Bidirectional relationship between sleep and sedentary behavior in adults with overweight or obesity: A secondary analysis

Effects of daily sleep on physical activity after cardiac surgery

BACKGROUND

Maintaining physical activity is challenging after cardiac surgery. Postsurgical cardiac patients often experience sleep problems showing a reciprocal interaction with physical activity. As sleep and physical activity show day-to-day variations, their daily relationships need to be assessed. However, no studies have examined daily sleep-physical activity relationships in postsurgical cardiac patients.

OBJECTIVES

This study aimed to examine the effects of daily sleep factors on daily physical activity after cardiac surgery.

METHODS

Among 33 patients who underwent cardiac surgery at least 10 weeks earlier, 5 sleep and 4 physical activity variables were measured using a wrist-worn ActiGraph for 7 days. Mixed-effects models were applied for data analyses.

RESULTS

Most participants were male (57.6 %), non-Hispanic whites (63.6 %) who had coronary artery bypass graft surgery (54.6 %). Participants averaged 60.8 ± 10.1 years of age and 85.7 ± 91.2 months since surgery. They slept for an average of 385.6 ± 74.6 min (6.4 ± 1.2 h). Among sleep factors, greater number of awakenings (NOA) predicted lower next-day sedentary time. Higher sleep efficiency (SE) was associated with lower next-day sedentary time when not controlling for covariates. Among the psychosocial, demographic, and clinical covariates, higher comorbidity index was associated with fewer kcals expended, less daily moderate-to-vigorous physical activity, and more daily sedentary time.

CONCLUSIONS

Daily SE and NOA and individual health status, including comorbidity, should be assessed over time to support improvement of daily physical activity after cardiac surgery. Researchers should examine the relationship between NOA and next-day sedentary time with larger samples. Such research should address multiple psychosocial, demographic, and clinical factors and the potential mediating role of sleep.

Temporal associations between nightly sleep with daytime eating and activity levels in free-living young adults

STUDY OBJECTIVES

This study aimed to quantify the temporal associations between nightly sleep quantity and timing with daytime eating behavior and activity levels in free-living (i.e. non-experimental) settings.

METHODS

Generally healthy young adults (N = 63; 28.9 ± 7.1 years) completed concurrent sleep (wrist actigraphy), eating (photo-assisted diet records), and activity (waist actigraphy) assessments over 14 days. Multilevel models quantified the associations between nightly sleep (total sleep time, timing of sleep and wake onset) with next-day eating behavior (diet quality, caloric intake, timing of eating onset/offset, eating window duration) and activity levels (total physical activity, sedentary time). Associations in the reverse direction (i.e. eating and activity predicting sleep) were explored. Models adjusted for demographic and behavioral confounders and accounted for multiple testing.

RESULTS

At within- and between-subject levels, nights with greater-than-average total sleep time predicted a shorter eating window the next day (all p ≤ 0.002). Later-than-average sleep and wake timing predicted within- and between-subject delays in next-day eating onset and offset, and between-subject reductions in diet quality and caloric intake (all p ≤ 0.008). At within- and between-subject levels, total sleep time was bidirectionally, inversely associated with sedentary time (all p < 0.001), while later-than-average sleep and wake timing predicted lower next-day physical activity (all p ≤ 0.008).

CONCLUSIONS

These data underscore the complex interrelatedness between sleep, eating behavior, and activity levels in free-living settings. Findings also suggest that sleep exerts a greater influence on next-day behavior, rather than vice versa. While testing in more diverse samples is needed, these data have potential to enhance health behavior interventions and maximize health outcomes.

Sleep, physical activity, and psychological outcomes in children and adolescents with pediatric onset multiple sclerosis

BACKGROUND

Sleep, physical activity (PA) and sedentary behavior (SED) have bidirectional associations with mental health in children. The relationships among sleep, PA, SED, with depressive and fatigue symptoms have not been investigated in Pediatric Onset Multiple Sclerosis (POMS) but are needed to inform sleep and PA behavior change interventions.

OBJECTIVES

(1) To describe sleep quality including: sleep efficiency, latency, total sleep time, number of awakenings, time in bed, and wake after sleep onset using actigraphy in children and adolescents ages 11 to 18 diagnosed with POMS, and to compare these sleep metrics to those of an age- and sex-matched non-MS group (2) To examine the relationship between time spent in sedentary, light (LIPA), moderate and vigorous PA (MVPA), sleep quality, with depression, fatigue, and quality of life in children and adolescents with POMS and an age and sex matched non-MS group.

METHODS

A cross-sectional study recruited children and adolescents with POMS ages 11 to 18 years followed at a tertiary pediatric hospital (Toronto, Canada) and an age and sex matched non-MS group from the general population. Participants were consented prior to initiation of study procedures. Participants wore an Actiwatch monitor and GT3X accelerometer and completed standardized questionnaires validated to capture data on sleep disturbances, depression, fatigue, and quality of life. Objective sleep data were collected using an Actiwatch including sleep efficiency, total sleep time, number of awakenings, wake after sleep onset (WASO), and sleep latency. A GT3X accelerometer was used to collect PA data including time spent in SED, light (LPA), and moderate to vigorous (MVPA) PA. Correlational analyses and tests of difference were used to compare the groups.

