Theoretical construct into blocks of actigraphic-derived sleep parameters

ABSTRACT Actigraphic parameters can provide indication of people’s sleep quality during their daily lives. However, there is a need for clear guidelines on the understanding of the different actigraphic parameters. The present study aims to propose a conceptual and theoretical framework for known actigraphic-derived parameters, which is able to describe the alternation between rest and wake phases during the nocturnal sleep, explaining their main characteristics and interrelations that can be replicated in future studies. Forty Sport Sciences students at the University of Milan (20 males; mean age ± SD, 22 ± 3 y) completed the validated Italian version of Morningness-Eveningness Questionnaire (MEQ) and wore an actigraph (Motion Watch 8®, Cambridge Neurotechnology, Cambridge, UK) for seven days. A framework was developed to depict the interactions between the actigraphic parameters and how they objectively describe sleep, according to which the parameters are organized into three different functional blocks related to different aspects of sleep. Correlations analyses were conducted to explore the relationships among the primary actigraphic parameters within and across the functional blocks. The proposed framework is a purely theoretical construct that provides a simple interpretation of known actigraphic parameters guiding researchers and practitioners in the use of these parameters either for research or clinical purposes.


Introduction
Sleep is an essential component of our life, whereas sleep disorders have deleterious effects on health (Gay et al. 2004;Knutsson 2003;Sack et al. 2007aSack et al. , 2007bSanthi et al. 2007). In fact, chronic insufficient sleep can result in adverse health consequences, including reduced glucose tolerance, increased blood pressure, and increased inflammatory markers (Knutson et al. 2007;Mullington et al. 2009). Sleep disorders, such as insomnia, frequent night awakenings, wandering at night, and unusual early morning awakenings, can also undermine the achievement of optimal amounts of sleep (Dowling et al. 2005).
The timing of nocturnal sleep is regulated by different neurochemical systems connected with each other, including the preoptic area and the suprachiasmatic nucleus, which receive afferences directly from retinal fibers (Buijs et al. 2003;Dijk and Lockley 2002;Sack 2009;Sothern et al. 2009). The suprachiasmatic nucleus is deemed as the main internal regulator of circadian rhythms and its degeneration can causes loss of entrainment with the daylight periodicity, the most important external synchronizer of the different rhythms (Buijs et al. 2003;Delaunay et al. 2000;Lee et al. 2000;Whitmore 2001), and desynchronization of endogenous behavioral and hormonal rhythms including the sleep-wake cycle. Hence, the endogenous rhythm of the sleep-wake cycle is, in normal conditions, synchronized with the alternation of day-night cycle, as well as other factors such as timing of meals and social routines. Such synchronization is important in order to maintain healthy sleep-wake patterns, as in fact its disruptions can lead to emergence of different sleep problems (Sack et al. 2007a(Sack et al. , 2007b. Actigraphy is a technique able to monitor these sleep-wake cycle, as well as daily motor activity (Ancoli-Israel et al. 2003;Calogiuri et al. 2013;Lehnkering et al. 2006;Paquet et al. 2007;Roveda et al. 2021Roveda et al. , 2018Roveda et al. , 2017Sadeh 2011). Compared to polysomnography, which is considered the "gold standard" for assessing people's sleep, actigraphy can conveniently monitor sleep in ecological conditions for days, weeks or even longer, providing critical information to assess a person's sleep-wake patterns (Ancoli-Israel et al. 2003). Furthermore, it represents a wieldy, objective, and cost-efficient method to monitor the sleep-wake cycle during daily life rather than in a laboratory environment (Sadeh et al. 1989).
The sleep-wake cycle, as measured by actigraphic monitoring, has an asymmetrical "square shape wave" (Barger et al. 2009;Dowling et al. 2005). In fact, a sleepwake cycle typically presents a phase of about 16 h with high and relatively frequent activity episodes, followed by a phase of about 8 h with lower and relatively infrequent activity episodes (Calogiuri et al. 2013). The periods of higher and lower activity assessed by the actigraph during the night are used to produce a series of parameters that represent the characteristics of a person's sleep. In particular, the sleep analysis allows for a night-by-night assessment of the sleep-wake patterns, generating parameters that provide a description of the phase, duration, quality of the nocturnal sleep and fragmentation of the sleep episodes (Calogiuri et al. 2013).
