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The Clinical Validation of the Athlete Sleep Screening Questionnaire: an Instrument to Identify Athletes that Need Further Sleep Assessment

The Clinical Validation of the Athlete Sleep Screening Questionnaire: an Instrument to Identify... Background: Previous research has established that general sleep screening questionnaires are not valid and reliable in an athlete population. The Athlete Sleep Screening Questionnaire (ASSQ) was developed to address this need. While the initial validation of the ASSQ has been established, the clinical validity of the ASSQ has yet to be determined. The main objective of the current study was to evaluate the clinical validity of the ASSQ. Methods: Canadian National Team athletes (N = 199; mean age 24.0 ± 4.2 years, 62% females; from 23 sports) completed the ASSQ. A subset of athletes (N = 46) were randomized to the clinical validation sub-study which required subjects to complete an ASSQ at times 2 and 3 and to have a clinical sleep interview by a sleep medicine physician (SMP) who rated each subjects’ category of clinical sleep problem and provided recommendations to improve sleep. To assess clinical validity, the SMP category of clinical sleep problem was compared to the ASSQ. Results: The internal consistency (Cronbach’s alpha = 0.74) and test-retest reliability (r = 0.86) of the ASSQ were acceptable. The ASSQ demonstrated good agreement with the SMP (Cohen’s kappa = 0.84) which yielded a diagnostic sensitivity of 81%, specificity of 93%, positive predictive value of 87%, and negative predictive value of 90%. There were 25.1% of athletes identified to have clinically relevant sleep disturbances that required further clinical sleep assessment. Sleep improved from time 1 at baseline to after the recommendations at time 3. Conclusions: Sleep screening athletes with the ASSQ provides a method of accurately determining which athletes would benefit from preventative measures and which athletes suffer from clinically significant sleep problems. The process of sleep screening athletes and providing recommendations improves sleep and offers a clinical intervention output that is simple and efficient for teams and athletes to implement. Keywords: Elite athletes, Sleep disturbances, Sleep interventions Key Points  Twenty-five percent of athletes were identified as needing further clinical sleep assessment. This is When athletes were rated based on the category of much lower than general sleep screening clinical sleep problem, there was good agreement questionnaires which have not been validated in elite between the ASSQ scoring system and the sleep athletes. medicine physician.  Sleep improved from baseline to after the sleep recommendations in those athletes with moderate to severe sleep difficulty. The ASSQ provides a valid and reliable tool to identify athletes for further sleep * Correspondence: [email protected] assessment and is easy and efficient for athletes and Centre for Sleep & Human Performance, 106-51 Sunpark Dr. SE, Calgary, AB the support team to administer. T2X 3V4, Canada Faculty of Kinesiology, University of Calgary, Calgary, AB, Canada Full list of author information is available at the end of the article © The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Bender et al. Sports Medicine - Open (2018) 4:23 Page 2 of 8 Background from a SMP [13]. However, no formal analyses of the clin- Sleep is a fundamental biological process that facilitates ical validity of the ASSQ were performed. recovery from the mental and physical demands of To address the lack of clinical validation, 199 Canadian high-performance sport [1, 2]. Recently, there has been a National Team athletes completed the ASSQ with 46 ath- proliferation of research exploring how sleep impacts re- letes randomized to partake in a standardized clinical sleep covery, training, and performance in elite athletes. Previ- interview from a SMP who was blinded to the ASSQ re- ous research has indicated elite athletes have a high sponses and SDS. At the end of the interview, the SMP prevalence of poor sleep quality [1, 3–8] and insufficient classified athletes into the level of clinical sleep problem, sleep quantity [9–11]. However, the quality of the research and those classifications were compared to the new ASSQ has been hampered by the investigative methods [3]. scoring system to determine the clinical validity of the In particular, the Pittsburgh Sleep Quality Index (PSQI), questionnaire. This manuscript describes the current ver- which is the primary questionnaire used to assess sleep in sion of the ASSQ and the methodology used to determine athletes [12], has not been validated in an athlete popula- the reliability of the instrument and the clinical validity. In tion [13, 14], is difficult to score, lacks information specific addition, the process of making sleep recommendations to athletes, and shows poor concordance rates with the based on the category of clinical sleep problem is described. clinical assessment of a sleep medicine physician (SMP; [13]). Another tool, the Athlete Sleep Behavior Question- Methods naire (ASBQ), is used to identify maladaptive sleep be- Participants and Procedures haviors in athletes [15]. The ASBQ shows promise to Canadian National Team carded athletes (N = 199) from differentiate sleep behaviors between athletes and controls both senior national teams and lower-level national teams but is still in development to determine valid cut-points participated in the study. The athletes were between the that are not based on the authors’ speculation. Further- ages of 18–36 from 23 different summer and winter sports. more, the ASBQ is not intended to be used as a clinical The corequestionnairewas thesameaspreviously sleep screening tool but instead to inform sleep hygiene published [13]; however, an additional question on caffeine recommendations for athletes. consumption was added, and a subset of athletes had The sport science community considers sleep to be an one additional question on electronic device use, see important part of the recovery process [16]; therefore, it Additional file 1. The consent form was located on the first is important to have a valid and reliable questionnaire pageoftheonlinesurvey(www.surveymonkey.com), and if that can be used as a first-line tool to screen and identify participants continued to complete the survey, it was an in- athletes with clinically relevant sleep problems and dication of their consent to participate. The study was per- possible sleep disorders. This allows quick intervention formed in accordance with the standards of ethics outlined only when necessary and differentiates those who may in the Declaration of Helsinki and approved by the Univer- only require education and behavioral interventions. sity of Calgary Conjoint Health Research Ethics Board. The Athlete Sleep Screening Questionnaire (ASSQ) was developed as a sleep screening tool to detect clinically sig- Clinical Validation Sub-study nificant sleep disturbances and daytime dysfunction and to Following the completion of the initial ASSQ at time 1 provide interventions based on the type and severity of the (T1), 65 athletes were randomized to the clinical validation problem that is detected in an athlete population [13]. The sub-study. Seventy-one percent of the athletes (N =46) details of the initial development of the ASSQ have been could comply with the requirements of the protocol. The previously published [13]. Briefly, a 15-item questionnaire protocol included 2 weeks of wrist-watch actigraphy, which was developed to assess both sleep and circadian factors of is not presented here (Readiband, Fatigue Science, Canada; sleep quantity, sleep quality, insomnia, and chronotype with [17]), an ASSQ completed at time 2 (T2), a standardized atimeframeof “over the recent past.” Answers from the clinical sleep interview with the SMP, a rating of the first seven questions were compiledto createa sleepdiffi- category of clinical sleep problem from the SMP, a more culty score (SDS) which categorized athletes into four cat- detailed follow-up interview if required, recommendations egories of clinical sleep problems—none, mild, moderate, for sleep interventions, and an ASSQ completed at time 3 and severe. The SDS system did not take into consideration (T3), see Fig. 1. chronotype (four questions) or other important factors of Once an athlete was randomized to the clinical validation sleep-disordered breathing and sleep and performance dur- sub-study, they were contacted to determine the actigraphy ing travel, but were used to guide the SMP as to who re- recording period which started after at least 1 day for every quired follow-up and further diagnostic testing. Based on time zone traveled recently to accommodate for any circa- the previous SDS cutoffs, 13% of the 349 athletes studied dian misalignment from recent travel. After the recording were classified as having moderate to severe clinical sleep period, athletes completed the ASSQ T2 to assess problems and required intervention recommendations test-retest reliability which occurred (102 ± 66 days) after Bender et al. Sports Medicine - Open (2018) 4:23 Page 3 of 8 follow-up interview to evaluate the problem further and to discuss the recommendations occurred. The recom- mendations were standardized based on the SMP’s rating of clinical sleep problem and the responses to the modi- fier questions. The recommendations included a general sleep education sheet on sleep quantity, quality, timing, and proper sleep hygiene for all the athletes. Also in- cluded were individualized recommendations depending on their responses to sleep duration, napping activity, in- somnia, and sleep-disordered breathing symptoms. If in- dicated, a travel and jet lag fatigue management education sheet, recommendations for an insomnia self-help