Functional analysis of bedtime procrastination in individuals with clinical insomnia: a behavior for digital emotional regulation?

Abstract

Bedtime procrastination (BP) has been identified as a significant factor contributing to poor sleep health. While previous studies have examined its behavioral and psychological correlates, its functional role in individuals with clinical insomnia remains unclear. This study aims to investigate the primary functions of BP in individuals with insomnia and examine its relationship with smartphone use. A total of 80 young adults (mean age 22.3 ± 2.4 years, 80% female) with clinical insomnia participated in the study. BP, insomnia severity, and emotion regulation were assessed using self-report questionnaires. Additionally, sleep patterns were monitored using a daily sleep diary and actigraphy. A structured interview using functional analysis was conducted to analyze the individual functions of BP, classifying responses into seven categories based on antecedents, behaviors, and consequences. Participants spent an average of 95.9 (SD = 38.3) minutes per day using their smartphones in the 3 h before bedtime. Most participants used their phones every day during this window (78.8%). The most common functions of BP were emotion regulation (49.3%), reward (14.3%), and sleep induction (10.7%). In addition, adaptive emotion regulation strategies significantly moderated the relationship between BP and smartphone use in the 3 h before bedtime (β = 0.34, 95% CI = [0.02–0.23]). Our findings suggest that for individuals with clinical insomnia, BP, which is largely driven by smartphone use, can serve as a tool for emotion regulation. Interventions targeting BP should incorporate strategies considering individual functions of BP, rather than merely restricting media use.

Statement of Significance.

This study explored functions of bedtime procrastination (BP) among individuals with insomnia, identifying emotional regulation, reward for oneself, and sleep induction as the main functions. By identifying emotion regulation as a primary function of BP for nearly half of the participants, the research suggests that digital emotion regulation may, in certain circumstances, serve as a strategic tool to reduce pre-sleep arousal and increase sleep readiness for individuals with insomnia. In contrast to previous studies that have considered BP as a habit or a consequence of digital media use, this study suggests that it may serve as an adaptive behavior to regulate negative emotions and induce sleep before bedtime in individuals with insomnia. These insights provide a more nuanced understanding of BP, suggesting a reconsideration of the entirely negative framing of the term.

Introduction

Insomnia, the most common sleep disorder, is marked by difficulty in initiating or maintaining sleep, which adversely affects daytime functioning [1]. One significant bedtime (BT) behavior that is associated with insomnia and also serves as a barrier to optimal sleep health is bedtime procrastination (BP) [2–4]. BP is defined as “going to bed later than intended, without having any external factors for doing so” [3]. In previous studies, BP has been considered a significant health-interfering behavior because of its associations with insufficient sleep and insomnia, and other psychological variables [2–4]. Additionally, BP has been associated with poor sleep health, as evidenced by self-reported measures such as decreased sleep recovery, delayed sleep onset, lower sleep efficiency (SE), and shorter total sleep time (TST) [5], making it an important target in both clinical and non-clinical populations.

Previous studies have reported on the potential link between BP and digital device use. Our research team found that individuals with high BP tend to use their smartphones an average of 61 min longer daily 3 h before bed compared to those with low levels of BP [2]. Other studies have reported that up to 90.6 per cent of adolescents use their smartphones in bed, with BP mediating the relationship between problematic smartphone use and sleep quality (SQ) [6]. These findings are supported by a recent meta-analysis confirming that using digital devices were consistently associated with BP, delayed sleep onset, and shorter overall sleep duration [7].

While these studies establish a clear behavioral link, the tendency to engage in BP via digital devices may depend on an individual’s emotional regulation style [8, 9]. Individuals with more passive emotional regulation styles may choose to watch media content rather than actively engage in problem-solving after a stressful event, leading to a gradual avoidance of social interactions and increased time awake in bed. Despite these associations, the role of emotion regulation style in the relationship between BP and smartphone use has not been empirically examined.

To bridge this gap, a functional approach can be utilized to address problematic behavior by identifying the underlying contingencies that maintain it [5, 10]. This approach conceptualizes behaviors—such as non-suicidal self-injury and binge eating—by analyzing the underlying processes that reinforce and maintain them [11, 12]. Within this framework, a behavior maintained over time is understood to serve a specific regulatory or communicative function for the individual [13]. Ultimately, while behavioral contexts can provide unique triggers and reinforcements, the function of the behavior remains the primary driver of repetition. When these regulatory behaviors occur within digital environments, they can be operationalized as digital emotion regulation (DER) [14]. Based on the current literature, DER can be operationalized as a process—the strategic use of digital affordances to influence affective states, which is consistent with previous theories, such as the Process Model of emotion regulation [14]. Within this theoretical framework, using digital devices to regulate emotions can involve stages such as situation selection (e.g. choosing to watch a video to avoid a negative emotion before bed) or attentional deployment (scrolling for distraction or sleep induction).

