Friday 12th August 2022

Daytime sleep behaviors and cognitive performance | NSS


Plain Language Summary

People living with HIV infection (PLWH) commonly experience daytime somnolence that may adversely synergize to increase comorbidity burden, reduce the quality of life, and reduce adherence to complex medical regimens. PLWH also frequently report cognitive complications such as cognitive decline. We studied whether daytime sleep behaviors, ie, daytime sleepiness or napping, are related to cognitive performance and whether the association is more profound in PLWH. From the UK Biobank cohort, we identified 562 PLWH and extracted 562 demographically similar uninfected people. They reported daytime sleepiness and napping frequencies and completed four cognitive tests. Firstly, among all 1124 participants, those with frequent daytime sleepiness or napping behaviors had worse cognitive functions than those without. Secondly, PLWH had worse cognitive functions than their uninfected counterparts. And thirdly, the association between frequent daytime sleepiness/napping behaviors and worse cognitive performance was much stronger in PLWH than in uninfected people. These results highlight the importance of sleep health in the context of HIV infection and imply complex mechanisms among HIV infection, daytime sleepiness/napping, and cognition, which need to be investigated further. Practitioners may use frequent daytime sleepiness/napping as an early phase signal for potential cognitive complications in PLWH. The current study also helps in identifying potential targets in terms of optimizing daytime sleep for cognitive benefits should a causal link be confirmed in future studies.


The advent of effective antiretroviral therapy (ART) has substantially increased the life expectancy of people living with HIV infection (PLWH). As PLWH age, cognitive impairment becomes an increasing clinical concern. Up to 50% of older (ie, aged 60 years) PLWH experience cognitive impairment,1,2 compromising adherence to complex medical regimens, successful independent living,3,4 and overall quality of life.5,6 It is thereore a critical need to identify mid- to late-life (ie, aged 40–69 years) risk factors predisposing PLWH to cognitive impairment.7

Sleep disturbance is highly prevalent in PLWH, affecting up to 70% of this population.8 Epidemiological studies have demonstrated higher prevalence of insomnia and obstructive sleep apnea syndrome in PLWH than in the general population.8 Prior evidence has also related sleep disturbance to cognitive impairment or dementia in populations such as older adults,9–13 as well as in PLWH.14 Daytime sleepiness and daytime napping, important aspects of the 24-hour sleep-wake cycle, are common in the general older population and particularly in aging PLWH.15 Excessive daytime sleepiness and napping can be a direct consequence of nighttime sleep disturbances, as well as factors separate from nighttime sleep, such as underlying disease processes and mood disorders.16,17 While a link between excessive daytime sleepiness or daytime napping and adverse health consequences including cognitive outcomes in the general population has been reported,16,18–21 such associations in aging PLWH have not been directly addressed.

We aimed to investigate: (1) whether poor daytime sleep behavior (DSB) burden (ie, more frequent daytime sleepiness or napping) is associated with worse cognitive performance in both PLWH and uninfected people, (2) whether PLWH have worse cognitive performance than uninfected people, and (3) whether HIV infection exacerbates the association between DSBs and cognition.

Materials and Methods

Study Design and Participants

This case-control study was based on individual-level data from the UK Biobank cohort.22 More than 500,000 middle- to older-aged participants (ie, aged 40–69 years) were recruited between 2006 and 2010 from 22 assessment centers across the United Kingdom. PLWH were identified at study baseline based on (1) HIV-1 antigen test (if seropositive; the tests cover 9,690 participants), (2) the International Classification of Disease version 10 (ICD-10; if with codes: B20-B24, F02.4, O98.7, R75, Z11.4, Z20.6, Z21, Z71.7, Z83.0, or with the keyword “HIV”; ICD records cover 437,506 participants), or (3) self-reported non-cancer illness (if with code 1439 or with keyword “HIV” or “AIDS”; this self-reported record covers 386,094 participants). The remaining UK Biobank participants who did not meet the above criteria were considered uninfected people. Participants who had missing data for the exposure variable (see details below), did not complete any cognitive tests (see details below), or were diagnosed with dementia were excluded.

At baseline, 502,507 participants were assessed in the UK Biobank. Among them, 2,386 had missing data for the exposure variable, or did not complete any one of the four cognitive tests. They were excluded. Afterwards, 226 participants with dementia were further excluded. In the remaining 499,895 participants, 18 were HIV seropositive; 313 had the ICD-10 codes related to HIV infection; and 465 self-reported themselves infected with HIV. After considering overlapped cases, the total number of unique PLWH participants was 567. Five of them were further excluded because of missing data in the variables used for propensity score (PS) matching (see details in “Extraction of matched uninfected people”), resulting in 562 PLWH included in the following analysis. From the remaining participants without identified infection, 562 matched uninfected people were extracted. Table 1 summarizes the covariates used in the PS model, as well as the exposure, and the outcome variables between the two groups. Supplemental Table 1 summarizes the additional three covariates, ie, education level, smoking behavior, and alcohol consumption behavior between the two groups.

Table 1 Demographics and Other Covariates Involved in the Propensity Score Matching, and Exposure and Outcome Variables for PLWH and Matched Uninfected People

Ethical approval for the UK Biobank study was received from the Northwest Multi-Center Research Ethics Committee. All participants provided written informed consent through electronic signature at study baseline. Data were collected and deidentified by the UK Biobank staff; they were transferred to and analyzed at the Brigham and Women’s Hospital. The Mass General Brigham institutional review board approved this specific study protocol with a final decision of not human subject study.

Extraction of Matched Uninfected People

To extract matched uninfected people, a PS model predicting PLWH based on age, sex, ethnicity, social-economic status, and comorbidities (ie, mental behavioral disorders, diseases of the nervous system, and diseases of the circulatory system) was developed. For each PLWH, an uninfected person was extracted based on the nearest neighbor of PS without replacement.

Ethnicity was dichotomized as white (those who reported “British”, “Irish”, or “any other white background”) or non-white. Social-economic status was based on Townsend deprivation index (TDI)23 at recruitment. The ICD-10 records were parsed to identify diseases of circulatory system (code I00-I99), mental/behavioral disorders (code F00-F99), and diseases of nervous system (code G00-G99). More detailed information regarding the ICD-10 codes and specific diseases within each of the categories can be found in Supplemental Methods. The three conditions were dichotomized as yes or no depending on whether any of these corresponding codes appeared in participants’ ICD-10 records prior to their dates of assessment. Participants who had missing data in any one of these variables were excluded prior to performing the analysis.

Assessment of Cognitive Function

Results from four cognitive tests that covered a majority of the UK Biobank participants24 were used in the current study, namely reaction time (RT) test, pairs matching test (PT), fluid intelligence (FI) test, and prospective memory (PM) test. They were administered at baseline through computerized touchscreen test batteries that were designed specifically for the UK Biobank to allow population-scale testing without examiner supervision.9,24,25

Reaction Time (RT) Test

This test was delivered as a timed test of symbol matching. It requested participants to respond with a button press when they identified the appearance of matching symbols in one pair of cards out of 12 pairs. The test score was the mean…


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