Translation,Adaptation,and Validation of the Athlete Sleep Behavior Questionnaire for Persian-Speaking Athletes

Abstract

Background:

The Athlete Sleep Behavior Questionnaire (ASBQ) is a valuable tool for assessing sleep-related issues in athletes. However, its psychometric properties in specific populations require thorough investigation.

Purpose:

To examine the psychometric properties of the ASBQ in a sample of Iranian athletes and nonathletes.

Study Design:

Cohort study (diagnosis); Level of evidence, 3.

Method:

A cross-sectional study was conducted with 978 participants (52% athletes, 48% nonathletes). Participants with a sleep disorder were excluded from the study. Data were collected on sociodemographic characteristics and participants were administered the ASBQ. Confirmatory factor analysis (CFA) was performed to evaluate the model fit. Cronbach alpha was used to assess internal consistency.

Results:

CFA results indicated acceptable model fit, with a comparative fit index of 0.91 and root mean square error of approximation of 0.075. Although some fit indices showed slight deviations, overall the factorial structure was supported. The Cronbach alpha for the overall scale was 0.876, demonstrating good internal consistency. Subscale reliabilities were also acceptable (behavioral: 0.720; environmental: 0.694; sports: 0.695).

Conclusion:

The findings of this study suggest that the Persian version of the ASBQ exhibits acceptable psychometric properties in this Iranian sample. These results support the use of the ASBQ for assessing sleep-related issues in Iranian athletes and provide valuable information for future research and interventions.

Sleep is a fundamental physiological process essential for human survival and well-being. ³⁰ In athletes, sleep plays a pivotal role in optimizing performance, facilitating recovery, and enhancing overall health. ³⁵ Sufficient sleep duration and quality are crucial for athletes to effectively adapt to the demands of rigorous training schedules, recover from strenuous exercise, and maintain optimal cognitive function.^7,14 However, Biggins et al ³ found that 16% of elite multisport athletes had clinically significant sleep problems, which were associated with poor sleep hygiene, increased health complaints, and mood disturbances. Juliff et al ¹⁸ revealed that a substantial proportion of athletes experience difficulty falling asleep before competitions, with 64% reporting disrupted sleep on at least 1 occasion over the past year. Inadequate sleep can have detrimental consequences for athletic performance, including decreased power output, reduced reaction time, impaired decision-making, and increased risk of injury.^6,27,29,31 Furthermore, sleep deprivation can negatively affect mood, increase the risk of depression and anxiety, and diminish overall well-being.^4,28

Given the critical role of sleep in athletic performance, it is essential for athletes to prioritize sleep hygiene and establish consistent sleep routines. However, the unique demands of athletic life, such as frequent travel, early-morning training sessions, and late-night competitions, can significantly disrupt sleep patterns.^8,33 Other factors, including jet lag, circadian rhythm dysregulation, stress, and caffeine consumption, can further exacerbate sleep disturbances in athletes. ²¹ Lastella et al ¹⁹ found that professional soccer players’ sleep and fatigue were most disrupted immediately after games and during flights. Players slept 3.6 hours less during flights compared with their sleep at home. The study underscores how travel and tight competition schedules negatively affect sleep and increase fatigue in athletes. To effectively address sleep-related issues in athletes, it is crucial to accurately assess their sleep behaviors and identify areas for improvement. While various sleep assessment tools exist, including polysomnography and wearable devices, these can be costly, time-consuming, and may not be readily accessible in all settings. ¹⁰

Self-report questionnaires offer a more practical and cost-effective alternative for assessing sleep in athletes. ¹⁶ Several general sleep questionnaires, such as the Pittsburgh Sleep Quality Index, ⁵ Epworth Sleepiness Scale, ¹⁷ and Sleep Hygiene Index (SHI), ²⁰ are widely used in the general population. However, these questionnaires may not adequately capture the unique sleep challenges faced by athletes, such as travel-related sleep disruptions, early-morning training schedules, and the effect of competition on sleep patterns. ¹⁶ In this regard, Halson ¹⁵ examined sleep disturbances in athletes and the lack of specific guidelines for assessing and addressing these issues. This study outlined that most sleep research in athletes is based on general population studies, such as those on insomnia, and lacks athlete-specific approaches. Recognizing this limitation, researchers have developed athlete-specific sleep questionnaires to better assess sleep quality and identify sleep-related issues in this population. The Athlete Sleep Screening Questionnaire (ASSQ) ² is one such tool, primarily designed for clinical screening of sleep disorders in athletes. While the ASSQ has shown promise in identifying athletes at risk for sleep disturbances, it may not comprehensively assess the full spectrum of sleep behaviors relevant to athletic performance, such as sleep hygiene practices, sleep routines, and the effect of specific training and competition demands on sleep. To address this gap, Driller et al ¹² developed the Athlete Sleep Behavior Questionnaire (ASBQ). The ASBQ was designed to provide a comprehensive assessment of athletes’ sleep behaviors by incorporating elements from the SHI, ²⁰ the International Classification of Sleep Disorders, ²⁶ and relevant research on sleep issues in athletes. ²² The questionnaire also includes specific recommendations for addressing identified sleep problems. ²³ The ASBQ focuses on assessing athletes’ sleep behaviors and identifying areas for improvement in sleep hygiene and routines, rather than primarily serving as a clinical screening tool for sleep disorders.