book or standardized online CBT-Insomnia program, a circadian re-entrainment program using light therapy and melatonin, and recommendations for fur- ther sleep testing or treatment in their local area were provided. When possible and with the athlete’s consent, the sport physician and lead integrated support team members would also get the results to help monitor the athlete’s progress on a more frequent basis. All but two athletes in the clinical validation sub-study completed an ASSQ T3 approximately 150 ± 67 days after the clinical Fig. 1 Study timeline and protocol. Cumulative days (mean ± SD) elapsed in the study (left dot boxes) between protocol procedures interview to assess if the sleep recommendations helped (right solid boxes) improve sleep as reflected in a reduction in the SDS. T1 but prior (1 ± 3 days) to the structured clinical sleep interview with the SMP. The structured clinical sleep inter- Statistical Analyses view was performed online through videoconferencing or To assess the sample characteristics, descriptive statistics over the phone. The SMP followed a standardized interview were determined for age, sex, sport, status on the senior na- sheet which was based on prior clinical experience working tional team or lower-level national team, years at the with athletes. It included questions about sleep history (e.g., current level, and training season the questionnaire was do they think they have a sleep problem), estimated sleep completed in. The median time taken to complete the parameters (e.g., sleep duration, naps, sleep latency, wake ASSQ (generated from www.surveymonkey.com)was also after sleep onset), sleep disorders (e.g., insomnia, delayed assessed. sleep phase syndrome, sleep-disordered breathing, periodic The internal consistency of the ASSQ items was esti- limb movement, parasomnias, and bruxism), timing of mated using Cronbach’s alpha. Test-retest correlations sleep (e.g., preferred bedtime and wake time, do they see compared the stability of the scale from the T1 to T2, themselves as a morning-type or evening-type), and if the prior to the recommendations taking place. athlete had issues with sleep or performance during travel. To determine if the questions for each scale could be After the interview was completed (average duration 9.9 ± summed for a score, unidimensionality of the latent trait 3 min), the SMP who was blinded to the results from the being measured (sleep difficulty and chronotype) was ASSQ at T1 and T2 rated the category of clinical sleep assessed using principal component analysis (PCA) [18]. problem based on how severe the athletes sleep distur- Exploratory factor analysis (EFA) was performed on both bances were (none, mild, mild to moderate, moderate, the sleep difficulty score and chronotype score. Pearson’s moderate to severe, and severe) and indicated which of the correlation coefficients were estimated between the ori- standardized recommendations were to be communicated ginal sleep difficulty score and the new sleep difficulty to the subject. score. Confirmatory factor analysis (CFA) was performed on both sleep difficulty and chronotype scores. Sleep Intervention Recommendations To determine if the sleep difficulty score and chronotype Athletes were emailed results based on the recommen- score were consistent across different groups, the data from dations from the SMP after the questionnaires were ex- the Canadian National Team athletes was compared to the amined (those athletes not randomized N = 153) or after data from a separate study in N = 1074 competitors from the interviews (N = 46). In those who the SMP classified the London Virgin Money Marathon who completed an ex- as moderate to severe (N = 16), a second more detailed panded version of the ASSQ (manuscript in preparation). Bender et al. Sports Medicine - Open (2018) 4:23 Page 4 of 8 For data from Canada and the UK, the CFA path coeffi- majority of athletes (68%) completed the ASSQ during their cients were compared, as well as the item correlations. competitive season with 24% in pre-season and 8% during Comparisons of level of clinical sleep problem between their rest season. the ASSQ and the SMP were performed using weighted The median time to complete the survey was 5 min kappa [19–21]. Weighted kappa was used as kappa (un- (range 1 to 268 min). There were four athletes who took weighted) does not consider the degree of disagreement. longer than 60 min to complete the survey (73, 186, 191, Reliability was estimated for the ASSQ scoring system and 268 min). It was assumed this was not a true measure with Cronbach’s alpha. Sensitivity, specificity, positive pre- of the time to complete the survey continuously; therefore, dictive value, and negative predictive value [22]wereesti- the median was used as a more appropriate measure of mated from the ASSQ scoring system sleep problem the time taken rather than the average time completed. categories to the SMP categories after the clinical inter- view. Sensitivity was estimated as the number of subjects Internal Consistency determined by the ASSQ scoring system as needing a clin- The internal consistency of the seven ASSQ items that ical intervention from the SMP (moderate and severe) di- made up the SDS was poor at T1 (Cronbach’s alpha = 0.58; vided by the number of subjects needing a clinical 95% CI 0.50 to 0.66). The two napping questions related to intervention as determined by the SMP. Specificity was es- how often the athlete napped and the duration of the nap timated by the number of subjects determined by the correlated poorly with the sleep difficulty score r =0.16 and ASSQ scoring system as not needing a clinical sleep inter- r = 0.04, respectively. Reliability and exploratory factor ana- vention (none, mild categories) divided by the number of lysis were then repeated without these questions. With the subjects not needing a clinical intervention as determined nap questions removed, the internal consistency of the five by the SMP. The positive predictive value was estimated items was acceptable (Cronbach’s alpha = 0.74; 95% CI by dividing the subjects for which there was agreement on 0.69 to 0.79). The average correlation with the total needing a clinical intervention (moderate, severe categor- score was r=0.69for thefiveitems with thelowest ies) by the actual number of subjects needing a clinical correlation for medication use (r = 0.42; item 6) and intervention as determined by the SMP. The negative pre- the highest correlation for satisfaction with quality of dictive value was estimated by dividing the subjects for Table 1 Participant characteristics which there was agreement on not needing a clinical intervention by the actual number of subjects not needing Sport N Sex F Age Years at level a clinical intervention as determined by the SMP. N (%) Mean ± SD Mean ± SD To assess the impact of the sleep recommendations, Alpine skiing 16 6 (38) 20.6 ± 3.0 2.8 ± 2.3 simple paired t tests were performed at T1 (baseline) Athletics 26 19 (74) 26.3 ± 4.1 4.5 ± 3.2 and T3 (post-recommendations) for the SDS and the Basketball 9 9 (100) 25.4 ± 5.1 6.3 ± 4.9 categories of clinical sleep problem. Biathlon 10 5 (50) 25.4 ± 2.9 5.1 ± 3.1 All statistical analyses were performed with R 3.3.3 (R Canoe/kayak 1 0 (0) 31 11 Core Team, Vienna, Austria). Descriptive statistics, Cron- bach’s alpha exploratory factor analysis and correlation es- Cross-country skiing 10 4 (40) 25.7 ± 4.9 5.3 ± 3.9 timates were performed with the Psych package [23]. CFA Cycling 6 5 (83) 27.3 ± 4.5 6 ± 4.8 were performed with the Lavaan package [24]. Inter-rater Diving 4 1 (25) 23.3 ± 3.1 5.0 ± 1.4 estimates and weighted kappa were performed with the Field hockey 22 22 (100) 22.5 ± 2.8 3.8 ± 2.3 IRR package [25]. Figure skating 12 6 (50) 24.5 ± 4.3 5.7 ± 3.1 Freestyle skiing 45 22 (49) 22.8 ± 3.6 4.6 ± 2.8 Results Sample Characteristics Golf 14 5 (36) 21.6 ± 2.5 3.2 ± 2.2 The ASSQ was administered to 199 Canadian National LT speed skating 1 0 (0) 24 4 Team carded athletes. The athletes were between the ages Luge 5 4 (80) 27.4 ± 6.8 6.8 ± 5.2 of 18–36 (mean age 24.0 ± 4.2 years) with 62% (N = 123) of Soccer 6 6 (100) 27.7 ± 4.9 6.8 ± 5.7 the sample females. The sample included representation of Swimming 3 2 (66) 20.1 ± 3.7 3.7 ± 3.8 athletes from 23 different summer and winter sports, see Triathlon 5 4 (80) 21.2 ± 3.3 2.3 ± 1.2 Table 1. Eighty-one percent (N =162) of the sample was on the senior national team of their sport with the remainder Wrestling 2 2 (100) 29.5 ± 6.4 7.5 ± 3.5 (N = 37) carded but on lower-level national teams. F female, LT long track One athlete from modern pentathlon included in athletics, one athlete from Sixty-seven percent of the sample (N = 133) had 5 years or skeleton included in luge, one athlete from rugby included in soccer, two less experience at the national team level, and 9% (N =19) athletes from ski jumping and two athletes from snowboarding included in had been at their current level for 10 years or more. The freestyle skiing Bender et al. Sports Medicine - Open (2018) 4:23 Page 5 of 8 sleep (r = 0.85, item 3). The factor loadings based on medication). The largest correlation was between item 3— the EFA (one factor, varimax rotation) for the SDS being satisfied with sleep quality—and item 5—trouble stay- five items were item 1 = 0.56, item 3 = 0.87, item 4 ing asleep. For the London Marathon data, the average cor- = 0.57, item 5 = 0.68, and item 6 = 0.27, with 40% of relation was 0.25. The smallest and largest correlations the variance explained. Although item 6 loaded were the same relationships as those seen in the Canadian weakly on the sleep difficulty factor, it was not National Team data. dropped from the scale as it positively contributed to the reliability measure. The PCA of the new SDS revealed Clinical Validity a strong first component (2.48) with all other components Cut-points