“One possible explanation for these findings is that parents do a good job of matching their decisions to give their children phones to the needs of their children and families,” said Robinson. “These findings should be seen as empowering parents to do what they think is right for their family.”
He noted that early phone acquisition was not associated with problems, but neither was late phone acquisition, and “If parents wanted to delay, we wouldn’t see negative effects of that either.”
Assessment of children’s well-being
When deciding to give a child a mobile phone, parents usually evaluate many factors, such as whether the child needs a phone to let parents know their whereabouts, access the Internet, or maintain social contacts; how much the phone may distract the child from sleep, homework, or other activities; and whether the child is mature enough to deal with risks such as exposure to social media, cyberbullying, or violent content online.
Previous research on the effects of children’s cell phone ownership has shown mixed results, with some studies suggesting phones impair or lower sleep while others show no effect. Previous studies were limited because most collected data at only one or two time points.
In the Stanford Medicine study, the children were 7 to 11 years old when the study began and 11 to 15 years old by the end of the research. Each child and their parent participated in the assessments at baseline and thereafter annually, for a total of five assessments per participant.
In each assessment, parents were asked if their children owned a mobile phone and if it was a smartphone. The midpoint between the last visit when the child did not have a phone and the first visit when they did have a phone was counted as the age of acquisition.
At each visit, the children completed a standardized questionnaire assessing symptoms of depression. Parents reported the child’s most recent school grades and the child’s usual bedtime and wake time on school and non-school nights; They also answered a questionnaire about their children’s daytime sleepiness. After each visit, the children wore accelerometers on their right hip for a week, and the data were used as an objective measure of sleep onset and sleep duration each night.
The analysis controlled for several potential confounding factors, including the child’s age at school start, the child’s sex and birth order, the country of birth of the child and the parents, the parents’ marital status and education level, household income, frequency of English at home, and how advanced the child was. during puberty.
This doesn’t mean that you can’t take your child’s phone away if you think he’s taking too long to fall asleep.
About 25% of children received phones by the age of 10.7, and 75% by the age of 12.6. Almost all children had phones by the age of 15. Of the children who owned phones, 99% owned smartphones by the end of the study. The timing of children’s phone purchases was similar to what was recorded in the US cross-sectional samples.
The scientists investigated whether children’s well-being outcomes differed based on whether they had their own cell phones and what happened to their well-being outcomes when they did get their own (going from not having a phone to having one). They also conducted analyzes to test whether the children’s well-being differed according to the age at which the children got their first mobile phone.
Initial comparisons of the phone-owning versus non-phone-owning condition showed some indication of the differences: While the depression scores for the whole group decreased over time, meaning they were less depressed, the decline was slower when the children owned the phones than without. Possible effects on sleep were also noted: Parents reported that children slept less on school nights when they had a phone than when they didn’t – although this observation was not supported by children’s sleep measurements from accelerometer data. . Accelerometer data showed that when kids don’t have phones, they sleep less on non-school nights.
There are no statistically significant differences
However, when the team controlled for the statistical effect of multiple comparisons on the same data set, none of these associations met the criteria for statistical significance.
The researchers conducted further analyzes to see if children’s characteristics interacted with phone ownership in explaining their well-being outcomes. Mobile phone ownership was associated with lower levels of depressive symptoms in boys than in girls, and less depression for children with lower versus higher sexual maturity. Phone ownership has also been associated with poorer sleep among children with higher maturity. These results highlight potential relationships to be examined more closely in future studies.
When the analyzes were conducted only on smartphones (versus any mobile phone), the results were similar.
The overall pattern of results indicates that, overall, technology ownership was not found to be associated in a positive or negative way with children’s well-being. The researchers note that it may be more important to study what kids do with their technology than just whether or not they own a phone.
“These are average trends at the population level,” Sun said. “There are still individual differences. That doesn’t mean you can’t take your child’s phone away if you think they’re taking too long to sleep.”
The team is conducting research on how people use their phones as part of the Stanford University School of Medicine Human Screen Project.
The scientists also note that the study did not give children unfettered access to phones, as their parents made decisions about their technology use.
“To the level we can measure, the timing itself [of acquiring a phone] It doesn’t seem to be a major factor because it happens in the broader context of parenting,” Robinson said. “It’s not an argument for kids to say to their parents, ‘Look, there are no traces of phones.'” Parents need to use their best judgment about what is appropriate for their children, as they apparently already do.”
The research was supported by the National Heart, Lung, and Blood Institute (grant U01HL103629), a Stanford Data Science Grant, the Stanford Maternal and Child Health Research Institute, and the Stanford Department of Pediatrics.
The research team includes members from Stanford BioX, the Stanford Cardiovascular Institute, the Stanford Wu Tsai Human Performance Alliance, the Stanford Maternal and Child Health Research Institute, and the Stanford Cancer Institute, as well as affiliates of the Stanford Institute for Human-Artificial Intelligence and the Stanford Woods Institute for the Environment.