Inter-Rater Reliability

To ensure accuracy of actigraphic scoring, 11 files (21.2%) were double scored by a second independent trained researcher. Discrepencies between scoring were cross- checked and all errors were minor and readily resolved. Differences greater than 15 minutes occurred in 1.2% of rest end times, and 4.9% of rest start times. These were double checked by a third independent person and resolved. An overall agreement of 93.9% was found, resulting in the decision not to double score the remaining files.

METHOD

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2.7.4 Actigraphy Variables

Once all actigraphy files were scored, variables that were to be used for analyses were selected and data were exported into databases. Table 2.1 summarises the night time sleep variables and daily activity variables used.

2.8 Data Management

2.8.1 Confidentiality

Participants were each allocated an identification number, which was written on questionnaires and diaries once returned. Page two of the Child/Family Questionnaire (Appendix B3) containing personal information was removed and securely stored separately. A database with families’ contact details, Actiwatch™ serial numbers and documentation tracking information was encrypted to ensure confidentiality. Variables were entered into databases using identification numbers only.

2.8.2 Data Entry

Questionnaire, diary and actigraphic data were initially entered into separate databases using SPSS 16.0 (SPSS Inc. Chicago, Illinois, USA). All questionnaire and diary data were double entered. A total of three (5.8%) questionnaires had incorrect entries, with the remaining 49 (94.2%) requiring no amendments. Double entry of daily diary data resulted in 358 days (98.4%) of matching data and errors on six days (1.6%). Any figures erroneously entered were corrected prior to analyses being carried out.

Diary and actigraphy databases were created for school days and non-school days, and school nights and non-school nights, based on the following criteria:

• School day = child attended school that day

• Non-school day = child did not attend school that day

• School night = child attended school the following day

• Non-school night = child did not attend school the following day

These data were then screened. Criteria for exclusion were if children were reported as being sick or taking medication on the day, or if the duration of time scored by the algorithm as invalid actigraphic activity counts (AC) during the daytime were greater than 180 minutes in total. One hundred and eighty minutes would represent

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≥20% of invalid activity for daily periods based on children sleeping for approximately 10 hours per day, and it was deemed unacceptable to have 20% or more of data in a day missing.

Table 2.1

Actigraphy Variables Selected for Analyses Actigraphy Variable Definition Sleep Variables:

Rest start time Start time of the first epoch in the rest interval. Sleep start time Start time of the first epoch in the sleep interval. Sleep onset latency Time elapsed between rest start time and the following

sleep start time.

Rest duration Time elapsed between rest start and rest end time. Sleep duration Time elapsed between sleep start time and final wake

time (sleep end).

Total sleep time Total number of epochs between the start and end of the sleep duration period scored as sleep by the Actiware® software multiplied by the epoch length in minutes.

Sleep efficiency (% sleep) Percentage of scored total sleep time within the sleep duration period.

Sleep end time Time at the end of the last epoch in the sleep interval. Snooze time Time elapsed between wake time and rest end time. Rest end time Time at the end of the last epoch in the rest interval. Percentage wake Percentage of scored total wake time for the sleep

interval.

Wake after sleep onset (WASO) Total number of epochs between the start and end of the sleep interval multiplied by epoch length in minutes. Sleep fragmentation index Sum of percent mobile and immobile bouts that are less

than 1 minute duration to the number of immobile bouts in the sleep interval.

Wake Variables:

Activity start time Start time of the first epoch in the active interval (i.e. beginning of waking activity for the day, following rise time).

Activity end time Time at the end of the last epoch in the active interval (i.e. the end of activity prior to rest interval).

Activity duration Time elapsed between the activity start and end times. Total activity count (AC) Sum of all valid physical activity counts for all epochs

from the start to the end of the active interval.

Average AC per minute Average of all valid physical activity counts for all epochs from the start to the end of the active interval divided by the epoch length in minutes.

Invalid activity time Total number of epochs between the start and end of the active interval exceeding the maximum possible value (invalid data due to hardware error or data corruption) plus the total number of epochs manually excluded multiplied by the epoch length in minutes.

METHOD

62 Of the potential 364 daytime periods of data (52 participants x 7 days), 26 days (7.1%) were excluded due to illness. A further 44 (12.1%) were not used due to invalid AC >180 minutes. Therefore 80.8% of daily data were usable. As some families completed seven nights of recording and others eight, there was a total of 379 potentially usable nights. Of these, 26 (6.9%) were excluded due to illness and one (0.3%) due to the actigraph being left off the child’s wrist at bedtime and being replaced at around midnight. This resulted in 92.9% of recordings at night being available for analysis. Resulting school and non-school data were averaged for each child and databases were set up with raw and averaged data (see Section 3.1).

2.9

Data Analysis

Data were analysed using SPSS 16.0 software and graphs were produced with PRISM 4.0 software (Graphpad Software, United States of America). P values < .05 were considered to be significant. The Shapiro-Wilk test of normality was applied to variables prior to identifying appropriate parametric or non-parametric statistical tests. The Shapiro-Wilk test was selected, as it yields exact significance values and has therefore been found to be more accurate than the Kolmogorov-Smirnov test (Field, 2005). Sleep propensity curves for school and non-school nights were calculated using purpose-built Excel software (Microsoft Office Excel 2007). Descriptive statistics (M and SD if normally distributed, or Mdn and range) and frequency distributions were produced for questionnaire, diary and actigraphy variables. Hypotheses (see Section 1.8) were investigated using statistical methods outlined below and parental feedback was summarised.