Interplay among bedtime procrastination, sleep patterns, and subjective wellbeing in the Indian population: An observational study

Abstract

BACKGROUND

Sleep deprivation is a common problem in society, and bedtime procrastination (BtP) has become a significant cause of poor sleep among healthy individuals across various countries.

AIM

To study BtP, sleep behavior, and subjective well-being in the Indian population.

METHODS

This was a cross-sectional study. The recruited participants were evaluated using the BtP Scale, World Health Organzation-5 Well-Being index, Patient Health Questionnaire-2, Generalized Anxiety Disorder 2-item, Munich Chronotype Questionnaire and Single-Item Sleep Quality Scale.

RESULTS

A total of 401 participants were recruited for the study. Symptoms of anxiety were higher in the female participants compared to males. Among females, there was a significant positive correlation between BtP score with symptoms of anxiety (r = 0.23) and depression (r = 0.15) and a negative correlation with subjective wellbeing (r = -0.23). A significant negative correlation was found between the ages of women and BtP score (r = -0.15). Among the male participants, there was a significant negative correlation of age with BtP score (r = -0.3) and anxiety (r = -0.19). Here, too, the BtP score was positively correlated with depression (r = 0.18) and anxiety (r = 0.35).

CONCLUSION

BtP worsens anxiety, depression, sleep quality, and subjective well-being. It needs to be targeted for the promotion and prevention of mental health.

Key Words: Bedtime procrastination; Sleep quality; Subjective well-being; Anxiety; Depression; Sleep

Core Tip: Bedtime procrastination (BtP) has a significant positive correlation with anxiety and depression, irrespective of sex. About 40% of people use electronic gadgets excessively (more than 6 hours daily). Females using electronic gadgets have significantly higher levels of anxiety than males. BtP negatively affects well-being and sleep quality in males.

INTRODUCTION

The Centre for Disease Control classifies sleep deprivation as a public health epidemic associated with the development of a variety of diseases[1]. The American Academy of Sleep Medicine and Sleep Research Society developed a consensus recommending a minimum of 7 hours of sleep per night for adults, otherwise associated with adverse health outcomes like metabolic disturbances, mental health disorders, and increased risk of death[2].

While the effects of good sleep are well known, incidences of poor sleep have increased in a post-pandemic era, with significant effects on the functionality of a person. An online survey reported that 61% of the 41000 respondents from India slept for less than 6 hours of uninterrupted sleep in the past year[3]. A survey done by Gupta et al[4]. after the coronavirus disease 2019 Lockdown in the Indian population, on a sample size of 958, observed a significant change in sleep habits compared to the pre-lockdown, regarding later bedtime, delayed sleep, and increased daytime napping. Reduced sleep and an increased incidence of depressive symptoms were observed across different occupational groups[4]. Dunn et al[5] reported poor sleep quality, prolonged latency, and excessive daytime sleepiness in the later years of college due to academic requirements among university students in India. Chronic sleep deprivation significantly affects memory span, decision-making, executive function, planning, social cognition, and verbal learning[6].

The term “Revenge Bedtime Procrastination” has gained fame post-pandemic on various social media platforms, glorifying it as a cause of sleep deprivation in healthy individuals. Zhang et al[7], in a study in 2020, evaluated the effects of smartphone addiction and BtP on the sleep quality of university students. They found a positive relationship between BtP and poor sleep quality. Similar studies have been conducted on the Indian population to show the detrimental effects of smartphone dependency on general health profile[8], sleep quality[9,10], cyberchondriac behavior, health anxiety and subjective well-being[11].

Kroese et al[12] first introduced this phenomenon called Bedtime Procrastination (BtP), defining it as “failing to go to bed at the intended time, while no external circumstances prevent a person from doing so”. BtP is widespread among young people[12,13]. Furthermore, studies were conducted to understand its prevalence and association with sleep quality, depression, and anxiety[10,14-17]. A meta-analysis by Hill et al[18] on BtP reported that BtP was moderately negatively associated with sleep duration [z = -0.31; 95% confidence interval (CI): -0.37 to -0.24] and sleep quality (z = -0.35; 95%CI: -0.42 to -0.27), and moderately positively associated with daytime fatigue (z = 0.32; 95%CI: 0.25 to 0.38)[18]. A study on students reported that higher general procrastination and higher BtP among students is associated with higher sleep insufficiency and daytime fatigue[19], which seems to impair the general well-being of the individuals. A study from Pakistan reported that BtP significantly worsens sleep disturbance and produces fatigue and mental health problems in students[20]. Similarly, another study on students reported that studyholics experience higher insomnia and BtP[21]. An Indian study reported that smartphone addiction and BtP often lead to dissatisfaction in life, significantly affecting overall well-being[22].