were made to the new 5-item SDS to having eigenvalues of less than 1.0, which indicated the categorize athletes into clinical sleep problem of none scale is unidimensional and can be summed for a score. (0–4), mild (5–7), moderate (8–10), and severe (11–17). The correlation between the new 5-item SDS with the When the SMP’s ratings of level of the clinical problem 7-item SDS was strong (r = 0.97, 95% CI 0.96 to 0.98). The were compared to the ASSQ scoring system (see Add- duration of the nap question was dropped from the itional file 2), the groups were not shown to be different questionnaire, but the nap frequency question (see (chi-square = 0.23, df = 3, p = 0.97), see Table 2. Agree- Additional file 1, item 2) was kept in the questionnaire to ment between the two rating systems was good (Cohen’s inform sleep education strategies. weighted kappa = 0.84, z = 5.68, p < 0.01). The sensitivity The internal consistency of the four questions from the of the ASSQ to detect clinically meaningful sleep prob- chronotype score (items 7–10) was acceptable (Cronbach’s lems (moderate to severe category) was 81% (95% CI alpha = 0.73; 95% CI 0.67 to 0.79). The average correlation 54.4 to 96.0%) when compared to the SMP ratings. The with the total score was r = 0.77 for the four items with specificity of the ASSQ scoring system to categorize ath- the lowest correlation for preferred time to bed (r =0.64; letes as not needing follow-up with the SMP (none to item 10) and the highest correlation for self-reported mild clinical sleep problem) was 93% (95% CI 77.9 to chronotype (r = 0.86, item 9). The factor loadings for the 99.2%). The positive predictive value of the ASSQ scor- chronotype four items based on the EFA (one factor, vari- ing system was 92% (95% CI 63.1 to 98.8%). The nega- max rotation) were item 7 = 0.66, item 8 = 0.52, item 9 = tive predictive value of the ASSQ scoring system was 0.88, and item 10 = 0.51, with 44% of the variance ex- 90% (95% CI 77.1 to 96.3%). When the ASSQ scoring plained. The PCA of the chronotype items revealed a system was applied to the entire sample, 25.1% of ath- strong first component (2.23) with all other components letes were identified as having a moderate or severe level having eigenvalues of less than 1.0 which indicated the of clinical sleep problem and recommendations were scale is unidimensional and can be summed for a score. made for further follow-up. The time between completing ASSQ T1 to T2 was 101 ± 66.2 days. There was a strong relationship (r = Impact on SDS After Sleep Recommendations 0.86, 95% CI 0.75–0.92) for the 5-item SDS from T1 to For all athletes in the sub-study who completed T3 (N = T2, indicating good stability of the SDS. There was a 44), there was an average reduction of SDS from T1 to strong relationship (r = 0.78, 95% CI 0.63–0.87) for the T3 of 1.5 points (t = 1.93, df = 80.72, p = 0.06). When the 4-item chronotype factor from T1 to T2 indicating good changes for each of the groups were assessed (see Fig. 2), stability of the chronotype score. the athletes who were in the none category did not im- prove after the recommendations (t = 0.78, df = 17, p = Stability of the ASSQ Across Populations 0.45). This is likely because there was no clinical sleep CFA revealed comparable factor loadings on the SDS items problem present. Those classified as having a mild for the Canadian National Team athletes to the London Marathon runners respectively (0.56 vs 0.42, item 1; 0.88 vs Table 2 ASSQ scoring system vs. sleep medicine physician 0.82, item 3; 0.57 vs 0.39, item 4; 0.68 vs 0.63, item 5; 0.27 ratings vs 0.25, item 6). The comparative fit index (CFI) was 0.98, ASSQ scoring system and the root mean square error of approximation (RMSEA) SMP rating None Mild Moderate Severe Total was 0.06 (95% CI 0.00 to 0.13). There was also good stabil- None 15 4 0 0 19 ity across the populations for the chronotype score. CFA re- Mild 3 6 2 0 11 vealed comparable factor loadings for both populations Moderate 0 3 9 1 13 (0.65 vs 0.52, item 7; 0.52 vs 0.52, item 8; 0.88 vs 0.90, item 9; 0.51 vs 0.43, item 10), with a CFI of 0.95 and a RMSEA Severe 0 0 1 2 3 of 0.15 (95% CI 0.07 to 0.24). For the Canadian data, the Total 18 13 12 3 average correlation was 0.35. The smallest correlation was Categories of clinical sleep problem between the sleep medicine physician between item 1 (sleep quantity) and item 6 (use of sleep (rows) and the ASSQ (columns). Perfect agreement is along the diagonal Bender et al. Sports Medicine - Open (2018) 4:23 Page 6 of 8 to determine the degree of the sleep problem and guide the intervention strategy; see Additional file 2. The other three items on napping frequency (item 2), caffeine use (item 15), and electronic device use (item 16) were not part of the scoring system but were kept in the ASSQ to inform sleep optimization strategies. Another objective of the current study was to assess the psychometric properties of the ASSQ. After the two nap questions were removed from the SDS, the internal consistency of the new 5-item SDS was acceptable. Test-retest reliability from T1 to T2 over a period of 101 days showed good stability (r = 0.86). The ASSQ also was stable across two different populations when the Can- adian National Team data were compared to a larger set Fig. 2 Changes in sleep difficulty score for each clinical sleep of runners (N = 1074) from the 2016 London Virgin problem category (none, n = 18; mild, n = 12; moderate, n = 11, and Money Marathon. severe, n = 3) from time 1 (T1) at baseline to time 3 (T3) after the sleep intervention recommendations In the current study, we found that 25% of athletes in the sample were identified as having clinically relevant sleep problems. This is similar to Tuomilehto et al. who clinical sleep problem had an average drop of 1.4 points on found approximately 21% of a sample of 107 professional the SDS, but this was not statistically significant (t = 0.78, Finnish hockey players have a sleep disorder as verified by df = 17, p = 0.11). Athletes who had a moderate (N = 11) or polysomnography [6]. However, when compared to stud- severe (N = 3) level of clinical sleep problem showed the ies which utilized the PSQI [12], our prevalence of athletes greatest improvement after the recommendations with with sleep problems was much lower than previous stud- an average drop of 3.9 points on the SDS (t =5.75, df ies which showed 40–50% of athletes had poor sleep [1, 4, = 13, p <0.01), see Fig. 2. 5, 7, 8]. A recent study confirmed poor concordance rate of the PSQI with the sleep evaluation of athletes by a SMP Discussion [13]. The discrepancies between the ASSQ and the PSQI The primary objective of this study was to assess the clin- could be due to the PSQI not being validated in an athlete ical validity of the ASSQ. After a standardized clinical population. Athletes are exposed to extensive monitoring sleep interview, the SMP categorized athletes into levels of of symptoms and could be more sensitive to reporting sleep problem and this was compared to the ASSQ rating symptoms than the general population [14]. This has im- system. There was no significant difference between the portant implications for both the research methodology ratings of the ASSQ and the SMP, which showed good and the clinical use of the PSQI in an athlete population. agreement between the categories, see Table 2. The ASSQ Over-identifying athletes that need clinical sleep interven- had good specificity of 93% but only an acceptable sensi- tions is inefficient and expensive, and those athletes with tivity of 81%. The measure of sensitivity did not take into more severe sleep issues may not get interventions in a consideration the modifiers which is the second part of timely manner. The ASSQ can be easily deployed by the the ASSQ’s scoring system, see Additional file 2.In 10 out athletes’ support staff and can quickly identify those who of 11 cases where the SMP rated the athlete as having a need further assessment and treatment. By utilizing the higher category of sleep problem than the ASSQ, there proper sleep intervention recommendations, athletes can were modifiers present. The reason the sleep-disordered begin to reduce sleep disturbances and optimize sleep. breathing and evening-type modifiers were not included Additionally, the sport and sleep science communities in the SDS was because the psychometric properties of now have a valid and reliable sleep screening tool to use in the questionnaire would be negatively impacted because this unique population. of a low prevalence of these problems occurring in ath- letes [26, 27]. However, case finding is critical because of Limitations the clinical significance, so having these as a part of the The most significant methodological limitation of this study secondary scoring system is important. For the questions was the choice to use one SMP as the “gold standard” by related to travel, this was not included in the SDS because which the clinical validity of the ASSQ was evaluated. not all athletes travel. It was included as a modifier to Ideally, the study would have used multiple SMPs to rate identify athletes who could benefit from specific interven- each athlete and subsequently perform inter-rater reliability tions to help sleep disturbance during travel. Therefore, testing. This research did not include another SMP because the SDS in conjunction with the modifiers should be used of the limited number of SMPs who specialize in evaluating Bender et al. Sports Medicine - Open (2018) 4:23 Page 7 of 8 sleep disturbances and sleep-related dysfunction in elite applicable to certain athlete groups (e.g., travel). They athletes. The decision to begin the exploration of the clin- are important to provide specific education and inter- ical validity of the ASSQ using one SMP allowed the re- vention recommendations. searchers to start the process, and