Markers of poor sleep quality increase sedentary behavior in college students as derived from accelerometry

Harmonization of three different accelerometers to classify the 24 h activity cycle

Increasing interest in measuring key components of the 24 h activity cycle (24-HAC) [sleep, sedentary behavior (SED), light physical activity (LPA), and moderate to vigorous physical activity (MVPA)] has led to a need for better methods. Single wrist-worn accelerometers and different self-report instruments can assess the 24-HAC but may not accurately classify time spent in the different components or be subject to recall errors.Objective. To overcome these limitations, the current study harmonized output from multiple complimentary research grade accelerometers and assessed the feasibility and logistical challenges of this approach.Approach. Participants (n= 108) wore an: (a) ActiGraph GT9X on the wrist, (b) activPAL3 on the thigh, and (c) ActiGraph GT3X+ on the hip for 7-10 d to capture the 24-HAC. Participant compliance with the measurement protocol was compared across devices and an algorithm was developed to harmonize data from the accelerometers. The resulting 24-HAC estimates were described within and across days.Main results. Usable data for each device was obtained from 94.3% to 96.7% of participants and 89.4% provided usable data from all three devices. Compliance with wear instructions ranged from 70.7% of days for the GT3X+ to 93.2% of days for the activPAL3. Harmonized estimates indicated that, on average, university students spent 34% of the 24 h day sleeping, 41% sedentary, 21% in LPA, and 4% in MVPA. These behaviors varied substantially by time of day and day of the week.Significance. It is feasible to use three accelerometers in combination to derive a harmonized estimate the 24-HAC. The use of multiple accelerometers can minimize gaps in 24-HAC data however, factors such as additional research costs, and higher participant and investigator burden, should also be considered.

The impact of the COVID-19 pandemic on physical activity habits at a residential university

OBJECTIVE

To assess changes in physical activity (PA) after a COVID-19 shutdown on a primarily residential university campus.

METHODS

Eighty students, faculty, and staff (FS) of a university (age: 32.2 ± 13.6 yr) who wore a consumer wearable technology (CWT) device completed an anonymous survey by inputting data for 30 days prior to- and 30 days following an academic break in 2020, in which the university transitioned to remote learning.

RESULTS

Steps decreased after spring break in all subjects (p < .001), but steps were impacted to a greater extent in students. 30-day, weekday, and weekend step averages all decreased in students (p < .001). FS were able to maintain their weekend step averages.

CONCLUSIONS

PA decreased in a university community after the COVID-19 shutdown. Students, no longer active transport for campus life, saw a greater impact on their PA. These changes could have an impact on health status.

Relationship of sleep regularity with device-based sedentary behavior time and physical activity time in working adults

OBJECTIVES

This study aimed to investigate the association of sleep regularity with time spent in sedentary behavior (SB) and physical activity (PA) in adults with full-time jobs.

METHODS

This was a cross-sectional study. Adults aged 21-64 years with full-time jobs were recruited between August 2019 and December 2020 in Taiwan. The time spent in SB/PA was assessed using triaxial accelerometers (Actigraph wGT3x-BT), and PA was further classified into light-intensity physical activity (LPA) and moderate-to-vigorous intensity physical activity (MVPA). Each participant recorded their daily sleep patterns and work hours in a log, which was further used to measure sleep variability and social jet lag. Linear regression was applied to examine the associations of indicators of sleep regularity with SB time and PA time.

RESULTS

A total of 192 adults (men = 28.13%; mean age = 38.56 ± 8.89 years) were included in this study. After adjusting for potential covariates, greater social jet lag was related to more SB time (unstandardized coefficient [B] = 14.39, P = .005) and less LPA time (B = -0.02, P = .010). No evidence of an association between other indicators of sleep regularity with SB/PA time was found.

CONCLUSIONS

These results provide evidence for a relationship between sleep regularity and SB/PA time in working adults. Maintaining regular sleep, especially a small social jet lag, was suggested to promote physical activity and avoid a sedentary lifestyle. Future research is recommended to examine work-related influences on the associations and bidirectional relationship between sleep regularity and SB/PA.