Actigraphy is commonly included within the diagnose of insomnia, circadian rhythms disorders and excessive sleepiness (American Sleep Disorders Association 1995). Actigraphy can also provide additional information on the specifics of sleep disorders, circadian rhythms disorders and sleep variability in patients with insomnia, information that would be difficult to obtain in any other practical way (Ancoli-Israel et al. 2003;Sadeh 2011).
Due to the increasing use of actigraphy for research and clinical purposes, guidelines for conducting and interpreting actigraphic assessments and analyses have been proposed (Ancoli-Israel et al. 2003;Calogiuri et al. 2013;Sadeh 2011). However, as debated by Smith in his 2015 letter "Why an actigraphy manual is needed," there is still a need in the literature for clear guidelines on how to interpret the detailed parametric portrait provided by actigraphy (Smith 2015). Frequently, the initial training in sleep actigraphy is focused only on the most relevant sleep parameters, whereas a more mature practitioner would benefit from a refined appreciation of the nuances of meaning of a wider parameter set. In addition, the ongoing evolution of the actigraphic technology and the presence on the market of actigraphic devices with different features may hinder the ability of the investigator to compare the results obtained in different studies (Smith 2015). To overcome this difficulty, a firmer grasp of the fundamental features of nocturnal sleep and a deeper insight into the hierarchical structure that exists among actigraphic parameters would certainly be helpful.
In the present study, our aim was to develop a purely theoretical framework conceptualizing and guiding the interpretation of known actigraphic parameters, a sort of handbook that defines main actigraphic parameters and their role in describing the quality and quantity of sleep, as well as standardize the reporting of findings. Such framework would be based on the foundational assumption that actigraphic parameters can provide an indirect evaluation of people's sleep and wake based on the presence (waking phases) or absence (rest phases) of movement. The "waking phases" are, in this context, identified as bouts prevalently spent in movement, though they may contain short phases of immobility. On the other hand, the "rest phases" are characterized by prolonged bouts in lack of movement, though they may also contain short phases of movement. Although different actigraphy devices may differ in the way they assess people's movement and compute actigraphic parameter based on them (different devices even provide different parameters), a conceptual and theoretical framework of actigraphic-determined sleep parameters can be applied to any actigraphy device, hence providing a very useful tool for using actigraphy in research and clinical contexts.
Hence, the present study aims to propose a simple conceptual framework for actigraphic-determined parameters depicting the nocturnal sleep, which can support the interpretation and use of known actigraphic parameters organizing them in a way that can be replicated in future studies. The framework is supported by a structural analysis of actigraphic data collected through one of the mainstream actigraphy devices (Motion Watch 8®, Cambridge Neurotechnology, Cambridge, UK), but its overarching constructs could be flexibly applied to parameters derived from other devices.

Participants
Forty university students (20 males and 20 females; mean age ± SD, 22 ± 3 y; 20 Neither-types, 10 Eveningtypes and 10 Morning-types) attending the School of Sport Sciences, University of Milan (academic year 2020/2021), were recruited. The chronotypical composition of our study group is similar to that can be usually found in the general population (i.e., 20% of Morning-types, 60% of Neither-types and 20% of Evening-types). To reduce the risk of possible confounders due to disruption of circadian rhythms and the decrease of sleep quality caused by the lockdown (Romdhani et al. 2022), the data were collected in the period June-July 2021, when the academic activities returned to normality (in presence lectures, without academic online activities). After introducing the modalities and purposes of the study, the participants signed an informed consent and completed the validated Italian version of Morningness-Eveningness Questionnaire (MEQ) (Zani et al. 1984) for the evaluation of their circadian typology. The main inclusion criterion was to maintain a regular and similar daily routine. All subjects followed similar university schedules, starting classes at 09:00 h, with no one working on the side of their studies. In addition, during the actigraphic monitoring, they were asked to abstain from leisure-time sport activities, while attending the same physical training sessions connected with the academic curricula. The participants reported they had no pathological conditions that could interfere with their sleep, nor were they under any pharmacological therapy. The study was approved by the Ethics Committee of the University of Milan, Italy, and performed in accordance with the 1964 Declaration of Helsinki.

