Introduction
It has been asked: what is sleep health, is it important, and can it be measured (Buysse, Reference Buysse2014)? Regarding the first question, broad support is given to defining sleep health as a multidimensional pattern of sleep and wakefulness that is well adapted to individual, social, and environmental demands needed. Regarding its importance, sleep health holds great promise in optimizing an individual’s mental and physical well-being and performance (Buysse, Reference Buysse2014; Cappuccio et al., Reference Cappuccio, Cooper, D’Elia, Strazzullo and Miller2011; Jennings et al., Reference Jennings, Muldoon, Hall, Buysse and Manuck2007). Regarding its measurement, however, which dimensions of sleep health can be measured at the self-report unit of analysis is less clear. The currently proposed dimensions of sleep health that can be accurately measured by self-report have been summarized in the acronym RU SATED: regularity, satisfaction, alertness, timing, efficiency, and duration (Buysse, Reference Buysse2014). Although these dimensions provide a solid framework for examining sleep health (Ravyts et al., Reference Ravyts, Dzierzewski, Perez, Donovan and Dautovich2021), we propose two additional psychological dimensions of sleep health for consideration: sleep resilience and sleep value. Validated and refined measures of these psychological dimensions of sleep health are lacking. This study was designed to validate measures of both and allow for exploratory analyses of these constructs in relation to several demographic, health, and sleep variables.
Sleep value
Sleep value refers to the amount of worth an individual places on their sleep (Kay et al., Reference Kay, Simmons, Nielson, Braithwaite and Esplin2023; Nielson et al., Reference Nielson, Taylor, Simmons, Decker, Kay and Cribbet2021). Many major sleep organizations recognize that promoting the value of sleep is essential to health (AASM, 2021; NSF, 2024; SRS, 2023). Sleep researchers have begun recognizing the importance of sleep value to sleep health promotion efforts at the individual and societal levels (Kay et al., Reference Kay, Simmons, Nielson, Braithwaite and Esplin2023; Nielson et al., Reference Nielson, Taylor, Simmons, Decker, Kay and Cribbet2021; Troxel & Romanelli, Reference Troxel and Romanelli2023; Wickwire, Reference Wickwire2016, Reference Wickwire2021). Kay and colleagues created a novel measure of sleep value called the Sleep Valuation Item Bank (SVIB). The SVIB included items selected to capture feelings, thoughts, and behaviors considered to reflect sleep value (Nielson et al., Reference Nielson, Taylor, Simmons, Decker, Kay and Cribbet2021). Exploratory factor analysis of the original SVIB revealed four latent factors of sleep value: wanting, prioritizing, preferring, and devaluing sleep (Kay et al., Reference Kay, Simmons, Nielson, Braithwaite and Esplin2023). Notably, high levels of ambivalence toward sleep’s value were demonstrated across demographic features. Gender differences were observed, with women having higher sleep wanting and lower sleep devaluation scores and men having higher sleep prioritizing scores (Kay et al., Reference Kay, Simmons, Nielson, Braithwaite and Esplin2023). Moreover, the study revealed that older adults tended to devalue sleep more than their younger counterparts. Employment status also played a role, as individuals with full-time jobs exhibited greater sleep value than those with other work statuses (Kay et al., Reference Kay, Simmons, Nielson, Braithwaite and Esplin2023). Regarding mental health, individuals experiencing decreased mental health, characterized by high levels of depression and insomnia, had higher levels of sleep devaluation despite valuing sleep in terms of wanting, prioritizing, and/or preferring it (Kay et al., Reference Kay, Simmons, Nielson, Braithwaite and Esplin2023). The finding that insomnia is associated with an ambivalent pattern of valuing sleep, with notably high levels of wanting sleep with high levels of sleep devaluing, raises the possibility that sleep value is multidimensional and there may be healthy patterns and levels of sleep value across its various dimensions, as opposed to sleep value being a “more is better,” monolithic construct. Thus, establishing these latent factors and their healthy patterns is critical. Based on these prior studies, our team identified the need to replace and refine several items and expand the SVIB to better capture a sleep liking or enjoyment dimension. This study employed the SVIB-2.0 that incorporated these updates. A major aim was to refine the factor structure of sleep value with the SVIB-2.0.
