Sleep quality is an important clinical construct since it is increasingly common for people to complain about poor sleep quality and its impact on daytime functioning. Moreover, poor sleep quality can be an important symptom of many sleep and medical disorders. However, objective measures of sleep quality, such as polysomnography, are not readily available to most clinicians in their daily routine, and are expensive, time-consuming, and impractical for epidemiological and research studies., Several self-report questionnaires have, however, been developed. The present review aims to address their psychometric properties, construct validity, and factorial structure while presenting, comparing, and discussing the measurement properties of these sleep quality questionnaires. A systematic literature search, from 2008 to 2020, was performed using the electronic databases PubMed and Scopus, with predefined search terms. In total, 49 articles were analyzed from the 5734 articles found. The psychometric properties and factor structure of the following are reported: Pittsburgh Sleep Quality Index (PSQI), Athens Insomnia Scale (AIS), Insomnia Severity Index (ISI), Mini-Sleep Questionnaire (MSQ), Jenkins Sleep Scale (JSS), Leeds Sleep Evaluation Questionnaire (LSEQ), SLEEP-50 Questionnaire, and Epworth Sleepiness Scale (ESS). As the most frequently used subjective measurement of sleep quality, the PSQI reported good internal reliability and validity; however, different factorial structures were found in a variety of samples, casting doubt on the usefulness of total score in detecting poor and good sleepers. The sleep disorder scales (AIS, ISI, MSQ, JSS, LSEQ and SLEEP-50) reported good psychometric properties; nevertheless, AIS and ISI reported a variety of factorial models whereas LSEQ and SLEEP-50 appeared to be less useful for epidemiological and research settings due to the length of the questionnaires and their scoring. The MSQ and JSS seemed to be inexpensive and easy to administer, complete, and score, but further validation studies are needed. Finally, the ESS had good internal consistency and construct validity, while the main challenges were in its factorial structure, known-group difference and estimation of reliable cut-offs. Overall, the self-report questionnaires assessing sleep quality from different perspectives have good psychometric properties, with high internal consistency and test-retest reliability, as well as convergent/divergent validity with sleep, psychological, and socio-demographic variables. However, a clear definition of the factor model underlying the tools is recommended and reliable cut-off values should be indicated in order for clinicians to discriminate poor and good sleepers.

The lack of quantitative criteria for identifying insomnia using actigraphy represents an unresolved limit for the use of actigraphy in a clinical setting. The current study was conducted to evaluate the most efficient actigraphic parameter in the assessment of insomnia and to suggest preliminary quantitative actigraphic criteria (QAC). Performing a retrospective study we recovered 408 actigraphic records from 3 sleep measure databases: 2 regarding insomnia patients (n = 126) and one normal sleepers (n = 282). We compared the 2 samples analyzing the following actigraphic sleep parameters: time in bed (TIB), sleep onset latency (SOL), total sleep time (TST), wake after sleep onset (WASO), sleep efficiency percentage (SE%), number of awakenings longer than 5 minutes (NA > 5) and mean motor activity (MA). Moreover, a linear discriminant function (LDF) was developed to identify and combine the most useful actigraphic sleep parameters to separate insomnia patients from normal sleepers. Using Youden index we calculated the preliminary QAC for each actigraphic sleep parameter and for LDF. Receiver operator characteristic (ROC) curves for classifying the accuracy of QAC were performed. All sleep parameters recorded by actigraphy significantly differentiated the 2 groups, except TIB. An LDF analysis showed that the most useful combination of actigraphic sleep parameters to assess insomnia was TST, SOL, and NA > 5, which obtained the best ROC and the best balance between positive and negative predictive values compared to any single actigraphic parameter. Actigraphy provided a satisfactory objective measurement of sleep quality in insomnia patients. The combination of TST, SOL, and NA > 5 proved the best way to assess insomnia using actigraphy. Acknowledging that the lack of a technological standard and some methodological limitations prevent us generalizing our results, we recommend additional studies on larger populations using different actigraph models.

The present study aimed to compare two commercially available actigraphs, with a concurrent polysomnographic (PSG) recording. Twelve healthy volunteers (six women; age range 19–28 yrs) simultaneously wore the Basic Mini‐Motionlogger® and Actiwatch® for seven overnight polysomnographic recordings. Comparisons of the following sleep measures were focused on: sleep onset latency (SOL), total sleep time, wake after sleep onset, and sleep efficiency. Both devices underestimated SOL in comparison to PSG, but they had similar performance compared to PSG for the other sleep measures. A limit of the study is that the results can be only generalized to healthy young subjects.

The present study aimed to analyze season of birth effects on preferred sleep-wake cycle timing as assessed by Morningness-Eveningness Questionnaire (MEQ). The MEQ was administered to a sample of 5,720 university students (3,851 Italians and 1,869 Spaniards; 3,877 female and 1,843 male; mean age 22.23 ± 2.98 years). Females preferred to go to bed significantly earlier and sleep longer than males, regardless of season of birth and nationality. Subjects born in spring and summer went to bed and reached midpoint of sleep later than subjects born in fall and winter. Nationality significantly affected all the sleep parameters considered except duration. Overall, the effect of the season of birth on sleep preference timing was significant but quantitatively small. We suggest an evolutionary context for the different contributions of genetic and environmental factors in modulating sleep-wake cycles in humans.