Morningness-Eveningness Questionnaire (MEQ)
The participants' chronotype was assessed using the Italian version of the MEQ (Zani et al. 1984), an instrument initially designed by Horne and Ostberg (1976). It includes 19 multiple-choice items regarding preferred time of day for sleep and activity, mood at bed time and after awakening, and the time of day during which the respondent, generally, feels most active. Each response is scored on a scale ranging between 0 and 6, with the total score ranging from 16 to 86. Lower scores indicate an "evening typology" (E-type), which can be differentiated in extreme (range, 16-30) and moderate E-types (range, 31-41), while higher scores indicate a "morning typology" (M-type), which can be differentiated in extreme (range, 70-86) and moderate M-types (range, 59-69). Scores between 42 and 58 identify an intermediate typology, also known as "neither-type" (N-type), which has no particular morning or evening preferences.

Actigraphic monitoring
The actigraphic monitoring was conducted using the Motion Watch 8®, a relatively new device that has shown high validity when tested against polysomnography and against other actigraph devices (Aili et al. 2017). The participants wore the actigraph on the nondominant wrist for seven days. Additionally, they compiled a daily diary recording their bed time, wake up time, rest bouts during the day, any nocturnal awakenings, abouts of moderate-to-vigorous physical activity (within the academic curricula), and the periods in which they removed the actigraph. The information derived from the diaries was cross-referenced with the objective data obtained from the actigraph before performing the analysis of actigraphic sleep parameters in order to more precisely identify the participants' bed and wake up time, as well as to identify possible abnormalities or prolonged spans with missing data. In order to reduce the risk of artifacts due to excessive interindividual variation, only the data collected during the weekdays (from Monday to Thursday) were retained for the final analysis. This choice was based on previous studies showing significant differences in the sleepwake patterns of individuals with different chronotype during the weekdays (Vitale et al. 2015). Moreover, the examination of the diaries revealed several participants did not conduct the actigraphic monitoring properly during the weekends (e.g., the device was removed for prolonged spans, or the participants engaged in highly irregular sleep-wake behavior).

Actigraphy-based sleep analysis
The actigraphic data were processed through the Actiwatch Sleep Analysis Software (CamNtech MotionWare 1.2.28, Cambridge Neurotechnology, Cambridge, UK). The rest-activity cycle, as measured through actigraphy, is characterized by an asymmetrical square-shaped wave (Barger et al. 2009;Dowling et al. 2005), with a phase of about 16 h with high and relatively frequent activity episodes (daily activity) followed by a phase of about 8 h with lower and relatively infrequent activity episodes (nocturnal rest) (Calogiuri et al. 2013). The data indicating activity and lack of activity assessed during the nocturnal rest phase are used by the Actiwatch software to produce, for each night, a series of parameters depicting the onset, duration, and quality of a person's sleep (Calogiuri et al. 2013).
All sleep parameters produced by the Actiwatch software are summarized in the supplementary material, with their respective definition, formula, and reference values for a healthy young adult population (18-25 years), if available from the literature (Hirshkowitz et al. 2015;Ohayon et al. 2017).

