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. Author manuscript; available in PMC: 2022 Jan 1.
Published in final edited form as: Nurs Res. 2021 Jul-Aug;70(4):310–316. doi: 10.1097/NNR.0000000000000506

Poor Sleep Predicts Increased Pain Perception among Adults with Mild Cognitive Impairment

Mary Elizabeth Bowen 1, Xiaopeng Ji 2, Mari A Griffioen 3
PMCID: PMC8222085  NIHMSID: NIHMS1673257  PMID: 33630768

Abstract

Background:

Older adults with mild cognitive impairment are at an increased risk for dementia of the Alzheimer’s type. These older adults also report poorer sleep and more pain than their cognitively intact adult counterparts. Poor sleep and pain are both symptoms associated with an increased risk for dementia in later life. Symptom science research in the direction of how poor sleep affects pain among older adults, especially those with mild cognitive impairment, is needed for the development of targeted sleep interventions to reduce pain and potentially delay/reduce the risk for Alzheimer’s disease in this population.

Objective:

To examine a predictive model of the relationship between poor sleep and pain perception among community-dwelling older adults with mild cognitive impairment.

Methods:

A longitudinal prospective design with 58 continuous matched sleep/pain observations of 15 older adults with mild cognitive impairment (MCI) for up to 6 months was used. Multilevel, mixed modeling, statistical techniques were used to examine the effects of prior-week sleep on subsequent pain perception. Pain perception (pain intensity, pain interference, and pain behavior) is measured by the Patient-Reported Outcomes Measurement Information System during monthly in-person visits. The ActiGraph GT3X+ was used to measure sleep (total sleep time, sleep efficiency, awakenings after sleep onset) objectively and continuously for up to 6 months along with other covariates (e.g., physical activity).

Results:

Increased awakenings after sleep onset in the prior week is associated with increased pain intensity, pain interference, and pain behavior. There was a trend toward sleep efficiency and increased pain intensity and sleep efficiency predicted increased pain interference and pain behavior. There was no relationship between prior week total sleep time and subsequent pain perception.

Discussion:

In this study, poor sleep in the prior week increased pain intensity, pain interference, and pain behavior. Interventions designed to decrease awakening after sleep onset and increase sleep efficiency specifically may effectively reduce pain in this population. Given that these symptoms are prevalent among older adults with MCI, sleep and pain interventions may also ameliorate some of the risk for Alzheimer’s disease in this population.

Keywords: chronic pain, cognition, dementia, frail elderly, sleep disruption

Pain remains a poorly understood but pervasive public health concern in later life. Pain is associated with cognitive and physical decline, increased morbidity, and higher health care costs (Institute of Medicine, Board on Health Sciences Policy, Committee on Advancing Pain Research, Care, and Education, 2011). While 41% of older adults with cognitive impairment (CI) report pain, previous work in this area has not examined this population of older adults (Binnekade et al., 2018). Prior efforts in this area have also focused on chronic pain (pain lasting >3 months), though pain perception (e.g., pain intensity, pain behavior, pain interference) may also be important. For example, pain interference is associated with increased analgesic use (Shade et al., 2019), depressive symptoms (Wang et al., 2018), higher mortality rates (Smith et al., 2018), and increased rates of dementia among community-dwelling older adults (Letzen et al., 2020). Increased pain intensity is associated with frailty (Reyes et al., 2019), accelerated brain aging (van der Leeuw et al., 2018), and suicidal ideation (Santos et al., 2020).

The etiology and site of chronic pain in older adults is wide-ranging (i.e., injury, joint pain, surgical pain) and is influenced by biological, psychological, and social factors (Engel, 1977; Institute of Medicine, Board on Health Sciences Policy, Committee on Advancing Pain Research, Care, and Education, 2011). While the exact underlying biological mechanism contributing to chronic pain is unclear, quantitative sensory testing (QST) allows for a mechanism-based evaluation of pain sensitivity (Rolke et al., 2006). QST has shown that sleep deprivation results in increased next-day thermal sensitivity in healthy adults (Schestatsky et al., 2013; Schuh-Hofer et al., 2013). Additionally, forced sleep restriction and sleep fragmentation alter functional connectivity of cognitive networks for pain modulation causing significant loss of pain inhibition and increased pain sensitivity in laboratory settings. It may be that sleep alters pain sensitivity and deactivates analgesic and activates hyperalgesic mechanisms through a variety of neurobiological mechanisms (Haack et al., 2020). However, findings derived from one-night experimental sleep protocols may not be generalizable to older adults.