While the SVIB captures broad dimensions of sleep value, it does not capture all aspects of the sleep valuation process. Although the SVIB captures self-reported attitudes (i.e., feelings, thoughts, and behaviors) about sleep’s value, we also recognized that the process and relative value of sleep may require additional measures. One way to determine the value an individual places on their sleep is to define it in terms widely understood: money. To this end, we developed the Monetary Sleep Value Scale, in which participants rated how much they would pay to obtain increments of more or better sleep and how much they would need to be paid to sacrifice increments of sleep. We also sought to determine the relative value sleep has in the broader value systems of individuals. To this end, we developed the Values Inventory, which asked participants to rate the value of a wide range of commonly held values, including sleep health. Each of these self-report measures was designed to capture the relative worth individuals place on their sleep, each providing unique information to help determine the sleep valuation process.
Sleep resilience
Resilience is typically associated with the ability to adapt to or bounce back from challenges. It involves not only the ability to withstand adverse events but also the capacity to adapt and thrive despite them (Southwick et al., Reference Southwick, Bonanno, Masten, Panter-Brick and Yehuda2014). As a novel concept, we define sleep resilience as the ability to function emotionally, cognitively, and physically in the presence of sleep or circadian rhythm disturbances. Examples of sleep resilience include individuals who seem to compensate following sleep deprivation to maintain the same levels of productivity and ultra-marathon runners who operate on little sleep (Gattoni et al., Reference Gattoni, Girardi, O’Neill and Marcora2021).
Upon investigating existing measures, we found a need for questionnaires addressing sleep resilience, which prompted us to create our own. We developed the Sleep Resilience Questionnaire, which evaluates an individual’s ability to feel and function resiliently, in terms of physical, emotional, and cognitive domains, in the presence of past or future sleep/circadian disruption. This measure includes self-reported sleep resilience which encompasses both a retrospective estimation (i.e., how resilient one feels to past or current sleep/circadian disruption) and a prospective estimation (i.e., a belief in one’s resilience to future sleep/circadian disruption). Our motivation to develop this measure stemmed from a desire to understand why specific individuals exhibit greater resilience to sleep/circadian disturbances than others, as well as specific populations of interest, including parents of young children, shift workers, and competitive athletes. Our aim was to incorporate a diverse range of items that assess physical, emotional, and cognitive aspects of sleep resilience, both in the present/past and projected into the future. Previous research focused on individuals’ vulnerability to sleep disturbance (Van Dongen et al., Reference Van Dongen, Baynard, Maislin and Dinges2004), where this measure aims to capture individuals’ resilience to sleep disturbances. Given the absence of established measures for sleep resilience, we sought to draw insights from existing definitions and measurements of resilience. The questionnaire underwent numerous revisions during its development and was piloted by research assistants at Brigham Young University, whose feedback contributed to refining the instrument.
In summary, the measures developed and the data collected in this study aim to address overlooked dimensions of sleep health. Once validated, these self-report measures will enable us to determine how sleep value and sleep resilience can be leveraged to improve overall health and well-being. To this end, we also included measures in our survey of demographic variables, sleep disturbance, sleep health, mental health, and physical health.
Methods
Procedures
This project titled “The Sleep Resilience and Variance in Sleep Valuation (SRVIV) Study” was funded by an internal grant from the College of Family, Home, and Social Sciences and was approved by the Institutional Review Board at Brigham Young University (IRB# IRB2023-146). Data were collected by a Qualtrics team via an anonymous online survey. Participant names, signatures, respondents’ IP Address, specific location data, and contact information were not collected in the survey. A feasibility sample of 500 adult participants was sought based on funding availability and to meet the statistical guideline of 10 subjects per question on the largest questionnaire in the study (i.e., the Sleep Resilience Questionnaire) ensuring stability and reliability in the analyses, as recommended by Kline (Kline, Reference Kline2014). Although a prior power analysis was not conducted, this sample size was also determined to be consistent with studies that used factor analysis and structural equation modeling (Ravyts & Dzierzewski, Reference Ravyts and Dzierzewski2024). The survey was open to adult participants within the continental United States, and the Qualtrics team was instructed to obtain quotas for the following categories: age: 18–34 (30%), 35–54 (32%), and 55+ (38%); gender: male (48%), female (52%), and non-binary (natural fallout); and region of the United States: Northeast (17%), Midwest (21%), West (24%), and South (38%).
