Technologies for "ehealth is an emerging field in the - PowerPoint PPT Presentation

Conflict of Interest Disclosures for Speakers 2018 NAMS Annual Meeting 1. I do not have any relationships with any entities producing , marketing, re-selling, or distributing health care goods or services consumed by, or used on, patients, OR 2. I have the following relationships with entities producing , marketing, re-selling, or distributing health care goods or services x consumed by, or used on, patients. Type of Potential Conflict Details of Potential Conflict Grant/Research Support Online Cognitive - Behavioral Consultant Speakers’ Bureaus Therapy Treatment for Insomnia Financial support Other BeHealth Solutions, LLC 3. The material presented in this lecture has no relationship with any of these potential conflicts, OR x 4. This talk presents material that is related to one or more of these potential conflicts, and the following objective references are provided as support for this lecture: 1. Espie, C. A., Kyle, S. D., Williams, C., Ong, J. C., Douglas, N. J., Hames, P., & Brown, J. S. (2012). A randomized, Lee Ritterband, PhD placebo - controlled, trial of online cognitive behavioral therapy for chronic insomnia disorder delivered via an automated media - rich Professor & Director web application. Sleep, 35(6), 769–781. 2. Vincent, N., & Lewycky, S. (2009). Logging on for better sleep: RCT of the effectiveness of online treatment for insomnia. Sleep, The Center for Behavioral Health & Technology 32(6), 807–815. 3. Ho, F. Y. Y., Chung, K. F., Yeung, W. F., Ng, T. H., Kwan, K. S., Yung, K. P., & Cheng, S. K. (2015). Self-help cognitive-behavioral University of Virginia School of Medicine therapy for insomnia: a meta-analysis of randomized controlled trials. Sleep medicine reviews , 19 , 17-28. eHealth Overview Technologies for "e‐health is an emerging field in the intersection of Behavioral Sleep Medicine medical informatics, public health and business, referring to health services and information delivered Internet interventions or enhanced through the Sensors Internet and related technologies." Apps Other devices Telehealth Eysenbach, G. (2001). What is e‐health? Journal of Medical Internet Research , 3(2), e20. 1

Technologies for Stepped Care Behavioral Sleep Medicine An evidence‐based stepped care model for CBT (2009) illustrating how patients might be allocated to resources in Internet interventions relation to assessed need, to achieve Sensors optimal service provision. Arrows represent self‐ Apps correcting referral movements. Other devices Telehealth Espie CA. "Stepped care": A health technology solution for delivering cognitive behavioral therapy as a first line insomnia treatment. Sleep . 2009;32:1549‐1558. Technologies for Technologies for Behavioral Sleep Medicine Behavioral Sleep Medicine Consumable Interventions vs eHealth Interventions Internet interventions Internet interventions “To reduce health disparities, interventions are required that can be used again and Sensors Sensors again without losing their therapeutic power, that can reach people even if local health care systems do not provide them with needed health care, and that can be shared globally without taking resources away from the populations where the interventions Apps Apps were developed.” Other devices Other devices Telehealth Telehealth Muñoz, R. F. (2010). Using Evidence‐Based Internet Interventions to Reduce Health Disparities Worldwide. Journal of Medical Internet Research , 2010;12(5):e60. 2

Mobile CBT-I Mobile CBT-I Mobile Mobile List of mobile phone sensors and their attributes List of features Scalable Passive Sleep Scalable Passive Sleep Monitoring Using Monitoring Using Mobile Phones: Mobile Phones: Opportunities and Opportunities and Obstacles Obstacles Saeb S, Cybulski TR, Saeb S, Cybulski TR, Schueller SM, Kording Schueller SM, Kording KP, Mohr DC KP, Mohr DC J Med Internet Res J Med Internet Res 2017;19(4):e118 2017;19(4):e118 http://www.jmir.org/20 http://www.jmir.org/20 17/4/e118 17/4/e118 Mobile CBT-I Mobile CBT-I Review Mobile Result: Using all available sensor features, the average accuracy of classifying whether a 10‐ min segment was 91.8% after correction, Feeling validated yet? A “…review of studies conducted in adult Scalable Passive Sleep corresponding to an average median absolute scoping review of the use of populations using consumer‐targeted Monitoring Using deviation of 38 min for sleep start time wearable technology or mobile devices consumer‐targeted wearable Mobile Phones: detection and 36 min for sleep end time. designed to measure and/or improve and mobile technology to Opportunities and sleep.” measure and improve sleep Obstacles Conclusions: • ”…mobile phones provide adequate sleep “…majority of studies focused on monitoring in typical use cases…” Baron, Duffecy, Berendsen, Saeb S, Cybulski TR, validating technology to measure sleep • “…several types of data artifacts…likely Mason, Lattiee, Manalo Schueller SM, Kording (n = 23) or were observational studies impose a ceiling on the accuracy of sleep Sleep Medicine Reviews KP, Mohr DC (n = 10). Few studies were used to prediction for certain subjects. Future Available Online December identify sleep disorders (n = 2), evaluate J Med Internet Res research will need to focus more on the 20, 2017 response to interventions (n = 3) or deliver 2017;19(4):e118 understanding of people’s behavior in their interventions (n = 5).” https://www.sciencedirect.co http://www.jmir.org/20 natural settings in order to develop sleep m/science/article/pii/S108707 17/4/e118 monitoring tools that work reliably in all 9216301496 cases for all people.” 3

