Open Access Original
Article

DOI: 10.7759/cureus.47236

A Comparison of Sleep Duration Accuracy
Between Questionnaire and Accelerometer in
Middle Childhood

Review began 10/03/2023
Review ended 10/12/2023

Kanae Kanda 1 , Tomohiro Hirao 1 , Nlandu R. Ngatu 1 , Akitsu Murakami 2 , Yusuke Yamadori 3 , Katsunori
Yokoyama 4 , Yoichi Hoshikawa 5 , Tetsuo Minamino 6

Published 10/17/2023© Copyright 2023
Kanda et al. This is an open access article
distributed under the terms of the Creative
Commons Attribution License CC-BY 4.0.,
which permits unrestricted use, distribution,and reproduction in any medium, provided
the original author and source are credited.

1. Department of Public Health, Faculty of Medicine, Kagawa University, Miki, JPN 2. Department of Palliative Care,
Faculty of Medicine, Kagawa University, Miki, JPN 3. Department of Anesthesiology, Takamatsu Red Cross Hospital,
Takamatsu, JPN 4. Department of Health and Welfare, Kagawa Prefecture Tosan Health and Welfare Office, Takamatsu,
JPN 5. Department of Health and Welfare, Kagawa Prefectural Government Office, Takamatsu, JPN 6. Department of
Cardiorenal and Cerebrovascular Medicine, Faculty of Medicine, Kagawa University, Miki, JPN
Corresponding author: Kanae Kanda, kanda.kanae@kagawa-u.ac.jp

Abstract
PurposeHealthy sleep is vital to children’s well-being, and assessing sleep efficiently and accurately can help
understand children's lifestyles. Due to the difficulty in objectively measuring sleep duration using wearable
sensors in large-scale surveys of children, self-administered questionnaires are often used in Japan;
however, their accuracy is uncertain. We evaluated and compared the accuracy of questionnaire-based sleep
times to those of wearable sensors.

Methods
This observational study was conducted between November 2019 and January 2020. A self-administeredquestionnaire on lifestyle habits and ActiGraph GT3X+ (ActiGraph, Inc., Pensacola, USA) accelerometer data
were collected from 40 fourth-grade elementary school students in Kagawa Prefecture, Japan. We analyzed
measurements for 256 days out of 280 days (40 persons × 7 days) after excluding days when the rate ofwearing the accelerometer was < 90%.

Results
The median sleep duration per accelerometry was 453 minutes, and the median time in bed was 519
minutes. Questionnaire-based time in bed was 11 minutes longer, with relatively high inter-individual
variability. The difference in bedtime was 26 minutes earlier, and wake-up time was 12 minutes earlier for
the questionnaire. The average sleep efficiency was 87.4%, and one-third of the children had sleep efficiency< 85%.

Conclusion
The difference in sleep duration by questionnaire compared to accelerometry was approximately 10 minutes,
suggesting the questionnaire may determine sleep duration with accuracy.

Categories: Preventive Medicine, Public Health, Epidemiology/Public Health
Keywords: sleep duration, questionnaire, physical activity, middle childhood, children, accelerometer

Introduction
It has long been reported that Japanese adults and children sleep the shortest hours in the world [1]. In a
survey of Japanese children's life rhythm over time elementary through high school students have been
going to bed later and sleeping shorter hours [2]. The average sleep time of children from first grade to highschool tends to get shorter every year, from 9 h 15 min in the first grade to 6 h 30 min in high school.
Epidemiological studies have reported an association between short sleep duration and the development ofobesity [3]. In school-aged children, a disrupted lifestyle due to nighttime activity, sedentary activities, anddecreased physical activity (PA) are associated with the onset of obesity [4,5]. Sleeping less than 8 h per day
increases the risk of developing obesity 2.87 times, and longer screen times devoted to gaming and otheractivities are associated with a higher prevalence of obesity [6].
These current conditions have led to the formulation of guidelines by relevant academic societies in Japanfor the prevention of obesity starting in childhood. For example, the treatment guidelines for childhood
obesity recommend that school-aged children should avoid sleeping less than 8 h per day and long periods of

