Measuring Subjective Sleep Quality: A Review

In the original paper [ 21 ] a cut-off score of 5 was proposed to distinguish between poor and good quality sleepers. This cut-off was used in four papers, supporting known-group validity [ 31 , 34 , 38 , 39 ]. Using ROC curve analysis, the reviewed papers did not systematically confirm this cut-off value [ 25 , 36 , 40 ], but cut-offs greater than 5, such as 6, 7, 8.5 or 11 were more useful in balancing between sensitivity and specificity. Most probably, this differentiation reflects the different populations taken into account and the clinical use of the global PSQI score. The cut-off of 11 was a very severe cut-off used for detecting insomnia patients [ 25 ], even if a value equal to 8.5 was a sufficient cut-off for detecting the severity of symptoms in a sample of insomnia patients (211 out of 261) [ 32 ]. According to a specific population (university students [ 40 ] or sleep-disorder adult patients [ 29 ]), a cut-off of 6 or 7 seemed to be able to distinguish insomniacs. Even if further research is needed to clarify the application and the use of specific cut-off points, shows that the PSQI had a good construct validity as demonstrated by known-group differences on the basis of proposed cut-off values or other sleep disorder assessments. It is worth noting that the group comparisons were performed according to different cut-off parameters, such as depression level, suggesting the association between poor sleep and different psychological or medical variables [ 23 , 26 , 28 , 30 , 31 , 33 , 35 , 36 ]. Finally, four studies performed regression analyses in order to detect which variable(s) could predict poor sleep; depression, anxiety, and stress were able to predict poor sleep quality [ 34 , 38 ]. As regards gender, females appeared to report a higher PSQI score (and also for C1 and C5 components; [ 39 , 41 ]) even if this result was not confirmed in other studies [ 27 , 35 ]. Only [ 27 ] reported the role of age and literacy in predicting sleep quality but these results need further research.

As shown in , the Cronbach’s alpha was on average equal to 0.76 [ 42 ]. It should be noted that the Cronbach’s alpha increased, on average, by 2 points, excluding C6 [ 30 , 39 , 40 ], supporting the fact that this component is problematic when defining the global score. The reliability of the PSQI was also shown by all corrected item-to-total or component-to-total correlations which ranged on average between 0.31 and 0.66, that is, from moderate to high correlations. Only in [ 38 ] were the corrected component-to-total correlations low (0.10–0.40). Only six studies tested the reliability of the tool over the time, suggesting that the PSQI was reliable over different periods (from 2 weeks to 14–16 weeks between administrations). The test-retest correlations were high (mean correlations = 0.64) and no difference between administrations was reflected in the mean value scores. The PSQI global score correlated with other sleep measures, such as the ISI, Ford Insomnia Response to Stress Test, and Glasgow Sleep Effort Scale [ 29 , 31 , 38 ], but not with ESS or Snore, Tired, Observed, Pressure, Body mass index, Age, Neck, Gender (STOP-BANG) [ 29 , 31 ]. The PSQI global score correlated with different tools measuring well-being from different perspectives (e.g., Beck Depression Inventory or Health Survey Short Form 36) with coefficient correlations ranging from −0.40 to 0.72 [ 30 , 33 , 34 , 37 , 38 ]. The correlations between the PSQI score and objective measures of sleep appeared to be more problematic, with a small number of exceptions such as the correlation between global score and Stage 2 latency (r = 0.294), Slow Wave Sleep latency (r = 0.524), Stage 1% (r = 0.327) and Stage 2% (r = −0.349) obtained by PSG [ 36 ], between sleep latency measured by both PSG and PSQI (r = 0.225) or between sleep efficiency measured by both PSG and PSQI (−0.331) [ 32 ].

Among different sample types (from non-clinical individuals to different medical populations), with a vast range of numerosity (from 50 to 3.742) comprising a wide age range (18–80+) and different language versions (English, Chinese, Greek, Korean, Italian, Spanish, Sinhala, European Portuguese, Malay, Kurdish, and Arabic), the most interesting result is related to the factor structure underlying the PSQI, using different factorial analyses ( ). In the present review, six papers [ 23 , 25 , 26 , 27 , 28 , 29 ] reported unidimensionality, six studies indicated a 2-factor model [ 30 , 31 , 32 , 33 , 34 , 35 ] and two investigations found a three-factor model [ 36 , 37 ]. The remaining articles in did not show a unique factor model [ 38 , 39 , 40 , 41 ]. In the two-factor solution, the C2, C3, and C4 loaded on one factor (i.e., a sort of sleep efficiency factor) whereas C5 and C7 loaded on the other factor (i.e., a version of the daytime disturbance factor). The C1 was often an added component of the factor containing C2–C4 while C6 either added to a factor comprising C5 and C7 or was deleted because of low (<0.40) loading value [ 30 , 31 , 32 , 33 , 34 ]. The only exception was the study conducted in cancer patients [ 35 ] with a factor labelled Perceived Sleep Quality with C1, C2, C5, C6, and C7, and the other factor labelled Sleep Efficiency with C3 and C4. Inter-factor correlation was on average 0.476. By contrast, the three-factor solution indicated that the Sleep Efficiency factor included C3 and C4, the Perceived Sleep Quality factor included C1, C2 and C6, and the Daily Disturbance factor included C5 and C7, with correlations between first and second factors (mean 0.465), between second and third factors (mean 0.58), and between first and third factors (mean 0.415) [ 26 , 33 , 36 , 39 ]. Alternative models included the same factors with the exclusion of C6. Importantly, a single study reported a different three-factor structure for male and female adults [ 41 ]. Specifically, for men F1 was determined by C1, C3, and C4, F2 by C5 and C7, and F3 included C2 and C6; for women F1 was determined by C1, C5, and C7, F2 included C3 and C4, and F3 was loaded by C2 and C6. This result could indicate the presence of gender differences in sleep quality.

The Pittsburgh Sleep Quality Inventory (PSQI; [ 21 ]) is the most commonly used measure of subjective self-report sleep quality for two main reasons. It was not only developed to quantify sleep quality [ 21 ] but also, in the majority of studies that validate a sleep questionnaire, the PSQI has been used as convergent validity, suggesting that the PSQI can be considered as an accepted reference or gold standard for self-perceived sleep quality. In addition, it is the most widely used sleep health assessment tool in both clinical and non-clinical populations [ 11 ]. In the present review, it was the questionnaire with the highest number of studies investigating its psychometric properties, beyond factor structure. The PSQI consisted of 24 questions or items to be rated, relating to the past month (0–3 for 20 items while 4 items were open-ended), 19 of which were self-reported and 5 of which required secondary feedback from a room or bed partner. Only 19 items (15 rated 0–3 and 4 open-ended) were used for the evaluation of sleep quality as perceived by the individuals. The open-ended items were also scored as categorical values (rated 0–3) as per the range of values reported by the patients. These 19 self-reported items were then used to generate scores, which ranged from 0 (no difficulty) to 3 (severe difficulty), representing the PSQI’s seven components: sleep quality (C1), sleep latency (C2), sleep duration (C3), habitual sleep efficiency (C4), sleep disturbance (C5), use of sleep medications (C6), and daytime disturbance (C7). The scores for each component were summed to get a total score, also termed the global score (range 0–21), providing an efficient summary of the respondent’s sleep experience and quality. Panayides et al. [ 23 ] not only revised the original 4-point Likert scale with a more optimal 3-point Likert scale that is more appropriate for a non-clinical sample, but also proposed a 16-item version following two calibrations using the Rasch model [ 24 ]. In contrast, Chien et al. [ 25 ] proposed a revised PSQI: short form Chinese version or SC_PSQI with nine items (sleep latency, habitual sleep efficiency, sleep disturbances, sleep interruptions, use of sleeping medication, daytime dysfunction, days of insomnia, fatigue upon awakening in bed, and earlier awakening). In addition, scoring of answers was changed from 0–3 to 0–2, and the score total amounted to 18.