Data base and statistical analysis
To render our line of thought more appealing and concrete, we illustrated its use in the real world by resorting to the actigraphic sleep parameters derived in the group of 40 subjects already described in the Participants section. It is worth emphasizing that such description of the sleep parameters in a sample of young and healthy volunteers was only aimed to provide a proof of concept of our theoretical construct. We took advantage of these experimental data to better highlight the meaning of the various parameters, as well as their interrelations within them. In addition, we were able to provide some insights into the statistical distribution of the various parameters, as well as into the degree of correlation between some selected pairs of them. Although such statistical analyses pertain to a sample of healthy, young subject and cannot be generalized, they nevertheless provide a starting point for investigators willing to apply our theoretical construct to their own experimental data.
The frequency distribution of each parameter was evaluated with the Shapiro-Wilk test in order to establish whether it was compatible with the Gaussian distribution. If not, the compatibility of the distribution with two asymmetric distributions (Log-normal and the Weibull) was evaluated. For all actigraphic parameters, descriptive statistics were computed as means and standard deviations (M ± SD) or medians and interquartile ranges (Median; Q1, Q3). Correlation analyses were performed to examine the relationships between selected pairs of actigraphic parameters within and across the different functional blocks, using Pearson's product-moment correlation coefficient (r) or Spearman rank correlation coefficient (r s ), depending on the data distribution. All statistical analyses were performed using SPSS 27 (IBM Corp. Released 2021, IBM SPSS Statistics for Mac, Armonk, NY: IBM Corp.). Statistical significance was assumed as p < .05.

Functional analysis of actigraphic sleep parameters
Our analysis of the actigraphic-base sleep parameters starts with a graphical representation of nocturnal sleep (Figure 1). This diagram, though highly simplified, allows one to appreciate that nocturnal sleep is not a homogeneous state with continuous immobility, but rather a composite and fragmented state, alternating between phases of sleep/wake and movement/immobility (Irwin 2015). The ideas conveyed by Figure 1 provided the foundations for the development of a theoretical and conceptual framework that facilitates the analysis of the known parameters organizing them in a way that can be replicated in future studies. In fact we found convenient to group the parameters into families. Thus, we compartimentalized the various parameters into three different groups, or "functional blocks," each one related with a different facet of the nocturnal sleep: • Block 1: sleep's beginning, end, and duration; • Block 2: sleep/wake phases alternation; • Block 3: movement/immobility alternation. Each functional block was represented in a diagram, thus making easier for the user to grasp the meaning of each parameter and its relationship with the other parameters belonging to the same functional block. Within each functional block, the parameters placed at the top of the diagram are basic parameters from which the remaining parameters are mathematically derived. The functional relationships between the basic and the derived parameters is represented in the diagram, as well as in the supplementary material.  (up to 30-min and ≥85%, respectively). The values of Wake After Sleep Onset (about 64 min) appeared, however, somewhat high compared to the reference values (no more than 20 min).

Functional blocks
Block 1: sleep's beginning, end, and duration Functional block 1 (Figure 2a) illustrates actigraphic parameters depicting the sleep's onset, end, and duration. It includes: Block 1 covers predominantly parameters obtained automatically from the Actiwatch software, but also a few parameters derived from the participants'diary. Specifically, the parameters within the DIARY section refer to self-reported Bed Time (BT) and Wake Time   actigraph's software and quantifies the time spent awake (as estimated through an algorithm based on the actigraphic data) within the time span between Sleep start and Sleep end. The particular program used in this study, provides this parameter both in minutes and as a percentage of TST. Actual Sleep Time (AST) is calculated as the difference between TST and WASO, quantifying the time "actually" (as estimated based on the actigraphic data) spent sleeping during the night. Similarly to WASO, this parameter is provided both in minutes and as a percentage of TST. Finally, Sleep Efficiency (SE%), a broadly used parameter for evaluating sleep and its quality, expresses Actual Sleep Time as a percentage of Time in Bed.

Block 2: sleep/wake phases alternation
Nocturnal sleep is composed not only of a single uninterrupted phase of sleep, can contain nocturnal awakenings. Block 2 (Figure 2b) depicts such sleep/wake alternation (as identified by the software's algorithm based on the actigraphic data) in relation to the actigraphic parameters that quantify it. It includes:

