Light exposure behaviors predict mood, memory and sleep quality

doi:10.21203/rs.3.rs-2492096/v1

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Light exposure behaviors predict mood, memory and sleep quality

https://doi.org/10.21203/rs.3.rs-2492096/v1

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published 01 Aug, 2023

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Ample research has shown that light influences our emotions, cognition, and sleep quality. However, little research has investigated if different light exposure-related behaviors, such as the use of electric light during daytime, and use of gadgets before sleeping, influence those variables. Three-hundred-and-one Malaysian adults (Mean_Age±SD=28 ± 9) completed the Light Exposure Behavior Assessment (LEBA) 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 61.36% predictive power, revealed that increased use of wearable blue filters indoors and outdoors decreased the morningness affect (Direct effect, DE=-0.16) and sleep quality (Total effect, TE = 0.11). Increased time spent outdoors predicted positive affect (DE = 0.32) and early chronotype (DE: RI = 0.14, PT = 0.15, RT = 0.15). Increased use of gadgets before sleep predicted late chronotype (DE: RT=-0.26; RI=-0.23; PT=-0.24; MA=-0.13), increased negative affect, reduced sleep quality (DE = 0.13), and increased trouble in memory and concentration (TE = 0.20 & 0.23, respectively). Increased use of electric light in the morning and daytime predicted a positive affect (DE = 0.16) and sleep quality (DE=-0.16). The results provide valuable insights into developing a healthy light diet to promote health and wellness.

light exposure

light-related behaviors

non-visual effects of light

light diet

PLS-SEM

Scientific evidence published over the last four decades has shown that retinal light exposure influences our physiology, behavior, and emotion. More specifically, it modulates human sleep, circadian rhythms, alertness, mood, neuroendocrine and neurobehavioral functions ^1–5. These influences of light on human physiology and behaviors are collectively known as non-image-forming responses (NIF) of light. The melanopsin-enriched intrinsically photoreceptive retinal ganglion cells (ipRGCs), which are sensitive to short wavelength-enriched (blue-enriched, ~ 480nm) lights ⁶, mostly mediate the NIF effects of light.

Light’s influence on chronotype, sleep quality and mood

With the advent of artificial light and self-luminous displays, our retinal light exposure is no more limited to the natural day-night cycle. An extensive body of research suggests that the imbalance of light and dark exposure disrupts our circadian system ⁷. Subsequently, this disruption gives rise to a series of adverse consequences, including decreased sleep quality, mood, and an alteration of sleeping habits ^7–9. Since the natural light-dark cycle is the most vital zeitgeber to synchronize our body clock to the astronomical day, altering this cycle forces us to have different chronotype-disposition for activity early or late in the day ¹⁰. Research shows that bright light exposure at night results in a late chronotype ¹¹, and bright light exposure in the morning leads to an early chronotype ^12,13. Increased nighttime light exposure is also associated with decreased sleep quality ^14,15. However, several studies have reported better nighttime sleep quality after exposure to bright light in the morning in an office environment ^8,9,16. Further, Brain regions such as limbic areas and the hypothalamic-pituitary-adrenal axis responsible for regulating mood are susceptible to circadian regulation ¹⁷. Thus, it is reasonable to anticipate that the disruption of circadian regulation will disrupt mood regulation ¹⁷. Bright light exposure in the morning leads to increased positive mood; however, exposure to bright light in the afternoon enhances negative mood ^18–21.

Light exposure, memory, and concentration

Several studies have confirmed that retinal light exposure activates the hippocampus, which is closely associated with memory functions ^22–24. Thus, researchers anticipate that retinal light exposure would influence memory. Vandewalle, et al. ²⁵ observed that, compared to green light exposure, blue light exposure improves working memory performance (N = 18). Alkozei, et al. ²⁶ reported enhanced verbal memory for a 30-minute blue light exposure (N = 12) compared to amber light. Huiberts, et al. ²⁷ offered further evidence that light influences memory-based task performances, whereby participants performed better in simple than complex tasks under bright light conditions (N = 64). Retinal light exposure is also associated with improved concentration. Kretschmer, et al. ²⁸ observed an improved concentration among night shift workers (N = 32) under a bright light condition (300–3000 lux). Sleegers, et al. ²⁹, in their studies on the effects of light in classroom environments, concluded a beneficial influence of a dynamic light environment on students’ concentration (N = 181).

