Professor Carol A. Landis DNSc, RN, FAAN, Box 357266, Biobehavioral Nursing and Health Systems, University of Washington, Seattle, WA, 98195-7266, USA. Tel.: 206 616 1908; fax: 206 543 4771; e-mail: email@example.com
The aims of this study were to evaluate sensitivity, specificity and accuracy with an epoch-by-epoch comparison of polysomnography (PSG) and actigraphy with activity counts scored at low, medium and high thresholds, and to compare PSG-derived total sleep time (TST), sleep efficiency (SE) and wake after sleep onset (WASO) to the same variables derived from actigraphy at low, medium and high thresholds in 9- to 11-year-old children with juvenile idiopathic arthritis (JIA), asthma and healthy control children. One night of PSG and actigraphy were recorded. Pairwise group comparisons for sensitivity showed significant differences at the low [Tukey’s honest significant difference (HSD) P < 0.002], medium (P < 0.001) and high thresholds (P < 0.001) between JIA and asthma groups, and at the high threshold between JIA and controls (P < 0.009). Significant differences were found for specificity at the low (P < 0.001), medium (P < 0.001) and high thresholds (P < 0.001) between JIA and asthma groups, and between JIA and controls (low, P < 0.002: medium, P < 0.002: high, P < 0.008 threshold). PSG TST, WASO and SE were not significantly different among the groups, but significant group differences were found for actigraphy TST, WASO and SE at all three thresholds. Actigraphy showed the least overestimation or underestimation of sleep or wakefulness at the medium threshold for TST and WASO for all three groups. Compared to PSG, actigraphy was most accurate in the identification of sleep from wakefulness in 9- to 11-year-old healthy children, and less accurate in children with JIA and asthma.
Polysomnography (PSG) is the most accurate method to measure sleep stages and wakefulness. It requires that participants either come to a sleep laboratory or be connected to portable PSG equipment at home, creating a considerable burden to participants and increasing study costs. Although the most accurate sleep measure, PSG data are usually obtained for 1 or 2 consecutive nights in a laboratory setting and thus provides only a limited view of sleep, which may not represent a subject’s customary sleep at home. Nevertheless, PSG is the ‘gold standard’ in sleep research and in the clinical evaluation of suspected sleep disorders.
In contrast to PSG, actigraphs, worn at the wrist or ankle, are relatively unobtrusive, inexpensive and can record subjects’ sleep and wake patterns for multiple days and nights in the home environment. Wrist actigraphy has become a widely used technology for estimating sleep–wake patterns in community-based research (Berger et al., 2008; Littner et al., 2003; Morgenthaler et al., 2007; Sadeh and Acebo, 2002). The actigraph is a battery-operated device worn on the non-dominant wrist and programmed for continuous limb movement data collection 24 h a day for consecutive days or weeks at a time. Several types of actigraphs are commercially available and differ in the type of motion sensor used for the detection and recording of limb movements. Most modern actigraphs use accelerometers and have an event marker, which a participant presses when the actigraph is removed or replaced, and at bedtime and waketime. Limb movement (activity) counts can be recorded and stored in the actigraph in varying (e.g. 15 s, 30 s or 1 min) or fixed epoch lengths. Activity counts are downloaded from the actigraph to a computer for processing and scoring into epochs of sleep and wake using algorithms that are unique to the particular software associated with the device. The software and scoring algorithms for actigraphs vary in how the activity counts are summarized for each epoch (Jean-Louis et al., 2001; Pollak et al., 1998; Sadeh and Acebo, 2002; Sadeh et al., 1995). Some software programs provide operator-determined activity count thresholds to specify for the discrimination of epochs of sleep and wake (Cole et al., 1992; Jean-Louis et al., 1999; Kushida et al., 2001). The differences in the devices and algorithms can pose significant challenges when comparing actigraph-derived sleep and wake parameters across different study populations.