To the best of our knowledge, only one study has been conducted in India by Shukla et al[23] to determine the prevalence of BtP among Indian university students. Despite the absence of published research exploring the causes and impacts of BtP, we hypothesize that this phenomenon is widespread within the general Indian population. It contributes to diminished subjective sleep quality, adversely affects mental health, and compromises subjective well-being. Our study assessed the prevalence of BtP among individuals in India. Additionally, we examined the association of BtP with sleep quality, psychological health, and overall well-being.

MATERIALS AND METHODS

Study design

The study was a web-based cross-sectional study conducted under the Department of Psychiatry of a tertiary centre in Lucknow. A self-reporting questionnaire was created using Google Forms. Convenience sampling was used to collect data. To reach a broader audience, unrestricted by location and occupation, we conducted an online survey and made it accessible on various social media platforms. The questionnaire was circulated to all the contacts in the university, including students, professors, and workers, using the email ID database available on the university portal. Further, the questionnaire was made available to a larger population by circulating the questionnaire link on social media networking sites like WhatsApp, Twitter, Facebook, Instagram, and Telegram. Duplication of respondents was eliminated as only a single response was admissible for one email ID.

Participants residing in India and between 18 and 60 years of age were included in the study. The exclusion criteria included patients reporting diagnosed psychiatric disorders (except personality disorders), sleeping disorders, or respiratory illnesses like asthma, chronic obstructive pulmonary disease. The sample size for this study was found to be 385, considering the expected prevalence of 50% and a 95%CI with a 5% margin of error in an infinite population using the iface sample size estimation software (https://sampsize.sourceforge.net/iface/). The study was conducted following the Declaration of Helsinki and approved by the Institutional Ethics Committee of King George's Medical University, Lucknow, Uttar Pradesh, India, with Ethical approval reference code: 128th ECM IIA/P3 with Letter No. 26/Ethics/2024 dated 02-04-2024.

Study instruments

We created a Google Form with multiple parts. The first part included personal details like email ID, age, sex, residence, highest completed level of education, comorbidities, and recreational drug use practices. The tools used in the study were validated in the Indian population and were used in previous studies.

BtP scale: A nine-item questionnaire developed by Kroese et al[24], with a five-point Likert scale. It consists of five negative and four positive statements. Each item can score from 1 (almost never) to 5 (almost always), while items 2, 3, 7, and 9 are reverse-coded. Hence, the range of total scores is 9-45, with more significant scores indicating a higher frequency of engaging in BtP.

World Health Organization-5 Wellbeing Index: This is a simple tool for screening general well-being. It consists of a five-item questionnaire with five statements answered on a Likert scale[25].

Patient Health Questionnaire-2: This is a two-item scale used to inquire about the frequency of depressed mood and anhedonia. Its score ranges from 0 to 6. A score of 3 or more is indicative of likely depression[26].

Generalized Anxiety Disorder 2-item: This consists of two questions the subject must answer for the initial screening of generalized anxiety disorder. The score ranges from 0 to 6, and 3 is the preferred cut-off[27].

Munich Chronotype Questionnaire: This self-reporting questionnaire tests typical sleep behavior over the past four weeks for work and work-free days separately[28].

Single-item Sleep Quality Scale: It is a self-rated, global sleep quality assessment tool. The questionnaire requires the respondent to rate the overall quality of sleep over a 7-day recall period on a discretizing visual analogue scale, whereby the respondent marks an integer score from 0 to 10, according to the following five categories: 0 = terrible, 1-3 = poor, 4-6 = fair, 7-9 = good, and 10 = excellent[29].

Statistical analysis

The data obtained from Google Forms were tabulated in Excel sheets, and the prevalence of BtP was determined and compared for different subgroups. The data were checked for their distribution patterns. As the data were normally distributed, we applied a parametric test to analyze the data.

The sociodemographic and clinical characteristics are presented in percentages and proportions. Our study analyzed categorical variables using Pearson's χ² test and continuous variables using the t-test. Pearson's correlational analysis evaluated the association of BtP with other outcome variables. P < 0.05 was considered statistically significant. We performed Statistical analysis using the Statistical Package for Social Sciences (version 24.0; IBM Corp., Armonk, NY, United States).