Participants provided informed consent electronically. Individuals under the age of 18 were screened prior to participation. Participants completed several questionnaires including basic demographics, psychological functioning, and sleep. The questionnaires contained in the survey were administered in the following order: Demographics questionnaire, Sleep Resilience Questionnaire, Values Inventory, Patient Reported Outcomes Measurement Information System Sleep-Disturbances – Short Form (PROMIS-SD), PROMIS Sleep-Related Impairment – Short Form (PROMIS-SRI), SVIB-2.0, Monetary Sleep Value Scale, PROMIS Depression – Short Form, and PROMIS Anxiety – Short Form. To help ensure valid responses, we used Bot detection (reCAPTCHA), prevented multiple submissions, and did not allow indexing. After participants completed the study, the Qualtrics team distributed rewards to participants based on the time commitment of the research study at an estimated rate of $5.23 an hour. For the 25-minute survey, the average estimated reward was $2.18 per participant, but the actual amount that each participant received is unknown to the researchers. Data were collected from July 13, 2023, to August 9, 2023.
Figure 1 displays the participant selection flowchart. The Qualtrics team ran a “soft launch” to pilot the survey on 20 participants. Based on the data, we made minor changes to the wording of the SVIB-2.0. Specifically, we changed the scale wording from “Strongly Disagree, Disagree, Neither Agree Nor Disagree, Agree, and Strongly Agree” to “Does not describe me, Describes me slightly well, Describes me moderately well, Describes me very well, and Describes me extremely well.” Due to these changes, the pilot participants were excluded from the final analysis dataset.
Figure 1. Participant selection flowchart
After obtaining IRB approval to make this modification to the survey, we instructed the Qualtrics team to fully launch the study and collect an initial dataset with 500 participants. We were allowed to review the dataset for quality and judged that 188 of the responses were invalid, as they were either not fully completed or were completed in less time than could be thoughtfully done based on pilot runs with students in our lab (i.e., less than 420 seconds), or respondents ranked more than 16 items as ‘Of utmost value’ on the Values Inventory. The Qualtrics research team replaced these invalid responses with 196 new participants, and we approved receipt from them of a raw dataset that included 508 participants.
We took further steps to create the analysis dataset for this study. We developed an exclusion codebook to flag and remove potentially invalid responses. Participants were automatically excluded if they responded with alphabetical text entries on survey items that requested numerical values or entered nonsensical responses in text entries. Some responses were less clearly invalid, and we allowed three flags before we excluded them from the analysis sample. Responses were flagged for (1) having no standard deviation on questionnaires that variance would be expected including the PROMIS-SRI, Sleep Resilience Questionnaire blocks one and four, the Values Inventory, or the SVIB-2.0 – Value items and devalue items; (2) high responses when the average of the SVIB-2.0 value block plus the average SVIB-2.0 devalue block equaled less than 1 or over 6, meaning that participants answered with only low or only high answers on questions that typically would be high on one and low on the other; (3) contradictory responses on negatively and positively worded items of the PROMIS-SD (item 8 + item 6 >6 or =0); and (4) contradictory responses on demographic variables (i.e., responding that they were married and not married on demographic variables). We excluded an additional 53 participants using the exclusion codebook. The final sample after all exclusions consisted of 455 respondents.
