bdata.bib

@poster{frank2017sensing,
  title={Sensing depression: Using smartphone sensors to predict changes in depression severity},
  author={Frank, Ellen and Merrill, Michael and Aung, Hane and Soares, Claudio and Kennedy, Sidney and Matthews, Mark and Kupfer, David and Lalovic, Aleksandra and Choudhury, Tanzeem},
  booktitle={Neuropsychopharmacology},
  volume={43},
  pages={S346--S346},
  year={2017},
  organization={NATURE PUBLISHING GROUP MACMILLAN BUILDING, 4 CRINAN ST, LONDON N1 9XW, ENGLAND},
  venue= {The 56th Annual Meeting of the American College of Neuropsychopharmacology}
}


@poster{frank2016sensing,
  title={Continuous Behavioral Data as a Potential Depression Biomarker},
  author={Frank, Ellen and Merrill, Michael and Aung, Hane and Soares, Claudio and Kennedy, Sidney and Matthews, Mark and Kupfer, David and Lalovic, Aleksandra and Choudhury, Tanzeem},
  booktitle={Neuropsychopharmacology},
  year = {2016},
  organization={NATURE PUBLISHING GROUP MACMILLAN BUILDING, 4 CRINAN ST, LONDON N1 9XW, ENGLAND},
  venue= {The 56th Annual Meeting of the American College of Neuropsychopharmacology}
}

@inproceedings{tseng2016ubicomp,
    author = {Tseng, Vincent W. S. and Merrill, Michael and Wittleder, Franziska and Abdullah, Saeed and Aung, Min Hane and Choudhury, Tanzeem},
    title = {Assessing Mental Health Issues on College Campuses: Preliminary Findings from a Pilot Study},
    year = {2016},
    isbn = {9781450344623},
    publisher = {Association for Computing Machinery},
    address = {New York, NY, USA},
    url = {https://doi.org/10.1145/2968219.2968308},
    doi = {10.1145/2968219.2968308},
    booktitle = {Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct},
    pages = {1200–1208},
    numpages = {9},
    keywords = {predictive modeling, mobile sensing, mental health, behavioral intervention, mHealth},
    location = {Heidelberg, Germany},
    series = {UbiComp ’16},
	venue = {Ubicomp}
}

@article{ben-zeev_crosscheck_2017,
	title = {{CrossCheck}: {Integrating} self-report, behavioral sensing, and smartphone use to identify digital indicators of psychotic relapse},
	volume = {40},
	issn = {1095-158X},
	shorttitle = {{CrossCheck}},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5593755/},
	doi = {10.1037/prj0000243}, 
	abstract = {Objective
                This purpose of this study was to describe and demonstrate CrossCheck, a multimodal data collection system designed to aid in continuous remote monitoring and identification of subjective and objective indicators of psychotic relapse
                Methods
                Individuals with schizophrenia-spectrum disorders received a smartphone with the monitoring system installed along with unlimited data plan for 12 months. Participants were instructed to carry the device with them and to complete brief self-reports multiple times a week. Multi-modal behavioral sensing (i.e., physical activity, geospatial activity, speech frequency and duration) and device use data (i.e., call and text activity, app use) were captured automatically. Five individuals who experienced psychiatric hospitalization were selected and described for instructive purposes.

                Results
                Participants had unique digital indicators of their psychotic relapse. For some, self-reports provided clear and potentially actionable description of symptom exacerbation prior to hospitalization. Others had behavioral sensing data trends (e.g., shifts in geolocation patterns, declines in physical activity) or device use patterns (e.g., increased nighttime app use, discontinuation of all smartphone use) that reflected the changes they experienced more effectively.

                Conclusion
                Advancements in mobile technology are enabling collection of an abundance of information that until recently was largely inaccessible to clinical research and practice. However, remote monitoring and relapse detection is in its nascency. Development and evaluation of innovative data management, modeling, and signal-detection techniques that can identify changes within an individual over time (i.e. unique relapse signatures) will be essential if we are to capitalize on these data to improve treatment and prevention.},
	number = {3},
	urldate = {2020-06-25},
	journal = {Psychiatric Rehabilitation Journal},
	venue = {Psychiatric Rehabilitation Journal},
	author = {Ben-Zeev, Dror and Brian, Rachel and Wang, Rui and Wang, Weichen and Campbell, Andrew T. and Aung, Min S. H. and Merrill, Michael and Tseng, Vincent W. S. and Choudhury, Tanzeem and Hauser, Marta and Kane, John M. and Scherer, Emily A.},
	month = sep,
	year = {2017},
	pmid = {28368138},
	pmcid = {PMC5593755},
	pages = {266--275},
	file = {PubMed Central Full Text PDF:/Users/michaelmerrill/Zotero/storage/BM5A6Q6N/Ben-Zeev et al. - 2017 - CrossCheck Integrating self-report, behavioral se.pdf:application/pdf}
}


@inproceedings{wang_crosscheck_2016,
	address = {Heidelberg Germany},
	title = {{CrossCheck}: toward passive sensing and detection of mental health changes in people with schizophrenia},
	isbn = {978-1-4503-4461-6},
	shorttitle = {{CrossCheck}},
	url = {https://dl.acm.org/doi/10.1145/2971648.2971740},
	doi = {10.1145/2971648.2971740},
	abstract = {Early detection of mental health changes in individuals with serious mental illness is critical for effective intervention. CrossCheck is the first step towards the passive monitoring of mental health indicators in patients with schizophrenia and paves the way towards relapse prediction and early intervention. In this paper, we present initial results from an ongoing randomized control trial, where passive smartphone sensor data is collected from 21 outpatients with schizophrenia recently discharged from hospital over a period ranging from 2-8.5 months. Our results indicate that there are statistically significant associations between automatically tracked behavioral features related to sleep, mobility, conversations, smartphone usage and self-reported indicators of mental health in schizophrenia. Using these features we build inference models capable of accurately predicting aggregated scores of mental health indicators in schizophrenia with a mean error of 7.6\% of the score range. Finally, we discuss results on the level of personalization that is needed to account for the known variations within people. We show that by leveraging knowledge from a population with schizophrenia, it is possible to train accurate personalized models that require fewer individual-specific data to quickly adapt to new users.},
	language = {en},
	urldate = {2020-06-25},
	booktitle = {Proceedings of the 2016 {ACM} {International} {Joint} {Conference} on {Pervasive} and {Ubiquitous} {Computing}},
	publisher = {ACM},
	author = {Wang, Rui and Aung, Min S. H. and Abdullah, Saeed and Brian, Rachel and Campbell, Andrew T. and Choudhury, Tanzeem and Hauser, Marta and Kane, John and Merrill, Michael and Scherer, Emily A. and Tseng, Vincent W. S. and Ben-Zeev, Dror},
	month = sep,
	year = {2016},
	pages = {886--897},
	file = {Wang et al. - 2016 - CrossCheck toward passive sensing and detection o.pdf:/Users/michaelmerrill/Zotero/storage/88HEZH9G/Wang et al. - 2016 - CrossCheck toward passive sensing and detection o.pdf:application/pdf},
	venue = {Ubicomp}
}