Towards Precision Stress Management: Design and Evaluation of a Practical Wearable Sensing System for Monitoring Everyday Stress

Tian, Hanqin; Rogers, Jeffrey M.; Chang, Hung‐Yang; Ball, Marion J.; Walter, Kimberly N.; Sun, Si; Chen, Ching-Hua; Xiang-rong, Zhu

doi:10.2196/iproc.8441

Cited by 14 publications

(14 citation statements)

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“…Intelligent prompts guided by sensor-driven machine learning algorithms that adapt to the user’s context may be beneficial for increasing user engagement [ 77 - 79 ]. It has also been suggested that tools to unobtrusively gauge and manage day-to-day stress may be improved by considering contextual information [ 34 , 40 , 80 ]. However, other research has shown that after 20 days of receiving machine learning suggestions, participants favored self-selecting their intervention, potentially seeking novelty after the algorithm became too locked in or limited in offerings [ 78 ].…”

Section: Discussionmentioning

confidence: 99%

Digital Prompts to Increase Engagement With the Headspace App and for Stress Regulation Among Parents: Feasibility Study

Militello¹,

Sobolev²,

Okeke³

et al. 2022

JMIR Form Res

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Background Given the interrelated health of children and parents, strategies to promote stress regulation are critically important in the family context. However, the uptake of preventive mental health is limited among parents owing to competing family demands. Objective In this study, we aim to determine whether it is feasible and acceptable to randomize digital prompts designed to engage parents in real-time brief mindfulness activities guided by a commercially available app. Methods We conducted a 30-day pilot microrandomized trial among a sample of parents who used Android smartphones. Each day during a parent-specified time frame, participants had a 50% probability of receiving a prompt with a message encouraging them to engage in a mindfulness activity using a commercial app, Headspace. In the 24 hours following randomization, ecological momentary assessments and passively collected smartphone data were used to assess proximal engagement (yes or no) with the app and any mindfulness activity (with or without the app). These data were combined with baseline and exit surveys to determine feasibility and acceptability. Results Over 4 months, 83 interested parents were screened, 48 were eligible, 16 were enrolled, and 10 were successfully onboarded. Reasons for nonparticipation included technology barriers, privacy concerns, time constraints, or change of mind. In total, 80% (8/10) of parents who onboarded successfully completed all aspects of the intervention. While it is feasible to randomize prompt delivery, only 60% (6/10) of parents reported that the timing of prompts was helpful despite having control over the delivery window. Across the study period, we observed higher self-reported engagement with Headspace on days with prompts (31/62, 50% of days), as opposed to days without prompts (33/103, 32% of days). This pattern was consistent for most participants in this study (7/8, 87%). The time spent using the app on days with prompts (mean 566, SD 378 seconds) was descriptively higher than on days without prompts (mean 225, SD 276 seconds). App usage was highest during the first week and declined over each of the remaining 3 weeks. However, self-reported engagement in mindfulness activities without the app increased over time. Self-reported engagement with any mindfulness activity was similar on days with (40/62, 65% of days) and without (65/103, 63% of days) prompts. Participants found the Headspace app helpful (10/10, 100%) and would recommend the program to others (9/10, 90%). Conclusions Preliminary findings suggest that parents are receptive to using mindfulness apps to support stress management, and prompts are likely to increase engagement with the app. However, we identified several implementation challenges in the current trial, specifically a need to optimize prompt timing and frequency as a strategy to engage users in preventive digital mental health.

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Section: Discussionmentioning

confidence: 99%

Digital Prompts to Increase Engagement With the Headspace App and for Stress Regulation Among Parents: Feasibility Study

Militello¹,

Sobolev²,

Okeke³

et al. 2022

JMIR Form Res

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“…We further assume that the local controllers act at a faster time-scale than the optimization time-scale, so that they can be considered to act instantaneously 5 . Feedback can be collected via onboard sensors or wearables that infer user's (dis)comfort [68].…”

Section: Numerical Examplementioning

confidence: 99%

Distributed Personalized Gradient Tracking with Convex Parametric Models

Notarnicola¹,

Simonetto²,

Farina³

et al. 2020

Preprint

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We present a distributed optimization algorithm for solving online personalized optimization problems over a network of computing and communicating nodes, each of which linked to a specific user. The local objective functions are assumed to have a composite structure and to consist of a known time-varying (engineering) part and an unknown (userspecific) part. Regarding the unknown part, it is assumed to have a known parametric (e.g., quadratic) structure a priori, whose parameters are to be learned along with the evolution of the algorithm. The algorithm is composed of two intertwined components: (i) a dynamic gradient tracking scheme for finding local solution estimates and (ii) a recursive least squares scheme for estimating the unknown parameters via user's noisy feedback on the local solution estimates. The algorithm is shown to exhibit a bounded regret under suitable assumptions. Finally, a numerical example corroborates the theoretical analysis.

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“…Users' feedback comes as a noisy sample of their comfort function, and the noise is modeled as a zero-mean Gaussian variable with variance σ " 0.1. Feedback in this example can come in different ways: it can come at low frequency, if the users are asked to hit the break or the accelerator every time they feel too close or too far from the vehicle in front, or it can come at higher frequency, if the users are equipped with heart rate/breathing rate sensors (which can be in smartwatches or incorporated in the seat of the vehicles [43]) which may be used as proxies of stress and discomfort.…”

Section: Numerical Evaluationmentioning

confidence: 99%

Personalized Optimization with User's Feedback

Simonetto,

Dall'Anese,

Monteil

et al. 2019

Preprint

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This paper develops an online algorithm to solve a time-varying optimization problem with an objective that comprises a known time-varying cost and an unknown function. This problem structure arises in a number of engineering systems and cyber-physical systems where the known function captures time-varying engineering costs, and the unknown function models user's satisfaction; in this context, the objective is to strike a balance between given performance metrics and user's satisfaction. Key challenges related to the problem at hand are related to (1) the time variability of the problem, and (2) the fact that learning of the user's utility function is performed concurrently with the execution of the online algorithm. This paper leverages Gaussian processes (GP) to learn the unknown cost function from noisy functional evaluation and build pertinent upper confidence bounds. Using the GP formalism, the paper then advocates timevarying optimization tools to design an online algorithm that exhibits tracking of the oracle-based optimal trajectory within an error ball, while learning the user's satisfaction function with no-regret. The algorithmic steps are inexact, to account for possible limited computational budgets or real-time implementation considerations. Numerical examples are illustrated based on a problem related to vehicle platooning.

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Towards Precision Stress Management: Design and Evaluation of a Practical Wearable Sensing System for Monitoring Everyday Stress

Cited by 14 publications

References 0 publications

Digital Prompts to Increase Engagement With the Headspace App and for Stress Regulation Among Parents: Feasibility Study

Digital Prompts to Increase Engagement With the Headspace App and for Stress Regulation Among Parents: Feasibility Study

Distributed Personalized Gradient Tracking with Convex Parametric Models

Personalized Optimization with User's Feedback

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