Thus, sleep may be a promising nonpharmacological pain intervention. There are few longitudinal studies in this area and most have focused on sleep among healthy adults, how pain affects sleep outcomes, and/or one type or source of pain (e.g., tension headaches; Dorsey et al., 2019). While the relationship between pain and sleep is likely reciprocal, a growing body of literature shows the relationship between poor sleep and subsequent pain is more robust (Edwards et al., 2008; Finan et al., 2013). For example, decreased sleep efficiency (SE) and increased wake after sleep onset (WASO) are associated with an increased risk of new-onset cases of chronic pain in pain-free adults, worsening the long-term prognosis of existing headache and chronic musculoskeletal pain, and influencing daily fluctuations in acute pain (Boardman et al., 2006; Finan et al., 2013; Ødegård et al., 2011). While the sleep mechanisms influencing pain perception are unclear, clinical and epidemiological evidence suggests older adults with chronic pain report shorter total sleep time (TST < 7 hr), decreased SE, and increased WASO (Finan et al., 2013). Older adults with mild CI (MCI) may be more vulnerable to the poor sleep/pain association. These older adults report poorer sleep than their counterparts, which, coupled with other lifestyle factors—such as reduced physical activity—may accelerate cognitive decline (Beaulieu-Bonneau & Hudon, 2009; Bowen, 2012). Longitudinal studies show that older adults with < 6.5 hr of TST have an increased risk for CI in the next decade compared to their counterparts (Keage et al., 2012). Focusing on older adults with MCI is a nursing priority given that 10%–20% of older adults have MCI and 50% of these cases will develop into Alzheimer’s disease in the next 3 years (Cooper et al., 2015).

Consistent with a biopsychosocial model of pain (Engel, 1977; Institute of Medicine, Board on Health Sciences Policy, Committee on Advancing Pain Research, Care, and Education, 2011), the aim of this longitudinal study was to examine a predictive model of poor sleep (e.g., decreased TST, increased WASO, decreased SE) and pain perception (pain intensity, pain interference, and pain behavior) among older adults with MCI, accounting for other social and lifestyle factors (e.g., gender, race, education, physical activity), which may influence this relationship. In the short term, the findings from this study may have implications for the development of tailored interventions to improve poor sleep-related pain among older adults with MCI. In the long-term, study findings may provide a basis for subsequent testing and research on nonpharmacological sleep interventions aimed at delaying/reducing the risk of Alzheimer’s disease in this population.

Methods

Design and Sample

A longitudinal prospective study design was used. Following approval from the institutional review board (#1346150–7), subjects were recruited from three senior centers and a nurse-managed, primary care clinic in the northeastern region of the United States. Interested potential subjects contacted study staff with information provided in brochures and/or displays in common rooms which were in each setting. Potential subjects were provided explanations of procedures, aims, and written consent documents to read and discuss with study staff. Recruitment, enrollment, and data collection occurred over the course of 9 months. To participate in this study subjects were ≥ 60 years old, met the criteria for MCI (Montreal Cognitive Assessment [MoCA] scores > 19 and < 26; Wood et al., 2020), were ambulatory, and community-dwelling. Exclusion criteria included an acute health event in the past year or the diagnosis of a debilitating chronic disease (e.g., Parkinson’s) that would affect collection of activity data. Subjects wore the ActiGraph GT3X-BT continuously for up to 6 months for data collection. At baseline subjects were provided written instruction on wearing the ActiGraph and given the opportunity to ask questions. Other data were collected using a Research Electronic Data Capture (REDCap) database during monthly in-person visits when ActiGraph data were downloaded and batteries charged. Tools were administered in person each month utilizing established protocols.

Measures

The dependent variable of interest was pain perception (pain intensity, pain interference, and pain behavior) measured by self-reported pain in the last 7 days using the Patient-Reported Outcomes Measurement Information System (PROMIS) Computer Adaptive Tests (CAT). Pain intensity measures current, average, and worst pain; pain interference measures the effects of pain on social, cognitive, emotional, physical, and recreational aspects of life; pain behavior measures external manifestations of pain. The PROMIS CAT calculate a T-score where 50 represents the population mean and 10 points represent one SD from the mean (higher scores indicate worse pain; Deyo et al., 2016). The measures are reliable (α = 0.91) and valid (r > 0.75) in persons with pain and MCI (Bartlett et al., 2015; Levin et al., 2015).