How to cite this article
Kanda K, Hirao T, Ngatu N R, et al. (October 17, 2023) A Comparison of Sleep Duration Accuracy Between Questionnaire and Accelerometer in
Middle Childhood. Cureus 15(10): e47236. DOI 10.7759/cureus.47236

screen time [7].
Local governments, local medical associations, boards of education, and schools are collaborating to provide
checkups for children to screen for health conditions, aiming to prevent lifestyle-related diseases [8]. For
children and guardians who require improvement for obesity, and have abnormal blood test results (elevatedhemoglobin A1c, elevated triglycerides, low high-density lipoprotein (HDL) cholesterol) or lifestyle-relateddiseases, lifestyle guidance is provided with follow-up observation and a visit to a medical institution isrecommended. This pediatric lifestyle-related disease prevention checkup also includes a survey on diet,sleeping time, and exercise time.
Of concern in this survey was sleep time measurement accuracy. Sleep time can be accurately measured bypolysomnogram (PSG) [9] or accelerometer. Accelerometers are small devices worn on the wrist that canmeasure acceleration in three dimensions and can analyze sleep, as well as PA [10]. In recent health researchstudies, accelerometers made by ActiGraph, Inc., have been widely used because of their high accuracy andreliability [11]. Moreover, sleep measurement using this accelerometer has been reported to be as sensitive
as a PSG in school-aged children [12,13]. Since it is difficult and impractical to have many children wear
accelerometers at one time in situations such as health checkups, surveys are commonly conducted. The
questionnaire is intended to allow children to reflect on their daily habits by completing the questionnairethemselves. However, the accuracy of the sleeping hours on the questionnaires answered by the children
themselves has not been verified to date. Therefore, the purpose of this study was to verify the accuracy of
sleep duration obtained from a questionnaire by distributing a self-administered questionnaire and an
accelerometer, the "ActiGraph GT3X+" (ActiGraph, Inc., Pensacola, USA) to fourth-grade elementary school
students.

Materials And Methods
Participants and procedures
The study used an observational research design with children. The study participants were fourth-grade
elementary school students aged 9-10 years living in Kagawa Prefecture, Japan. The participating children
and their parents or guardians were informed of the risks and benefits of this study, and written informed
consent was provided by a parent or guardian for each participant. Those who did not provide consent andchildren who refused to wear the accelerometers were excluded. Participants were kept anonymous with aresearch ID attached so that the authors could not identify individual participants. The study was conductedfrom November 2019 through January 2020. The study period did not include the holiday seasons.

MeasuresSleep Duration
Sleep duration was evaluated using a triaxial accelerometer (ActiGraph GT3X+) [10]. Children were
instructed to wear the accelerometer on their non-dominant hand 24 h/day for seven consecutive days andto remove it only when bathing, showering, or swimming. The wristband type is more practical andcomfortable than a hip-worn accelerometer [14] and is reportedly reliable and valid for measuring overall PA
in children [15].
The following sleep analysis parameters were measured: bedtime and wake time, time in bed (TIB), sleep
onset latency (SOL), total sleep time (TST), waking after sleep onset (WASO), frequency of the average
duration of waking periods, total amount of body movement, and sleep efficiency (SE). The Cole-Kripke
algorithm [16] was used; sleep and wakefulness were determined using 60-s epochs (time blocks). In using
the GT3X+, we decided to apply the Cole-Kripke algorithm for sleep analysis, which was validated in asample of elementary school students to be more sensitive to sleep detection than the Sadeh algorithm [17].
Sleep duration included only nocturnal sleep. Bedtime and wake time were determined manually byexamining individual actograms. Manual scoring has been found to correlate more strongly with PSG
readings than computer-based automated analysis [18].
TIB and TST are calculated by body movements during sleep. SE refers to the ratio of time spent asleep to
TIB and is calculated as follows: TST/TIB x 100 (%). The higher the SE value, the better the sleep quality. The
cutoff value was 85% SE, which is the standard for determining good and bad SE [19]. The measured sleep
analysis parameters, which have been described elsewhere in the literature [20], were defined as follows:a) Sleep timing:
Bedtime: time the participant goes to bed.
Wake time: time the participant gets out of bed.
Sleep onset: clock time of the first consecutive minute scored as evening sleep.

