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J Sleep Med > Volume 20(3); 2023 > Article
Huang, Lin, and Yang: Prioritization of Sleep: Discrepancies Between Attitude and Practice



With the promotion of sleep through education, an increasing recognition of the importance of sleep among the general public has been observed. However, whether this increased recognition affects sleep practices is unknown. To provide direction for future sleep education programs, this study aimed to explore the consistency between people’s prioritization of sleep among their daily life activities and their actual practice when sleep conflicts with other activities.


The study was a cross-sectional online survey with 898 adults (male:female=372:526; age=39.74±10.04 years). The participants were asked to complete an online survey that required them to 1) rate how important sleep was; 2) prioritize daily life activities including sleep, work, family activities, leisure activities, social activities, and exercise; and 3) recall the actions they undertook at the most recent instance when sleep conflicted with each of the other activities.


Although 70.7% of the participants prioritized sleep by placing it in their top three daily life activities, only 35.7% and 38.1% chose to go to sleep when it conflicted with work and family activities, respectively. Gender and occupational factors also significantly influenced the discrepancy between sleep attitudes and practices.


Our study demonstrated an inconsistency between reporting sleep as a priority in daily life activities and actual behavioral practices when sleep conflicted with other activities. The results suggest that sleep education should not only emphasize the importance of sleep but also provide practical solutions for circumstances when sleep conflicts with other activities.


Accumulating data have demonstrated the importance of sleep in overall well-being. Studies have reported the negative impact of insufficient sleep on psychological health, such as fatigue [1-4], inattention [5-8], forgetfulness [9,10], irritability [11], and depression [12-14]. Moreover, sleep deprivation also affects physical health, resulting in obesity, metabolic syndrome, and cardiovascular disease [15-17]. On the other hand, optimized sleep has been identified to be related to enhanced memory consolidation [18-20] and improved workplace performance [21-23]. Neuroscience research has demonstrated that sleep is important for the elimination of harmful proteins from the brain that may be related to the development of Alzheimer’s disease [24,25]. With the development of modern technology, sleep education has become more accessible to many people. The benefits of adequate sleep have become common knowledge among the general population [26]. Therefore, people are expected to prioritize sleep in their daily life activities. However, according to a survey by the National Sleep Foundation [27] and a national survey in the United States of America [28], the actual hours of sleep do not exhibit an increasing trend but rather a decreasing pattern over the years. Sleep education should be promoted to highlight the importance of sleep [29].
However, the question remains–does sufficient knowledge guarantee the practice of corresponding behaviors? The knowledge-attitude-behavior model [30] considers that knowledge is essential for changes in attitude and behavior and suggests that health-related behaviors could be promoted by providing knowledge through education. However, other models of health-related behavior suggest that an individual’s behavior is not solely influenced by knowledge or cognitive belief, but also by emotional and motivational, social factors along with the economic environment [31,32]. Therefore understanding how important people consider sleep relative to other daily life activities, as well as how they prioritize sleep in behavioral practice is essential. This will provide a direction for the design of sleep education programs.
Therefore, we conducted an online survey to understand how people prioritize their sleep among different daily activities as well as the actions they take when sleep conflicts with other commitments. If these two aspects align, suggesting that the more significance individuals place on sleep, the more likely they prioritize sleep when it conflicts with other activities. Moreover, sleep education should be designed to convey the importance of sleep. However, if an individual’s beliefs regarding the importance of sleep are not consistent with their behavior, then sleep education focusing on the benefits of sleep might have a limited impact. Furthermore, the skills to facilitate individuals’ control over time and behavior might be an essential component in the design of sleep education programs.