we certainly encourage further validity and reliability testing of the ASSQ. Sleep-Disordered Breathing If an athlete answers yes Although the chronotype score showed good psycho- to item 13 (loud snoring) or item 14 (sleep apnea), they metric properties, one limitation in this study was that it should be further evaluated. was not validated with existing chronotype question- naires or biological markers of circadian phase. Future Travel If the athlete answers yes to item 11 (sleep dis- research could test the ASSQ chronotype score against turbance), education on travel management is recom- existing questionnaires for cut-points of morningness mended. If the athlete answers yes to item 12 and eveningness and verify these cut-points against bio- (performance issues), the problem is likely more serious logical markers of circadian phase. and may require further assessment and treatment. Another limitation of our study was the lack of object- ive markers of sleep disturbance. The protocol did in- Chronotype Sleep difficulty is more common in ath- clude actigraphy over a 2-week period; however, the letes who are evening types. Add the scores from purpose of the study was to examine the clinical validity items 7–10 to get the chronotype score for evening- of the questionnaire, which actigraphy cannot assess. ness. Totals ≤ 4 indicate the athlete is an evening type Polysomnography may have been more appropriate to and may require further assessment and treatment confirm the presence of sleep disorders but is typically (e.g., bright light therapy, melatonin). only one night of data whereas the questionnaire asks about sleep patterns across a much longer period (“the Items of Interest recent past”). In addition, the feasibility of using poly- Items of interest use the responses to specific items to somnography in this study would have been extremely inform sleep optimization strategies. For example, if an difficult because athletes participated from places across athlete is only getting 6–7 h of sleep (item 1), and not Canada, and a minority of the athletes were training and napping frequently throughout the week (item 2), a rec- competing in locations around the world. Again, the ommendation to increase nighttime sleep duration and purpose of the questionnaire is to identify athletes need- napping would be warranted. ing further sleep assessment and is not intended to diag- nose athletes with sleep disorders. Conclusions The psychometric properties of the data (reliability, Instructions for ASSQ Usage (See Additional file 2) test-retest, and association with independent expert It is recommended to assess the SDS first (items 1, 3, 4, judgment) suggest strongly that the ASSQ can detect 5, and 6), then the modifiers of sleep-disordered breath- clinically meaningful sleep disturbances in an elite ing (items 13 and 14), travel (items 11 and 12), and athlete population. The ASSQ is easy to administer, chronotype (items 7–10), and finally evaluate the items quick to complete, and can be scored remotely. Most of interest (e.g., item 2, 15, 16) to inform more specific importantly, it provides support staff the capability to sleep optimization strategies. understand when further follow-up with a SMP or qualified sleep professional is necessary. We found Sleep Difficulty Score (SDS) that theprevalenceof clinicallymeaningfulsleep dis- The SDS is used to classify athletes into the level of clin- turbances was much lower than with previously used ical sleep problem (none, mild, moderate, severe) based tools and caution researchers and clinicians about on the response to items 1, 3, 4, 5, and 6 with poorer sleep using tools that have not been properly validated in indicative of a higher score. Responses to those items are an athlete population. With sleep screening, recom- summed, and scores of 0–4 are classified as the “none” mendations, and proper follow-up, athletes can im- category, scores of 5–7 are classified as the “mild” cat- prove their sleep for the benefit of better health and egory, scores of 8–10 are classified as the moderate cat- performance. egory, and scores of 11–17 are classified as the severe category. Those athletes classified as having a moderate or Additional File severe sleep problem should be further evaluated. Additional file 1: Athlete Sleep Screening Questionnaire (ASSQ). (PDF 13 kb) Modifiers Additional file 2: ASSQ sleep difficulty score (SDS) scoring key. The modifiers are not included in the SDS because (DOCX 22 kb) they occur less frequently and are not always Bender et al. Sports Medicine - Open (2018) 4:23 Page 8 of 8 Abbreviations Received: 18 January 2018 Accepted: 24 May 2018 ASSQ: Athlete Sleep Screening Questionnaire; CFA: Confirmatory factor analysis; CFI: Comparative fit index; EFA: Exploratory factor analysis; PCA: Principal component analysis; RMSEA: Root mean square error of References approximation; SDS: Sleep difficulty score; SMP: Sleep medicine physician; 1. Samuels C. Sleep, recovery, and performance: the new frontier in high- T1: Time 1; T2: Time 2; T3: Time 3 performance athletics. Phys Med Rehabil Clin N Am. 2009;20(1):149–59. 2. Venter RE. Role of sleep in performance and recovery of athletes: a review article. South Afr J Res Sport Phys Ed Recreation. 2012;34(1):167–84. Acknowledgements 3. Gupta L, Morgan K, Gilchrist S. Does elite sport degrade sleep quality? A We thank the athletes for participating in the study and the funding sources systematic review. Sports Med. 47(7):1317–33. for their support. 4. Fietze I, Strauch J, Holzhausen M, Glos M, Theobald C, Lehnkering H, et al. Sleep quality in professional ballet dancers. Chronobiol Int. 2009;26(6):1249–62. Funding https://doi.org/10.3109/07420520903221319. The study was supported by Mitacs and Own the Podium through the 5. Swinbourne R, Gill N, Vaile J, Smart D. Prevalence of poor sleep quality, Mitacs Accelerate postdoctoral internship awarded to Dr. Bender to work on sleepiness and obstructive sleep apnoea risk factors in athletes. Eur J Sport this study. Sci. 2016;16(7):850–8. 6. Tuomilehto H, Vuorinen V-P, Penttilä E, Kivimäki M, Vuorenmaa M, Venojärvi M, et al. Sleep of professional athletes: underexploited potential to improve Availability of Data and Materials health and performance. J Sports Sci. 2016:1–7. Data could be made available upon request. 7. Knufinke M, Nieuwenhuys A, Geurts SA, Coenen AM, Kompier MA. Self- reported sleep quantity, quality and sleep hygiene in elite athletes. J Sleep Authors’ Contributions Res. 2017;27(1):78–85. AB, DL, PW, and CS contributed to the study design and interpretation of 8. Monma T, Ando A, Asanuma T, Yoshitake Y, Yoshida G, Miyazawa T et al. the results. AB and CS contributed to the data collection. AB and DL Sleep disorder risk factors among student athletes. Sleep. Med. 2018;44:76-81. contributed to the data analysis. AB, DL, and CS contributed to the writing 9. Lastella M, Roach GD, Halson SL, Sargent C. Sleep/wake behaviours of elite and revision of the manuscript. All co-authors meet the criteria for author- athletes from individual and team sports. Eur J Sport Sci. 2015;15(2):94–100. ship, have seen and agreed upon the contents of the final manuscript, and 10. Leeder J, Glaister M, Pizzoferro K, Dawson J, Pedlar C. Sleep duration and certify that the submission is original work. All authors read and approved quality in elite athletes measured using wristwatch actigraphy. J Sports Sci. the final manuscript. 2012;30(6):541–5. 11. Sargent C, Halson S, Roach GD. Sleep or swim? Early-morning training severely restricts the amount of sleep obtained by elite swimmers. Eur J Authors’ Information Sport Sci. 2014;14(sup1):S310–S5. Dr. Amy Bender is a Clinical Program Director of Athlete Services at CSHP, 12. Buysse DJ, Reynolds CF, Monk TH, Berman SR, Kupfer DJ. The Pittsburgh Calgary, AB, Canada. She is also an Adjunct Assistant Professor in the Faculty of sleep quality index: a new instrument for psychiatric practice and research. Kinesiology at the University of Calgary, Calgary, AB, Canada. Psychiatry Res. 1989;28(2):193–213. Mr. Doug Lawson has a graduate degree in Medical Science (Medical 13. Samuels C, James L, Lawson D, Meeuwisse W. The Athlete Sleep Screening Education) and helps with statistical analyses at the CSHP, Calgary, AB, Canada. Questionnaire: a new tool for assessing and managing sleep in elite Dr. Penny Werthner is a Professor and Dean of the Faculty of Kinesiology at athletes. Br J Sports Med. 2016;50(7):418–22. https://doi.org/10.1136/ the University of Calgary, Calgary, AB, Canada. bjsports-2014-094332. Dr. Charles Samuels is a Medical Director at the CSHP, Calgary, AB, Canada. 14. Bender AM, Samuels CH. Comment on: “does elite sport degrade sleep He is also an Assistant Clinical Professor in the Faculty of Medicine and quality? A systematic review”. Sports medicine (Auckland, NZ). 2017. Adjunct Professor in the Faculty of Kinesiology at the University of Calgary, 15. Driller MW, Mah CD, Halson SL. Development of the athlete sleep behavior Calgary, AB, Canada. questionnaire: a tool for identifying maladaptive sleep practices in elite athletes. Sleep Sci. 2018;11(1):37–44. Ethics Approval and Consent to Participate 16. Halson SL. Nutrition, sleep and recovery. Eur J Sport Sci. 2008;8(2):119–26. The study was performed in accordance with the standards of ethics 17. Dunican IC, Murray K, Slater JA, Maddison KJ, Jones MJ, Dawson B, et al. outlined in the Declaration of Helsinki. This study was approved by the Laboratory and home comparison of wrist-activity monitors and University of Calgary Conjoint Health Research Ethics Board, and all polysomnography in middle-aged adults. Sleep Biol Rhythms. 2018;16(1):85–97. participants gave informed consent. 18. Cooper JC. Factor analysis: an overview. Am Stat. 1983;37(2):141–7. 