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Sleep offset: clock time of the first consecutive minute scored as morning waking time.
b) Sleep quantity:
SOL: time between bedtime and sleep onset.
Sleep period time (SPT): time elapsed between sleep onset and offset (excludes SOL).
WASO: number of minutes scored as awake between sleep onset and offset.
TST: represents true sleep time calculated as SPT minus WASO.
TIB: time elapsed from getting into bed to getting out of bed (includes SOL).
c) Sleep quality:
SE: percent of time spent asleep between sleep onset and offset. This measure excludes sleep latency. SE =TST/SPT or TST/TIB. This study used the latter definition of SE (TST/TIB).

Physical activity
PA, number of steps, activity intensity, and activity duration, were measured using the ActiGraph GT3X-BT.
Using Freedson’s cut points for children [21,22], the measured PA was divided into four levels: sedentary,light, moderate, and vigorous. We note that actigraphy uses an inclinometer (body position analysis) todetermine whether the subject is in a standing, sitting, or lying position. Since sedentary behavior
determined by actigraphy also includes TIB, in this study, the duration of sedentary activity minus TIB was
defined as “sedentary time.”

Questionnaire
At the beginning of this project, a form to record data for seven days was distributed and the children were
instructed to complete this questionnaire themselves each day. Participants were advised to fill out the formby the end of the day so that their memory of that day would be fresh. We advised parents to help theirchildren complete the daily sleep questionnaires, if needed. The accelerometers and questionnaire forms
were collected after one week. The questionnaire comprised the following four questions:“At what time did you go to bed yesterday and at what time did you get up this morning?”“Please tell us how long you spent walking to school today.”“Did you go to a sports club or swimming today?”“How many hours did you spend looking at game consoles, smartphones, televisions, tablets, computers, orother screens today?”
The primary outcome measures evaluated in this study were sleep duration by accelerometry and the degreeof accuracy (error and error distribution) of sleep duration assessed by the questionnaire. Secondary outcome
measures were the associations between sleep duration, number of steps, and sitting time.

Sample size estimation
In a previous study on sleep in children [23], the average sleep duration for children was 522 min (8 h 42min), with a standard deviation of 30 min. Assuming an error of ± 10 min and a reliability of 95%, therequired number of participants was calculated as 34. Considering possible study dropouts and the numberof children per class, the required number of participants was set at 40.

Ethical considerations
The study was conducted according to the guidelines of the Declaration of Helsinki and was approved by the
ethics committee of the Kagawa University School of Medicine (approval no.: 2019-141; approval date: July29, 2019). The benefits and risks of the study were explained to the participants using an explanatory
document. After ensuring that the participants fully understood this explanation, their free and voluntary
informed consent to participate in this study was obtained in writing. Since this study was conducted withchildren, the children’s parents/guardians were regarded as legal representatives and their consent for theirchildren to participate in the study was obtained as well. To ensure the participants fully understood thestudy items were explained as clearly as possible.

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Statistical analyses
All data measured with the wearable accelerometer were analyzed using ActiLife software (v.6.13.4,
ActiGraph, Inc., Pensacola, USA). The duration of wear per day was calculated. The days with a wearing rate
< 90% were excluded from the analysis. The measurement error for sleep duration assessed by questionnaire
and on accelerometry was determined, and differences were tested by t-test. Pearson’s correlation
coefficient analysis was used to evaluate the associations between sleep duration and some lifestyle-related
factors. A p-value of < 0.05 was considered statistically significant. JMP Pro 16.1 statistical software (SASInstitute, Cary, NC, USA) was used for statistical processing.