The participants were recruited via an online survey platform (Survey Monkey Inc., San Mateo, CA, USA). The inclusion criteria were as follows: 1) age >18 years and 2) full-time employment. Online informed consent was obtained from all participants before their inclusion in the study. Initially, 914 participants participated in this study. However, 16 participants were excluded from the analyses because of missing data. The final sample included 898 participants (372 males and 526 females) aged 20–68 years (mean: 39.74 years; standard deviation [SD]: 10.04 years).
The survey collected demographic data and three main aspects concerning sleep priorities in attitudes and practices. Demographic data included age, sex, marital status, educational level, occupational level, occupational category, daily working hours, and individual monthly income. First, the participants were asked to answer “How important is sleep to you?” on a five-point Likert scale ranging from “0–not important” to “5–very important.” Second, they were requested to prioritize six life activities–sleeping, work, family activities, leisure activities, social activities, and exercise–based on their importance. Attitude priority scores were derived from the rankings. A score of 6 was assigned if sleep was placed as the priority, 5 if the second priority, 4 if the third priority, 3 if the fourth priority, 2 if the fifth priority, and 1 if the final priority. Third, participants were asked to recall the last time their sleep conflicted with each of the other daily activities listed in the second part and to report the activity they finally prioritized. The sleep priority practice scores were derived from the aforementioned ratings. For each conflicting situation, one point was given if the participant chose to go to sleep, and no point was given if the participant chose another activity. The sum of the points plus one was considered as the priority sleep practice score. Thus, the participant would have a score of 6 if they chose sleep for all conflicting situations and would have a score of 1 if they chose other activities for all items. The discrepancy between attitude and practice scores was calculated by subtracting the attitude score from the practice score.
The chi-square test was used to examine whether differences were identified between priority attitudes and practices. An independent sample t-test was conducted to explore the effect of gender on the priority and discrepancy scores. Analysis of variance (ANOVA) was used to compare differences in sleep attitudes, sleep practices, and discrepancies among occupational ranks, occupational categories, and ranges of individual income. Pearson’s correlation was used to explore the association between daily working hours and discrepancy scores. Statistical analyses were performed using IBM SPSS Statistics V25.0 (IBM Corp., Armonk, NY, USA).

Ethical committee permission statement

The study was approved by the Ethics Review Committee of the National Cheng-Chi University (Application No. NCCU-REC-201702-I001; Approval Date: 8th February, 2017) and was performed following the ethical standards of the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards. Informed consent was obtained from all the participants.


The descriptive results of demographic information, including sex, marital status, level of education, occupational rank, occupational category, and individual monthly income, are presented in Table 1. The gender ratio between males and females was 1:1.41. The marital status demonstrated that 51.2% of the participants were married and 42.7% were single. Additionally, for educational level, 81.8% of participants had college degrees. The average daily working hours were 8.79 hours (SD=1.69). Furthermore, among the occupational ranks, 61.9% were general staff, 31.9% were managers, and 5.4% were selfemployed workers. The occupational categories were quite diverse, with 14.5% in the medical health industry, 14.3% in the educational service industry, 13.8% in the traditional manufacturing industry, 13.3% in the electronic information industry, and 10.4% in the general service industry. The individual monthly income received by 54.2% was between New Taiwan dollar (NT$) 30,000–59,999, 21.4% received between NT$ 60,000–89,999, and 12% received under NT$ 29,999 or above NT$ 90,000.
Regarding the rating of the importance of sleep, the survey data demonstrated that over 98.3% of the participants prioritized sleep to varying degrees. According to the results, 48.7% of participants (n=437) considered sleep as “extremely important”, 30.2% (n=271) considered sleep as “very important”, and 19.4% (n=174) considered sleep as “important;” however, only 1.8% of the participant considered sleep as “not important” (0.6%, n=5) or “extremely unimportant” (1.2%, n=11).
In terms of the participants’ sleep attitude priority, the blue bar presented in Fig. 1 illustrates that 28.2% of the participants prioritized sleep as the first priority, 21.6% as the second, 20.9% as the third, 16.4% as the fourth priority, and 3.8% as the last priority. The orange bar in Fig. 1 represents the participants’ sleep practice priority, with 10.8% (n=97) of participants choosing to go to sleep as the first priority, 20.2% (n=181) as the second, 33.2% (n=298) as the third, 22.0% (n=198) as the fourth, 11.2% (n=101) as the fifth, and 2.6% (n=23) as the last priority.
The discrepancy scores between attitude and practice priorities are displayed in Fig. 2. The Chi-Square test exhibited a significant inconsistency between attitude priority and practice priority (χ2=311.755, p<0.001). Only 34.6% (n=311) of participants displayed behavior that was consistent with their sleep attitude; 44.0% (n=395) tended to sacrifice sleep even with high priority for sleep in attitude, 21.4% (n=192) chose to go to sleep even with high ranking for other daily life activities. Regarding the main reasons for the conflicts, our survey identified that 64.3% and 61.9% of participants sacrificed sleep for work and family activities, respectively. A small proportion of participants sacrificed sleep for leisure activities (38.0%), social activities (28.4%), and exercise (17.9%) (Fig. 3).
As for the demographic data, the results (Table 2) demonstrated no significant difference between the sexes in sleep attitude (t=-1.721, p=0.086) and sleep practice priority (t=1.031, p=0.303) but a significant difference in the discrepancy score was observed (t=-2.623, p<0.01). Females choose to sacrifice sleep more than males. In addition, Pearson correlations demonstrated that daily working hours had a modest significant correlation with discrepancy scores (r=0.078, p<0.05), but no significant correlation was observed with sleep attitude (r=0.045, p=0.18) or sleep practice (r=-0.039, p=0.25). Age had a low significant correlation with sleep attitude (r=-0.130, p< 0.01) and discrepancy score (r=-0.155, p<0.01), but no significant correlation with sleep practice (r=0.033, p=0.327). Regarding individual income levels, the results of ANOVA analyses (Table 2) exhibited no significant differences between individuals with different income ranges in terms of sleep attitude, sleep practice, and discrepancy score. Concerning the occupational ranks, a significant difference was observed between different occupational ranks regarding sleep attitude and the discrepancy score. However, no significant difference was present between occupational ranks regarding sleep practice. Post-hoc tests displayed that compared to general staff, middle-level managers and self-employed workers had significantly lower sleep priority attitude scores. General staff and lower-level managers also had significantly higher discrepancy scores compared to middle-level managers and self-employed workers. This implies that general staff and lower-level managers tended to sacrifice more sleep, while middle-level managers and self-employed workers tended to sacrifice less sleep. With regard to occupational categories, the results of the ANOVA tests (Supplementary Fig. 1 in the online-only Data Supplement) exhibited significant differences in sleep attitude (female=1.831, p<0.05) and discrepancy score (female=2.888, p<0.001). Among all occupational categories, workers in the mass media communication industry had high sleep priority attitude scores, whereas workers in the agriculture, forestry, fishing, and hunting industries, along with the unemployed workers, had low sleep priority attitude scores. However, the discrepancy score demonstrated that mass media communication industry workers had significantly high scores, while the agriculture, forestry, fishing, and hunting industries, as well as the accommodation and catering industries, had significantly low scores.


