Improved Sleep Detection Through the Fusion of Phone Agent and Wearable Data Streams

Martínez, Gonzalo J.; Mattingly, Stephen M.; Young, Jessica L; Faust, Louis; Dey, Anind K.; Campbell, Andrew T.; Choudhury, Munmun De; Mirjafari, Shayan; Nepal, Subigya; Robles-Granda, Pablo; Saha, Koustuv; Striegel, Aaron

doi:10.1109/percomworkshops48775.2020.9156211

Cited by 16 publications

(8 citation statements)

References 22 publications

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“…This paper contextualizes the potential of leveraging pervasive technologies for this new work paradigm to enable new forms of personnel management. Pervasive technologies include ubiquitous technologies such as wearables, bluetooth, and smartphone based sensors, as well as online technologies such as social media and crowd-contributed online platforms -these technologies have shown significant promises for passively understanding wellbeing both longitudinally and at scale [13,21,23,24,25,26,27,28,29,30].…”

Section: Introductionmentioning

confidence: 99%

Social Media and Ubiquitous Technologies for Remote Worker Wellbeing and Productivity in a Post-Pandemic World

Swain

Saha

Abowd

et al. 2020

2020 IEEE Second International Conference on Cognitive Machine Intelligence (CogMI)

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In light of the ongoing COVID-19 pandemic, remote work styles have become the norm. However, these work settings introduce new intricacies in worker behaviors. The overlap between work and home can disrupt performance. The lack of social interaction can affect motivation. This elicits a need to implement novel methods to evaluate and enhance remote worker functioning. The potential to unobtrusively and automatically assess such workers can be fulfilled by social media and ubiquitous technologies. This paper situates recent research in the new context by extending our insights for increased remote interaction and online presence. We present implications for proactive assessment of remote workers by understanding day-level activities, coordination, role awareness, and organizational culture. Additionally, we discuss the ethics of privacy-preserving deployment, employer surveillance, and digital inequity. This paper aims to inspire pervasive technologies for the new future of work.

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Section: Introductionmentioning

confidence: 99%

Social Media and Ubiquitous Technologies for Remote Worker Wellbeing and Productivity in a Post-Pandemic World

Swain

Saha

Abowd

et al. 2020

2020 IEEE Second International Conference on Cognitive Machine Intelligence (CogMI)

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“…This possibility allows wearables to generate the observed similar wake times for snoozers and non-snoozers, even if snoozers are cognitively alert prior to getting out of bed. Indeed, a recent study of the Tesserae data reports that wearables overestimate sleep compared to self-report by ~46 min, but that discrepancy can be reduced to 8 min if wearable data is corrected by cell phone usage [ 62 ], which could reflect cognitive alertness in the absence of movement. This reflects our survey findings, with ~75% of alarm and snoozing originated from a cell phone, and the average time spent snoozing before getting out of bed was 26.93 min.…”

Section: Discussionmentioning

confidence: 99%

“…In addition to understanding the physiology of snoozing, future work should consider how to measure the prevalent behavior of snoozing in-situ. Smartphone and wearable combinations offer a promising avenue to measure snoozing naturalistically, and incorporating these streams together could detect snoozing and reduce discrepancies between self-report, phone usage, and wearables as in [ 62 ]. Widespread measurement would also allow for an assessment of snoozing’s broader impact on society.…”

