A systematic review and meta-analysis of cognitive and behavioral interventions to improve sleep health in adults without sleep disorders

The Process of Developing a Sleep Health Improvement Plan: a Lab-Based Model of Self-Help Behavior

BACKGROUND

Although self-help strategies to improve sleep are widely accessible, little is known about the ways in which individuals interact with these resources and the extent to which people are successful at improving their own sleep based on sleep health recommendations. The present study developed a lab-based model of self-help behavior by observing the development of sleep health improvement plans (SHIPs) and examining factors that may influence SHIP development.

METHOD

Sixty healthy, young adults were identified as poor sleepers during one week of actigraphy baseline and recruited to develop and implement a SHIP. Participants viewed a list of sleep health recommendations through an eye tracker and provided information on their current sleep health habits. Each participant implemented their SHIP for 1 week during which sleep was assessed with actigraphy.

RESULTS

Current sleep health habits, but not patterns of visual attention, predicted SHIP goal selection. Sleep duration increased significantly during the week of SHIP implementation.

CONCLUSIONS

Findings indicate that the SHIP protocol is an effective strategy for observing self-help behavior and examining factors that influence goal selection. The increase in sleep duration suggests that individuals may be successful at extending their own sleep, though causal mechanisms have not yet been established. This study presents a lab-based protocol for studying self-help sleep improvement behavior and takes an initial step toward gaining knowledge required to improve sleep health recommendations.

Examining mediators of intervention efficacy in a randomised controlled m-health trial to improve physical activity and sleep health in adults

Objectives: Examining mediators of intervention efficacy in an m-health intervention targeting physical activity and sleep in 160 Australian adults.Design: Nationwide randomised controlled trial.Main outcome measures: Moderate- and vigorous-intensity physical activity (MVPA), assessed using the Active Australia Questionnaire; sleep quality (Pittsburgh Sleep Quality Index); and sleep hygiene practices (Sleep Hygiene Index). Hypothesised psychosocial (e.g. self-efficacy) and behavioural (i.e. MVPA, sleep quality, sleep hygiene) mediators were tested on primary endpoint data at 3 months using bias-corrected bootstrapping (PROCESS 2 for SPSS). All outcomes and mediators were assessed using self-report.Results: At three months, the intervention had significantly improved sleep quality (d = 0.48, 95% CI: -2.26, -0.33, p = 0.009) and sleep hygiene (d = 0.40, 95% CI: -3.10, -0.19, p = 0.027). Differences in MVPA were not significant (d = 0.24, 95% CI: -35.53, 254.67, p = 0.139). Changes in MVPA were mediated by self-efficacy, perceived capability, environment, social support, intentions and planning, some of which showed inconsistent mediation (suppression). None of the hypothesised psychosocial factors mediated sleep outcomes. Changes in sleep hygiene mediated changes in sleep quality.Conclusions: Several psychosocial factors mediated changes in physical activity but not in sleep outcomes. Mediation effects of sleep hygiene on sleep quality highlight the importance of providing evidence-based strategies to improve sleep quality.

Which sleep hygiene factors are important? comprehensive assessment of lifestyle habits and job environment on sleep among office workers

BACKGROUND

Although several lifestyle habits are associated with sleep, it is unclear which factors are important. Among office workers, the effect of job environment should also be considered. The multivariate analyses on the effects of lifestyle habits and job environment on sleep among office workers was conducted.

METHODS

A cross-sectional survey of 6,342 employees from 29 companies was conducted in 2017-2019. Complete responses and informed consent were provided by 5,640 participants. The survey examined demographic variables, sleep schedules, Pittsburgh Sleep Quality Index (PSQI), Brief Job Stress Questionnaire (BJSQ), and lifestyle habits.

