Effects of non-pharmacological interventions on sleep in chronic low back pain: A systematic review and meta-analysis of randomised controlled trials.

Low back pain is a leading cause of disability worldwide and adults with chronic low back pain (≥12weeks) commonly experience sleep impairments (e.g., insomnia, sleep disturbance). This study examined the effects of non-pharmacological interventions on sleep in adults with chronic low back pain. Six databases (PubMed, CINAHL, SPORTDiscus, PsycINFO, EMBASE, CENTRAL) were searched from inception to 2 June 2021 for randomised controlled trials. Pairwise random-effect meta-analysis estimated standardised mean difference (Hedges' g) at end-of-intervention follow-up. Nineteen studies (participants: 1348) were included. When compared to control, non-pharmacological interventions improved sleep (g [95%CI]: -0.33 [-0.56, -0.11], p = 0.004, small effect, I2 = 59.3%; n = 879; studies: n = 13; GRADE: low). This small improvement in sleep was associated with a moderate reduction in pain intensity (-0.69 [-1.00, -0.38], p < 0.001, I2 = 75.3%; n = 812; studies: n = 12; GRADE: very low) and no changes in back-related disability (-0.50 [-1.13, 0.14], p = 0.129, I2 = 91.4%; n = 517; studies: n = 6; GRADE: low). Notably, all eligible studies reported interventions primarily aimed to reduce pain, although our search criteria were not limited to pain interventions. Key limitations were data paucity and high risk of bias. Future research should investigate sleep-based interventions (i.e., those purposely designed to improve sleep) using subjective and objective measures across a range of sleep domains (CRD42021275227).

Effects of pharmacotherapy on sleep-related outcomes in adults with chronic low back pain: A systematic review and meta-analysis of randomised controlled trials.

Background: Adults with chronic low back pain (CLBP) suffer impaired sleep. Medications for CLBP can impact sleep which in turn may influence treatment outcomes. This systematic review and meta-analysis examined the effects of pharmacotherapy (any type) on sleep in adults with CLBP. Methods: In this systematic review and meta-analysis, we searched PubMed, CINAHL, SPORTDiscus, PsycINFO, EMBASE, and CENTRAL from inception to 10 July 2022. Randomised controlled trials that investigated the effects of pharmacotherapy on sleep in adults with CLBP were included. Manual citation search of relevant systematic reviews and included studies were also conducted. Mean change from baseline for sleep outcomes (e.g., sleep quality, total sleep time, wake after sleep onset) was the effect of interest. Pairwise inverse-variance random effect meta-analysis was performed to impute pooled estimates (Hedges' g or risk ratios). The Hartung-Knapp-Sidik-Jonkman method was used where there were ≤5 studies. The Grading of Recommendations Assessment, Development, and Evaluation (GRADE) system was used for evaluating the certainty of evidence. This study was registered with PROSPERO (CRD42022309419). Findings: Assessment of 3959 records resulted in nine studies (n = 2927) being included. Pharmacotherapy for CLBP management had a small, yet unlikely clinically significant, effect on improving sleep in adults with CLBP, when compared to placebo (g [95% CI]: -0.23 [-0.37, -0.09], p = .0009; I 2  = 30.1%; n = 1433; studies: n = 8; GRADE: low). Notably, no eligible studies investigated the effect of sleep medications in this population, despite being within the scope of this review. Interpretation: Pharmacotherapy used to manage CLBP provided improvements in sleep in adults with CLBP. Given that these effects were small and unlikely clinically significant, clinicians could consider alternative treatments (e.g., non-pharmacological interventions) for managing sleep in adults with CLBP. However, low to very low certainty of evidence precluded strong conclusions. To improve certainty of evidence and confidence in the effect estimates, future research needs to use robust method to minimise bias. Additional research evaluating multiple sleep characteristics, using both validated objective and subjective measures, is also warranted to further investigate the influence of distinct sleep parameters. Funding: The Summer Research Scholarship from the Appleton Institute, Central Queensland University, Australia.

Lifestyle Behaviors and Suicide-Related Behaviors in Adolescents: Cross-Sectional Study Using the 2019 YRBS Data.

Objective: The purpose of this research was to investigate the prevalence of lifestyle behaviors and suicide-related behaviors and the association between them using a nationally representative sample of adolescents from the USA. Methods: 13,677 high school students aged 14-18 years were included in this cross-sectional study. The research data were retrieved from the Youth Risk Behavior Surveillance System Survey in 2019. All data on age, sex, grade, race, physical activity, television time, fruit intake, and suicide-related behavior were self-reported by students. Logistic regression models were adopted to examine the association between lifestyle behaviors and the suicide-related behaviors. Results: Students who played video/computer games for ≥2 h had higher risk of suicide attempt (OR = 1.55, 95%CI: 1.30-1.85). Daily sleep duration of ≤8 h was positively associated with considering a suicide attempt (OR = 1.99, 95%CI: 1.62-2.43). In addition, participants who did not engage in any sport team were more likely to report considering a suicide attempt (OR = 1.50, 95%CI: 1.24-1.81). Conclusion: This research suggests that some lifestyle behaviors (e.g., time for video or computer use, sleep duration, sports team participation, regular breakfast intake, and substance use) are associated with increased risk of suicidal behavior and ideation in high school students. To identify the specific effect of multiple lifestyle factors in influencing the risk of suicide-related behaviors in high school students, longitudinal studies are warranted in future.

