ReviewDevelopmental psychobiology2019
Automated sensing of daily activity: A new lens into development.
Review in Developmental psychobiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
42 citing papers in PubMed, 81 citations in OpenAlex.
- Variation in Everyday Maternal Sensitivity Predicts Infants' Real-Time Physiological Regulation.Developmental psychobiology · 2026Article
- Bridging Machine Learning and Event-Based Analyses to Assess Maternal Contingent Responses to Infant Nondistress and Distress Vocalizations From Home Audio Recordings.Developmental science · 2026Article
- Linking temporal dynamics of children's natural behavior to brain development.Developmental cognitive neuroscience · 2026Article
- Thinking Critically About Algorithms for Automated Detection of Behavior: 11 Guidelines for Social and Behavioral Scientists.Developmental science · 2026Article
- Everyday maternal sensitivity to distress is dynamic and systematically varies across contexts.Child development · 2026Article
- Non-Linear Changes in Face Availability During Naturalistic Playtime Across the First Years: Insights From Head-Mounted Cameras and Automated Face Detection.Developmental science · 2026Article
- Trade-Offs Between Simplifying Inertial Measurement Unit-Based Movement Recordings and the Attainability of Different Levels of Analyses: Systematic Assessment of Method Variations.JMIR mHealth and uHealth · 2025Article
- Reaching While Learning to Sit: Capturing the Kinematics of Co-Developing Skills at Home.Developmental psychobiology · 2024Article
- Infant-directed communication: Examining the many dimensions of everyday caregiver-infant interactions.Developmental science · 2024Article
- Long-form recording of infant body position in the home using wearable inertial sensors.Behavior research methods · 2024Article
- Automatic assessment of infant carrying and holding using at-home wearable recordings.Scientific reports · 2024Article
- Evaluating Users' Experiences of a Child Multimodal Wearable Device: Mixed Methods Approach.JMIR human factors · 2024Article
- Maternal contingent responses to distress facilitate infant soothing but not in mothers with depression or infants high in negative affect.Developmental psychology · 2024Article
- Preliminary Technical Validation of LittleBeats™: A Multimodal Sensing Platform to Capture Cardiac Physiology, Motion, and Vocalizations.Sensors (Basel, Switzerland) · 2024Article
- Childhood conduct problems and parent-child talk during social and nonsocial play contexts: a naturalistic home-based experiment.Scientific reports · 2024Article
- Spontaneous infant crying modulates vagal activity in real time.Developmental psychobiology · 2023Article
- Infant crying predicts real-time fluctuations in maternal mental health in ecologically valid home settings.Developmental psychology · 2023Article
- Early triadic interactions in the first year of life: a systematic review on object-mediated shared encounters.Frontiers in psychology · 2023Review
- Auditory chaos classification in real-world environments.Frontiers in digital health · 2023Article
- Early development in autism: How developmental cascades help us understand the emergence of developmental differences.Advances in child development and behavior · 2023Article
Corrections and comments
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Authors and funding
1 author at 1 institution in 1 country.
Funding
Abstract
Rapidly maturing technologies for sensing and activity recognition can provide unprecedented access to the complex structure daily activity and interaction, promising new insight into the mechanisms by which experience shapes developmental outcomes. Motion data, autonomic activity, and "snippets" of audio and video recordings can be conveniently logged by wearable sensors (Lazer et al., 2009). Machine learning algorithms can process these signals into meaningful markers, from child and parent behavior to outcomes such as depression or teenage drinking. Theoretically motivated aspects of daily activity can be combined and synchronized to examine reciprocal effects between children's behaviors and their environments or internal processes. Captured over longitudinal time, such data provide a new opportunity to study the processes by which individual differences emerge and stabilize. This paper introduces the reader to developments in sensing and activity recognition with implications for developmental phenomena across the lifespan, sketching a framework for leveraging mobile sensors for transactional analyses that bridge micro- and longitudinal- timescales of development. It finishes by detailing resources and best practices to facilitate the next generation of developmentalists to contribute to this emerging area.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.