skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


Title: Using Smartphone Sensor Data to Assess Inhibitory Control in the Wild: Longitudinal Study
Background Inhibitory control, or inhibition, is one of the core executive functions of humans. It contributes to our attention, performance, and physical and mental well-being. Our inhibitory control is modulated by various factors and therefore fluctuates over time. Being able to continuously and unobtrusively assess our inhibitory control and understand the mediating factors may allow us to design intelligent systems that help manage our inhibitory control and ultimately our well-being. Objective The aim of this study is to investigate whether we can assess individuals’ inhibitory control using an unobtrusive and scalable approach to identify digital markers that are predictive of changes in inhibitory control. Methods We developed InhibiSense, an app that passively collects the following information: users’ behaviors based on their phone use and sensor data, the ground truths of their inhibition control measured with stop-signal tasks (SSTs) and ecological momentary assessments (EMAs), and heart rate information transmitted from a wearable heart rate monitor (Polar H10). We conducted a 4-week in-the-wild study, where participants were asked to install InhibiSense on their phone and wear a Polar H10. We used generalized estimating equation (GEE) and gradient boosting tree models fitted with features extracted from participants’ phone use and sensor data to predict their stop-signal reaction time (SSRT), an objective metric used to measure an individual’s inhibitory control, and identify the predictive digital markers. Results A total of 12 participants completed the study, and 2189 EMAs and SST responses were collected. The results from the GEE models suggest that the top digital markers positively associated with an individual’s SSRT include phone use burstiness (P=.005), the mean duration between 2 consecutive phone use sessions (P=.02), the change rate of battery level when the phone was not charged (P=.04), and the frequency of incoming calls (P=.03). The top digital markers negatively associated with SSRT include the standard deviation of acceleration (P<.001), the frequency of short phone use sessions (P<.001), the mean duration of incoming calls (P<.001), the mean decibel level of ambient noise (P=.007), and the percentage of time in which the phone was connected to the internet through a mobile network (P=.001). No significant correlation between the participants’ objective and subjective measurement of inhibitory control was found. Conclusions We identified phone-based digital markers that were predictive of changes in inhibitory control and how they were positively or negatively associated with a person’s inhibitory control. The results of this study corroborate the findings of previous studies, which suggest that inhibitory control can be assessed continuously and unobtrusively in the wild. We discussed some potential applications of the system and how technological interventions can be designed to help manage inhibitory control.  more » « less
Award ID(s):
1840025
PAR ID:
10339863
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
JMIR mHealth and uHealth
Volume:
8
Issue:
12
ISSN:
2291-5222
Page Range / eLocation ID:
e21703
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; (, Journal of Medical Internet Research)
    null (Ed.)
    Background With nearly 20% of the US adult population using fitness trackers, there is an increasing focus on how physiological data from these devices can provide actionable insights about workplace performance. However, in-the-wild studies that understand how these metrics correlate with cognitive performance measures across a diverse population are lacking, and claims made by device manufacturers are vague. While there has been extensive research leading to a variety of theories on how physiological measures affect cognitive performance, virtually all such studies have been conducted in highly controlled settings and their validity in the real world is poorly understood. Objective We seek to bridge this gap by evaluating prevailing theories on the effects of a variety of sleep, activity, and heart rate parameters on cognitive performance against data collected in real-world settings. Methods We used a Fitbit Charge 3 and a smartphone app to collect different physiological and neurobehavioral task data, respectively, as part of our 6-week-long in-the-wild study. We collected data from 24 participants across multiple population groups (shift workers, regular workers, and graduate students) on different performance measures (vigilant attention and cognitive throughput). Simultaneously, we used a fitness tracker to unobtrusively obtain physiological measures that could influence these performance measures, including over 900 nights of sleep and over 1 million minutes of heart rate and physical activity metrics. We performed a repeated measures correlation (rrm) analysis to investigate which sleep and physiological markers show association with each performance measure. We also report how our findings relate to existing theories and previous observations from controlled studies. Results Daytime alertness was found to be significantly correlated with total sleep duration on the previous night (rrm=0.17, P<.001) as well as the duration of rapid eye movement (rrm=0.12, P<.001) and light sleep (rrm=0.15, P<.001). Cognitive throughput, by contrast, was not found to be significantly correlated with sleep duration but with sleep timing—a circadian phase shift toward a later sleep time corresponded with lower cognitive throughput on the following day (rrm=–0.13, P<.001). Both measures show circadian variations, but only alertness showed a decline (rrm=–0.1, P<.001) as a result of homeostatic pressure. Both heart rate and physical activity correlate positively with alertness as well as cognitive throughput. Conclusions Our findings reveal that there are significant differences in terms of which sleep-related physiological metrics influence each of the 2 performance measures. This makes the case for more targeted in-the-wild studies investigating how physiological measures from self-tracking data influence, or can be used to predict, specific aspects of cognitive performance. 