Results
Table 1 provides the characteristics of the analytic sample. Descriptive statistics were presented primarily as
medians and interquartile ranges, per day basis. One participant was excluded due to refusal to wear theaccelerometer. In total, 40 participants, with a mean age of 9.7 (0.5) years, 26 boys (65.0%) and 14 girls(35.0%), were in the final analysis. All participants were Japanese citizens. The median body mass index(BMI) was 17.1 (interquartile range [IQR], 16.1-18.4) and was relatively higher in girls than in boys: 18 (IQR,
16.5-18.4) vs. 16.7 (IQR, 16-18). We evaluated results for the measurements of a total of 280 days (40participants × 7 days); 256 days were included in the analyses because we excluded days when the rate ofwearing the accelerometer was < 90%.

All (n = 40)

Boys (n = 26)

Girls (n = 14)

Parameter
Median(IQR)Median(IQR)Median(IQR)

Height (cm)136(131-141)

136(132-140)136.2(131-146.2)

Weight (kg)31

(29.7-34)

31(29-32.3)

33(30.2-35)

BMI (kg/m2)

17.1(16.1-18.4)

16.7(16-18)18(16.5-18.4)

TABLE 1: Participant descriptive characteristics
Participants, n = 40; samples analyzed, 256 days. SD: standard deviation; IQR: interquartile range; BMI: body mass index

Table 2 shows the aggregate results for the accuracy of sleep duration. The sleep outcome variables were
bedtime, wake time, TIB, TST, and SE. We noted that 53.1% of all participants went to bed between 21:30
and 22:00 and that the difference in bedtime was 26 min earlier using the questionnaire (p < .0001). In
addition, 53.9% of all participants woke up before 7:00 a.m., with 6:30 a.m. being the most common wake-up
time. The difference in wake-up time was 12 min earlier for the questionnaire (p < .0001). The median TIB(the time spent in bed) was 519 min (IQR, 489-547) by accelerometry and 530 min (IQR, 507-559) by thequestionnaire. The TIB measurement was 11 min longer (IQR, -19 to -41 min, p = .015) when using thequestionnaire. The median TST (the time spent sleeping), as measured by the accelerometer, was 453 min(IQR, 416-489). SE was 87.4%, and one-third of all participants had a SE < 85%.

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Questionnaire survey

ActiGraph GT3X+

Difference

Median(IQR)Median(IQR)Median(IQR)p-value

In bed

21:50(21:30-22:08)

22:00(21:34-22:22)

0:26(0:14-0:46)< 0.0001

Out bed

6:30(6:20-7:00)

6:33(6:16-7:03)

0:12(0:05-0:34)< 0.0001

TIB (min)530(507-559)

519(489-547)

11(-19-41)0.0015

TST (min)

---

---

453

(416-489)

---

---

---

SE (%)

---

---

88

(83-92)

---

---

---

Sedentary time (min)

---

---

244

(181-332)

---

---

---

Number of steps

---

---

14971

(12344-17176)

---

---

---

Walking time to and from school (min)12(0-30)

---

---

---

---

---

Availability of sports club activities (yes, %)40(15.6%)

---

---

---

---

---

Screen time (min)90(50-150)

---

---

---

---

---

Variables

TABLE 2: Sleep time from the questionnaire survey versus accelerometer
Participants, n = 40; samples analyzed, 256 days. IQR: interquartile range; TIB: time in bed (from the time into bed to the time out of bed); SE: sleep
efficiency, the percent of time asleep between sleep onset and offset excluding sleep latency; SE=TST/TIB (includes sleep onset latency); sedentary time,
static sitting or standing activity; screen time, using items such as games, smartphones, personal computers, tablets, televisions.

