Multilevel models for exploring the effects of daytime physical

Multilevel models were fit to explore the effect of physical activity (overall level of physical activity, running, walking, standing, sitting, lying down) on subsequent sleep (SQ, SE, TST). Table 5.5 presents a summary of the results for these models.

As presented in Table 5.5, the overall level of physical activity and the amount of time spent running, walking, standing, sitting and lying down were not significant predictors of subsequent SQ and SE. Among all predictors, the overall level of physical activity (p = 0.028), the amount of time spent running (p = 0.021) and lying down (p = 0.0008) were independently found to be significant predictors of TST. Those who reported lower levels of physical activity, less time running and more time lying down during the day were also those who reported longer total sleep time during the night; those who reported higher levels of physical activity, more time running and more time lying down during the day were also those who reported shorter total sleep time during the night. Intuitively, this suggests a simple reciprocity of opportunity such that the less time people being active during the day (e.g., lower overall level of physical activity, more lying down), the longer they sleep during the night. Figure 5.2 presents results summary of the multilevel models for exploring the relationship from last night’s sleep and next day physical activity, and from daytime physical activity to nighttime sleep.

Table 5.5Results of the multilevel models for exploring the effects of daytime physical activity on subsequent sleep

LRT

Outcome Predictor Fixed

coefficient -Log likelihood P- value AIC SQ C 6.644 1594 n.a 3197.7 C + Level of PA 6.649, -0.001 1594 0.971 3199.7 C + Running 6.634, 0.006 1594 0.832 3199.6 C + Walking 6.636, 0.001 1594 0.957 3199.7 C + Standing 6.688, -0.011 1594 0.703 3199.5 C + Sitting 6.407, 0.038 1594 0.243 3198.3 C + Lying down 6.801, -0.042 1593 0.136 3197.5 SE C 93.175 3010 n.a 6029.3 C + Level of PA 93.198, -0.004 3010 0.978 6031.3 C + Running 93.273, -0.069 3010 0.685 6031.1 C + Walking 94.495, -0.264 3009 0.096 6028.5 C + Standing 92.329, 0.212 3009 0.189 6029.6 C + Sitting 91.610, 0.251 3009 0.167 6029.4 C + Lying down 93.586, -0.11 3010 0.478 6030.8 TST C 444.688 5092 n.a 10192.1 C + Level of PA 467.986, -4.534 5089 0.028* 10189.3 C + Running 451.531, -4.836 5089 0.021* 10188.8 C + Walking 452.112, -1.488 5091 0.454 10193.5 C + Standing 446.509, -0.456 5092 0.822 10194.1 C + Sitting 434.681, 1.610 5091 0.474 10193.6 C + Lying down 420.850, 6.441 5086.5 0.0008*** 10183.0 Notes: LRT= Likelihood ratio test. *p < 0.05. **p < 0.01. ***p < 0.001. - Log

likelihood= the negative log maximum likelihood. C= Constant. Level of PA= Level of physical activity. SQ= sleep quality. SE= Sleep efficiency. TST= Total sleep time. n.a= Not available.

Figure 5.2 Results summary of the multilevel models for exploring the relationship from last night’s sleep and next day physical activity (dotted arrows), and from daytime physical activity to nighttime sleep (solid arrows).

SQ= Sleep quality. TST= Total sleep time.

+/- = Indication of the relationship. “Nighttime”/ “Daytime”= Timeframe of sleep and physical activity. *p< 0.05. **p< .01. ***p< .001.

5.4 Discussion

Multilevel models were fitted to explore the within-person temporal relationship between different indices of nighttime sleep and daytime physical activity in 118 healthy young adults. The findings did not find significant associations between sleep and overall level of physical activity. However, there were significant associations between sleep and different types of physical activity. When physical activity was categorised by type, individuals who had worse sleep quality the previous night spent more time lying

down the next day and, those who spent longer time lying down during the day had longer total sleep time on the subsequent night. Further, those who had shorter total sleep time spent more time walking the next day and those who spent less time on overall level of physical activity and running during the day had longer total sleep time the subsequent night. Although the findings reflect a bidirectional and temporal relationship between sleep and physical activity in healthy young adults, the findings suggest a possible different role for sleep quality and total sleep time in influencing physical activity the next day.