The independent variable of interest is poor sleep, measured by TST (the total number of minutes categorized as “sleep” during each sleep period where poor sleep = a TST of < 7 hours/night), SE (the ratio between TST and the total nightly time in bed where poor sleep is a SE of < 95%) and WASO (the total amount of time awake between sleep onset and offset with poor sleep = WASO > 30 min/night; Ohayon et al., 2017). Sleep variables were measured by the ActiGraph GT3X-BT; (ActiGraph LLC, USA), a valid and reliable measure of sleep that has been widely used among community-dwelling older adults with/out CI (Khou et al., 2018). Subjects wore the ActiGraph continuously on their non-dominant wrist for up to 6 months. Raw data were downloaded and analyzed using the ActiLife 6 software (ActiGraph LLC) with 60-s epoch length. The Cole-Kripke algorithm was applied to classify every 60 s epoch and the Tudor-Locke algorithm was used to detect primary sleep periods (at least 160 min). Other sleep episodes (e.g., daytime naps) were excluded. Nonwear time was determined by the Troiano wear time validation algorithm. Sleep data were included if there were at least three valid nights of data (≥ 10 hr of nighttime wear time and TST ≥ 160 min/week). Physical activity data were included if waking wear time ≥ 10 hr/day. Missing or invalid data were excluded from data analysis. For bivariate analyses and receiver operator characteristic (ROC) curves, sleep cut offs were used to determine sensitivity and specificity of sleep variables (Lichstein et al., 2003). In multivariate analyses poor sleep was measured continuously.

Additional variables in this study included age, gender, race, education, chronic pain (pain lasting > 3 months measured at baseline) and physical activity (measured continuously by ActiGraph GT3X-BT). Physical activity was categorized by percent time/day spent in physical activity (e.g., < 30%, 30%–50% [reference category], and > 50%) using Locally Weighted Scatterplot Smoothing (LOWESS) plots.

Data Analysis

Multilevel mixed modeling statistical techniques were used to examine prior-week sleep and pain perception, account for data nested within subjects over time, and adjust for potential confounders. The last 7 days of pain were matched with the prior week’s average sleep data. For each pain outcome a model building approach was used, and sleep variables were added separately to address multicollinearity issues. Subjects were fitted as random effects. Goodness-of-fit statistics (Akaike information criterion [AIC] and the Bayesian information criterion [(BIC]) were examined for each model. All analyses were performed in Stata 16.

There are few sample size requirements for multilevel mixed models as it is the total number of person-by-time observations that is most important; at least three measures per subject are required for longitudinal analysis of multilevel data (Muthén & Curran, 1997). In this study, 15 adults with MCI and at least three valid sleep data points in the week prior to pain assessment wore an ActiGraph continuously (24/7) to provide daytime/nighttime data to be matched with real-time pain perception for up to 6 months (mean = 4 months), totaling 58 paired observations of sleep and pain for predictive analytics. Examinations of pain data in multilevel models showed no significant differences between assessment time points with and without paired sleep data available (p > 0.05). The average number of pairs for those with (n = 3.5) and without chronic pain (n = 4.3) were similar.

Results

Descriptive Statistics

As shown in Table 1, the subjects were about 75 years old; 47% were non-Latino Black and 73% had more than a high school education. The seven subjects who reported chronic pain had increased pain intensity (t = −2.88, p = .01), interference (t = −2.56, p = .02) and behavior (t = − 2.26, p = .04). Non-Latino Whites had higher pain intensity (t = −3.05, p = .01) and behavior (t = −3.07, p = .01). The average MoCA score at baseline was 22.9 (min–max = 20–25; SD = 1.7). At baseline, subjects slept an average 6.77 hours/night in the week preceding pain assessment, with SE ranging from 88% to 97%. The average WASO time was 25 min. Subjects with increased WASO (> 30 min) had increased pain intensity (t = −3.18, p = .01), interference (t =−2.38, p = .03) and behavior (t = −3.09, p = .01). Most participants in the study (n = 9) spent 30%–50% of the day in physical activity at baseline.

Table 1.

Baseline bivariate relationships describing sleep and pain perception in the sample (N=15)