As expected, our survey demonstrated that most participants recognized the importance of sleep. When compared with other activities in life, sleep was the top priority for nearly 30% of the participants, meanwhile more than 70% of the participants placed sleep in the top three priorities. However, this endorsement of the importance of sleep in their attitude was not reflected in their sleep practices. Only approximately 37% of participants chose to go to sleep when it conflicted with work or family activities. Although more participants chose to go to sleep when it conflicted with social or leisure activities or exercise, a quarter to a third of the participants sacrificed their sleep for these activities.
These results do not fully support the knowledge-attitudebehavior model, which predicts that attitudes guide people’s behavior. These findings suggest that going to sleep when it conflicts with other activities, is not merely a rational decisionmaking process based on a decisional balance of pros and cons. Several theories have been proposed to explain people’s engagement in health-related behaviors. For example, the health belief model [33] suggests that engagement in health-related behaviors depends not only on the perceived benefits of action but also on perceived barriers to action. Therefore, situational factors, such as social pressures from the job, family, or friends, might impede the intention to go to sleep in light of its perceived benefits. Other theories such as the theory of reasoned action [34] and the theory of planned behavior [35,36] further consider social influence (perceived subjective norms) and self-control (perceived behavioral control). Therefore, the decision to go to sleep is influenced by both social and environmental factors. However, some theories have also focused on motivational and emotional factors. For example, self-regulation theory [37] emphasizes the role of internal desires. According to this theory, attitude is a distal cause of intention, mediated by desire. Therefore, in light of the attitude that sleep is more important, people may still stay awake while performing other desirable activities. These theories explain why sleep is deprioritized in certain situations. Future studies should explore the influences of the aforementioned factors on sleep practices.
Our study also demonstrated an unexpected but interesting finding: 21.4% of the participants chose to go to sleep when it conflicted with other activities, even though they did not rank sleep as a high priority. However, what influences these individuals to prioritize sleep remains unknown. One possibility is that the participants were too sleepful to remain awake for other activities. Moreover, to investigate levels of sleepiness and possible sleep disorders in these individuals in future studies would be interesting.
Further examination of the associations between the participants’ background information and their sleep priorities also provided some insightful findings. All demographic variables collected demonstrated no significant influence on the sleep priority practice score, suggesting that the tendency to sacrifice sleep for other activities is a common phenomenon across different ages, genders, occupations, and socioeconomic levels. However, sleep priority attitudes were discovered to differ among participants of different occupational ranks and categories. Specifically, general staff and lower-level managers prioritized sleep more than middle-level managers and selfemployed workers. Media and communication workers had high sleep priority attitude scores, whereas agriculture, forestry, fishing, and hunting workers and unemployed participants had low attitude scores.
Individuals with high sleep priority attitude scores also exhibited a high discrepancy between attitude and behavioral practices. Several factors may have contributed to the differences in results. First, age and opportunity to receive sleep education may have played a role. Age displayed a significant negative correlation with sleep priority attitudes and discrepancy scores. As general staff and lower-level managers tend to be young, they may possess a strong awareness of sleep health and therefore value sleep more. Following from that, workers in the media and communication industries might have greater opportunities to be exposed to sleep education information leading to high ratings on the importance of sleep. Conversely, workers in agriculture, forestry, fishing, and hunting may have limited exposure to sleep education, resulting in a comparatively lower prioritization of sleep. Another possible reason is that general staff, lower-level managers, and media and communications workers may have long or less flexible work schedules. Daily working hours displayed a minor but significant positive correlation (r=0.078) with the discrepancy scores. Furthermore, a previous study demonstrated that perceived time control is an important factor in occupational stress [38]. Therefore, low perceived time control in these populations may have led to increased sleep disturbances and concerns about getting sufficient sleep. Another finding of the current study was that female participants had significantly higher discrepancy scores than male participants. Although, female participants did not sacrifice sleep for other activities more than male participants did had a near-significant trend (p=0.87) of higher sleep priority attitude than male participants. The prevalence of insomnia is higher in females than in males [39]. A possibility exists that increased sleep difficulties in females compared to males lead to greater sleep value. A somewhat unexpected negative finding was that individual income level did not have an impact on either the attitude or practice of prioritizing sleep. These results suggest that insufficient practice in getting enough sleep is not simply driven by practical concerns such as financial needs or family burdens, but also by other cognitive, motivational, and emotional factors. The study also highlighted the need to further understand the factors that might impede the practice of healthy sleep habits in different subgroups.
In summary, this study demonstrates that people sacrifice their sleep for other activities in life, especially work and family activities, despite emphasizing the importance of sleep. Although the current study did not assess the reasons why participants chose to sacrifice sleep, the study has important implications. First, although sleep education has become more accessible and is promoted due to the increased recognition of the importance of sleep, most programs focus on knowledge of the mechanism and function of sleep and the pathology of sleep disorders, as well as principles of sleep hygiene practice. Although this understanding could be crucial in encouraging individuals to prioritize sleep over other activities, it is equally essential to offer them strategies to reduce conflicts between sleep and other daily obligations (e.g., time management techniques), deal with social pressure, or resist the urge to continue engaging in other activities when it is time to go to bed. Second, the findings of our study indicate that work and family activities are the two activities for which people tend to sacrifice sleep, and an individual staying awake until a later time due to job demands is understandable. These findings highlight the importance of sleep education in the workplace. A growing body of literature emphasizes the positive influence of sufficient sleep on employees’ work performance, problems at work, and safety issues associated with sleep deprivation [40,41]. Therefore, the promotion of sleep-friendly work policies is required. In addition, studies on the influence of family members on sleep are limited. Future studies are required to investigate the effect of factors, such as family duties and interactions, on an individual’s sleep. Third, the study demonstrated that although insufficient healthy sleep practice was common across all participants, the contributing factors might be different for some subgroups. Further studies are required to explore the specific factors in these subgroups. In addition, sleep education needs to consider the specific factors of the target populations.
Although this study has important implications, the study also has some limitations. First, we conducted an online survey. The sample might have been limited to people familiar with Internet technology. Therefore, the generalizability of our results is limited. Second, sleep practices were measured retrospectively. Recall bias may been incorporated in their report. Future studies using daily recalls should be conducted to confirm our findings. Third, sleep attitudes and practices may be affected by sleep habits and disorders. For example, individuals with insomnia tend to change their sleep schedules, have excessive bed rest, and take more daytime naps. However, our survey did not include questions about sleep habits and sleep disorders; hence, the influence of these factors cannot be ruled out. Finally, our study is more descriptive and demonstrates this discrepancy without exploring the factors underlying the phenomenon. Future research should further explore the influence of individual characteristics and/or social factors, as well as different cognitive and emotional factors, on the priority and practice of sleep.