For PA per day as measured by accelerometry, the median sedentary time was 699 min (IQR, 646-790).
Median sedentary time calculated by subtracting TIB from total sedentary time was 244 min (IQR, 181-332).
The median number of steps taken was 14,971. When compared to the time spent walking as derived fromthe questionnaire, we found that most steps were taken on weekdays during a school day (between homeand school). The median screen time as assessed by the questionnaire was 90 min (IQR, 50-150); 41.4% of
the participants had more than 2 h per day of screen time. Only 15.6% of the total participants answered thatthey engaged in sports activities (sports clubs or swimming) on the day of the survey. Twenty-four (60%) of
the respondents did not exercise regularly on any day of the week.
As a secondary analysis, we analyzed associations between sleep duration and lifestyle-related factors. A
single correlation analysis using Pearson's correlation coefficients showed a weak negative correlation
between sleep duration and sedentary time. However, the correlation coefficient was low and did notprovide definitive evidence of an association (r = -0.249, p < 0.0001) (Table 3).

CovariaterpBody mass index (kg/m2)

0.058

0.3617

In bed

-0.0410.5164Out of bed

0.162

0.0098Sedentary time

-0.249<0.0001

Number of steps-0.0380.5420Availability of sports activities

0.031

0.6216

Screen time

-0.0690.2762TABLE 3: Relationships between total sleep time and lifestyle factors
N = 40; samples analyzed, 256 days. Shown are the r values of Pearson’s correlation coefficient with accompanying p values.

Discussion
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This study examined the difference between sleep duration measured objectively by accelerometry andsubjectively using a questionnaire. We also evaluated whether lifestyle-related factors, such as the numberof steps taken per day and the sedentary time, were associated with sleep duration. To accomplish theseaims, we measured sleep duration for one week in children aged 9-10 years using accelerometry and ourstudy questionnaire, within a community-based study. The difference between sleep duration in children
who responded to the questionnaire and the objective time measured by accelerometry was approximately
10 min, suggesting that the questionnaire provided an accurate determination of sleep duration.
Objective measurements using accelerometry or PSG are difficult to obtain with large samples, especiallywhen measuring data for children. One small study compared diary entries to accelerometer estimates andalthough the bedtime differed by approximately 20 min, the TST differed by only 0.7 min, demonstrating astrong correlation (r = 0.93) between the two modalities [24]. This result suggests that a questionnaire canprovide reasonably accurate results within pediatric research. However, since differences between estimatesderived from accelerometry and questionnaires vary from individual to individual, it is necessary to devise
questions that lead to the best accuracy. It is important to note that there is a difference between the time
one gets into bed and the time of actual sleep onset [25]. Since the questionnaire allows the individual todecide which time will be considered the beginning of sleep, measurement errors are likely to occur.
Moreover, when using a questionnaire, responses are affected by recall bias [26]. Therefore, it is necessary toanswer the day’s questions on the same day or as close to that day as possible so that the respondent’s
memory will be clear.
SE was used to measure sleep quality. Although no baseline for SE has been established, a recent report ofpolysomnographic parameters scored using the American Academy of Sleep Medicine criteria showed a SE of
85.7% for the total sample [27]; the average SE in middle childhood (5-12 years) was 89.6% using the same
ActiGraph model as in our study [13]. For the children in our sample, the average was 87.4%, similar toprevious studies. A concern was that approximately one-third of the children in the study sample had an SEof < 85%, suggesting that they may either have been unable to fall asleep after going to bed or may havebeen engaged in another activity before going to sleep. TIB, which is used to calculate SE, includes timespent engaging in non-sleep-related activities, such as reading, watching television, or lying in bed andthinking about the next day; this can distort SE measurements if taken literally [25]. These results suggestthat when asked about sleep duration in a questionnaire, some individuals recorded the time they went to
bed and others recorded the time they think they fell asleep. Therefore, our suggestion is that when asking
about sleep duration in a questionnaire, it is necessary to provide a definition of sleep to the subjects inadvance to avoid differences.
Regarding the association between sleep duration and lifestyle-related factors, we found evidence of an
association only with sedentary time; no associations with other factors. Previous studies using actigraphy
found that children engage in much sedentary behavior and little moderate-to-vigorous PA at school [28].
Moreover, a higher proportion of the school day spent in %MVPA relative to %sedentary times and %LPA wassignificantly associated with higher tests for gross motor development total scores [29]. In terms of mental
health, shorter sleep duration in children has been shown to increase the risk of symptoms of emotional andbehavioral disorders measured two years later [30]. Thus sleep is closely related to the amount of PA and tosedentary behavior, which are essential items when assessing lifestyle and health behaviors in children. In
this study, we believe the reason sedentary activity was moderately associated and not PA, despite somechildren taking part in sports activities, was due to the small percentage of children involved in sports
activities, and the effect of longer sitting time at school was more significant.