Sample Characteristics Pain intensity Pain interference Pain behaviors
M (SD) M (SD) M (SD)
Age, M (SD) 75.5 (8.4) 40.9 (9.0) 46.4 (7.5) 46.9 (10.1)
Sex, n (%)
 Female 11 (73) 42.0 (9.4) 46.8 (7.50) 47.7 (10.2)
 Male 4 (27) 37.8 (8.2) 45.3 (8.7) 44.9 (11.2)
Race, n (%)
 Non-Latino White 8 (53) 46.1 (6.8) ** 49.1 (5.5) 52.9 (7.5) **
 Non-Latino Black 7 (47) 34.8 (7.6) 43.1 (8.6) 40.2 (8.5)
Education, n (%)
 ≤high school 4 (27) 45.3 (9.9) 50.4 (9.5) 51.3 (11.1)
 Vocational school 3 (20) 43.5 (11.1) 45.41 (5.9) 47.8 (10.9)
 2-year degree 4 (27) 38.9 (9.5) 46.8 (9.5) 45.0 (11.2)
 4-years of college or + 4 (27) 36.4 (6.8) 42.6 (4.6) 43.9 (10.2)
Chronic pain, n (%)
 No 8 (53) 35.78 (7.3)* 42.4 (3.5)* 42.1 (9.5) *
 Yes 7 (47) 46.7 (7.3) 50.9 (7.3) 52.5 (8.0)
MoCA score, M (SD) 22.9 (1.7) 40.9 (9.0) 46.4 (7.5) 46.9 (10.1)
Total Sleep Timea
 ≥7 hours 9 (60) 39.8 (8.9) 45.2 (8.7) 45.7 (10.2)
 <7 hours 6 (40) 44.6 (9.2) 49.8 (10.0) 51.8 (8.8)
Sleep Efficiencya
 ≥95% 6 (40) 38.0 (8.2) 44.2 (9.4) 43.9 (9.6)
 <95% 9 (60) 44.2 (9.2) 48.9 (9.1) 50.9 (9.5)
WASOa
 ≤ 30 min 10 (66.7) 37.6 (8.3) ** 43.6 (8.4) * 43.8 (9.0) **
 >30 min 5 (33.3) 49.9 (2.7) 53.9 (7.0) 56.89 (3.5)
Time Spent in Physical Activity/Daya
 <30% 4 (26.7) 40.9 (12.0) 45.5 (0.6) 44.7 (10.9)
 30–50% 9 (60.0) 42.8 (8.4) 48.7 (10.3) 50.3 (9.3)
  >50% 2 (13.3) 38.7 (11.2) 42.7 (5.6) 45.4 (14.2)
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Note. MoCA= Montreal Cognitive Assessment; WASO = wake after sleep onset (min).

a

Weekly average sleep and activity data one-week preceding pain perception.

*

p ≤ .05,

**

p ≤ .01.

Multilevel Results

In multilevel models (Table 2) there was no relationship between TST and pain perception (p > .05). However, subjects with increased WASO were associated with increased pain intensity (β = 0.13, p = .01), interference (β = 0.11, p = .02), and behavior (β = 0.11, p = .01). Increased SE was associated with decreased pain interference (β = −0.64, p = .03) and behavior (β = −0.49, p = 0.01) in the following week. There was a trend toward significance between SE and pain intensity (β = −0.76, p = .07). These sleep and subsequent pain associations were independent of the other factors considered in the models. In addition, < 30% of daily physical activity time was associated with increased pain intensity (β = 4.48, p = .02), behavior (β = 4.76, p = .03) and interference (β = 2.92, p = .05; compared to 30%–50% of time). Participants with ≥ 50% of time spent in physical activity were associated with increased pain perception (β = 4.03–4.64, p < .01).

Table 2.

Multi-level models examining the relationship between prior-week sleep disruption and pain perception (N=15; 58 observations)

