The SCREENS pilot trial (www.clinicaltrials.gov – NCT03788525) is a two-arm parallel group cluster randomized trial with two intervention groups and no control group. Data for this trial was collected between October 2018 and March 2019. The overall purpose of the pilot trial was to assess compliance to the included interventions and the included measurement protocol, as well as feasibility of the survey-based recruitment strategy.
The collection of data was reported to the local data protection department SDU RIO (ID: 10.391) in agreement with the rules of the Danish Data Protection Agency.
Families in the municipality of Middelfart in Denmark were invited to participate if they had at least one child aged 6-10 years residing in the household (n=1686). A digital letter with a survey concerning screen media habits in the family was sent directly to a randomly chosen parent in each household in October 2018. In addition to the survey, a short description of the SCREENS pilot trial was provided on the final page. Here, respondents could note if they were interested in hearing more about the study. Based on the survey responses, families were eligible to participate if the randomly chosen parent’s total screen media use was above the median amount (2.7 hrs/day) based on all respondents (n=394), and if all children in the household were older than 3.9 years. The latter was to avoid potential disturbances of sleep measurement due to an infant or toddler’s pattern of nocturnal awakening.
Eligibility to the trial was assessed further during a phone call. Families had to meet the following inclusion criteria:
- At least one child and one adult in each family had to participate
- All participants had to be able to heavily restrict total leisure screen media use
- Families had to consider their habitual screen media use a problem and be motivated to reduce it for a two-week-period
- Non-participating family members of the household had to respect the conditions which the participants had to follow
Exclusion of participants was based on the following criteria:
- If adults or children resided only part time in the household
- If participants had been diagnosed with a sleep or stress disorder within the last 12 months
- If adults in the household worked night hours
- If family members were not able to do physical activities as part of daily living
- If a family member was diagnosed with a neuropsychiatric or autism spectrum disorder
- If family members were already participating in other studies
Eligible families were informed about the content of the trial at a meeting in their home. Three additional meetings were planned with families who were willing to participate. The purposes of these meetings were to set up baseline measurements (baseline day 1), perform randomization procedure (baseline day 8), and collect measurement equipment (experiment day 15). A phone call was also planned to remind participants to start follow-up measurements (experiment day 8) (Figure 1).
Twelve families with at least one child and one adult were deemed sufficient to investigate compliance to- and feasibility of the intervention and assessment methodology. Also, we considered this sample size large enough for potential problems with the intervention or assessment methods to emerge. An a priori sample size calculation was not considered relevant because hypothesis testing of efficacy was not an aim in the pilot trial.
Included families were randomized to one of two screen media restriction interventions; a general restrict or evening restrict group. Those in the general restrict group had to hand over smartphones and tablets and restrict all leisure screen media use for entertainment purposes to a maximum of three hours/week/person for two weeks. Families randomized to the evening restrict group had to remove all leisure screen media use after six PM for two weeks. A more thorough description of the components of the intervention can be found elsewhere (www.clinicaltrial.gov (NCT04098913 under “Arms and interventions”).
The random sequence generation was performed by Odense Patient Explorative Network Randomise (OPEN R). An online platform provided by OPEN R was used to perform randomization in the home of the participants ensuring allocation concealment until the screen time intervention was assigned. The randomization was made in alternating blocks of two-four families and was stratified by sibling status (only child/not only child) in the household.
Families underwent an extensive measurement protocol at baseline and follow-up spanning seven consecutive days (Figure 1). This paper will focus on the EEG-based sleep assessments. Details on the remaining components of the measurement protocol can be found elsewhere.10
Sleep was objectively measured in the households for three consecutive measurement nights at baseline and follow-up using a single channel EEG-based sleep equipment (Zmachine). Participants were instructed to wear the equipment on the first night (only at baseline) and the last three nights of the measurement protocol. If a family started baseline measurements on a Wednesday, the sleep assessment nights were Wednesday (test night), Sunday, Monday, and Tuesday). Participants were instructed to attach three sensors to the back of the head approximately 30 minutes prior to bedtime which was defined as “when you are lying in bed and you are ready to close your eyes and go to sleep”. One sensor was placed on the neck below the hair line (ground) and one behind each ear on the differential mastoids (signal).8 We custom-made an elastic pocket for the Zmachine, which the participants were instructed to attach to an elastic waist belt at bedtime (Figure 2). We developed the pocket such that the device and its cables could be fixated to the belt and thus eliminate the risk of wire entanglement around the neck during sleep. This solution was mainly developed for children in whom the device had not been tested prior. Participants were instructed to connect the cable to the sensors and the Zmachine just before bedtime.
