OBJECTIVES: Sleep disordered breathing (SDB) is very common in older people and is known to be associated with complaints of impaired daily functioning, including excessive daytime sleepiness and ...cognitive impairments. As part of a larger study on SDB and aging, it became possible to examine the relationship between SDB and cognition in older men and women.
DESIGN: A population‐based longitudinal study.
SETTING: In‐home interviews and home sleep recordings in the greater San Diego area.
PARTICIPANTS: Community‐dwelling people age 65 and older with high risk for SDB were originally studied from 1981 through 1985 and then followed every 2 years. Data from the 46 subjects who completed Visit 3 and Visit 4 are presented.
MEASUREMENTS: Subjects were interviewed in the home about their sleep and medical condition before each visit. Cognitive function was assessed using the Mini‐Mental State Examination (MMSE). Daytime sleepiness was based on self‐report. Objective sleep was recorded in the home and scored for sleep, apneas and hypopneas, and oximetry variables.
RESULTS: Increases in respiratory disturbance index (RDI) (P = .036) and increases in daytime sleepiness (P = .002) were associated with decreases in cognitive performance (i.e., increases in cognitive impairment). Increases in RDI were also associated with increases in daytime sleepiness (P = .012). Change in MMSE scores was therefore regressed onto changes in RDI, daytime sleepiness, age, and education, resulting in decreases in MMSE scores being associated with increases in daytime sleepiness (P = .019) but not with changes in RDI (P = .515). There was no significant relationship between changes in oxygen saturation levels and changes in MMSE.
CONCLUSIONS: The results of this study suggest that declining cognitive function is associated primarily with increases in daytime sleepiness. Although cognitive decline was also associated with increases in RDI, this association did not hold in the more inclusive model which also included variable of SDB, oximetry, sleep and subjective report. One theoretical model could suggest that any relationship between SDB and cognitive function may be mediated by the effect of SDB on daytime sleepiness. These results suggest that older patients suffering from mild to moderate SDB may benefit from the treatment of SDB, even if they are not markedly hypoxemic.
Mild sleep disordered breathing is very common in the elderly, but little is known about the course of the disorder over time. Twenty-four elderly people from a population-based study were recorded ...three times over an 8.5-year period. There were no significant changes in either apnea index or in respiratory disturbance index (RDI) over time, even when controlled for body mass index. For most subjects, there was great variability over time in the number of respiratory disturbances. The sensitivity of RDI > or = 15 at visit 1 predicting RDI > or = 15 at visit 3 was only 20%. The predictive value was 50%. Sleep disordered breathing measured at a single point in time is rather weakly predictive of the severity of breathing disorder 4-8 years later.
Digital technologies such as smartphones are transforming the way scientists conduct biomedical research using real-world data. Several remotely-conducted studies have recruited thousands of ...participants over a span of a few months. Unfortunately, these studies are hampered by substantial participant attrition, calling into question the representativeness of the collected data including generalizability of findings from these studies. We report the challenges in retention and recruitment in eight remote digital health studies comprising over 100,000 participants who participated for more than 850,000 days, completing close to 3.5 million remote health evaluations. Survival modeling surfaced several factors significantly associated(P < 1e-16) with increase in median retention time i) Clinician referral(increase of 40 days), ii) Effect of compensation (22 days), iii) Clinical conditions of interest to the study (7 days) and iv) Older adults(4 days). Additionally, four distinct patterns of daily app usage behavior that were also associated(P < 1e-10) with participant demographics were identified. Most studies were not able to recruit a representative sample, either demographically or regionally. Combined together these findings can help inform recruitment and retention strategies to enable equitable participation of populations in future digital health research.
Obstructive sleep apnea is a prevalent medical condition with potentially serious medical and psychosocial consequences. Nasal continuous positive airway pressure (CPAP) is the treatment-of-choice ...for this condition and has been shown to reduce the frequency of nocturnal respiratory events, improve sleep architecture and decrease daytime sleepiness. Patient nonadherence has been shown to be approximately 50% at one year, therefore limiting the effectiveness of nasal CPAP therapy. Previous studies examining the determinants of adherence to CPAP have limited the variables studied to disease status, patient (sociodemographic), and treatment variables, with no reliable predictors found. The current study investigated the relationship between Social Cognitive Theory (SCT) and Transtheoretical Model (TM) variables and objectively measured CPAP adherence over a one month time period. SCT variables included measures of self-efficacy, outcome expectations, social support and knowledge. TM variables included measures of stage of change, decisional balance index (a summary of the pros and cons of engaging in the behavior) and processes of change. Measures were taken at CPAP fitting, one week post-fitting, and one month post-fitting. Fifty-one patients that were prescribed CPAP as part of their clinical care at the VASDHS agreed to be studied. CPAP pressure was used as a covariate in all regression analyses. SCT measured at time 1 was not predictive (R2 = 0.330, p = 0.092), while measured at time 2 was predictive of CPAP adherence at one month (R2 = 0.234, p = 0.019). TM measured at time 1 was not predictive (R2 = 0.003, p = 0.769) while measured at time 2 was predictive of CPAP adherence at one month (R2 = 0.209, p = 0.001). The Decisional Balance Index (from TM) individually accounted for a significant amount of variance in objective CPAP adherence in the above analyses. Future behavioral interventions designed to increase CPAP adherence may prove to be effective if based on these models.
PDF