RESULTS

Mean values were as follows: age, 36.9 years (±10.2); PSQI, 6.52 (±2.83); and total sleep time, 6h06m (±1h40m) on work days and 7h39m (±1h58m) on free days. After adjusting for job environment and demographic variables, irregular meal time (1.45-2.86), not eating vegetables every day (1.35), nightcap (2.74-3.55), weight gain (1.20-1.42), lack of sunlight in the morning in the bedroom (1.48-1.60), waking up before dawn (2.18), electronic display use in bed (1.50), and daily caffeine intake (1.27) were significantly associated with sleep disturbance. Irregular meal time (1.51-2.37), lack of morning breakfast (1.74-2.95), having dinner within 2 hours before bed time (0.49-0.64), not eating vegetables every day (1.52), lack of sunlight exposure in the morning (1.43-2.01), and caffeine use every day (1.42) were also associated with eveningness (p<.01).

CONCLUSION

Each sleep hygiene factor had a different effect size. Sleep hygiene interventions to promote worker sleep health should prioritize factors in accordance with effect size.

Right care, first time: a highly personalised and measurement-based care model to manage youth mental health

Mood and psychotic syndromes most often emerge during adolescence and young adulthood, a period characterised by major physical and social change. Consequently, the effects of adolescent-onset mood and psychotic syndromes can have long term consequences. A key clinical challenge for youth mental health is to develop and test new systems that align with current evidence for comorbid presentations and underlying neurobiology, and are useful for predicting outcomes and guiding decisions regarding the provision of appropriate and effective care. Our highly personalised and measurement-based care model includes three core concepts: ▶ A multidimensional assessment and outcomes framework that includes: social and occupational function; self-harm, suicidal thoughts and behaviour; alcohol or other substance misuse; physical health; and illness trajectory. ▶ Clinical stage. ▶ Three common illness subtypes (psychosis, anxious depression, bipolar spectrum) based on proposed pathophysiological mechanisms (neurodevelopmental, hyperarousal, circadian). The model explicitly aims to prevent progression to more complex and severe forms of illness and is better aligned to contemporary models of the patterns of emergence of psychopathology. Inherent within this highly personalised approach is the incorporation of other evidence-based processes, including real-time measurement-based care as well as utilisation of multidisciplinary teams of health professionals. Data-driven local system modelling and personalised health information technologies provide crucial infrastructure support to these processes for better access to, and higher quality, mental health care for young people. CHAPTER 1: MULTIDIMENSIONAL OUTCOMES IN YOUTH MENTAL HEALTH CARE: WHAT MATTERS AND WHY?: Mood and psychotic syndromes present one of the most serious public health challenges that we face in the 21st century. Factors including prevalence, age of onset, and chronicity contribute to substantial burden and secondary risks such as alcohol or other substance misuse. Mood and psychotic syndromes most often emerge during adolescence and young adulthood, a period characterised by major physical and social change; thus, effects can have long term consequences. We propose five key domains which make up a multidimensional outcomes framework that aims to address the specific needs of young people presenting to health services with emerging mental illness. These include social and occupational function; self-harm, suicidal thoughts and behaviours; alcohol or other substance misuse; physical health; and illness type, stage and trajectory. Impairment and concurrent morbidity are well established in young people by the time they present for mental health care. Despite this, services and health professionals tend to focus on only one aspect of the presentation - illness type, stage and trajectory - and are often at odds with the preferences of young people and their families. There is a need to address the disconnect between mental health, physical health and social services and interventions, to ensure that youth mental health care focuses on the outcomes that matter to young people. CHAPTER 2: COMBINING CLINICAL STAGE AND PATHOPHYSIOLOGICAL MECHANISMS TO UNDERSTAND ILLNESS TRAJECTORIES IN YOUNG PEOPLE WITH EMERGING MOOD AND PSYCHOTIC SYNDROMES: Traditional diagnostic classification systems for mental disorders map poorly onto the early stages of illness experienced by young people, and purport categorical distinctions that are not readily supported by research into genetic, environmental and neurobiological risk factors. Consequently, a key clinical challenge in youth mental health is to develop and test new classification systems that align with current evidence on comorbid presentations, are consistent with current understanding of underlying neurobiology, and provide utility for predicting outcomes and guiding decisions regarding the provision of appropriate and effective care. This chapter outlines a transdiagnostic framework for classifying common adolescent-onset mood and psychotic syndromes, combining two independent but complementary dimensions: clinical staging, and three proposed pathophysiological mechanisms. Clinical staging reflects the progression of mental disorders and is in line with the concept used in general medicine, where more advanced stages are associated with a poorer prognosis and a need for more intensive interventions with a higher risk-to-benefit ratio. The three proposed pathophysiological mechanisms are