The current study demonstrated an inconsistency between the reported prioritization of sleep and behavioral practices when sleep conflicts with other daily life activities. Although people recognize the importance of sleep and prioritize it over other activities, they tend to sacrifice sleep when it conflicts with work and family activities. The results suggest that sleep education should not only emphasize the importance of sleep but also provide practical solutions when sleep conflicts with other activities. Furthermore, sleep education should be promoted in workplaces to eliminate conflicts between sleep and work demands.

Supplementary Materials

The online-only Data Supplement is available with this article at https://doi.org/10.13078/jsm.230019.
Supplementary Figure 1.
The sleep attitude, sleep practice and the discrepancy between attitude priority score and practice priority score of different occupational categories. Both left and right panels represent duplicate content differently. A: The red bar represents the discrepancy score is above average; the green bar represents the discrepancy score is below average; the blue bar represents the discrepancy score is not only below average, but also negative. The orange dotted line represents the average. The Mass media Communication industry worker tended to have more sleep sacrificed and agriculture, forestry, fishing and hunting industry worker tended to have less sleep sacrificed. B: The blue bar represents the sleep priority attitude and the orange bar represents the sleep priority practice. Similarly, the mass media communication industry worker tended to value sleep more and the agriculture, forestry, fishing and hunting industry worker tended to value sleep less.


Conflicts of Interest
The authors have no potential conflicts of interest to disclose.
Author Contributions
Conceptualization: Ya-Chuan Huang, Chien-Ming Yang. Data curation: Ya-Chuan Huang. Formal analysis: all authors. Funding acquisition: Chien- Ming Yang. Methodology: Tzu-Ting Lin, Chien-Ming Yang. Project administration: all authors. Resources: Tzu-Ting Lin, Chien-Ming Yang. Supervision: Tzu-Ting Lin, Chien-Ming Yang. Visualization: all authors. Writing—original draft: Ya-Chuan Huang. Writing—review & editing: all authors.
Funding Statement
The project is partially supported by National Science and Technology Council.


We thank our colleagues Yin-Chu Su (True Colors Psychotherapy Clinic) and Fan-Chi Hsiao (Department of Counseling, Clinical and Industrial/ Organizational Psychology, Ming Chuan University) who provided insight and expertise that greatly assisted the research.

Fig. 1.
Comparison of the attitude and practice priorities of sleep. Blue bars represent participants’ priority of sleep and orange bars represent participants’ practice priority derived from their actual practice rating. Most participants ranked sleep as the second priority, but most participants practiced sleep as the third priority.
Fig. 2.
The discrepancy between attitude priority score and practice priority score of sleep. The negative value (blue bars) indicates higher attitude priority than behavioral priority and positive value (green bars) indicate higher behavioral priority than attitude priority.
Fig. 3.
The percentage of participants who chose to go to sleep or to do a conflicted activity. The blue bars represent the percentage of participants who chose the conflicted activities and the orange bars represent the percentage of participants who chose sleep. When being asked to recall the last time, sleep was conflicting with diferent daily activity, the majority chose work or family activities instead of sleep.
Table 1.
Descriptive statistics of demographic variables (n=898)
Variables Values
Age, yr (range) 39.74 ± 10.04 (20–68)
Daily working hours (range) 8.79 ± 1.69 (2–20)
 Male 372 (41.4)
 Female 526 (58.6)
Marital status
 Single 383 (42.7)
 Cohabit 30 (3.3)
 Married 460 (51.2)
 Separated 8 (0.9)
 Widowed 4 (0.4)
 Divorced 13 (1.5)
Educational level
 Middle school 5 (0.6)
 High school 58 (6.5)
 Associate degree 100 (11.1)
 Bachelor’s degree 418 (46.5)
 Graduate degree 317 (35.3)
Occupational rank
 General staff 534 (61.9)
 Lower-level manager 133 (15.4)
 Middle-level manager 99 (11.5)
 Higher-level executive 43 (5.0)
 Self-employed worker 47 (5.4)
 Others 7 (0.8)
Occupational category
 Electronic information 119 (13.3)
 General services 93 (10.4)
 Traditional manufacturing 124 (13.8)
 Logistics warehousing 13 (1.4)
 Retail trade 47 (5.2)
 Construction civil engineering 34 (3.8)
 Finance insurance 42 (4.7)
 Medical health 130 (14.5)
 Educational services 128 (14.3)
 Political social welfare 9 (1.0)
 Mass media communication 13 (1.4)
 Agriculture, forestry, fishing, & hunting 3 (0.3)
 Travel & leisure 8 (0.9)
 Accommodation & catering 11 (1.2)
 Legal & accounting industry 11 (1.2)
 Armed forces 5 (0.6)
 Public administration 66 (7.3)
 Student 3 (0.3)
 Unemployed 1 (0.1)
 Others 38 (4.2)
Individual income (monthly)
 Under NT$ 29,999 (≈USD 1,000) 109 (12.1)
 NT$ 30,000–59,999 (≈USD 2,000) 487 (54.2)
 NT$ 60,000–89,999 (≈USD 3,000) 192 (21.4)
 Above NT$ 90,000 (≈USD 3,000) 110 (12.2)

Values are presented as mean±standard deviation (range) or n (%).