Initial studies have shown promising psychometric properties of the ASBQ, suggesting its value for assessing sleep behaviors in athletes. ¹¹ However, further research is needed to validate the ASBQ across diverse athletic populations and cultural contexts. The present study aimed to rigorously translate and culturally adapt the ASBQ for Persian-speaking athletes and conduct a comprehensive psychometric evaluation, including confirmatory factor analysis (CFA) and reliability analyses. It was hypothesized that the Persian version of the ASBQ would demonstrate good psychometric properties comparable to those of the original version.

Methods

Participants

This study utilized a cross-sectional design to investigate the psychometric properties of the Persian version of the ASBQ in a sample of Iranian athletes and nonathletes. Participants were recruited from various universities, sports clubs, and community centers in Tehran, Iran. Inclusion criteria included individuals aged 18 years or older. Exclusion criteria included individuals with diagnosed sleep disorders (eg, insomnia and sleep apnea) and those with cognitive impairments that would preclude their participation in the study. A total of 978 (336 males, 642 females) individuals participated in the study; 512 were athletes (defined as individuals actively engaged in organized sports at least 3 times per week) and 466 were nonathletes. This study was approved by the Ethics Committee of Farhangian University (code IR.CUF.1403.1112). All participants provided written informed consent before participating in the study.

Measures

Sociodemographic Questionnaire

Sociodemographic data, including age, sex, marital status, occupation, educational level, and current level of physical activity, were recorded for each participant.

Athlete Sleep Behavior Questionnaire

The original English version of the ASBQ ¹² consists of 18 items assessing various aspects of athletes’ sleep behaviors, including sleep hygiene practices, sleep routines, and the effect of training and competition on sleep. The items are rated on a 5-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree).

Sleep Hygiene Index

The SHI ²⁰ is a 13-item self-report measure designed to assess individual adherence to recommended sleep hygiene practices. Each item is rated on a 5-point Likert scale ranging from 0 (never) to 4 (always), quantifying the frequency of specific sleep-related behaviors. These behaviors encompass a range of factors, including the regularity of sleep and wake-up times, the effect of daytime naps, presleep physical activity, bedtime routines, sleep environment (light, noise, and temperature), and the influence of substances like caffeine and alcohol. Higher scores on the SHI indicate poorer sleep hygiene practices.

Translation and Cultural Adaptation

The translation and cultural adaptation process followed the guidelines proposed by the World Health Organization:

• Forward translation: Two independent bilingual translators, fluent in both English and Persian, translated the original English version of the ASBQ into Persian.

• Back translation: Two independent bilingual translators, blind to the original English version, back-translated the Persian versions into English.

• Committee review: A committee of experts, including linguists (1 with 10+ years of experience in translation and linguistics), sleep specialists (2 with 15+ years of experience in sleep medicine), and sport psychologists (2 with 12+ years of experience in sports psychology), reviewed the forward and back-translated versions for accuracy, clarity, and cultural appropriateness.

• Cognitive interviews: Cognitive interviews were conducted with a small sample of 15 participants to assess the clarity, comprehensibility, and cultural relevance of the Persian version of the ASBQ. Based on the feedback from the review committee and cognitive interviews, minor adjustments were made to the Persian version of the ASBQ to ensure clarity, cultural appropriateness, and conceptual equivalence to the original English version.

Data Collection

Data were collected through self-administered questionnaires distributed both online and in paper format. Participants completed the questionnaires either online or in person, depending on their preference. Detailed instructions were provided to ensure that all participants understood how to complete the questionnaires.