The significant associations of different types of sleep (i.e., sleep quality and total sleep time) and types of physical activity (i.e., walking, lying down) are consistent with the findings of the previous studies that examined within-person association between sleep and physical activity among individuals with and without bipolar disorder (McGlinchey et al., 2014) and pain-free older adults (Dzierzewski et al., 2014). However the findings of the present study did not find significant association between perception of poor/better sleep quality and overall level of physical activity. A possible explanation for this might be that the effect faded when all kinds of physical activity were aggregated. It is interesting to note that in the present study, poor sleep quality predicted subsequent sedentary behaviour that was lying down but not overall level of physical activity and dynamic physical activity (i.e., running). This may be explained by the fact that sedentary behaviours and dynamic physical activity could be influenced by qualitatively different factors. Using self-administered questionnaire, Burton, Turrell, Oldenburg, and Sallis (2005) conducted a survey to examine the relative contributions of

psychological, social and environmental variables to different types of physical activity (e.g., walking, total activity, moderate- and vigorous- intensity physical activity) among 1827 adults aged 18 to 65 years old. They found that physical health, anticipated competitiveness, and time management barriers contributed more to the vigorous- intensity physical activity. Meanwhile the neighbourhood environment such as perceived safety and ambiance contributed more to walking. In addition, results from Schmid et al.’s study (2009) demonstrated that healthy individuals who experienced sleep impairment were more likely to lower the intensity of physical activity levels and reduce the daytime physical activity. Taken together, potentially different kinds of physical activity appear to systematically vary in terms of contexts, demands, or characteristics.

Surprisingly, the significant positive association between lying down and total sleep time is not expected. According to the Two-process Model of Sleep Regulation (Borbely, 1982), the homeostatic sleep drive is accumulated throughout the day. Lying down (i.e., a state of inactivity) during the day would weaken the homeostatic sleep drive and disrupt circadian rhythm such as decreasing in the light exposure and irregular of rest-activity cycles and physical activity (Van Someren & Riemersma-Van Der Lek, 2007; Youngstedt, Kripke, & Elliott, 2002). Severe reduction in homeostatic sleep drive and a shift in circadian rhythms could influence total sleep time (Dijk, Duffy & Czeisler, 2000). Gellis, Park, Stotsky, and Taylor (2014) reported that it was common among university students to have irregular sleep-wake schedule and use bed for activities other than sleep such as reading and watching television in bed. In addition, as a group,

the characteristics of participants in the present study reflect typical sleep pattern of this age group with longer total sleep time (≥7 hours), higher sleep efficiency, shorter sleep onset latency and less awakenings during the night. Groeger et al. (2004) reported that individuals aged 16 to 24 years slept more than 7 hours every night. Hence, the long duration of total sleep time might be related with the participants’ developmental sleep need following greater time spent lying down in the day (Hirshkowitz et al., 2015).

With regard to sleep efficiency, the findings indicated that sleep efficiency was not a significant predictor of daytime physical activity the next day and none of the daytime physical activity variables predicted sleep efficiency the subsequent night. This was unexpected, as sleep efficiency has been commonly considered an indicator of sleep continuity and correlated with subjective sleep quality (Akerstedt et al., 1994; Kaplan et al., 2017; Keklund & Akerstedt, 1997). This finding is contradictory to that of Lambiase et al.'s (2013), in which a significant temporal association between greater sleep efficiency (derived from actigraph) and greater physical activity the next day among older women was found. There are several possible explanations for the non- significant finding in the present study. First, it seems possible that there was a qualitative difference between different types of sleep indices (i.e., sleep efficiency, sleep quality, total sleep time). For example, perception of sleep quality might carry a stronger influence on physical activity the next day (Tang & Sanborn, 2014). Individuals may retrospectively perceive their sleep experience based on their feeling on waking in the morning such as feeling “unrefreshed” and “tired” which in turn decreases their subsequent physical activity. Argyropoulos et al. (2003) demonstrated that there was a