Functional assessment or functional behavior assessment is the systematic process of identifying functional relationships between environmental events and the occurrence or nonoccurrence of a target behavior [15, 16]. Based on a previous study by our research team, we found emotional regulation (31.3%), reward (26.5%), and social interaction and belongingness (18.1%) were the most frequent functions of BP in individuals without insomnia symptoms [17]. Thus, a majority of individuals engaged in BP to reduce negative emotions or avoid situations or thoughts that caused negative emotions. Individuals with low self-regulation may use electronic devices to distract themselves from distressing thoughts or emotions, particularly through social media or streaming content [8, 18].

In contrast to individuals without sleep disturbances, individuals with clinical levels of insomnia may have different functions of BP compared to those who do not experience sleep disturbance. While our previous work identified that non-clinical individuals primarily engage in BP for self-reward or social belonging [19], we hypothesize that the presence of clinical insomnia symptoms may reconfigure the functional nature of this behavior. According to previous studies, insomnia patients commonly experience hyperarousal before bed [9, 20], which interferes with sleep initiation. Consequently, individuals with insomnia often come to perceive their bed as an aversive stimulus due to the repeated association between BT and sleep difficulties, leading to frustration and discomfort throughout the night [21]. Thus, they may perceive BT as a negative experience associated with stress and frustration, ultimately increasing the likelihood of BP as a form of avoidance. Indeed, recent studies have suggested that individuals with insomnia may use BP as a strategy to increase sleep pressure before sleep [22]. Unlike individuals who do not experience insomnia, BP in insomnia patients may serve a function similar to safety behaviors that may not be beneficial in the long-term [23]. For instance, delaying BT to avoid frustration associated with sleeplessness may paradoxically heighten cognitive arousal and perpetuate insomnia by reinforcing maladaptive sleep patterns [5].

Therefore, the current study explores the possibility that BP can serve a potentially functional or strategic behavior before bed in individuals with insomnia, which may be different from individuals who do not experience sleep disturbance. This study had two aims: (1) to explore the functions of BP among individuals with clinical insomnia and (2) to examine whether emotion regulation style moderates the relationship between BP and smartphone use before BT.

Materials and methods

Participants

Participants were recruited between July 2020 and September 2021 in Seoul, South Korea using advertisements through online community postings and offline fliers. Eighty participants who met criteria for clinical levels of insomnia and endorsed frequent engagement in BP were selected to participate in the study.

This study was advertised targeting young adults in their 20s based on the previous study that noted that general procrastination behavior was most common in early adulthood [24]. A total of 620 volunteers completed the screening survey for this study. Individuals who scored 32 or lower on the Bedtime Procrastination Scale (BPS) and who scored 14 or lower on the Insomnia Severity Index (ISI) were excluded from the study (n = 311). The exclusion criteria for BPS were based on previous studies using a 33 score for the cut-off score [2].

Next, telephone screening interviews were conducted to ensure that participants met inclusion criteria. During the telephone screening, 83 individuals who did not experienced insomnia symptoms based on DSM-5 criteria and 7 individuals who did not engage in BP were excluded. In addition, individuals who declined to participate in this study (n = 94), who had past suicide attempts (n = 6), individuals taking psychotropic medication (n = 3), and individuals with severe psychopathology such as psychosis (n = 8) or bipolar disorder (n = 1) were screened. The specific exclusion criteria were: (a) history of suicide attempts; (b) previous diagnosis of bipolar disorder and schizophrenia; (c) currently taking psychotropic or participating in psychotherapy for mental disorder; and (d) being a shift worker. Finally, a total of 80 participants enrolled in this study, excluding 27 participants who withdrew before visit 1 (Figure 1).

Figure 1.

Study flowchart.

Procedure

During visit 1, participants completed a baseline questionnaire packet. Additionally, they were provided with detailed instructions on how to complete a daily sleep diary. Every participant was then asked to participate in a structured interview that lasted approximately 1 h, including a functional analysis of their BP behavior.

A functional analysis of BP was conducted by a master’s level student who was trained with the study protocol by a licensed clinical psychologist (S.S.) prior to starting the study. During the functional analysis, the trained interviewer asked the participant to identify a recent situation where the participant engaged in prolonged BP (>1 h). Functional analysis for the study consisted of (1) Antecedents (A): situations, thoughts, and emotions before engaging in BP (2) Behaviors (B): specific behavior associated with postponing BT, and (3) Consequences (C): the participant’s emotions and behavior following engaging in BP, focusing on reinforcing factors that perpetuated BP. Researchers used structured interview questions to investigate the function of BP. For example, “What thoughts and emotions did you experience before engaging in bedtime procrastination?”, “What activities do you engage in when you go to bed later than your intended bedtime?”, “What do you perceive is an advantage of BP?”. Every interview session was recorded, and individual functions of BP based on responses of the participants were classified into seven categories based on a previous study [17].