Data Analysis

Data were analyzed using IBM SPSS Statistics Version 28.0 (IMB). Descriptive statistics were computed to summarize the demographic characteristics of the sample. CFA was performed using Amos Version 28.0 (IBM) to evaluate the factorial structure of the Persian version of the ASBQ. The adequacy of the sample size for CFA was assessed using the Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy and Bartlett test of sphericity. The fit of the CFA model was evaluated using several goodness of fit indices: (1) comparative fit index (CFI), (2) goodness of fit index (GFI), (3) adjusted goodness of fit index (AGFI), (4) normed fit index (NFI), and (5) incremental fit index (IFI). Values ≥0.90 indicate a good fit, while values >0.95 are considered excellent. Additionally, the root mean square error of approximation (RMSEA) was used, where values <0.08 suggest a good fit and those <0.05 indicate an excellent fit. The root mean square residual (RMR) was also considered, with values <0.05 reflecting a good fit. Internal consistency reliability was evaluated using the Cronbach alpha coefficient for both the overall scale and each subscale of the ASBQ, with values >0.70 indicating good reliability and those >0.80 signifying excellent reliability. To assess the convergent validity of the ASBQ, the SHI was administered to all participants.

Results

The study sample consisted of 978 individuals, of whom 336 (34%) were male and 642 (66%) were female. Of the sample, 512 (52%) individuals were athletes and 466 (48%) were nonathletes. The mean age of the sample was 32.05 years (SD, 7.178 years). In terms of marital status, 287 (29%) individuals were single, 644 (66%) were married, 27 (3%) were divorced, 8 (1%) were widowed, and 12 (1%) did not provide an answer.

To conduct a CFA, the adequacy of the sample size was first examined. The KMO measure of sampling adequacy was reported as 0.927. Additionally, the chi-square value in the Bartlett test of sphericity was 5230.645, with 153 degrees of freedom and a significance level of .001. Based on the results of the KMO and Bartlett tests, the sample size was deemed adequate for CFA. Figure 1 presents the results of the CFA. It should be noted that items 2, 3, and 11 were excluded from the final analysis due to reasons such as low and nonsignificant correlations with other items constituting the factor in question, or due to skewness values exceeding an absolute value of 3.

Figure 1.

Confirmatory factor analysis of the sleep behavior questionnaire. The Athlete Sleep Behavior Questionnaire (ASBQ) is represented by items ASBQ1 to ASBQ18, which correspond to the 18 questions included in the analysis. Additionally, the symbol e indicates the error values associated with the responses.

Table 1 reports the factor loadings and t test values for each observed variable on the latent constructs. All observed variables exhibit factor loadings ≥0.40, and the corresponding t values surpass 1.96. These results demonstrate that the observed variables (questionnaire items) have strong loadings on the latent constructs (questionnaire dimensions) and can adequately represent these constructs.

Table 1

Summary of the Confirmatory Factor Analysis Model for the Sleep Behavior Questionnaire ^a

Subscale	Item	Beta	t	P Value
Behavioral factors	ASBQ4: I consume alcohol within 4 hours of going to bed	.44	—	—
	ASBQ6: I go to bed feeling thirsty	.47	11.05	.001
	ASBQ8: I use light-emitting technology in the hour leading up to bedtime	.49	11.34	.001
	ASBQ12: I wake to go to the bathroom more than once per night	.75	13.02	.001
	ASBQ13: I wake myself and/or my bed partner with my snoring	.78	12.37	.001
Environmental factors	ASBQ1: I take afternoon naps lasting two or more hours	.45	—	—
	ASBQ5: I go to bed at different times each night (more than ±1 hour variation)	.67	12.15	.001
	ASBQ15: I get up at different times each morning (more than ±1 hour variation)	.70	12.74	.001
	ASBQ16: At home, I sleep in a less than ideal environment	.43	9.92	.001
	ASBQ17: I sleep in foreign environments (e.g hotel rooms)	.45	10.29	.001
	ASBQ18: Travel gets in the way of building a consistent sleep-wake routine	.42	9.75	.001
Sports factors	ASBQ7: I go to bed with sore muscles	.67	—	—
	ASBQ9: I think, plan and worry about my sporting performance when I am in bed	.40	11.88	.001
	ASBQ10: I think, plan and worry about issues not related to my sport when I am in bed	.67	19.52	.001
	ASBQ14: I wake myself and/or my bed partner with my muscle twitching	.66	20.90	.001

a

ASBQ1 to ASBQ18 correspond to the 18 questions. ASBQ, Athlete Sleep Behavior Questionnaire.