Pain Intensity Pain Interference Pain Behaviors
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8 Model 9 Model 10 Model 11 Model 12
β (SE) β (SE) β (SE) B (SE) β (SE) B (SE) β (SE) β (SE) β (SE) B (SE) B (SE) β (SE)
Age 0.4 (0.1)*** 0.5 (0.1)*** 0.6 (0.1)*** 0.6 (0.1)*** 0.4 (0.1)* 0.5 (0.1)*** 0.6 (0.1)*** 0.6 (0.1)*** 0.4 (0.2)* 0.5 (0.1)*** 0.5 (0.1)*** 0.6 (0.1)***
Male 0.9 (1.6) 2.9 (2.3) 2.5 (1.8) 3.3 (1.9) 1.2 (2.8) 4.8 (3.3) 4.3 (2.5)† 4.9 (2.6)† −0.7 (3.2) 4.3 (3.5) 2.25 (3.2) 2.93 (3.3)
Non-Latino Black 4.2 (1.7) * −3.7 (1.6)* 3.6 (1.5)* 3.7 (1.7)* 4.5 (1.8)* 5.6 (1.9)* 5.5 (1.7)** 5.6 (1.7)** 1.4 (2.8) 2.3 (2.8) 1.8 (2.7) 1.9 (2.8)
Education
Vocational school −6.6 (3.1)* −6.0 (2.3)* −5.8 (1.8)* −6.8 (1.7)*** −9.6 (3.5)** −9.1 (3.0)** −8.8 (2.1)*** −9.7 (2.1)*** −9.5 (2.9)** −9.9 (1.7)*** −8.3 (1.8)*** −9.2 (1.7)***
2-year degree −2.8 (1.9) −4.0 (2.1)* −2.1 (2.1) −3.4 (2.1) 1.5 (4.2) −0.6 (3.7) 1.2 (2.7) 0.1 (2.6) −0.1 (4.1) −3.9 (3.0) −0.6 (3.1) −1.4 (3.2)
≥ 4-years college −2.3 (3.0) * −5.3 (2.2)* −6.2 (2.5)* −6.6 (2.6)* −2.6 (2.9) −7.2 (5.0) −7.9 (4.0)* −8.4 (4.1)* −0.7 (4.8) −5.3 (3.5) −5.6 (3.4) −6.1 (3.4)
Chronic pain 15.1 (1.8)*** 13.1 (2.2)** 11.1 (2.3)*** 11.5 (2.1)*** 17.3 (2.9)*** 14.7 (2.4)*** 12.9 (2.3)*** 13.2 (2.3)*** 15.8 (2.7)*** 13.4 (2.9)*** 11.7 (2.9)*** 11.7 (2.8)***
Time Spent in Physical Activity/Daya
<30% 4.5* (1.9) 3.7* (1.6) 2.0 (1.3) 2.8* (1.4)* 2.9 (1.5)* 1.7 (1.7) 0.2 (1.7) 0.8 (1.5) 4.8* (2.2)* 4.0 (2.3)† 2.3 (2.4) 2.7 (2.3)
>50% 4.0† (2.6) 5.4* (2.7) 5.2* (2.7) 5.1* (2.6) 4.0† (2.6) 4.9† (2.6) 4.7† (2.6) 4.7† (2.6) 4.6 (2.9) 5.6† (3.2) 5.6† (3.2) 5.5† (3.2)
TST (minutes) 0.0 (0.0) 0.0 (0.0) 0.0 (0.0)
SE (%) −0.8† (0.3) −0.6* (0.3) −0.5* (0.3)
WASO (minutes) 0.1* (0.1) 0.1* (0.0) 0.1* (0.1)
Model Fit
AIC 475.7 389.5 385.9 386.9 473.8 395.9 393.8 394.1 508.5 423.2 423.9 423.7
BIC 505.1 418.4 414.8 415.8 503.2 424.7 422.7 422.9 537.9 452.1 452.8 452.6
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Notes: TST = total sleep time (minutes); SE = sleep efficiency (percent); WASO = wake after sleep onset (minutes).

a

30–50% is the reference category

†

p = .07,

*

p ≤ .05,

**

p ≤ .01,

***

p ≤ .001

In ad hoc analyses, ROC curves based on significant results were used to determine whether decreased SE and increased WASO had good sensitivity and specificity to subsequent pain perceptions in this population. Pain was dichotomized so that participants scoring one standard deviation above the mean signified increased pain perception (HealthMeasures, n.d.). None of the significant sleep variables had good sensitivity and specificity so we additionally examined SE and pain intensity. SE had good sensitivity (75.6%) and fair specificity (61.5%) to pain intensity (AUC, 0.73). Examining the cut off that maximizes the sensitivity and specificity of the measure, participants with less than 93% SE/night in the prior week were associated with increased pain intensity.

Discussion

In this longitudinal study utilizing a biopsychosocial model of pain with up to 6 months of continuous sleep and pain measurement (58 observations), decreased SE (pain interference and pain behavior only) and increased WASO were associated with increased pain perception. In addition, 30%–50% average daily time spent in physical activity was independently associated with reduced pain perception. Previous work on sleep and pain has largely focused on how pain may be associated with poor sleep. A growing body of work supports this study’s findings that poor sleep is a robust predictor of subsequent pain (Edwards et al., 2008; Finan et al., 2013). This may be due, in part, to functional connectivity of cognitive networks for pain modulation, significant loss of pain inhibition, and increased pain sensitivity following increased WASO. Increased WASO is one of the criteria to diagnose insomnia (Letzen et al., 2020; Lichstein et al., 2003). Mitigating insomnia symptoms and improving SE are associated with reduced next-day and long-term pain interference and pain severity among adults and older adults with chronic pain (Tang et al., 2012; Vitiello et al., 2014). Poor sleep may also affect pain processing by sensitizing peripheral nociceptors, altering descending pain modulation, and/or affecting central inhibitory and facilitating mechanisms, causing a state of generalized hyperalgesia (Sivertsen et al., 2015).

This study’s findings are in contrast with studies reporting a relationship between TST and pain. This may be because these studies have largely focused on young adults, healthy older adults, or relied on self-reports of TST (Edwards et al., 2008; Sivertsen et al., 2015). Healthy older adults may have more endurance to decreased TST compared to younger adults and changes in TST may be a less sensitive predictor (Duffy et al., 2009). In addition, daytime napping is more prevalent among older adults; daytime naps may attenuate decreased TST-related hyperalgesia, confounding the relationship between TST and pain perception (Li et al., 2018).

