Abstract
Objective
We explored the relationship between the Sleep Hygiene Practices Scale (SHPS) and sleep quality and sleep-related impairment in Black and Latinx adults with type 2 diabetes (T2DM).
Methods
Forty Black and Latinx adults with T2DM participated. Self-reported measures include the Pittsburg Sleep Quality Index (PSQI), Patient Reported Outcomes Measurement Information System (PROMIS) Sleep Disturbance (SD) and Sleep-Related Impairment (SRI) measures, and SHPS (domains include sleep schedule and timing, arousal-related behaviors, poor eating/drinking habits prior to sleep, and poor sleep environment).
Results
SHPS Cronbach’s alpha coefficients were 0.58 (schedule), 0.78 (arousal), 0.29 (eating), 0.81 (environment) and 0.88 (overall for four domains). SHPS scores correlated with PSQI (Pearson correlation r = 0.67, 95% CI [0.44, 0.81], PROMIS-SD (r = 0.61 [0.36–0.77]), and PROMIS-SRI (r = 0.43, [0.13–0.65]). There remained a significant relationship between sleep hygiene and both sleep quality and sleep-related impairment adjusting for hemoglobin A1c, age, and body mass index in regression models.
Conclusions
We observed moderate correlations between sleep quality and sleep-related impairment with sleep hygiene using the SHPS in Black and Latinx adults with T2DM.
Keywords: Sleep quality, Type 2 diabetes, Sleep hygiene, Black/African Americans, Latinx
Highlights
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The American Diabetes Association recommends people with type 2 diabetes have assessments to identify sleep health issues.
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The Sleep Hygiene Practices Scale is used to evaluate sleep habits, daily activities affecting sleep, and sleep environments.
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SHPS has moderate correlations with sleep quality and sleep-related impairment in Black and Latino adults with type 2 diabetes.
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The relationship between SHPS, sleep quality, and sleep-related impairment remained after adjusting for HbA1c, age, and BMI.
1. Introduction
Sleep quality and duration are modifiable risk factors contributing to poor glycemic control in people with type 2 diabetes mellitus (T2DM) [1]. Given this association, the 2022 American Diabetes Association (ADA) guidelines recommend people with T2DM receive “assessment of sleep pattern and duration” by healthcare professionals to identify sleep problems [2]. However, the recommendations are neither specific, nor provide clear guidance on which evidence-based instruments should be used for clinical assessment. A reliable, validated sleep hygiene instrument for clinical assessment could reveal modifiable behaviors and environmental factors that are related to sleep quality.
Sleep Hygiene is defined as a “set of behavioral and environmental recommendations intended to promote healthy sleep.” [3] Several sleep hygiene instruments have been developed to measure sleep hygiene [[4], [5], [6], [7], [8], [9]]. One instrument, the Sleep Hygiene Practices Scale (SHPS), is a 30-item four-domain self-report Likert scale that evaluates an individual’s sleep habits, daily life activities that influence sleep, and sleep environments [7]. It has been validated in good sleepers and in people with insomnia [7]. Clinically, it has the potential to identify areas of improvement for sleep hygiene, with minimal burden in terms of time and effort required for patients and providers to complete the assessments. Despite its potential, there is no research using the SHPS scale in Black and Latinx populations with T2DM. This is of particular relevance in these communities, where individuals experience worse sleep quality, shorter sleep duration [[10], [11], [12]] and experience higher levels of blood glucose [13].
This observational study evaluated the relationship between SHPS, self-reported sleep quality and sleep-related impairment, and hemoglobin A1c (HbA1c) in Black and Latinx people with poorly controlled T2DM (HbA1c ≥ 8%). We included three sleep quality measures (Pittsburg Sleep Quality Index [PSQI], Patient Reported Outcomes Measurement Information System Sleep Disturbance [PROMIS-SD]), and Patient Reported Outcomes Measurement Information System Sleep-Related Impairment [PROMIS-SRI].
2. Methods
2.1. Study design and sample
This observational, cross-sectional study was conducted at the University of Illinois Hospital and Health Sciences System (UI Health), an academic medical center in Chicago, IL. The participants were recruited from a concurrent randomized, controlled trial (RCT) on diabetes self-management using mobile health technology [14]. At routine study visits, individuals were invited to participate in a separate observational trial and eligible, interested participants were consented separately. Forty English-speaking Black and Latinx individuals (aged 21–75) with poorly controlled T2DM (HbA1c ≥ 8%) completed the self-reported sleep quality and sleep hygiene instruments, administered by trained research personnel via interview. They received $40 for completing the additional surveys.
The study protocol was approved by the University of Illinois Chicago Institutional Review Board (Protocol # 2016-0380).