The goodness of fit indices for the observed model were as follows: 0.91 for CFI, indicating a good fit; 0.075 for RMSEA, also indicating a good fit; 0.93 for GFI, reflecting a good fit; 0.89 for AGFI, suggesting a less than adequate fit (poor); 0.90 for NFI, indicating a good fit; 0.91 for IFI, demonstrating a good fit; and 0.056 for RMR, which suggests a less than adequate fit (poor).

Based on the CFI (0.91) and RMSEA (0.075), 2 critical indices that both reflected a good fit, the model fit of the CFA for the ASBQ is good. The GFI, NFI, and IFI also indicated a good model fit, although the AGFI and RMR deviated somewhat from the acceptable level. Overall, because most indices suggest a good fit for the observed model, the factor structure of the ASBQ is supported.

Cronbach alpha was used to assess the reliability of the questionnaire. The overall Cronbach alpha for all items was 0.876, indicating good reliability. Specifically, the Cronbach alpha for the behavioral factors subscale was 0.720, reflecting good reliability. In contrast, the Cronbach alpha for the environmental factors subscale was 0.694, indicating poor reliability, while the sports factors subscale also demonstrated a Cronbach alpha of 0.695, suggesting poor reliability. Although both the environmental and sports subscales were slightly below 0.70, the overall Cronbach alpha exceeding this threshold allows for the acceptable judgment of reliability. It is noteworthy that Cronbach alpha is influenced by the number of items; therefore, reducing the number of items may result in a decrease in alpha, which does not necessarily imply poor reliability.

To examine the convergent validity of the ASBQ, correlations between the total score of the ASBQ and SHI were analyzed. The results revealed a moderate, statistically significant positive correlation between the ASBQ and SHI (r = 0.71; P < .001). This finding supports the convergent validity of the ASBQ, as it demonstrates a significant association between the ASBQ scores, which assess overall sleep behaviors, and SHI scores, which specifically assess sleep hygiene practices. This positive correlation suggests that the ASBQ effectively captures aspects of sleep behavior related to sleep hygiene as measured by the SHI.

Discussion

This study aimed to adapt and validate the ASBQ for use in a Persian-speaking population. The major findings demonstrate that the Persian version of the ASBQ exhibits acceptable psychometric properties, as evidenced by an overall Cronbach alpha of 0.876, indicating good reliability. The behavioral factors subscale achieved a Cronbach alpha of 0.720, reflecting adequate reliability, while the environmental (Cronbach alpha = 0.694) and sports (Cronbach alpha = 0.695) factors subscales were slightly below the commonly accepted threshold of 0.70. Despite this, the overall reliability of the ASBQ supports its use as a reliable and valid tool for assessing sleep behaviors in this population. Furthermore, the goodness of fit indices for the observed model provided additional validation. The CFI was 0.91, and the RMSEA was 0.075, both indicating a good fit. The GFI was 0.93, while the NFI and IFI were 0.90 and 0.91, respectively, further supporting a good model fit. However, the AGFI was 0.89, suggesting a less than adequate fit, and the RMR was 0.056, also indicating a less than adequate fit. Overall, despite the deviations in the AGFI and RMR from acceptable levels, the majority of indices suggest a good fit for the observed model. This reinforces the adequacy of the factor structure of the ASBQ, highlighting its relevance and applicability for understanding sleep behaviors among athletes in Persian-speaking contexts.

In a review study, Vlahoyiannis et al ³⁴ critically assessed sleep monitoring methods used in athletic populations, comparing them with the gold standard. They discussed the advantages and limitations of various tools, such as polysomnography and actigraphy. The review highlights several underreported factors affecting athletes’ sleep, including sleep environment, napping, and daily habits like nutrition, caffeine, and alcohol intake. It emphasizes the importance of considering these factors in future research to improve sleep assessments and allow for better comparisons across studies. Practical recommendations are made to enhance the quality of sleep studies in athletes. However, many athletes struggle to achieve sufficient sleep quality and duration, often due to poor sleep habits.^10,21 This underscores the importance of monitoring and controlling these sleep practices to achieve optimal athletic performance. ¹⁶ The ASBQ was specifically designed for this purpose and has undergone translation and validation in multiple languages. However, a Persian version of the ASBQ was previously lacking. Utilizing a rigorous translation methodology, the present study conducted a cross-cultural adaptation of the ASBQ, resulting in the development of an accurate and easily understandable ASBQ.