In this study, older adults with MCI who spent ≤ 30% or ≥ 50% of their time/day in physical activity were associated with increased pain perception. Though focused on back pain and/or healthy adults, this finding is consistent with other studies documenting a curvilinear relationship between physical activity and pain (Heneweer et al., 2009). Future work examining physical activity thresholds and ways to effectively incorporate physical activity in therapeutic doses in this population may lead to additional interventions to reduce pain—irrespective of sleep habits. This study also found a trend toward decreased SE and pain intensity; older adults with at least 93% SE/night were associated with reduced next-day pain intensity. This threshold is higher than the cut-off value (> 85%) for good sleep quality recommended by the National Sleep Foundation (Ohayon et al., 2017) but similar to findings from previous work on older adults (e.g., SE > 92%; Levenson et al., 2013).

There are several limitations to consider before interpreting results. First, while the subjects in this study had 6 months of continuous sleep data with five repeated measures of pain perception (58 observations), it remains that this study utilizes a small sample size and may be prone to type II error (Muthén & Curran, 1997). Second, sleep data were not cross validated with self-reports of sleep onset and offset which may reduce the accuracy of TST and SE measures.

Conclusion

This study adds to emergent research suggesting a robust relationship in the direction of poor sleep and subsequent pain and has implications for sleep interventions aiming to reduce pain. Given that older adults with MCI have a higher prevalence of both poor sleep and pain, sleep interventions may also delay/reduce the risk for Alzheimer’s disease.

Acknowledgement:

Research reported in this publication was supported by a National Institute of Nursing Research Omics Associated with Self-Management Interventions for Symptoms (OASIS) Pilot Study Award Number P30NR016579 and by the College of Health Sciences at the University of Delaware. This work does not necessarily reflect the views of the Department of Veterans Affairs.

Footnotes

Ethical Conduct of Research: Institutional Review Board from the University of Delaware (#1346150-7) approval was obtained prior to enrolling participants.

The authors have no conflicts of interest to report.

Contributor Information

Mary Elizabeth Bowen, Associate Dean of Research, Associate Professor, University of Delaware School of Nursing, Newark, DE, and, Research Health Scientist, Corporeal Michael J. Crescenz VA Medical Center, Department of Veterans Affairs, Philadelphia, PA.

Xiaopeng Ji, University of Delaware School of Nursing, Newark, DE.

Mari A. Griffioen, University of Delaware School of Nursing, Newark, DE.