Interrelation of chronotype, mood, sleep quality, memory and concentration

Ample research has found that chronotype influences sleep quality. Juda, et al. ³⁰ found that workers with early chronotypes had shorter sleep duration and more sleep disturbances than late chronotypes (N = 371 shift workers). Moreover, late chronotypes had poor sleep quality with non-regular sleeping habits during weekdays due to the misalignment of their preferred activity period vs. real-world demands ^31–33. Chronotypes can also influence our memory and concentration ^34–36. Schmidt, et al. ³⁴ reported an interaction effect of chronotype and time of day on memory (N = 32). The memory performance of those with early chronotype was better in the morning. In the same vein, the memory performance of those with late chronotype was better in the afternoon ^37,38. Researchers have termed it the synchrony effect. Research has also indicated that sleep quality is contingent on mood ³⁹. Positive affect- a state of pleasurable engagement with the environment, is associated with improved sleep patterns ^40,41. However, negative affect leads to sleep deprivation, poor sleep quality, and reduced cognitive functioning ^42–46. Poor sleep quality also reduces memory functions and concentration ^47–51.

The current study

Acknowledging the influence of retinal light exposure on our health and well-being, many researchers have tried to quantify healthy light exposure. They have given recommendations for a healthy indoor light environment ⁵². However, little effort is visible to study light exposure-related behaviors, which could be an active agent modifying our retinal light exposure. People can control their light exposure through different behaviors by actively seeking or avoiding certain types of light exposure. Understanding these behaviors is essential to develop a healthy light diet-a pattern of light exposure promoting health, wellness, and work performance. In that vein, we have developed the Light Exposure Behavior Assessment (LEBA) ⁵³ tool that categorizes five different types of behavior. First, the propensity of wearing blue light filter glasses indoors and outdoors (LEBA B1). Second, the tendency to spend time outdoors (LEBA B2). Third, the usage of smart gadgets on the bed before sleeping (LEBA B3). Fourth, our inclination to control environmental light before bedtime (LEBA B4). Finally, using electric light sources throughout the day (LEBA B5). However, whether these categorizations of behaviors would predict different aspects of our health, memory and concentration are still unknown.

We posed the following questions: What are the influences of LEBA categories on (a) chronotype, (b) mood, (c) sleep quality, and (d) memory and concentration? To answer these questions, we proposed a theoretical framework (Fig. 1) based on the literature reviewed. We used the partial least squares structural equation modeling (PLS-SEM) - most suited to formulate such a predictive model ^54,55. Predicting relationships using PLS-SEM is a two-step process where first, a measurement model is used to assess the reliability and validity of the latent variables used in the model. Second, a structural model is used to investigate the precited relationships of the latent structures. In the structural model, (i) the direct effects (DE): influence unmediated by any other constructs in the model, (ii) indirect effects (IE): influences mediated by at least one intervening construct in the model and (iii) total effects (TE): sums of direct and indirect effects of a given construct can be estimated ⁵⁶.

We predicted that five types of LEBA behavior categories would directly influence chronotype (H1), mood (H2), and sleep quality (H3). We also predicted that mood (H4) and chronotype (H5) would influence sleep quality. Sleep quality (H6), mood (H7), and chronotype (H8) would affect memory and concentration. LEBA categories would directly influence memory and concentration (H9). Lastly, we predicted that LEBA categories would exhibit a significant total effect on sleep quality (H10), memory, and concentration (H11).

Structural validity

Table 1 presents the fit indices of the scales used in this study. Light Exposure Behavior Assessment (LEBA), Pittsburgh Sleep Quality Index (PSQI), Morningness-Eveningness Questionnaire (MEQ), and Positive and Negative Affect Scales (PANAS) exhibited acceptable to a good fit in terms of CFI and TLI (> 0.95 or .90), RMSEA (< 0.08 or 0.06), and SRMR(< 0.08).

Table 1

Structural validity of the scales used in the PLS-SEM model assessed using ESEM.
		Degrees of freedom, p	Comparative Fit Index (CFI)	Tucker-Lewis Index (TLI)	Root mean square error of approximation (RMSEA;90% CI)	Standardized root mean square residual (SRMR)
LEBA	57.04	73, p < 0.001	0.994	0.987	0.06(0.0-0.074)	0.04
PSQI	19.84	8, p = 0.011	0.966	0.910	0.07(0.03–0.11)	0.07
MEQ	91.50	101, p < 0.001	0.970	0.949	0.04(0.03–0.06)	0.04
PANAS	293.76	151, p < 0.001	0.992	0.990	0.06(0.05–0.07)	0.06

