Drug Use

Studies


Johns Hopkins

PI: August Holtyn

Cocaine Use

Users:

25

Person-Days:

350

Samples:

18 billion

Sensors

• AutoSense
• MotionSense
• Phone

Description

This study is designed to extend previous work in the development of methods to automatically detect the timing of cocaine use from cardiac interbeat interval and physical activity data derived from wearable, unobtrusive mobile sensor technologies.


Publications

  1. Edison Thomaz, Abdelkareem Bedri, Temiloluwa Prioleau, Irfan Essa and Gregory D Abowd.
    Exploring Symmetric and Asymmetric Bimanual Eating Detection with Inertial Sensors on the Wrist. In Proceedings of the 1st Workshop on Digital Biomarkers. 2017, 21–26. URL, DOI BibTeX

    @inproceedings{Thomaz:2017:ESA:3089341.3089345,
    	author = "Thomaz, Edison and Bedri, Abdelkareem and Prioleau, Temiloluwa and Essa, Irfan and Abowd, Gregory D.",
    	title = "Exploring Symmetric and Asymmetric Bimanual Eating Detection with Inertial Sensors on the Wrist",
    	booktitle = "Proceedings of the 1st Workshop on Digital Biomarkers",
    	year = 2017,
    	pages = "21--26",
    	publisher = "ACM",
    	abstract = "Motivated by health applications, eating detection with off-the-shelf devices has been an active area of research. A common approach has been to recognize and model individual intake gestures with wrist-mounted inertial sensors. Despite promising results, this approach is limiting as it requires the sensing device to be worn on the hand performing the intake gesture, which cannot be guaranteed in practice. Through a study with 14 participants comparing eating detection performance when gestural data is recorded with a wrist-mounted device on (1) both hands, (2) only the dominant hand, and (3) only the non-dominant hand, we provide evidence that a larger set of arm and hand movement patterns beyond food intake gestures are predictive of eating activities when L1 or L2 normalization is applied to the data. Our results are supported by the theory of asymmetric bimanual action and contribute to the field of automated dietary monitoring. In particular, it shines light on a new direction for eating activity recognition with consumer wearables in realistic settings.",
    	doi = "10.1145/3089341.3089345",
    	url = "https://md2k.org/images/papers/biomarkers/p21-thomaz.pdf"
    }
    
  2. James M Rehg, Susan A Murphy and Santosh Kumar (eds.).
    Detecting Eating and Smoking Behaviors Using Smartwatches
    . pages 175–201, Springer International Publishing, 2017. URL, DOI BibTeX

    @inbook{Parate2017,
    	pages = "175--201",
    	title = "Detecting Eating and Smoking Behaviors Using Smartwatches",
    	publisher = "Springer International Publishing",
    	year = 2017,
    	author = "Parate, Abhinav and Ganesan, Deepak",
    	editor = "Rehg, James M. and Murphy, Susan A. and Kumar, Santosh",
    	abstract = "Inertial sensors embedded in commercial smartwatches and fitness bands are among the most informative and valuable on-body sensors for monitoring human behavior. This is because humans perform a variety of daily activities that impacts their health, and many of these activities involve using hands and have some characteristic hand gesture associated with it. For example, activities like eating food or smoking a cigarette require the direct use of hands and have a set of distinct hand gesture characteristics. However, recognizing these behaviors is a challenging task because the hand gestures associated with these activities occur only sporadically over the course of a day, and need to be separated from a large number of irrelevant hand gestures. In this chapter, we will look at approaches designed to detect behaviors involving sporadic hand gestures. These approaches involve two main stages: (1) spotting the relevant hand gestures in a continuous stream of sensor data, and (2) recognizing the high-level activity from the sequence of recognized hand gestures. We will describe and discuss the various categories of approaches used for each of these two stages, and conclude with a discussion about open questions that remain to be addressed.",
    	booktitle = "Mobile Health: Sensors, Analytic Methods, and Applications",
    	doi = "10.1007/978-3-319-51394-2_10",
    	url = "https://doi.org/10.1007/978-3-319-51394-2_10"
    }
    
  3. Abdelkareem Bedri, Richard Li, Malcolm Haynes, Raj Prateek Kosaraju, Ishaan Grover, Temiloluwa Prioleau, Min Yan Beh, Mayank Goel, Thad Starner and Gregory Abowd.
    EarBit: Using Wearable Sensors to Detect Eating Episodes in Unconstrained Environments. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 1(3):37:1–37:20, 2017. URL, DOI BibTeX