RESULTS

The total number of participants in the study was 401, of whom 176 were males and 224 were females (Figure 1). The general characteristics of the dataset are given in Table 1. The result shows that 2.8% of men went to sleep between 9 PM and 10 PM, 52% between 10 PM and 12 PM, 25% between 12 AM and 1 AM, and a few after 1 AM (18%). Among women, 3% sleep before 10 PM, 45% between 10 PM and 12 PM, 26% between 12 AM and 1 AM, and about 23% go to sleep after 1 AM. About 51% of males woke up between 6 AM and 8 AM, 20% between 8 AM and 9 AM, and about 30% woke up after 9 AM. Among women, 50% woke up between 6 AM and 8 AM, 19% between 8 AM and 9 AM, and only 12% woke up after 9 AM (Figure 2). More than 40% of the participants use their electronic gadgets for more than 6 hours per day on average, and professional work is the single most common reason for using them (Table 2).

Figure 1 Flow diagram showing recruitment of participants to the study.

Figure 2 Bedtime and wake-up time of male and female participants.

Table 1 Sociodemographic characteristics of the participants.

Domain	n (%)
Age group, years
< 25	191 (47.63)
25-50	196 (48.87)
> 50	14 (3.49)
Sex
Male	176 (43.89)
Female	224 (55.86)
Prefer not to say	1 (0.25)
Occupation
Students pursuing a professional course	150 (37.4)
Salaried jobs	143 (35.6)
Students of the general stream	40 (9.9)
Self-employed	30 (7.48)
Unemployed	15 (3.74)
Housewife	8 (1.99)
Others	15 (3.74)
Religion
Hindu	361 (90)
Muslim	20 (4.98)
Sikh	4 (0.99)
Others	16 (3.99)
Domicile
Urban	322 (80.29)
Semi-urban	51 (12.71)
Rural	28 (6.98)

Table 2 Usage of electronic gadgets among the study participants, n (%).

Question asked	Total responses	Male	Female
Do you have a personal electronic gadget?	401	176	224
How many hours in a day do you spend on electronic gadgets?
Less than 2 hours	21 (5.2)	9	12
2-4 hours	85 (21.2)	39	46
4-6 hours	134 (33.4)	55	79
6-8 hours	96 (23.9)	46	50
More than 8 hours	65 (16.2)	27	37
The single major reason for using an electronic gadget
Official/professional work	123 (30.6)	71	52
Study	114 (28.4)	39	75
Social media	82 (20.4)	32	50
Entertainment	76 (18.9)	32	44
Study and entertainment	1 (0.2)	0	1
Both official and entertainment	2 (0.49)	1	1
Finding job	1 (0.2)	0	1
All of the above	2 (0.49)	2	0

Symptoms of anxiety were significantly higher in the female group (2.19 ± 1.5) compared to the male group (1.5 ± 1.38) (t [398] = 4.4; P < 0.05). Subjective well-being was slightly higher in males (54.7 ± 20.02) than in females (52.03 ± 20.0) (t [398] = 1.98; P = 0.04). A significantly higher number of females (n = 74) screened positive for depression using Generalized Anxiety Disorder 2-item (GAD-2) (χ² = 9.409, P = 0.002; Table 3). The effect size of GAD-2 reached a mild to moderate level in the sex-wise comparison (Table 3).

Table 3 Comparison of anxiety, depression and subjective wellbeing level between female and male participants, n (%).

Variables	Females (n = 224), mean ± SD	Males (n = 176), mean ± SD	Test of significance	Effect size (Cohen’s d); 95%CI
GAD-2	2.191 ± 1.545	1.534 ± 1.389	t = 4.412, df = 398, P = 0.0001	0.444 (0.245 to 0.644)
PHQ-2	1.987 ± 1.531	1.721 ± 1.384	t = 1.799, df = 398, P = 0.0728	0.181 (-0.017 to 0.379)
WHO-5	52.035 ± 20.003	54.704 ± 20.022	t = 1.983, df = 398, P = 0.048	-0.133 (-0.331 to 0.064)
GAD-2			χ2 = 9.409	Risk ratio = 1.71
Screen positive1	74 (33.03)	34 (19.32)	P = 0.002	Risk difference = 0.137
Screen negative	150 (66.97)	142 (80.68)
PHQ-2			χ2 = 0.862	Risk ratio = 1.162
Screen positive1	68 (30.36)	46 (26.14)	P = 0.353	Risk difference = 0.042
Screen negative	156 (69.64)	130 (73.86)

¹Number of positive test results (cut-off score: 3). CI: Confidence interval; GAD-2: Generalized Anxiety Disorder 2-item; PHQ-2: Patient Health Questionnaire-2 item; SD: Standard deviation; WHO-5: World Health Organization-5 Well-being Index.