3.2. The Sleep-Disorder Scales

Sleep disorders are among the most prevalent complaints in primary medical care and in the general population [4,5,6,7,8,9,10,11,12]. Epidemiological data indicate that insomnia is the most frequent sleep complaint [3]. Insomnia disorder is characterized by difficulty falling asleep, difficulty staying asleep, early morning awakening, and clinical distress or impairments of daily activities [3,13,14,15]. Sleep disturbance and associated daytime symptoms occur at a frequency of three nights or more per week for at least three months. In addition, sleep disorders compromise the sleep-wake cycle and they can affect sleep (hyposomnia) and/or wake (hypersomnia). In this section, we decided to group altogether all scales evaluating more specific alterations of sleep, such as insomnia or different sleep disorders, and complaints of the sleep-wake cycle.

The Athens Insomnia Scale (AIS) [43] is a self-reported questionnaire designed to measure the severity of insomnia based on the diagnostic criteria of the International Classification of Diseases, 10th revision (ICD-10) [44]. There are two versions of the scale: the AIS-8 and the AIS-5. For the eight-item scale, the first five items (assessing difficulty with sleep induction, awakening during the night, early morning awakening, total sleep time, and overall quality of sleep) correspond to criterion A (“complaint of difficulty falling asleep, maintaining sleep or non-refreshing sleep”) for the diagnosis of insomnia according to ICD-10 [44], while the last three items pertain to the consequences of insomnia the next day (problems with sense of well-being, functioning, and sleepiness during the day) according to criterion C of ICD-10 (“the sleep disturbance results in marked personal distress or interference with personal functioning in daily living”). The brief 5-item version is made up of the first five items. In both versions, participants are asked to score each item from 0 (no problem at all) to 3 (very serious problem) if they have experienced any difficulty sleeping at least three times a week during the past month. The total score of the AIS-8 ranges from 0 to 24 while that of the AIS-5 ranges from 0 to 15. Within the selected papers that regard AIS, a great quantity of the studies was performed in Asiatic countries, without modification of the original version. As regards the factor structure of the AIS-8 ( ), three studies provided support for unidimensionality [45,46,47] (a mean variance of 67.58% explained; an average range of factor loadings of between 0.54 and 0.85), while three other studies reported a better fit with the two-factor model [48,49,50] with a Nocturnal factor (items 1–5; mean factor loadings ranging between 0.55 and 0.87) and a Daytime Dysfunction factor (items 6–8; mean factor loadings ranging between 0.54 and 0.93), with a mean inter-factor correlation equal to 0.70. Two studies using the AIS-5 found a 1-factor model [45,49] with an average of 57.16% of variance explained, confirming the latent presence of the Nocturnal factor in the full AIS version. The sample size and the type of population assessed (generally trauma patients in studies assessing the two-factor model) may be responsible for these divergent results in the factor analysis. The mean reliability of AIS-8 was 0.86 while that of AIS-5 was 0.84 (Cronbach’s alphas for the two supposed factors were on average higher than 0.80 for the Nocturnal factor and above 0.70 for the Daytime Dysfunction factor). A good internal homogeneity was also demonstrated (mean item-total correlation range 0.56–0.80). With different temporal intervals (from 1 week to about 3 months), the mean ICC of the AIS-8 was 0.78 and that of the AIS-5 was 0.68, suggesting a good test-retest reliability. Finally, both versions of AIS showed convergent/divergent validity (many correlations of a moderate level > 0.30; from −0.53 to +0.85 in range) with different sleep scales, such as PSQI and ISI, and with different psychological variables, such as anxiety or depression [45,46,47,48,49,50], but not with Alcohol Use Disorders Identification Test (AUDIT) or socioeconomic status [46]. The validity of the AIS was also confirmed by known-group differences between patients (psychiatric, insomnia or cancer patients taking opioids) and control or non-insomnia groups ( ). Age (older adults) and gender (women) differences were found in the total AIS score as well as for both factors [50]. When a specific cut-off score was proposed, we observed that for AIS-8 values in the range between 5 and 9 [46,48,49] could discriminate between insomnia and non-insomnia groups ( ) with a mean sensitivity equal to 80% and a mean specificity equal to 82%, in line with the proposed cut-offs in the original study [44,51]. The different cut-off values reflected the insomnia patients involved in the study and the severity of their insomnia symptoms [46,48,49]. It is worth noting that Enomoto et al. [49] reported a cut-off of 4 for AIS-5.

