IOS Crowd–Sensing Won’t Hurt a Bit!: AWARE Framework and Sustainable Study Guideline for iOS Platform

Nishiyama, Yuuki; Ferreira, Denzil; Eigen, Yusaku; Sasaki, Wataru; Okoshi, T.; Nakazawa, Jin; Dey, Anind K.; Sezaki, Kaoru

doi:10.1007/978-3-030-50344-4_17

Cited by 22 publications

(11 citation statements)

References 20 publications

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“…Delphi uses the AWARE open source framework ( 44 , 45 ) to collect raw data from smartphone sensors. Sensors enabled in the current study included Battery, GPS, Screen (on and off), and Timezone.…”

Section: Methodsmentioning

confidence: 99%

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Predicting Symptoms of Depression and Anxiety Using Smartphone and Wearable Data

et al. 2021

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Background: Depression and anxiety are leading causes of disability worldwide but often remain undetected and untreated. Smartphone and wearable devices may offer a unique source of data to detect moment by moment changes in risk factors associated with mental disorders that overcome many of the limitations of traditional screening methods.Objective: The current study aimed to explore the extent to which data from smartphone and wearable devices could predict symptoms of depression and anxiety.Methods: A total of N = 60 adults (ages 24–68) who owned an Apple iPhone and Oura Ring were recruited online over a 2-week period. At the beginning of the study, participants installed the Delphi data acquisition app on their smartphone. The app continuously monitored participants' location (using GPS) and smartphone usage behavior (total usage time and frequency of use). The Oura Ring provided measures related to activity (step count and metabolic equivalent for task), sleep (total sleep time, sleep onset latency, wake after sleep onset and time in bed) and heart rate variability (HRV). In addition, participants were prompted to report their daily mood (valence and arousal). Participants completed self-reported assessments of depression, anxiety and stress (DASS-21) at baseline, midpoint and the end of the study.Results: Multilevel models demonstrated a significant negative association between the variability of locations visited and symptoms of depression (beta = −0.21, p = 0.037) and significant positive associations between total sleep time and depression (beta = 0.24, p = 0.023), time in bed and depression (beta = 0.26, p = 0.020), wake after sleep onset and anxiety (beta = 0.23, p = 0.035) and HRV and anxiety (beta = 0.26, p = 0.035). A combined model of smartphone and wearable features and self-reported mood provided the strongest prediction of depression.Conclusion: The current findings demonstrate that wearable devices may provide valuable sources of data in predicting symptoms of depression and anxiety, most notably data related to common measures of sleep.

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Section: Methodsmentioning

confidence: 99%

“…Increased security is achieved with application permissions, certificates, user authentication, and the use of secure network connections to access and transfer the logged data between the client and the dashboard. For further information on the AWARE framework see Ferreira et al ( 44 ) and Nishiyama et al ( 45 ).…”

Section: Methodsmentioning

confidence: 99%

Predicting Symptoms of Depression and Anxiety Using Smartphone and Wearable Data

et al. 2021

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“…Thus, we can easily test the application with such phone models to achieve higher execution stability. Third, thanks to the iOS AWARE Framework [16,29], we can implement and deploy an application that can continuously collect various sensor data in spite of the fact that iOS is a rather strict environment as a sensing platform than Android.…”

Section: Methodsmentioning

confidence: 99%

Nation-wide Mood: Large-scale Estimation of People's Mood from Web Search Query and Mobile Sensor Data

Okoshi

Sasaki

Kawane³

et al. 2021

Preprint

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The ability to estimate the current affective statuses of web users has considerable potential for the realization of user-centric services in the society. However, in real-world web services, it is difficult to determine the type of data to be used for such estimation, as well as collecting the ground truths of such affective statuses. We propose a novel method of such estimation based on the combined use of user web search queries and mobile sensor data. The system was deployed in our product server stack, and a large-scale data analysis with more than 11,000,000 users was conducted. Interestingly, our proposed "Nation-wide Mood Score, " which bundles the mood values of users across the country, (1) shows the daily and weekly rhythm of people's moods, (2) explains the ups and downs of people's moods in the COVID-19 pandemic, which is inversely synchronized to the number of new COVID-19 cases, and (3) detects the linkage with big news, which may affect many user's mood states simultaneously, even in a fine-grained time resolution, such as the order of hours. CCS CONCEPTS• Human-centered computing → Smartphones; • Information systems → Web log analysis; Information applications.

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“…Thus, we can easily test the application with such phone models to achieve higher execution stability. Third, thanks to the iOS AWARE Framework [12,23], we can implement and deploy an application that can continuously collect various sensor data in spite of the fact that iOS is a rather strict environment as a sensing platform than Android.…”

Section: Methodsmentioning

confidence: 99%

NationalMood: Large-scale Estimation of People's Mood from Web Search Query and Mobile Sensor Data

Okoshi,

Sasaki,

Kawane

et al. 2020

Preprint

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The ability to estimate current affective statuses of web users has considerable potential towards the realization of user-centric opportune services. However, determining the type of data to be used for such estimation as well as collecting the ground truth of such affective statuses are difficult in the real world situation. We propose a novel way of such estimation based on a combinational use of user's web search queries and mobile sensor data. Our large-scale data analysis with about 11,000,000 users and 100 recent advertisement log revealed (1) the existence of certain class of advertisement to which mood-status-based delivery would be significantly effective, (2) that our "National Mood Score" shows the ups and downs of people's moods in COVID-19 pandemic that inversely correlated to the number of patients, as well as the weekly mood rhythm of people. CCS CONCEPTS• Human-centered computing → Smartphones; • Information systems → Web log analysis; Information systems applications.

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IOS Crowd–Sensing Won’t Hurt a Bit!: AWARE Framework and Sustainable Study Guideline for iOS Platform

Cited by 22 publications

References 20 publications

Predicting Symptoms of Depression and Anxiety Using Smartphone and Wearable Data

Predicting Symptoms of Depression and Anxiety Using Smartphone and Wearable Data

Nation-wide Mood: Large-scale Estimation of People's Mood from Web Search Query and Mobile Sensor Data

NationalMood: Large-scale Estimation of People's Mood from Web Search Query and Mobile Sensor Data

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