Feasibility of Measuring Screen Time, Activity, and Context Among Families With Preschoolers: Intensive Longitudinal Pilot Study

Abstract

Background Digital media has made screen time more available across multiple contexts, but our understanding of the ways children and families use digital media has lagged behind the rapid adoption of this technology. Objective This study evaluated the feasibility of an intensive longitudinal data collection protocol to objectively measure digital media use, physical activity, sleep, sedentary behavior, and socioemotional context among caregiver-child dyads. This paper also describes preliminary convergent validity of ecological momentary assessment (EMA) measures and preliminary agreement between caregiver self-reported phone use and phone use collected from passive mobile sensing. Methods Caregivers and their preschool-aged child (3-5 years) were recruited to complete a 30-day assessment protocol. Within 30-days, caregivers completed 7 days of EMA to measure child behavior problems and caregiver stress. Caregivers and children wore an Axivity AX3 (Newcastle Upon Tyne) accelerometer to assess physical activity, sedentary behavior, and sleep. Phone use was assessed via passive mobile sensing; we used Chronicle for Android users and screenshots of iOS screen time metrics for iOS users. Participants were invited to complete a second 14-day protocol approximately 3-12 months after their first assessment. We used Pearson correlations to examine preliminary convergent validity between validated questionnaire measures of caregiver psychological functioning, child behavior, and EMA items. Root mean square errors were computed to examine the preliminary agreement between caregiver self-reported phone use and objective phone use. Results Of 110 consenting participants, 105 completed all protocols (105/110, 95.5% retention rate). Compliance was defined a priori as completing ≥70%-75% of each protocol task. There were high compliance rates for passive mobile sensing for both Android (38/40, 95%) and iOS (64/65, 98%). EMA compliance was high (105/105, 100%), but fewer caregivers and children were compliant with accelerometry (62/99, 63% and 40/100, 40%, respectively). Average daily phone use was 383.4 (SD 157.0) minutes for Android users and 354.7 (SD 137.6) minutes for iOS users. There was poor agreement between objective and caregiver self-reported phone use; root mean square errors were 157.1 and 81.4 for Android and iOS users, respectively. Among families who completed the first assessment, 91 re-enrolled to complete the protocol a second time, approximately 7 months later (91/105, 86.7% retention rate). Conclusions It is feasible to collect intensive longitudinal data on objective digital media use simultaneously with accelerometry and EMA from an economically and racially diverse sample of families with preschool-aged children. The high compliance and retention of the study sample are encouraging signs that these methods of intensive longitudinal data collection can be completed in a longitudinal cohort study. The lack of agreement between self-reported and objectively measured mobile phone use highlights the need for additional research using objective methods to measure digital media use. International Registered Report Identifier (IRRID) RR2-36240

The utility of behavioral biometrics in user authentication and demographic characteristic detection: a scoping review

“…Specifically with the passive-sensing application Chronicle, data are not associated with IP addresses or phone numbers and only indicate the type of application used (e.g., educational, social media), not the information on websites visited or the content of messages and emails. Parents are comfortable with using passive sensing technology when participating in a research study, as indicated by a feasibility study reporting no dropouts due to privacy concerns in using this technology [ 171 ]. While passive sensing applications have been shown to be accepted for use by families, future research can examine the extent to which families are comfortable with sensor tracking technology (e.g., accelerometer, gyroscope, touch) continuously monitoring user behavior on shared mobile devices.…”

Managing Screen Use in the Under-Fives: Recommendations for Parenting Intervention Development

“…While child or parent self-report and parent proxy-report have been the most common methods of assessment in research to date, studies of self-report of screen use suggest low validity (Boase & Ling, 2013), where adults tend to under-report their use of television (Clark et al, 2009) and smartphones (Lee et al, 2017). Recent studies of parents suggest there is poor correspondence between caregiver self-report and objective measures of child and parent screen use (Barr et al, 2020;Parker et al, 2022;Radesky et al, 2020). Current work on objective measures of screen use holds promise in terms of obtaining more accurate measures of child exposure (e.g., Radesky et al, 2020;Vadathya et al, 2022), however, given the number of devices a child may access in the home and in the community, both actively and passively, the task of estimating screen exposure is complex.…”