Among males, there was a significant negative correlation between the age of participants with BtP score (r = -0.3) and anxiety (r = -0.19). Sleep latency had a significant negative correlation with subjective well-being (r = -0.24) and sleep quality (r = -0.24), and a positive correlation with procrastination time (r = 0.4). Sleep duration had a significant negative correlation with procrastination time (r = -0.27). A significant positive correlation was reported between procrastination time and BtP score (r = 0.2), depression (r = 0.27), and anxiety (r = 0.29), whereas a negative correlation was reported with subjective well-being (r = -0.19) and sleep quality (r = -0.3). BtP score had a significant positive correlation with depression (r = 0.18) and anxiety (r = 0.35), whereas a negative correlation was seen with sleep quality (r = -0.25; Table 4).

Table 4 Association of bedtime procrastination with other study variables among males (n = 176).

Variables	Age of the participants	Sleep latency in minutes	Hours of sleep	Procrastination time (minutes)	BtP score	PHQ-2 score	GAD-2 score	WHO-5 score
Sleep latency in minutes	R = -0.0056	-	-	-	-	-	-	-
	P = 0.937
Hours of sleep	R = -0.0941	R = -0.044	-	-	-	-	-	-
	P = 0.215	P = 0.562
Procrastination time (minutes)	R = -0.0822	R = 0.4034	R = -0.276	-	-	-	-	-
	P = 0.279	P < 0.00001c	P = 0.0002c
BtP score	R = -0.396	R = 0.0242	R = -0.0614	R = 0.2003	-	-	-	-
	P < 0.00001c	P = 0.750	P = 0.421	P = 0.0076b
PHQ-2 score	R = -0.192	R = 0.1203	R = -0.0586	R = 0.2708	R = 0.1891	-	-	-
	P = 0.011a	P = 0.1117	P = 0.444	P = 0.0003c	P = 0.0119a
GAD-2 score	R = -0.1098	R = 0.1011	R = -0.1297	R = 0.2961	R = 0.3561	R = 0.5769	-	-
	P = 0.146	P = 0.181	P = 0.088	P = 0.00007c	P < 0.00001c	P < 0.00001c
WHO-5 score	R = 0.1021	R = - 0.246	R = 0.0469	R = -0.1948	R = -0.1471	R = -0.3933	R = -0.2971	-
	P = 0.177	P = 0.0009c	P = 0.536	P = 0.0095b	P = 0.0515	P < 0.00001c	P = 0.00006c
SISS (sleep quality)	R = 0.0505	R = -0.2487	R = 0.1403	R = -0.3029	R = -0.2555	R = -0.2362	R = -0.2811	R = 0.418
	P = 0.506	P = 0.0009c	P = 0.063	P = 0.00004c	P = 0.0006c	P = 0.0016b	P = 0.00016c	P < 0.00001c

^aP < 0.05.

^bP < 0.01.

^cP < 0.001.

BtP: Bedtime procrastination; GAD-2: Generalized Anxiety Disorder 2-item; PHQ-2: Patient Health Questionnaire-2 item; SISS: Single-item Sleep Quality Scale; WHO-5: World Health Organization-5 Well-being Index. .

A significant negative correlation was found between the ages of the female participants and BtP score (r = -0.15), depressive symptoms (r = -0.16), anxiety symptoms (r = -0.15), and sleep quality (r = -0.13). Sleep latency had a significant negative correlation with duration of sleep (r = -0.15), subjective well-being (r = -0.14), and sleep quality (r = -0.13) and a significant positive correlation with procrastination time (r = 0.17). Sleep duration had a significant negative correlation with BtP score (r = -0.18) and a significant positive correlation with sleep quality (r = 0.32). A significant positive correlation was found between procrastination time and BtP score (r = 0.15) and anxiety (r = 0.19). BtP score had a significant positive correlation with depression (r = 0.15) and anxiety (r = 0.23), whereas a negative correlation was seen with subjective wellbeing (r = -0.23) and sleep quality (r = -0.24). Anxiety and depression had a significant negative correlation with sleep quality and subjective wellbeing (Table 5).

Table 5 Association of bedtime procrastination with other study variables among females (n = 224).