    @article{Bedri:2017:EUW:3139486.3130902,
    	author = "Bedri, Abdelkareem and Li, Richard and Haynes, Malcolm and Kosaraju, Raj Prateek and Grover, Ishaan and Prioleau, Temiloluwa and Beh, Min Yan and Goel, Mayank and Starner, Thad and Abowd, Gregory",
    	title = "EarBit: Using Wearable Sensors to Detect Eating Episodes in Unconstrained Environments",
    	journal = "Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.",
    	year = 2017,
    	volume = 1,
    	number = 3,
    	pages = "37:1--37:20",
    	abstract = "Chronic and widespread diseases such as obesity, diabetes, and hypercholesterolemia require patients to monitor their food intake, and food journaling is currently the most common method for doing so. However, food journaling is subject to self-bias and recall errors, and is poorly adhered to by patients. In this paper, we propose an alternative by introducing EarBit, a wearable system that detects eating moments. We evaluate the performance of inertial, optical, and acoustic sensing modalities and focus on inertial sensing, by virtue of its recognition and usability performance. Using data collected in a simulated home setting with minimum restrictions on participants’ behavior, we build our models and evaluate them with an unconstrained outside-the-lab study. For both studies, we obtained video footage as ground truth for participants activities. Using leave-one-user-out validation, EarBit recognized all the eating episodes in the semi-controlled lab study, and achieved an accuracy of 90.1% and an F1-score of 90.9% in detecting chewing instances. In the unconstrained, outside-the-lab evaluation, EarBit obtained an accuracy of 93% and an F1-score of 80.1% in detecting chewing instances. It also accurately recognized all but one recorded eating episodes. These episodes ranged from a 2 minute snack to a 30 minute meal.",
    	doi = "10.1145/3130902",
    	publisher = "ACM",
    	url = "https://md2k.org/images/papers/biomarkers/a37-bedri.pdf"
    }
    
  4. J Poncela-Casasnovas, B Spring, D McClary, A C Moller, R Mukogo, C A Pellegrini, M J Coons, M Davidson, S Mukherjee and Nunes L A Amaral.
    Social embeddedness in an online weight management programme is linked to greater weight loss. the Journal of the Royal Society Interface 12(104), 2015. URL BibTeX

    @article{Poncela-Casasnovas2015,
    	author = "J. Poncela-Casasnovas and B. Spring and D. McClary and A.C. Moller and R. Mukogo and C.A. Pellegrini and M.J. Coons and M. Davidson and S. Mukherjee and L.A. Nunes Amaral",
    	title = "Social embeddedness in an online weight management programme is linked to greater weight loss",
    	journal = "the Journal of the Royal Society Interface",
    	year = 2015,
    	volume = 12,
    	number = 104,
    	abstract = "The obesity epidemic is heightening chronic disease risk globally. Online weight management (OWM) communities could potentially promote weight loss among large numbers of people at low cost. Because little is known about the impact of these online communities, we examined the relationship between individual and social network variables, and weight loss in a large, international OWM programme. We studied the online activity and weight change of 22 419 members of an OWM system during a six-month period, focusing especially on the 2033 members with at least one friend within the community. Using Heckman's sample-selection procedure to account for potential selection bias and data censoring, we found that initial body mass index, adherence to self-monitoring and social networking were significantly correlated with weight loss. Remarkably, greater embeddedness in the network was the variable with the highest statistical significance in our model for weight loss. Average per cent weight loss at six months increased in a graded manner from 4.1% for non-networked members, to 5.2% for those with a few (two to nine) friends, to 6.8% for those connected to the giant component of the network, to 8.3% for those with high social embeddedness. Social networking within an OWM community, and particularly when highly embedded, may offer a potent, scalable way to curb the obesity epidemic and other disorders that could benefit from behavioural changes.",
    	school = "Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL 60208, USA HHMI, Northwestern University, Evanston, IL 60208, USA Northwestern Institute on Complex Systems, No",
    	url = "http://dx.doi.org/10.1098/rsif.2014.0686"
    }
    
  5. F Cordeiro, D Epstein, E Thomaz, E Bales, A K Jagannathan, G D Abowd and J Fogarty.
    Barriers and Negative Nudges: Exploring Challenges in Food Journaling. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (CHI ’15). 2015. URL BibTeX

