When You Don't Have Chronotype Data: Sleep Questionnaires as a Circadian Window

Although closely related to sleep, circadian rest-activity rhythms represent an independent dimension of human physiological and behavioral functioning, and disruptions in these rhythms have been associated with adverse physical and mental health outcomes across the lifespan.[1] However, assessing circadian rhythms is methodologically complex, often requiring extended actigraphic monitoring or laboratory-based methods such as salivary melatonin sampling to estimate dim light melatonin onset (DLMO).[2] [3] To address this, constructs like chronotype – defined as an individual's preferred timing for activity and rest – have been operationalized,[4] enabling the development of self-report measures widely used in sleep medicine and chronobiology, such as the morningness-eveningness questionnaire (MEQ) and the Munich chronotype questionnaire (MCTQ).[5] [6] [7]

From a theoretical standpoint, it is crucial to distinguish between biological circadian rhythms – measurable through markers such as DLMO or core body temperature[8] under free-running conditions, where external synchronizers are minimized – and real-world, socially regulated daily rhythms. The latter reflects the interplay between endogenous predispositions and external factors such as work schedules, artificial light exposure, geographic latitude, and cultural habits. Accordingly, measures like sleep timing preferences or chronotype estimates from self-report questionnaires capture a ‘manifested’ rhythm rather than a direct readout of the circadian phase and should be interpreted with these contextual influences in mind.

Furthermore, while chronotype assessments have proved valuable for exploring links between circadian preference and health outcomes, most large-scale population studies lack these measures, thereby limiting the ability to examine circadian factors in epidemiological research.[1]

As a partial remedy, it may be promising to identify proxies of circadian preference within existing sleep questionnaires. The Pittsburgh Sleep Quality Index (PSQI),[9] a well-established self-report measure of sleep quality, includes items assessing habitual sleep and wake times over the past month, and similar information is also captured in large-scale surveys such as the Centers for Disease Control's National Health and Nutrition Examination Survey (NHANES).[10] Though such timing data correlate only modestly with biological circadian markers like DLMO,[2] questionnaire-derived estimates of sleep onset (bedtime + sleep latency), sleep offset (wake-up time), and sleep midpoint (the average of sleep onset and offset) may still provide unique circadian information beyond standard subjective sleep parameters (see [Fig. 1]).

Fig. 1 Clock-based illustration showing how sleep onset, sleep offset, and sleep midpoint can be derived from self-reported sleep data. The example refers to a fictitious subject who reported a bedtime of 10:15 PM, a sleep latency of 15 minutes, and a wake-up time of 6:00 AM. The light grey shaded area represents the sleep period, while the clock hands indicate the calculated times of sleep onset (10:30 PM), sleep offset (6:00 AM), and sleep midpoint (2:15 AM).

In support of this approach, we examined previously collected data from 1,234 university students (mean age = 23.3 ± 2.4 years; 87.3% women, 12% men, 0.7% undisclosed). Sleep onset, offset, and midpoint calculated from self-reported habitual bedtime, sleep latency, and wake-up time, were significantly but only moderately correlated with sleep duration (r = -.23, r = .34, and r = .06, respectively; all p-values < .05). In multiple linear regression models controlling for sleep duration, sleep latency, and subjective sleep quality, sleep midpoint (selected as the most representative circadian proxy) was significantly associated with mental health-related quality of life (HRQoL) (β = -0.13, p = .004), but not with physical HRQoL (β = –0.02, p = .60). Furthermore, after classifying participants into early, intermediate, and late chronotype groups based on the 33^rd and 66^th percentiles of sleep midpoint, ANOVAs showed that late chronotypes reported significantly lower mental HRQoL compared to both early and intermediate chronotypes (F = 8.5, p < .001; post-hoc p-values < .001 and .02), and lower physical HRQoL compared to intermediates (F = 4.7, p = .01; post-hoc p = .007).

We propose that circadian proxies derived from instruments like the PSQI or other sleep timing questions are worth reporting and investigating when exploring associations between sleep and health outcomes, especially in large datasets lacking direct chronotype or objective circadian data. While such proxies do not capture the intrinsic circadian phase, they reflect socially expressed patterns of sleep-wake behavior – manifestations of circadian preference shaped by real-life constraints – which remain highly relevant for both research and public health purposes. Building on this approach, similar strategies may be applicable to other self-report instruments, and prospective sleep diaries may be particularly promising for estimating subjective circadian timing and its variability over longer intervals.

 

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Conflicts of Interest

The author declares no conflicts of interest.

Ethical Committee Permission and Informed Consent

The data collected from human participants reported in this study were detailed in previous publications. All procedures were approved by the competent Ethics Committee at Sapienza University of Rome (protocol number 0000/2021). All participants provided informed consent.

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Publication History

Received: 14 April 2025

Accepted: 05 June 2025

Article published online:
04 August 2025