Variables	Age of the participants	Sleep latency in minutes	Hours of sleep	Procrastination time (minutes)	BtP score	PHQ-2 score	GAD-2 score	WHO-5 score
Sleep latency in minutes	R = -0.0881	-	-	-	-	-	-	-
	P = 0.189
Hours of sleep	R = -0.1091	R = -0.1573	-	-	-	-	-	-
	P = 0.104	P = 0.0187a
Procrastination time (minutes)	R = 0.0244	R = 0.1732	R = -0.1257	-	-	-	-	-
	P = 0.716	P = 0.0094b	P = 0.0597
BtP score	R = -0.1518	R = 0.1042	R = -0.1812	R = 0.1541	-	-	-	-
	P = 0.0229a	P = 0.1199	P = 0.0066b	P = 0.0210a
PHQ-2 score	R = -0.1671	R = 0.0839	R = 0.016	R = 0.1128	R = 0.1578	-	-	-
	P = 0.0123a	P = 0.211	P = 0.8117	P = 0.0921	P = 0.0181a
GAD-2 score	R = -0.1593	R = 0.054	R = -0.0522	R = 0.1904	R = 0.2303	R = 0.6355	-	-
	P = 0.0172a	P = 0.4212	P = 0.4387	P = 0.0042	P = 0.0005c	P < 0.00001c
WHO-5 score	R = 0.038	R = -0.1486	R = 0.0581	R = -0.0769	R = -0.2326	R = -0.6183	R = -0.4855	-
	P = 0.572	P = 0.0257a	P = 0.3868	P = 0.2511	P = 0.0004c	P < 0.00001c	P < 0.00001c
SISS	R = -0.1335	R = -0.1347	R = 0.3271	R = -0.1253	R = -0.2419	R = -0.2163	R = -0.3065	R = 0.3785
	P = 0.045a	P = 0.0435a	P < 0.00001c	P = 0.0618	P = 0.0003c	P = 0.0011b	P < 0.00001c	P < 0.00001c

^aP < 0.05.

^bP < 0.01.

^cP < 0.001.

BtP: Bedtime procrastination; GAD-2: Generalized Anxiety Disorder 2-item; PHQ-2: Patient Health Questionnaire-2 item; WHO-5: World Health Organization-5 Well-being Index; SISS: Single-item Sleep Quality Scale.

DISCUSSION

This demographic analysis revealed a predominance of participants aged 18 to 50, primarily from urban areas, and a slight female majority. In males, there was a decrease in BtP scores and depressive symptoms with increasing age. A similar trend was noted in the female group, in addition to a decrease in GAD-2 score and an overall improvement in perceived sleep quality with age.

BtP is widespread among young people[12,13]. The age-related trends observed in our male and female cohorts suggest that older adults may adopt healthier sleep practices, possibly due to increased awareness of the consequences of poor sleep or shifts in lifestyle priorities. This might also be because young adults, mostly university students or in their early careers, have compromised sleep due to academic burdens or career progression. A cross-sectional study intending to detect excessive daytime sleepiness among college students found that students taking professional courses and science streams seem more at risk[30].

In our study, there was no significant difference between the sexes in reporting perceived poor sleep quality using the single-item Sleep Quality Scale. However, females had fewer hours of sleep. A study conducted by Shad et al[31] in college students of medical and non-medical backgrounds found that sleep quality did not significantly differ by sex.

Various studies have had different findings in this arena. Two studies reported that females had significantly better sleep times and quality than males[32,33]. A study by Kaur[34] found that sleep quality was worse among female undergraduate non-medical students.

Our analysis revealed increased screening rates for anxiety and depression and decreased perceived sleep quality and overall well-being score with increasing BtP score. Two studies found an independent association between depression and BtP, where the severity of depression increased with increasing BtP, eventually contributing to depression[15,16].

Furthermore, studies have shown that anxiety has a positive effect on BtP[14], which is expected since people with high anxiety levels also have high avoidance behavior and may regulate negative emotions through BtP[35]. A study done by Li et al[36] reported that higher procrastination scores were associated with higher risks of insomnia symptoms.

While for students, the leading cause of procrastination was studying, professionals procrastinated mainly for leisure or work completion. Other responses included stress, surrounding atmosphere, irregular schedule, and children. In a qualitative study by Nauts et al[37], individuals with BtP reported that when they were tired and stressed at the end of the day, they opted to delay their bedtime and engage in different activities despite knowing its negative impact on their sleep. They distinguished between deliberate procrastination, mindless procrastination, and strategic delay.