2.2. Measures
Participants provided sociodemographic data, completed obstructive sleep apnea (OSA) risk assessment with STOP-BANG, an 8-item screener with yes/no responses to questions on snoring, tiredness, observed apnea, high blood pressure, body mass index (BMI), age, neck circumference, and male gender [15], and indicated use of pain, sleep, or psychotropic medications [16]. Sleep quality was obtained using the Pittsburg Sleep Quality Index (>5 indicates poor sleep) [17] and PROMIS Sleep Disturbance (SD) (T-score >50 indicates worse disturbance) [18]. Sleep-related impairment was determined using the PROMIS Sleep-Related Impairment (SRI) (T-score >50 indicates worse daytime impairment due to sleep problems) [18]. Sleep hygiene was collected using the Sleep Hygiene Practices Scale (SHPS) [7]. The SHPS included 30 items covering four domains: (1) sleep schedule and timing, (2) arousal-related behaviors, (3) poor eating/drinking habits prior to sleep, and (4) poor sleep environment. (See Appendix A) A higher SHPS score indicates more maladaptive sleep hygiene practices. The concurrent RCT measured weight and height (to compute body mass index, BMI). At time of consent for the observational study, HbA1c was obtained by venipuncture at the UI Health Clinical Research Center from a certified phlebotomist. The blood samples were transported to a professional outside laboratory for processing.
2.3. Statistical analysis
Descriptive statistics were calculated for sociodemographic measures. Scoring of the PSQI, PROMIS-SD, PROMIS-SRI, and SHPS were calculated per directions in original sources. All scales resulted in one summary score, excluding SHPS which yielded a total score and four SHPS subscale scores. Pearson’s correlation coefficients with 95% confidence intervals were computed for each combination of different sleep quality measures and SHPS, and for hemoglobin A1c and SHPS. Cronbach’s alpha coefficient was also computed for total SHPS. Three multivariable linear regression models were constructed to predict sleep quality or sleep-related impairment (with PSQI, PROMIS-SD, or PROMIS-SRI as the dependent variable). The SHPS score was an independent variable, and we adjusted for HbA1c, age, and BMI. R software (version 4.2.0) [19] and SAS (version 9.4)[20] were used for analyses.
3. Results
3.1. Study population characteristics
Descriptive statistics of the study cohort are shown in Table 1. Of the 40 participants, 80% were women, 85% were Black, 15% were Latinx, and 60% had annual household income of <$20000 per year. The mean (standard deviation, SD) age was 52.2 (8.2) years, BMI was 36.9 (8.5) kg/m2, and average hemoglobin A1c was 9.9 (1.7) %. Sixty percent of participants either reported a history of or were at high risk of obstructive sleep apnea. Also, 12.5% took some form of medication to assist with sleep, 35% took medication for pain, and 10.3% took an antidepressant regularly. Mean (SD) of total SHPS was 78.8 (22.3), and for each domain were: schedule 21.5 (6.2), arousal 25.2 (8.9), eating 13.5 (4.0), and environment 18.1 (8.4). Mean (SD) for PSQI was 8.7 (4.0). Mean T-score (SD) for PROMIS measures were: PROMIS-SD 47.8 (9.9), PROMIS-SRI 47.1 (10.5).
Table 1.
Descriptive statistics of study cohort.
| Variable | Overall (N = 40) | |
|---|---|---|
| Age, mean years (SD) | 52.2 | (8.24) |
| BMI, mean kg/m2 (SD) | 36.9 | (8.47) |
| Hemoglobin A1c, Mean (SD) (%) | 8.9 | (1.54) |
| PSQI Score, Mean (SD) | 7.9 | (3.60) |
| PHQ9 Score, Mean (SD) | 6.3 | (5.91) |
| Gender, N (%) | ||
| Male | 8 | (20.0) |
| Female | 32 | (80.0) |
| Ethnicity, N (%) | ||
| Latinx/Hispanic | 6 | (15.0) |
| African American | 34 | (85.0) |
| Education, N (%) | ||
| Less than High School | 6 | (15.0) |
| High School diploma/GED | 14 | (35.0) |
| Some college/Graduated college | 20 | (50.0) |