    more » « less
  2. ; ; ; ; ; ; ; ; ; (, JMIR Formative Research)
    Background The physical and emotional well-being of women is critical for healthy pregnancy and birth outcomes. The Two Happy Hearts intervention is a personalized mind-body program coached by community health workers that includes monitoring and reflecting on personal health, as well as practicing stress management strategies such as mindful breathing and movement. Objective The aims of this study are to (1) test the daily use of a wearable device to objectively measure physical and emotional well-being along with subjective assessments during pregnancy, and (2) explore the user’s engagement with the Two Happy Hearts intervention prototype, as well as understand their experiences with various intervention components. Methods A case study with a mixed design was used. We recruited a 29-year-old woman at 33 weeks of gestation with a singleton pregnancy. She had no medical complications or physical restrictions, and she was enrolled in the Medi-Cal public health insurance plan. The participant engaged in the Two Happy Hearts intervention prototype from her third trimester until delivery. The Oura smart ring was used to continuously monitor objective physical and emotional states, such as resting heart rate, resting heart rate variability, sleep, and physical activity. In addition, the participant self-reported her physical and emotional health using the Two Happy Hearts mobile app–based 24-hour recall surveys (sleep quality and level of physical activity) and ecological momentary assessment (positive and negative emotions), as well as the Perceived Stress Scale, Center for Epidemiologic Studies Depression Scale, and State-Trait Anxiety Inventory. Engagement with the Two Happy Hearts intervention was recorded via both the smart ring and phone app, and user experiences were collected via Research Electronic Data Capture satisfaction surveys. Objective data from the Oura ring and subjective data on physical and emotional health were described. Regression plots and Pearson correlations between the objective and subjective data were presented, and content analysis was performed for the qualitative data. Results Decreased resting heart rate was significantly correlated with increased heart rate variability (r=–0.92, P<.001). We found significant associations between self-reported responses and Oura ring measures: (1) positive emotions and heart rate variability (r=0.54, P<.001), (2) sleep quality and sleep score (r=0.52, P<.001), and (3) physical activity and step count (r=0.77, P<.001). In addition, deep sleep appeared to increase as light and rapid eye movement sleep decreased. The psychological measures of stress, depression, and anxiety appeared to decrease from baseline to post intervention. Furthermore, the participant had a high completion rate of the components of the Two Happy Hearts intervention prototype and shared several positive experiences, such as an increased self-efficacy and a normal delivery. Conclusions The Two Happy Hearts intervention prototype shows promise for potential use by underserved pregnant women. 