Magalhães et al[38] further distinguished between BtP and while-in-bed procrastination. Their study reported that 59.5% of participants engaged in while-in-bed procrastination, while the rest procrastinated before bed. From the data collected by the short Munich Chronotype Questionnaire tool, we calculated BtP and while-in-bed procrastination among the participants. BtP could only be calculated among participants with different workdays and weekends. While in-bed procrastination seems highly prevalent among the participants, with 117 males and females reporting more than 15 minutes of procrastination while in bed. The individuals with more procrastination time had higher BtP scores and fewer hours of sleep and perceived sleep quality as poor. They also had higher scores for anxiety and depression and low scores for perceived general well-being. More procrastination time was related to increased sleep latency.

Alarm dependency was seen in 71.75% (287) of participants. 50% of participants took over 5 minutes to get out of bed after waking up. 32% of participants had a delayed sleep schedule on weekends only.

Our data mainly comprised students pursuing professional courses (37.4%) and salaried persons (35.6%). A longitudinal study done to find a correlation between work settings and sleep revealed that persons without children had longer and better sleep when doing work from home (WFH) than those in work from office (WFO)[39]. Also, WFH persons had higher well-being compared to WFO. Although in our study, participants with WFH mentioned children as a cause of delay in going to bed, but had equal risk of high BtP scores, poor sleep, and low well-being scores.

All participants had personal electronic gadgets; 40% spent more than 6 hours on gadgets; 30.6% used electronic devices mainly for professional work, 28.4% for studies, 20.4% for social media usage, and 18.9% mainly for entertainment. A total of 110 participants (27.5%) used their gadgets before going to sleep or in bed, while 84 used them late at night before going to bed. We found no correlation between using electronic gadgets before bedtime and sleep disturbances or poor mental health.

Sleep latency had a significant negative correlation with sleep duration, subjective well-being, sleep quality and a significant positive correlation with procrastination time. The time taken to fall asleep is an important indicator of distress. The longer time takes to fall asleep, more is the distress and the poorer the quality of sleep. These distressing sleep latency prolongation and BtP are expected to adversely affect the subjective well-being.

The prevalence of BtP was evident in the respondents, although fewer participants preferred leisure to make up for work or studies. More procrastination time leads to altered sleep quality by affecting sleep latency, which affects individuals' mental and overall well-being. No other risk factor was identified in the study that predisposed the individuals to BtP or poor sleep quality.

Our study included an array of healthy individuals belonging to different sectors of the profession and of different ages. As the existing studies are limited to specific groups of populations (students, professionals, females, adolescents) of different backgrounds, and BtP is studied with reference to different variables (sleep quality, quality of life, subjective well-being, smartphone use, subjective satisfaction), the findings are grossly heterogeneous.

We tried to address problems related to BtP, as there is a lack of such data from the Indian population. Since most of our sample came from a professional background, we cannot generalize our findings to a larger population subset. Also, we worked with a limited sample size. Since it was a cross-sectional study, we cannot conclude our study regarding an individual's BtP and cognitive dysfunction.

This study had several limitations. One major limitation was the small sample size. The study design was cross-sectional in nature, which fails to establish causality. The participants were mostly between 18 and 50 years old, and the inclusion criteria were up to the age of 60; hence, the findings cannot be generalized to older adults. Due to the limitations of the sampling technique (convenient sampling and web-based survey), there is unequal representation of all socioeconomic strata. The data mainly consists of urban population respondents, more likely due to the web-based format of the survey. The 2-item screening tools for anxiety and depression have been used. A broader survey, including a larger sample, to study the sleep patterns of the general Indian population, can help develop better strategies for preventing mental health problems.

Our study covered multiple aspects of sleep, including perceived quantity, quality, and latency, its associated effect on mental and physical well-being, and the reason behind its insufficiency. Revenge BtP can be identified as one cause. People's awareness of its detrimental effects might affect their nighttime habits and help them in self-management.

CONCLUSION

We conclude that there is a positive correlation between increased BtP score and depression and anxiety, and a negative correlation with the feeling of subjective well-being. BtP score was associated with poor sleep quality and higher sleep latency. Previous literature suggests similar detrimental effects of electronic gadgets and work pressure affecting sleep quality, but only a handful come from the Indian subcontinent. We also noted that BtP and poor sleep quality were higher in the younger age group than in older people. Moreover, we found that many people attributed their BtP to professional rather than leisure activities. BtP has become a rising problem in our society, and more work is required to address it and spread awareness among society.