NT$, New Taiwan dollar; USD, US dollar

Table 2.
Subgroup comparison analyses for the sleep priority attitude, sleep priority practice, and discrepancy score
N Mean SD T/F p
Gender difference
 Sleep priority attitude 1.031 0.303
  Male 372 3.95 1.26
  Female 526 3.86 1.21
 Sleep priority practice -1.721 0.086
  Male 372 4.22 1.45
  Female 526 4.39 1.45
 Discrepancy score -2.623 <0.01*
  Male 372 0.27 1.45
  Female 526 0.53 1.42
Individual income per month
 Sleep priority attitude 0.204 0.894
  Under NT$ 29,999 109 4.32 1.439
  NT$ 30,000–59,999 487 4.35 1.456
  NT$ 60,000–89,999 192 4.25 1.458
  Above NT$ 90,000 110 4.32 1.465
 Sleep priority practice 0.654 0.587
  Under NT$ 29,999 109 3.92 1.270
  NT$ 30,000–59,999 487 3.85 1.223
  NT$ 60,000–89,999 192 3.98 1.173
  Above NT$ 90,000 110 3.94 1.356
 Discrepancy score 1.276 0.281
  Under NT$ 29,999 109 0.40 1.522
  NT$ 30,000–59,999 487 0.50 1.429
  NT$ 60,000–89,999 192 0.27 1.406
  Above NT$ 90,000 110 0.38 1.471
Occupational rank
 Sleep priority attitude 4.415 <0.01*
  General staff 534 4.44 1.425
  Lower-level manager 133 4.35 1.503
  Middle-level manager 99 3.91 1.471
  Higher-level executive 43 4.09 1.411
  Self-employed worker 47 3.83 1.479
  Others 7 3.29 0.756
 Sleep priority practice 0.663 0.652
  General staff 534 3.90 1.223
  Lower-level manager 133 3.75 1.252
  Middle-level manager 99 3.90 1.274
  Higher-level executive 43 3.84 1.413
  Self-employed worker 47 4.04 1.215
  Others 7 3.43 0.976
 Discrepancy score 5.050 <0.001
  General staff 534 0.55 1.358
  Lower-level manager 133 0.59 1.562
  Middle-level manager 99 0.01 1.607
  Higher-level executive 43 0.26 1.529
  Self-employed worker 47 -0.21 1.267
  Others 7 -0.14 1.464

T-test results presented for gender differences. Analysis of variance (AVOVA) results presented for different levels of monthly individual income and occupational level.

* p<0.01;