Measurement model

We excluded one item from the LEBA (item 04) and four items from MEQ (items 06, 10,16,12) due to weak factor loadings (< 0.40; Supplementary Table S1). The results of the refitted measurement model assessment are shown in Supplementary Table S2. The sleep efficiency (SE) factor of PSQI exhibited poor reliability in terms of Cronbach’s \(\alpha\) (\(\alpha\)=0.48) but had satisfactory construct reliability (CR = 0.79). All other factors exhibited acceptable to satisfactory internal consistency in terms of Cronbach’s \(\alpha\) (0.51–0.94) and construct reliability (0.72–0.96). Concerning convergent validity, 8 out of 13 constructs had average variance extracted (AVE)> 0.50 (except LEBA B2, negative affect, perceived sleep quality, peak time and retiring). However, all 13 constructs had CR > 0.60 and AVE < CR. This indicated acceptable reliability and convergent validity of all constructs in the model.

To establish the discriminant validity, we calculated the square root of each construct’s AVE and compared them to their corresponding inter-construct correlation (Supplementary Table S3). All constructs’ square root of AVE were greater than their inter-construct correlation indicating satisfactory discriminant validity. Further evidence of the discriminant validity of the constructs was drawn by heterotrait-monotrait (HTMT) analysis. Supplementary Table S4 presents the HTMT values and indicates satisfactory discriminant validity (HTMT < 0.80) for all 13 constructs.

Structural model

Variance inflation factors (VIF) for all constructs were < 3, indicating no possible collinearity. Figure 2 and Table 2 depict significant (t > 1.96) direct effects and total effects observed in our model. All direct effects of the structural model are provided in Supplementary Table S5.

Table 2

Direct and total effects estimated in the PLS-SEM model (Only significant structural relationships are reported, t > 1.96).
Hypothesis	Path Coefficients	Original Est.		Bootstrap Mean		Bootstrap SD		t	2.5% CI	97.5% CI	Results
Direct effects
H1: Light exposure-related behaviors -> Chronotype											Supported
H1	LEBA B1 -> MA	-0.16		-0.16		0.06		2.44	0.28	0.03
	LEBA B2 -> PT	0.15		0.15		0.07		2.27	0.02	0.28
	LEBA B2 -> RT	0.15		0.15		0.06		2.29	0.02	0.27
	LEBA B2 -> RI	0.14		0.14		0.06		2.33	0.02	0.25
	LEBA B3 -> PT	-0.24		-0.24		0.05		4.39	0.35	0.14
	LEBA B3 -> MA	-0.13		-0.13		0.06		2.24	0.24	0.01
	LEBA B3 -> RT	-0.26		-0.27		0.05		4.83	0.37	0.16
	LEBA B3 -> RI	-0.23		-0.23		0.06		3.79	0.35	0.11
H2: Light exposure-related behaviors -> Mood
H2	LEBA B2 -> PA	0.32		0.32		0.05		6.21	0.22	0.42	Supported
	LEBA B5 -> PA	0.16		0.16		0.06		2.45	0.03	0.28
	LEBA B3 -> NA	0.17		0.17		0.06		2.84	0.05	0.29
H3: Light exposure-related behaviors -> Sleep Quality
H3	LEBA B3 -> PSQ	0.13		0.13		0.06		2.24	0.01	0.24	Supported
H3	LEBA B5 -> PSQ	-0.16		-0.16		0.06		-2.59	-0.27	-0.03	Supported
H4: Mood -> Sleep quality
H4	PA -> PSQ	-0.18		-0.18		0.06		-3.02	-0.30	-0.06	Supported
	PA -> SE	0.22		0.21		0.07		3.08	0.07	0.35
	NA -> PSQ	0.28		0.29		0.06		4.83	0.17	0.40
H5: Chronotype -> Sleep Quality
H5	MA -> PSQ	-0.20		-0.20		0.06		-3.31	-0.31	-0.08	Supported
H6: Sleep quality -> Memory and Concentration
H6	PSQ -> Memory	0.17		0.18		0.06		3.11	0.07	0.29	Supported
H6	PSQ -> Concentration	0.26		0.26		0.06		4.60	0.15	0.37	Supported
H7: Mood -> Memory and Concentration
H7	NA -> Memory	0.38		0.38		0.06		6.63	0.26	0.49	Supported
H7	NA -> Concentration	0.33		0.32		0.06		5.87	0.21	0.43	Supported
H8: Chronotype -> Memory and Concentration
H8	Chronotype ->work performance	Details are provided Supplementary Table 5									Not supported
H9: Light exposure related behavior -> Memory and Concentration
H9	Light exposure related behavior -> Memory and Concentration	Details are provided Supplementary Table 5									Not supported
Total Effects
Hypothesis	Path Coefficients	Original Est.		Bootstrap Mean		Bootstrap SD		T Stat.	2.5% CI	97.5% CI	Results
H10: LEBA to Sleep quality
H10	LEBA B1 -> PSQ	0.11	0.12		0.05		2.06		0.01	0.22	Supported
	LEBA B3 -> PSQ	0.21	0.21		0.06		3.53		0.09	0.32
	LEBA B5 -> PSQ	-0.17	-0.17		0.07		-2.38		-0.30	-0.02
H11: LEBA to Memory and Concentration
H11	LEBA B3 -> Memory	0.20	0.19		0.06		3.12		0.06	0.31	Supported
H11	LEBA B3 -> Concentration	0.23	0.23		0.06		3.89		0.11	0.34	Supported

