Big Data Opportunities and Challenges in Monitoring Health Behaviors in the Home and Environment

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1 Holly Jimison, PhD, FACMI IPA, Health Scientist Administrator OBSSR Big Data Opportunities and Challenges in Monitoring Health Behaviors in the Home and Environment 2012 mhealth Training Institute

2 Home Monitoring Data 2

3 Opportunities Need for scalable and low-cost approaches to providing a spectrum of care solutions to the home. Understanding and improving health behavior change becomes even more important. Requires new methods in continuous monitoring and meaningful interpretation of behaviors. Potential to collect massive amounts of data from a variety of sources. The unobtrusive and continuous monitoring of behaviors in natural settings is largely unexplored 2012 mhealth Training Institute 3

4 Challenges Big Data: large, diverse, complex, longitudinal, and distributed data sets generated from instruments, sensors, computer interactions, video Large-scale, diverse, and high-resolution data sets just-in-time decision-making and intervention Low cost sensors, noise, bias, context dependencies Need new statistical and mathematical algorithms, prediction techniques, and modeling methods, as well as multidisciplinary approaches to data analysis and new technologies for sharing data Need advances in machine learning, data mining, fusion algorithms, modeling and visualization 2012 mhealth Training Institute 4

5 Business Big Data Existing forms of data on human behaviors from unobtrusive monitoring in the real world Global Positioning Systems Purchasing Behaviors Computer Interactions Social Networks (Facebook has > 40 billion photos) Searching Behaviors 2012 mhealth Training Institute 5

6 Tracking Searches Big data analysis can detect epidemics before the medical reports - CDC Data - Google Estimates Jeremy Ginsberg 1, Matthew H. Mohebbi 1, Rajan S. Patel 1, Lynnette Brammer 2, Mark S. Smolinski 1 & Larry Brilliant 1 Detecting influenza epidemics using search engine query data Nature 457, (19 February 2009) mhealth Training Institute 6

7 Health Related Current Health Monitoring and Interventions in the Home & Environment Clinical monitoring for chronic conditions Diabetes, Asthma, Heart Failure Wellness (diet, exercise, smoking, etc.) Safety (older adults, e.g., falls) 2012 mhealth Training Institute 7

8 Health Related New Health Monitoring and Interventions in the Home & Environment Behavioral measures important for both wellness and the management of chronic conditions Sleep management Medication management Socialization Mood management Activity monitoring» Movement, interactive exercise» Eating behaviors» 2012 mhealth Training Institute 8

9 Examples Examples of New Behavioral Measures (used in remote coaching research) Activity Monitoring in the Home Cognitive Monitoring Motor Speed Sleep Monitoring Socialization Skype, phone, s Physical Exercise Depression 2012 mhealth Training Institute 9

10 Home health based on unobtrusive, continuous monitoring Behavioral Markers = Continuous Monitoring + Computational Models

11 Activity Monitoring in the Home Sensor Events Private Home Bedroom Bathroom Living Rm Front Door Kitchen Hayes, ORCATECH 2007

12 Activity Monitoring in the Home Sensor Events Residential Facility Bedroom Bathroom Living Rm Front Door Kitchen Hayes, ORCATECH 2007

13 Measuring Gait in the Home Unobtrusive gait measurement in-home with passive infrared (PIR) sensors - Hagler, et al., IEEE Trans Biomed Eng, 2010 Four restricted view PIR sensors Measure gait velocity whenever a subjects passes through the sensor-line Deployed for the Intelligent Systems for Assessing Aging Changes (ISAAC) study 200+ subjects monitored for up to 4 years and counting 13

14 Subject 1 Austin et al, Sept EMBC (Gait) 1

15 Subject 2 Austin et al, Sept EMBC (Gait) 2

16 Socialization Measure Skype use, use, phone use Feedback for Cognitive Health Coaching 16

17 Sleep Disorders Prevalence 9% middle aged women 24% middle aged men 80% undiagnosed Overnight in Sleep Lab Expensive Obtrusive Inconvenient In-home Assessment Screening Tool Night-to-Night Variability Normal Sleeping Environment 17

18 Load cells sensors 18

19 Respiration Model M Force 19

20 Apnea Detection 5. Z. Beattie, C. Hagen, M. Pavel, and T. Hayes, Unobtrusive Monitoring of Sleep Apnea," SLEEP 2011 Abstract 25th Anniversary Meeting of the Associated Professional Sleep Societies, LLC, Minneapolis, Minnesota, Jun 11 Jun 15,

21 Home PC use trends detect change in those with MCI 20 Intact MCI Mean 1.5 hours on computer/per day at baseline month Mean Days on Computer Over time: Less use days per month Less use time when in session More variable in use pattern over time Kaye, et al. AAICK, ay 201e, 1 et Months of Continuous Monitoring al. 2011

22 Monitoring Cognition 22

23 Example 2012 mhealth Training Institute 23

24 Model Recall Next Search for Move to Next Target Next Target Target t R + t ( nd, ) + t S M S. Hagler et al., mhealth Training Institute 24

25 Cognition S X = 27((a +τ X ) + 7τ X + χ X R S b) +(34sec ) N X R 2 = 0.78 p < S. Hagler et al., mhealth Training Institute 25

26 Cognitive Modeling Example: Memory Adaptive Memory Games Short-term memory Working memory Spatial memory Abstract reasoning B A C D A E B C F B G H D G E I B A C D D E B B F F G H D D D B A C C D E E B E F G H H H B A B C D D E D E F G F F B C D D E F E E Characterize Memory Capacity Intervening number of events Intervening time Memory load Simple Memory Model: Discrete Buffer Misha Pavel, Probability of Correct Probability of Correct Subject 1020, N = Intervening Number of Events Intervening Time [sec] Characterize Memory Capacity with a Single Parameter