References

  1. Bartlett SJ, Orbai A-M, Duncan T, DeLeon E, Ruffing V, Clegg-Smith K, & Bingham CO III. (2015). Reliability and validity of selected PROMIS measures in people with rheumatoid arthritis. PLoS ONE, 10, e0138543. 10.1371/journal.pone.0138543 [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Beaulieu-Bonneau S, & Hudon C (2009). Sleep disturbances in older adults with mild cognitive impairment. International Psychogeriatrics, 21, 654–666. 10.1017/S1041610209009120 [DOI] [PubMed] [Google Scholar]
  3. Binnekade TT, Scherder EJA, Maier AB, Lobbezoo F, Overdorp EJ, Rhebergen D, Perez RSGM, & Oosterman JM (2018). Pain in patients with different dementia subtypes, mild cognitive impairment, and subjective cognitive impairment. Pain Medicine, 19, 920–927. 10.1093/pm/pnx162 [DOI] [PubMed] [Google Scholar]
  4. Boardman HF, Thomas E, Millson DS, & Croft PR (2006). The natural history of headache: Predictors of onset and recovery. Cephalalgia, 26, 1080–1088. 10.1111/j.1468-2982.2006.01166.x [DOI] [PubMed] [Google Scholar]
  5. Bowen ME (2012). A prospective examination of the relationship between physical activity and dementia risk in later life. American Journal of Health Promotion, 26, 333–340. 10.4278/ajhp.110311-QUAN-115 [DOI] [PubMed] [Google Scholar]
  6. Cooper C, Sommerlad A, Lyketsos CG, & Livingston G (2015). Modifiable predictors of dementia in mild cognitive impairment: A systematic review and meta-analysis. American Journal of Psychiatry, 172, 323–334. 10.1176/appi.ajp.2014.14070878 [DOI] [PubMed] [Google Scholar]
  7. Deyo RA, Ramsey K, Buckley DI, Michaels L, Kobus A, Eckstrom E, Forro V, & Morris C (2016). Performance of a patient reported outcomes measurement information system (PROMIS) short form in older adults with chronic musculoskeletal pain. Pain Medicine, 17, 314–324. 10.1093/pm/pnv046 [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Dorsey SG, Griffioen MA, Renn CL, Cashion AK, Colloca L, Jackson-Cook CK, Gill J, Henderson W, Kim H, Joseph PV, Saligan L, Starkweather AR, & Lyon D (2019). Working together to advance symptom science in the precision era. Nursing Research, 68, 86–90. 10.1097/NNR.0000000000000339 [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Duffy JF, Willson HJ, Wang W, & Czeisler CA (2009). Healthy older adults better tolerate sleep deprivation than young adults. Journal of the American Geriatrics Society, 57, 1245–1251. 10.1111/j.1532-5415.2009.02303.x [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Edwards RR, Almeida DM, Klick B, Haythornthwaite JA, & Smith MT (2008). Duration of sleep contributes to next-day pain report in the general population. PAIN®, 137, 202–207. 10.1016/j.pain.2008.01.025 [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Engel GL (1977). The need for a new medical model: A challenge for biomedicine. Science, 196, 129–136. 10.1126/science.847460 [DOI] [PubMed] [Google Scholar]
  12. Finan PH, Goodin BR, & Smith MT (2013). The association of sleep and pain: An update and a path forward. Journal of Pain, 14, 1539–1552. 10.1016/j.jpain.2013.08.007 [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Haack M, Simpson N, Sethna N, Kaur S, & Mullington J (2020). Sleep deficiency and chronic pain: Potential underlying mechanisms and clinical implications. Neuropsychopharmacology, 45, 205–216. 10.1038/s41386-019-0439-z [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. HealthMeasures. (n.d.). PROMIS® score cut points. Retrieved from https://www.healthmeasures.net/score-and-interpret/interpret-scores/promis/promis-score-cut-points
  15. Heneweer H, Vanhees L, & Picavet HSJ (2009). Physical activity and low back pain: A U-shaped relation? Pain, 143, 21–25. 10.1016/j.pain.2008.12.033 [DOI] [PubMed] [Google Scholar]
  16. Institute of Medicine, Board on Health Sciences Policy, Committee on Advancing Pain Research, Care, and Education. (2011). Relieving pain in America: A blueprint for transforming prevention, care, education, and research. National Academies Press. [PubMed] [Google Scholar]
  17. Keage HAD, Banks S, Yang KL, Morgan K, Brayne C, & Matthews FE (2012). What sleep characteristics predict cognitive decline in the elderly? Sleep Medicine, 13, 886–892. 10.1016/j.sleep.2012.02.003 [DOI] [PubMed] [Google Scholar]
  18. Khou CS, Rendzia B, & Watts A (2018). Evaluation of waist-worn actigraphy monitors for the assessment of sleep in older adults with and without Alzheimer’s disease. Journal of Rehabilitation and Assistive Technologies Engineering, 5, 2055668318777989. 10.1177/2055668318777989 [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Letzen JE, Remeniuk B, Smith MT, Irwin MR, Finan PH, & Seminowicz DA (2020). Individual differences in pain sensitivity are associated with cognitive network functional connectivity following one night of experimental sleep disruption. Human Brain Mapping, 41, 581–593. 10.1002/hbm.24824 [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Levenson JC, Troxel WM, Begley A, Hall M, Germain A, Monk TH, & Buysse DJ (2013). A quantitative approach to distinguishing older adults with insomnia from good sleeper controls. Journal of Clinical Sleep Medicine, 9, 125–131. 10.5664/jcsm.2404 [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Levin JB, Aebi ME, Smyth KA, Tatsuoka C, Sams J, Scheidemantel T, & Sajatovic M (2015). Comparing patient-reported outcomes measure information system depression scale with legacy depression measures in a community sample of older adults with varying levels of cognitive functioning. American Journal of Geriatric Psychiatry, 23, 1134–1143. 10.1016/j.jagp.2015.04.004 [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Li J, Vitiello MV, & Gooneratne NS (2018). Sleep in normal aging. Sleep Medicine Clinics, 13, 1–11. 10.1016/j.jsmc.2017.09.001 [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Lichstein KL, Durrence HH, Taylor DJ, Bush AJ, & Riedel BW (2003). Quantitative criteria for insomnia. Behaviour Research and Therapy, 41, 427–445. 10.1016/S0005-7967(02)00023-2 [DOI] [PubMed] [Google Scholar]