The American Academy of Sleep Medicine has issued practice parameters on the use of actigraphy, which includes use of them in children with sleep disorders and other pediatric populations, including children with chronic illnesses (Littner et al., 2003; Morgenthaler et al., 2007). Agreement between actigraphy and PSG has been reported in a study of infants (Insana et al., 2010), healthy school-aged children <9 years old (Spruyt et al., 2011) and in children with sleep-disordered breathing (Hyde et al., 2007), but to our knowledge studies have not reported similar comparisons in healthy children 9–11 years old, or in children with chronic illnesses such as arthritis or asthma. Chronic illnesses vary in underlying disease mechanisms and manifestation of symptoms that may have differential effects on sleep and wake patterns. For example, joint inflammation and pain in JIA represent different pathophysiological processes compared to bronchoconstriction and difficulty breathing in asthma. Varying disease-related symptoms could influence movement patterns of the children throughout the night. PSG studies in JIA have shown sleep fragmentation (e.g. sleep stage shifts, more arousals and awakenings, increased limb movements), snoring, mild sleep-disordered breathing and daytime sleepiness (Lopes et al., 2008; Passarelli et al., 2006; Ward et al., 2008, 2010; Zamir et al., 1998) but less is known about sleep as measured by actigraphy in JIA. Compared to healthy school-aged children, PSG studies in asthma have shown decreased total sleep time, more arousals and awakenings (Ramagopal et al., 2008; Sulit et al., 2005), and actigraphy studies have shown decreased total night-time sleep, more daytime napping and higher activity counts during sleep (Kieckhefer et al., 2008; Sadeh et al., 1995). Without studies of concordance between PSG and actigraphy in JIA or asthma it is unclear, for example, which threshold for scoring actigraphy would yield the most sensitive, specific and accurate measure of sleep and of nocturnal wakefulness. Further, given the known developmental changes in sleep as children mature, concerns about the ability of actigraphy to accurately evaluate sleep quality in school-aged children (Spruyt et al., 2011), increasing use of actigraphy in community-based clinical studies and a lack of reports in the literature comparing actigraphy to PSG in children with chronic illness, there is a need for further validation studies. In 9- to 11-year-old children with JIA, asthma and healthy control children, the aims of this study were: (1) to evaluate sensitivity, specificity and accuracy with an epoch-by-epoch comparison of (PSG and actigraphy with activity counts scored at low, medium and high thresholds and (2) to compare PSG-derived total sleep time (TST), sleep efficiency (SE) and wake after sleep onset (WASO) to the same variables derived from actigraphy at low, medium and high thresholds.
This study is a secondary analysis of data obtained from two research projects that examined sleep quality in children with JIA (Ward et al., 2008, 2010) and in children with and without asthma (Kieckhefer et al., 2008, 2009). Human subjects approval for the studies was obtained from the Institutional Review Boards at Seattle Children’s Hospital (JIA study) and the University of Washington (asthma study). From 2002 to 2007, children 9–11 years of age with JIA (n = 34), with asthma (n = 19) and controls (n = 18) were studied. Children in either study were excluded if they had a diagnosis of a psychiatric condition, diabetes, cancer or cystic fibrosis or were diagnosed with an upper respiratory infection or acute exacerbation of asthma within the past 2 weeks.
For both studies, each child and a parent slept in the University of Washington School of Nursing sleep research laboratory. They were scheduled to arrive at the laboratory approximately 2–3 h prior to the child’s usual bedtime. A schedule for bedtime and rise time was established based on a child’s usual school-night schedule, except during summer months, when most children followed a similar schedule every night of the week. PSG and actigraphy were recorded simultaneously for each child using standard laboratory protocols. In the JIA study average bedtime was 21:25 hours and rise time was 07:13 hours, in the asthma study average bedtime was 21:15 hours and rise time was 07:30 hours.
Both studies used the same laboratory PSG protocols. Details for the PSG recordings have been published previously (Ward et al., 2008, 2010). In brief, electrodes for electro-oculogram, electrocardiogram, electromyelogram and leg movements were placed according to laboratory protocols based on published standards (Rechtschaffen and Kales, 1968). Electroencephalogram electrodes were positioned at two frontal (F7, F8), two central (C3, C4) and two occipital (O1, O2 locations (international 10–20 system of measurement) and linked to reference electrodes placed over each mastoid bone (A1, A2). Nasal airflow was monitored with a pressure cannula placed in the nose (Pro-Tech Services, Inc., Mukilteo, WA, USA) and respiratory effort was measured by piezo respiratory effort bands placed around the chest and abdomen (Pro-Tech Services Inc.). Oxygen saturation was measured from the left or right index finger with a pulse oximeter (Nonin XPod, Nonin Med, Plymouth, MN, USA). Electrophysiological signals were recorded and digitized using the Somnologica data acquisition recording system (A10 recorders; Embla, Broomfield, CO, USA) and displayed and stored on a desktop (Dell Pentium III) computer. All digitized data were acquired and stored unfiltered and displayed continuously in 30-s intervals during each recording. PSG data from each laboratory night were scored (Somnological software version 3.1.2) manually into 30-s epochs of wake and sleep stages by an experienced technologist according to standard criteria (Rechtschaffen and Kales, 1968).