• Wake Bouts (WB); • mean Length of Wake Bouts (LWB); • Wake After Sleep Onset (WASO); • Sleep Bouts (SB); • mean Length of Sleep Bouts (LSB); • Actual Sleep Time (AST); • Total Sleep Time (TST).
The top-left part of the figure shows the parameters indicative of a WAKE state: Wake Bouts (WB), i.e. the number of estimated waking episodes during TST, and their mean duration (mean Length of Wake Bouts, LWB). The product between these two parameters (Wake Bouts * mean Length of Wake Bouts) would therefore correspond to the WASO. On the top-right, the block presents the parameters indicative of the SLEEP phase: Sleep Bouts (SB), i.e., the number of estimated sleep episodes during TST, and their mean duration (mean Length of Sleep Bouts, LSB). The product between these two parameters (Sleep Bouts * mean Length of Sleep Bouts) would therefore correspond to the Actual Sleep Time. This emphasizes how the overall nocturnal sleep (i.e., the time between falling asleep and waking up, which is quantified by TST) is indeed a summative alternation of the time spent sleeping (estimated and quantified by Actual Sleep Time) and the time spent awake (estimated and quantified by WASO). On the top-left, block 3 shows an ACTIVITY/MOVING TIME section: the actigraph records both the Total Activity during moving phases (TA, expressed in counts) and the Time spent in Movement (MM, expressed in minutes, or Moving Time%, expressed as a percentage of TST). The ratio between these two parameters (Total Activity during moving phases/Time spent in Movement) generates Mean Activity during the Moving Minutes (MAMM). Furthermore, the ratio between Total Activity during moving phases and TST returns the Mean Activity (MA) level during TST. On the top-right of block 3, the IMMOBILE TIME contains three parameters: Immobile Minutes (IM; i.e., the total number of minutes with no movement), the number of Immobility Phases (IP; i.e., the number of continuous and immobile epochs) and the number of One-Minute Immobile phases (OMI, i.e., the number of phases without movement with a duration of less than one minute). The ratio between One-Minute Immobile phases and number of Immobility Phases results in One-Minute Immobile phases expressed as a percentage of the Total Number of Phases in Immobility (OMI%). The sum of Time spent in Movement and Immobile Minutes results in TST, emphasizing the interrelation between the phases of movement and immobility. The actigraph uses the alternating phases of movement/immobility to detect phases of sleep/wake throughout the overall nocturnal sleep period. The actigraph's software quantifies this through the parameter Fragmentation Index (FI), which is calculated as the sum between Moving Time% and One-Minute Immobile phases%. Figure 3 presents the scatter plots, with correlation coefficients end significance values, for selected pairs of actigraphic parameters within and across the different functional blocks. For instance, Figure 3a shows the relationship between the Time in Bed (i.e., time spent in bed, even if awake) and Actual Sleep Time (i.e., the time actually spent sleeping). In accordance with the conceptual framework described above (Block 1), these two parameters do not overlap, because there is a latency time between the Bed Time and the time in which the subject starts to sleep as well as between the wake up and the Wake Time. In addition, being the alternation of rest and wake cycles a natural characteristic of the nocturnal sleep, not all the night rest can be considered actual sleep. For this reason, Time in Bed is always greater than Actual Sleep Time. The Pearson's correlation coefficient between these two parameters is 0.81, indicating a strong effect size. In fact, the cloud of data is closely scattered around the diagonal with no visible outliers, indicating that, in general, the participants in the study had relatively low levels of SOL and WASO. Figure 3b shows the relationship between SE% and SOL. The Spearman's correlation is negative (r s : −0.44), indicating that an increment of SOL corresponds to a worsening of SE%. Figure 3c evaluates the relationship between the Total Activity during moving phases (Total activity counts) and the Moving Minutes. The two parameters showed a strong positive correlation, yet non-perfect (r s : 0.72), emphasizing how they describe similar but distinct aspects of sleep. Figure 3d investigates the predictive ability of SOL towards the FI. A weak and not significant correlation was found, indicating that, in our sample, the SOL is not indicative of FI (r s : 0.16). Figure 3e shows the relationship between SE% and FI. SE% and FI are actigraphic indicators of sleep quality: high values of SE% indicate good quality of sleep, while high values of FI indicate poor sleep. Accordingly, a strong and negative correlation is found between the two parameters (r s : −0.65). Figure 3f indicates the relationship between FI with Wake Bouts normalized for TST (NB: this parameter was not automatically generated by the actigraph software but computed by the researchers). The correlation is strong (r: 0.63), indicating that the ratio Wake Bouts/ TST could be considered a new index of sleep fragmentation, an alternative to the classic FI.