19. Cohen J. Weighted kappa: nominal scale agreement provision for scaled disagreement or partial credit. Psychol Bull. 1968;70(4):213. Consent for Publication 20. Sim J, Wright CC. The kappa statistic in reliability studies: use, interpretation, Amy Bender, Doug Lawson, Penny Werthner, and Charles Samuels grant the and sample size requirements. Phys Ther. 2005;85(3):257. right to Sports Medicine Open to publish this manuscript. 21. Viera AJ, Garrett JM. Understanding interobserver agreement: the kappa statistic. Fam Med. 2005;37(5):360–3. Competing Interests 22. Brenner H, Gefeller O. Variation of sensitivity, specificity, likelihood ratios and The authors, Doug Lawson and Penny Werthner, declare that they have no predictive values with disease prevalence. Stat Med. 1997;16(9):981–91. competing interests. Charles Samuels is the principal owner of the Centre for 23. Revelle W. Psych: procedures for psychological, psychometric, and Sleep and Human Performance (CSHP). Amy Bender is employed full-time by personality research. Evanston: Northwestern University; 2014. p. 165. the CSHP. As a result of this research, a method for sleep screening and pro- 24. Lavaan RY. An R package for structural equation model and more. Version 0. viding recommendations for athletes will be developed and commercialized 5–12 (BETA). Ghent: Ghent University; 2012. for profit. 25. Gamer M, Lemon J, Gamer MM, Robinson A, Kendall’s W. Package ‘irr’. Various coefficients of interrater reliability and agreement. 2012. 26. Rice TB, Dunn RE, Lincoln AE, Tucker AM, Vogel RA, Heyer RA, et al. Sleep- Publisher’sNote disordered breathing in the National Football League. Sleep. 2010;33(6):819–24. Springer Nature remains neutral with regard to jurisdictional claims in 27. Lastella M, Roach GD, Halson SL, Sargent C. The chronotype of elite athletes. published maps and institutional affiliations. J Hum Kinet. 2016;54(1):219–25. Author details Centre for Sleep & Human Performance, 106-51 Sunpark Dr. SE, Calgary, AB T2X 3V4, Canada. Faculty of Kinesiology, University of Calgary, Calgary, AB, Canada. Faculty of Chiropractic, D’Youville College, Buffalo, NY, USA. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Sports Medicine - Open Springer Journals

The Clinical Validation of the Athlete Sleep Screening Questionnaire: an Instrument to Identify Athletes that Need Further Sleep Assessment

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Springer Journals
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Copyright © 2018 by The Author(s).
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Medicine & Public Health; Sports Medicine
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2199-1170
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10.1186/s40798-018-0140-5
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Abstract

Background: Previous research has established that general sleep screening questionnaires are not valid and reliable in an athlete population. The Athlete Sleep Screening Questionnaire (ASSQ) was developed to address this need. While the initial validation of the ASSQ has been established, the clinical validity of the ASSQ has yet to be determined. The main objective of the current study was to evaluate the clinical validity of the ASSQ. Methods: Canadian National Team athletes (N = 199; mean age 24.0 ± 4.2 years, 62% females; from 23 sports) completed the ASSQ. A subset of athletes (N = 46) were randomized to the clinical validation sub-study which required subjects to complete an ASSQ at times 2 and 3 and to have a clinical sleep interview by a sleep medicine physician (SMP) who rated each subjects’ category of clinical sleep problem and provided recommendations to improve sleep. To assess clinical validity, the SMP category of clinical sleep problem was compared to the ASSQ. Results: The internal consistency (Cronbach’s alpha = 0.74) and test-retest reliability (r = 0.86) of the ASSQ were acceptable. The ASSQ demonstrated good agreement with the SMP (Cohen’s kappa = 0.84) which yielded a diagnostic sensitivity of 81%, specificity of 93%, positive predictive value of 87%, and negative predictive value of 90%. There were 25.1% of athletes identified to have clinically relevant sleep disturbances that required further clinical sleep assessment. Sleep improved from time 1 at baseline to after the recommendations at time 3. Conclusions: Sleep screening athletes with the ASSQ provides a method of accurately determining which athletes would benefit from preventative measures and which athletes suffer from clinically significant sleep problems. The process of sleep screening athletes and providing recommendations improves sleep and offers a clinical intervention output that is simple and efficient for teams and athletes to implement. Keywords: Elite athletes, Sleep disturbances, Sleep interventions Key Points  Twenty-five percent of athletes were identified as needing further clinical sleep assessment. This is When athletes were rated based on the category of much lower than general sleep screening clinical sleep problem, there was good agreement questionnaires which have not been validated in elite between the ASSQ scoring system and the sleep athletes. medicine physician.  Sleep improved from baseline to after the sleep recommendations in those athletes with moderate to severe sleep difficulty. The ASSQ provides a valid and reliable tool to identify athletes for further sleep * Correspondence: [email protected] assessment and is easy and efficient for athletes and Centre for Sleep & Human Performance, 106-51 Sunpark Dr. SE, Calgary, AB the support team to administer. T2X 3V4, Canada Faculty of Kinesiology, University of Calgary, Calgary, AB, Canada Full list of author information is available at the end of the article © The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Bender et al. Sports Medicine - Open (2018) 4:23 Page 2 of 8 Background from a SMP [13]. However, no formal analyses of the clin- Sleep is a fundamental biological process that facilitates ical validity of the ASSQ were performed. recovery from the mental and physical demands of To address the lack of clinical validation, 199 Canadian high-performance sport [1, 2]. Recently, there has been a National Team athletes completed the ASSQ with 46 ath- proliferation of research exploring how sleep impacts re- letes randomized to partake in a standardized clinical sleep covery, training, and performance in elite athletes. Previ- interview from a SMP who was blinded to the ASSQ re- ous research has indicated elite athletes have a high sponses and SDS. At the end of the interview, the SMP prevalence of poor sleep quality [1, 3–8] and insufficient classified athletes into the level of clinical sleep problem, sleep quantity [9–11]. However, the quality of the research and those classifications were compared to the new ASSQ has been hampered by the investigative methods [3]. scoring system to determine the clinical validity of the In particular, the Pittsburgh Sleep Quality Index (PSQI), questionnaire. This manuscript describes the current ver- which is the primary questionnaire used to assess sleep in sion of the ASSQ and the methodology used to determine athletes [12], has not been validated in an athlete popula- the reliability of the instrument and the clinical validity. In tion [13, 14], is difficult to score, lacks information specific addition, the process of making sleep recommendations to athletes, and shows poor concordance rates with the based on the category of clinical sleep problem is described. clinical assessment of a sleep medicine physician (SMP; [13]). Another tool, the Athlete Sleep Behavior Question- Methods naire (ASBQ), is used to identify maladaptive sleep be- Participants and Procedures haviors in athletes [15]. The ASBQ shows promise to Canadian National Team carded athletes (N = 199) from differentiate sleep behaviors between athletes and controls both senior national teams and lower-level national teams but is still in development to determine valid cut-points participated in the study. The athletes were between the that are not based on the authors’ speculation. Further- ages of 18–36 from 23 different summer and winter sports. more, the ASBQ is not intended to be used as a clinical The corequestionnairewas thesameaspreviously sleep screening tool but instead to inform sleep hygiene published [13]; however, an additional question on caffeine recommendations for athletes. consumption was added, and a subset of athletes had The sport science community considers sleep to be an one additional question on electronic device use, see important part of the recovery process [16]; therefore, it Additional file 1. The consent form was located on the first is important to have a valid and reliable questionnaire pageoftheonlinesurvey(www.surveymonkey.com), and if that can be used as a first-line tool to screen and identify participants continued to complete the survey, it was an in- athletes with clinically relevant sleep problems and dication of their consent to participate. The study was per- possible sleep disorders. This allows quick intervention formed in accordance with the standards of ethics outlined only when necessary and differentiates those who may in the Declaration of Helsinki and approved by the Univer- only require education and behavioral interventions. sity of Calgary Conjoint Health Research Ethics Board. The Athlete Sleep Screening Questionnaire (ASSQ) was developed as a sleep screening tool to detect clinically sig- Clinical Validation Sub-study nificant sleep disturbances and daytime dysfunction and to Following the completion of the initial ASSQ at time 1 provide interventions based on the type and severity of the (T1), 65 athletes were randomized to the clinical validation problem that is detected in an athlete population [13]. The sub-study. Seventy-one percent of the athletes (N =46) details of the initial development of the ASSQ have been