Strengths and limitations
The current observational study of children investigated the accuracy of sleep measured objectively using
accelerometry and subjectively using an epidemiological survey questionnaire. This is a substantial strengthof this research because this comparison has not been previously clarified. However, our results should beconsidered in relation to the limitations of the present research.
First, this study was limited by its small sample size. Since the study was conducted within a small
population sample in one community in Japan, generalizations should be made with caution. Similarly, thechildren enrolled in this study were limited to fourth graders, and the results regarding their lifestyle androutines cannot be applied to all elementary school children. Second, because the period for measurement
was one week, only short-term and transitory results may have been detected. Since we analyzed the datafor one aggregated week, we were unable to examine the characteristics of days of the week or of weekdays
and holidays in detail. Third, although SE could be calculated from accelerometers, we were not able todetermine the factors that affect SE. Finally, because the purpose of this study was to examine sleep
duration in detail and this was an observational study, we did not examine the effects of short sleep
durations or late bedtimes on lifestyle.

Conclusions
The results of this observational study indicated that, in reference to sleep parameters at ages 9-10 years,
questionnaire measurements may be comparable to those derived from accelerometry. This suggests that a

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These findings provide implications for public health interventions and preventive medical initiatives.

Additional Information
Author Contributions
All authors have reviewed the final version to be published and agreed to be accountable for all aspects of thework.
Acquisition, analysis, or interpretation of data: Kanae Kanda, Tomohiro Hirao, Yusuke Yamadori
Drafting of the manuscript: Kanae Kanda
Concept and design: Tomohiro Hirao, Nlandu R. Ngatu, Akitsu Murakami, Katsunori Yokoyama, Yoichi
Hoshikawa, Tetsuo Minamino
Critical review of the manuscript for important intellectual content: Tomohiro Hirao, Nlandu R.
Ngatu, Yusuke Yamadori, Akitsu Murakami, Katsunori Yokoyama, Yoichi Hoshikawa, Tetsuo Minamino

Disclosures
Human subjects: Consent was obtained or waived by all participants in this study. Ethics Committee of the
Kagawa University School of Medicine issued approval 2019-141. Animal subjects: All authors haveconfirmed that this study did not involve animal subjects or tissue. Conflicts of interest: In compliancewith the ICMJE uniform disclosure form, all authors declare the following: Payment/services info: This
research was funded by the Japan Agency for Medical Research and Development (AMED), initials of theauthors who received each award; Tetsuo Minamino, grant number; 20gk0110048h0002.
https://www.amed.go.jp/en/index.html. The funders had no role in study design, data collection andanalysis, decision to publish, or preparation of the manuscript. Financial relationships: All authors havedeclared that they have no financial relationships at present or within the previous three years with anyorganizations that might have an interest in the submitted work. Other relationships: All authors havedeclared that there are no other relationships or activities that could appear to have influenced thesubmitted work.

Acknowledgements
We thank all study participants, research assistants, and colleagues.

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2023 Kanda et al. Cureus 15(10): e47236. DOI 10.7759/cureus.47236

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