    @conference{cordeiro2015barriers,
    	author = "F. Cordeiro and D. Epstein and E. Thomaz and E. Bales and A.K. Jagannathan and G.D. Abowd and J. Fogarty",
    	title = "Barriers and Negative Nudges: Exploring Challenges in Food Journaling",
    	booktitle = "Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (CHI ’15)",
    	year = 2015,
    	publisher = "Submission",
    	note = "Accepted to International ACM Conference on Human Factors in Computing Systems (CHI) 2015, Seoul, Korea",
    	abstract = "Although food journaling is understood to be both important and difficult, little work has empirically documented the specific challenges people experience with food journals. We identify key challenges in a qualitative study combining a survey of 141 current and lapsed food journalers with analysis of 5,526 posts in community forums for three mobile food journals. Analyzing themes in this data, we find and discuss barriers to reliable food entry, negative nudges caused by current techniques, and challenges with social features. Our results motivate research exploring a wider range of approaches to food journal design and technology.",
    	url = "http://depstein.net/pubs/fcordeiro_chi15.pdf"
    }
    
  6. J Steglitz, M Sommers, M R Talen, L K Thornton and B Spring.
    Evaluation of an electronic health record-supported obesity management protocol implemented in a community health center: a cautionary note. Journal of the American Medical Informatics Association, 2015. URL BibTeX

    @article{rohtua,
    	author = "J. Steglitz and M. Sommers and M.R. Talen and L.K. Thornton and B. Spring",
    	title = "Evaluation of an electronic health record-supported obesity management protocol implemented in a community health center: a cautionary note",
    	journal = "Journal of the American Medical Informatics Association",
    	year = 2015,
    	abstract = "Objective: Primary care clinicians are well-positioned to intervene in the obesity epidemic. We studied whether implementation of an obesity intake protocol and electronic health record (EHR) form to guide behavior modification would facilitate identification and management of adult obesity in a Federally Qualified Health Center serving low-income, Hispanic patients. Materials and Methods In three studies, we examined clinician and patient outcomes before and after the addition of the weight management protocol and form. In the Clinician Study, 12 clinicians self-reported obesity management practices. In the Population Study, BMI and order data from 5000 patients and all 40 clinicians in the practice were extracted from the EHR preintervention and postintervention. In the Exposure Study, EHR-documented outcomes for a sub-sample of 46 patients actually exposed to the obesity management form were compared to matched controls. Results Clinicians reported that the intake protocol and form increased their performance of obesity-related assessments and their confidence in managing obesity. However, no improvement in obesity management practices or patient weight-loss was evident in EHR records for the overall clinic population. Further analysis revealed that only 55 patients were exposed to the form. Exposed patients were twice as likely to receive weight-loss counseling following the intervention, as compared to before, and more likely than matched controls. However, their obesity outcomes did not differ. Conclusion Results suggest that an obesity intake protocol and EHR-based weight management form may facilitate clinician weight-loss counseling among those exposed to the form. Significant implementation barriers can limit exposure, however, and need to be addressed.",
    	publisher = "The Oxford University Press",
    	url = "http://dx.doi.org/10.1093/jamia/ocu034"
    }
    
  7. Edison Thomaz, Irfan Essa and Gregory D Abowd.
    A Practical Approach for Recognizing Eating Moments with Wrist-mounted Inertial Sensing. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing. 2015, 1029–1040. URL, DOI BibTeX

    @inproceedings{Thomaz:2015:PAR:2750858.2807545,
    	author = "Thomaz, Edison and Essa, Irfan and Abowd, Gregory D.",
    	title = "A Practical Approach for Recognizing Eating Moments with Wrist-mounted Inertial Sensing",
    	booktitle = "Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing",
    	year = 2015,
    	pages = "1029--1040",
    	publisher = "ACM",
    	abstract = "Recognizing when eating activities take place is one of the key challenges in automated food intake monitoring. Despite progress over the years, most proposed approaches have been largely impractical for everyday usage, requiring multiple on-body sensors or specialized devices such as neck collars for swallow detection. In this paper, we describe the implemen-tation and evaluation of an approach for inferring eating mo-ments based on 3-axis accelerometry collected with a popu-lar off-the-shelf smartwatch. Trained with data collected in a semi-controlled laboratory setting with 20 subjects, our sys-tem recognized eating moments in two free-living condition studies (7 participants, 1 day; 1 participant, 31 days), with F-scores of 76.1% (66.7% Precision, 88.8% Recall), and 71.3%(65.2% Precision, 78.6% Recall). This work represents a contribution towards the implementation of a practical, au-tomated system for everyday food intake monitoring, with applicability in areas ranging from health research and food journaling.",
    	doi = "10.1145/2750858.2807545",
    	url = "http://doi.acm.org/10.1145/2750858.2807545"
    }
    