Correlations within and across the functional blocks
Finally, the last two figures exhibit the relationship between SE% and Wake Bouts (Figure 3g) and FI and Wake Bouts (Figure 3h). It is known that a reduction of SE% corresponds to an increase of FI with subsequent awakenings experienced by the subject during the night rest. Accordingly, the correlations for our sample show that Wake Bouts is inversely correlated with SE% (r s : −0.57) and directly correlated with FI (r: 0.55).

Discussion
Sleep is not a homogeneous and monolithic state, but rather a composite and fragmented state, regulated by the interplay of two major processes, one that promotes sleep and one that maintains wakefulness (Gillette and Abbott, 2019;Irwin 2015). Within each process there are periods of movement and immobility: the former prevalent during the phases of sleep while the latter during the waking phases (Dijk and Landolt 2019). These movements can be used, through actigraphic assessments, to indirectly estimate people's duration and quality of the sleep.
The starting point for our study was the need for a purely theoretical and conceptual framework for known actigraphic parameters, which can serve as a manual for data interpretation. Our proposed framework is composed of three functional blocks, each containing a number of actigraphic parameters describing different aspects of sleep, organizing them in a way that can be replicated in future studies. These blocks explain the relationships between the various actigraphic parameters, their meaning and what they describe. The framework is set to facilitate the interpretation of actigraphic parameters and avoid inaccuracies in using and interpreting these parameters, either for research or for clinical purposes.
The first functional block of actigraphic parameters includes parameters that depict the sleep's onset, end, and duration. This block includes SE%, which is one of the principal parameters used in literature to quantify sleep quality (Ohayon et al. 2017). The second functional block describes the alternating of sleep phases and waking phases, where the sleep phases are expected to be longer than the wake phases. An essential parameter within this block is WASO, which quantifies the overall amount of time spent in nocturnal awakenings. The third functional block includes parameters that describe the presence of movement and immobility phases. The phases of movement obviously prevail during the waking phases, while immobility prevails during the phases of sleep. This type of sleep fragmentation, and its impact in terms of sleep quality, is depicted by another important actigraphic parameter, the FI.
The statistical analysis presented alongside the conceptual model, which was based on actigraphic monitoring among 40 healthy young adults, also provides important insights. In particular, the descriptive analysis highlights that some parameters, including SE% and SOL, typically have non-normal frequency distributions. This has practical implications with respect to statistical analyses because whenever parameters having nonnormal distribution are compared among different groups, non-linear normalizing data transformations or non-parametric tests are recommended. The correlation analyses, which explored the degree of association existing between selected pairs of parameters, both within and between functional blocks, adds further insight to the understanding of our conceptual framework. The correlations give an overview of how the different parameters describe different aspects of the nocturnal sleep and underline how all the relationship among the differ parameters. Defining the reciprocal relationships between the different actigraphic parameters allow to have a practical tool to verify how the actigraphic parameters affect each other.
An issue that emerged from the analyses relates to the likely overestimation that actigraphy devices make about the number and duration of the nocturnal awakenings. Actigraphy provides an indirect estimate of sleep and wake based on the individual's movement, which might not always match the actual sleep and wake states. Because of this, some actigraphy software estimate the parameter Number of Awakenings longer than 5-minutes (NA > 5), which has shown higher validity in detecting nocturnal awakenings (Natale et al. 2009) and for which clear reference values are available (Ohayon et al. 2017). Unfortunately, this parameter is not provided in all the actigraphy software, such as the one used for this particular study.