Table 2

ReliabilityConstruct Validity
Study and Abbreviation of QuestionnairePopulationDimensionalityInternal ConsistencyTest-RetestConvergent/Divergent ValidityKnown-Group Validity
(Mean Value)Cut-Off Score or ROC CurveVersionGómez-Benito et al., 2011 [45]: AIS-8 and AIS-5A total of 323 individuals (227 females); mean age of 30.29 years: 167 students (138 females; mean age of 20.50 years), 77 psychiatric outpatients (38 females; mean age of 40.16 years) and 79 community sample (56 females; mean age of 43.42 years)AIS-8: 1-factor model (51.50% of variance explained) and factor loading ranging from 0.488 to 0.858.
AIS-5: 1-factor model (54.33% of variance explained) and factor loading ranging from 0.614 to 0.818Cronbach’s alpha for AIS-8 was 0.86. Cronbach’s alpha for AIS-5 was 0.79.For AIS-8 Cronbach’s alphas for undergraduates, patients and the general population were 0.81, 0.89, and 0.86, respectively.
For AIS-5 Cronbach’s alpha for undergraduates, patients and the general population were 0.66, 0.83, and 0.78, respectivelyA 1-month test-retest for AIS-8 (ICC = 0.75) and for AIS-5 (ICC = 0.64)AIS-8 and AIS-5 correlated with BDI (0.53 and 0.46, respectively), with BAI (0.49 and 0.42 respectively) and with GHQ-12 (0.54 and 0.44 respectively)For AIS-8: students (4.83) < community sample (6.20) < psychiatric patients (8.12).
For AIS-5: students (2.82) < community sample (4.22) or psychiatric patients (5.22)
SpanishOkajima et al., 2013 [48]: AIS-JA total of 640 individuals (371 females); mean age of 48.8 years: 477 outpatients with chronic insomnia (253 women; mean age of 47.9 years) and 163 individuals who scored less than 6 points on PSQI-J (54 women; mean age of 51.3 years)A 2-factor structure: Nocturnal Sleep Problems (AIS-J-nocturnal, items 1–5; factor loading 0.33–0.87) and Daytime Dysfunction (AIS-J-daytime, items 6–8; factor loading 0.45–0.94).Inter-factor correlation was 0.62Cronbach’s alpha for total score was 0.88 and for F1 and F2 it was 0.85 and 0.78, respectively
There was a correlation between AIS-J and PSQI-J (0.81) and between AIS-J and ISI-J (0.85).
In patients with insomnia there were correlations between AIS-J and PSQI-J (0.57) and between AIS-J and ISI-J (0.58) but no significant correlations were found in patients with depression or those with anxiety disorder (rs n.a.)Insomnia group with higher scores than control group for AIS-J (11.81 vs. 2.64), Nocturnal (8.12 vs. 1.63) and Daytime (3.65 vs. 1.01) factors. Insomnia, depression and anxiety disorder groups (above 11) > healthy controls (less than 3). Same results for Nocturnal factor (above 6 for patients and below 2 for controls). For Daytime factors, depression and anxiety disorder groups (above 4) had higher scores than primary insomnia (about 3) and controls (about 1)AIS-J: ROC curve for insomnia (primary and secondary) with AUC = 0.96 for cut-off of 5.5 and sensitivity = 92% and specificity = 93% (LR+ = 13.62 and LR− = 0.09.
AIS-J-nocturnal: ROC curve for insomnia (primary and secondary) with AUC = 0.97 for cut-off of 3.5 and sensitivity = 93% and specificity = 94% (LR+ = 16.73 and LR− = 0.07).
For primary insomnia AIS-J > 5.5 with AUC = 0.97, sensitivity = 93%, specificity = 93%, LR+ = 13.78, LR− = 0.08; AIS-J—nocturnal > 3.5 with AUC = 0.97, sensitivity = 94%, specificity = 94%, LR+ = 16.88, LR− = 0.07JapaneseJeong et al., 2015 [46]: AIS-8A total of 221 firefighters and rescue workers (14 women); mean age of 40.3 yearsA 1-factor structure (95.73% of variance explained) with factor loading of each item ranging from 0.51 to 0.82Cronbach’s alpha of 0.88. Mean item-total correlation coefficient was 0.73 (0.56–0.84 range)A 1 week test-retest with ICC for total score equal to 0.94 (0.58–0.95 range)AIS-8 correlated with PSQI (0.82), ISI (0.85), ESS (0.29), SF-36 mental component summary (−0.53), SF-36 physical component summary (−0.37), but not with AUDIT-C (0.10) or socioeconomic status (0.01)Based on Structured Clinical Interview for the DSM-IV-TR and a structured clinical interview for insomnia, participants were divided into non-insomnia, participants with insomnia symptoms (group 1), individuals with disturbed daily functioning (group 2) and those in group 2 who had symptoms even during off-duty periods.Non-insomnia group (4.1) < group 1 (9.3) < group 2 (10.1)ROC curve for group 1 with cut-off score AIS-8 = 6, AUC = 0.87, sensitivity = 87%, specificity = 72%, LR+ = 3.07, LR− = 0.18;
ROC curve for group 2 with cut-off score AIS-8 = 8, AUC = 0.84, sensitivity = 73%, specificity = 79%, LR+ = 3.50, LR− = 0.35; ROC curve for group 3 with cut-off score AIS-8 = 9, AUC = 0.85, sensitivity = 71%, specificity = 84%, LR+ = 4.45, LR− = 0.34KoreanEnomoto et al., 2018 [49]: AIS-8 and AIS-5A total of 144 outpatients with a history of pain (86 females); mean age of 53.3 yearsAIS-8: 2-factor model without item 8 with poor factor loading: Nocturnal Sleep Problem (items 1–5) and Daytime Dysfunction (6–7).
AIS-5: 1-factor (Nocturnal Sleep Problem) model with a covariation between item 1 and item 5 (0.30) and factor loading > 0.60Cronbach’s alpha for AIS-8 was 0.87 and for AIS-5 was 0.89.
For nocturnal sleep problems the Cronbach’s alpha was 0.89 and for daytime dysfunction was 0.66An 87.4 day test-retest with overall ICC of 0.64 for AIS-8, 0.72 for the AIS-5 and nocturnal sleep problems and 0.54 for daytime dysfunctionAIS-8 correlated with NRS (0.36), PDAS (0.46), HADS-anxiety (0.54), HADS-depression (0.64), PCS-total (0.36), PCS-rumination (0.23), PCS-magnification (0.37), PCS-helplessness (0.35), PSEQ (−0.47).
AIS-5 correlated with NRS (0.35), PDAS (0.37), HADS-anxiety (0.42), HADS-depression (0.52), PCS-total (0.26), PCS-rumination (0.17), PCS-magnification (0.27), PCS-helplessness (0.24), PSEQ (−0.35)Based on the semi-structured interview data, participants were divided into an insomnia group and non-insomnia group.
Insomnia group (AIS-8 = 11.4; AIS-5 = 7.1) > non-insomnia group (AIS-8 = 5.2; AIS-5 = 2.7)ROC curve to detect insomnia with AIS-8 = 8, AUC = 0.82, sensitivity = 72%, specificity = 85%.
ROC curve to detect insomnia with AIS-5 = 4, AUC = 0.82, sensitivity = 78%, specificity = 70%JapaneseIwasa et al., 2018 [50]: AIS-SJA total of 50,547 community dwellers who lived in the evacuation zone designated by the government for Fukushima Dalichi NPP Incident (27.669 women); mean age of 52.9 yearsA 2-factor model:
F1 or Nocturnal (items 1–5; factor loading from 0.71 to 0.87) and F2 or Daytime (items 6–8; factor loading from 0.62 to 0.91). Inter-factor correlation was 0.77Cronbach’s alpha for all 8 items was 0.81. Cronbach’s alpha for F1 was 0.80 and for F2 was 0.76
Correlations appeared between total AIS-SJ score and K6 scale (0.60), PCL-S (0.60), mental illness (0.36), self-rated health (0.51), experiencing tsunami (0.10), experiencing NPP incident (0.18), bereavement (0.17), housing damage (0.13) and losing job (0.15). There was the same correlation pattern for F1 (0.10–0.54 range) and for F2 (0.08–0.56 range)Young men (2.76) had similar AIS-SJ score to that of old men (2.82). Older women (3.49) had higher AIS-SJ score than young women (3.19). Women (3.27) had higher AIS-SJ score than men (2.80).
Older adults (2.06) had higher Nocturnal score than younger adults (1.86). Women (2.12) had higher Nocturnal score than men (1.80). Younger adults (1.12) had higher Daytime score than older adults (1.09). Women (1.21) had higher Daytime score than men (1.00)
JapaneseLin et al., 2020 [47]: AIS-8 and ISIA total of 573 patients with cancer at stage III or IV (247 females); mean age of 61.3 yearsAIS-8: 1-factor structure (adequate average variance extracted = 0.56) with factor loadings from 0.61 to 0.87 and Rasch-derived infit (0.81 to 1.17) and outfit (0.79 to 1.14) mean square fitted the underlying construct; no substantial DIF was found across the sex (DIF contrast = −0.43 to 0.43) or insomnia condition (DIF contrast = −0.23 to 0.19).