The functions of BP were grouped into seven categories (1) emotion regulation—BP to reduce or avoid negative emotions; (2) reward—to give oneself “me time” or “self-reward” for working hard during the day; (3) social interaction and belonging—BP to feel connected to another person or group; (4) Information and knowledge acquisition—to stay informed through the news or other content; (5) Sleep induction—behaviors designed to reduce arousal and induce sleep; (6) Sense of accomplishment—BP to feel a sense of accomplishment for having accomplished something; and (7) Pleasure seeking—BP simply for pleasure.

After a week of measuring sleep patterns, all participants returned for visit 2. During visit 2, participants received sleep education and feedback based on their 1-week sleep diary and results of the questionnaire. Interviews were led by a licensed clinical psychologist with a Diplomate of Behavioral Sleep Medicine certification (S.S.) and three master’s level graduate students trained in behavioral sleep medicine. The next step involved training graduate-level research assistants on the functional classification framework for BP. Following training, coders practiced applying the functional categories using pilot cases prior to coding study data. Throughout the study, coders received ongoing supervision from the research team. All participant interviews were audio-recorded, and recordings from 80 participants were independently reviewed by three trained research assistants to classify the functional categories of BP. Coding was guided by a pre-defined standardized coding framework, with each functional category assessed for presence or absence. Ambiguous cases were discussed with the research team until consensus was reached.

Measures

Bedtime Procrastination Scale

The BPS was developed by Kroese and colleagues (2014) and measures the degree of BP. The BPS consists of 9 items that describe sleep-related behaviors and habits that indicate high or low level of BP. The items were rated on a five-point Likert scale from 1 (almost never) to 5 (almost always). The BPS total score range is 9 to 45 points, with higher scores reflecting more BP.

Sleep diary

Participants were asked to complete a weekly sleep diary for 4 weeks after the first visit. They were asked to record bedtime procrastination duration (BPD), which was operationally defined as the difference from the time initially planned to go to bed and lights off (LO). Planned BT was defined as the time participants planned to go to bed [25] and was assessed each morning using the following question: “What time did you to bed?” Additional sleep parameters such as sleep onset latency (SOL), wake after sleep onset, TST, time in bed, BT, light off time (LO), wake time (WT), SE, SQ, napping time, and feeling refreshed upon awakening (scale 1–5) were also collected. Information regarding the use of sleep aids or medications was obtained via the sleep diary. Participants were asked each morning: “Did you take any over-the-counter or prescribed medications to help you sleep?” Only three participants (3.8%) reported using sleep-related medications during the study period; therefore, medication use was not included as a covariate in the analyses.

Insomnia Severity Index

The ISI was developed by Bastien, Vallières, and Morin [26] and is composed of 7 items that measure the severity of insomnia during the past 2 weeks. The ISI is rated on a five-point Likert scale from 0 to 4. The total score range is 0–28, and higher scores reflect greater insomnia severity. In the present study, participants who scored below 15 were excluded [27].

Emotion Regulation Strategy Questionnaire

The Emotion Regulation Strategy Questionnaire (ERSQ) was developed by Lee and Kwon [28] and is composed of 69 items that measure individual emotion regulation strategies. The ERSQ is rated on a seven-point Likert scale from 0 to 6. The total score range is 0–414. The ERSQ could separate subscales into two dimensions, depending on whether the emotion regulation strategies used by individuals effectively reduce negative emotions as follows: (a) adaptive emotion regulation strategy (ERSQ_A): an emotion regulation strategy that is effective in controlling negative emotions; (b) maladaptive emotion regulation strategy (ERSQ_M): emotion regulation strategies that may be temporarily effective in regulating negative emotions, but in the long-term maintain and exacerbate negative emotions [29]. Example items include: “I try to think about my situation in a reasonable way” (adaptive), and “I tend to think only about negative outcomes that may happen in the future” (maladaptive). The Cronbach’s α for the ERSQ scale was 0.85 in this study.

Smartphone Addiction Scale-Short Version (SAS-SV)

The Smartphone Addiction Scale-Short Version (SAS-SV) was developed by Kwon and colleagues (2013). This questionnaire composed of 10 items that assessed smartphone addiction using 1 (strongly disagree) to 6 (strongly agree) scales. The total scores were calculated by adding up all the scores given by the participants on each item and the total score ranged from 10 to 60. This scale is not used to diagnose smartphone addiction but to measure the risks associated with smartphone addiction. The Cronbach’s α for the SAS scale was 0.81 in this study.

Actigraphy

Actigraphy (Readiband; Fatigue Science, Honolulu, USA) was used to measure objective sleep of the participant. The device is based on the Sleep, Activity, Fatigue, and Task Effectiveness (SAFTE) model, a computerized biomathematical model that predicts changes in “cognitive effectiveness” based on the sleep/wake schedule. The device estimated objective sleep indicators such as TST, BT, and WT. The model became known as the SAFTE model. It was subsequently prospectively validated across a wide range of sleep conditions, from total sleep deprivation to normal sleep [30].