SD, standard deviation; NT$, New Taiwan dollar


1. Gates M, Wingert A, Featherstone R, Samuels C, Simon C, Dyson MP. Impact of fatigue and insufficient sleep on physician and patient outcomes: a systematic review. BMJ Open 2018;8:e021967. https://doi.org/10.1136/bmjopen-2018-021967.
crossref pmid pmc
2. Borragan G, Guerrero-Mosquera C, Guillaume C, Slama H, Peigneux P. Decreased prefrontal connectivity parallels cognitive fatigue-related performance decline after sleep deprivation. An optical imaging study. Biol Psychol 2019;144:115-124. https://doi.org/10.1016/j.biopsycho.2019.03.004.
3. Samkoff JS, Jacques CH. A review of studies concerning effects of sleep deprivation and fatigue on residents’ performance. Acad Med 1991;66:687-693. https://doi.org/10.1097/00001888-199111000-00013.
crossref pmid
4. Sagun S, DeCicco D, Badami V, et al. iSleepFirst: burnout, fatigue, and wearable-tracked sleep deprivation among residents staffing the medical intensive care unit. Sleep Breath 2023;27:2491-2497. https://doi.org/10.1007/s11325-023-02839-8.
crossref pmid
5. Becker SP, Epstein JN, Tamm L, et al. Shortened sleep duration causes sleepiness, inattention, and oppositionality in adolescents with attentiondeficit/hyperactivity disorder: findings from a crossover sleep restriction/extension study. J Am Acad Child Adolesc Psychiatry 2019;58:433-442. https://doi.org/10.1016/j.jaac.2018.09.439.
crossref pmid
6. Hennig T, Krkovic K, Lincoln TM. What predicts inattention in adolescents? An experience-sampling study comparing chronotype, subjective, and objective sleep parameters. Sleep Med 2017;38:58-63. https://doi.org/10.1016/j.sleep.2017.07.009.
crossref pmid
7. Gruber R, Michaelsen S, Bergmame L, et al. Short sleep duration is associated with teacher-reported inattention and cognitive problems in healthy school-aged children. Nat Sci Sleep 2012;4:33-40. https://doi.org/10.2147/nss.s24607.
crossref pmid pmc
8. Song T, Xu L, Peng Z, et al. Total sleep deprivation impairs visual selective attention and triggers a compensatory effect: evidence from event-related potentials. Cogn Neurodyn 2023;17:621-631. https://doi.org/10.1007/s11571-022-09861-8.
crossref pmid
9. Cousins JN, Fernandez G. The impact of sleep deprivation on declarative memory. Prog Brain Res 2019;246:27-53. https://doi.org/10.1016/bs.pbr.2019.01.007.
crossref pmid
10. Yin Y, Chen S, Song T, Zhou Q, Shao Y. Cognitive load moderates the effects of total sleep deprivation on working memory: evidence from event-related potentials. Brain Sci 2023;13:898. https://doi.org/10.3390/brainsci13060898.
crossref pmid pmc
11. Poznanski B, Cornacchio D, Coxe S, Pincus DB, McMakin DL, Comer JS. The link between anxiety severity and irritability among anxious youth: evaluating the mediating role of sleep problems. Child Psychiatry Hum Dev 2018;49:352-359. https://doi.org/10.1007/s10578-017-0769-1.
crossref pmid
12. Holsboer-Trachsler E, Seifritz E. Sleep in depression and sleep deprivation: a brief conceptual review. World J Biol Psychiatry 2000;1:180-186. https://doi.org/10.3109/15622970009150589.
crossref pmid
13. Roberts RE, Duong HT. The prospective association between sleep deprivation and depression among adolescents. Sleep 2014;37:239-244. https://doi.org/10.5665/sleep.3388.
crossref pmid pmc
14. Riemann D, Krone LB, Wulff K, Nissen C. Sleep, insomnia, and depression. Neuropsychopharmacology 2020;45:74-89. https://doi.org/10.1038/s41386-019-0411-y.
crossref pmid
15. Guo X, Zheng L, Wang J, et al. Epidemiological evidence for the link between sleep duration and high blood pressure: a systematic review and meta-analysis. Sleep Med 2013;14:324-332. https://doi.org/10.1016/j.sleep.2012.12.001.
crossref pmid
16. Buxton OM, Marcelli E. Short and long sleep are positively associated with obesity, diabetes, hypertension, and cardiovascular disease among adults in the United States. Soc Sci Med 2010;71:1027-1036. https://doi.org/10.1016/j.socscimed.2010.05.041.
crossref pmid