ISI: 1-factor model (adequate average variance extracted = 0.54) with factor loadings from 0.61 to 0.81 and Rasch-derived infit (0.72 to 1.14) and outfit (0.76 to 1.11) mean square fitted the underlying construct; no substantial DIF was found across the sex (DIF contrast = −0.12 to 0.48) or insomnia condition (DIF contrast = −0.19 to 0.33)AIS-8: satisfactory internal consistency (ω = 0.88), high composite reliability (0.91), low standard error of measurement (2.57), corrected item-total correlations from 0.56 to 0.76, separation reliability (0.88 and 0.84) and separation indices (2.75 and 2.30) were acceptable.
ISI: satisfactory internal consistency (ω = 0.79), high composite reliability (0.89), low standard error of measurement (2.00), corrected item-total correlations from 0.43 to 0.67, separation reliability (0.98 and 0.78) and separation indices (7.20 and 2.71) were acceptableA 2-week test-retest reliability for AIS-8 was satisfactory (0.72 to 0.82) and ICC = 0.82.
2-week test-retest reliability for ISI was satisfactory (0.72 to 0.82) and ICC = 0.79AIS-8 and ISI were mutually correlated (0.64).
AIS-8 was correlated with ESAS (0.38), HADS-anxiety (0.58), HADS-depression (0.56), KPSS (−0.50), GHQ-12 (0.61), ESS (0.62) and PSQI (0.55).ISI correlated with ESAS (0.41), HADS-anxiety (0.53), HADS-depression (0.62), KPSS (−0.41), GHQ-12 (0.54), ESS (0.64), and PSQI (0.58)AIS-8 and ISI: patients who took opioids had the highest AIS scores (9.48 and 10.33) followed by those taking non-opioid analgesics (7.13 and 7.60) and those taking other medications (7.29 and 6.41).
AIS < 7 (insomniacs) vs. AIS > 7 (non-insomniacs): difference for actigraph data of TST, SE, bedtime, wake time, SOL, and WASO.
ISI < 9 (insomniacs) vs. ISI > 9 (non insomniacs): difference for actigraph data of TST, SE, bedtime, wake time, SOL, and WASOROC curve with cut-off score of AIS-8 = 7 for detecting insomnia with AUC = 0.86, sensitivity = 86% and specificity = 81%.
ROC curve with cut-off score of ISI = 9 for detecting insomnia with AUC = 0.82, sensitivity = 86% and specificity = 83%.
AIS-8 and ISI cut-offs were consistent with psychiatric diagnosis based on DSM-IVPersianYu, 2010 [58]: ISI-CA total of 585 Chinese community-dwelling older people (474 females); mean age of 74.3 yearsA 2-factor model (61.40% of the total variance) with factor loadings ranging from 0.56 to 0.85: F1 (items 1–4) assessing severity of sleeping difficulties; F2 (items 5–7) assessing daytime interference and distress associated with insomnia as well as how noticeable the sleeping problem wasCronbach’s alpha of 0.81. Corrected item-to-total correlation for the items was in the range of 0.33–0.67.Cronbach’s alpha for F1 = 0.788. Cronbach’s alpha for F2 = 0.640
ISI-C correlated with CPSQI (0.686) and with sleep efficiency derived from CPSQI item scores (−0.583)Depressed adults reported by GDS had higher scores on the ISI-C (12.61) than those without this problem (9.19).
Poorer sleepers defined by the CPSQI cut-off point ≥ 5 had a higher ISI-C score than those of normal sleepers [mean not reported]
ChineseFernandez-Mendoza et al., 2012 [62]: ISIA total of 500 adults from the general population (307 females); mean age of 39.13 yearsA 3-factor model with Impact of Insomnia (items 5–7), Sleep Dissatisfaction (items 1, 4, 7) and Night-time Sleep Difficulties (items 1–3).Mean inter-item correlations were 0.35 (night-time sleep difficulties), 0.59 (impact of insomnia) and 0.50 (sleep dissatisfaction).
Inter-factor correlations were found between Impact of Insomnia and Sleep Dissatisfaction (0.61), between Sleep Dissatisfaction and Night-time Sleep Difficulties (0.83) and between Impact of Insomnia and Night-time Sleep Difficulties (0.48)Cronbach’s alpha was 0.82.
Cronbach’s alpha of factors was 0.60 (for night-time sleep difficulties), 0.81 (for impact of insomnia) and 0.75 (for sleep dissatisfaction).
Corrected item-to-total correlation for the items ranged from 0.47 to 0.71
ISI correlated with PSQI (0.68), ESS (0.18), POMS-fatigue (0.40), POMS-depression (0.34) and POMS-anxiety (0.38).
Impact of insomnia correlated with PSQI (0.49), ESS (0.26), POMS-fatigue (0.45), POMS-depression (0.38) and POMS-anxiety (0.41).
Sleep dissatisfaction correlated with PSQI (0.68), ESS (0.11), POMS-fatigue (0.34), POMS-depression (0.27) and POMS-anxiety (0.33).
Sleep difficulties correlated with PSQI (0.62), POMS-fatigue (0.23), POMS-depression (0.22) and POMS-anxiety (0.23), but not ESS (0.04)
85% of subjects classified as insomniacs in the ISI total score ≥8 were classified as poor sleepers in the PSQI total score > 5 and 33% of non-insomniacs were classified as poor sleepersSpanishSadeghniiat-Haghighi et al., 2014 [59]: ISI-PA total of 1037 patients referred to a sleep disorder clinic (301 females; mean age of 45.4 years) and 50 hospital staff (31 females; mean age of 32.1 years)A 2-factor model (60.58% of variance observed):
F1: item 1a, item 1b, item 1c and item 2 (factor loading ranged from 0.57 to 0.77);
F2: items 3–5 (factor loading ranged from 0.64 to 0.83)Cronbach’s alpha in patients was 0.78.
Corrected item-total correlations ranged from 0.35 to 0.63
ISI-P correlated with ESS (0.12), BDI (0.42) and PSQI (0.74)
ISI total score correlated with PSG variables such as WASO (0.12) and SE (−0.13). Item 1a correlated with WASO (0.17), EMA (0.14), TST (−0.22), TWT (0.19) and SE (−0.24). Item 1b correlated with WASO (0.12), TST (−0.12) and SE (−0.14). Item 1c correlated with SOL (−0.12), WAS (0.12), EMA (0.17), and SE (−0.10). Item 2 correlated with SE (−0.10).
Item 1a correlated with C2-PSQI (0.61), item 1b correlated with C5-PSQI (0.18), item 1c correlated with C5-PSQI (0.13), item 2 correlated with C1-PSQI (0.41), and item 3 correlated with C7-PSQI (0.43)Patient group (15.90) > control group (10.10)
PersianDragioti et al., 2015 [54]: ISI-4A total of 836 patients with chronic pain (269 men with mean age of 50 years and 567 women with mean age of 45 years)And 1-factor model (63.1% of variance explained) with factor loadings ranging from 0.598 to 0.880.
In 109 men the 1-factor solution was confirmed (66.8% of variance explained with factor loadings from 0.591 to 0.905).In 225 women the 1-factor solution was confirmed (62.4% of variance explained with factor loadings from 0.597 to 0.880). In 502 patients the 1-factor solution was found with only 4 items (items 2, 4, 5 and 7: factor loadings from 0.72 to 0.88) and no sex difference was foundCronbach’s alpha of ISI-4 was 0.88.
Component-to-total score correlations were high (0.65–0.80).
Inter-component analysis revealed correlations between items from 0.53 to 0.75
ISI-4 correlated with HADS-anxiety (0.37), HADS-depression (0.35), quality of sleep (0.65) and mental dimension of quality of life (−0.35).
No correlation with age (p > 0.05)No gender difference (men = 10.22 vs. women = 9.80).
Multiple linear regression analysis showed that both sex and pain duration affected the score of the ISI-4 whereas pain intensity was associated with the ISI-4 score
SwedishCastronovo et al., 2016 [63]: ISIA total of 272 consecutive patients (165 females) with insomnia diagnosis and enrolled in a CBT-I; mean age of 41.36 yearsA 3-factor model with Impact (items 3–5), Satisfaction (items 1a, 2, 5) and Severity (items 1a, 1b, 1c).
There were inter-factor correlations between Impact and Satisfaction (0.45), between Impact and Severity (0.25) and between Satisfaction and Severity (0.76). Factor loadings in absolute value ranged from 0.33 to 0.99Ordinal alpha was 0.75 with an increase to 0.76 with the first item removed.