Section: Discussionmentioning

confidence: 99%

Snoozing: an examination of a common method of waking

Mattingly

Martínez

Young

et al. 2022

Sleep

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Study Objectives Snoozing was defined as using multiple alarms to accomplish waking, and considered as a method of sleep inertia reduction that utilizes the stress system. Surveys measured snoozing behavior including who, when, how, and why snoozing occurs. In addition, the physiological effects of snoozing on sleep were examined via wearable sleep staging and heart rate activity, both over a long time scale, and on the days that it occurs. We aimed to establish snoozing as a construct in need of additional study. Methods A novel survey examined snoozing prevalence, how snoozing was accomplished, and explored possible contributors and motivators of snoozing behavior in 450 participants. Trait- and day-level surveys were combined with wearable data to determine if snoozers sleep differently than non-snoozers, and how snoozers and non-snoozers differ in other areas, such as personality. Results 57% of participants snoozed. Being female, younger, having fewer steps, having lower conscientiousness, having more disturbed sleep, and being a more evening chronotype increased the likelihood of being a snoozer. Snoozers had elevated resting heart rate and showed lighter sleep before waking. Snoozers did not sleep less than non-snoozers nor did they feel more sleepiness or nap more often. Conclusions Snoozing is a common behavior associated with changes in sleep physiology before waking, both in a trait- and state-dependent manner, and is influenced by demographic and behavioral traits. Additional research is needed, especially in detailing the physiology of snoozing, its impact on health, and its interactions with observational studies of sleep.

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“…Therefore, rather than looking at a single biomarker of the ANS, as is HRV, a more complete view of the ANS response could perhaps delineate a viable strategy for health monitoring unobtrusively in the wild. More broadly, approaches based on multimodality are more likely to yield successful outcomes in health monitoring, as recent studies show in other fields such as sleep monitoring [108], job performance monitoring [109,110], and personality prediction [111].…”

Section: Implications Of This Studymentioning

confidence: 99%

Alignment Between Heart Rate Variability From Fitness Trackers and Perceived Stress: Perspectives From a Large-Scale In Situ Longitudinal Study of Information Workers

Martínez¹,

Grover²,

Mattingly³

et al. 2022

JMIR Hum Factors

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Background Stress can have adverse effects on health and well-being. Informed by laboratory findings that heart rate variability (HRV) decreases in response to an induced stress response, recent efforts to monitor perceived stress in the wild have focused on HRV measured using wearable devices. However, it is not clear that the well-established association between perceived stress and HRV replicates in naturalistic settings without explicit stress inductions and research-grade sensors. Objective This study aims to quantify the strength of the associations between HRV and perceived daily stress using wearable devices in real-world settings. Methods In the main study, 657 participants wore a fitness tracker and completed 14,695 ecological momentary assessments (EMAs) assessing perceived stress, anxiety, positive affect, and negative affect across 8 weeks. In the follow-up study, approximately a year later, 49.8% (327/657) of the same participants wore the same fitness tracker and completed 1373 EMAs assessing perceived stress at the most stressful time of the day over a 1-week period. We used mixed-effects generalized linear models to predict EMA responses from HRV features calculated over varying time windows from 5 minutes to 24 hours. Results Across all time windows, the models explained an average of 1% (SD 0.5%; marginal R2) of the variance. Models using HRV features computed from an 8 AM to 6 PM time window (namely work hours) outperformed other time windows using HRV features calculated closer to the survey response time but still explained a small amount (2.2%) of the variance. HRV features that were associated with perceived stress were the low frequency to high frequency ratio, very low frequency power, triangular index, and SD of the averages of normal-to-normal intervals. In addition, we found that although HRV was also predictive of other related measures, namely, anxiety, negative affect, and positive affect, it was a significant predictor of stress after controlling for these other constructs. In the follow-up study, calculating HRV when participants reported their most stressful time of the day was less predictive and provided a worse fit (R2=0.022) than the work hours time window (R2=0.032). Conclusions A significant but small relationship between perceived stress and HRV was found. Thus, although HRV is associated with perceived stress in laboratory settings, the strength of that association diminishes in real-life settings. HRV might be more reflective of perceived stress in the presence of specific and isolated stressors and research-grade sensing. Relying on wearable-derived HRV alone might not be sufficient to detect stress in naturalistic settings and should not be considered a proxy for perceived stress but rather a component of a complex phenomenon.

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Improved Sleep Detection Through the Fusion of Phone Agent and Wearable Data Streams

Cited by 16 publications

References 22 publications

Social Media and Ubiquitous Technologies for Remote Worker Wellbeing and Productivity in a Post-Pandemic World

Social Media and Ubiquitous Technologies for Remote Worker Wellbeing and Productivity in a Post-Pandemic World

Snoozing: an examination of a common method of waking

Alignment Between Heart Rate Variability From Fitness Trackers and Perceived Stress: Perspectives From a Large-Scale In Situ Longitudinal Study of Information Workers

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