13 Smart Learning Using Big and Small Data for Mobile and IOT e-Health
631
mental health conditions such as Parkinson’s, Alzheimer’s, Huntington’s disease,
post-traumatic stress disorder (PTSD), and behavioral conditions such as autism
and ADHD. Further analysis of a patient’s speech or written notes can identify
additional indicators of meaning, syntax and intonation in language. Combining the
various measurements from heterogeneous mobile and IOT e-health data with other
clinical information sources such as magnetic resonance imagings (MRIs) and EEGs
can paint a more complete picture of the individual-specific characteristics for health
professionals to better identify, understand, and treat the underlying disease. The
important part of mobile and IOT e-health is to make what were once invisible signs
become clear signals of patients behavioral risk and how well they are responding
to interventions, complementing regular clinical visits with EMA assessments in
daily-life settings.
Acknowledgment Theme 2: The projects Witra-Care and Mobile Care Backup (MoCaB) are
funded by German Federal Ministry of Education and Research (grant numbers Witra-Care:
16SV6380; MoCaB: 16SV7472).
References
1. E.K. Choe, N.B. Lee, B. Lee, W. Pratt, J.A. Kientz, in Proceedings of the 32nd Annual ACM
Conference on Human Factors in Computing Systems. Understanding quantified-selfers’
practices in collecting and exploring personal data. (Toronto, ON, Canada, Apr. 26–May 1).
(ACM Press, New York, 2014), pp. 1143–1152
2. K. Shameer, M.A. Badgeley, R. Miotto, B.S. Glicksberg, J.W. Morgan, J.T. Dudley, Translational bioinformatics in the era of real-time biomedical, health care and wellness data streams.
Brief. Bioinform. 18(1), 105–124 (2017). https://doi.org/10.1093/bib/bbv118
3. B. Knowles, A. Smith-Renner, F. Poursabzi-Sangdeh, D. Lu, H. Alabi, Uncertainty in current and future health wearables. Commun. ACM 61(12), 62–67 (2018).
https://doi.org/10.1145/3199201
4. J.B. Bassingthwaighte, Strategies for the physiome project. Ann. Biomed. Eng. 28(8), 1043–
1058 (2000). https://doi.org/10.1114/1.1313771
5. X. Li, J. Dunn, D. Salins, G. Zhou, W. Zhou, S.M. Schüssler-Fiorenza Rose,
et al., Digital health: Tracking physiomes and activity using wearable biosensors reveals useful health-related information. PLoS Biol. 15(1), e2001402 (2017).
https://doi.org/10.1371/journal.pbio.2001402
6. R. Voelker, Smart watch detects seizures. JAMA 319(11), 1086 (2018).
https://doi.org/10.1001/jama.2018.1809
7. G.H. Tison, J.M. Sanchez, B. Ballinger, A. Singh, J.E. Olgin, M.J. Pletcher, et al., Passive
detection of atrial fibrillation using a commercially available smartwatch. JAMA Cardiol.
(2018). https://doi.org/10.1001/jamacardio.2018.0136
8. J.E. Dimsdale, Psychological stress and cardiovascular disease. J. Am. Coll. Cardiol. 51(13),
1237–1246 (2008)
9. D.M. Lloyd-Jones, Y. Hong, D. Labarthe, et al., Defining and setting national goals for
cardiovascular health promotion and disease reduction: The American Heart Association’s
strategic impact goal through 2020 and beyond. Circulation 121, 586–613 (2010)
10. B.H. Marcus, L.H. Forsyth, E.J. Stone, P.M. Dubbert, T.L. McKenzie, A.L. Dunn, S.N. Blair,
Physical activity behavior change: Issues in adoption and maintenance. Health Psychol. 19,
32–41 (2000)
11. P. Salmon, Effects of physical exercise on anxiety, depression, and sensitivity to stress: A
unifying theory. Clin. Psychol. Rev. 21(1), 33–61 (2001)
631
mental health conditions such as Parkinson’s, Alzheimer’s, Huntington’s disease,
post-traumatic stress disorder (PTSD), and behavioral conditions such as autism
and ADHD. Further analysis of a patient’s speech or written notes can identify
additional indicators of meaning, syntax and intonation in language. Combining the
various measurements from heterogeneous mobile and IOT e-health data with other
clinical information sources such as magnetic resonance imagings (MRIs) and EEGs
can paint a more complete picture of the individual-specific characteristics for health
professionals to better identify, understand, and treat the underlying disease. The
important part of mobile and IOT e-health is to make what were once invisible signs
become clear signals of patients behavioral risk and how well they are responding
to interventions, complementing regular clinical visits with EMA assessments in
daily-life settings.