Actigraphy has been shown to be a valid instrument for identifying rhythms as well as sleep disturbances (Ancoli-Israel et al. 2003;Morgenthaler et al. 2007). The use of actigraphic parameters, as presented in our proposed conceptual framework, can provide an overall estimate of people's sleep behavior. For example, referring to the practical interpretations of the parameters of block 1, assuming that a person has an adequate Time in Bed (e.g., for healthy adults, seven to nine h, Hirshkowitz et al. 2015), it can be assumed that higher values of SE% (≥85%, Ohayon et al. 2017) are normally accompanied by lower values of SOL (no more than 30 min, Ohayon et al. 2017) and WASO (no more than 20 min, Ohayon et al. 2017), resulting in better quality of the sleep. Such a scenario would indicate that a person not only obtained an exactly amount of sleep, but also that there is absence of major sleep disturbances such as difficulty in falling and staying asleep. Referring to the practical interpretations of the parameters of block 2, to a certain extent, some amounts of wakefulness during the overall nocturnal sleep phase are normal. In general, however, fewer Wake Bouts and a smaller mean Length of Wake Bouts indicate a better quality of sleep. As mentioned above, it should be noted that actigraph devices tend to overestimate the Wake Bouts. For example, according to the reference values provided by the National Sleep Foundation, healthy young adults should record one or three nocturnal awakening maximum (Ohayon et al. 2017), whereas actigraphic assessments tend to return a higher number of Wake Bouts. In addition, referring to the practical interpretations of the parameters of block 3, moving during the nocturnal sleep period not necessarily indicate a state of wake. Movements while asleep are in fact not uncommon, especially during the phases of REM sleep (Ohayon et al. 2017). In general, however, lower levels of activity during the sleep period are desirable. This is the case also for the parameter FI, which is particularly useful to quantify the extent to which the nocturnal sleep is disturbed by movements (and possibly awakenings). Unfortunately, while guidelines have been provided for several of the parameters, to the best of our knowledge, specific normative values for FI are yet not available in the literature.
The broad range of parameters provided by actigraphy allow to make assessments relative to the specific nature of different types of sleep disturbances. According with Natale et al. (2009), a combination of Actual Sleep Time, SOL, and Number of Awakenings longer than 5-minutes emerged as the best way to support insomnia diagnoses using actigraphy. Other studies (Edinger et al. 2004;Lichstein et al. 2003;Schutte-Rodin et al. 2008) proposed different combinations of actigraphic parameters to support insomnia diagnoses. For example, average SOL and WASO > 30 min, occurring ≥3 nights per week for ≥6 months indicate presence of insomnia. High values of SOL, WASO, mean movements, and Number of Awakenings longer than 5-minutes, alongside lower level of SE% are also considered characteristics of insomnia. In addition, SE% <85%, TST <6.5 h and frequent awakenings or other sleep complaints could also be indicators of insomnia.
Our study is mainly theoretical in nature and is intended to provide a conceptual framework useful to facilitate the interpretation of actigraphic studies. We illustrated a practical application of such theoretical construct by analyzing the actigraphic data collected in a sample of young, healthy subjects. Such analysis of experimental data, however, is only a proof of concept and is subject to some inherent limitations. Firstly, the investigation only involved undergraduate college students of Caucasian ethnicity. Given that sleep is influenced by both genetic and environmental factors and are known to depend on age, the statistical distributions of the parameters, as well as the experimental correlations among them are not necessarily generalizable to groups of different ethnicity and age. Nevertheless, the key message of our study is that normality cannot be given for granted and that the statistical distribution of each parameter must be ascertained in each specific case. In future studies the robustness of these proposed functional blocks could be better tested in samples of individuals with sleep disorders. The second limitation relates to the use of only one actigraph device, the Motion Watch 8® (Cambridge Neurotechnology, Cambridge, UK). Parameters provided by this device, however, are partially comparable with other actigraph models. Thirdly, we performed a short actigraphic monitoring, excluding the weekend days. Optimal duration of actigraphic monitoring should last at least one week, including weekend days. However, in our study, the shortened monitoring was a practical trade-off to ensure effective and reliable assessments that, in line with the purpose of the study, could reflect at best healthy and stable sleep routines.
In conclusion, the theoretical and conceptual framework of actigraphic-derived sleep structure proposed in this study, which can be flexibly adapted to any actigraph device, may prove beneficial to elucidate the subtle nuances of meaning of the various sleep parameters and to deepen the insight about the functional, as well as experimental, relationships among them. Hopefully, this framework will have both heuristic potential and pragmatic utility, thus allowing the clinical researcher to gain an increased confidence and effectiveness in planning and interpreting actigraphy-based sleep studies.

Disclosure statement
No potential conflict of interest was reported by the authors.