neurodevelopmental abnormalities, hyperarousal and circadian dysfunction, which, over time, have illness trajectories (or pathways) to psychosis, anxious depression and bipolar spectrum disorders, respectively. The transdiagnostic framework has been evaluated in young people presenting to youth mental health clinics of the University of Sydney's Brain and Mind Centre, alongside a range of clinical and objective measures. Our research to date provides support for this framework, and we are now exploring its application to the development of more personalised models of care. CHAPTER 3: A COMPREHENSIVE ASSESSMENT FRAMEWORK FOR YOUTH MENTAL HEALTH: GUIDING HIGHLY PERSONALISED AND MEASUREMENT-BASED CARE USING MULTIDIMENSIONAL AND OBJECTIVE MEASURES: There is an urgent need for improved care for young people with mental health problems, in particular those with subthreshold mental disorders that are not sufficiently severe to meet traditional diagnostic criteria. New comprehensive assessment frameworks are needed to capture the biopsychosocial profile of a young person to drive highly personalised and measurement-based mental health care. We present a range of multidimensional measures involving five key domains: social and occupational function; self-harm, suicidal thoughts and behaviours; alcohol or other substance misuse; physical health; and illness type, stage and trajectory. Objective measures include: neuropsychological function; sleep-wake behaviours and circadian rhythms; metabolic and immune markers; and brain structure and function. The recommended multidimensional measures facilitate the development of a comprehensive clinical picture. The objective measures help to further develop informative and novel insights into underlying pathophysiological mechanisms and illness trajectories to guide personalised care plans. A panel of specific multidimensional and objective measures are recommended as standard clinical practice, while others are recommended secondarily to provide deeper insights with the aim of revealing alternative clinical paths for targeted interventions and treatments matched to the clinical stage and proposed pathophysiological mechanisms of the young person. CHAPTER 4: PERSONALISING CARE OPTIONS IN YOUTH MENTAL HEALTH: USING MULTIDIMENSIONAL ASSESSMENT, CLINICAL STAGE, PATHOPHYSIOLOGICAL MECHANISMS, AND INDIVIDUAL ILLNESS TRAJECTORIES TO GUIDE TREATMENT SELECTION: New models of mental health care for young people require that interventions be matched to illness type, clinical stage, underlying pathophysiological mechanisms and individual illness trajectories. Narrow syndrome-focused classifications often direct clinical attention away from other key factors such as functional impairment, self-harm and suicidality, alcohol or other substance misuse, and poor physical health. By contrast, we outline a treatment selection guide for early intervention for adolescent-onset mood and psychotic syndromes (ie, active treatments and indicated and more specific secondary prevention strategies). This guide is based on experiences with the Brain and Mind Centre's highly personalised and measurement-based care model to manage youth mental health. The model incorporates three complementary core concepts: ▶A multidimensional assessment and outcomes framework including: social and occupational function; self-harm, suicidal thoughts and behaviours; alcohol or other substance misuse; physical health; and illness trajectory. ▶Clinical stage. ▶Three common illness subtypes (psychosis, anxious depression, bipolar spectrum) based on three underlying pathophysiological mechanisms (neurodevelopmental, hyperarousal, circadian). These core concepts are not mutually exclusive and together may facilitate improved outcomes through a clinical stage-appropriate and transdiagnostic framework that helps guide decisions regarding the provision of appropriate and effective care options. Given its emphasis on adolescent-onset mood and psychotic syndromes, the Brain and Mind Centre's model of care also respects a fundamental developmental perspective - categorising childhood problems (eg, anxiety and neurodevelopmental difficulties) as risk factors and respecting the fact that young people are in a period of major biological and social transition. Based on these factors, a range of social, psychological and pharmacological interventions are recommended, with an emphasis on balancing the personal benefit-to-cost ratio. CHAPTER 5: A SERVICE DELIVERY MODEL TO SUPPORT HIGHLY PERSONALISED AND MEASUREMENT-BASED CARE IN YOUTH MENTAL HEALTH: Over the past decade, we have seen a growing focus on creating mental health service delivery models that better meet the unique needs of young Australians. Recent policy directives from the Australian Government recommend the adoption of stepped-care services to improve the appropriateness of care, determined by severity of need. Here, we propose that a highly personalised approach enhances stepped-care models by incorporating clinical staging and a young person's current and multidimensional needs. It explicitly aims to prevent progression to more complex and severe forms of illness and is better aligned to contemporary models of the patterns of emergence of psychopathology. Inherent within a highly personalised approach is the incorporation of other evidence-based processes, including real-time measurement-based care and use of multidisciplinary teams of health professionals. Data-driven local system modelling and personalised health information technologies provide crucial infrastructure support to these processes for better access to, and higher quality of, mental health care for young people.