PSG parameters of interest included: (1) total sleep time defined as the amount of time in non-rapid eye movement (NREM) Stages 1–4 and REM; (2) sleep efficiency expressed as a ratio of total sleep time/time in bed (e.g. from lights off to lights on); and (3) wake after sleep onset expressed as a percentage of wake during sleep period time (e.g. time from lights out until final awakening).
Concurrent with PSG, each child wore an actigraph (Actiwatch 64™; Mini-Mitter Philips Respironics, Bend, OR, USA) placed on the non-dominant wrist during each laboratory night. Actiwatch 64™ has an accelerometer that senses the occurrence and degree of motion in all directions. This motion is converted into an electric signal and digitally integrated to derive an activity count. Actigraphy data were recorded in 15-s epochs (JIA) or 30-s epochs (asthma and controls), inspected visually and screened of artifacts prior to running the scoring program. For the JIA data, prior to applying the scoring algorithm the raw activity counts in 15-s epochs were combined to create 30-s epochs.
Activity counts were scored using the Actiware software version 3.4 (Mini-Mitter Philips Respironics, Inc.). The Actiware software scoring algorithm to discriminate activity counts into epochs of sleep and wake computes a weighted sum of activity in the current epoch, preceding four epochs and the following four epochs (Mini-Mitter Philips Respironics, Inc.). This software has three different sensitivity thresholds. If the total activity count is above the defined threshold the epoch is scored as wake, and if the total activity count is equal to or below the sensitivity threshold the epoch is scored as sleep. The threshold sensitivity values include: (1) low, defined as 80 activity counts per epoch; (2) medium, defined as 40 activity counts per epoch; and (3) high, defined as 20 activity counts per epoch (Table 1).
The actigraphy sleep interval was set manually for each record using PSG lights out and on for sleep onset and offset, respectively. Sleep onset was defined as the first 10-min period in which no more than one epoch was scored as mobile, and sleep offset was defined as the last 10-min period in which no more than one epoch was scored as mobile. Actigraphy parameters of interest included (1) total sleep time, defined as the number of minutes scored as sleep between sleep onset and sleep offset during the sleep interval (similar to PSG sleep period time); (2) sleep efficiency, defined as the ratio of total sleep time/time in bed; and (3) wake after sleep onset, defined as the percentage of scored total wake time during the sleep interval multiplied by 100. We calculated the three actigraphy sleep parameters using each of the threshold sensitivities.
PSG and actigraphy data processing for analysis
PSG and actigraphy data were synchronized to National Institute of Standards and Technology (NIST) using the same computerized system for each recording session so that each 30-s epoch of PSG could be matched epoch-by-epoch with actigraphy for comparison. Each scored PSG record was exported from Somnologica as a text file and read into spss (version 15.0; SPSS Inc, Chicago, IL, USA) and wake and sleep stage data were collapsed into binary variables (e.g. wake = 0; REM and sleep Stages 1, 2, 3, 4 = 1). Each scored actigraphy record was exported as a text file from Actiware and read subsequently into an Excel (Microsoft Office, version 2007) worksheet and also read into spss as binary sleep or wake variables. Agreement and disagreement between actigraphy and PSG was assessed for each matched 30-s epoch. When the actigraph data agreed with PSG data, the epoch was coded as true sleep (TS) or true wake (TW). When the actigraph data did not agree with PSG data it was scored as false sleep (FS) or false wake (FW).