| Income, N (%) | ||
| Less than $10,000 | 16 | (40.0) |
| $10,000-$20,000 | 8 | (20.0) |
| Greater than $20,000 | 14 | (35.0) |
| Refused to Answer | 2 | (5.0) |
| Risk for Sleep Apnea, N (%) | ||
| Low | 18 | (45.0) |
| Intermediate | 1 | (2.5) |
| High | 21 | (52.5) |
| Ever been diagnosed OSA, N (%) | ||
| Yes | 10 | (25) |
| No | 30 | (75) |
| Sleep Medication, N (%) | ||
| Yes | 5 | (12.5) |
| No | 34 | (85.0) |
| Don’t Know | 1 | (2.5) |
| Pain Medication, N (%) | ||
| Yes | 14 | (35.0) |
| No | 25 | (62.5) |
| Don’t know | 1 | (2.5) |
| Depression Medication, N (%) | ||
| Yes | 4 | (10.3) |
| No | 35 | (89.7) |
3.2. Sleep quality measures and the sleep hygiene practice scales
Cronbach’s alpha coefficient for SHPS domains were: 0.58 (schedule), 0.78 (arousal), 0.29 (eating), and 0.81 (environment). For the overall SHPS measure, the Cronbach’s alpha coefficient was 0.88. The overall SHPS score positively correlated with sleep quality (PSQI Pearson correlation r = 0.67, 95% CI [0.44, 0.81], PROMIS-SD r = 0.61 [0.36–0.77]), and PROMIS-SRI r = 0.43, [0.13–0.65]. Table 2 highlights correlations between sleep quality and individual SHPS domain scores. After adjusting for hemoglobin A1c, age, and BMI in the multivariable linear regression model, overall SHPS scores remained significant predictors of sleep quality for PSQI (B = 0.225, p<0.001), PROMIS-SD (B = 0.261, p<0.001), and PROMIS-SRI (B = 0.195, p<0.01), (Table 3). There was no significant correlation between overall or individual SHPS scores and hemoglobin A1c.
Table 2.
Correlations between sleep quality/sleep-related impairment and the sleep hygiene practices scale.
| Measure | Sleep Hygiene Practices Scale (SHPS) |
||||
|---|---|---|---|---|---|
| Pearson’s coefficient (r), [95% CI] | |||||
| Overall Score | Schedule | Arousal | Eating | aEnvironment | |
| PSQI | 0.67 | 0.56 | 0.61 | 0.51 | 0.43 |
| [0.44,0.81] | [0.30, 0.75] | [0.37, 0.78] | [0.23, 0.71] | [0.13, 0.66]. | |
| PROMIS-SD | 0.61 | 0.51 | 0.60 | 0.31 | 0.47 |
| [0.36,0.77] | [0.23,0.71] | [0.35,0.76] | [-0.0049,0.56] | [0.19,0.69] | |
| PROMIS-SRI | 0.43 | 0.47 | 0.44 | 0.36 | 0.28 |
| [0.13,0.65] | [0.18–0.68] | [0.14,0.66] | [0.05,0.60] | [-0.15,0.47] | |
Subscore data missing for the environment domain for one participant.
Table 3.
Multiple regression model for predictors of sleep quality or sleep-related impairment (PSQI, PROMIS-SD, PROMIS-SRI).
| Variable | PSQI |
PROMIS-SD |
PROMIS-SRI |
p-value | Beta Coeff |
p-value |
|---|---|---|---|---|---|---|
| Beta Coeff |
p-value | Beta Coeff |
||||
| [95% CI] | [95% CI] | [95% CI] | ||||
| SHPS | 0.225b | <0.0001 | 0.261b | <0.0001 | 0.195a | 0.009 |
| [0.139, 0.312] | [0.141,0.381] | [0.052,0.338] | ||||
| A1c | 0.19658 | 0.747 | 0.56972 | 0.502 | 0.14084 | 0.889 |
| [-1.031, 1.424] | [-1.137, 2.276] | [-1.891, 2.173] | ||||
| Age | 0.24855 | 0.045 | 0.13982 | 0.406 | 0.18214 | 0.364 |
| [0.006, 0.491] | [-0.198, 0.478] | [-0.220, 0.584] | ||||
| BMI | 0.14567 | 0.247 | -0.00106 | 0.995 | -0.06726 | 0.745 |
| [-0.106, 0.397] | [-0.350, 0.348] | [-0.483, 0.349] |
Unadjusted R.2.
Unadjusted R.2.
4. Discussion
Among this studied Black and Latinx uncontrolled T2DM population, moderate correlations were observed between sleep quality/impairment and sleep hygiene which remained significant after adjusting for hemoglobin A1c, age, and BMI. The strength of these relationships varied among validated measures of sleep quality and sleep-related impairment (PSQI, PROMIS-SD, PROMIS-SRI). SHPS correlations with PROMIS-SRI were weaker, and non-significant in the environment domain. Collectively, findings support the utility of most of the SHPS domains in sleep hygiene assessment, with additional knowledge of its relationship to sleep quality and sleep-related impairment.