Technologies for Mobile CBT-I Behavioral Sleep Medicine Empirical Evidence Rate My Sleep: Examining the “…to examine the information and functions found within sleep apps, Information, Function, and Basis determine if the information is based on in Empirical Evidence Within empirical evidence…” Sleep Applications for Mobile Devices N = 76 Sleep apps found in the Google Internet interventions Play store Lee‐Tobin, P. A., Ogeil, R. P., Savic, Sensors M., & Lubman, D. I. • Only 32.9% of sleep apps contained (2017). Journal of Clinical Sleep empirical evidence supporting their Apps Medicine , 13 (11), 1349‐1354. claims, • 15.8% contained clinical input, and • 13.2% contained links to sleep Other devices http://jcsm.aasm.org/ViewAbstra literature ct.aspx?pid=31126 Telehealth Mobile CBT-I Mobile CBT-I CBT‐I Coach Apps Review Kuhn, E., Weiss, B. J., Taylor, K. L., Sample size based on the intervention methods (N=16) Hoffman, J. E., Ramsey, K. M., Manber, R., … Trockel, M. (2016). CBT‐ I Coach: A Description and Clinician Mobile Phone Perceptions of a Mobile App for Interventions for Sleep Cognitive Behavioral Therapy for Disorders and Sleep Insomnia . Journal of Clinical Sleep Medicine , 12 (4), 597–606. Quality: Systematic http://doi.org/10.5664/jcsm.5700 Review Koffel, E., Kuhn, E., Petsoulis, N., Shin JC, Kim J, Grigsby‐ Erbes, C. R., Anders, S., Hoffman, J. E., ... & Polusny, M. A. (2016). A Toussaint D randomized controlled pilot study of JMIR Mhealth Uhealth CBT‐I Coach: feasibility, acceptability, 2017;5(9):e131 and potential impact of a mobile http://mhealth.jmir.org phone application for patients in cognitive behavioral therapy for /2017/9/e131 insomnia . Health informatics journal . https://doi.org/10.1177/ 1460458216656472 4

Mobile CBT-I Mobile CBT-I The Sleepcare App “Win‐Win aSleep” [WWaS] Mobile Phone‐Delivered Cognitive Behavioral Therapy for Insomnia: A Randomized Waitlist Controlled Mobile Application–Assisted Cognitive Behavioral Therapy for Trial. Insomnia in an Older Adult Horsch, C. H., Lancee, J., Griffioen‐ Both, F., Spruit, S., Fitrianie, S., Chen Yong‐Xiang, Hung Yi‐Ping, and Chen Hsi‐Chung. Neerincx, M. A., … Brinkman, W.‐P. Telemedicine and e‐Health. March 2016, 22(4): 332‐334. (2017). Journal of Medical Internet https://doi.org/10.1089/tmj.2015.0064 Research , 19(4), e70. http://doi.org/10.2196/jmir.6524 Technologies for Mobile CBT-I Behavioral Sleep Medicine Sleep Ninja A Smartphone App for Adolescents With Sleep Disturbance: Development of the Sleep Ninja Internet interventions Werner‐Seidler A, O'Dea B, Shand Sensors F, Johnston L, Frayne A, Fogarty AS, Christensen H Apps JMIR Ment Health 2017;4(3):e28 http://mental.jmir.org/2017/3/e28 Other devices Telehealth 5