A sleep hygiene and yoga intervention conducted in affordable housing communities: Pilot study results and lessons for a future trial

BACKGROUND

and purpose: Inadequate sleep is highly prevalent among socioeconomically disadvantaged and racial/ethnic minority communities and is often related to maladaptive sleep behaviors and stress. There is scant research investigating the delivery of these interventions in underserved communities. The purpose of this study was to develop and test the feasibility and acceptability of a sleep education and yoga intervention for socioeconomically disadvantaged and racial/ethnic diverse adults.

MATERIALS AND METHODS

We present quantitative and qualitative data from a single-arm sleep education and yoga pilot study (n = 17) conducted in two affordable housing communities, and the multi-modal process we employed to refine the intervention for a future trial.

RESULTS

Participants were age 43.6 years on average (±19.3 years) and 88.2% were female. Nearly 56% identified as non-Hispanic Black and 19% as Hispanic/Latino. Results showed significant pre/post-intervention improvements in sleep duration (5.4 ± 1.2 h/night vs 6.9 ± 1.7 h/night; p < 0.01), sleep-related impairment (-8.15; p < 0.01), sleep disturbance (-5.95; p < 0.01), and sleep hygiene behaviors (-5.50; p < 0.01).

CONCLUSION

This study indicates intervention acceptability and improvements in sleep and sleep hygiene. Future randomized controlled trials are needed to assess efficacy.

Hózhó: Promoting sleep health among Navajo caregivers

OBJECTIVES

This study explored the feasibility and acceptability of a sleep health education intervention for caregivers of children with developmental disabilities (DD).

DESIGN

This mixed-methods pilot study utilized repeated measures and caregiver interviews.

SETTING

The intervention occurred in the homes of caregivers who live on the Navajo Nation.

PARTICIPANTS

Fifteen caregivers of children with DD aged from birth to 3 years old participated.

INTERVENTION

The intervention consisted of three 1-hour home-based sessions. Educational modules were tailored to specific sleep issues of the caregiver and their child(ren), as well as the unique environmental and cultural features of Navajo families.

MEASUREMENTS

Quantitative measures included a sleep habits questionnaire, pre- and postmeasures of learning, and the SF-12 HRQoL. Quantitative data were analyzed with frequencies and repeated measures analyses with p .05. Qualitative comments regarding facilitators and detractors to healthy sleep were transcribed verbatim and categorized into themes.

RESULTS

Caregiver sleep duration increased by 2 hours (5.8±1.8 to 7.8±1.9, p = .005). Caregivers also reported improved physical (45.0±8.2 to 52.8+8.7 p = .001) and mental HR-QoL (41.8±8.9 to 49.3±10.9, p = .002), and enhanced knowledge of sleep disorders (13.4±4.0 to 20.7±5.6) and healthy sleep habits (15.7±4.1 to 25.4±3.4 each p = .005). Many participants reported better sleep quality in their children with earlier bedtimes and less night waking.