To Nap or Not to Nap? A Systematic Review Evaluating Napping Behavior in Athletes and the Impact on Various Measures of Athletic Performance.

PURPOSE: The objective of this systematic review was to 1) determine how studies evaluated napping behavior in athletes (frequency, duration, timing and measurement); 2) explore how napping impacted physical performance, cognitive performance, perceptual measures (eg, fatigue, muscle soreness, sleepiness and alertness), psychological state and night-time sleep in athletes. METHODS: Five bibliographic databases were searched from database inception to 11 August 2020. Observational and experimental studies comprising able-bodied athletes (mean age ≥12 years), published in English, in peer-reviewed journal papers were included. The Downs and Black Quality Assessment Checklist was used for quality appraisal. RESULTS: Thirty-seven studies were identified of moderate quality. Most studies did not include consistent information regarding nap frequency, duration, and timing. Napping may be beneficial for a range of outcomes that benefit athletes (eg, physical and cognitive performance, perceptual measures, psychological state and night-time sleep). In addition, napping presents athletes with the opportunity to supplement their night-time sleep without compromising sleep quality. CONCLUSION: Athletes may consider napping between 20 to 90 min in duration and between 13:00 and 16:00 hours. Finally, athletes should allow 30 min to reduce sleep inertia prior to training or competition to obtain better performance outcomes. Future studies should include comprehensive recordings of nap duration and quality, and consider using sleep over a 24 hour period (daytime naps and night-time sleep period), specifically using objective methods of sleep assessment (eg, polysomnography/actigraphy).

Vigorously Cited: A Bibliometric Analysis of the 500 Most Cited Physical Activity Articles.

BACKGROUND: To date, no citation analysis has been conducted in the physical activity field, which can contribute to assess the impact of this research field and identify knowledge gaps. Therefore, this study aimed to identify the 500 most cited physical activity publications and report their bibliometric characteristics. METHODS: The Web of Science database (all database indexes) was searched, and bibliometric characteristics were imported and calculated. RESULTS: A total of 520 publications were ranked as the top 500. The sum of the citations was 326,258, and the average citation density was 41.0 (45.1) citations per year. Original research articles constituted the major portion of included publications (53.7%; 170,774 citations). Papers reporting relationship of physical activity with health were the most prevalent type of publication included (43.7%; 141,027 citations). Journal impact factor had a weak but significant positive correlation with citation density (r = .12; P = .006). The United States was ranked first in terms of the contributions from institutions and authors contributing to the most cited physical activity papers. CONCLUSIONS: Top physical activity publications are well cited compared with other health behavior fields. Original research reporting on the associations between physical activity and health has a higher citation impact compared with other types of original research within the physical activity field. The physical activity research field continues to expand rapidly as newer publications attract more citations in a shorter time span compared with older publications.

Sleep and physical activity in university students: A systematic review and meta-analysis.

University students have low levels of physical activity and report disturbances to sleep, which are independently associated with poor health outcomes. Some research suggests that there is a bi-directional relationship between sleep and physical activity in adults. However, the relationship between sleep and physical activity in university students has not yet been evaluated. Therefore, the aim of this systematic review and meta-analysis was to qualitatively synthesise and quantitatively evaluate the evidence for the association between sleep and physical activity in university students. Twenty-nine eligible studies were included, with a total of 141,035 participants (43% men and 57% women). Only four studies used device-based measures of sleep and/or physical activity, with the remainder including self-report measures. Qualitative synthesis found that the majority of studies did not find any association between sleep and physical activity in university students. However, random-effects meta-analysis showed that moderate-to-high intensity physical activity was associated with lower PSQI scores (e.g., better sleep quality) [r = -0.18, 95% CI (-0.37, 0.03), p = 0.100]. Further, a weak negative association between moderate-to-vigorous physical activity level and sleep duration was also found [r = -0.02, 95% CI (-0.16, 0.12), p = 0.760]. As the findings of this review are predominantly derived from cross-sectional investigations, with limited use of device-based measurement tools, further research is needed to investigate the relationship between sleep and physical activity in university students. Future studies should employ longitudinal designs, with self-report and device-based measures, and consider the intensity and time of physical activity as well as records of napping behaviour.