Predicted relationships

Table 3 shows that, in line with our predictions, LEBA categories exhibited direct effects on chronotype (H1), mood (H2) and sleep quality (H3). Among the four factors of chronotype, we observed a negative direct effect of LEBA B1 on morning affect (MA; our bodily responses in the morning; \(\beta\)= -0.16). LEBA B2 exhibited a direct effect on positive affect (\(\beta\)= 0.32) and chronotype factors: peak time (PT; body’s peak time for different activities; \(\beta\)= 0.15), retiring (RT; the time when our body starts to prepare for sleeping; \(\beta\)= 0.15), and rising time (RI; the time when our body prepares for waking up; \(\beta\)= 0.14). In contrast, LEBA B3 directly, but negatively, influenced the four chronotype factors: PT (\(\beta\)= -0.24), MA (\(\beta\)= -0.13), RT (\(\beta\)= -0.26) and RI (\(\beta\)= -0.23). LEBA B3 increased negative affect (\(\beta\)= 0.17) and decreased perceived sleep quality (PSQ; \(\beta\)= 0.13). However, LEBA B5 significantly increased positive affect (\(\beta\)= 0.16) and PSQ (\(\beta\)= -0.16). Both positive and negative affect directly influenced sleep quality (H4), where positive mood increased both the sleep efficiency (SE; \(\beta\)= 0.22) and PSQ (\(\beta\)= -0.18), whereas negative affect decreased PSQ ( \(\beta\)= 0.28).

Chronotype directly influenced sleep quality (H5), where increased morning affect (MA) was observed to increase PSQ (\(\beta\)= -0.20). A negative influence of PSQ was observed on memory and concentration (H6), where poor PSQ was predicted to increase trouble in memory (\(\beta\)= 0.17) and concentration (\(\beta\)= 0.26). Increased negative affect predicted a deteriorated memory and concentration (H7; memory=0.38; concentration=0.33). No significant direct effect of chronotype (H8) and light exposure-related behaviors (H9) was observed on memory and concentration. We observed significant total effects of light exposure-related behaviors on sleep quality (H10). LEBA B1 and B3 were predicted to decrease PSQ (\(\beta\)= 0.11 and \(\beta\)= 0.21, respectively). However, LEBA B5 was associated with an improvement in PSQ (\(\beta\)= -0.17). Lastly, significant total effects of light exposure-related behaviors on memory and concentration were observed (H11). LEBA B3 was predicted to increase trouble in memory and concentration (\(\beta\)= 0.20 and \(\beta\)= 0.23, respectively).

Explanatory and predictive Power of the fitted model.

Our fitted model exhibited substantial R² for PSQ (26.79%) and trouble in concentration (30.35%). Moderate R² was observed for PA (13.85%) and trouble in memory (25.51%). Adequate R² was observed for PT (10.96%) and RT (12.45%). Our model exhibited weak R² for MA, RI, SE and NA. \(PL{S}_{predict}\) function indicated our model had medium predictive power with 61.36% of the indicators having RMSE value lower than the LM benchmark.

This study investigated the relationship between light exposure behavior, chronotype, sleep quality, mood, memory and concentration. To test the relationships, we conceptualized a framework based on the existing literature and used PLS-SEM to assess the direct and total effects of light exposure behaviors on the aforementioned variables.

Our measurement model indicated acceptable reliability and validity of the scales used to measure chronotype, sleep quality and mood. Two factors: sleep efficiency (SE) and rising (RI), had Cronbach’s α < 0.60 but exhibited satisfactory construct reliability (> 0.60). These two factors were composed of only two items each, which might be a contributor to the low Cronbach’s alpha coefficient. Further, we used two single global items to capture the essence of constructs: trouble in memory and concentration. The use of such global single items allowed us to reduce participants’ cognitive demands required to respond to our survey and increased the response rate with fewer missing parts ⁵⁷. Typically, single global items are known to be reliable with good predictive validity and allow the participants to consider the key features of the given construct ^58–61.