Statistical analysis

Statistical analyses were performed using SPSS 21.0 version (IBM Corp., Armonk, NY, USA). Descriptive statistics and Pearson’s correlation analysis were conducted. Pearson’s correlation coefficient was calculated to investigate the relationship between BP and other variables that was measured by questionnaires, sleep diary, and actigraphy.

A qualitative analysis of the function of BP was conducted to estimate the function of BP. First, the functions of BP were classified into seven categories. Next, each participant’s functional areas were coded 1 if they had the function and were coded 0 if they did not report endorsing the function. Finally, a frequency analysis of multiple responses was conducted to estimate the function of BP in clinical insomnia patients. The statistical analyses were performed using SPSS 21.0 version (IBM Corp., Armonk, NY, USA).

Moderation analysis was conducted using model 1 in PROCESS [31]. Analyses were bootstrapped with 5000 replications to calculate standard errors and 95% confidence intervals. A total of two models were constructed with two types of emotion regulation strategies (M) entered as moderators. In model 1, BPS was set as the predictor (X), phone time 3 h before BT as the outcome (Y), and adaptive emotion regulation strategy (based on the ERSQ) as the moderator (M); in model 2, BPS was set as the predictor (X), total minutes of smartphone use in the 3 h before BT as the outcome (Y), and maladaptive emotion regulation strategy was added as the moderator. All of the variable’s scores were mean-centered in the current moderation analyses to avoid multicollinearity given the high intercorrelations. Finally, the simple slope tests were conducted to further validate the moderation effects. Subsequently, Johnson-Neyman analysis was performed to identify regions of significance in the conditional effect, where the independent variable’s effect on the dependent variable becomes significant or nonsignificant across levels of the moderator [32].

Results

Demographic and clinical information

Demographic information is presented in Table 1. Participants were on average 22.3 years (SD = 2.4), and 80 per cent of the sample was female. Participants’ total minutes of smartphone use in the 3 h before BT averaged 95.9 (SD = 38.3, range = 13–180) minutes per day. Most participants reported using their phone 7 days (78.8%) per week during the 3 h window before BT.

Table 1.

Demographic and clinical characteristics (N = 80)

Variables	n or M	% or SD
Gender (%)
Female	64	80
Male	16	20
Age	22.26	2.36
Education (%)
High school graduation	2	2.5
University student	67	83.75
Bachelor’ degree	6	7.5
Master’s student	5	6.25
Marital status (%)
Married	1	1.25
Single	79	98.75
Employment status (%)
Unemployed	7	8.75
Employed	2	2.5
Student	71	88.75
Total minutes of smartphone use in the 3 h before bedtime	95.85	38.33
Average days of smartphone use (%)
4 days	3	3.75
5 days	4	5
6 days	10	12.5
7 days	63	78.75
Questionnaires
BPS	38.10	3.32
ISI	16.19	3.14
ERSQ_A	207.09	31.81
ERSQ_M	84.75	19.34
SAS	35.90	6.66
Sleep diary
BPD (min)	101.72	64.27
SOL (min)	18.83	17.82
WASO (min)	12.28	16.04
TST (min)	399.74	61.26
TIB (min)	508.63	90.51
SQ	3.17	0.69
SE (%)	79.67	9.46
FRESH	2.82	0.68
NAP (min)	34.94	42.35
BT	02:00:11	1:29:12
LO	02:56:26	1:29:00
WT	10:15:26	1:32:13
Actigraphy (n = 77)
TST_A (min)	383.02	67.57
BT_A	02:49:45	1:42:45
WT_A	10:18:24	1:30:00

Total minutes of Smartphone use in the 3 h before bedtime, average use time of phones for 3 h before bedtime; Average days of smartphone use, average use days of phone 3 h before bedtime per week; BPS, Bedtime Procrastination Scale; ISI, Insomnia Severity Index; ERSQ_A, adaptive emotion regulation strategy; ERSQ_M, maladaptive emotion regulation strategy; SAS, smart phone addiction scale; BPD, bedtime procrastination duration; SOL, sleep onset latency; WASO, wake after sleep onset; TST, total sleep time; TIB, time in bed; SE, sleep efficiency; SQ, sleep quality; FRESH, feeling refreshed upon awakening; BT, bedtime; LO, lights off; WT, wake time; NAP, napping time; TST_A, total sleep time of actigraphy; BT_A, bedtime of actigraphy; WT_A, wake time of actigraphy.

Based on sleep diaries, participants reported an average BPD of 101.7 (SD = 64.3) minutes daily. Average TST was 399.7 (SD = 61.3) minutes, SE was 79.7% (SD = 9.5), BT was 2:00 a.m., LO was 2:56 a.m, and WT was 10:15 a.m. Based on actigraphy, average TST was 383 (SD = 67.6) minutes, BT was 2:49 a.m., and WT was 10:18 am.