Participants
The final analysis sample included 455 respondents. Sample characteristics were determined using a demographics questionnaire that included gender (male, female, non-binary), age (18–86), race and ethnicity (multiple-response allowed for American Indian/Alaska Native, Asian/Asian American, Black/African American, Native Hawaiian or Other Pacific Islander, Hispanic/Latino/Latina, White, and other, where participants were given the option to specify further), education (less than a high school diploma, high school degree or equivalent (e.g., GED), some college, associate degree, bachelor’s degree, master’s degree, professional degree or doctorate degree), number of dependents (e.g., “How many dependents do you claim? – dependents include any qualifying children or relatives claimed on your taxes,” 0–15), household income (less than $10,000, $10,000–$40,000, $40,001–$90,000, $90,001–$190,000, more than $190,000), marital status (multiple-response allowed for single, in a committed relationship, married, widowed, divorced, separated, cohabiting, never married, open relationship), mental health numerical rating scale (i.e., “Rate your mental health on a scale 0–100 where 0 = worst imaginable mental health state, 100 = best imaginable mental health state”), and physical health numerical rating scale (i.e., “Rate your physical health on a scale 0–100 where 0 = worst imaginable physical health state, 100 = best imaginable physical health state”). Numerical rating scales for self-reported mental health show a significant correlation with other mental health indicators such as the Patient Health Questionnaire and Geriatric Depression Scale (Ahmad et al., Reference Ahmad, Jhajj, Stewart, Burghardt and Bierman2014). Numerical rating scales for self-reported physical health demonstrate strong construct validity (Liang, Reference Liang1986).
In the final analysis sample, 53% identified as female, 46% as male, and 1% as non-binary, 82% were white, 13% as black/African American, and 50% were married. The participants’ education levels ranging from less than a high school diploma to doctorate and professional degrees, ages (Mean = 45.4), and annual income ranging from <$10,000 to >$190,000 were represented. See Table 1 for the full demographic features including estimates for central tendency (means and frequency) and variance.
Table 1. Demographic features of the analysis sample (N = 455)
Note: n(%) and M(SD).
a Indicates that participants could select multiple options.
b Indicates that mental and physical health was rated on a scale from 0 to 100.
Measures
Sleep Valuation Item Bank 2.0
Sleep value was determined using the SVIB-2.0 (see Table 2 for full item bank). The SVIB-2.0 questionnaire has 60 items presented in two separate questionnaire blocks, 44 valuing items on block one and 16 devaluing items on block two. The first iteration of the SVIB demonstrated an acceptable reliability coefficient, with a Cronbach’s alpha of 0.92 (Nielson et al., Reference Nielson, Taylor, Simmons, Decker, Kay and Cribbet2021), but had several items with poor face validity or factor loadings. In addition, although it had items such as “I enjoy sleeping.” the original SVIB failed to capture a factor of sleep liking or sleep enjoyment. It was proposed that to capture an intrinsic valuation of sleep additional items may be needed (Kay et al., Reference Kay, Simmons, Nielson, Braithwaite and Esplin2023). The revision of the SVIB involved the removal of items that exhibited poor factor loadings and the introduction of new items specifically designed to capture the construct of liking or enjoying sleep. Participants were asked, “For each item, indicate how well the statement describes you,” and participants responded on a 5 point Likert-type scale ranging from “Does not describe me” to “Describes me extremely well.” For the block 1, higher scores indicate more value, and for block 2 lower scores indicate higher sleep value. The items aimed to understand how participants valued their sleep in terms of wanting, liking, devaluing, prioritizing, and preferring.
Table 2. Sample distributions of the Sleep Valuation Item Bank 2.0 (SVIB-2.0)
Note: Items were rated on a 0–4 scale. For block 1, higher scores indicate more value, and for block 2 lower scores indicate higher sleep value.
Monetary Sleep Value Scale
The Monetary Sleep Value Scale included 16 items presented in two blocks that asked participants to assign a dollar value to give up sleep, obtain more sleep, or get better sleep. See Table 3 for the complete questionnaire. In developing this questionnaire, we aimed to translate the value individuals place on their sleep into a monetary figure to gain insights into how individuals perceive their sleep value. In this approach, we sought to explore a different dimension of the sleep valuation process, which may allow for a more comprehensive understanding of how individuals value their sleep in relation to money. This measure has two blocks – the first block’s questions focus on how much money participants would need to be paid to give up sleep, ranging from one hour to seven nights of sleep (184 hours of wakefulness). The second block’s questions focus on how much money participants would pay to gain additional sleep ranging from one hour of sleep for one night to undisturbed and refreshing sleep for the rest of their lives. Participants were allowed to respond with the following categories, block one – “I would do it for free,” “I would do it for $1–10,” I would do it for $11–100,” “I would do it for $101–1000,” “I would not do this for any monetary value you could offer,” block two – “I would not be willing to pay anything for this,” “I would pay $1–10,” “I would pay $11–100 for this,” “I would pay $101–1000 for this,” and I would pay more than $1000 for this.” Then, participants were asked to specify further how much money for each question.