These findings are consistent with the results observed in the two other studies of SHPS. In the first validation of an English version of SHPS among a Taiwanese study population, (Yang et al.) [7], only three domains (schedule, arousal, and environment) in good sleepers and one domain (arousal) in people with insomnia correlated with sleep quality (measured by PSQI). In the validation of a Japanese version of SHPS in a Japanese population [21] there was adequate internal consistency, test-retest reliability, and clinical validity of the measure (though no evaluation of any relationship with sleep quality) among people with insomnia and good sleepers. Similar to our results, “eating/drinking behavior” did not have adequate internal reliability. This may be related to a lack of differentiation of the various behaviors addressed (e.g., caffeinated drinks vs. alcohol at nighttime) in this domain. Also, there may be complex habits observed in those with T2DM, such as engaging in bedtime snacks to avoid nocturnal hypoglycemia.
In general, sleep hygiene assessments have not been widely studied [[4], [5], [6], [7], [8], [9]]. Most research on these instruments focus on clinical use validation [[4], [5], [6], [7], [8], [9]]. For example, the Sleep Hygiene Index (SHI) [6] is a 13-item validated scale with positive associations with sleep quality and good psychometric properties in various populations [[22], [23], [24]]. However, there are only a few studies validating SHI or other sleep hygiene assessments in people with chronic diseases, such as chronic pain [25] and breast cancer [26]. Furthermore, none of the validated sleep hygiene assessments have been studied in people with T2DM or in Black and Latinx populations.
Like other validated instruments, the SHI may be limited in its ability to identify intervention targets because of its complex questions with multiple sleep concerns grouped together (e.g., “double-barreled” questions) [6]. On the other hand, the SHPS can be employed by clinicians and researchers to help identify targets for intervention with its categorized domains. In T2DM, glycemic control may improve through better sleep quality and duration (and less daytime impairment) contributing to healthier self-care behaviors and having other direct effects on glucose metabolism [27]. Sleep extension and education to improve sleep quality demonstrate promising results in improving glycemic control [28]. Additionally, strong rationale exists for future studies with tailored, personalized, and culturally relevant sleep hygiene interventions among diverse T2DM populations [29]. Black and Latinx communities are at a higher risk for environmental hazards related to structural racism that can affect sleep quality and duration [30]. These hazards include chronic stress exposure, shift work demands, and behavioral factors influenced by low socioeconomic status. This directly contributes to suboptimal physical activity and dietary patterns that represent principal diabetes-related behaviors [31,32].
To our knowledge, this is the first study to evaluate the SHPS in Black and Latinx populations and those with T2DM. However, study limitations exist. It is a small, observational study so the results may lack generalizability to other populations and settings. Additionally, other psychometric properties (e.g., external validity, predictive validity, and test-retest reliability) were not explored. Finally, given the small sample size, it is difficult to measure individual effects of sociodemographics, depression, and stressful life events on sleep quality, though they would be highly relevant to consider in the studied population.
In summary, we found the Sleep Hygiene Practices Scale (SHPS) to be associated with different measures of sleep quality and sleep-related impairment in a Black and Latinx population with uncontrolled T2DM. Further assessment is needed of the SHPS in larger, diverse populations with various chronic health conditions. The SHPS may help identify target areas for behavioral intervention, though research is needed to determine if assessment and subsequent intervention results in a corresponding change in sleep quality and other clinical outcomes.
Funding
This work is funded, in part, by a research supplement from the National Institute of Diabetes and Digestive and Kidney Disease (R01DK108141S), a career development award from the National Heart, Lung, and Blood Institute (K01HL149775), and The Central Society of Clinical and Translational Research (CSCTR) Early Career Development Award. Additionally, the project described was supported by the National Center for Advancing Translational Sciences, National Institutes of Health, through Grant UL1TR002003. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Financial Support disclosure
None of the authors have financial support to disclose.
Clinical trial
This manuscript does not report a clinical trial.
CRediT authorship contribution statement
Alana Biggers: Writing – original draft, Preparation, Data curation, Formal analysis, Reviewing, Editing. Isaye Barton: Formal analysis, Writing – review & editing. Julia Henkins: Data curation, Formal analysis, Writing – review & editing. Hajwa Kim: Formal analysis, Writing – review & editing. Rose Perez: Writing – review & editing. Jason Ong: Writing – review & editing. Lisa K. Sharp: Writing – review & editing. Ben S. Gerber: Supervision, Formal analysis, Writing – review & editing.
Declaration of competing interest
None of the authors have a conflict of interest to disclose.
This data was presented at the Society of General Internal Medicine (SGIM) 2022 Conference from April 6–9, 2022 in Orlando, FL.
Footnotes
Supplementary data to this article can be found online at https://doi.org/10.1016/j.sleepx.2023.100066.
Appendix A. Supplementary data
The following is the Supplementary data to this article:
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