could comply with the requirements of the protocol. The previously published [13]. Briefly, a 15-item questionnaire protocol included 2 weeks of wrist-watch actigraphy, which was developed to assess both sleep and circadian factors of is not presented here (Readiband, Fatigue Science, Canada; sleep quantity, sleep quality, insomnia, and chronotype with [17]), an ASSQ completed at time 2 (T2), a standardized atimeframeof “over the recent past.” Answers from the clinical sleep interview with the SMP, a rating of the first seven questions were compiledto createa sleepdiffi- category of clinical sleep problem from the SMP, a more culty score (SDS) which categorized athletes into four cat- detailed follow-up interview if required, recommendations egories of clinical sleep problems—none, mild, moderate, for sleep interventions, and an ASSQ completed at time 3 and severe. The SDS system did not take into consideration (T3), see Fig. 1. chronotype (four questions) or other important factors of Once an athlete was randomized to the clinical validation sleep-disordered breathing and sleep and performance dur- sub-study, they were contacted to determine the actigraphy ing travel, but were used to guide the SMP as to who re- recording period which started after at least 1 day for every quired follow-up and further diagnostic testing. Based on time zone traveled recently to accommodate for any circa- the previous SDS cutoffs, 13% of the 349 athletes studied dian misalignment from recent travel. After the recording were classified as having moderate to severe clinical sleep period, athletes completed the ASSQ T2 to assess problems and required intervention recommendations test-retest reliability which occurred (102 ± 66 days) after Bender et al. Sports Medicine - Open (2018) 4:23 Page 3 of 8 follow-up interview to evaluate the problem further and to discuss the recommendations occurred. The recom- mendations were standardized based on the SMP’s rating of clinical sleep problem and the responses to the modi- fier questions. The recommendations included a general sleep education sheet on sleep quantity, quality, timing, and proper sleep hygiene for all the athletes. Also in- cluded were individualized recommendations depending on their responses to sleep duration, napping activity, in- somnia, and sleep-disordered breathing symptoms. If in- dicated, a travel and jet lag fatigue management education sheet, recommendations for an insomnia self-help book or standardized online CBT-Insomnia program, a circadian re-entrainment program using light therapy and melatonin, and recommendations for fur- ther sleep testing or treatment in their local area were provided. When possible and with the athlete’s consent, the sport physician and lead integrated support team members would also get the results to help monitor the athlete’s progress on a more frequent basis. All but two athletes in the clinical validation sub-study completed an ASSQ T3 approximately 150 ± 67 days after the clinical Fig. 1 Study timeline and protocol. Cumulative days (mean ± SD) elapsed in the study (left dot boxes) between protocol procedures interview to assess if the sleep recommendations helped (right solid boxes) improve sleep as reflected in a reduction in the SDS. T1 but prior (1 ± 3 days) to the structured clinical sleep interview with the SMP. The structured clinical sleep inter- Statistical Analyses view was performed online through videoconferencing or To assess the sample characteristics, descriptive statistics over the phone. The SMP followed a standardized interview were determined for age, sex, sport, status on the senior na- sheet which was based on prior clinical experience working tional team or lower-level national team, years at the with athletes. It included questions about sleep history (e.g., current level, and training season the questionnaire was do they think they have a sleep problem), estimated sleep completed in. The median time taken to complete the parameters (e.g., sleep duration, naps, sleep latency, wake ASSQ (generated from www.surveymonkey.com)was also after sleep onset), sleep disorders (e.g., insomnia, delayed assessed. sleep phase syndrome, sleep-disordered breathing, periodic The internal consistency of the ASSQ items was esti- limb movement, parasomnias, and bruxism), timing of mated using Cronbach’s alpha. Test-retest correlations sleep (e.g., preferred bedtime and wake time, do they see compared the stability of the scale from the T1 to T2, themselves as a morning-type or evening-type), and if the prior to the recommendations taking place. athlete had issues with sleep or performance during travel. To determine if the questions for each scale could be After the interview was completed (average duration 9.9 ± summed for a score, unidimensionality of the latent trait 3 min), the SMP who was blinded to the results from the being measured (sleep difficulty and chronotype) was ASSQ at T1 and T2 rated the category of clinical sleep assessed using principal component analysis (PCA) [18]. problem based on how severe the athletes sleep distur- Exploratory factor analysis (EFA) was performed on both bances were (none, mild, mild to moderate, moderate, the sleep difficulty score and chronotype score. Pearson’s moderate to severe, and severe) and indicated which of the correlation coefficients were estimated between the ori- standardized recommendations were to be communicated ginal sleep difficulty score and the new sleep difficulty to the subject. score. Confirmatory factor analysis (CFA) was performed on both sleep difficulty and chronotype scores. Sleep Intervention Recommendations To determine if the sleep difficulty score and chronotype Athletes were emailed results based on the recommen- score were consistent across different groups, the data from dations from the SMP after the questionnaires were ex- the Canadian National Team athletes was compared to the amined (those athletes not randomized N = 153) or after data from a separate study in N = 1074 competitors from the interviews (N = 46). In those who the SMP classified the London Virgin Money Marathon who completed an ex- as moderate to severe (N = 16), a second more detailed panded version of the ASSQ (manuscript in preparation). Bender et al. Sports Medicine - Open (2018) 4:23 Page 4 of 8 For data from Canada and the UK, the CFA path coeffi- majority of athletes (68%) completed the ASSQ during their cients were compared, as well as the item correlations. competitive season with 24% in pre-season and 8% during Comparisons of level of clinical sleep problem between their rest season. the ASSQ and the SMP were performed using weighted The median time to complete the survey was 5 min kappa [19–21]. Weighted kappa was used as kappa (un- (range 1 to 268 min). There were four athletes who took weighted) does not consider the degree of disagreement. longer than 60 min to complete the survey (73, 186, 191, Reliability was estimated for the ASSQ scoring system and 268 min). It was assumed this was not a true measure with Cronbach’s alpha. Sensitivity, specificity, positive pre- of the time to complete the survey continuously; therefore, dictive value, and negative predictive value [22]wereesti- the median was used as a more appropriate measure of mated from the ASSQ scoring system sleep problem the time taken rather than the average time completed. categories to the SMP categories after the clinical inter- view. Sensitivity was estimated as the number of subjects Internal Consistency determined by the ASSQ scoring system as needing a clin- The internal consistency of the seven ASSQ items that ical intervention from the SMP (moderate and severe) di- made up the SDS was poor at T1 (Cronbach’s alpha = 0.58; vided by the number of subjects needing a clinical 95% CI 0.50 to 0.66). The two napping questions related to intervention as determined by the SMP. Specificity was es- how often the athlete napped and the duration of the nap timated by the number of subjects determined by the correlated poorly with the sleep difficulty score r =0.16 and ASSQ scoring system as not needing a clinical sleep inter- r = 0.04, respectively. Reliability and exploratory factor ana- vention (none, mild categories) divided by the number of lysis were then repeated without these questions. With the subjects not needing a clinical intervention as determined nap questions removed, the internal consistency of the five by the SMP. The positive predictive value was estimated items was acceptable (Cronbach’s alpha = 0.74; 95% CI by dividing the subjects for which there was agreement on 0.69 to 0.79). The average correlation with the total needing a clinical intervention (moderate, severe categor- score was r=0.69for thefiveitems with thelowest ies) by the actual number of subjects needing a clinical correlation for medication use (r = 0.42; item 6) and intervention as determined by the SMP. The negative pre- the highest correlation for satisfaction with quality of dictive value was estimated by dividing the subjects for Table 1 Participant characteristics which there was agreement on not needing a clinical intervention by the actual number of subjects not needing Sport N Sex F Age Years at level a clinical intervention as determined by the SMP. N (%) Mean ± SD Mean ± SD To assess the impact of the sleep recommendations, Alpine skiing 16 6 (38) 20.6 ± 3.0 2.8 ± 2.3 simple paired t tests were performed at T1 (baseline) Athletics 26 19 (74) 26.3 ± 4.1 4.5 ± 3.2 and T3 (post-recommendations) for the SDS and the Basketball 9 9 (100) 25.4 ± 5.1 6.3 ± 4.9 categories of clinical sleep problem. Biathlon 10 5 (50) 25.4 ± 2.9 5.1 ± 3.1 All statistical analyses were performed with R 3.3.3 (R Canoe/kayak 1 0 (0) 31 11 Core Team, Vienna, Austria). Descriptive statistics, Cron- bach’s alpha exploratory factor analysis and correlation es- Cross-country skiing 10 4 (40) 25.7 ± 4.9 5.3 ± 3.9 timates were performed with the Psych package [23]. CFA Cycling 6 5 (83) 27.3 ± 4.5 6 ± 4.8 were performed with the Lavaan package [24]. Inter-rater Diving 4 1 (25) 23.3 ± 3.1 5.0 ± 1.4 estimates and weighted kappa were performed with the Field hockey 22 22 (100) 22.5 ± 2.8 3.8 ± 2.3 IRR package [25]. Figure skating 12 6 (50) 24.5 ± 4.3 5.7 ± 3.1 Freestyle skiing 45 22 (49) 22.8 ± 3.6 4.6 ± 2.8 Results Sample Characteristics Golf 14 5 (36) 21.6 ± 2.5 3.2 ± 2.2 The ASSQ was administered to 199 Canadian National LT speed skating 1 0 (0) 24 4 Team carded athletes. The athletes were between the ages Luge 5 4 (80) 27.4 ± 6.8 6.8 ± 5.2 of 18–36 (mean age 24.0 ± 4.2 years) with 62% (N = 123) of Soccer 6 6 (100) 27.7 ± 4.9 6.8 ± 5.7 the sample females. The sample included representation of Swimming 3 2 (66) 20.1 ± 3.7 3.7 ± 3.8 athletes from 23 different summer and winter sports, see Triathlon 5 4 (80) 21.2 ± 3.3 2.3 ± 1.2 Table 1. Eighty-one percent (N =162) of the sample was on the senior national team of their sport with the remainder Wrestling 2 2 (100) 29.5 ± 6.4 7.5 ± 3.5 (N = 37) carded but on lower-level national teams. F female, LT long track One athlete from modern pentathlon included in athletics, one athlete from Sixty-seven percent of the sample (N = 133) had 5 years or skeleton included in luge, one athlete from rugby included in soccer, two less experience at the national team level, and 9% (N =19) athletes from ski jumping and two athletes from snowboarding included in had been at their current level for 10 years or more. The freestyle skiing Bender et al. Sports Medicine - Open (2018) 4:23 Page 5 of 8 sleep (r = 0.85, item 3). The factor loadings based on medication). The largest correlation was between item 3— the EFA (one factor, varimax rotation) for the SDS being satisfied with sleep quality—and item 5—trouble stay- five items were item 1 = 0.56, item 3 = 0.87, item 4 ing asleep. For the London Marathon data, the average cor- = 0.57, item 5 = 0.68, and item 6 = 0.27, with 40% of relation was 0.25. The smallest and largest correlations the variance explained. Although item 6 loaded were the same relationships as those seen in the Canadian weakly on the sleep difficulty factor, it was not National Team data. dropped from the scale as it positively contributed to the reliability measure. The PCA of the new SDS revealed Clinical Validity a strong first component (2.48) with all other components Cut-points were made to the new 5-item SDS to having eigenvalues of less than 1.0, which indicated the categorize athletes into clinical sleep problem of none scale is unidimensional and can be summed for a score. (0–4), mild (5–7), moderate (8–10), and severe (11–17). The correlation between the new 5-item SDS with the When the SMP’s ratings of level of the clinical problem 7-item SDS was strong (r = 0.97, 95% CI 0.96 to 0.98). The were compared to the ASSQ scoring system (see Add- duration of the nap question was dropped from the itional file 2), the groups were not shown to be different questionnaire, but the nap frequency question (see (chi-square = 0.23, df = 3, p = 0.97), see Table 2. Agree- Additional file 1, item 2) was kept in the questionnaire to ment between the two rating systems was good (Cohen’s inform sleep education strategies. weighted kappa = 0.84, z = 5.68, p < 0.01). The sensitivity The internal consistency of the four questions from the of the ASSQ to detect clinically meaningful sleep prob- chronotype score (items 7–10) was acceptable (Cronbach’s lems (moderate to severe category) was 81% (95% CI alpha = 0.73; 95% CI 0.67 to 0.79). The average correlation 54.4 to 96.0%) when compared to the SMP ratings. The with the total score was r = 0.77 for the four items with specificity of the ASSQ scoring system to categorize ath- the lowest correlation for preferred time to bed (r =0.64; letes as not needing follow-up with the SMP (none to item 10) and the highest correlation for self-reported mild clinical sleep problem) was 93% (95% CI 77.9 to chronotype (r = 0.86, item 9). The factor loadings for the 99.2%). The positive predictive value of the ASSQ scor- chronotype four items based on the EFA (one factor, vari- ing system was 92% (95% CI 63.1 to 98.8%). The nega- max rotation) were item 7 = 0.66, item 8 = 0.52, item 9 = tive predictive value of the ASSQ scoring system was 0.88, and item 10 = 0.51, with 44% of the variance ex- 90% (95% CI 77.1 to 96.3%). When the ASSQ scoring plained. The PCA of the chronotype items revealed a system was applied to the entire sample, 25.1% of ath- strong first component (2.23) with all other components letes were identified as having a moderate or severe level having eigenvalues of less than 1.0 which indicated the of clinical sleep problem and recommendations were scale is unidimensional and can be summed for a score. made for further follow-up. The time between completing ASSQ T1 to T2 was 101 ± 66.2 days. There was a strong relationship (r = Impact on SDS After Sleep Recommendations 0.86, 95% CI 0.75–0.92) for the 5-item SDS from T1 to For all athletes in the sub-study who completed T3 (N = T2, indicating good stability of the SDS. There was a 44), there was an average reduction of SDS from T1 to strong relationship (r = 0.78, 95% CI 0.63–0.87) for the T3 of 1.5 points (t = 1.93, df = 80.72, p = 0.06). When the 4-item chronotype factor from T1 to T2 indicating good changes for each of the groups were assessed (see Fig. 2), stability of the chronotype score. the athletes who were in the none category did not im- prove after the recommendations (t = 0.78, df = 17, p = Stability of the ASSQ Across Populations 0.45). This is likely because there was no clinical sleep CFA revealed comparable factor loadings on the SDS items problem present. Those classified as having a mild for the Canadian National Team athletes to the London Marathon runners respectively (0.56 vs 0.42, item 1; 0.88 vs Table 2 ASSQ scoring system vs. sleep medicine physician 0.82, item 3; 0.57 vs 0.39, item 4; 0.68 vs 0.63, item 5; 0.27 ratings vs 0.25, item 6). The comparative fit index (CFI) was 0.98, ASSQ scoring system and the root mean square error of approximation (RMSEA) SMP rating None Mild Moderate Severe Total was 0.06 (95% CI 0.00 to 0.13). There was also good stabil- None 15 4 0 0 19 ity across the populations for the chronotype score. CFA re- Mild 3 6 2 0 11 vealed comparable factor loadings for both populations Moderate 0 3 9 1 13 (0.65 vs 0.52, item 7; 0.52 vs 0.52, item 8; 0.88 vs 0.90, item 9; 0.51 vs 0.43, item 10), with a CFI of 0.95 and a RMSEA Severe 0 0 1 2 3 of 0.15 (95% CI 0.07 to 0.24). For the Canadian data, the Total 18 13 12 3 average correlation was 0.35. The smallest correlation was Categories of clinical sleep problem between the sleep medicine physician between item 1 (sleep quantity) and item 6 (use of sleep (rows) and the ASSQ (columns). Perfect agreement is along the diagonal Bender et al. Sports Medicine - Open (2018) 4:23 Page 6 of 8 to determine the degree of the sleep problem and guide the intervention strategy; see Additional file 2. The other three items on napping frequency (item 2), caffeine use (item 15), and electronic device use (item 16) were not part of the scoring system but were kept in the ASSQ to inform sleep optimization strategies. Another objective of the current study was to assess the psychometric properties of the ASSQ. After the two nap questions were removed from the SDS, the internal consistency of the new 5-item SDS was acceptable. Test-retest reliability from T1 to T2 over a period of 101 days showed good stability (r = 0.86). The ASSQ also was stable across two different populations when the Can- adian National Team data were compared to a larger set Fig. 2 Changes in sleep difficulty score for each clinical sleep of runners (N = 1074) from the 2016 London Virgin problem category (none, n = 18; mild, n = 12; moderate, n = 11, and Money Marathon. severe, n = 3) from time 1 (T1) at baseline to time 3 (T3) after the sleep intervention recommendations In the current study, we found that 25% of athletes in the sample were identified as having clinically relevant sleep problems. This is similar to Tuomilehto et al. who clinical sleep problem had an average drop of 1.4 points on found approximately 21% of a sample of 107 professional the SDS, but this was not statistically significant (t = 0.78, Finnish hockey players have a sleep disorder as verified by df = 17, p = 0.11). Athletes who had a moderate (N = 11) or polysomnography [6]. However, when compared to stud- severe (N = 3) level of clinical sleep problem showed the ies which utilized the PSQI [12], our prevalence of athletes greatest improvement after the recommendations with with sleep problems was much lower than previous stud- an average drop of 3.9 points on the SDS (t =5.75, df ies which showed 40–50% of athletes had poor sleep [1, 4, = 13, p <0.01), see Fig. 2. 