Results indicated that the structural model had satisfactory explanatory power (R² > 0.10) for all factors except for morning affect (MA), rising behaviors (RI), sleep efficiency (SE) and negative affect (NA). These four factors exhibited weak R². However, overall, our model exhibited satisfactory predictive relevance, and most relationships were in line with our predictions. Results indicated that LEBA categories influenced chronotype (H1), mood (H2), and sleep quality (H3). Mood (H4) and chronotype (H5). Sleep quality(H6) and mood (H7) exhibited significant direct influences on memory and concentration. However, chronotype did not exhibit any significant direct influence on memory and concentration (H8). We did not observe any significant direct effect of LEBA categories on memory and concentration (H9). However, significant total effects of LEBA categories were observed on sleep quality (H10), memory and concentration (H11).

Increased use of blue filters indoors and outdoors (LEBA B1) predicted a decrease in the morning affect (DE= -0.16) and sleep quality (TE = 0.11), indicating the necessity of the blue light component to synchronize our body clock with the natural light-dark cycle. A group of photoreceptors in our eye- intrinsically photoreceptive retinal ganglion cells (ipRGCs) are highly sensitive to blue light ^2,62. These ipRGCs receive signals from the light and send them to the suprachiasmatic nucleus (SCN) of the brain, the so-called master clock of our body clock to align our inner rhythm with the astronomical cycle. Hence, deprivation of blue light during daytime misguides our circadian rhythm. Figueiro, et al. ⁶³ in their study, reported that blue-enriched light exposure throughout the day promotes better alignment of the circadian rhythm with the earth’s 24-h light-dark cycle. Studies also reported a delay in nighttime melatonin onset due to blue-depleted daytime light exposure causing a phase shift in our circadian rhythm ⁶⁴.

Spending time outdoors (LEBA B2) led to an improved mood by increasing the positive affect (DE = 0.32) and promoted early chronotype (DE: RI = 0.14, PT = 0.15, RT = 0.15). Similar results were reported in previous studies ^8,65. Burns, et al. ⁶⁵, using a bio-bank of 400,000 UK participants, conferred that time spent in outdoor light improved mood and promoted early chronotypes. An, et al. ⁶⁶ reported a reduced depressive mood among workers when more sunlight is available in the environment. Further, Figueiro, et al. ⁸ reported reduced depressive symptoms for light exposures with high circadian efficiency-ability to entrain our body clock like the sunlight. Collectively, these findings suggest that sleep and mood-related problems are rooted in people’s behaviors that guide their outdoor light exposure.

Increased use of smart gadgets on the bed before sleeping (LEBA B3) was predicted to promote late chronotype (DE: RT=-0.26; RI=-0.23; PT=-0.24; MA=-0.13), negative affect (DE = 0.17) and reduced sleep quality (DE = 0.13). Significant total effects were observed on trouble in memory, concentration, and sleep quality (TE = 0.20, 0.23, & 0.21, respectively). Previous studies reported adverse effects of using smart devices in bed on sleep quality and mood ^67–69. The self-luminous display of smart gadgets often emits blue lights. Exposure to these blue lights at night is directly associated with reduced cognitive functioning, mood, circadian phase shift and reduced sleep quality ^70–74.

Results indicated that the increased use of electric light in the morning and daytime (LEBA B5) improved positive affect (DE = 0.16) and sleep quality (DE=-0.16, TE=-0.17). A similar conclusion was also drawn in the works of Figueiro, et al. ⁸, where increased circadian effective daytime light exposure was reported to improve sleep quality and mood among office workers (N = 109). Several studies independently demonstrated that inadequate daytime light exposure led to greater melatonin suppression at night, thus causing a phase shift, more nighttime awakening sleep deprivation and poor sleep quality ^75–77. Studies based on real-world settings such as offices and schools collectively also indicated that increased electric light exposure improved mood and sleep quality ^9,78,79. Brown, et al. ⁵², in their attempt to provide a consensus-based recommendation for healthy light exposure for indoor usage, indicated a requirement of at least 250 melanopic equivalent daylight illuminance to mitigate the adverse effects of reduced sunlight exposure. Interestingly, we did not observe any influence of Controlling the light environment before bedtime (LEBA B4) on sleep, emotion and cognition. This factor had three items that investigate how we control the light emitted from our devices before our bedtime, such as using blue light filter applications or dimming the monitor one hour before sleep. But, recent recommendations indicated that investigations related to light in a sleep environment should consider a time span of three hours prior to sleep ⁵², which could a contributing factor to such findings.