The corrected item-to-total correlation for the items ranged from 0.49 to 0.74After CBI-I treatment the ordinal alpha was 0.73Correlations between the severity ratings obtained in the first three items of the ISI with corresponding quantitative estimates of SOL (0.44 ISI1a), WASO (0.33 ISI1b), NAWK (0.28 ISI1b), EMA (0.44 ISI 1c) obtained from the sleep diaries, the total ISI score and SE (−0.28) variable from the sleep diary correlated with the Impact scale and SOL (0.14; but not with NAWK, WASO, EMA, SE), Satisfaction scale and SOL (0.36), WASO (0.19), EMA (0.19), SE (−0.26; but not with NAWK) and between Severity scale and SOL (0.21), NAWK (0.20), WASO (0.32), EMA (0.45), SE (−0.39)Follow-up evaluations from baseline and follow-up after a CBT-I treatment: percentage of responses 3, 4, and 5 decreased, indicating a general improvement in patients’ conditions. Total score of the ISI and scores obtained in each scale were lower after CBT-I
ItalianGerber et al., 2016 [55]: ISIStudy 2: 862 students (639 women) with mean age of 24.7 years.
Study 3: 533 employees of the police force and emergency response service corps (122 women) with mean age of 41.2 yearsStudies 2 and 3:
1-factor solution for men and women with factor loadings from 0.11 to 0.90 (males) and from 0.11 to 0.89 (females).
Item 5 with factor loadings of 0.31 and 0.29 for Study 2 and of 0.26 and 0.26 for Study 3.
Item 6 with factor loadings of 0.11 and 0.11 for Study 2 and of 0.13 and 0.13 for Study 3Study 2: Cronbach’s alpha was 0.77 in the total sample, 0.78 for men and 0.76 for women.
Inter-item correlations were above the critical value of 0.20 (with the exclusion of item 6). Item-total correlations were found for men and women (with mean correlations of 0.51 for men and 0.49 for women)
Study 3: Cronbach’s alpha was 0.81 in the total sample, 0.81 for men and 0.82 for women
Inter-item correlations exceeded the critical value of 0.20 (with few cases for item 6). Item-total correlations were on average correlations of 0.55 for men and 0.56 for women
Study 2: for males correlations were found between total ISI scores and each item and PSQI with 0.14–0.55 range with few exceptions (below 0.10). Total ISI correlated with all sleep variables of PSQI (range −0.19 and 0.54) with the exception of sleep duration. There were correlations between all items and total scores (0.31–0.50 range; but not item 6) with Depression scale.The same pattern appeared for females (0.13–0.69). ISI correlated with the Depression scale (0.19–0.51 range).
Study 3: for males there were correlations between ISI and each item with PSQI (0.11–0.69 range with few exception with r < 0.10). ISI and each item correlated with SF-12 (from −0.20 to −0.46). The same pattern was evident in females with PSQI (0.18–0.75 range) and with SF-12 (from −0.22 to −0.45 with the exception of item 6)Study 2: women (6.80) > men (5.91) for total ISI score. There were also gender differences for item 1 and item 2 but not for the other items.Study 3: no gender difference was evident for the total ISI score (women: 7.00 and men: 6.97) and there were no differences for any itemCut-off:
0–7 no clinically significant insomnia,8–14 sub-threshold insomnia,
15–21 clinical insomnia (moderate severity)
22–28 clinical insomnia (severe)GermanDieck et al., 2018 [56]: ISIA total of 700 participants (573 females); mean age of 32.16 yearsA 3-factor model:
F1 (items 2, 4, 7), F2 (items 1), F3 (items 5–7). Item 3 was not assigned to one factor. Correlations between F1 and F3 (0.744), but not for F1 and F2 (0.209) or between F2 and F3 (0.180).
1-factor model estimated with the quartimin rotation method and weighted least squares method: factor loadings from 0.409 (item 3) to 0.901 (item 7)Cronbach’s alpha of the ISI was 0.83 and the item-total correlation ranged from 0.36 to 0.77A 2-week test-retest for total score of the ISI was 0.77, and the individual items ranged from 0.51 to 0.73 with only item 3 (0.54) and item 6 (0.51 showed weak test-retest.
Test-retest with PSQI ≤5 (0.31–0.58) and PSQI > 5 (0.42–0.78). Test-retest with PSQI > 5 and BDI-II < 20 (0.35–0.74)Correlations between ISI and PSQI (0.79, 0.61, 0.77), between ISI and BDI-II including sleep item (0.55, 0.37, 0.66), between ISI and BDI-II excluding sleep item (0.56, 0.36, 0.64) and between ISI and SRS (0.36)
Based on PSQI value ≤5 ROC curve with cut-off ISI > 10 with AUC = 0.94, sensitivity = 91% and specificity = 84% (LR+ = 5.86; LR− = 0.10). Based on BDI-II < 20 ROC curve with cut-off score ISI > 10 with sensitivity = 87.28%, specificity = 84.72% (LR+ = 5.71, LR− = 0.15)GermanKaufmann et al., 2019 [57]: ISIA total of 83 Operation Enduring Freedom/Operation Iraqi Freedom/Operation New Dawn veterans with a history of TBI (11 females); mean age of 32.5 years1-factor model (69.0% of variance explained) with total eigenvalues = 4.83 and factor loadings > 0.70Cronbach’s alpha was 0.92
ISI total score was correlated with NSI (0.76) and BDI-II (0.56) sleep item, the PSQI global score (0.76) and with the ESS total score (0.32).
The ISI was correlated with the PSQI individual component score (ranging from 0.324 to 0.791, with the exclusion of C6 with 0.226).
ISI total score correlated with BAI (0.450), PCL-M total with sleep item excluded (0.513) and BDI-II with sleep item excluded (0.476)
Categorical scores:0–7 as no insomnia
8–14 as sub-threshold insomnia
15–21 as moderate insomnia
22–28 as severe insomnia.
Cut-off of ISI > 11 with 67.5% were classified as having clinical insomnia.
Based on PSQI cut-off score of > 8 to indicate elevated insomnia symptoms, ROC curve with AUC = 0.87, sensitivity = 81% and specificity = 71% with cut-off ISI > 11.5English
(American)Dieperink et al., 2020 [61]: ISI-DKA total of 249 patients with a medical condition (158 females); mean age of 58.2 yearsA 2-factor model with Severity factor (items 1–4; factor loadings from 0.57 to 0.88) and Impact factor (items 5–7; factor loadings from 0.73 to 0.90) and correlation between factors (0.88).
3-factor model with Severity factor (items 1–3; factor loadings from 0.59 to 0.92), Impact factor (items 5–7; factor loadings from 0.72 to 1.30) and Dissatisfaction factor (items 1, 4, 7; factor loadings from −0.36 to 0.85) and inter-factor correlations between severity and dissatisfaction (0.94), between severity and impact (0.81) and between dissatisfaction and impact (0.95)Cronbach’s alpha was 0.90 with item-total correlation interval between 0.52–0.80 and a mean value of 0.71.When item 3 was deleted the Cronbach’s alpha increased to 0.91.
For the 2-factor model, the Cronbach’s alpha of Severity factor was 0.83 and that of Impact factor was 0.88.
For the 3-factor model the Cronbach’s alpha of Severity factor was 0.75, that of Dissatisfaction factor was 0.81 and that of Impact factor was 0.8817.1 days test-retest with ICC = 0.90, with SEM = 2.52, SDC = 6.99 and LoA = 0.05
No gender difference (male = 9.36 vs. female = 10.74).
Responders ≥70 years old (8.85) had lower ISI-DK scores compared to younger responders (10.65).Responders with EQ VAS score < 83.7 (11.21) had a higher ISI-DK score compared to responders with a higher EQ VAS score (7.18). Responders with anxiety/depression (12.50) had a higher IS-DK score compared to responders with no problem (8.23).Responders with pain/discomfort problems (11.21) had higher ISI-DK scores compared to responders with no problem (7.44)Using the insomnia cut-offs 25.4% had moderate insomnia (15–21) and 2.4% had severe insomnia (22–28)DanishManzar et al., 2020 [60]: ISIA total of 406 substance-using community dwelling adults (54 females); mean age of 27 yearsA 2-factor model with the incorporation of modification indices to covary the error terms (cumulative variance explained = 63.41%): F1 (items 1–3; factor loading from 0.67 to 0.76) and F2 (items 4–7; factor loading from 0.51 to 0.80). Inter-factor correlation was 0.52Cronbach’s of F1 was 0.68 and of F2 was 0.78.The item-total correlations of the ISI were 0.47–0.72. Inter-item (homogeneity) correlations ranged from 0.11 to 0.57 with the exception of the correlation between item 1 and item 7
All of the item scores of the ISI, both factor scores and the ISI total score, correlated with the meta-cognition total score (0.16–0.44 range) and its factor scores: meta-memory (0.19–0.35 range) and meta-concentrations (0.10–0.44 range)