27 Activity Monitoring in the Home Cognitive Monitoring Examples Adaptive Computer Games Divided Attention, Planning, Memory, Verbal Fluency, +++ Linguistic Complexity s, phone Motor Speed Speed of Walking, Computer Typing, Mouse Movements Sleep Monitoring Depression affect on phone, linguistic analysis Medication Management Context aware reminding Socialization Skype, phone, s Physical Exercise Interactive video 2012 mhealth Training Institute 27

28 Closing the Loop Health interventions based on Big Data monitoring in the home and environment 2012 mhealth Training Institute 28

29 Monitoring->Care Training Pulmonary Function GPS EEG SpO 2 Coaching Chronic Care Social Networks Health Information Decision Support Population Statistics Epidemiology Evidence Posture ECG Gait Blood Pressure Inference Balance Step Size Step Height Performance Prediction Early Detection Datamining M Pavel, H Watclar, Ref 29

30 Remote Coaching Platform System to facilitate a single coach in managing a large number of clients Multiple modules not single condition or approach User assessment user model tailored data sharing, action plan, coaching messages Semi-automated tailored messaging based on home monitoring and self-report data 2012 mhealth Training Institute 30

31 Health Coaching Platform Architecture

32 Family Interface Safety monitoring Soft alerts Team-based care Socialization Health Coaching Platform Architecture

33 Scalable Approach to Delivering Health Interventions to the Home Platform for delivering sustained health interventions to the home Lifestyle interventions need variety, fun Tailoring based on monitoring Incorporate principles of health behavior change Optimal use of lower cost and local personnel Integrate family & informal caregivers into the health care team (untapped resource)

34 Modular Software for Multiple Protocols Cognitive Exercise (computer game format) Novelty exercise Physical Exercise Sleep Management Socialization Medication Management Mood Management (depression)

35 Physical Activity Module Recommended chair exercises Feedback from time in YouTube and simultaneous motion sensor triggers 35

36 Sleep Module Tailor intervention Measure sleep quality using motion sensors Assess sleep issues Anxiety Napping during day Sleep hygiene 36

37 Socialization Protocols for Cognitive Health Web cams and Skype software given to participants and their remote family partner Frequent spontaneous use among participants

38 Participant Home Page Participant home page Messages from coach Featured story Weekly goals Activities Surveys Access modules Physical Activity Sleep Socialization Novelty Mental Exercises Cognitive Games Coaching Process Participant Materials 38

39 Big Data Implications New analysis and modeling techniques required for behavioral biomarker discovery Sophisticated data models to infer patient state Need to model context Sophisticated user models to tailor intervention Privacy / Security tailored data sharing Multiple care team members (including family) Who sees what data? Appropriate summarization 2012 mhealth Training Institute 39

40 Challenges Big Data Challenges with Behavior Monitoring Data Acquisition: Constraints on monitoring systems Frequent data, but noisy and context dependent Models of sensors, noise, context Robust estimation and classification framework Information fusion from multiple sources Sampling and interpolation: sparse, event-based data Addressing alert fatigue: Containment of false alarms 2012 mhealth Training Institute

41 Models for Big Data Increasing Knowledge Statistical modeling black box Regression Association assessment Unsupervised clustering Functional equations Invariances System properties Process models Biomechanical models Cardiovascular physiology Visual information processing 2012 mhealth Training Institute 41

42 Models for Big Data Decision models (alternatives, utilities) Time series models Graphical models Markov models Bayesian networks Network analysis Simulation models mhealth Training Institute 42

43 Models for Big Data Decision models (alternatives, utilities) Time series models Graphical models Markov models Bayesian networks Network analysis Simulation models. When do you use what? What variables do you include? What features are important? How do I get started? Big Data Training 2012 mhealth Training Institute 43

44 Big Data Skill Sets Sensor characterization (accuracy, bias, drift sampling rate, setting, etc.) Intelligent data sampling Data cleaning / missing data / understanding Data visualization techniques, data representation Data storage / transfer Privacy / security of data Modeling techniques Analysis methods, sensor fusion 2012 mhealth Training Institute

45 Big Data Skill Sets Sensor characterization (accuracy, bias, drift sampling rate, setting, etc.) Intelligent data sampling Data cleaning / missing data / understanding Data visualization techniques, data representation Data storage / transfer Privacy / security of data Modeling techniques Analysis methods, sensor fusion Clinical or health relevance Managing multidisciplinary teams, IRB, etc mhealth Training Institute

46 Take Home Messages Medicine is shifting to out-of-hospital/clinic care. Need for low-cost behavioral metrics. The unobtrusive and continuous monitoring of behaviors in natural settings is largely unexplored. Many opportunities to develop new metrics and more effective interventions (physical activity, sleep, depression, socialization, cognition, eating behaviors, etc.) Big Data Issues/Challenges noise / bias / context; intelligent sampling; modeling of sensors (reliability/accuracy of data); fusion algorithms; data mining; visualization 2012 mhealth Training Institute 46

47 Questions? Contact: Holly Jimison, PhD, FACMI OBSSR / NIH 2012 mhealth Training Institute 47

48 48 NSF Medication Administration and Adherence 9/20/ 2012

49 Medication Management Goal: Enhance context aware reminding optimized using a decision-theoretic approach Context awareness: Sensing a patient s location and actions Inferring a patient s state, activities and contexts Decision-theoretic framework: 1.Objective function: Expected utility of medication adherence process 2.Utility of drugs and alerts 3.Probability of medication taking 49

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