Utilizing daily mood diaries and wearable sensor data to predict depression and suicidal ideation among medical interns

Horwitz, Adam G.; Czyz, Ewa K.; Al‐Dajani, Nadia; Dempsey, Walter; Zhuo, Zhao; Nahum‐Shani, Inbal; Sen, Srijan

doi:10.1016/j.jad.2022.06.064

Cited by 18 publications

(24 citation statements)

References 37 publications

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“…1 for the study selection process). A total of 75 full texts were screened, revealing eight studies investigating the predictive value of passive sensing for the prediction of STB [25][26][27][28][29][30][31][32], six trials focusing on the feasibility of passive sensing [27,[33][34][35][36][37], and ve study protocols [38][39][40][41][42]. All papers were published between 2019 and 2022.…”

Section: Search Resultsmentioning

confidence: 99%

“…The study characteristics are displayed in Table 1. Sample sizes ranged from 25 [31] to 2881 participants [29]. Half of the studies were based on clinical populations, i.e., participants were in inpatient or outpatient treatment [25,28,31,32].…”

Section: Prediction Studiesmentioning

confidence: 99%

“…One study recruited adolescents [32], whereas the other studies were in adult populations. [25,32], and four studies used both [29][30][31]. The passive data collection interval varied between seven [28, 32] and 92 days [29,30].…”

Section: Prediction Studiesmentioning

confidence: 99%

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Passive Sensing for the Prediction of Suicidal Thoughts and Behaviors: A Systematic Review and Recommendations for Future Research

Büscher,

Winkler,

Mocellin

et al. 2024

Preprint

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Predicting suicidal thoughts and behaviors (STB) remains challenging. The use of passive sensing data gathered through smartphones and wearables may contribute to overcoming current limitations in STB prediction. In this systematic review, we explored the feasibility and predictive validity of passive sensing for STB. On October 18, 2022, we systematically searched Medline, Embase, Web of Science, PubMed, and PsycINFO. Studies were eligible if they reported on the association between STB and passive sensing through smartphones or wearables, or on the feasibility of passive sensing in this context. The risk of bias was assessed by two independent researchers using the PROBAST tool. Out of 1765 unique records, we identified eight prediction studies, six feasibility studies, and five protocols. Studies found that electrodermal activity, sleep characteristics, heart rate variability, and app usage were associated with STB. However, results on the incremental value of passive data beyond self-report are inconsistent. Risk of bias ratings revealed major shortcomings in methodology and reporting. Studies indicated that passive sensing is feasible in terms of user satisfaction and adherence. In conclusion, there is only limited evidence on the predictive value of passive sensing for the prediction of STB. We highlight important quality characteristics for future research.

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Section: Search Resultsmentioning

confidence: 99%

Section: Prediction Studiesmentioning

confidence: 99%

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Passive Sensing for the Prediction of Suicidal Thoughts and Behaviors: A Systematic Review and Recommendations for Future Research

Büscher,

Winkler,

Mocellin

et al. 2024

Preprint

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“…They could reach an accuracy of 96.5 %. Horwitz et al [9] has designed a system that can predict depression and suicidal ideation among medical interns. For the data collection, they used Fitbit and for the prediction of suicidal ideation based on depression detection.…”

Section: Literature Reviewmentioning

confidence: 99%

Detection of suspicious activity using mobile sensor data and Modified Sub-space K-NN for criminal investigations

Aloni¹,

Shekhawata²

2022

YMER

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With the bulk availability of mobile sensors, the data collected from them mustn’t be wasted. Nowadays the creation of black-box software that collects this data is not a very difficult task. It is possible to detect suspicious unlawful events using this black-box data. In this paper, we present a novel way of doing forensic investigation using a modified sub-space K-NN (MSK) algorithm. The MSK algorithm is capable of detecting suspicious activities from mobile sensor data. Using this technique, we could detect any normal activity versus suspicious activity with 99.7 % accuracy. We expect the future researcher to develop on this idea and build a solid digital forensic system capable of doing bias-free decisions. Keywords: Forensic, Mobile sensor data, Black box, mobile data collection

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“…SI also increases during medical internship and physicians are more likely than the general population to die by suicide (Goldman, Shah, & Bernstein, 2015). A recent investigation demonstrated that intensive longitudinal data (ILD) collected each day over a 60 day period during the first quarter of medical internship improves the prediction of eventual depression and SI at the end of the quarter, over and above initial symptom- and trait-level indicators (Horwitz et al, 2022). Yet, data collection past a certain point in time may be providing only limited additional predictive utility, while potentially contributing to respondent burden and drop out.…”

mentioning

confidence: 99%

Using machine learning with intensive longitudinal data to predict depression and suicidal ideation among medical interns over time

et al. 2022

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Background Use of intensive longitudinal methods (e.g. ecological momentary assessment, passive sensing) and machine learning (ML) models to predict risk for depression and suicide has increased in recent years. However, these studies often vary considerably in length, ML methods used, and sources of data. The present study examined predictive accuracy for depression and suicidal ideation (SI) as a function of time, comparing different combinations of ML methods and data sources. Methods Participants were 2459 first-year training physicians (55.1% female; 52.5% White) who were provided with Fitbit wearable devices and assessed daily for mood. Linear [elastic net regression (ENR)] and non-linear (random forest) ML algorithms were used to predict depression and SI at the first-quarter follow-up assessment, using two sets of variables (daily mood features only, daily mood features + passive-sensing features). To assess accuracy over time, models were estimated iteratively for each of the first 92 days of internship, using data available up to that point in time. Results ENRs using only the daily mood features generally had the best accuracy for predicting mental health outcomes, and predictive accuracy within 1 standard error of the full 92 day models was attained by weeks 7–8. Depression at 92 days could be predicted accurately (area under the curve >0.70) after only 14 days of data collection. Conclusions Simpler ML methods may outperform more complex methods until passive-sensing features become better specified. For intensive longitudinal studies, there may be limited predictive value in collecting data for more than 2 months.

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Utilizing daily mood diaries and wearable sensor data to predict depression and suicidal ideation among medical interns

Cited by 18 publications

References 37 publications

Passive Sensing for the Prediction of Suicidal Thoughts and Behaviors: A Systematic Review and Recommendations for Future Research

Passive Sensing for the Prediction of Suicidal Thoughts and Behaviors: A Systematic Review and Recommendations for Future Research

Detection of suspicious activity using mobile sensor data and Modified Sub-space K-NN for criminal investigations

Using machine learning with intensive longitudinal data to predict depression and suicidal ideation among medical interns over time

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