular measure for monitoring sleep quality. In practice, classical sleep quality assessment scales and screening tools have contributed significantly to research on sleep quality risk assessment and enhancement in athletes ( Mah et al, 2018 ; Bender et al, 2018a , b ; Halson, 2019 ; Rebello et al, 2022 ; Suppiah et al, 2022 ), facilitating the development and application of sleep science in sports. Yet, researchers have grown increasingly conscious that the scales used to assess sleep quality in athletes were designed largely for clinical use, despite the fact that the vast majority of populations tested did not include patients with clinical issues.…”

“…Scales are the most widely used approach to evaluate sleep quality in research. Studies examining athletes’ sleep quality, identifying factors influencing sleep quality, and implementing interventions, such as Cranial Electrotherapy Stimulation and sleep hygiene, have primarily relied on scales, such as the Pittsburgh Sleep Quality Index ( Van Ryswyk et al, 2017 ; Kwon et al, 2019 ; Driller et al, 2022 ; Suppiah et al, 2022 ; Monma et al, 2023 ). While these scales have facilitated significant advancements in sleep quality research among athletes, their limitations and potential biases should not be overlooked.…”

Relación de trastornos del sueño durante viajes competitivos con calidad de sueño, sexo y modalidad deportiva en universitarios. (Relationship of sleep disorders during competitive travels with sleep quality, sex and sports modality in university students.)

“…Por otra parte, también se han reportado datos contradictorios en deportistas jóvenes, por ejemplo, en el estudio de Suppiah et al (2021) se encontró que los atletas de deporte de equipo informaron una CS significativamente peor que los atletas de deportes individuales. De igual manera, Suppiah et al (2022) identificaron en un modelo para predecir la CS en deportistas jóvenes que la participación en un deporte de equipo resultaba en una mayor probabilidad de tener una CS deficiente. Por otro lado, estos resultados varían en dependencia del componente del el Índice de Calidad de Sueño de Pittsburgh (ICSP) analizado; en una muestra de casi 500 deportistas olímpicos se encontró que los atletas de deportes de equipo informaron tiempos de latencia de inicio del sueño significativamente más prolongados que los deportistas individuales, pero también, una mayor duración total del sueño (Halson et al, 2022).…”

“…Dentro de los sistemas de seguimiento del sueño de los deportistas se incluyen las medidas de auto informe a través de cuestionarios validados ya que representan una alternativa lógica sobre los factores que contribuyen a las alteraciones del sueño y pueden determinar su calidad (Suppiah et al, 2022). Uno de los cuestionarios más utilizados para la valoración de CS es el ICSP ya que su administración permite a los profesionales determinar el historial de sueño de los deportistas e identificar posibles fuentes de alteraciones del sueño en función de las respuestas y puntuaciones de los componentes individuales del cuestionario (Suppiah et al, 2021).…”