  8. E Thomaz, C Zhang, I Essa and G D Abowd.
    Inferring Meal Eating Activities in Real World Settings from Ambient Sounds: A Feasibility Study. In Proceedings of the 20th International Conference on Intelligent User Interfaces (IUI '15). 2015, 427–431. URL BibTeX

    @inproceedings{Thomaz:2015:IME:2678025.2701405,
    	author = "E. Thomaz and C. Zhang and I. Essa and G.D. Abowd",
    	title = "Inferring Meal Eating Activities in Real World Settings from Ambient Sounds: A Feasibility Study",
    	booktitle = "Proceedings of the 20th International Conference on Intelligent User Interfaces (IUI '15)",
    	year = 2015,
    	pages = "427--431",
    	publisher = "ACM",
    	abstract = "Dietary self-monitoring has been shown to be an effective method for weight-loss, but it remains an onerous task despite recent advances in food journaling systems. Semi-automated food journaling can reduce the effort of logging, but often requires that eating activities be detected automatically. In this work we describe results from a feasibility study conducted in-the-wild where eating activities were inferred from ambient sounds captured with a wrist-mounted device; twenty participants wore the device during one day for an average of 5 hours while performing normal everyday activities. Our system was able to identify meal eating with an F-score of 79.8% in a person-dependent evaluation, and with 86.6% accuracy in a person-independent evaluation. Our approach is intended to be practical, leveraging off-the-shelf devices with audio sensing capabilities in contrast to systems for automated dietary assessment based on specialized sensors.",
    	url = "http://doi.acm.org/10.1145/2678025.2701405"
    }
    
  9. C A Pellegrini, S A Hoffman, L M Collins and B Spring.
    Optimization of remotely delivered intensive lifestyle treatment for obesity using the Multiphase Optimization Strategy: Opt-IN study protocol.. Contemporary Clinical Trials 38(2):251-9, 2014. URL BibTeX

    @article{pellegrini2014b,
    	author = "C.A. Pellegrini and S.A. Hoffman and L.M. Collins and B. Spring",
    	title = "Optimization of remotely delivered intensive lifestyle treatment for obesity using the Multiphase Optimization Strategy: Opt-IN study protocol.",
    	journal = "Contemporary Clinical Trials",
    	year = 2014,
    	volume = 38,
    	number = 2,
    	pages = "251-9",
    	abstract = "Obesity-attributable medical expenditures remain high, and interventions that are both effective and cost-effective have not been adequately developed. The Opt-IN study is a theory-guided trial using the Multiphase Optimization Strategy (MOST) to develop an optimized, scalable version of a technology-supported weight loss intervention. OBJECTIVE: Opt-IN aims to identify which of 5 treatment components or component levels contribute most meaningfully and cost-efficiently to the improvement of weight loss over a 6 month period. STUDY DESIGN: Five hundred and sixty obese adults (BMI 30-40 kg/m(2)) between 18 and 60 years old will be randomized to one of 16 conditions in a fractional factorial design involving five intervention components: treatment intensity (12 vs. 24 coaching calls), reports sent to primary care physician (No vs. Yes), text messaging (No vs. Yes), meal replacement recommendations (No vs. Yes), and training of a participant's self-selected support buddy (No vs. Yes). During the 6-month intervention, participants will monitor weight, diet, and physical activity on the Opt-IN smartphone application downloaded to their personal phone. Weight will be assessed at baseline, 3, and 6 months. SIGNIFICANCE: The Opt-IN trial is the first study to use the MOST framework to develop a weight loss treatment that will be optimized to yield the best weight loss outcome attainable for $500 or less.",
    	url = "http://www.ncbi.nlm.nih.gov/pubmed/24846621"
    }
    

 

 

 

Copyright © 2018 MD2K. MD2K is supported by the National Institutes of Health Big Data to Knowledge Initiative (Grant #1U54EB020404)
Team: Cornell Tech, GA Tech, Harvard, U. Memphis, Northwestern, Ohio State, UCLA, UCSD, UCSF, UMass, U. Michigan, U. Utah, WVU