Two sets of analytical methods were used to calculate PSG and actigraphy agreement using spss. An epoch-by-epoch agreement analysis provided estimates of sensitivity, specificity and accuracy parameters. Sensitivity was defined as the proportion of all epochs scored as sleep by PSG that were also scored as sleep by actigraphy (true sleep). Specificity was defined as the proportion of all epochs scored as wake by PSG that were also scored as wake by actigraphy (true wake). Accuracy was the proportion of all PSG scored epochs correctly identified by actigraphy. False-positive fraction (FPF) was defined as the fraction or probability of actigraphy reporting sleep when PSG detects wakefulness (Pepe, 2003). The second set of analytical methods involved group comparisons of sleep parameters (e.g. TST, SE and WASO) estimated with PSG and with actigraphy.
Data were analyzed using spss for Windows version 15.0. The first set of analysis was conducted to address group differences in clinical characteristics (e.g. age and sex) using a separate chi-square test for each variable. Statistical significance was set at P < 0.05 (two-sided).
For the first aim, we examined sensitivity, specificity and accuracy with an epoch-by-epoch comparison between PSG and actigraphy scored at low, medium, high thresholds among the three groups. Contingency tables were used to count the number of occurrences of true sleep, true wake, false sleep and false wake. Estimates of sensitivity, specificity and accuracy were derived from these occurrences. General linear model (GLM) analyses (i.e. repeated/related measures analyses) for the epoch-by-epoch agreement measures (sensitivity, specificity and accuracy) were used to evaluate the main effects and interactions with threshold as within-subject factor and group the between-subject factor. To aid interpretation, GLM analyses (repeated/related measures) were followed by a series of univariate one-way analyses of variance (anovas), with pairwise group comparisons executed using Tukey’s honest significant difference (HSD).
For the second aim, we examined whether PSG-derived TST, SE and WASO differed from the same variables derived by actigraphy at the low, medium and high thresholds among the three groups. A series of univariate one-way anovas, with pairwise group comparisons executed using Tukey’s HSD, were used to evaluate the differences in TST, SE and WASO between PSG and actigraphy at the low, medium and high thresholds.
Finally, the Bland–Altman concordance technique was used to determine if a meaningful agreement could be found between concurrent PSG and each of actigraphy threshold for TST and WASO. Bland–Altman plots represent graphically the difference between two measurement techniques (on y-axes) against the average of the two techniques (on the x-axis). Mean difference represents the bias between the two measures. Standard deviation of the bias (SD) provides an estimate of the scale of the variation of the difference between the two measures. For each of the sleep parameter comparisons, the average of actigraphy at a specific threshold and PSG was plotted on the x-axis, and the difference between actigraphy at a specific threshold and PSG was plotted on the y-axis for each group. A positive bias thus indicates an overestimation of the variable with actigraphy; a negative bias indicates an underestimation of the variable with actigraphy.
No group differences were found for age, but sex differences were found (χ2 = 18.7, P < 0.001) (see Table 2). As is characteristic of JIA, there were more girls in the JIA group (85%) compared to the asthma (31.6%) and control (38.9%) groups. Of the 34 children with JIA, 27% had pauciarticular disease and 71% had polyarticular disease. Children diagnosed with asthma were well or partially controlled based on frequency of symptoms and pulmonary function testing (http://www.nhlbi.nih.gov/guidelines/asthma/asthgdln.pdf).
Table 2. Demographic and clinical characteristics
JIA (n= 34)
Asthma (n= 19)
Controls (n= 18)
Data are mean ± standard deviation or n (%).
*χ2 = 18.7, P < 0.001. JIA, juvenile idiopathic arthritis.
10.2 ± 0.82
9.8 ± 0.86
9.8 ± 0.71
Sex, n (%)*
Ethnicity, n (%)
To address aim 1, Table 3 shows the sensitivity, specificity and accuracy values (mean ± SD) for the epoch-by-epoch agreement between PSG and actigraphy at the low, medium and high thresholds for each group.
Table 3. Sensitivity, specificity, accuracy of epoch-by-epoch comparison polysomnography and actigraphy
JIA (n = 34)
Asthma (n = 19)
Controls (n = 18)
Data are mean ± standard deviation. All P = pairwise comparison, Tukey’s honest significant difference.
*P < 0.002, juvenile idiopathic arthritis (JIA) versus asthma.
†P < 0.001, JIA versus asthma.
‡Both P < 0.001, JIA versus asthma; JIA versus control.
