Predictable Privacy-Preserving Mobile Crowd Sensing

Liu, Alex X.; Li, Rui

doi:10.1007/978-3-030-58896-0_12

Cited by 2 publications

(4 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This study will contribute to the scientific and clinical efforts to understand, detect, and prevent STB in youth. While most previous studies used intensive self-reports, previous studies have also demonstrated the usefulness of predicting symptoms in patients with major depressive disorder and mood prediction in patients using machine learning algorithms with multimodal data collection from mobile devices [23][24][25][26]50]. However, this is one of the first studies to investigate proximal predictors of youth STB using real-time mobile passive sensing, including communication and activity patterns, and clinical and self-reported assessments over 6 months.…”

Section: Discussionmentioning

confidence: 99%

“…Recognizing objective web-based and offline social behavior patterns preceding STB may be particularly beneficial for adolescents. Pioneering studies have demonstrated that passive mobile sensing by actigraphy [23][24][25][26] may predict STB. An existing study demonstrated the usefulness of predicting symptoms in patients with major depressive disorder through passive data collection (electrodermal activity, sleep patterns, motion, communication, location changes, and phone usage patterns) from built-in sensors in phones and a wearable device [23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

“…Pioneering studies have demonstrated that passive mobile sensing by actigraphy [23][24][25][26] may predict STB. An existing study demonstrated the usefulness of predicting symptoms in patients with major depressive disorder through passive data collection (electrodermal activity, sleep patterns, motion, communication, location changes, and phone usage patterns) from built-in sensors in phones and a wearable device [23][24][25][26]. Another study demonstrated the feasibility of predicting patients' moods using machine learning algorithms with privacy-preserving techniques through multimodal data collection (text, audio, and sensor data) from patients' mobile devices [23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

“…An existing study demonstrated the usefulness of predicting symptoms in patients with major depressive disorder through passive data collection (electrodermal activity, sleep patterns, motion, communication, location changes, and phone usage patterns) from built-in sensors in phones and a wearable device [23][24][25][26]. Another study demonstrated the feasibility of predicting patients' moods using machine learning algorithms with privacy-preserving techniques through multimodal data collection (text, audio, and sensor data) from patients' mobile devices [23][24][25][26]. However, to our knowledge, there are no studies investigating proximal predictors of youth STB via real-time mobile passive sensing, including communication and activity patterns, and clinical and self-reported assessments over 6 months.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Real-Time Real-World Digital Monitoring of Adolescent Suicide Risk During the Six Months Following Emergency Department Discharge: Protocol for an Intensive Longitudinal Study

Barzilay,

Fine,

Akhavan

et al. 2023

JMIR Res Protoc

View full text Add to dashboard Cite

Background Suicide is the second leading cause of death in adolescents, and self-harm is one of the strongest predictors of death by suicide. The rates of adolescents presenting to emergency departments (EDs) for suicidal thoughts and behaviors (STBs) have increased. Still, existing follow-up after ED discharge is inadequate, leaving a high-risk period for reattempts and suicide. There is a need for innovative evaluation of imminent suicide risk factors in these patients, focusing on continuous real-time evaluations with low assessment burden and minimal reliance on patient disclosure of suicidal intent. Objective This study examines prospective longitudinal associations between observed real-time mobile passive sensing, including communication and activity patterns, and clinical and self-reported assessments of STB over 6 months. Methods This study will include 90 adolescents recruited on their first outpatient clinic visit following their discharge from the ED due to a recent STB. Participants will complete brief weekly assessments and be monitored continuously for their mobile app usage, including mobility, activity, and communication patterns, over 6 months using the iFeel research app. Participants will complete 4 in-person visits for clinical assessment at baseline and at the 1-, 3-, and 6-month follow-ups. The digital data will be processed, involving feature extraction, scaling, selection, and dimensionality reduction. Passive monitoring data will be analyzed using both classical machine learning models and deep learning models to identify proximal associations between real-time observed communication, activity patterns, and STB. The data will be split into a training and validation data set, and predictions will be matched against the clinical evaluations and self-reported STB events (ie, labels). To use both labeled and unlabeled digital data (ie, passively collected), we will use semisupervised methods in conjunction with a novel method that is based on anomaly detection notions. Results Participant recruitment and follow-up started in February 2021 and are expected to be completed by 2024. We expect to find prospective proximal associations between mobile sensor communication, activity data, and STB outcomes. We will test predictive models for suicidal behaviors among high-risk adolescents. Conclusions Developing digital markers of STB in a real-world sample of high-risk adolescents presenting to ED can inform different interventions and provide an objective means to assess the risk of suicidal behaviors. The results of this study will be the first step toward large-scale validation that may lead to suicide risk measures that aid psychiatric follow-up, decision-making, and targeted treatments. This novel assessment could facilitate timely identification and intervention to save young people’s lives. International Registered Report Identifier (IRRID) DERR1-10.2196/46464

show abstract