EthiopianNatale et al., 2014 [66]: MSQA total of 1830 university students and their parents/grandparents (1073 women); mean age of 35.70 yearsA 2-factor model (49.8% of variance explained): Wake dimension (items 4, 5, 8, 9; factor loadings from 0.52 to 0.83) and Sleep dimension (items 1–3, 7, 10; factor loadings from 0.51 to 0.75). Only item 6 (snoring) had a loading value of 0.39 and it was not loaded on any dimension.
This 9-item solution was a better model in comparison to the 10-item solutionCronbach’s alpha for the MSQ was 0.77. The average inter-item correlation (homogeneity) was 0.26, ranging between −0.01 and 0.58.
Cronbach’s alpha of wake dimension was 0.75 and that of homogeneity 0.44.
Cronbach’s alpha of sleep dimension was 0.75 and homogeneity amounted to 0.37

Based on SDQ, healthy participants obtained lower scores in the wake dimension (11.92) in comparison to participants compatible with EDS (17.27).
Healthy participants obtained lower scores in the sleep dimension (12.48) in comparison to participants compatible with impaired sleep quality (18.75)ROC curve with cut-off value for wake dimension > 14 with AUC = 0.83, sensitivity = 78%, specificity = 74%, PPV = 0.29 and NPV = 0.96.
ROC curve with cut-off value for sleep dimension > 16 with AUC = 0.82, sensitivity = 73%, specificity = 80%, PPV = 0.40, and NPV = 0.94ItalianKim, 2017 [67]: MSQ-InsomniaA total of 470 students from six nursing colleges in South Korea (437 females); mean age of 21.40 yearsA 1-factor model (56.0% of the variance explained): MSQ-Insomnia with 4 items (items 1–4) loading from 0.33 (item 3: taking sleeping pills and tranquilizers) to 0.89 (item 2: awakening early in the morning and unable to sleep again)Cronbach’s alpha of MSQ-Insomnia was 0.69. Item-total correlations ranged from 0.30 (item 3) to 0.68 (item 2).
Cronbach’s alpha increased to 0.73 if item 3 was deletedTest-retest with ICC = 0.84.
No difference in MSQ-Insomnia score between baseline and retestMSQ-Insomnia correlated with PSQI (0.69), as well as each item of MSQ-Insomnia correlated with PSQI (item1: 0.71, item 2: 0.52, item 3: 0.22, item 4: 0.42).
MSQ-Insomnia correlated with PSS (0.31) as well as each item of MSQ-Insomnia correlated with PSS (item 1: 0.26, item 2: 0.25, item 3: 0.11, item 4: 0.24)
Based on the PSQI cut-off score of 8.5, 98 students were classified as poor sleepers. The MSQ-Insomnia had a good level of predictive validity (AUC = 0.85) to predict poor sleepersKoreanTibubos et al., 2020 [69]: JSS-4A total of 2515 representative individuals of the German population (1350 females); mean age of 50.53 yearsA 1-factor model (eigenvalue of 3.10) accounting for 77.5% of total variance. Factor loadings ranged between 0.83 and 0.93.
1-factor solution confirmed with CFA (standardized factor loadings ranged between 0.71 and 0.95)Cronbach’s alpha of 0.90 and Mc Donald’s omega of 0.90.
Corrected item-total correlations ranged from 0.69 (item 4) to 0.86 (item 3) in total sample (range 0.67–0.86 in men and 0.70–0.86 in women)
Correlations were present between JSS-4 total and sex (0.10), age (0.28), household income (−0.19), BSI-18 total score (0.51), BIS-18 anxiety (0.42), BSI-18 depression (0.41), and BSI-18 somatic symptom load (0.45).There was the same correlation pattern for item 1 (from −0.17 to 0.43), item 2 (from −0.17 to 0.43), item 3 (from −0.20 to 0.44), and item 4 (from −0.15 to 0.49)Women (4.23) showed a higher JSS-4 total than men (3.37).
There was a linear increase of JSS-4 total score from 14–20 years (2.03) to ≥71 years (5.92).Not living with a partner (4.31) induced a higher JSS-4 score than living with a partner (3.47).
JSS-4 total decreased as education level increased (from less education (≤8 years: 4.52 to university students: 1.74).
Being retired (5.43) or unemployed (4.67) determined a higher JSS-4 total score than being a student (1.96) or worker (2.87).
There was a linear decrease of JSS-4 total score from <€1000 household income (5.78) to ≥€2500 (2.81).
Multivariate analysis showed that sleep problems were moderately linked (0.46) with global psychological distress in the specified model. As expected, female (β = 0.05 and 0.08, respectively), older (β = 0.24 and β = 0.05, respectively), and low income (β = −0.18 and β = −0.21, respectively) individuals were more likely to report sleep problems and psychological distressAccording to the significant differences in JSS-4 scores between age groups and both sexes, norm values for the total sample as well as for each combination of age and sex separately were provided in percentile ranks:
JSS-4 score = 0 corresponded to 33 percentile in total sample; JSS-4 score = 20 corresponded to 100 percentile in total sampleGermanManzar et al., 2018 [71]: LSEQ-MA total of 424 Ethiopian university students (74 females); mean age of 21.87 years1-factor model with cumulative variance rule > 40%. 2-factor model with eigenvalue > 1 and scree plot: F1 (items getting to sleep 1–3, quality of sleep 1–2) and F2 (items awake following sleep 1–2, behaviour following wakening 1–2). The behaviour following wakening item 3 did not load in any factor.
In confirmatory analysis no model had the best fit values even if the original 4-factor correlated model performed best in some values but not allCronbach’s alpha was 0.84.
Item-total correlations ranged from 0.60 to 0.69