CONCLUSIONS

Findings suggest that this tailored sleep education program is a culturally responsive approach to promoting caregiver sleep health and HR-QoL, as well as the sleep health of their children. Caregivers credited improved sleep to the support they received during visits and text messaging.

Effects of physical activity programs on sleep outcomes in older adults: a systematic review

BACKGROUND

One in two older adults report sleep problems, which not only cause fatigue, but also negatively affect general functioning, activities of daily living, and physical and mental health. Although it is known that physical activity is positively associated with sleep in older adults, the effects of physical activity programs on sleep in older adults has not been reviewed. The aim of this systematic review was to systematically review the effects of physical activity programs on sleep in generally healthy older adults aged 60+ years.

METHODS

Searches were performed in PubMed, Embase, Web of Science, SPORTDiscus, PEDro and CINAHL. The methodological quality of the included studies was rated using the 'Quality Assessment Tool for Quantitative Studies'. Only studies of moderate and strong quality were included. This review was registered in PROSPERO (CRD42018094007).

RESULTS

Fourteen studies met the inclusion criteria (six randomised controlled trials and eight pretest-posttest studies). Of these studies, five were moderate and nine were strong quality studies. Mean age of study samples ranged from 64 to 76 years. Exercise programs included various activities aimed at improving mobility, endurance and strength. Intervention duration ranged from 2 weeks to 12 months. Eleven studies used subjective measures of sleep, two used objective measures and one used both. Sixteen different sleep outcomes were reported. All but one study, found at least one significant improvement on sleep outcomes. No significantly detrimental effects were reported. Effect sizes, calculated in ten studies, ranged from 0,34-1,55 and were substantial (≥0,8) in four studies.

CONCLUSIONS

This systematic review suggests that exercise programs positively affect various aspects of sleep in generally healthy older adults. More specifically, moderate intensity exercise programs, with a frequency of three times per week and a duration of 12 weeks up to 6 months, showed the highest number of significant improvements in different sleep outcomes in older adults. Furthermore, programs that offered single exercise types, such as Baduanjin, Tai chi and the silver yoga program, or a combination of exercises showed the highest proportion of significant versus reported effects on sleep outcomes.

Diet and sleep health: a scoping review of intervention studies in adults

BACKGROUND

Recent research has demonstrated an association between dietary intake and sleep health that can influence chronic disease risk factors. A scoping review of research studies investigating dietary intake and sleep was undertaken to determine the extent and scope of research in laboratory-based, free-living and mixed settings. Additionally, this review determines how well subpopulations and geographical locations are represented and the methodologies used to assess outcome measures.

METHODS

Five online databases were used to identify papers published between 1970 and 2017. Included studies were those conducted in adults and reported both outcomes of interest: (i) sleep health, including sleep restriction and sleep hygiene and (ii) dietary outcomes, including altered nutrients, dietary patterns and supplements.

RESULTS

In total, 129 publications were included with the majority being dietary interventions investigating sleep outcomes (n = 109) with fewer being sleep interventions investigating and reporting dietary outcomes (n = 20). Dietary interventions were most often carried out in free-living environments, in contrast to sleep interventions that were most often carried out in laboratory-based environments. The majority of dietary interventions investigated use of a supplement (n = 66 studies), which was predominantly caffeine (n = 49). Sleep interventions investigated sleep duration only, with the majority (n = 17) investigating the effect of partial sleep restriction under 5.5 h per night on dietary intake, while three studies investigating total sleep deprivation.

CONCLUSIONS

Investigating broader aspects of dietary such as overall diet quality and dietary patterns and other components of sleep health such as quality,  timing and sleep hygiene are important aspects for future research.