    more » « less
  3. ; ; ; ; ; ; ; (, Proceedings of the Human Factors and Ergonomics Society Annual Meeting)
    Vigilance refers to an individual’s ability to maintain attention over time. Vigilance decrement is particularly concerning in clinical environments where shift work and long working hours are common. This study identifies significant factors and indicators for predicting and monitoring individuals’ vigilance decrement. We enrolled 11 participants and measured their vigilance levels by recording their reaction times while completing the Psychomotor Vigilance Test. Additionally, we measured participants’ physiological responses and collected their sleep deprivation data, demographic information, and self-reported anxiety levels. Using repeated-measures correlation analysis, we found that decreased vigilance levels, indicated by longer reaction times, were associated with higher electrodermal activity ( p < .01), lower skin temperature ( p < .001), shorter fixation durations ( p < .05), and increased saccade frequency ( p < .05). Moreover, sleep deprivation significantly affected vigilance decrement ( p < .001). Our findings provide the potential to develop a predictive model of vigilance decrements using physiological signals collected from non-intrusive devices, as an alternative to current behavior-based methods. 
    more » « less
  4. ; ; ; (, Journal of Experimental Biology)
    ABSTRACT Oxygen deprivation during embryonic development can permanently remodel the vertebrate heart, often causing cardiovascular abnormalities in adulthood. While this phenomenon is mostly damaging, recent evidence suggests developmental hypoxia produces stress-tolerant phenotypes in some ectothermic vertebrates. Embryonic common snapping turtles (Chelydra serpentina) subjected to chronic hypoxia display improved cardiac anoxia tolerance after hatching, which is associated with altered Ca2+ homeostasis in heart cells (cardiomyocytes). Here, we examined the possibility that changes in Ca2+ cycling, through the sarcoplasmic reticulum (SR), underlie the developmentally programmed cardiac phenotype of snapping turtles. We investigated this hypothesis by isolating cardiomyocytes from juvenile turtles that developed in either normoxia (21% O2; ‘N21’) or chronic hypoxia (10% O2; ‘H10’) and subjected the cells to anoxia/reoxygenation, in either the presence or absence of SR Ca2+-cycling inhibitors. We simultaneously measured cellular shortening, intracellular Ca2+ concentration ([Ca2+]i), and intracellular pH (pHi). Under normoxic conditions, N21 and H10 cardiomyocytes shortened equally, but H10 Ca2+ transients (Δ[Ca2+]i) were twofold smaller than those of N21 cells, and SR inhibition only decreased N21 shortening and Δ[Ca2+]i. Anoxia subsequently depressed shortening, Δ[Ca2+]i and pHi in control N21 and H10 cardiomyocytes, yet H10 shortening and Δ[Ca2+]i recovered to pre-anoxic levels, partly due to enhanced myofilament Ca2+ sensitivity. SR blockade abolished the recovery of anoxic H10 cardiomyocytes and potentiated decreases in shortening, Δ[Ca2+]i and pHi. Our novel results provide the first evidence of developmental programming of SR function and demonstrate that developmental hypoxia confers a long-lasting, superior anoxia-tolerant cardiac phenotype in snapping turtles, by modifying SR function and enhancing myofilament Ca2+ sensitivity. 
    more » « less
  5. ; ; ; (, ACM Transactions on Computing for Healthcare)
    New modes of technology are offering unprecedented opportunities to unobtrusively collect data about people's behavior. While there are many use cases for such information, we explore its utility for predicting multiple clinical assessment scores. Because clinical assessments are typically used as screening tools for impairment and disease, such as mild cognitive impairment (MCI), automatically mapping behavioral data to assessment scores can help detect changes in health and behavior across time. In this article, we aim to extract behavior markers from two modalities, a smart home environment and a custom digital memory notebook app, for mapping to 10 clinical assessments that are relevant for monitoring MCI onset and changes in cognitive health. Smart-home-based behavior markers reflect hourly, daily, and weekly activity patterns, while app-based behavior markers reflect app usage and writing content/style derived from free-form journal entries. We describe machine learning techniques for fusing these multimodal behavior markers and utilizing joint prediction. We evaluate our approach using three regression algorithms and data from 14 participants with MCI living in a smart-home environment. We observed moderate to large correlations between predicted and ground-truth assessment scores, ranging from r = 0.601 to r = 0.871 for each clinical assessment. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email