The Zmachine algorithm categorizes the EEG signal on 30-second epoch basis into five different categories 1) Wake, 2) Light sleep (Stage N1 & N2), 3) Deep sleep (Stage N3), 4) Rapid eye movement sleep (REM-sleep) and 5) sensor problem (if the sensor connection fails). Kaplan et al. found that the Zmachine algorithm has sensitivity (95.5 %) and specificity (92.5 %) when compared to polysomnographic technologists in scoring sleep and wake in adults.7 Wang et al. compared the Z-PLUS algorithm in conjunction with the Z-ALG algorithm to sleep stages scored by polysomnographic technologists and found that it has high sensitivity ranging between 72 % to 91 % in adults.7, 8
Participants also reported bedtimes and time of awakening each day allowing crude calculation of self-reported total sleep time.
Total sleep time was defined as the sum of time scored as light, deep, and REM sleep. Sleep onset latency was calculated as the time from application of the Zmachine equipment to the first epoch scored as sleep. Wake after sleep onset was calculated as the amount of time scored as wake between the first and the last epoch scored as sleep.
Assessment of feasibility
A priori, participants completing at least 2 out of 3 nights at both baseline and follow-up, were defined as compliant (see NCT04098913 at www.clinicaltrial.gov under “Secondary outcome measures”). In more specific terms; participants were only considered compliant if they provided 2 out of 3 nights with complete sleep data from the Zmachine.
- A night with complete sleep data was defined as a night in which less than 10 % of the epochs were scored as sensor problems.
Episodes with equipment failure (e.g. due to low battery or disconnection of the cable) was identified by manually looking through the sleep data records in cases where there was a difference of more than one hour between self-reported total sleep time and objectively measured total sleep time. Subsequently, a night was excluded if the sleep data record stopped during the night and no data was collected for the rest of the night (a strong indicator of equipment failure).
Adults also completed a questionnaire on behalf of themselves and each child concerning the perceived feasibility of the sleep assessment. The questionnaire was developed by the authors based on experiences with internal testing of the equipment. The questionnaire contained a variety of questions regarding the use of the sleep equipment, e.g. “to which degree were you bothered by the sleep equipment before, during, or following sleep?” (see Table 3 for an overview of the questions).
Baseline characteristics were computed using medians and inter quartile ranges for continuous variables and proportions for categorical variables. Characteristics are presented separately for children and adults; within the two intervention groups and for both groups combined.
Degree of compliance to the sleep protocol was calculated as proportions. Perceived feasibility was reported by calculating proportions in each response category.
A mean based on all nights with complete sleep data was calculated for everyone at baseline and follow-up for all sleep parameters. The means are based on sleep data from at least two and maximum three nights for each individual at baseline and follow-up, respectively. Group means and standard deviations were calculated for all sleep parameters at baseline and follow-up. Pearson correlation coefficients were calculated for the correlation between baseline and follow-up scores. Mean group changes and standard deviations in sleep parameters were calculated by subtracting group mean at baseline from group mean at follow-up. Not all sleep parameters followed a strict normal distribution; nevertheless, we present means and standard deviations to inform future sample size calculations. Medians and interquartile ranges for all sleep parameters are also given in Additional file 2. We performed supplementary sample size estimations based on the standard deviations of- and the correlation between the baseline and follow-up scores when scores from both groups were pooled (Additional file 3).
All statistical computations were performed in STATA IC 16 software (Statacorp).