Summary: Insomnia nosology has significantly evolved since the Diagnostic and Statistical Manual (DSM)‐III‐R first distinguished between ‘primary’ and ‘secondary’ insomnia. Prior International Classification of Sleep Disorders (ICSD) nosology ‘split’ diagnostic phenotypes to address insomnia's heterogeneity and the DSM nosology ‘lumped’ them into primary insomnia, while both systems assumed causality for insomnia secondary to health conditions. In this systematic review, we discuss the historical phenotypes in prior insomnia nosology, present findings for currently proposed insomnia phenotypes based on more robust approaches, and critically appraise the most relevant ones. Electronic databases PsychINFO, PubMED, Web of Science, and references of eligible articles, were accessed to find diagnostic manuals, literature on insomnia phenotypes, including systematic reviews or meta‐analysis, and assessments of the reliability or validity of insomnia diagnoses, identifying 184 articles. The data show that previous insomnia diagnoses lacked reliability and validity, leading current DSM‐5‐TR and ICSD‐3 nosology to ‘lump’ phenotypes into a single diagnosis comorbid with health conditions. However, at least two new, robust insomnia phenotyping approaches were identified. One approach is multidimensional‐multimethod and provides evidence for self‐reported insomnia with objective short versus normal sleep duration linked to clinically relevant outcomes, while the other is multidimensional and provides evidence for two to five clusters (phenotypes) based on self‐reported trait, state, and/or life‐history data. Some approaches still need replication to better support whether their findings identify true phenotypes or simply different patterns of symptomatology. Regardless, these phenotyping efforts aim at improving insomnia nosology both as a classification system and as a mechanism to guide treatment.

Across all ages, individuals with Down syndrome (DS) experience high rates of sleep problems as well as cognitive impairments. This study sought to investigate whether circadian rhythm disruption was also experienced by people with DS and whether this kind of sleep disorder may be correlated with cognitive performance. A cross-sectional study of 101 participants (58 with DS, 43 with typical development) included individuals in middle childhood (6–10 years old), adolescence (11–18 years old), and young adulthood (19–26 years old). Sleep and markers of circadian timing and robustness were calculated using actigraphy. Cognitive and behavioral data were gathered via a novel touchscreen battery (A-MAP^TM, Arizona Memory Assessment for Preschoolers and Special Populations) and parent questionnaire. Results indicated that children and adolescents with DS slept the same amount as peers with typical development, but significant group differences were seen in phase timing. The circadian robustness markers, interdaily stability and intradaily variability of sleep-wake rhythms, were healthiest for children regardless of diagnostic group and worst for adults with DS. Amplitude of the 24-h activity profile was elevated for all individuals with DS. In analyses of the correlations between sleep quality, rhythms, and cognition in people with DS, interdaily stability was positively correlated with reaction time and negatively correlated with verbal and scene recall, a finding that indicates increased stability may paradoxically correlate with poorer cognitive outcomes. Further, we found no relations with sleep efficiency previously found in preschool and adult samples. Therefore, the current findings suggest that a thorough examination of sleep disorders in DS must take into account age as well as circadian robustness to better understand sleep-cognitive correlations in this group.

The long-term effects of sleep health and shift work on cognitive performance are unclear. In addition, research has been limited by small sample sizes and short follow-up periods. We conducted one of the largest examinations of the longitudinal influence of sleep health dimensions and shift work on cognitive performance in people of middle and old age using data from the UK Biobank. The hypothesis was that poor sleep health and shift work would predict lower cognitive performance. Self-reported sleep duration, daytime sleepiness, insomnia symptoms, chronotype and shift work status were assessed as predictors at baseline. Cognitive performance was operationalised by a touchscreen test battery at follow-up between 7.4 ± 2.2 and 9.0 ± 0.9 years after baseline assessment, depending on the specific task. Models were performed for each cognitive domain including relevant confounders (e.g., depression). The alpha level was set at p<0.01 for all analyses. The study sample comprised 9,394 participants for the reasoning task, 30,072 for the reaction time task, 30,236 for the visual memory task, 2,019 for the numeric memory task and 9,476 for the prospective memory task. Shift work without night shifts (ß= -2.0x10- ¹ ± 6.5x10- ², p=0.002) and with night shifts (ß= -1.9x10- ¹ ± 7.2x10- ², p=0.010) predicted a significantly reduced performance in the reasoning task. Short sleep duration (ß= -2.4x10- ¹ ± 7.9x10- ², p=0.003) and shift work without night shifts (ß= -3.9x10- ¹ ± 1.2x10- ¹, p=0.002) predicted a significantly lower performance in the task probing prospective memory. Our results suggest that, after controlling for confounding variables, shift work and short sleep duration are important predictors for cognitive performance in people of middle and old age. Further work is required to examine causal mechanisms of the observed associations.

The objective of this study was to understand the associations of sleep and cardiorespiratory fitness with hippocampal volume and global cognition among older adults (n = 30, age = 65.8 years, female = 73.3%). Wrist actigraphy provided objective measures of nighttime sleep including sleep duration, average wake bout length (WBL; sleep disturbance), and wake-to-sleep transition probability (WTSP; sleep consolidation). Cardiorespiratory fitness was quantified via cycle exercise using a modified heart rate recovery approach. Magnetic resonance imaging was used to determine hippocampal volume and the Mini-Mental State Examination was used to assess global cognition. Fitness moderated associations of sleep with hippocampal volume and cognitive performance, whereby the association of WBL—an index of poor sleep—with hippocampal atrophy was stronger among less-fit individuals, and the association of sleep duration with cognitive performance was stronger among more-fit individuals. Across the fitness levels, a longer WBL was associated with lower cognitive performance, and a higher WTSP—an index of more consolidated sleep—was associated with greater hippocampal volume. Sleep and fitness were unrelated to the volume of an amygdala control region, suggesting a degree of neuroanatomical specificity. In conclusion, higher cardiorespiratory fitness may attenuate sleep disturbance-related hippocampal atrophy and magnify the cognitive benefits of good sleep. Prospective studies are needed to confirm these findings.