As predicted, we observed that sleep quality was contingent upon mood (H4) and chronotype (H5). Sleep quality (H6) and mood (H7) predicted trouble in memory and concentration. However, we did not observe any significant direct effect of chronotype (H8) on memory and concentration. Several studies reported the influence of chronotype on memory and concentration ^34–36. These influences are highly dependent on the time of day, with a diurnal variation of performance differences observed between early and late chronotypes. Since our aim was to assess typical memory and concentration problems in the past month, we were not able to specify the diurnal variation in performance. This could be the leading cause of our model not finding any significant relationship among chronotype, memory and concentration.

Some limitations of this study should be noted. First, our PLS-SEM-based model is fitted on a female-dominated sample which hinders the generalizability of the findings. Future studies with gender-balanced samples with higher representativeness of the Malaysian population are suggested to increase the generalizability. Second, morning affect (MA), rising behaviors (RI), sleep efficiency (SE) and negative affect (NA) exhibited weak R² in our fitted model. Studies with larger samples might yield better explanatory power for these four factors.

This research aims to examine whether light exposure-related behaviors could predict chronotype, sleep quality, mood, memory, and concentration to provide insight into developing a healthy light diet. To attain this goal, a conceptual framework was developed, and a partial least square structural equation modeling was applied to a sample of 301 Malaysian Residents. All constructs used in the model exhibited acceptable reliability and validity. Results indicated that wearing blue light filters during the daytime and using smart gadgets in bed before sleep is detrimental to chronotype, mood, sleep quality, memory and concentration. However, spending time outdoors promotes mood and early chronotype. Also, the usage of electric light in the morning and during the daytime promotes mood and sleep quality. Collectively, these findings will facilitate the development of a healthy light diet to facilitate mental health and wellness.

Participants

We conducted a large-scale online survey on Malaysian residents. The exclusion-inclusion criteria for respondents to be included in this study were: (1) any Malaysian resident aged > 18 and able to read and write English (2) no physiological and psychological disorder (self-reported). Three hundred and sixty-six adults completed the survey. The completion rate of our survey was 87% (45 participants' data was excluded due to incompleteness). We further excluded 19 participants based on our exclusion-inclusion criteria. Thus, we used data from 301 participants for further processing.

A priori power analysis was conducted to determine the sample size adequacy with G^*Power 3.0 ⁸⁰. To achieve an effect size of 0.15 ⁸¹ and 80% statistical power and \(\alpha\)=0.05, for a multiple liner regression with 13 predictors, a total sample size of 131 individuals was needed. Further, the maximum number of items per factor in our model was six. In the PLS-SEM-based analysis, to detect a minimum \({R}^{2}\) value of 0.10 for a factor with six items with 80% statistical power and α = 0.05, at least 130 participants are required ⁸². Our sample size exceeded these recommendations. Out of 301 participants (Age_Mean±SD=28±9), 72.43% (218) were female, ranging in age from 18 to 59 (Age_Mean±SD=26.85±8.07), and 27.57% (83) were male with an age range between 18 to 74 years (Age_Mean±SD=30.35±12.14). 78.66% of the participants were unmarried. The majority of the participants were students (71.42%) and of intermediate chronotype (68%). Table 3summarizes the demographics of the participants.

Table 3

Demographics of the participants (N = 301).
Characteristic	Female, N = 218 Mean (SD) or N(%)	Male, N = 83 Mean (SD) or N(%)
Age	27 (8)	30 (12)
Religion
Atheist	23 (11%)	7 (8.4%)
Buddhist	99 (45%)	35 (42%)
Christian	36 (17%)	13 (16%)
Hindu	21 (9.6%)	11 (13%)
Muslim	39 (18%)	17 (20%)
Ethnicity
Malaysian Chinese	138 (63%)	46 (55%)
Malaysian Indian	19 (8.7%)	13 (16%)
Malaysian Malay	26 (12%)	7 (8.4%)
Others	35 (16%)	17 (20%)
Marital Status
Single	180 (83%)	56 (67%)
Married	37 (17%)	27 (33%)
Divorced	1 (0.5%)	0 (0%)
Education
Doctor of Philosophy (PhD)	43 (20%)	13 (16%)
Master’s degree	38 (17%)	22 (27%)
Postgrad diploma	1 (0.5%)	0 (0%)
Bachelor’s degree	129 (59%)	41 (49%)
Diploma	7 (3.2%)	7 (8.4%)
Occupation
Student	165 (76%)	50 (60%)
Work	42 (19%)	31 (37%)
Neither	11 (5.0%)	2 (2.4%)
Community Stance	7.07 (1.87)	7.00 (1.85)
Sleep Quality
Good Sleep	69 (32%)	24 (29%)
Poor Sleep	149 (68%)	59 (71%)
Chronotype
Definite Evening	8 (3.7%)	1 (1.2%)
Intermediate	144 (66%)	60 (72%)
Moderate Evening	43 (20%)	13 (16%)
Moderate Morning	23 (11%)	9 (11%)

