Actigraphy

To determine total PA performed each day, total activity counts from the ActiGraph, total number of steps/day from the Yamax, and total MET-min/day from the PA log were utilized in analyses. ActiGraph data were screened for non-wear time using 60 consecutive zero counts/minute. ActiGraph cutpoints of 0-50 counts/min were considered to be sedentary behaviour (based on cutpoints suggested by Esliger et al. [14 ] and Crouter et al. [15 (link)] and found to be representative of sitting/lying behaviour [16 ]), 51-759 counts/min were considered to be light PA [17 (link)], 760-1951 counts/min were considered to be moderate lifestyle PA, and ≥1952 counts/min were considered to be moderate- to vigorous-intensity PA [8 (link)]. For the PA log, activities with MET values ranging from 1-2.99 were considered sed/light, ≥3 were considered moderate- to vigorous-intensity PA [18 (link)]. The resulting data for the PA log was total MET-min/day, and MET-min/day in each intensity category. Non-normally distributed data were transformed using log + 1 for inferential analyses. Descriptive statistics were presented in their raw form (i.e., not transformed).
To address the main purpose of this paper, Spearman-Brown Prophecy Formulas based on ICC for all 21 days and a reliability of .80 were used to predict the number of days of complete data needed to represent total PA (Yamax steps, total ActiGraph counts, and total PA log MET-min/day), sedentary behaviour (total minutes from accelerometer with ≤ 50 activity counts/min), light-intensity(ActiGraph min/day and PA log MET-min/day), moderate-intensity (ActiGraph min/day), vigorous-intensity (ActiGraph min/day), and moderate-to vigorous-intensity (ActiGraph min/day and PA log MET-min/day) PA. These analyses were repeated to calculate a reliability of 0.85, 0.9, and 0.95.
To address the secondary aim of this paper, a repeated measures analysis of variance (RMANOVA) with post hoc pairwise comparisons where necessary was used to determine between day differences in mean PA level (i.e., the seven days of the week) for each intensity and for all instruments. To derive daily mean PA estimates from the 21 days, data from each day of the week was averaged (e.g., the mean of the three Mondays included in the monitoring period were averaged to determine the average PA behaviour on Monday). Analyses were completed using SAS version 9.1 (Cary, IL) and SPSS version 17 (Chicago, IL). Statistical significance for all analyses was set at p < .05.

Activity was measured using the ActiGraph GT1M (ActiGraph, Florida, USA), a small (3.8×3.7×1.8 cm), lightweight (27 g) uni-axial accelerometer that measures volumes and patterns of activity. The ActiGraph has been extensively validated in children [26] , [27] , [28] , and is robust when used in large-scale studies in children [3] , [4] , [5] , [8] . A 15-second sampling epoch was selected in order to optimize the ability to capture the sporadic nature of children’s activity [1] . Children were asked to wear the accelerometer on an elasticated belt on the right hip for seven consecutive days during all waking hours, except during bathing or swimming. Accelerometers were posted to families who were asked to return it as soon as possible after the monitoring period using a supplied pre-paid envelope. Accelerometers were distributed between May 2008 and August 2009.
Ethical approval for the MCS accelerometer study was granted by the Northern and Yorkshire Research Ethics Committee (REC number: 07/MRE03/32). The MCS data for surveys 1 to 4 are currently available via the Economic and Social Data Service; the MCS accelerometer data will be also be available shortly at the beginning of 2013.

At the midpoint of the study period (after 1 week), the EG1 received a face-to-face, 45–60-min lasting session with a member of the Laboratory for Sleep, Cognition and Consciousness Research of the University of Salzburg, who was trained for the “sleep education intervention.” General information about sleep, basic principles of sleep hygiene, as well as general advice for better sleep (stimulus control), were explained and discussed by the use of an information booklet. In addition, the “coach” showed and explained the participants' sleep data of the first week as recorded from the actigraph. Furthermore, the sleep data feedback addressed the PSQI value at study entrance. The EG2 received sleep data feedback only, at the same point of time in the study protocol (namely 1 week after study entrance). Finally, the CG was simply monitored for the 2-week period with actigraphy and daily morning and evening protocols via smartphone app and only had a personal appointment to discuss the data at the end of the study period (cf. Figure 1).

Objective sleep data were measured by using ©wGT3X-BT ActiGraphs (ActiGraph™; Pensacola, FL, USA), which capture continuous, high-resolution physical activity and provide sleep/wake analysis. ActiGraph data were processed using the ©ActiLife software and the implemented Cole–Kripke algorithm (38 (link)) with a pre-defined sleep epoch length of 60 s; we here focused on SE and SOL of that algorithm. In addition, participants were asked to keep a daily sleep log, where individual lights on/off was recorded. Participants were instructed to provide these timings immediately before switching off and after turning on the lights.