Publications by authors named "Kaya de Barbaro"

14 Publications

  • Page 1 of 1

Sensing everyday activity: Parent perceptions and feasibility.

Infant Behav Dev 2021 Feb 16;62:101511. Epub 2021 Jan 16.

Department of Psychology, University of Texas at Austin, United States.

Mobile and wearable sensors provide a unique opportunity to capture the daily activities and interactions that shape developmental trajectories, with potential to revolutionize the study of development (de Barbaro, 2019). However, developmental research employing sensors is still in its infancy, and parents' comfort using these devices is uncertain. This exploratory report assesses parent willingness to participate in sensor studies via a nationally representative survey (N = 210) and live recruitment of a low-income, minority population for an ongoing study (N = 359). The survey allowed us to assess how protocol design influences acceptability, including various options for devices and datastream resolution, conditions of data sharing, and feedback. By contrast, our recruitment data provided insight into parents' true willingness to participate in a sensor study, with a protocol including 72 h of continuous audio, motion, and physiological data. Our results indicate that parents are relatively conservative when considering participation in sensing studies. However, nearly 41 % of surveyed parents reported that they would be at least somewhat willing to participate in studies with audio or video recordings, 26 % were willing or extremely willing, and 14 % reported being extremely willing. These results roughly paralleled our recruitment results, where 58 % of parents indicated interest, 29 % of parents scheduled to participate, and 10 % ultimately participated. Additionally, 70 % of caregivers stated their reason for not participating in the study was due to barriers unrelated to sensing while about 25 % noted barriers due to either privacy concerns or the physical sensors themselves. Parents' willingness to collect sensitive datastreams increased if data stayed within the household for individual use only, are shared anonymously with researchers, or if parents receive feedback from devices. Overall, our findings suggest that given the correct circumstances, mobile sensors are a feasible and promising tool for characterizing children's daily interactions and their role in development.
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http://dx.doi.org/10.1016/j.infbeh.2020.101511DOI Listing
February 2021

A Real-Time Eating Detection System for Capturing Eating Moments and Triggering Ecological Momentary Assessments to Obtain Further Context: System Development and Validation Study.

JMIR Mhealth Uhealth 2020 12 18;8(12):e20625. Epub 2020 Dec 18.

Georgia Institute of Technology, Atlanta, GA, United States.

Background: Eating behavior has a high impact on the well-being of an individual. Such behavior involves not only when an individual is eating, but also various contextual factors such as with whom and where an individual is eating and what kind of food the individual is eating. Despite the relevance of such factors, most automated eating detection systems are not designed to capture contextual factors.

Objective: The aims of this study were to (1) design and build a smartwatch-based eating detection system that can detect meal episodes based on dominant hand movements, (2) design ecological momentary assessment (EMA) questions to capture meal contexts upon detection of a meal by the eating detection system, and (3) validate the meal detection system that triggers EMA questions upon passive detection of meal episodes.

Methods: The meal detection system was deployed among 28 college students at a US institution over a period of 3 weeks. The participants reported various contextual data through EMAs triggered when the eating detection system correctly detected a meal episode. The EMA questions were designed after conducting a survey study with 162 students from the same campus. Responses from EMAs were used to define exclusion criteria.

Results: Among the total consumed meals, 89.8% (264/294) of breakfast, 99.0% (406/410) of lunch, and 98.0% (589/601) of dinner episodes were detected by our novel meal detection system. The eating detection system showed a high accuracy by capturing 96.48% (1259/1305) of the meals consumed by the participants. The meal detection classifier showed a precision of 80%, recall of 96%, and F1 of 87.3%. We found that over 99% (1248/1259) of the detected meals were consumed with distractions. Such eating behavior is considered "unhealthy" and can lead to overeating and uncontrolled weight gain. A high proportion of meals was consumed alone (680/1259, 54.01%). Our participants self-reported 62.98% (793/1259) of their meals as healthy. Together, these results have implications for designing technologies to encourage healthy eating behavior.

Conclusions: The presented eating detection system is the first of its kind to leverage EMAs to capture the eating context, which has strong implications for well-being research. We reflected on the contextual data gathered by our system and discussed how these insights can be used to design individual-specific interventions.
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http://dx.doi.org/10.2196/20625DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7775824PMC
December 2020

Finding Structure in Time: Visualizing and Analyzing Behavioral Time Series.

Front Psychol 2020 24;11:1457. Epub 2020 Jul 24.

