# Actimetry-Derived 24 h Rest-Activity Rhythm Indices Applied to Predict MCTQ and PSQI
## 4 Discussion  
This study assessed the relationship between the wrist acitometry-derived 24 h rhythm of motor activity indices and the sleep-wake rhythm characteristics obtained by a questionnaire survey. Parametric (MESOR, \(A\), and \(\varphi\)) and nonparametric (IS, IV, L5, L5t, M10, M10t, AcoB, and AciB) indices were used as the objective indicators of the 24 h rhythm of motor activity. The indicators obtained using the MCTQ (MSFc, SJL, SID, SIE) and PSQI global score were used as the subjective indicators of the sleep-wake rhythm and sleep quality. In general, a closer relationship was noted between the nonparametric indicators of the 24 h rhythm of motor activity and the sleep-wake rhythm characteristics. According to our data, the IS, L5t, and M10t were predictors of the MSFsc and SID, and the AciB was a predictor of SJL and the PSQI global score (Figure 3). Of the parametric indicators, only \(\varphi\) showed a significant association with the SIE. Based on this, it can be concluded that the nonparametric indicators of the 24 h rhythm of motor activity are more suitable for assessing the state of the circadian system. Therefore, in the further development of the circadian misalignment concept and its contribution to various pathologies, it is necessary to take this circumstance into account. In previous studies, nonparametric indicators of the 24 h rhythm of motor activity were actively used to assess the state of the circadian system in patients with chronic diseases. It has been shown that women with breast cancer have significantly higher values of IV [43] and low values of the IS [44], M10 [43; 44], and L5 [43]. Low IS values are associated with elevated blood pressure and a higher incidence of arterial hypertension [45]. High IV values were observed in individuals with bipolar disorders [46]. Low IS values and high IV values are associated with increased mortality [47]. The parametric indicators of the 24 h rest-activity rhythm have been used relatively less frequently. A decrease in the MESOR and \(A\) values was observed in women with breast cancer [43; 44] and in people suffering from a binge eating disorder [48]. In [49], it was suggested that when approximating a time series of motor activity using the Cosinor method, a significant part of the trait variability was considered as unexplained. However, from our point of view, a more fundamental reason for the low sensitivity of the parametric indicators is that the 24 h rest-activity rhythm itself is a complex integral indicator, which, along with the circadian system, is associated with a number of other external and internal factors. As an output, activity is non-sinusoidal and incorporates mapping components that vary in timing and duration \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model \#** & **Dependent Variable** & **Predictors** & \(B\) & \(\beta\) & \(p\) & \(\Delta R^{2}\) \\ \hline
1 & MSFsc & M10t & 0.252 & 0.278 & 0.015 & 0.077 \\
2 & MSFsc & L5t & 0.338 & 0.316 & 0.005 & 0.100 \\
3 & MSFsc & IS & \(-\)0.021 & \(-\)0.245 & 0.017 & 0.060 \\
4 & SJL & AciB & 0.320 & 0.241 & 0.032 & 0.145 \\
5 & SID & M10t & \(-\)0.257 & \(-\)0.322 & 0.003 & 0.080 \\
6 & SID & L5t & \(-\)0.340 & \(-\)0.361 & 0.001 & 0.105 \\
7 & SID & IS & 0.042 & 0.313 & 0.003 & 0.093 \\
8 & SIE & \(\varphi\) & \(-\)0.043 & \(-\)0.308 & 0.007 & 0.095 \\
9 & PSQI & AciB & 0.990 & 0.293 & 0.013 & 0.169 \\ \hline \hline \end{tabular}  
* A series of multiple regression analyses were performed using ‘sleep-wake rhythm characteristics (MSFc, SJL, SID, SIE, PSQI)’ as dependent variables and ‘24 h rest-activity rhythm characteristics (MESOR, \(A\), \(\varphi\), M10t, 2 + In(L5), L5t, IS, IV, AcoB, 1 + In(AciB))’ as independent variables (predictors) adjusted for ‘age’, ‘sex’, and ‘BMI’; a stepwise inclusion procedure was used; \(B\): non-standardized regression coefficient; \(\beta\): standardized regression coefficient; \(p\): significance of \(B\); \(\Delta R^{2}\): portion of the variance accounted for by separate predictors in the model.  
\end{table}