The ASBQ demonstrated good internal consistency, with Cronbach alpha and McDonald omega coefficients around 0.7. These coefficients suggest that the items of the ASBQ effectively measure a single underlying construct related to sleep behavior among Persian-speaking athletes. The Cronbach alpha in our study was higher than those of the original version (0.63), Brazilian version (0.78), ¹³ Turkish version (0.62), ⁹ and Japanese version (0.65). ³² Our findings suggest that the Persian version of the ASBQ is a reliable and valid tool for assessing sleep-related issues in Iranian athletes, making it a valuable resource for future research and interventions. The study is aligned with previous research, such as the work by Rabin et al, ²⁴ which also emphasized the importance of assessing sleep health in athletes. This is further supported by studies such as that by Samuels et al, ²⁵ who developed the ASSQ, a tool specifically designed to assess sleep in elite athletes, and that by Baize et al, ¹ who validated the French versions of the ASBQ and the Athens Insomnia Scale for competitive athletes.

Interestingly, the positive factor loadings for all items indicate that they are effectively measuring the same underlying construct of sleep-related behaviors. The results were consistent with previous studies, which reported factor loadings ranging from 0.418 to 0.825 in the Turkish ASBQ version ⁹ and from 0.45 to 0.61 in the original ASBQ version. ¹² The construct validity of the ASBQ was supported by CFA, which revealed that all 18 items of the ASBQ loaded significantly onto a single latent factor (ie, a 1-factor solution), indicating that they collectively measure the overall sleep behavior of athletes. It is worth noting that while the initial CFA model had fit indices below the recommended thresholds, the removal of 2 potentially misfitting items (ASBQ4 and ASBQ13) improved the model fit. ASBQ4, which involves alcohol consumption within 4 hours before sleep, might not be culturally aligned with Persian Muslim athletes, and it is possible that many athletes might not identify as alcohol consumers or that the proportion of athletes who consume alcohol is small. This observation was further validated by the IRT results, indicating that ASBQ4 is the most difficult for respondents in terms of their responses, potentially affecting the accuracy of their answers. Similarly, the CFA indicated that ASBQ 13, “I wake myself and/or my bed partner with my snoring,” may not be reliably contributing to the measurement of the construct being assessed by the ASBQ. Therefore, further examination, potential rewording, or refinement of ASBQ13 to improve its performance within the scale is warranted. This highlights the need for continuous evaluation and refinement of the ASBQ items to enhance the model's accuracy, thereby ensuring its appropriateness across diverse populations and cultural contexts.

The IRT analysis also revealed that the majority of items have higher absolute tau values. This reflects their capacity to effectively differentiate among individuals characterized by varying trait levels, making them valuable for assessing the intended construct (ie, athletes’ sleep behavior). The strong discriminatory power of the ASBQ items further contributes to the construct validity and overall measurement accuracy of the questionnaire.

Limitations

While this study provides promising results regarding the adaptation and validation of the ASBQ for Persian-speaking athletes, several limitations should be acknowledged. First, the generalizability of our findings may be limited due to the specific sample population, which consisted of 52% athletes and 48% nonathletes; this distribution may not fully represent the wider athletic community or various sporting disciplines in Iran. Additionally, the exclusion of participants with diagnosed sleep disorders may have introduced a selection bias, potentially affecting the generalizability of the findings to all athletes. Although the overall Cronbach alpha of 0.876 indicates good reliability, the environmental (Cronbach alpha = 0.694) and sports (Cronbach alpha = 0.695) subscales fell below the commonly accepted threshold of 0.70, suggesting that the measurement of these domains may require further refinement. Furthermore, the CFA revealed that certain items, specifically ASBQ4 (regarding alcohol consumption) and ASBQ13 (related to snoring), may not culturally resonate with the target population, thus affecting their reliability and validity. Future research should consider these limitations by including a more diverse and representative sample, potentially addressing cultural nuances associated with sleep behaviors and refining the questionnaire to enhance its psychometric properties.

Conclusion

The ASBQ is a valuable tool for assessing sleep issues in athletes, but its psychometric properties need examination in specific populations. This cross-sectional study investigated the ASBQ's psychometric properties in 978 Iranian athletes (52%) and nonathletes (48%). Participants with sleep disorders were excluded. Sociodemographic data and the ASBQ were collected. CFA and the Cronbach alpha were used to assess model fit and internal consistency. CFA showed acceptable fit (CFI = 0.91; RMSEA = 0.075). The overall Cronbach alpha was 0.876, with acceptable subscale reliabilities (behavioral: 0.720; environmental: 0.694; sports: 0.695). The Persian ASBQ demonstrates acceptable psychometric properties in this Iranian sample, supporting its use for assessing sleep problems in Iranian athletes and informing future research and interventions.