Department of Psychology, University of Groningen, Groningen, Netherlands.

The temporal structure of behavior contains a rich source of information about its dynamic organization, origins, and development. Today, advances in sensing and data storage allow researchers to collect multiple dimensions of behavioral data at a fine temporal scale both in and out of the laboratory, leading to the curation of massive multimodal corpora of behavior. However, along with these new opportunities come new challenges. Theories are often underspecified as to the exact nature of these unfolding interactions, and psychologists have limited ready-to-use methods and training for quantifying structures and patterns in behavioral time series. In this paper, we will introduce four techniques to interpret and analyze high-density multi-modal behavior data, namely, to: (1) visualize the raw time series, (2) describe the overall distributional structure of temporal events (Burstiness calculation), (3) characterize the non-linear dynamics over multiple timescales with Chromatic and Anisotropic Cross-Recurrence Quantification Analysis (CRQA), (4) and quantify the directional relations among a set of interdependent multimodal behavioral variables with Granger Causality. Each technique is introduced in a module with conceptual background, sample data drawn from empirical studies and ready-to-use Matlab scripts. The code modules showcase each technique's application with detailed documentation to allow more advanced users to adapt them to their own datasets. Additionally, to make our modules more accessible to beginner programmers, we provide a "Programming Basics" module that introduces common functions for working with behavioral timeseries data in Matlab. Together, the materials provide a practical introduction to a range of analyses that psychologists can use to discover temporal structure in high-density behavioral data.
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http://dx.doi.org/10.3389/fpsyg.2020.01457DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7393268PMC
July 2020

Optimal sampling strategies for characterizing behavior and affect from ambulatory audio recordings.

J Fam Psychol 2020 Dec 9;34(8):980-990. Epub 2020 Apr 9.

Department of Psychology.

Advances in mobile and wearable technologies mean it is now feasible to record hours to days of participant behavior in its naturalistic context, a great boon for psychologists interested in family processes and development. While automated activity recognition algorithms exist for a limited set of behaviors, time-consuming human annotations are still required to robustly characterize the vast majority of behavioral and affective markers of interest. This report is the first to date which systematically tests the efficacy of different sampling strategies for characterizing behavior from audio recordings to provide practical guidelines for researchers. Using continuous audio recordings of the daily lives of 11 preschool-aged children, we compared sampling techniques to determine the most accurate and efficient approach. Results suggest that sampling both low and high frequency verbal and overt behaviors is best if samples are short in duration, systematically rather than randomly selected, and sampled to cover at least 12.5% of recordings. Implications for assessment of real-world behavior are discussed. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
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http://dx.doi.org/10.1037/fam0000654DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7544678PMC
December 2020

Longform recordings of everyday life: Ethics for best practices.

Behav Res Methods 2020 10;52(5):1951-1969

Department of Communication Sciences and Disorders, Northwestern University, 2240 Campus Dr., Frances Searle Building, Room 3-358, Evanston, IL, 60208, USA.

Recent advances in large-scale data storage and processing offer unprecedented opportunities for behavioral scientists to collect and analyze naturalistic data, including from underrepresented groups. Audio data, particularly real-world audio recordings, are of particular interest to behavioral scientists because they provide high-fidelity access to subtle aspects of daily life and social interactions. However, these methodological advances pose novel risks to research participants and communities. In this article, we outline the benefits and challenges associated with collecting, analyzing, and sharing multi-hour audio recording data. Guided by the principles of autonomy, privacy, beneficence, and justice, we propose a set of ethical guidelines for the use of longform audio recordings in behavioral research. This article is also accompanied by an Open Science Framework Ethics Repository that includes informed consent resources such as frequent participant concerns and sample consent forms.
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http://dx.doi.org/10.3758/s13428-020-01365-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7483614PMC
October 2020

Automated Detection of Infant Holding Using Wearable Sensing: Implications for Developmental Science And Intervention.

Proc ACM Interact Mob Wearable Ubiquitous Technol 2019 Jun;3(2)

The University of Texas at Austin.

