Ample research has shown that light influences our emotions, cognition, and sleep quality. However, little work has examined whether different light exposure-related behaviors, such as daytime exposure to electric light and nighttime usage of gadgets, especially before sleep, influence sleep quality and cognition. Three-hundred-and-one Malaysian adults (MeanAge±SD = 28 ± 9) completed the Light Exposure Behavior Assessment tool that measured five light exposure behaviors. They also completed the Morningness-Eveningness Questionnaire, Positive and Negative Affect Schedule, Pittsburgh Sleep Quality Index, and single items assessing trouble in memory and concentration. A partial least square structural equation model, showing 72.72% predictive power, revealed that less use of wearable blue filters outdoors during the day and more within one hour before sleep predicted early peak time (direct effect = -0.25). Increased time spent outdoors predicted a positive affect (direct effect = 0.33) and a circadian phase advancement (direct effect: rising time = 0.14, peak time = 0.20, retiring time = 0.17). Increased use of mobile phone before sleep predicted a circadian phase delay (direct effect: retiring time = -0.25; rising time = -0.23; peak time = -0.22; morning affect = -0.12), reduced sleep quality (direct effect = 0.13), and increased trouble in memory and concentration (total effect = 0.20 and 0.23, respectively). Increased use of tunable, LED, or dawn-simulating electric light in the morning and daytime predicted a circadian phase advancement (direct effect: peak time = 0.15, morning affect = 0.14, retiring time = 0.15) and good sleep quality (direct effect = -0.16). The results provide valuable insights into developing a healthy light diet to promote health and wellness.
Light exposure is an essential driver of health and well-being, and individual behaviours during rest and activity modulate physiologically relevant aspects of light exposure. Further understanding the behaviours that influence individual photic exposure patterns may provide insight into the volitional contributions to the physiological effects of light and guide behavioural points of intervention. Here, we present a novel, self-reported and psychometrically validated inventory to capture light exposure-related behaviour, the Light Exposure Behaviour Assessment (LEBA). An expert panel prepared the initial 48-item pool spanning different light exposure-related behaviours. Responses, consisting of rating the frequency of engaging in the per-item behaviour on a five-point Likert-type scale, were collected in an online survey yielding responses from a geographically unconstrained sample (690 completed responses, 74 countries, 28 time zones). The exploratory factor analysis (EFA) on an initial subsample (n = 428) rendered a five-factor solution with 25 items (wearing blue light filters, spending time outdoors, using a phone and smartwatch in bed, using light before bedtime, using light in the morning and during daytime). In a confirmatory factor analysis (CFA) performed on an independent subset of participants (n = 262), we removed two additional items to attain the best fit for the five-factor solution (CFI = 0.95, TLI = 0.95, RMSEA = 0.06). The internal consistency reliability coefficient for the total instrument yielded McDonald's Omega = 0.68. Measurement model invariance analysis between native and non-native English speakers showed our model attained the highest level of invariance (residual invariance CFI = 0.95, TLI = 0.95, RMSEA = 0.05). Lastly, a short form of the LEBA (n = 18 items) was developed using Item Response Theory on the complete sample (n = 690). The psychometric properties of the LEBA indicate the usability for measuring light exposure-related behaviours. The instrument may offer a scalable solution to characterise behaviours that influence individual photic exposure patterns in remote samples. The LEBA inventory is available under the open-access CC-BY license. Instrument webpage: https://leba-instrument.org/ GitHub repository containing this manuscript: https://github.com/leba-instrument/leba-manuscript .