Functional analysis of BP in insomnia patients

Data from 80 participants were used for analysis, but since duplicate responses were allowed, the total frequency of responses was 140. The sum of the response ratios for each item was 175%. Among the total frequency of responses, the most common functions of BP were emotional regulation (49.3%), reward (14.3%), and sleep induction (10.7%; see Figure 2).

Figure 2.

Function of bedtime procrastination (N = 80).

Correlation analysis between BP and emotion regulation

BPS scores were positively correlated with the Smartphone Addiction Scale (SAS) scores (r = .45, p < .001) and ISI scores (r = .28, p < .05; Table 2). BPD measured by sleep diaries was positively correlated with average smartphone use 3 h before BT (r = .57, p < .001) and ISI (r = .25, p < .05).

Table 2.

Correlations between bedtime procrastination and questionnaires (N = 80)

Variables		1	2	3	4	5	6	7	8
1	BPS	1
2	BPD (min)	0.14	1
3	Total minutes of smartphone use in the 3 h before bedtime	0.15	.57 ***	1
4	Average days of smartphone use	0.05	−0.05	.30**	1
5	ISI	.28 *	.25 *	0.14	0.12	1
6	ERSQ_A	0.07	−0.09	−.25 *	−.31 **	−0.05	1
7	ERSQ_M	0.15	0.08	0.04	0.08	0.09	−0.16	1
8	SAS	.45 ***	.23 *	.36 **	0.2	0.22	−0.14	.25*	1

Note. ^*p < .05, ^**p < .01, ^***p<.001. BPD, bedtime procrastination duration; BPS, Bedtime Procrastination Scale; Total minutes of smartphone use in the 3 h before bedtime, average use time of phones for 3 h before bedtime; Average days of smartphone use, average use days of phone 3 h before bedtime per week; ISI, Insomnia Severity Index; ERSQ_A, adaptive emotion regulation strategy; ERSQ_M, maladaptive emotion regulation strategy; SAS, smart phone addiction scale.

Additionally, average use time of phones 3 h before BT was negatively correlated with adaptive emotion regulation strategies (ERSQ_A; r = .−25, p < .05). Adaptive emotion regulation strategies (ERSQ_A) were significantly negatively correlated with the daily average smartphone usage time (r = −.31, p < .01).

Moderation analysis

Results indicated that adaptive emotion regulation strategies (ERSQ_A) were associated with variation in the pattern of association between BPS and average total minutes of smartphone use in the 3 h before BT (see Table 3). Specifically, the association between BPS and average smartphone use differed depending on the level of ERSQ_A (β = 0.34, 95% CI = [0.02, 0.22]). However, maladaptive emotion regulation strategy (ERSQ_M) did not show a moderating pattern in this association.

Table 3.

Moderation analysis (N = 80)

Dependent variable	Predictor variable	β	se	t	p	95% CI
Total minutes of smartphone use in the 3 h before bedtime	BPS	0.24	1.27	2.14	.04*	(0.19–5.26)
	ERSQ_A	−0.26	0.13	−2.48	.02*	(−0.57 - 0.06)
	BPS*ERSQ_A	0.34	.05	2.41	.02*	(0.02–0.22)
	BPS	0.24	1.33	1.24	.22	(−0.99–4.30)
	ERSQ_M	0.02	0.23	0.18	.86	(−0.41–0.49)
	BPS*ERSQ_M	0.02	0.07	0.16	.88	(−0.13–0.15)

Note. ^*p < .05. se, standard errors; 95% CI, 95% confidence interval; BPS, bedtime procrastination scale; ERSQ_A, adaptive emotion regulation strategy; ERSQ_M, maladaptive emotion regulation strategy.

To further explore this interaction, conditional associations of BPS on smartphone use were examined at three levels based on ERSQ_A scores: 1 SD below the mean, at the mean, and 1 SD above the mean (Table 4). At the mean level of ERSQ_A, the association was positive (β = 0.24, 95% CI = [0.19, 5.26]). At 1 SD above the mean, the association was even stronger (β = 0.57, 95% CI = [2.01, 11.24]). However, at 1 SD below the mean, the association was not significant (β = −0.10, 95% CI = [−4.68, 2.33]). These patterns suggest that the co-occurrence of BP and smartphone use was more pronounced among individuals with higher levels of adaptive emotion regulation.

Table 4.

Conditional indirect effects model predicting smartphone time (N = 80)

Variable		β	se	t	p	95% CI
ERSQ_A	−1SD	−0.10	1.76	−.67	.51	(−4.68–2.33)
	M	0.24	1.27	2.14	.03*	(0.19–5.26)
	+1SD	0.57	2.32	2.86	<.01**	(2.01–11.24)

Note. ^*p < .05, ^**p < .01. se, standard errors; 95% CI, 95% confidence interval; Smartphone time, use time of smartphones for 3 h before bedtime; ERSQ_A, adaptive emotion regulation strategy. Conditional indirect effect of adaptive emotion regulation strategy at mean and ±1SD on use time of smartphones for 3 h before bedtime.