Measures

Light exposure behavior assessment

Light exposure-related behaviors were measured using the short form of the Light Exposure Behavior Assessment ⁵³. The short form contains five factors with 18 items. Light Exposure Behavior Assessment (LEBA) measures the propensity of different light exposure-related behaviors in the last one month retrospectively using a five-point Likert-type response scale (1 = never; 2 = rarely; 3 = sometimes; 4 = often; 5 = always). All 18 items of LEBA and the participants’ responses to them are shown in Fig. 3.

Positive and negative affect schedule

The positive and negative affect schedule (PANAS)⁸³ was used to measure positive and negative affect. PANAS comprises two 10-item mood scales measuring positive affect (PA) and negative affect (NA). In this study, participants retrospectively rate their positive and negative affect based on the last month using a five-point Likert-type response scale (1 = very slightly/not at all; 2 = a little; 3 = moderately; 4 = quite a bit; 5 = extremely).

Memory and concentration

We used two global single items with four-point Likert-type response options investigating trouble in memory and concentration. These global single items asked the participants about the propensity of their memory and concentration difficulty in the last month (0 = Absent; 1 = Slight; 2 = Moderate; 3 = Severe).

Pittsburgh sleep quality index

We used the Pittsburgh Sleep Quality Index (PSQI)⁸⁴ to measure the participants' sleep quality. PSQI measures seven domains of sleep to differentiate “poor” from “good” sleep. Participants responded to the PSQI using Likert-type response options ranging from 0 to 3, whereby 3 reflects the negative extreme on the Likert Scale. A sum of scores ≥ 5 indicates poor sleep quality. The latent structure of PSQI was reported to vary from one factor to three factors ^84,85. Dunleavy, et al. ⁸⁶, in their study, recommended using a two-factor model: perceived sleep quality (PSQ) and sleep efficiency (SE) while measuring the sleep quality among Singapore citizens. In this study, we followed their recommended structure. A higher score in the PSQ factor would indicate decreased perceived sleep quality. In contrast, a higher score in the SE factor would indicate higher sleep efficiency.

Morningness-eveningness questionnaire

Chronotype was measured using Morningness-Eveningness questionnaire (MEQ)⁸⁷. MEQ consists of 19 questions, and the scores range from 16 to 86. A higher score indicates a higher morning propensity. Caci, et al. ⁸⁸ reported a four-factor structure of MEQ: peak time (PT), morning affect (MA), retiring (RT) and rising (RI) in s student sample (N = 456). Items in PT investigate the body’s peak time for different activities. MA investigates our bodily responses in the morning. RT captures the time when our body starts to prepare for sleeping. Lastly, RI investigates the time when our body prepares for waking up.

Procedure

The project was approved by Monash University Human Research Ethics Committee (Project ID: 14786). The research was performed in accordance with relevant guidelines/regulations of the Declaration of Helsinki. Informed consent was obtained from all participants before data collection. This was a cross-sectional, fully anonymous online survey. Participants were invited via email and social media (i.e., LinkedIn, Twitter, and Facebook) with the attachment of an Explanatory Statement, in which we mentioned that their participation would be voluntary and that they could withdraw from participation at any time without being penalized. If the participants expressed happiness with the statement, a survey link was sent to them. At the beginning of the survey, their consent was recorded digitally. The survey took around 15 to 20 minutes, for which they were not compensated. The survey was completed between April and November, 2022.

Analytic strategies

We used R version 4.1.2; ⁸⁹ and several statistical packages, including esemComp ⁹⁰, “SEMinR” ⁹¹ and tabledown ⁹² for our analysis.

Structural validity of the scales

We gathered structural validity evidence of LEBA, PSQI, MEQ and PANAS scales in our sample using the exploratory structural equation modeling (ESEM)⁹³. ESEM intricates the computational advantages of exploratory and confirmatory factor analysis by allowing the items to cross-load to represent the data more realistically and offering fit indices to assess the model fit. To assess the model fit, we followed the guidelines of Hu and Bentler ⁹⁴: comparative fit index (CFI) and the Tucker Lewis index (TLI): acceptable fit\(\ge 0\).90, good fit \(\ge 0\).95; the root mean square error of approximation (RMSEA): acceptable fit <0.08, good fit < 0.06; and the standardized root mean square (SRMR): acceptable fit < 0.10, good fit<0.08.