Based on GAD-7 score, normal participants had higher scores than those with moderate anxiety for LSEQ-M total score (66.92 vs. 52.65, respectively) and for all items with the exception of behavior, following wakening item 2ROC curve with LSEQ-M score cut-off value of 52.6, with AUC = 0.95, sensitivity = 94% and specificity = 80%Ethiopian (Mizan)Ricketts et al., 2019 [73]: SLEEP-50A total of 234 patients with Trichotillomania (227 females; mean age of 32.30 years), 170 patients with Excoriation disorder (162 females; mean age of 36.40 years) and 146 healthy adults (145 females; mean age of 38.60 years)Trichotillomania: 9-factor model with Sleep Apnea (items 1–8; factor loadings range 0.41–0.89), Insomnia (items 9–16; factor loadings range 0.50–0.90), Narcolepsy (items 17–21; factor loadings 0.52–0.69), Restless Legs/Periodic Limb Movement Disorder (items 22–28; factor loadings range 0.49–0.95), Circadian Rhythm Sleep Disorder (items 26–28; factor loadings range 0.49–0.73), Sleepwalking (items 29–31; factor loadings range 0.56–0.84), Nightmares (items 32–36; factor loadings range 0.39–0.65 with poor loading for item 35); Factors influencing sleep (items 37–43; factor loadings range 0.37–0.80 with poor loading for item 39), Impact of sleep complaints on daily functioning (items 44–50; factor loading range 0.59–0.94).
Inter-factor correlations ranged from −0.21 to 0.76).
Excoriation disorder:
9-factor model with Sleep Apnea (items 1–8; factor loadings range 0.32–0.80), Insomnia (items 9–16; factor loadings range 0.63–0.83), Narcolepsy (items 17–21; factor loadings 0.54–0.83), Restless Legs/Periodic Limb Movement Disorder (items 22–28; factor loadings range 0.64–0.92), Circadian Rhythm Sleep Disorder (items 26–28; factor loadings range 0.44–0.70), Sleepwalking (items 29–31; factor loadings range 0.59–1.12), Nightmares (items 32–36; factor loadings range 0.36–0.58 with poor loading for item 35); Factors influencing sleep (items 37–43; factor loadings range 0.17–0.87 with poor loading for item 39), Impact of sleep complaints on daily functioning (items 44–50; factor loading range 0.69–0.86).
Inter-factor correlations ranged from −0.81 to 0.81)Trichotillomania:Cronbach’s alpha for the full scale was 0.91. Alphas for the individual subscales were:
Sleep apnea (0.70), insomnia (0.85), narcolepsy (0.47), restless legs/PLMD (0.74), circadian rhythm sleep disorder (0.55), sleepwalking (0.39), nightmares (0.65), factors influencing sleep (0.56), and impact of sleep complaints on daily functioning (0.87).
Excoriation disorder:
Cronbach’s alpha for the full scale was 0.89. Alphas for the individual subscales were:
Sleep apnea (0.67), insomnia (0.85), narcolepsy (0.63), restless legs/PLMD (0.74), circadian rhythm sleep disorder (0.47), sleepwalking (0.66), nightmares (0.66), factors influencing sleep (0.41), and impact of sleep complaints on daily functioning (0.86)
Trichotillomania: there were correlations between SLEEP-50 overall complaints (0.71) and SLEEP-50 impact (0.63) subscales and the global PSQI. Similarly, correlations were found between SLEEP-50 overall complaints (0.62) and SLEEP-50 impact (0.60) subscales and PSQI overall sleep quality subscale (ranging from 0.16 to 0.75).
Excoriation disorder: correlations were present between SLEEP-50 overall complaints (0.58) and impact (0.56) subscales and the global PSQI. Similarly, correlations were found between SLEEP-50 overall complaints (0.44) and impact (0.60) subscales and PSQI overall sleep quality subscales (ranging from 0.10 to 0.65)There were differences between groups based on SLEEP-50 cut-off scores ( ≥ 15) for sleep apnea (trichotillomania 17.1%, excoriation 19.7% and control 6.5%), narcolepsy (trichotillomania 32.9%, excoriation 36.3% and control 16.1%), restless leg/periodic limb movement disorder (trichotillomania 19.7%, excoriation 24.4% and control 11.4%), circadian rhythm sleep disorder (trichotillomania 13.8%, excoriation 12.1% and control 5.1%) and with more than one sleep disorder (trichotillomania 63.6%, excoriation 66.5% and control 39.0%)
English (American)Open in a separate window

Similar to the AIS, the Insomnia Severity Index (ISI) measures perceived insomnia severity, focusing on the level of disturbance to the sleep pattern, consequences of insomnia, and the degree of concern and distress related to the sleep problem [52]. Its content corresponds in part to the diagnostic criteria of insomnia outlined in the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) [53]. The ISI comprises seven items that assess the severity of sleep-onset and sleep maintenance difficulties (both nocturnal and early morning awakening), satisfaction with current sleep pattern, interference with daily functioning, noticeability of impairment attributed to the sleep problem and degree of distress or concern caused by the sleep problem. Each of these items is rated on a five-point Likert scale (0 = not at all; 4 = extremely) and the time interval is “in the past 2 weeks”. Total scores range from 0 to 28 with high scores indicating greater insomnia severity. This tool is available in three different versions: patient (self-administered), significant other, and clinician. All included papers referred to the patient’s version. In the original validation study, different categories were provided: 0–7, no significant insomnia, 8–14, subthreshold insomnia, 15–21, moderate insomnia, and 22–28, severe insomnia [52]. Concerning the factor structure, we found four studies proposing a 1-factor model [47,54,55,56,57] (mean 62.03% of total variance and mean factor loadings ranging from 0.47 to 0.83). It should be noted that Dragioti et al. [54] proposed a four-item version (items 2, 4, 5, 7), especially for patients with chronic pain. However, three studies reported the 2-factor solution [58,59,60] (Severity of sleeping difficulties with items 1–4 or alternatively items 1a, 1b, 1c, and 2; Impact of insomnia with items 5–7 or, alternatively, items 3–5). This solution generally explained 61.80% of variance with mean factor loadings ranging from 0.50 to 0.90 for both factors and mean inter-factor correlation of 0.50 ( ). In medical patients, Dieperink et al. [61] considered the 3-factor model, in which Severity of Nighttime Sleep Difficulties (items 1–3 with factor loadings > 0.59), Impact of Insomnia (items 5–7 with factor loadings > 0.72), and Sleep Dissatisfaction/Satisfaction (items 1, 4, 7 with factor loadings > 0.36) correlated with each other with values greater than 0.80. This solution was confirmed in other studies [56,62,63] ( ), even if Dieck et al. [56] reported a different composition of factors (F1: items 2, 4, 7; F2: item 1; F3: items 5, 7) and a single correlation between F1 and F3 (0.794). The different numerosity of the samples and the specific characteristics of recruited participants might explain these different results regarding the latent structure of the ISI. The reliability of ISI was good with a mean Cronbach’s alpha of 0.82 and mean corrected item-to-total correlations ranging from 0.47 to 0.66. Importantly, the test-retest reliability was significant in clinical and nonclinical populations [56]. In general, test-retest reliability after 2 weeks was satisfactory (mean ICC = 0.82) [47,56,61] and the ordinal alpha remained above the critical value of 0.70 after a CBT-I treatment [63]. The ISI exhibited significant correlations with several sleep questionnaires such as AIS and PSQI (but low correlation coefficients with ESS) and with different psychological, health, and psychopathological questionnaires. The range of all correlations was between −0.58 and 0.79 ( ). Sadeghniiat-Haghighi et al. [59] reported a specific correlation pattern with PSG variables. Indeed, the first three items were associated with PSG variables to a greater extent than the total ISI score was, which correlated only with WASO and SE, even if the correlation coefficients for the first three items were small (<±0.30). In addition, Castronovo et al. [63] reported how the first three items of the ISI were associated with quantitative estimates of sleep parameters ( ) obtained from the sleep diaries with moderate correlations, supporting the premise that the first three items have a diagnostic role. As regards validity, the studies demonstrated known-group differences on the basis of different criteria, such as PSQI or depression [47,58,59,61]. In one study [55] women had a higher ISI score than men, and in another study [54] sex was a predictive factor of ISI score, but this gender effect was not systematically confirmed [54,55,61]. Importantly, Castronovo et al. [63] found that ISI was sensitive to change after a specific CBT-I treatment, with a reduction of the higher scores in each item. In our selected papers, only three studies performed a ROC curve analysis; these reported that ISI cut-off was in the range between 9 and 11 with a mean sensitivity of 86% and mean specificity of 80% [47,56,57], depending on the population considered and PSQI cut-off used [47,55,57,59,62]. Taking into account that one study reported an agreement of about 85% between ISI ≥ 8 and PSQI > 5 [62] in the detection of “poor sleepers”, the cut-off values proposed in reviewed articles were in the subthreshold insomnia categories within the 8–14 range [52].

