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Min, Jun-Ki; Doryab, Afsaneh; Wiese, Jason; Amini, Shahriyar; Zimmerman, John; Hong, Jason I.

doi:10.1145/2556288.2557220

Cited by 161 publications

(40 citation statements)

References 34 publications

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“…Researchers have used smartphones to assess and predict academic performance [65], used them to detect sleep and sleep quality [36], and personality traits [13], to passively sense and detect mental health changes (e.g., schizophrenia [64], lack of social interaction [18]), and to detect habitual behaviors such as smoking [52]. It is noteworthy that substance use (e.g., cocaine usage [10], cigarette smoking [49], heroin craving [20]) can be detected using machine learning applied to data from wearable sensors.…”

Section: Smartphone-based Behavior Modelingmentioning

confidence: 99%

Detecting Drinking Episodes in Young Adults Using Smartphone-based Sensors

Bae

Ferreira

Suffoletto

et al. 2017

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

120

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Alcohol use in young adults is common, with high rates of morbidity and mortality largely due to periodic, heavy drinking episodes (HDEs). Behavioral interventions delivered through electronic communication modalities (e.g., text messaging) can reduce the frequency of HDEs in young adults, but effects are small. One way to amplify these effects is to deliver support materials proximal to drinking occasions, but this requires knowledge of when they will occur. Mobile phones have built-in sensors that can potentially be useful in monitoring behavioral patterns associated with the initiation of drinking occasions. The objective of our work is to explore the detection of daily-life behavioral markers using mobile phone sensors and their utility in identifying drinking occasions. We utilized data from 30 young adults aged 21-28 with past hazardous drinking and collected mobile phone sensor data and daily Experience Sampling Method (ESM) of drinking for 28 consecutive days. We built a machine learning-based model that is 96.6% accurate at identifying non-drinking, drinking and heavy drinking episodes. We highlight the most important features for detecting drinking episodes and identify the amount of historical data needed for accurate detection. Our results suggest that mobile phone sensors can be used for automated, continuous monitoring of at-risk populations to detect drinking episodes and support the delivery of timely interventions. CCS Concepts: • Human-centered computing ➝ Human computer interaction (HCI) ➝ Empirical studies in HCI

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Section: Smartphone-based Behavior Modelingmentioning

confidence: 99%

Detecting Drinking Episodes in Young Adults Using Smartphone-based Sensors

Bae

Ferreira

Suffoletto

et al. 2017

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

120

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“…Mobile phones can measure location, distance traveled, social interactions (phone call and short message service: SMS), application usage, and acceleration and light levels. Researchers have used wearable sensors and/or mobile phone data to understand factors such as personality type [1], mood [2, 3], sleep [3, 4, 5] and self-reported stress [6, 7]. In previous work, we collected 5 days of data (wearable sensor, mobile phone and surveys) from 18 participants and were able to classify them into high and low perceived stress groups using machine learning [6].…”

Section: Introductionmentioning

confidence: 99%

“…We also found features in mobile phone usage, wearable sensor and survey data that were significantly related to perceived stress level using correlation analysis. In the current study, we have increased our sampling period to ~30-days per person and our population to 66 participants to collect more intensive multi-modal data including perceived stress, sleep, personality, physiological, behavioral and social interaction data that are important factors in academic performance, sleep, stress, and mental health in addition to what were monitored on the phone in previous studies [4, 5, 8, 9, 10]. …”

Section: Introductionmentioning

confidence: 99%

“…While previous studies have examined multiple days and nights (from one week to a couple months) of daily behaviors including sleep, activity and social interactions, [4, 5, 8, 9, 10], few studies have examined capturing both internal and external factors to understand which factors are related and how, and fewer have used objective sensor data. Understanding these associations can be used to design tools to reduce stress, improve academic performance, sleep quality, and mental health.…”

Section: Introductionmentioning

confidence: 99%

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Recognizing academic performance, sleep quality, stress level, and mental health using personality traits, wearable sensors and mobile phones

Sano

Phillips

et al. 2015

2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN)

202

129

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What can wearable sensors and usage of smart phones tell us about academic performance, self-reported sleep quality, stress and mental health condition? To answer this question, we collected extensive subjective and objective data using mobile phones, surveys, and wearable sensors worn day and night from 66 participants, for 30 days each, totaling 1,980 days of data. We analyzed daily and monthly behavioral and physiological patterns and identified factors that affect academic performance (GPA), Pittsburg Sleep Quality Index (PSQI) score, perceived stress scale (PSS), and mental health composite score (MCS) from SF-12, using these month-long data. We also examined how accurately the collected data classified the participants into groups of high/low GPA, good/poor sleep quality, high/low self-reported stress, high/low MCS using feature selection and machine learning techniques. We found associations among PSQI, PSS, MCS, and GPA and personality types. Classification accuracies using the objective data from wearable sensors and mobile phones ranged from 67–92%.

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“…Although much research has focused on tracking for general health and wellness (e.g., [30][31][32]), people who live with a chronic illness have further tracking requirements central to illness management that provide a rich opportunity for understanding through research. Furthermore, research examining the lived experiences of self-tracking find that this act of tracking is often coordinated or influenced by communication with health experts (e.g., [24,28,33]), with peers (e.g., [34]), with friends and acquaintances (e.g., [35]), among family members (e.g., [36]), and with workplace colleagues and by workplace programs (e.g., [37]).…”

Section: Social Factors Of Self-managementmentioning

confidence: 99%

“It’s Not Just Technology, It’s People”: Constructing a Conceptual Model of Shared Health Informatics for Tracking in Chronic Illness Management (Preprint)

Vizer¹,

Eschler²,

Koo³

et al. 2018

Preprint

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Background: Chronic illness is a substantial public health burden and the day-to-day work of a person with chronic illness is central to illness management. However, although management programs are available to train people to manage their chronic illnesses, those programs rarely provide people with sufficient guidance on implementing fine-grained management strategies. This lack of guidance transfers much of the burden of day-to-day management practices to people with chronic illness. Objective: To decrease the management burden many people have created technological solutions, but we claim that to be maximally effective and tailored to people's needs, those solutions need to be informed by a model that integrate patient work and self-management frameworks with personal health informatics models. We see an opportunity to improve personal health informatics models to (1) incorporate the ongoing nature of management work, and (2) append a social dimension to more accurately depict the context of illness management. Methods:We used qualitative methods to analyze the chronic illness management practices of 63 people through the lenses of self-management and informatics frameworks. Results: Analysis results are distilled into a new Conceptual Model of Shared Health Informatics. We describe the people and work involved in chronic illness management and contribute a Conceptual Model of Shared Health Informatics depicting the process of chronic illness management in a social context. Conclusions: Through insight into management work provided by our Conceptual Model of Shared Health Informatics, technology designers and implementers can improve the quality of chronic illness management tools to ensure confident and capable management of chronic illness.

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Toss 'n' turn

Cited by 161 publications

References 34 publications

Detecting Drinking Episodes in Young Adults Using Smartphone-based Sensors

Detecting Drinking Episodes in Young Adults Using Smartphone-based Sensors

Recognizing academic performance, sleep quality, stress level, and mental health using personality traits, wearable sensors and mobile phones

“It’s Not Just Technology, It’s People”: Constructing a Conceptual Model of Shared Health Informatics for Tracking in Chronic Illness Management (Preprint)

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