Physical contact is critical for children's physical and emotional growth and well-being. Previous studies of physical contact are limited to relatively short periods of direct observation and self-report methods. These methods limit researchers' understanding of the natural variation in physical contact across families, and its specific impacts on child development. In this study we develop a mobile sensing platform that can provide objective, unobtrusive, and continuous measurements of physical contact in naturalistic home interactions. Using commercially available motion detectors, our model reaches an accuracy of 0.870 (std: 0.059) for a second-by-second binary classification of holding. In addition, we detail five assessment scenarios applicable to the development of activity recognition models for social science research, where required accuracy may vary as a function of the intended use. Finally, we propose a grand vision for leveraging mobile sensors to access high-density markers of multiple determinants of early parent-child interactions, with implications for basic science and intervention.
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http://dx.doi.org/10.1145/3328935DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6657512PMC
June 2019

Automated sensing of daily activity: A new lens into development.

Authors:
Kaya de Barbaro

Dev Psychobiol 2019 04 18;61(3):444-464. Epub 2019 Mar 18.

Department of Psychology, The University of Texas at Austin, Austin, Texas.

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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http://dx.doi.org/10.1002/dev.21831DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7343175PMC
April 2019

Chronodes: Interactive Multifocus Exploration of Event Sequences.

ACM Trans Interact Intell Syst 2018 Feb;8(1)

Georgia Institute of Technology.

The advent of mobile health (mHealth) technologies challenges the capabilities of current visualizations, interactive tools, and algorithms. We present Chronodes, an interactive system that unifies data mining and human-centric visualization techniques to support explorative analysis of longitudinal mHealth data. Chronodes extracts and visualizes frequent event sequences that reveal chronological patterns across multiple participant timelines of mHealth data. It then combines novel interaction and visualization techniques to enable multifocus event sequence analysis, which allows health researchers to interactively define, explore, and compare groups of participant behaviors using event sequence combinations. Through summarizing insights gained from a pilot study with 20 behavioral and biomedical health experts, we discuss Chronodes's efficacy and potential impact in the mHealth domain. Ultimately, we outline important open challenges in mHealth, and offer recommendations and design guidelines for future research.
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http://dx.doi.org/10.1145/3152888DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5835550PMC
February 2018

New meanings of thin-skinned: The contrasting attentional profiles of typical 12-month-olds who show high, and low, stress reactivity.

Dev Psychol 2018 05 22;54(5):816-828. Epub 2018 Jan 22.

Department of Psychology, University of Cambridge.

Previous research is inconsistent as to whether a more labile (faster-changing) autonomic system confers performance advantages, or disadvantages, in infants and children. To examine this, we presented a stimulus battery consisting of mixed static and dynamic viewing materials to a cohort of 63 typical 12-month-old infants. While viewing the battery, infants' spontaneous visual attention (looks to and away from the screen) was measured. Concurrently, arousal was recorded via heart rate (HR), electrodermal activity, head velocity, and peripheral movement levels. In addition, stress reactivity was assessed using a mild behavioral stressor (watching a video of another infant crying). We found that infants who were generally more attentive showed smaller HR increases to the stressor. However, they also showed greater phasic autonomic changes to attractive, attention-getting stimulus events, a faster rate of change of both look duration and of arousal, and more general oscillatory activity in arousal. Finally, 4 sessions of attention training were applied to a subset of the infants (24 trained, 24 active controls), which had the effect of increasing visual sustained attention. No changes in HR responses to stressor were observed as a result of training, but concomitant increases in arousal lability were observed. Our results point to 2 contrasting autonomic profiles: infants with high autonomic reactivity to stressors show short attention durations, whereas infants with lower autonomic reactivity show longer attention durations and greater arousal lability. (PsycINFO Database Record
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http://dx.doi.org/10.1037/dev0000428DOI Listing
May 2018

Contingencies Between Infants' Gaze, Vocal, and Manual Actions and Mothers' Object-Naming: Longitudinal Changes From 4 to 9 Months.

Dev Neuropsychol 2016 Jul - Dec;41(5-8):342-361

a Department of Cognitive Science , University of California San Diego , San Diego , California.

Infants' early motor actions help organize social interactions, forming the context of caregiver speech. We investigated changes across the first year in social contingencies between infant gaze and object exploration, and mothers' speech. We recorded mother-infant object play at 4, 6, and 9 months, identifying infants' and mothers' gaze and hand actions, and mothers' object naming and general utterances. Mothers named objects more when infants vocalized, looked at objects or the mother's face, or handled multiple objects. As infants aged, their increasing object exploration created opportunities for caregiver contingencies and changed how gaze and hands accompany object naming over time.
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http://dx.doi.org/10.1080/87565641.2016.1274313DOI Listing
August 2017

Infant Attention Is Dynamically Modulated With Changing Arousal Levels.