Partial least squares structural equation modeling

Measurement model assessment. First, we assessed the quality of the measurement model. We excluded items with factor loading < 0.40 to increase the robustness of the measurement model ⁹¹. Second, we estimated the internal consistency reliability estimates of each construct. We reported both the lower bound estimate of reliability- Cronbach’s \(\alpha\) coefficient and the upper bound estimate of reliability-construct reliability (CR). Both Cronbach’s \(\alpha\) and CR coefficient values range between 0 to 1, where higher values represent better reliability. As a general guideline, Cronbach’s \(\alpha\) above 0.70 is considered satisfactory ^95,96 and a value above 0.50 is considered acceptable ⁹⁷. CR coefficient value of 0.60 and above indicates a satisfactory reliability ⁹¹.

Third, we assessed the convergent and discriminant validity of the measurement model. For convergent validity, we used the average variance extracted (AVE) value of each construct. AVE ≥ 0.50 or AVE < 0.50 with a CR > 0.60 and AVE < CR indicate an acceptable convergent validity ⁹⁸. For discriminant validity, we compared the square root of the AVE of a construct with its corresponding correlation with other constructs ⁹⁸. The square root of the AVEs of each construct should be higher than its correlation with other constructs. We have also reported the bootstrapped heterotrait-monotrait ratio (HTMT) of correlations of the construct as additional proof of discriminant validity. For conceptually similar constructs, the HTMT value should be < 0 .90, and for constructs that are conceptually distinct, the HTMT value should be < 0.80 ⁹⁹.

Structural model assessment. First, we assessed the collinearity of the constructs in our structural model by calculating variance inflation factor (VIF) values. VIF > 3 indicates probable collinearity issues ⁹⁹. Next, we estimated the direct effects (DE) and total effects (TE) of the structural model using a bootstrapping approach with 10000 sub-samples and reported the significant total effects (t > 1.96) observed in our model. Lastly, we reported the adjusted \({R}^{2}\) as a measure of the explanatory power. For assessing the explanatory power, we followed the guidelines of Falk and Miller ¹⁰⁰: \({R}^{2}\) values \(\ge\) 0.10 indicates adequate explanatory power. Further, we have categorized the \({R}^{2}\) values following the guidelines of Cohen ⁸¹: 0.02 (weak), 0.13 (moderate), and 0.26 (substantial). For predictive relevance, we assessed the fitted model’s predictive power by K-fold cross-validation using the \(PL{S}_{predict}\) function from the “SEMinR” package ⁹¹. \(PL{S}_{predict}\) provides the root-mean-square error (RMSE) and respective linear-regression model benchmarks (LM) for all indicators. We assessed the model’s predictive power by following the guideline of Hair ⁹¹: (i) high predictive power: all indicators in the fitted PLS-SEM model have lower RMSE values compared to the LM (ii) medium predictive power: the majority(≥ 50%) of the indicators have lower RMSE values than LM (iii) low predictive power: less than 50% of the indicator have lower RMSE value than LM (iv) no predictive power: no indicator has lower RMSE value than LM model. Figure 4 depicts the analysis steps we followed.

Ethical Considerations

The project received ethics clearance from Monash University Human Research Ethics Committee (Project ID: 14786). Informed consent was obtained from all participants. Participants were assured about their voluntary participation and the right to withdraw at any time.

Author contributions

M.S., V.K., and S.H. designed the study and planned for data collection. M.S. conducted the formal analysis and prepared all figures and tables. M.S., V.K., and S.H. wrote the main manuscript text. All authors reviewed the manuscript

Data availability statement

The datasets generated and/analyzed during the current study are available in the GitHub repository, https://github.com/ILLMMU/Study2

Conflict of Interest

The authors declare no competing interests.

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No competing interests reported.

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Light exposure behaviors predict mood, memory and sleep quality

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Abstract

Figures

Introduction

Results

Predicted relationships

Discussion

Conclusion

Methods

Measures

Light exposure behavior assessment

Positive and negative affect schedule

Memory and concentration

Pittsburgh sleep quality index

Morningness-eveningness questionnaire

Structural validity of the scales

Partial least squares structural equation modeling

Declarations

References

Additional Declarations

Supplementary Files

Status:

Journal Publication

Version 1