§Both P < 0.002, JIA versus asthma; JIA versus control.
¶Both P < 0.002, JIA versus asthma; JIA versus control.
**Both P < 0.008, JIA versus asthma; JIA versus control.
0.98 ± 0.01
0.97 ± 0.02
0.98 ± 0.01
0.96 ± 0.02
0.94 ± 0.03
0.95 ± 0.02
0.93 ± 0.03
0.88 ± 0.04
0.90 ± 0.04
0.38 ± 0.20
0.60 ± 0.16
0.55 ± 0.15
0.51 ± 0.21
0.73 ± 0.13
0.69 ± 0.15
0.62 ± 0.18
0.80 ± 0.11
0.77 ± 0.15
0.88 ± 0.07
0.90 ± 0.04
0.90 ± 0.04
0.89 ± 0.06
0.89 ± 0.04
0.90 ± 0.03
0.88 ± 0.05
0.87 ± 0.04
0.87 ± 0.02
Significant main effects were found for threshold (F(2,68) = 226, P < 0.001) and group (F(2,68) = 10.1, P < 0.001), and the group × threshold interaction was also significant (F(4,136) = 5.8, P < 0.001). Pairwise group comparisons showed significant differences at the low (Tukey’s HSD P < 0.002), medium (P < 0.001) and high thresholds (P < 0.001) between JIA and asthma groups. Significant group differences also were found at the high threshold between and control groups (P < 0.009).
Significant main effects were found for threshold (F(2,68) = 626, P < 0.001) and group (F(2,68) = 11.5, P < 0.001) and the group × threshold interaction also was significant (F(4,136) = 4.3, P < 0.002). Pairwise group comparisons showed significant differences for specificity at the low (P < 0.001), medium (P < 0.001) and high thresholds (P < 0.001) between JIA and asthma groups and at the low (P < 0.002), medium (P < 0.002) and high (P < 0.008) thresholds between JIA and control groups.
Table 3 shows accuracy data for the three groups, which did not differ by group at any threshold. Fig. 1(a–c) shows 1-specificity [false-positive fraction (FPF)] versus sensitivity by group, at the low, medium and high thresholds, respectively. The FPF was higher in the JIA group at all thresholds compared to the asthma and control groups, which suggests that actigraphy identified sleep when PSG did not.
Sleep parameters concordance
To address aim 2, Table 4 shows PSG-derived and actigraphy-derived TST, SE and WASO with the latter scored at low, medium and high thresholds for the three groups. PSG-derived TST (F(2,68) = 0.43, P = 0.65), SE (F(2,68) = 1.1, P = 0.36), WASO (F(2,68) = 2.1, P = 0.14) did not differ among the groups. However, significant group differences were found for actigraphy TST at medium (F(2,68) = 4.0, P < 0.02) and high (F(2,68) = 5.3, P < 0.008) thresholds; actigraphy SE at the low (F(2,68) = 8.2, P < 0.001), medium (F(2,68) = 9.7, P < 0.001) and high (F(2,68) = 10.7, P < 0.001) thresholds; and actigraphy WASO at the low (F(2,68) = 13.9, P < 0.001), medium (F(2,68) = 14.5, P < 0.001) and high (F(2,68) = 14.6, P < 0.001) thresholds.
Table 4. Sleep parameters between polysomnography and actigraphy
JIA (n = 34)
Asthma (n = 19)
Controls (n = 18)
Data are mean ± standard deviation. All P = pairwise comparison, Tukey’s honest significant difference.
*P = 0.05 juvenile idiopathic arthritis (JIA) versus asthma.
†Both P < 0.04 JIA versus asthma, JIA versus control.
‡P < 0.001 JIA versus asthma.
§P < 0.001 JIA versus asthma; P < 0.04,JIA versus control.
¶P < 0.001 JIA versus asthma; P < 0.02 JIA control.
**P < 0.001 JIA asthma; P = 0.007JIA versus control.
††P < 0.001 JIA versus asthma; P = 0.005JIA control.
‡‡P < 0.001 JIA versus asthma; P = 0.003 JIA versus control.