  24. Muthén BO, & Curran PJ (1997). General longitudinal modeling of individual differences in experimental designs: A latent variable framework for analysis and power estimation. Psychological Methods, 2, 371–402. 10.1037/1082-989X.2.4.371 [DOI] [Google Scholar]
  25. Ødegård SS, Sand T, Engstrøm M, Stovner LJ, Zwart J-A, & Hagen K (2011). The long-term effect of insomnia on primary headaches: A prospective population-based cohort study (HUNT–2 and HUNT–3). Headache: The Journal of Head and Face Pain, 51, 570–580. 10.1111/j.1526-4610.2011.01859.x [DOI] [PubMed] [Google Scholar]
  26. Ohayon M, Wickwire EM, Hirshkowitz M, Albert SM, Avidan A, Daly FJ, Dauvilliers Y, Ferri R, Fung C, Gozal D, Hazen N, Krystal A, Lichstein K, Mallampalli M, Plazzi G, Rawding R, Scheer FA, Somers V, & Vitiello MV (2017). National Sleep Foundation’s sleep quality recommendations: First report. Sleep Health, 3, 6–19. 10.1016/j.sleh.2016.11.006 [DOI] [PubMed] [Google Scholar]
  27. Reyes PO, Perea EG, & Marcos AP (2019). Chronic pain and frailty in community-dwelling older adults: A systematic review. Pain Management Nursing, 20, 309–315. 10.1016/j.pmn.2019.01.003 [DOI] [PubMed] [Google Scholar]
  28. Rolke R, Magerl W, Campbell KA, Schalber C, Caspari S, Birklein F, & Treede R-D (2006). Quantitative sensory testing: A comprehensive protocol for clinical trials. European Journal of Pain, 10, 77–88. 10.1016/j.ejpain.2005.02.003 [DOI] [PubMed] [Google Scholar]
  29. Santos J, Martins S, Azevedo LF, & Fernandes L (2020). Pain as a risk factor for suicidal behavior in older adults: A systematic review. Archives of Gerontology and Geriatrics, 87, 104000. 10.1016/j.archger.2019.104000 [DOI] [PubMed] [Google Scholar]
  30. Schestatsky P, Dall-Agnol L, Gheller L, Stefani LC, Sanches PRS, de Souza IC, Torres IL, & Caumo W (2013). Pain–autonomic interaction after work-induced sleep restriction. European Journal of Neurology, 20, 638–646. 10.1111/ene.12011 [DOI] [PubMed] [Google Scholar]
  31. Schuh-Hofer S, Wodarski R, Pfau DB, Caspani O, Magerl W, Kennedy JD, & Treede R-D (2013). One night of total sleep deprivation promotes a state of generalized hyperalgesia: A surrogate pain model to study the relationship of insomnia and pain. Pain, 154, 1613–1621. 10.1016/j.pain.2013.04.046 [DOI] [PubMed] [Google Scholar]
  32. Shade MY, Herr K, & Kupzyk K (2019). Self-reported pain interference and analgesic characteristics in rural older adults. Pain Management Nursing, 20, 232–238. 10.1016/j.pmn.2019.03.001 [DOI] [PubMed] [Google Scholar]
  33. Sivertsen B, Lallukka T, Petrie KJ, Steingrímsdóttir ÓA, Stubhaug A, & Nielsen CS (2015). Sleep and pain sensitivity in adults. Pain, 156, 1433–1439. 10.1097/j.pain.0000000000000131 [DOI] [PubMed] [Google Scholar]
  34. Smith D, Wilkie R, Croft P, & McBeth J (2018). Pain and mortality in older adults: The influence of pain phenotype. Arthritis Care & Research, 70, 236–243. 10.1002/acr.23268 [DOI] [PubMed] [Google Scholar]
  35. Tang NKY, Goodchild CE, Sanborn AN, Howard J, & Salkovskis PM (2012). Deciphering the temporal link between pain and sleep in a heterogeneous chronic pain patient sample: A multilevel daily process study. Sleep, 35, 675–687. 10.5665/sleep.1830 [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. van der Leeuw G, Ayers E, Leveille SG, Blankenstein AH, van der Horst HE, & Verghese J (2018). The effect of pain on major cognitive impairment in older adults. Journal of Pain, 19, 1435–1444. 10.1016/j.jpain.2018.06.009 [DOI] [PubMed] [Google Scholar]
  37. Vitiello MV, McCurry SM, Shortreed SM, Baker LD, Rybarczyk BD, Keefe FJ, & Von Korff M (2014). Short-term improvement in insomnia symptoms predicts long-term improvements in sleep, pain, and fatigue in older adults with comorbid osteoarthritis and insomnia. PAIN®, 155, 1547–1554. 10.1016/j.pain.2014.04.032 [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Wang J, Dietrich MS, Simmons SF, Cowan RL, & Monroe TB (2018). Pain interference and depressive symptoms in communicative people with Alzheimer’s disease: A pilot study. Aging & Mental Health, 22, 808–812. 10.1080/13607863.2017.1318258 [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Wood JL, Weintraub S, Coventry C, Xu J, Zhang H, Rogalski E, Mesulam M-M, & Gefen T (2020). Montreal cognitive assessment (MoCA) performance and domain-specific index scores in amnestic versus aphasic dementia. Journal of the International Neuropsychological Society, 26, 927–931. 10.1017/S135561772000048X [DOI] [PMC free article] [PubMed] [Google Scholar]

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