Table 3. Sample distributions for the Monetary Sleep Value Scale items
Note: Items were rated on a 0–4 scale. For block 1, higher scores indicate higher payments to lose sleep, while for block 2, higher scores indicate higher payments to gain sleep.
Values Inventory
The Values Inventory is a novel 38-item questionnaire we developed to measure participants’ terminal values, including sleep health. See Table 4 for the entire questionnaire. This questionnaire was included to get at the relative value placed on sleep health compared to other commonly held values. Participants were asked to first rate their terminal values on a 5 point Likert-type scale ranging from “Not at all valuable” to “Of utmost value.” After the participants had ranked the 38 items, they were asked to rank their top 10 “Of utmost value” items. We requested respondents to limit their “Of utmost value” rankings to 10 items; however, some participants ranked up to 16 items “Of utmost value.” Those who ignored this requirement and ranked more than 16 items were excluded by the Qualtrics team before sending us the dataset. At the end of this survey, participants were asked the following question, “If you could be just as productive at work, school, and other important areas of your life, feel just as good, stay healthy, and get your social needs met, regardless of how much sleep you got; how much sleep would you choose to regularly obtain across the 24-hour day?” Our preliminary assessment of the scale showed that the central tendency for this question is 8.43 hours (SD 3.6). This question aimed to understand the value individuals assign to sleep based on ideal time allocation.
Table 4. Sample distributions for the Values Inventory items
Note: Items were rated on a 0–4 scale. Higher scores indicate higher value. *Indicates item derived from Milton Rokeach’s work.
In developing the Values Inventory, we initiated our process by revisiting the terminal values identified by Milton Rokeach in 1974 (Rokeach, Reference Rokeach1974). Recognizing that these values were nearly five decades old, we aimed to update and refine them to reflect contemporary societal norms. To achieve this, we consulted the internet for lists of commonly held values, reviewed lists of values outlined in common behavioral activation activities, and conducted brainstorming sessions within the lab to identify commonly held values. We generated a preliminary list and piloted the questionnaire with research assistants in the lab. Their feedback prompted us to make necessary revisions and enhancements to the questionnaire.
Sleep Resilience Questionnaire
The Sleep Resilience Questionnaire is a novel 50-item self-report measure created to measure participants’ present/past and future resilience to sleep and circadian rhythm disturbances. Participants were asked to rate how much they are impacted by sleep disturbances in different areas of physical, emotional, and cognitive feeling and functioning on a 5 point Likert-type scale ranging from “Not at all” to “A great deal.” Lower values indicate greater sleep reslience. Table 5 shows the entire questionnaire. The questionnaire was presented as four questionnaire blocks: (1) 13 questions for a retrospective estimate of sleep resilience in terms of feelings, (2) 13 questions for a retrospective estimate of sleep resilience in terms of functioning, (3) 12 questions for a prospective estimate of sleep resilience in terms of feeling, and (4) 12 questions for a prospective estimate of sleep resilience in terms of functioning. In the first two blocks, participants are asked, “Think of the times over the past year when you were not sleeping well. How much was your daily functioning impacted by the sleep disturbance in the following areas?” In the second two blocks, participants were asked, “If you were to have sleep difficulties in the future, how much will the sleep disturbances impact your daytime functioning in the following areas?” Then, participants responded to questions that measured their ability to feel and function well physically, cognitively, and emotionally.
Table 5. Sample distributions for the Sleep Resilience Questionnaire items
Note: Each item was rated on a scale of 0–4. Lower values indicate greater sleep reslience.