5, 7, 8]. A recent study confirmed poor concordance rate of the PSQI with the sleep evaluation of athletes by a SMP Discussion [13]. The discrepancies between the ASSQ and the PSQI The primary objective of this study was to assess the clin- could be due to the PSQI not being validated in an athlete ical validity of the ASSQ. After a standardized clinical population. Athletes are exposed to extensive monitoring sleep interview, the SMP categorized athletes into levels of of symptoms and could be more sensitive to reporting sleep problem and this was compared to the ASSQ rating symptoms than the general population [14]. This has im- system. There was no significant difference between the portant implications for both the research methodology ratings of the ASSQ and the SMP, which showed good and the clinical use of the PSQI in an athlete population. agreement between the categories, see Table 2. The ASSQ Over-identifying athletes that need clinical sleep interven- had good specificity of 93% but only an acceptable sensi- tions is inefficient and expensive, and those athletes with tivity of 81%. The measure of sensitivity did not take into more severe sleep issues may not get interventions in a consideration the modifiers which is the second part of timely manner. The ASSQ can be easily deployed by the the ASSQ’s scoring system, see Additional file 2.In 10 out athletes’ support staff and can quickly identify those who of 11 cases where the SMP rated the athlete as having a need further assessment and treatment. By utilizing the higher category of sleep problem than the ASSQ, there proper sleep intervention recommendations, athletes can were modifiers present. The reason the sleep-disordered begin to reduce sleep disturbances and optimize sleep. breathing and evening-type modifiers were not included Additionally, the sport and sleep science communities in the SDS was because the psychometric properties of now have a valid and reliable sleep screening tool to use in the questionnaire would be negatively impacted because this unique population. of a low prevalence of these problems occurring in ath- letes [26, 27]. However, case finding is critical because of Limitations the clinical significance, so having these as a part of the The most significant methodological limitation of this study secondary scoring system is important. For the questions was the choice to use one SMP as the “gold standard” by related to travel, this was not included in the SDS because which the clinical validity of the ASSQ was evaluated. not all athletes travel. It was included as a modifier to Ideally, the study would have used multiple SMPs to rate identify athletes who could benefit from specific interven- each athlete and subsequently perform inter-rater reliability tions to help sleep disturbance during travel. Therefore, testing. This research did not include another SMP because the SDS in conjunction with the modifiers should be used of the limited number of SMPs who specialize in evaluating Bender et al. Sports Medicine - Open (2018) 4:23 Page 7 of 8 sleep disturbances and sleep-related dysfunction in elite applicable to certain athlete groups (e.g., travel). They athletes. The decision to begin the exploration of the clin- are important to provide specific education and inter- ical validity of the ASSQ using one SMP allowed the re- vention recommendations. searchers to start the process, and we certainly encourage further validity and reliability testing of the ASSQ. Sleep-Disordered Breathing If an athlete answers yes Although the chronotype score showed good psycho- to item 13 (loud snoring) or item 14 (sleep apnea), they metric properties, one limitation in this study was that it should be further evaluated. was not validated with existing chronotype question- naires or biological markers of circadian phase. Future Travel If the athlete answers yes to item 11 (sleep dis- research could test the ASSQ chronotype score against turbance), education on travel management is recom- existing questionnaires for cut-points of morningness mended. If the athlete answers yes to item 12 and eveningness and verify these cut-points against bio- (performance issues), the problem is likely more serious logical markers of circadian phase. and may require further assessment and treatment. Another limitation of our study was the lack of object- ive markers of sleep disturbance. The protocol did in- Chronotype Sleep difficulty is more common in ath- clude actigraphy over a 2-week period; however, the letes who are evening types. Add the scores from purpose of the study was to examine the clinical validity items 7–10 to get the chronotype score for evening- of the questionnaire, which actigraphy cannot assess. ness. Totals ≤ 4 indicate the athlete is an evening type Polysomnography may have been more appropriate to and may require further assessment and treatment confirm the presence of sleep disorders but is typically (e.g., bright light therapy, melatonin). only one night of data whereas the questionnaire asks about sleep patterns across a much longer period (“the Items of Interest recent past”). In addition, the feasibility of using poly- Items of interest use the responses to specific items to somnography in this study would have been extremely inform sleep optimization strategies. For example, if an difficult because athletes participated from places across athlete is only getting 6–7 h of sleep (item 1), and not Canada, and a minority of the athletes were training and napping frequently throughout the week (item 2), a rec- competing in locations around the world. Again, the ommendation to increase nighttime sleep duration and purpose of the questionnaire is to identify athletes need- napping would be warranted. ing further sleep assessment and is not intended to diag- nose athletes with sleep disorders. Conclusions The psychometric properties of the data (reliability, Instructions for ASSQ Usage (See Additional file 2) test-retest, and association with independent expert It is recommended to assess the SDS first (items 1, 3, 4, judgment) suggest strongly that the ASSQ can detect 5, and 6), then the modifiers of sleep-disordered breath- clinically meaningful sleep disturbances in an elite ing (items 13 and 14), travel (items 11 and 12), and athlete population. The ASSQ is easy to administer, chronotype (items 7–10), and finally evaluate the items quick to complete, and can be scored remotely. Most of interest (e.g., item 2, 15, 16) to inform more specific importantly, it provides support staff the capability to sleep optimization strategies. understand when further follow-up with a SMP or qualified sleep professional is necessary. We found Sleep Difficulty Score (SDS) that theprevalenceof clinicallymeaningfulsleep dis- The SDS is used to classify athletes into the level of clin- turbances was much lower than with previously used ical sleep problem (none, mild, moderate, severe) based tools and caution researchers and clinicians about on the response to items 1, 3, 4, 5, and 6 with poorer sleep using tools that have not been properly validated in indicative of a higher score. Responses to those items are an athlete population. With sleep screening, recom- summed, and scores of 0–4 are classified as the “none” mendations, and proper follow-up, athletes can im- category, scores of 5–7 are classified as the “mild” cat- prove their sleep for the benefit of better health and egory, scores of 8–10 are classified as the moderate cat- performance. egory, and scores of 11–17 are classified as the severe category. Those athletes classified as having a moderate or Additional File severe sleep problem should be further evaluated. Additional file 1: Athlete Sleep Screening Questionnaire (ASSQ). (PDF 13 kb) Modifiers Additional file 2: ASSQ sleep difficulty score (SDS) scoring key. The modifiers are not included in the SDS because (DOCX 22 kb) they occur less frequently and are not always Bender et al. Sports Medicine - Open (2018) 4:23 Page 8 of 8 Abbreviations Received: 18 January 2018 Accepted: 24 May 2018 ASSQ: Athlete Sleep Screening Questionnaire; CFA: Confirmatory factor analysis; CFI: Comparative fit index; EFA: Exploratory factor analysis; PCA: Principal component analysis; RMSEA: Root mean square error of References approximation; SDS: Sleep difficulty score; SMP: Sleep medicine physician; 1. Samuels C. Sleep, recovery, and performance: the new frontier in high- T1: Time 1; T2: Time 2; T3: Time 3 performance athletics. Phys Med Rehabil Clin N Am. 2009;20(1):149–59. 2. Venter RE. Role of sleep in performance and recovery of athletes: a review article. South Afr J Res Sport Phys Ed Recreation. 2012;34(1):167–84. Acknowledgements 3. Gupta L, Morgan K, Gilchrist S. Does elite sport degrade sleep quality? A We thank the athletes for participating in the study and the funding sources systematic review. Sports Med. 47(7):1317–33. for their support. 4. 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Understanding interobserver agreement: the kappa statistic. Fam Med. 2005;37(5):360–3. Competing Interests 22. Brenner H, Gefeller O. Variation of sensitivity, specificity, likelihood ratios and The authors, Doug Lawson and Penny Werthner, declare that they have no predictive values with disease prevalence. Stat Med. 1997;16(9):981–91. competing interests. Charles Samuels is the principal owner of the Centre for 23. Revelle W. Psych: procedures for psychological, psychometric, and Sleep and Human Performance (CSHP). Amy Bender is employed full-time by personality research. Evanston: Northwestern University; 2014. p. 165. the CSHP. As a result of this research, a method for sleep screening and pro- 24. Lavaan RY. An R package for structural equation model and more. Version 0. viding recommendations for athletes will be developed and commercialized 5–12 (BETA). Ghent: Ghent University; 2012. for profit. 25. Gamer M, Lemon J, Gamer MM, Robinson A, Kendall’s W. Package ‘irr’. Various coefficients of interrater reliability and agreement. 2012. 26. Rice TB, Dunn RE, Lincoln AE, Tucker AM, Vogel RA, Heyer RA, et al. Sleep- Publisher’sNote disordered breathing in the National Football League. Sleep. 2010;33(6):819–24. Springer Nature remains neutral with regard to jurisdictional claims in 27. Lastella M, Roach GD, Halson SL, Sargent C. The chronotype of elite athletes. published maps and institutional affiliations. J Hum Kinet. 2016;54(1):219–25. Author details Centre for Sleep & Human Performance, 106-51 Sunpark Dr. SE, Calgary, AB T2X 3V4, Canada. Faculty of Kinesiology, University of Calgary, Calgary, AB, Canada. Faculty of Chiropractic, D’Youville College, Buffalo, NY, USA.

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Sports Medicine - Open – Springer Journals

Published: Jun 4, 2018

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