The study, published in the Journal of Activity, Sedentary and Sleep Behaviors, tackled two questions at once. First, how many days and hours of parent reporting are needed before the app’s estimates of physical activity, sedentary behavior, and sleep become statistically reliable? Second, do the app’s classifications actually correspond to what a body-worn accelerometer records during the same moments of a child’s day? Both questions matter because the first years of life are a period of explosive development in which movement habits begin to form, and health guidelines from the World Health Organization already prescribe targets for sleep, sitting, and activity in children under five, even though the tools for measuring compliance remain crude.

The researchers drew on the My Little Moves cohort study, recruiting families through daycare centers, youth health services, and community organizations across the Netherlands. In total, 324 children, averaging about 22 months of age, contributed at least two days of app data for the reliability analysis. A smaller subgroup of 75 children, averaging about 20 months, also wore two Axivity AX3 accelerometers, one on the left wrist and one on the right hip, for eight consecutive days. The devices are tiny, weighing just 11 grams, and captured raw triaxial acceleration at 50 Hz around the clock, except during bathing. Parents were asked to log their child’s activities in the app for seven consecutive days, from midnight to midnight.

The app itself works like a digital time-use diary. Parents select from eleven activity categories, including personal care, eating and drinking, active transport, passive transport, playing, screen use, sitting or lying calmly, and sleeping, and record the start and stop time of each activity in five-minute increments. Follow-up questions probe the intensity of play, the child’s posture, and the context, such as location. Crucially, the app adapts its content to the child’s developmental stage: parents enter the child’s age and motor milestones, such as rolling over, sitting, crawling, or walking, and the activity categories and questions adjust accordingly. Reporting takes parents roughly 10 to 30 minutes per day, a burden the researchers acknowledge is not trivial.

To determine the minimum reporting time, the team applied the Spearman-Brown prophecy formula to single-day intraclass correlations, testing every combination of daily reporting hours from 12 to 24 and monitoring periods from two to seven days. The results were specific. Reliable estimates of sleep required at least two days of 20 reported hours. Physical activity required two days of 23 hours. Sedentary behavior proved the most demanding, needing four days of 17 hours. For the overall composition of the 24-hour day, in which the three behaviors are analyzed together as mutually exclusive shares of time, two days of 23 hours sufficed. Notably, the study also found significant differences between weekdays and weekend days for most behaviors, underscoring the importance of capturing both in any monitoring window.

The validity analysis took an unusual route, because there is no gold standard for measuring movement behaviors in infants and toddlers, and no validated cut-points exist for translating raw acceleration into activity classes across the full 0-to-4 age range. Instead, the researchers tested hypotheses about how accelerometer-derived acceleration should behave if the app’s classifications were meaningful. The central prediction: acceleration should be lowest during app-reported sleep, intermediate during sedentary behavior, and highest during physical activity. Using the open-source GGIR software, they computed two metrics, the Euclidean norm minus one (ENMO) and the mean amplitude deviation (MAD), for both hip and wrist placements, and matched every five-second epoch of acceleration to the parent-reported activity in progress at that moment.

The main hypothesis held up cleanly. Across both sensor placements and both metrics, acceleration differed significantly among the three behaviors in exactly the predicted order, with p-values below 0.001. The explained variance was moderate to substantial, reaching an R-squared of 0.37 for wrist-worn MAD, suggesting that the app’s broad behavioral categories map onto genuinely different patterns of body movement. The researchers then pushed further, formulating 55 sub-hypotheses about expected similarities and differences in acceleration across the app’s finer activity categories. Consistent support emerged for 21 of them. Active play and active transport reliably produced higher acceleration than sitting, personal care, eating, and passive screen use, while sleep produced lower acceleration than nearly everything else.

But the finer-grained comparisons also exposed instructive failures. Passive transport, meaning a child being pushed in a stroller or carried in a car seat, generated significantly higher acceleration than other sedentary categories and was indistinguishable from genuine physical activity. The reason is a known blind spot of accelerometry in early childhood: the sensor records the motion of whoever is moving the child, not just the child’s own movement. The finding, the authors argue, is a clear signal that current accelerometer processing methods need rethinking for the youngest age groups. Screen use posed its own puzzle: passive screen use showed the lowest acceleration of all sedentary activities, echoing laboratory findings in older children, while active screen use, such as dancing in front of a television, was reported only three times in the entire dataset, too rarely to draw conclusions.

The study has honest limitations. The comparison sample of 75 children falls short of the roughly 100 participants recommended for measurement-instrument validation studies, and most participating parents were highly educated women, which may limit generalizability. Parents reported activities in blocks of about 30 minutes, making the app insensitive to rapid posture changes such as brief episodes of tummy time, an issue for monitoring infant-specific recommendations. Excluding the categories for unknown activities and time with other caregivers, which accounted for 3.4 percent of reports, may have introduced systematic bias, particularly since many young children spend long hours in daycare outside parental sight. Attrition was also notable: 22 percent of parents who started never logged any activities at all.

Even so, the practical takeaway is concrete. Two full days of app reporting, of at least 23 hours each, are enough to obtain a reliable picture of how a child under four divides the day between moving, sitting, and sleeping, and the app’s categories behave in physiologically plausible ways when checked against objective motion data. The authors recommend combining the app with accelerometry in future research, since the two tools capture complementary information: sensors quantify movement continuously, while the app supplies the context, the type of activity, and the setting that sensors cannot see. They also call for data-driven machine learning models to translate raw acceleration into behavior estimates for infants and toddlers, including daytime naps, and for clearer scientific definitions of what physical activity even means in children who cannot yet walk. For a field long forced to extrapolate from tools built for school-aged children, the message is that measuring the earliest years properly is finally within reach, one parent-reported five-minute block at a time.

Subject of Research: Reliability and validity of a parent-report mobile app for assessing 24-hour movement behaviors in children aged 0 to 4 years

Article Title: Assessing 24-h movement behaviors in early childhood (0–4 years): Reliability of the My Little Moves app and comparison with accelerometry

Article References: Lettink, A., Arts, J., Gubbels, J. S., Altenburg, T. M., & Chinapaw, M. J. M. (2025). Assessing 24-h movement behaviors in early childhood (0–4 years): Reliability of the My Little Moves app and comparison with accelerometry. Journal of Activity, Sedentary and Sleep Behaviors, 4(1), Article 5. https://doi.org/10.1186/s44167-025-00075-x

Image Credits: AI Generated

DOI: 10.1186/s44167-025-00075-x

Keywords: physical activity, sedentary behavior, sleep, infants, toddlers, preschoolers, accelerometry, mobile app, time-use diary, measurement validity, 24-hour movement behaviors, child health