Patient Reported Outcomes Measurement Information System (PROMIS)
Sleep quality was measured using the PROMIS-SD -Short Form, a 7-item self-report measure, and the PROMIS-SRI – Short Form, an 8-item self-report measure. The PROMIS measures have a high internal consistency (α = .92 and .89, respectively) (Chimenti et al., Reference Chimenti, Rakel, Dailey, Vance, Zimmerman, Geasland, Williams, Crofford and Sluka2021). Anxiety severity was measured using the PROMIS Emotional Distress – Anxiety – Short Form, a 7-item self-report measure. Depression severity was determined by using the PROMIS Emotional Distress – Depression – Short Form, an 8-item self-report measure. These measures have strong internal consistency (α = .93 and .95, respectively) (Pilkonis et al., Reference Pilkonis, Choi, Reise, Stover, Riley and Cella2011).
Discussion
This study provides a rich resource for answering several current and future questions on the psychology of sleep and circadian rhythms. Sleep value may have implications on how psychosocial and cultural factors influence sleep and circadian health disparities and may be a psychological factor important to consider in the evaluation and treatment of sleep and circadian rhythm disorders. Sleep resilience may play a role in how sleep and circadian rhythms impact psychological functioning including motivation, emotion, cognition, and performance and may inform how sleep resilience can be harnessed to improve psychological, biological, and social outcomes.
In the immediate future, we aim to answer several questions with this dataset including whether these novel scales of sleep value and resilience are valid. We also plan to use the SVIB-2.0 to solidify the factor structure of sleep value. In addition, we also plan to explore whether there are distinct sleep value profiles and how different demographics might vary across each profile. We will also explore how sleep health relates to sleep value and other commonly held values using the Values Inventory. Finally, we plan to explore how different levels of sleep resilience relate to demographics, sleep disturbance, and sleep-related impairment. The primary limitations of this study are that the sample was collected exclusively within the United States, the study’s cross-sectional design, and the inclusion of only self-report measures.
Many questions about sleep value and sleep resilience remain and the Final Analysis Sample Dataset is shared at the Community Site as a resource for other investigators to pursue them. We also share these questionnaires for researchers to employ in their own studies. Suggested questions that future research may consider pursuing include the following: What are the benefits of sleep value? How does sleep valuation relate to treatment seeking for sleep problems, willingness to pay for sleep therapy, and adherence to treatment recommendations? Can sleep value inform sleep health promotion efforts or forecast treatment outcomes? Can sleep valuation be utilized to assess if treatment effectively substituted maladaptive beliefs and attitudes about sleep with more adaptive ones? Can resilience to sleep disturbance be enhanced or taught?
Data availability statement
This data is available on the Sleep Psychology Research Directions community site. For the Monetary Sleep Value Scale, we provided only the Likert scale responses. The open response values can be obtained upon request to the corresponding author.
Authorship contributions (CRediT)
Dustin Sherriff made substantial contributions to this paper, which include the conception and design of the project, acquisition, analysis, and interpretation of the data. In addition, he was heavily involved in drafting the manuscript.
Levi Ward made a substantial contribution to this paper that includes the conception and design of the project analysis and interpretation of the data.
Danika Calvin made a substantial contribution to this paper, which includes the conception and design of the project
Bryce Klingonsmith made substantial contributions to this paper, which include the conception and design of the project.
Daniel B. Kay made substantial contributions to this paper, which include the conception and design of the project, acquisition, analysis, and interpretation of the data. In addition, he was heavily involved in drafting the manuscript.
Financial support
This study was funded with internal funds provided by the College of Family Home and Social Science, Department of Psychology, and Generous Donations to Brigham Young University. The undergraduate students, Dustin Sherriff, Bryce Klingonsmith, Danika Calvin, and Levi Ward were funded by Experiential Learning Funds provided by the Department of Psychology at Brigham Young University.
Competing interests
None.
Ethics statement
This project was approved by the Institutional Review Board at Brigham Young University (IRB# IRB2023-146). Study participants gave informed consent to take part in the study.
Open access
Comments
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