The Mini Sleep Questionnaire (MSQ) [64] is a short questionnaire that can be used to screen sleep disorders in the population and considers complaints regarding both sleep and wake at the same time. The original version was composed of seven items that evaluate symptoms of hypersomnia, and one item on sleep maintenance. Subsequently, three items regarding symptoms of insomnia were added. Thus, the final 10-item version assesses both insomnia and excessive daytime sleepiness. Each item is scored on a seven-point Likert scale ranging from 1 (never) to 7 (always), and takes into account the past seven days. The total sum of scores is divided into four levels of sleep difficulties: 10–24, good sleep quality; 25–27, mild sleep difficulties; 28–30, moderate sleep difficulties; ≥31, severe sleep difficulties [65]. The total score offers an estimate of sleep quality, with higher scores reflecting more serious sleeping problems. However, Natale et al. [66] found two factors explaining about 50% of total variance with loading values higher than 0.50, with the exclusion of item 6 (snoring) ( ). The authors labeled Wake (items 4, 5, 8, 9) and Sleep (items 1, 2, 3, 7, 10) dimensions. Thus, the MSQ could be considered a good tool for screening sleep disorders in the population because it consists of two subscales that investigate sleep quality and daytime sleepiness [66]. By contrast, Kim [67] assessed the psychometric properties of MSQ-Insomnia which is composed of four items (difficulty falling asleep, awakening early in the morning and unable to sleep again, taking sleeping pills and tranquilizers, and waking up during the night) with factor loading values higher than 0.50, with the exception of item 3 in the single factor. As reported in , Natale et al. [66] reported higher Cronbach’s alphas for both factors, with a good internal homogeneity (0.44 for wake factor and 0.37 for sleep factor) while Kim [67] reported a Cronbach’s alpha of 0.69 with an increase of alpha if item 3 was deleted (0.73). The item-total correlation was ≥0.30. Furthermore, the Korean version of MSQ for insomnia subscale [67] correlated with both PSQI (0.22–0.71 range) and Perceived Stress Scale or PSS (0.11–0.31 range), while Natale et al. [66] reported that healthy participants obtained lower scores in the wake dimension of the MSQ in comparison to participants, a result that was compatible with excessive daytime sleepiness; they also found that healthy participants obtained lower scores in the sleep dimension of the MSQ in comparison to participants, compatible with impaired sleep quality ( ). Finally, Natale et al. [66] indicated that Wake > 14 and Sleep > 16 were optimal values for detecting hypersomnia and insomnia problems, respectively. Kim [67] evaluated the predictive validity of the MSQ-Insomnia for poor sleepers determined by the diagnostic cut-off on the Korean PSQI score (>8.5 points, [32]), and concluded that it gave a good level of predictive validity.

Finally, in this section we included three tools used for the diagnosis of sleep disorders including insomnia. The first questionnaire was the Jenkins Sleep Scale (JSS) [68]. The JSS is an efficient instrument for the evaluation of the most common symptoms in the general population [68]. JSS is a simple, self-reported, and non-time-consuming scale to be used in daily practice, clinical research, and epidemiologic studies. The questionnaire consists of four items that assess sleep problems over the preceding 4 weeks, with questions regarding trouble falling asleep, trouble staying asleep, frequent awakenings during the night, and subjective feelings of fatigue and sleepiness despite having had a typical night’s rest [69]. The respondents answer the questions using a 6-point Likert-type scale from 0 (not at all) to 5 (22 to 31 days). The total scores range from 0 to 20, and higher scores indicate a greater number of sleep problems [68,69]. In a large representative German sample [69], JSS-4 showed and confirmed the 1-factor solution, explaining a large variance with high factor loading values ( ). The JSS-4 proved excellent reliability and it demonstrated good construct validity with regard to mental health, suggesting that sleep problems and psychological distress comprising anxiety, depression, and somatization were moderately related to each other. In addition, the JSS-4 total score was associated with sex, age, education, household income, cohabitation, and employment [69], not only with correlations but also with multivariate analysis. Interestingly, normative data of sleep problems were provided with the percentile rank of each value of the total score provided, allowing comparisons of the JSS-4 scores obtained with different groups of the general population stratified by sex and age. It is worth noting that Tibubos et al. [69] indicated that, in the total sample, a sum score equal to 2 corresponded to 51 in percentile rank, in line with the recommended cut-off of ≥2 to detect sleep disturbances, which corresponds to at least one troubled night per week [68]. The second self-report scale is the Leeds Sleep Evaluation Questionnaire (LSEQ) [70]. The LSEQ comprises ten self-rating 100 mm line analogue questions concerned with sleep and morning behavior and is relatively simple in its use. In the original study [70], factor analysis revealed four independent domains that pertained to sleep latency (or getting to sleep GTS: items 1–3), quality of sleep (QOS: items 4–5), awakening from sleep (AFS: items 6–7), and behavior following wakefulness (BFW: items 8–10). For each item of 100 mm visual analogue, 0 indicated the worst sleep condition and 100 suggested a normal state, and therefore lower scores of the LSEQ indicated poor sleep. In our review, we found an adapted LSEQ that had been administered to Ethiopian university students [71]. In this version (LSEQ-M), not only did the authors modify some items or word expressions (e.g., “usual” was replaced with “normal”), but also the reported score for each item was divided by 10 to determine an individual item score between 0 and 10. Such scores (between 0 and 10) for each item were added to obtain a LSEQ-M global score with a range of 0–100. Interestingly, the authors found 1-, 2-factor and 4-factor models according to different criteria (i.e., eigenvalue > 1, scree plot and cumulative variance > 40). The significant lower values of the LSEQ-M global score as well as those relating to all the items (with the exclusion of item 9) among students with a moderate level of anxiety established the diagnostic known-group validity of the tool. At the cut-off score of 52.6, the sensitivity and specificity of the LSEQ-M were 94% and 80%, respectively ( ) [71]. The third self-administered questionnaire is the SLEEP-50 [72], assessing a range of sleep complaints and disorders, including sleep apnea, insomnia, narcolepsy, restless leg syndrome/periodic limb movement disorder, circadian rhythm sleep disorder, sleep walking, and nightmares, in addition to factors which may disrupt sleep, and the impact of sleep complaints on daily functioning. Items are rated on a 4-point Likert scale, from 1 (not at all) to 4 (very much), and each item refers to the past 4 weeks. Items are summed to yield subscale totals and an overall total score, with higher scores indicative of poorer sleep functioning. Spoormaker et al. [72] reported that cut-off scores for each sleep subscale in conjunction with the impact subscale (i.e., greater than or equal to a score of 15) were used to establish the presence of a sleep disorder (i.e., whether symptoms reached a diagnostic threshold). Ricketts et al. [73] added psychometric properties of the SLEEP-50 in two medical conditions (Trichotillomania and Excoriation disorder) in which sleep problems may occur and influence disorder severity. As shown in , a similar 9-factor model was found for both Trichotillomania and Excoriation Disorder samples, with low factor loadings for item 35 on Factor 7 and item 39 on Factor 8. As far as internal consistency is concerned, values were similar between both groups of patients and comparable to those found in the initial investigation [72]. The internal consistency for the full SLEEP-50 scale was excellent in Trichotillomania and good in Excoriation Disorder, with moderate to strong convergent validity in the association with PSQI global score (from 0.25 to 0.75 and from 0.17 to 0.65 in Trichotillomania and Excoriation Disorder, respectively). The study showed that Trichotillomania and Excoriation Disorder groups exhibited sleep complaints and met a clinical threshold at higher rates (i.e., 63.6% for Trichotillomania and 66.5% for Excoriation Disorder) compared to the control group (39%), suggesting that the SLEEP-50 is a valid self-report tool which may serve to facilitate and standardize screening of multiple sleep complaints among individuals with hair-pulling and skin-picking disorders [73].