Using Johnson-Neyman analysis, the region where the moderating pattern of ERSQ_A was statistically detectable was identified. The conditional association between BPS and average smartphone use was significant when ERSQ_A scores exceeded −1.95 (p < .05), reflecting a pattern consistent with a stronger association between BPS and smartphone use at higher levels of ERSQ_A (see Supplementary Material S1).

Discussion

This study investigated functions of BP in young adults with clinical insomnia and explored BP as a potential functional and adaptive strategy for this population. Among our sample, most of the participants reported engaging in BP on average of 101 min per day. A majority of participants used their phones during this time, with mobile phone use averaging 95 min 3 h prior to their reported BT, which occurred most days during the week (78.8% of the sample reported using their phones 7 days of the week prior to BT). This suggests that BP is particularly prevalent among younger adults with clinical insomnia. Participants also reported a later BT (2:00 am) and WT (10:15 am) which may have reflected flexibility in their schedule as a majority of the sample were university students. Nearly half of the participants (49.3%) reported emotion regulation as a primary function of BP in this population. To the best of our knowledge, this is the first study to investigate specific functions of BP in individuals with insomnia.

Functions of BP in the clinical insomnia group

Based on the results of a functional analysis in our study, the most common functions of BP among individuals with clinical insomnia were emotional regulation (49.3%), compensation and reward (14.3%), and sleep induction (10.7%). These findings suggest that individuals with clinical insomnia primarily engage in BP to regulate negative emotions and to compensate for daily demands, which is broadly consistent with patterns observed in younger adults without insomnia [2]. However, unlike non-clinical populations, individuals with insomnia additionally reported using BP as a means of sleep induction, such as reducing arousal and facilitating sleep onset. In previous studies investigating individuals without insomnia, BP most frequently served functions related to emotional regulation (31.3%), rewarding oneself (26.5%), and social interaction and feelings of belonging (18.1%) [19]. Taken together, these findings indicate that while BP serves similar core functions across clinical and non-clinical populations—namely emotional regulation and rewarding oneself—individuals with insomnia uniquely engage in BP for the purpose of sleep induction.

This difference in the third most frequent function between clinical and non-clinical groups suggests that individuals with clinical insomnia may intentionally engage in BP in an effort to fall asleep more easily. This interpretation is consistent with prior studies indicating that individuals who experience prolonged SOL were more likely to engage in BP to increase sleep pressure or reduce pre-sleep frustration [22, 33]. Supporting this notion, qualitative research has described a pattern of “strategic delay”, in which individuals deliberately choose to go to bed later because they believe it will facilitate sleep [34]. Participants engaging in this form of BP reported reasons such as “I would be unable to sleep if I went to bed earlier” or “If I go to bed late, I will fall asleep more easily” [22]. Additionally, insomnia has been conceptualized as a disorder involving circadian misalignment, whereby individuals attempt to initiate sleep earlier than their endogenous circadian phase [35]. In such cases, engaging in passive activities before BT may serve as a way to pass time until sleep becomes physiologically possible.

While our study investigated main functions of BP, the temporal dynamics of BP warrant further investigation. Similar to many other detrimental health behaviors, such as alcohol consumption or smoking, BP may offer short-term affective utility while leading to significant long-term costs to sleep health. The high prevalence of emotion regulation as a primary function of BP in our study suggests that many individuals use BP to improve their mood before sleeping. After a highly stressful day, digital devices may serve as a low effort tool for decompression. However, there may be a trade-off of short-term relief over the long-term health benefits of a consistent sleep schedule [36]. Together, these findings suggest that BP in individuals with insomnia may function as a coping strategy for managing sleep difficulties, although whether this strategy is misguided and chronic engagement reinforces sleep disturbances remain uncertain.

The role of digital emotion regulation in BP among individuals with clinical insomnia

This study found that higher levels of BP were associated with more time spent on their smartphones within 3 h of their BT and smartphone addiction. Increasing use of digital technologies, including smartphones, social media, and streaming services, has significantly altered BT behaviors, consistent with previous research suggesting that digital media use significantly contributes to increased BP [18, 37]. This influence extends beyond simply curtailing TST, as digital media actively delays sleep onset by reinforcing engagement and interfering with sleep initiation. BP may not be merely a byproduct of digital media consumption; rather, digital emotional regulation may mediate this relationship. Our findings suggest that individuals with insomnia may engage in BP not only as a habitual delay but also as a functional behavior aimed at facilitating sleep.