Sleep deficiency and chronic pain: potential underlying mechanisms and clinical implications

Pain can be both a cause and a consequence of sleep deficiency. This bidirectional relationship between sleep and pain has important implications for clinical management of patients, but also for chronic pain prevention and public health more broadly. The review that follows will provide an overview of the neurobiological evidence of mechanisms thought to be involved in the modulation of pain by sleep deficiency, including the opioid, monoaminergic, orexinergic, immune, melatonin, and endocannabinoid systems; the hypothalamus-pituitary-adrenal axis; and adenosine and nitric oxide signaling. In addition, it will provide a broad overview of pharmacological and non-pharmacological approaches for the management of chronic pain comorbid with sleep disturbances and for the management of postoperative pain, as well as discuss the effects of sleep-disturbing medications on pain amplification.

Cannabis Expectancies for Sleep

Up to 35% of adults in the United States suffer from sleep disturbances, which covary with a host of negative mental and physical health outcomes. Previous research suggests that cannabis' sedative effects may be associated with improved sleep. The present study examined the self-reported effect of cannabis use on individual's sleep-related problems. Participants included 311 individuals recruited online, who reported both sleep-related problems and cannabis use. Analyses revealed that participants expected cannabis to decrease the incidence of sleep-related problems, including allowing participants to have an earlier bedtime, to fall asleep more quickly, and to have a longer night's sleep. Moreover, expectancies about the influence of cannabis on sleep negatively covaried with cannabis-related problems. These findings suggest that individuals believe using cannabis might positively influence their sleep quality and believing so may be protective against cannabis problems. Randomized control trials of cannabis for insomnia appear justified.

Feasibility and Preliminary Efficacy of an m-Health Intervention Targeting Physical Activity, Diet, and Sleep Quality in Shift-Workers

Poor health behaviors are prevalent in shift-workers, but few multiple health-behavior interventions consider their unique needs. This study aimed to (1) evaluate the feasibility and acceptability of an existing app-based intervention to improve physical activity, diet, and sleep quality in a shift-worker population, (2) estimate intervention effect in a four-week pilot randomized controlled trial (RCT) (ACTRN12618001785291). Shift-workers (18–65 years old) were randomized to intervention (n = 20) or wait-list (n = 20) groups. Outcomes included recruitment, engagement, attrition, usefulness ratings, System Usability Scale (SUS), qualitative interviews, and estimation of treatment effect (minutes of physical activity, diet quality, and sleep quality) using mixed model analysis. Recruitment took one week. App-use at week four was 55% (11/20), 85% (34/40) completed the four-week follow-up questionnaire, and 20% (4/20) of the intervention group completed the qualitative interview. The intervention was rated as slightly to moderately useful by 76.9% (10/13) of participants on a five-point scale. The SUS score was 62.7 (12.7) out of 100. Diet quality improved for the intervention (4.5 points; 95% confidence interval (CI) = 0.1, 8.9; p = 0.047) vs. the wait-list group, but not physical activity or sleep quality. Qualitative interviews found that a more tailored intervention, more accessible information, and interactive features were desired. The intervention was feasible in terms of recruitment, but modifications to increase engagement are needed.

Efficacy of an m-Health Physical Activity and Sleep Health Intervention for Adults: A Randomized Waitlist-Controlled Trial

INTRODUCTION

Interventions that improve both physical activity and sleep quality may be more effective in improving overall health. The purpose of the Synergy Study is to test the efficacy of a mobile health combined behavior intervention targeting physical activity and sleep quality.

STUDY DESIGN

Randomized, waitlist-controlled trial.

SETTING/PARTICIPANTS

This study had an app-based delivery mode, Australia-wide. The participants were 160 adults who reported insufficient physical activity and poor sleep quality in an eligibility survey.

INTERVENTION

The intervention was a mobile app providing educational resources, goal setting, self-monitoring, and feedback strategies. It included 12 weeks of personalized support including weekly reports, tool sheets, and prompts.