Total sleep time, min
485 ± 48.9
479 ± 66.9
468 ± 75.1
512 ± 39.0
481 ± 66.3
482 ± 68.7
496 ± 37.9
457 ± 66.2
462 ± 68.9
474 ± 37.0
428 ± 65.0
435 ± 72.3
Sleep efficiency, %
84 ± 8.9
81 ± 10.1
82 ± 8.4
89 ± 5.2
81 ± 9.3
84 ± 7.5
86 ± 5.7
77 ± 9.9
80 ± 8.1
82 ± 6.3
72 ± 9.9
75 ± 8.9
Wake after sleep onset, %
11.3 ± 8.6
16.2 ± 10.3
14.0 ± 6.0
5.0 ± 3.2
12.1 ± 8.1
9.8 ± 3.1
8.0 ± 4.3
16.5 ± 9.0
13.7 ± 4.3
12.1 ± 5.5
21.8 ± 9.2
18.8 ± 5.6
Bland–Altman plots for TST and WASO between PSG and actigraphy for the low, medium and high thresholds are shown in Fig. 2 for the three groups. Compared to the low and high thresholds, actigraphy showed the least amount of overestimation or underestimation in the identification of TST and WASO at the medium threshold for all three groups. Mean difference for TST at the medium threshold was 11 min for JIA, −20 min for asthma and −8 min for controls. Mean difference for WASO at the medium threshold was −3 min for JIA, 0.61 min for asthma and −0.42 min for controls. In contrast, TST at the high threshold showed the least underestimation in the JIA group (−10.9 min) but the most underestimation in the asthma group (−50.1 min). At the high threshold, WASO was only slightly overestimated (0.75 min) in the JIA group, but it was overestimated (5.8 min) in the asthma group.
The findings from this study are the first report of PSG and actigraphy concordance in 9- to11-year-old children with JIA, asthma and healthy controls. Overall, findings showed that actigraphy had the least overestimation or underestimation of sleep or wakefulness at the medium threshold for TST and WASO for all three groups. However, compared to PSG, actigraphy was accurate in the identification of sleep and wakefulness in 9- to 11-year-old healthy children at all three thresholds, and less accurate in children with JIA and asthma. Our results highlight the importance of evaluating actigraphy-determined sleep parameters at each threshold when using a device and software that enables this level of discrimination, as sensitivity, specificity and accuracy may differ with children with various chronic illnesses. As noted by others, actigraphy is not as accurate a measure of sleep parameters compared to PSG for certain clinical populations (Hyde et al., 2007; Insana et al., 2010; Owens et al., 2009; Paquet et al., 2007; Sitnick et al., 2008; Spruyt et al., 2011). Our findings of low specificity in the identification of nocturnal wakefulness across all three groups is consistent with those of others (Hyde et al., 2007; Paquet et al., 2007; Sitnick et al., 2008). These observations could be attributed to the probability of more sleep epochs compared to wake epochs identified during the night-time hours. However, in comparison to the other groups the false-positive fraction was higher in the JIA group, which suggests that actigraphy may be less likely to identify wakefulness in JIA. This finding might be explained by sleep fragmentation and brief episodes of wake associated with mild sleep-disordered breathing that we have identified in JIA (Ward et al., 2010) and as noted in children with obstructive sleep apnea (Hyde et al., 2007).
Concordance between actigraphy and PSG for sensitivity to measure sleep was high for the three groups. This finding is not surprising, as the probability of actigraphy identifying sleep epochs compared to wake epochs are high during the night and improve with longer sleep duration.
Unlike sensitivity, specificity was lower in JIA children. As mentioned above, this finding may be related to mild sleep-disordered breathing that we recently reported in JIA (Ward et al., 2010). When children with arthritis are awake they may move less during sleep, particularly if they have painful joints leading actigraphy identification of sleep when the child is actually awake. Alternatively, as there were more girls in the JIA group compared to asthma and control groups, group differences in actigraphy recognition of wakefulness might be related to girls but not boys being more likely to lie awake without moving, as has been noted in adult women with insomnia (Lichstein et al., 2006). Unfortunately, we did not have a sufficient sample to evaluate gender differences within the groups. JIA and asthma are chronic conditions that probably impact sleep quality differently, and more research is necessary to replicate our findings and explore reasons behind group differences. Previous studies in infants and children have also shown that actigraphy is less accurate in identifying wake episodes in certain clinical populations (Insana et al., 2010; Owens et al., 2009; Sitnick et al., 2008; Spruyt et al., 2011).
