BeWell: Sensing Sleep, Physical Activities and Social Interactions to Promote Wellbeing

Lane, Nicholas D.; Lin, Mu; Mohammod, Mashfiqui; Yang, Xiao Hua; Lu, Hong; Cardone, Giuseppe; Ali, Shahid; Doryab, Afsaneh; Berke, Ethan M.; Campbell, Andrew T.; Choudhury, Tanzeem

doi:10.1007/s11036-013-0484-5

Cited by 143 publications

(94 citation statements)

References 36 publications

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“…There is a rich literature on analyzing smartphone sensing data for smart health applications, focusing on physical, behavioral, or mental health [5, 24, 30-32, 34, 39]. For instance, BeWell [24] is a personal health monitoring app that analyzes physical activities, sleep, and social interactions in order to provide feedback on user lifestyle. An approach was developed in [5] to automatically recognize stress from smartphone's social interaction data, weather data, and self-reported personality information.…”

Section: Related Workmentioning

confidence: 99%

Joint Modeling of Heterogeneous Sensing Data for Depression Assessment via Multi-task Learning

Lü

Chen

Yue

et al. 2018

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

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Depression is a common mood disorder that causes severe medical problems and interferes negatively with daily life. Identifying human behavior patterns that are predictive or indicative of depressive disorder is important. Clinical diagnosis of depression relies on costly clinician assessment using survey instruments which may not objectively reflect the fluctuation of daily behavior. Self-administered surveys, such as the Quick Inventory of Depressive Symptomatology (QIDS) commonly used to monitor depression, may show disparities from clinical decision. Smartphones provide easy access to many behavioral parameters, and Fitbit wrist bands are becoming another important tool to assess variables such as heart rates and sleep efficiency that are complementary to smartphone sensors. However, data used to identify depression indicators have been limited to a single platform either iPhone, or Android, or Fitbit alone due to the variation in their methods of data collection. The present work represents a large-scale effort to collect and integrate data from mobile phones, wearable devices, and self reports in depression analysis by designing a new machine learning approach. This approach constructs sparse mappings from sensing variables collected by various tools to two separate targets: self-reported QIDS scores and clinical assessment of depression severity. We propose a so-called heterogeneous multi-task feature learning method that jointly builds inference models for related tasks but of different types including classification and regression tasks. The proposed method was evaluated using data collected from 103 college students and could predict the QIDS score with an R 2 reaching 0.44 and depression severity with an F1-score as high as 0.77. By imposing appropriate regularizers, our approach identified strong depression indicators such as time staying at home and total time asleep.

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Section: Related Workmentioning

confidence: 99%

Joint Modeling of Heterogeneous Sensing Data for Depression Assessment via Multi-task Learning

Lü

Chen

Yue

et al. 2018

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

View full text Add to dashboard Cite

show abstract

“…A combination of sensors and smartphone applications allow automated recognition, so that time spent in proximity to another specific person can be quantified and reciprocal influences on behavior can be observed and modified 37 . A great deal of progress has also been made in sensing length and quality of social interactions using ambient sounds via smartphones or other wearable technologies 38 . Research has repeatedly shown that sensed and perceived environments do not necessarily overlap, and both have impact upon behavior 39 .…”

Section: Fast-paced Development Of Mobile Technologies For Obesity Prmentioning

confidence: 99%

Innovations in the Use of Interactive Technology to Support Weight Management

et al. 2015

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New and emerging mobile technologies are providing unprecedented possibilities for understanding and intervening on obesity-related behaviors in real time. However, the mobile health (mHealth) field has yet to catch up with the fast-paced development of technology. Current mHealth efforts in weight management still tend to focus mainly on short message systems (SMS) interventions, rather than taking advantage of real-time sensing to develop Just-In-Time, Adaptive Interventions (JITAIs). This paper will give an overview of the current technology landscape for sensing and intervening on three behaviors that are central to weight management; diet, physical activity, and sleep. Then five studies that really dig into the possibilities that these new technologies afford will be showcased. We conclude with a discussion of hurdles that mHealth obesity research has yet to overcome, and a future-facing discussion.

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“…Previous work has addressed various issues with such notifications and other interruptions [18], including the effects of interruptions during phone calls [4], while the subject is engaged in interactive tasks [17], and multitasking [13]. Recently, researchers have developed automated schemes to deliver notifications depending on phone activity [11], a user's context, notification content, sender identity [31], and types of device interactions or physical activities [25,33], typically with the goal to reduce cognitive workload.…”

Section: Related Workmentioning

confidence: 99%

“…User surveys have been used as a primary tool for data collection in various user studies on topics such as addictive behavior [32,48,49], pain [52], health [3,39,45], well-being [25,61], customer satisfaction [14], and system usability analysis [19]. For example, in the health and well-being area, researchers rely on surveys to check how factors such as mood [62], social interactions, sleeping habits, levels of physical activity [61], life satisfaction levels [9], and spirituality [37,55] affect the health and well-being of an individual or an entire community.…”

Section: Introductionmentioning

confidence: 99%

Design Factors of Longitudinal Smartphone-based Health Surveys

Vhaduri

Poellabauer

2017

J Healthc Inform Res

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Phone-based surveys are increasingly being used in healthcare settings to collect data from potentially large numbers of subjects, e.g., to evaluate their levels of satisfaction with medical providers, to study behaviors and trends of specific populations, and to track their health and wellness. Often, subjects respond to such surveys once, but it has become increasingly important to capture their responses multiple times over an extended period to accurately and quickly detect and track changes. With the help of smartphones, it is now possible to automate such longitudinal data collections, e.g., push notifications can be used to alert a subject whenever a new survey is available. This paper investigates various design factors of a longitudinal smartphone-based health survey data collection that contribute to user compliance and quality of collected data. This work presents the design recommendations based on analysis of data collected from 17 subjects over a 1-month period.

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BeWell: Sensing Sleep, Physical Activities and Social Interactions to Promote Wellbeing

Cited by 143 publications

References 36 publications

Joint Modeling of Heterogeneous Sensing Data for Depression Assessment via Multi-task Learning

Joint Modeling of Heterogeneous Sensing Data for Depression Assessment via Multi-task Learning

Innovations in the Use of Interactive Technology to Support Weight Management

Design Factors of Longitudinal Smartphone-based Health Surveys

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