significant relationship between perception of sleep quality and feelings on waking in 40 patients with depression. Second, the relationship between sleep efficiency and physical activity might be confounded by mood. McCrae et al. (2008) showed that nights with worse sleep quality were followed by days with more negative affect and less positive affect in older adults (aged ≥60 years). However, morning mood and morning pain were non-significant predictors in the temporal relationship between sleep and physical activity among heterogeneous patients with chronic pain (Tang & Sanborn, 2014). Smith et al. (2009) suggested that sleep impairment might interact with multiple mechanisms to influence physical activity. Further studies, which take mood, pain and other psychological variables such as tiredness, motivation and fatigue into account, will need to be undertaken.

Strengths and limitations

A key strength of this study is its daily process design that involved repeated monitoring of sleep and physical activity at specific times over seven days for time- lagged data analysis. This study was specifically designed for participants to monitor their sleep and physical activity in their own natural sleeping and living environment in which has the advantage for more ecological validity of the findings. Therefore participants were free to carry out their daily activity outside the controlled laboratory setting and did not require alteration of their daily activity and sleep. However, there are several limitations to this present study. First, although the advantage of paper-and- pencil physical activity diary was that it was inexpensive, easy to administer and easily

accessible, the limitation was that it did not provide information or data on the hour of the day when the activities were performed continuously. In addition, it lacks of objectivity and precision. Future studies should consider using objective assessment in addition to subjective assessment to maximise the recording precision of the daily data over an extended time period with less intrusion and minimal discomfort for the participants. For example the use of a physical activity-monitoring recorder could help minimise participants’ recall bias as it could automatically detect participants’ movement. Second, the extent to which the results can be generalised to the clinical population such as patients with chronic pain or treatment seeking individuals remains to be determined in future research as the current study was carried out in healthy young adults drawn from a university setting. Nevertheless, it should be noted that the present sample reflected findings that were free from the influence of medical condition or medication.

Besides, physical activity pattern in pain-free individuals might be different from individuals with chronic pain (e.g., Spenkelink, Hutten, Hermens, & Greitemann, 2002; van den Berg-Emons et al., 2007). Spenkelink et al. (2002) found that patients with chronic low back pain demonstrated a lower physical activity level specifically in the evening compared to pain-free individuals. Tang and Sanborn (2014) also found that there was a gradual decline in physical activity from 4.00pm to 4.00am in a heterogeneous chronic pain sample. But it is important to note that the gradual decline was observed within the context of human circadian rhythm and there was no pain-free control group as comparison. Finally, whilst a minimum of 7 days physical activity

monitoring is a substantive period of time to ensure a reliable estimate of regular physical activity pattern (Trost, Pate, Freedson, Sallis, & Taylor, 2000), a longer monitoring period would be important for future study to allow for adaptation particularly if it is being conducted in clinical patients as they could have different physical activity pattern such as increased activity fluctuations compared to healthy individuals (Verbunt, Huijnen, & Seelen, 2012). Patients with chronic pain’s daily physical activity levels have often been described as a saw-tooth pattern (Hasenbring et al., 2006; Verbunt et al., 2012). The “saw-tooth pattern” refers to the huge between-day variation in physical activity among patients with chronic pain.

Conclusion

In conclusion, the findings of the present study highlight the presence of a potential temporal relationship between sleep and physical activity at the within-person association. Specifically, the findings revealed that nights of worse sleep quality were followed by days of greater time spent on lying down, which subsequently followed by nights of longer total sleep time. However these findings may need to be verified with the objective measure. The next crucial step is to replicate the present study in people with chronic pain and correct the limitations discussed. These methodological improvements will be discussed in Chapter 6.