These results suggest that emotion regulation, a main function of BP that was endorsed by nearly half of our participants, can act as a potential mechanism linking BP and smartphone use before BT. Smartphones can be used to avoid aversive BT routines or enable individuals to manage emotional distress through digital means [38]. According to Gross’s process model of emotion regulation processes [39], BP due to smartphone use may reflect a number of various regulatory strategies. Individuals may attempt to select or modify situations to avoid emotional discomfort (situation selection/modification), divert attention from distressing thoughts (attention placement), or downregulate emotional responses (response regulation) by delaying BT and engaging with digital content. This may especially apply to insomnia patients who find attempting to sleep as aversive or frustrating. Thus, BP in this population may function as a strategic component of a multi-stage DER process, aimed at facilitating sleep by promoting de-arousal before BT.

In addition, individuals with insomnia may use digital devices to regulate emotions before bed, reducing pre-sleep arousal, and ultimately increasing sleep readiness. Prior studies have demonstrated that individuals with insomnia tend to engage in prolonged digital device use before BT, suggesting that DER may be associated with sleep-related behaviors [8]. Thus, it raises the question of whether BP is universally maladaptive.

The impact of digital engagement can be positive or negative depending on individual regulatory strategies. Active engagement fosters social connectedness and elicits positive emotions, whereas passive use, characterized by mindless scrolling, can increase social comparison and negative affect [40]. Our findings indicate that adaptive emotion regulation strategies significantly moderated the relationship between BP and smartphone use. Notably, individuals with higher levels of adaptive emotion regulation strategies were more likely to use their smartphone before bed for BP, suggesting that for insomnia patients, BP may be a functional strategy for inducing sleep. This finding is particularly interesting considering the unique characteristics of increased emotional reactivity and emotion dysregulation in insomnia, which leads affected individuals to seek external means to manage stress and negative emotions [41]. Since individuals with insomnia often experience pre-sleep arousal and cognitive hyperactivity [42], utilizing smartphones may be a strategic attempt to prepare for sleep and stabilize mood.

This result highlights a shift from our previous work with non-clinical samples [17], where BP was primarily driven by reward-seeking. For those with insomnia, the pre-sleep period often acts as a conditioned aversive stimulus due to chronic hyperarousal [43]. In this context, individuals with higher adaptive regulatory skills may utilize digital devices to promote de-arousal and induce sleep by distracting themselves from negative emotions associated with sleep. Consequently, BP in this sample is reframed as a purposeful effort toward DER—an active attempt to alleviate negative affect—rather than a mere failure of self-control. Digital devices, particularly smartphones, provide an easily accessible medium for regulation, lowering the threshold for engagement and reinforcing BP as a coping strategy for managing affective states. Ultimately, for individuals with insomnia who possess adaptive emotion regulation skills, digital engagement may serve as a functional tool for managing sleep-related stress and facilitating sleep onset. Although the present study did not differentiate specific smartphone activities during BP, future research should examine how distinct forms of digital engagement differentially impact pre-sleep arousal and BP.

Furthermore, reliance on DER among individuals with insomnia may be influenced by underlying circadian misalignment. Research suggests that many individuals with insomnia attempt to initiate sleep at a time misaligned with their endogenous circadian phase [35]. In such cases, digital media use may serve as a means of passing time until sleep becomes physiologically possible. This is consistent with findings that individuals who take longer to fall asleep are more likely to engage in passive digital activities before BT, possibly as a means of increasing sleep pressure [7]. Taken together, these patterns suggest that BP among individuals with insomnia may reflect a compensatory coping strategy in response to difficulties initiating sleep, rather than a purely maladaptive form of BP.

In light of our findings, the term “bedtime procrastination,” which has generally been associated with negative connotations of self-regulatory failure, may not adequately account for the functional complexity of this behavior. Considering BP is often driven by specific individual functions and motivations, a more neutral term—such as “bedtime postponement”—may be more appropriate in reflecting its diverse regulatory functions.

Implications for insomnia treatment: The role of digital emotion regulation in BP

Traditional Cognitive Behavioral Therapy for Insomnia (CBT-I) primarily focuses on improving sleep hygiene, stimulus control, and cognitive restructuring [44]. However, existing CBT-I does not adequately address BP and the impact of digital devices on sleep and emotion regulation. In fact, most of the CBT-I modules were developed at a time when smart devices did not exist, and updates in the treatment may need to be made to reflect modern society. Research suggests that the issue lies not in the mere use of digital media, but in how it is utilized as a tool for emotion regulation [14].

Previous research has highlighted that CBT-I largely emphasizes important behavioral principles [44], but does not fully account for the influence of DER on individuals with insomnia. Findings from this study suggest that BP may be reinforced by DER. This indicates that simply restricting digital media use may not be sufficient in improving sleep outcomes. Instead, interventions should incorporate emotional processing skills to effectively reduce BP. This perspective supports more personalized, function-based approaches to improving sleep health in individuals with insomnia. A distinction should be made between healthy and maladaptive DER strategies, as interventions targeting BP and emotion regulation concurrently may be more effective than traditional approaches.