Acknowledgment Theme 2: The projects Witra-Care and Mobile Care Backup (MoCaB) are
funded by German Federal Ministry of Education and Research (grant numbers Witra-Care:
16SV6380; MoCaB: 16SV7472).
References
1. E.K. Choe, N.B. Lee, B. Lee, W. Pratt, J.A. Kientz, in Proceedings of the 32nd Annual ACM
Conference on Human Factors in Computing Systems. Understanding quantified-selfers’
practices in collecting and exploring personal data. (Toronto, ON, Canada, Apr. 26–May 1).
(ACM Press, New York, 2014), pp. 1143–1152
2. K. Shameer, M.A. Badgeley, R. Miotto, B.S. Glicksberg, J.W. Morgan, J.T. Dudley, Translational bioinformatics in the era of real-time biomedical, health care and wellness data streams.
Brief. Bioinform. 18(1), 105–124 (2017). https://doi.org/10.1093/bib/bbv118
3. B. Knowles, A. Smith-Renner, F. Poursabzi-Sangdeh, D. Lu, H. Alabi, Uncertainty in current and future health wearables. Commun. ACM 61(12), 62–67 (2018).
https://doi.org/10.1145/3199201
4. J.B. Bassingthwaighte, Strategies for the physiome project. Ann. Biomed. Eng. 28(8), 1043–
1058 (2000). https://doi.org/10.1114/1.1313771
5. X. Li, J. Dunn, D. Salins, G. Zhou, W. Zhou, S.M. Schüssler-Fiorenza Rose,
et al., Digital health: Tracking physiomes and activity using wearable biosensors reveals useful health-related information. PLoS Biol. 15(1), e2001402 (2017).
https://doi.org/10.1371/journal.pbio.2001402
6. R. Voelker, Smart watch detects seizures. JAMA 319(11), 1086 (2018).
https://doi.org/10.1001/jama.2018.1809
7. G.H. Tison, J.M. Sanchez, B. Ballinger, A. Singh, J.E. Olgin, M.J. Pletcher, et al., Passive
detection of atrial fibrillation using a commercially available smartwatch. JAMA Cardiol.
(2018). https://doi.org/10.1001/jamacardio.2018.0136
8. J.E. Dimsdale, Psychological stress and cardiovascular disease. J. Am. Coll. Cardiol. 51(13),
1237–1246 (2008)
9. D.M. Lloyd-Jones, Y. Hong, D. Labarthe, et al., Defining and setting national goals for
cardiovascular health promotion and disease reduction: The American Heart Association’s
strategic impact goal through 2020 and beyond. Circulation 121, 586–613 (2010)
10. B.H. Marcus, L.H. Forsyth, E.J. Stone, P.M. Dubbert, T.L. McKenzie, A.L. Dunn, S.N. Blair,
Physical activity behavior change: Issues in adoption and maintenance. Health Psychol. 19,
32–41 (2000)
11. P. Salmon, Effects of physical exercise on anxiety, depression, and sensitivity to stress: A
unifying theory. Clin. Psychol. Rev. 21(1), 33–61 (2001)