RESULTS

25 POMS (21F; 16.6 years ±1.1 yrs., median Expanded Disability Status Scale (EDSS) =1.5, IQR=1) and 25 Non-MS (22 F; 16±1.3 yrs.) took part. POMS had higher BMI (T= -5.1, P<0.001) compared to Non-MS. No differences in sleep efficiency (MS mean = 87%, vs. 88%) sleep time (MS Mean = 7.3 hrs. vs. 7.4 hrs.,), WASO (MS mean=37 mins. vs. 36 mins), latency (MS mean=15 mins vs. 11 mins), SED (MS mean =763 mins. vs. 730 mins) or PA (MS, mean LPA = 68 mins. vs 60 mins; MS mean MVPA = 12.7 mins. vs. 12.4 mins). Within POMS, higher sleep efficiency was associated with more SED (SR= 0.4, p = 0.05), while higher sleep efficiency was associated with less SED in Non-MS (SR = -0.7, p< 0.0). In children with POMS, less sleep time, shorter sleep onset latency and more WASO was associated with more SED (SR range = -0.45 to -0.58, P< 0.01). Higher sleep efficiency was associated with less fatigue. Less WASO was associated with lower depression, lower fatigue (SR = 0.67, p<0.01) and better quality of life (SR= -0.6, p<0.01). Greater LPA was associated with lower sleep onset latency (-0.45, p<0.05).

CONCLUSIONS

Children with POMS did not differ in Actiwatch monitored sleep quality metrics. However, within the POMS group sleep quality was associated with better fatigue, depression and QOL. Further, total sleep time, WASO and latency associated with time spent SED and LPA, which independently associate with mental health outcome. Longitudinal work should determine the temporal associations between WASO, sleep latency, sleep time, PA, and mental health outcomes and whether reallocation of specific sleep or PA behaviors (time to sleep, total sleep time, sedentary to MVPA) result in improved depression fatigue, or quality of life in children and adolescents with POMS.

Sedentary behavior and cardiovascular disease risk: An evolutionary perspective

A ubiquitous aspect of contemporary societies is sedentary behavior (SB), defined as low intensity activities in a seated, reclined, or supine posture. Leading public health agencies, including the World Health Organization, have recognized the strong association between SB and poor health outcomes, particularly cardiovascular disease. However, while public health agencies have begun to advocate for “reductions” in SB, the current US guidelines are typically vague and non-specific. There is good reasoning behind this non-committal advocacy—there is limited mechanistic and clinical evidence to support policy development. To guide SB policy development, it is important to first consider the origins and evolution of SB, including the following: 1) is SB really a novel/contemporary behavior? i.e., how has this behavior evolved? 2) how did our ancestors sit and in what contexts? 3) how does SB interact with 24-hour activity behaviors, including physical activity and sleep? 4) what other historical and contemporary facets of life interact with SB? and 5) in what context do these behaviors occur and how might they provide different evolutionarily novel stressors? This perspective article will synthesize the available evidence that addresses these questions and stimulate discussion pertaining to the lessons that we can learn from an historical and evolutionary perspective. Last, it will outline the gaps in current SB interruption literature that are hindering development of feasible SB reduction policy.

Bi-Directional, Day-to-Day Associations between Objectively-Measured Physical Activity, Sedentary Behavior, and Sleep among Office Workers

The bi-directional, day-to-day associations between daytime physical activity and sedentary behavior, and nocturnal sleep, in office workers are unknown. This study investigated these associations and whether they varied by weekday or weekend day. Among 324 Swedish office workers (mean age 42.4 years; 33.3% men), moderate-to-vigorous physical activity (MVPA), and sedentary behaviors and sleep (total sleep time (TST) and sleep efficiency (SE)) were ascertained by using accelerometers (Actigraph GT3X) over 8 days. Multilevel linear mixed models were used to assess the bi-directional, day-to-day, within-person associations. Additional analyses stratified by weekend/weekday were performed. On average, participants spent 6% (57 min) of their day in MVPA and 59% (9.5 h) sedentary, and during the night, TST was 7 h, and SE was 91%. More daytime sedentary behavior was associated with less TST that night, and reciprocally, more TST at night was associated with less sedentary behavior on the following weekday. Greater TST during the night was also associated with less MVPA the next day, only on weekdays. However, daytime MVPA was not associated with TST that night. Higher nighttime SE was associated with greater time spent sedentary and in MVPA on the following day, regardless if weekday or weekend day. Sleep may be more crucial for being physically active the following day than vice versa, especially on weekdays. Nevertheless, sedentary behavior's relation with sleep time may be bi-directional. Office workers may struggle with balancing sleep and physical activity time.