Independent and Combined Associations between Physical Activity and Sedentary Time with Sleep Quality among Chinese College Students

Objective: To investigate the independent and combined associations between physical activity and sedentary time with sleep quality among Chinese college students. Methods: A cross-sectional study was conducted among Chinese college students (N = 2347; M age = 20 years; 67.1% students were female). We used the International Physical Activity Questionnaire—Short Form, the Adolescent Sedentary Activity Questionnaire, and the Pittsburgh Sleep Quality Index to assess the subjects’ physical activity level, sedentary time, and sleep quality, respectively. Analyses were conducted using a multivariate logistic regression model. Result: Of the total participants, 48.6% had poor sleep quality and 10% were at low physical activity levels, and the mean (±SD) sedentary time was 5.33 ± 2.34 h/day. No significant association was found between physical activity and sleep quality (OR = 1.27, 95% CI: 0.95~1.70) among Chinese college students after adjustment for potential confounders. Sedentary time was significantly positively associated with poor sleep quality (OR = 1.37, 95% CI: 1.14~1.65). The risks for those with a low physical activity level and high sedentary time, and a moderate physical activity level and high sedentary time were 2.78 (OR = 2.78, 95% CI: 1.61~4.80) and 1.49 (OR = 1.49, 95% CI: 1.13~1.95) times higher, respectively, than those with a high physical activity level and low sedentary time. Conclusion: Among Chinese college students, high sedentary time was significantly negatively associated with sleep quality. A low physical activity level was insignificantly associated with sleep quality. A moderate physical activity level and high sedentary time, and a low physical activity level and high sedentary time were interactively associated with increased risks of poor sleep quality, respectively.

An Exploration of Domain-Specific Sedentary Behaviors in College Students by Lifestyle Factors and Sociodemographics

College students exhibit high levels of sedentary time and/or poor lifestyle factors (e.g., poor sleep, stress, physical inactivity). It is unknown; however, in what domains college students spend their sedentary time and whether there are associations between sedentary time and these lifestyle factors. This study examined sedentary behavior of college students by domains, current lifestyle factors and sociodemographics. Undergraduates (n = 272, M age = 20 years, 79% female) self-reported their sedentary behavior, sleep, stress, physical activity, anthropometrics and sociodemographics. Sedentary time was categorized as: total, recreational screen, education and social. Students reported spending > 12 h of their day sedentary on average, with over a third of this time spent in recreational screen time. All categories of sedentary time were significantly correlated with body mass index, and both total sedentary time and screen time were significantly correlated with sleep score, with poorer sleep quality associated with greater sedentary time. Physical activity was negatively correlated with social sedentary time only. Subgroups with elevated sedentary time included minority students, those with low parental education and students with overweight/obesity. Given the negative health impacts of sedentary behavior, college students would likely benefit from interventions tailored to this population which target reducing sedentary time, particularly recreational screen time.

Relationships between College Students' Sedentary Behavior, Sleep Quality, and Body Mass Index

PURPOSE

Sedentary behavior (SB), sleep efficiency (SE), sleep duration (SD), and body mass index (BMI) are crucial determinants of an individual's health. However, empirical evidence regarding associations between these factors in young adults living in China remains unknown. Therefore, the purpose of this study was to examine the relationships between accelerometer-measured SB, SE, SD, and BMI in Chinese college students.

METHODS

Two-hundred and twenty college students (115 females, Meanage = 20.29 years, SD = 2.37) were recruited from a south-central Chinese university. Participants' SB (daily % time spent in SB), SE (number of minutes of sleep duration/number of minutes in bed), and SD were assessed via wrist-worn ActiGraph GT9X Link accelerometers for one week. Body weight was measured using a digital weight scale, height was measured using a stadiometer, and BMI was calculated as weight (kg)/height (m2).

RESULTS

Participants' average time spent in SB was 76.52% (SD = 10.03), SE was 84.12% (SD = 4.79), and BMI was 20.67 kg/m2 (SD = 3.12), respectively. Regression analyses indicated that SB (β = -0.17, p = 0.01) and BMI (β = -0.20, p < 0.01) negatively predicted SE. In addition, BMI negatively predicted SD (β = -0.22, p < 0.01).

CONCLUSION

Prolonged SB (e.g., screen viewing, smartphone use, and computer playing) and higher BMI may link to shorter sleep duration and lower sleep efficiency in Chinese young adults. Future randomized controlled trials are needed to further confirm these findings. Given that increased BMI status and SB may relate to adverse health outcomes, more population-based intervention strategies seeking to lower BMI and reduce SB (e.g., nutrition education and physical activity promotion) are needed in this population.