Sleep parameters concordance
The Bland–Altman plots highlight the close concordance between PSG and actigraphy for both TST and WASO at the medium threshold in healthy children. This finding is inconsistent with a recent study that found poor concordance between PSG and actigraphy for both TST and WASO at the medium threshold in 4- to 9-year-old healthy children who wore the same Actiwatch device (Actiwatch 64™; Mini-Mitter Philips Respironics) (Spruyt et al., 2011). The difference in concordance may be attributed to the inclusion of younger children and/or the use of a 1-min epoch sampling in the Spruyt et al. study. The manufacturer recommends use of the medium threshold. However, further studies are needed on PSG and actigraphy concordance at low, medium and high thresholds to determine which threshold for scoring actigraphy would yield the most sensitive, specific and accurate measure of sleep and of nocturnal wakefulness in healthy children of different ages and to assess the impact of different recorded epoch lengths on these measures.
In JIA and asthma, the Bland–Altman analysis revealed striking differences in the underestimation of sleep scored at the high threshold. The asthma group showed nearly five times the amount of bias compared to the JIA group. This finding may be explained by more night-time movement, differing disease mechanisms and/or disease-related symptoms (frequent night cough) in asthma children. The difference in TST and WASO for the JIA children may also reflect particular distinct mechanisms, low disease activity or a combination of disease-related symptoms (pain, fatigue) and medication use. Unlike the JIA children, the asthma children had an underestimation of TST at the medium threshold and an underestimation of WASO at the low and medium thresholds. Unlike the JIA and healthy children, the low threshold for TST had the least amount of bias (3.6 min) in the asthma group. Future studies examining PSG and actigraphy concordance in children with chronic conditions may want to include videosomnography to understand the nature of the movements recorded by the actigraph. Our findings highlight that changes in sleep are probably not uniform across pediatric chronic conditions, and that recommendations regarding thresholds for scoring actigraphy from healthy children are not adequate for assuming the same quality of validity of actigraphy data in children with chronic conditions.
There are several limitations worth noting in this study. First, the small number of asthma (n = 19) and control (n = 18) children compared to the JIA (n = 34) children restricts the generalizability of the findings and the types of data analysis that could be conducted. Secondly, only one type of actigraph device was studied. Future studies could compare various actiwatch devices. Additional comparison studies are needed between PSG and actigraphy concordance in different pediatric chronic conditions compared to healthy control children.
In conclusion, there is a paucity of data on PSG and actigraphy concordance in children with chronic conditions. The extent of sleep disturbances as measured by actigraphy in pediatric chronic conditions has not been well studied. Given the unobtrusiveness and cost-effectiveness of actigraphy in pediatric chronic illness, it is important for clinicians and researchers to understand the differences in scoring threshold algorithms and their strengths and limitations. Although actigraphy may not be as sensitive for certain populations, it remains an ecologically useful device to measure aspects of sleep and wake. Additional research on PSG and actigraphy concordance will provide more nuanced use in children with chronic health conditions.
Declarations of Interest
None for all the authors.
The authors thank the children and families who helped with this research. We thank Robert Burr MSEE, PhD, Research Professor, for his assistance with the data analysis and Shao-Yu Tsai PhD, RN, Assistant Professor, National Taiwan University for her assistance with the data entry. We thank Linda Peterson, Research Coordinator, Dr Laurie Beitz MD and the staff in the Rheumatology Clinic for recruiting the participants. We thank Ernie Tolentino, Laboratory Manager, and the sleep laboratory staff, James Rothermel, Taryn Jenkins, David Krizan and Paul Wilkinson for recording, processing and scoring of the sleep data. We also thank Hieke Nuhsbaum for data entry and Salimah Man, Yuen Song, Tuyet Nguyen. Sarah Shapro, and Whitney Jewell for helping with data collection and processing. This research was supported by grants from the National Institute of Nursing Research, T32 NR00710, NR08136, NR08238, the Center for Women’s Health and Gender Research, NR04011, the National Center for Research Resources (NCRR), M01-RR-00037, the Center for Research on Management of Sleep Disturbances, NR011400 and Pulmonary Center Training grant, T72 MC 0007.