MAIN OUTCOME MEASURES

Outcomes were assessed at baseline, 3 months (primary), and 6 months (secondary endpoint). Self-reported minutes of moderate-to-vigorous intensity physical activity and sleep quality were co-primary outcomes. Resistance training; sitting time; sleep hygiene; sleep timing variability; insomnia severity; daytime sleepiness; quality of life; and depression, anxiety, and stress symptoms were secondary outcomes. Data were collected between June 2017 and February 2018 and analyzed in August 2018.

RESULTS

At 3 months, between-group differences in moderate-to-vigorous intensity physical activity were not statistically significant (p=0.139). Significantly more participants in the intervention group engaged in ≥2 days/week (p=0.004) of resistance training. The intervention group reported better overall sleep quality (p=0.009), subjective sleep quality (p=0.017), sleep onset latency (p=0.013), waketime variability (p=0.018), sleep hygiene (p=0.027), insomnia severity (p=0.002), and lower stress symptoms (p=0.032) relative to waitlist controls. At 6 months, group differences were maintained for sleep hygiene (p=0.048), insomnia severity (p=0.002), and stress symptoms (p=0.006). Differences were observed for bedtime variability (p=0.023), sleepiness (p<0.001), daytime dysfunction (p=0.039), and anxiety symptoms (p=0.003) at 6 months, but not 3 months.

CONCLUSIONS

This remotely delivered intervention did not produce statistically significant between-group differences in minutes of moderate-to-vigorous intensity physical activity. Significant short-term differences in resistance training and short- and medium-term differences in sleep health in favor of the intervention were observed.

TRIAL REGISTRATION

This study is registered at anzctr.org.au ACTRN12617000376347.

Time-of-day and days-on-shift predict increased fatigue over two-week offshore day-shifts

OBJECTIVES

The purpose of this study was to investigate the accumulation of fatigue over a two-week offshore period. In particular, the effects of (1) time-of-day and days-on-shift as well as (2) acute and chronic sleep loss on the rate at which fatigue accumulates were investigated.

METHODS

42 day-shift offshore workers were examined. Fatigue was measured using pre- and post-shift scores on the Karolinska Sleepiness Scale (KSS). Total sleep time was measured using actigraphy (Motionwatch8, Camntech). Data was analyzed using a linear mixed model analyses.

RESULTS

Average sleep loss per night was 92 min (95%CI: 89.6-94.0; p < .001). Mean cumulative sleep loss across the study was 21:20hrs (SD = 08:10hrs) over the 14 days. Chronic sleep loss was significantly related to a modest increase in sleepiness (KSS) across the shift (95%CI: 0.01-0.17; p = .020) and in post-shift scores (95%CI:.07-0.19; p < .001). Time-of-day (95%CI: 0.63 to -0.01; p = .042) and days-on-shift (95%CI: 0.03-0.08; p < .001) as well as their interaction (95%CI: 0.08 to -0.00; p = .027) influenced the rate at which fatigue accumulated over a two-week offshore period.

CONCLUSIONS

Pre- and post-shift fatigue accumulate in different ways over the two-week offshore period. The accumulation of post-shift fatigue scores was positively related to successive days-on-shift and chronic sleep loss. Our results suggest that prolonging offshore periods will likely result in elevated fatigue risk. Accumulating fatigue and sleep loss over two-week offshore periods should be considered in fatigue risk management plans and systems.

When research leads to learning, but not action in high performance sport

Research from sports science and related clinical and scientific fields produces knowledge that is often highly relevant to high performance sport. However, there exists a gap between current science and applied coaching or sports science practice. Addressing, and bridging, this gap from both sides of the research-application divide is an important ambition. In this commentary we discuss the wonderful, yet often unforgiving challenge of improving athlete performance as a practitioner or coach in high performance sport. Blending existing knowledge (that which we know works), transferable knowledge (that which has worked elsewhere), anecdotal observations (that which we have seen work) and innovation (where the risk: reward ratio of it working at all is high), to find the right solution, at the right time, for the specific (and often unique) individual is the challenging "art" of application. Here we explore this challenge related to the high performance sports system and the people within it, then more specifically to coaching and learning, and finally to practitioners and athletes using a case study example of applying sleep science to the high performance environment.