Child Dev 2017 03 2;88(2):629-639. Epub 2016 Dec 2.

University of East London.

Traditional accounts of developing attention and cognition emphasize static individual differences in information encoding; however, work from Aston-Jones et al. suggests that looking behavior may be dynamically influenced by autonomic arousal. To test this model, a 20-min testing battery constituting mixed photos and cartoon clips was shown to 53 typical 12-month-olds. Look duration was recorded to index attention, and continuous changes in arousal were tracked by measuring heart rate, electrodermal activity, and movement levels. Across three analyses, we found that continuous changes in arousal tracked simultaneous changes in attention measures, as predicted by the Aston-Jones model. It was also found that changes in arousal tended to precede (occur before) subsequent changes in attention. Implications of these findings are discussed.
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http://dx.doi.org/10.1111/cdev.12689DOI Listing
March 2017

Stress reactivity speeds basic encoding processes in infants.

Dev Psychobiol 2016 07 18;58(5):546-55. Epub 2016 Mar 18.

Medical Research Council Cognition and Brain Sciences Unit, Cambridge, UK.

Acute stress attenuates frontal lobe functioning and increases distractibility while enhancing subcortical processes in both human and nonhuman animals (reviewed by Arnsten [2009] Nature Reviews Neuroscience, 10(6):410-422). To date however these relations have not been examined for their potential effects in developing populations. Here, we examined the relationship between stress reactivity (infants' heart rate response to watching videos of another child crying) and infant performance on measures of looking duration and visual recognition memory. Our findings indicate that infants with increased stress reactivity showed shorter look durations and more novelty preference. Thus, stress appears to lead to a faster, more stimulus-ready attentional profile in infants. Additional work is required to assess potential negative consequences of stimulus-responsivity, such as decreased focus or distractibility. © 2016 Wiley Periodicals, Inc. Dev Psychobiol 58: 546-555, 2016.
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http://dx.doi.org/10.1002/dev.21399DOI Listing
July 2016

Sensorimotor Decoupling Contributes to Triadic Attention: A Longitudinal Investigation of Mother-Infant-Object Interactions.

Child Dev 2016 Mar-Apr;87(2):494-512. Epub 2015 Nov 27.

University of California, San Diego.

Previous developmental accounts of joint object activity identify a qualitative "shift" around 9-12 months. In a longitudinal study of 26 dyads, videos of joint object interactions at 4, 6, 9, and 12 months were coded for all targets of gaze and manual activity (at 10 Hz). At 12 months, infants distribute their sensorimotor modalities between objects handled by the parent and others controlled by the infant. Analyses reveal novel trajectories in distributed joint object activity across the 1st year. At 4 months, infants predominantly look at and manipulate a single object, typically held by their mothers. Between 6 and 9 months, infants increasingly decouple their visual and haptic modalities and distribute their attention between objects held by their mothers and by themselves. These previously unreported developments in the distribution of multimodal object activity might "bridge the gap" to coordinated joint activity between 6 and 12 months.
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http://dx.doi.org/10.1111/cdev.12464DOI Listing
January 2017

Micro-analysis of infant looking in a naturalistic social setting: insights from biologically based models of attention.

Dev Sci 2011 Sep 26;14(5):1150-60. Epub 2011 Jul 26.

Department of Cognitive Science, University of California, San Diego, La Jolla, CA 92093-0515, USA.

A current theory of attention posits that several micro-indices of attentional vigilance are dependent on activation of the locus coeruleus, a brainstem nucleus that regulates cortical norepinephrine activity (Aston-Jones et al., 1999). This theory may account for many findings in the infant literature, while highlighting important new areas for research and theory on infant attention. We examined the visual behaviors of n = 16 infants (6-7 months) while they attended to multiple spatially distributed targets in a naturalistic environment. We coded four measures of attentional vigilance, adapted from studies of norepinergic modulation of animal attention: rate of fixations, duration of fixations, latency to reorientation, and target 'hits'. These measures showed a high degree of coherence in individual infants, in parallel with findings from animal studies. Results also suggest that less vigilant infants showed greater habituation to the trial structure and more attentiveness to less salient stimuli during periods of high attentional competition. This pattern of results is predicted by the Aston-Jones model of attention, but could not be explained by the standard information processing model.
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http://dx.doi.org/10.1111/j.1467-7687.2011.01066.xDOI Listing
September 2011