17. Liew SC, Aung T. Sleep deprivation and its association with diseasesa review. Sleep Med 2021;77:192-204. https://doi.org/10.1016/j.sleep.2020.07.048.
crossref pmid
18. Besedovsky L, Schmidt EM, Linz B, Diekelmann S, Lange T, Born J. Signs of enhanced sleep and sleep-associated memory processing following the anti-inflammatory antibiotic minocycline in men. J Psychopharmacol 2017;31:204-210. https://doi.org/10.1177/0269881116658991.
crossref pmid
19. Sawangjit A, Kelemen E, Born J, Inostroza M. Sleep enhances recognition memory for conspecifics as bound into spatial context. Front Behav Neurosci 2017;11:28. https://doi.org/10.3389/fnbeh.2017.00028.
crossref pmid pmc
20. Oyanedel CN, Sawangjit A, Born J, Inostroza M. Sleep-dependent consolidation patterns reveal insights into episodic memory structure. Neurobiol Learn Mem 2019;160:67-72. https://doi.org/10.1016/j.nlm.2018.05.013.
crossref pmid
21. Barnes CM, Wagner DT. Changing to daylight saving time cuts into sleep and increases workplace injuries. J Appl Psychol 2009;94:1305-1317. https://doi.org/10.1037/a0015320.
crossref pmid
22. Hafner M, Stepanek M, Taylor J, Troxel WM, van Stolk C. Why sleep matters-the economic costs of insufficient sleep: a cross-country comparative analysis. Rand Health Q 2017;6:11.
23. Harrison Y, Horne JA. One night of sleep loss impairs innovative thinking and flexible decision making. Organ Behav Hum Decis Process 1999;78:128-145. https://doi.org/10.1006/obhd.1999.2827.
crossref pmid
24. Kam K, Parekh A, Sharma RA, et al. Sleep oscillation-specific associations with Alzheimer’s disease CSF biomarkers: novel roles for sleep spindles and tau. Mol Neurodegener 2019;14:10. https://doi.org/10.1186/s13024-019-0309-5.
crossref pmid pmc
25. Wang C, Holtzman DM. Bidirectional relationship between sleep and Alzheimer’s disease: role of amyloid, tau, and other factors. Neuropsychopharmacology 2020;45:104-120. https://doi.org/10.1038/s41386-019-0478-5.
crossref pmid
26. Chaput JP, Dutil C, Sampasa-Kanyinga H. Sleeping hours: what is the ideal number and how does age impact this? Nat Sci Sleep 2018;10:421-430. https://doi.org/10.2147/nss.s163071.
crossref pmid pmc
27. Hisler GC, Muranovic D, Krizan Z. Changes in sleep difficulties among the U.S. population from 2013 to 2017: results from the National Health Interview Survey. Sleep Health 2019;5:615-620. https://doi.org/10.1016/j.sleh.2019.08.008.
crossref pmid
28. Keyes KM, Maslowsky J, Hamilton A, Schulenberg J. The great sleep recession: changes in sleep duration among US adolescents, 1991-2012. Pediatrics 2015;135:460-468. https://doi.org/10.1542/peds.2014-2707.
crossref pmid pmc
29. Katz T, Malow BA. Sleep education and the importance of starting early. Sleep 2014;37:1033-1034. https://doi.org/10.5665/sleep.3754.
crossref pmid pmc
30. Bettinghaus EP. Health promotion and the knowledge-attitude-behavior continuum. Prev Med 1986;15:475-491. https://doi.org/10.1016/0091-7435(86)90025-3.
crossref pmid
31. Schwarzer R. Social-cognitive factors in changing health-related behaviors. Current Directions in Psychological Science 2001;10:47-51. https://doi.org/10.1111/1467-8721.00112.
32. Borrell LN. The role of social class on health behaviors and psychosocial factors: the United States experience. Soz Praventivmed 2005;50:193-194. https://doi.org/10.1007/s00038-005-5019-9.
crossref pmid
33. Janz NK, Becker MH. The health belief model: a decade later. Health Educ Q 1984;11:1-47. https://doi.org/10.1177/109019818401100101.
crossref pmid
34. Fishbein M. A theory of reasoned action: some applications and implications. Nebr Symp Motiv. 1980;27:65-116. https://doi.org/10.1177/109019818401100101.
crossref pmid
35. Ajzen I. The theory of planned behavior. Organizational Behavior and Human Decision Processes 1991;50:179-211. https://doi.org/10.1016/0749-5978(91)90020-T.
36. Kuhl J. From cognition to behavior: perspectives for future research on action control. Kuhl J, Beckmann J. Springer. 1985;267-275. https://doi.org/10.1007/978-3-642-69746-3_12.