Limitations

This study has several limitations that should be considered when interpreting its findings. First, the sample consisted solely of young adults in their 20s residing in Seoul, which may introduce potential biases related to age and regional characteristics. Consequently, the generalizability of the findings to other age groups or individuals from diverse geographic and cultural backgrounds may be limited. Additionally, as this study employed a cross-sectional design, it is difficult to establish causality between variables. For instance, it remains unclear whether BP results from smartphone use and emotion regulation difficulties or whether it serves as a contributing factor to these behaviors. Future research should adopt longitudinal studies or experimental designs to establish clearer causal relationships.

Second, this study has limitations concerning the measurement methods used. The primary variables—BP, emotion regulation, and smartphone use—were assessed using self-report questionnaires and interviews, which are inherently susceptible to recall bias, response bias, and social desirability bias. In particular, smartphone use was self-reported rather than passively tracked, which may lead to recall bias or under-reporting, especially for short or fragmented usage before BT. Furthermore, although this study confirmed that emotion regulation influences BP, it did not account for fluctuations in emotional states throughout the day (e.g. daytime stress, evening mood changes). Additionally, although actigraphy provides objective estimations of sleep, it cannot clearly distinguish quiet wakefulness from sleep, which may result in misclassification of sleep parameters. Moreover, actigraphy data were not formally cross-validated with sleep diary reports, and thus discrepancies between objective sleep estimates and subjective reports cannot be fully ruled out. Given the dynamic nature of emotion regulation, future research should incorporate methods such as ecological momentary assessment to track real-time emotional variations and their impact on BP. Additionally, objective measurement tools such as smartphone usage log data and physiological markers, as well as systematic cross-validation procedures between subjective and objective sleep measures, should be employed to enhance measurement accuracy and validity.

Third, functional classification of BP were conducted by trained researchers with a pre-defined manual. While coding was conducted independently between trained researchers, we did not collect data to calculate statistics for inter-rater reliability (i.e. Cohen’s kappa).

Fourth, this study was conducted during the early phase of the COVID-19 pandemic, which represents a unique contextual limitation. During this period, access to traditional social interactions and coping strategies was substantially restricted due to public health measures. As a result, digital device use, including smartphone use, may have served a more adaptive role for emotional regulation and social connection than it would under typical circumstances. Therefore, the functions of BP and smartphone use observed in this study may partly reflect pandemic-specific conditions, and caution is warranted when generalizing these findings to non-pandemic contexts. Furthermore, chronotype was not directly assessed in the present study. Given that young adults typically exhibit delayed circadian preferences, age-related circadian delay may represent an unmeasured but important factor influencing sleep timing, BP, and pre-BT digital device use. Although pre-BT screen use has been suggested to behaviorally delay sleep–wake timing through exposure to bright light and cognitively or emotionally engaging content, recent evidence indicates that the magnitude of its direct impact on sleep timing and SOL may be smaller than previously assumed, often <10 min or negligible [13]. Accordingly, the extent to which pre-BT screen use disrupts sleep may depend on contextual, behavioral, and individual difference factors, rather than exerting a uniformly adverse effect.

Finally, a further limitation is the lack of analysis regarding the specific content of smartphone use and motivational factors underlying BP. While this study identified a relationship between smartphone usage time and BP, it did not differentiate between various types of smartphone use (e.g. social networking, video streaming, gaming, web browsing). Moreover, the study did not explore motivational mechanisms that sustain BP. Some individuals may engage in BP as an intentional decision, while others may do so as an involuntary habit. Therefore, future research should distinguish the function and motivation of BP to provide a more nuanced understanding of the psychological mechanisms underlying BP.

These limitations emphasize the need for further research incorporating more diverse samples, robust methodological designs, and objective measurement tools to improve our understanding of the relationship between BP, smartphone use, and emotion regulation.

Conclusion

This study examined the functions of BP in individuals with clinical insomnia and its relationship with smartphone use and DER. The findings suggest that BP in individuals with insomnia is primarily driven by emotion regulation, followed by compensation and attempts to facilitate sleep onset. These results indicate that individuals with insomnia may use BP as a coping mechanism to manage emotional distress and sleep difficulties.

Additionally, smartphone use before BT was significantly correlated with BP, reinforcing previous findings that digital media consumption contributes to BP. Notably, individuals with higher levels of adaptive emotion regulation strategies also spend more time on their smartphone when engaging in more BP. This suggests that individuals with insomnia who use adaptive emotion regulation strategies may utilize their smartphones for DER to facilitate sleep induction. In clinical populations with insomnia, BP may be a functional strategy for digital emotional regulation.

Considering these findings, interventions for BP for insomnia patients should consider the individual functions of BP rather than merely restricting digital media use. Traditional CBT-I may need to be adapted to reflect the role of digital devices in sleep-related behaviors.

Disclosure statement

Financial disclosure: None declared.

Non-financial disclosure: None declared.

Data availability

The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request.