37. Leone L, Perugini M, Ercolani AP. A comparison of three models of altitude-behavior relationships in the studying behavior domain. European Journal of Social Psychology 1999;29(2-3):161-189. https://doi.org/10.1002/(SICI)1099-0992(199903/05)29:2/3%3C161::AIDEJSP919%3E3.0.CO;2-G.
38. Hsu YY, Bai CH, Yang CM, Huang YC, Lin TT, Lin CH. Long hours’ effects on work-life balance and satisfaction. Biomed Res Int 2019;2019:5046934. https://doi.org/10.1155/2019/5046934.
crossref pmid pmc
39. Zeng LN, Zong QQ, Yang Y, et al. Gender difference in the prevalence of insomnia: a meta-analysis of observational studies. Front Psychiatry 2020;11:577429. https://doi.org/10.3389/fpsyt.2020.577429.
crossref pmid pmc
40. Majekodunmi A, Landrigan CP. The effect of physician sleep deprivation on patient safety in perinatal-neonatal medicine. Am J Perinatol 2012;29:43-48. https://doi.org/10.1055/s-0031-1286184.
crossref pmid
41. Lockley SW, Barger LK, Ayas NT, et al. Effects of health care provider work hours and sleep deprivation on safety and performance. Jt Comm J Qual Patient Saf 2007;33(11 Suppl):7-18. https://doi.org/10.1016/s1553-7250(07)33109-7.
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