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

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Lee Ritterband, PhD Professor & Director The Center for Behavioral Health & Technology University of Virginia School of Medicine

Online Cognitive-Behavioral Therapy Treatment for Insomnia

2018 NAMS Annual Meeting

Conflict of Interest Disclosures for Speakers

• 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

consumed by, or used on, patients.

Type of Potential Conflict Details of Potential Conflict Grant/Research Support Consultant Speakers’ Bureaus Financial support Other BeHealth Solutions, LLC

• 3. The material presented in this lecture has no relationship with any of these potential conflicts, OR
• 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:

x x

1. Espie, C. A., Kyle, S. D., Williams, C., Ong, J. C., Douglas, N. J., Hames, P., & Brown, J. S. (2012). A randomized, placebo-controlled, trial of online cognitive behavioral therapy for chronic insomnia disorder delivered via an automated media-rich 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, 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 therapy for insomnia: a meta-analysis of randomized controlled trials. Sleep medicine reviews, 19, 17-28.

eHealth Overview

"e‐health is an emerging field in the intersection of medical informatics, public health and business, referring to health services and information delivered

• r enhanced through the

Internet and related technologies."

Eysenbach, G. (2001). What is e‐health? Journal of Medical Internet Research, 3(2), e20.

Internet interventions Sensors Apps Other devices Telehealth

Technologies for Behavioral Sleep Medicine

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Espie CA. "Stepped care": A health technology solution for delivering cognitive behavioral therapy as a first line insomnia treatment. Sleep. 2009;32:1549‐1558.

An evidence‐based stepped care model for CBT (2009) illustrating how patients might be allocated to resources in relation to assessed need, to achieve

• ptimal service
• provision. Arrows

represent self‐ correcting referral movements. Stepped Care

Internet interventions Sensors Apps Other devices Telehealth

Technologies for Behavioral Sleep Medicine

Internet interventions Sensors Apps Other devices Telehealth

Technologies for Behavioral Sleep Medicine

Consumable Interventions vs eHealth Interventions

“To reduce health disparities, interventions are required that can be used again and 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 were developed.”

Muñoz, R. F. (2010). Using Evidence‐Based Internet Interventions to Reduce Health Disparities Worldwide. Journal of Medical Internet Research, 2010;12(5):e60.

Internet interventions Sensors Apps Other devices Telehealth

Technologies for Behavioral Sleep Medicine

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Mobile CBT-I

Mobile

Scalable Passive Sleep Monitoring Using Mobile Phones: Opportunities and Obstacles Saeb S, Cybulski TR, Schueller SM, Kording KP, Mohr DC J Med Internet Res 2017;19(4):e118 http://www.jmir.org/20 17/4/e118

List of mobile phone sensors and their attributes Mobile CBT-I

Mobile

Scalable Passive Sleep Monitoring Using Mobile Phones: Opportunities and Obstacles Saeb S, Cybulski TR, Schueller SM, Kording KP, Mohr DC J Med Internet Res 2017;19(4):e118 http://www.jmir.org/20 17/4/e118

List of features Mobile CBT-I

Mobile

Scalable Passive Sleep Monitoring Using Mobile Phones: Opportunities and Obstacles Saeb S, Cybulski TR, Schueller SM, Kording KP, Mohr DC J Med Internet Res 2017;19(4):e118 http://www.jmir.org/20 17/4/e118

Result: Using all available sensor features, the average accuracy of classifying whether a 10‐ min segment was 91.8% after correction, corresponding to an average median absolute deviation of 38 min for sleep start time detection and 36 min for sleep end time. Conclusions:

• ”…mobile phones provide adequate sleep

monitoring in typical use cases…”

• “…several types of data artifacts…likely

impose a ceiling on the accuracy of sleep prediction for certain subjects. Future research will need to focus more on the understanding of people’s behavior in their natural settings in order to develop sleep monitoring tools that work reliably in all cases for all people.” Mobile CBT-I

Review

Feeling validated yet? A scoping review of the use of consumer‐targeted wearable and mobile technology to measure and improve sleep Baron, Duffecy, Berendsen, Mason, Lattiee, Manalo Sleep Medicine Reviews Available Online December 20, 2017 https://www.sciencedirect.co m/science/article/pii/S108707 9216301496

“…review of studies conducted in adult populations using consumer‐targeted wearable technology or mobile devices designed to measure and/or improve sleep.” “…majority of studies focused on validating technology to measure sleep (n = 23) or were observational studies (n = 10). Few studies were used to identify sleep disorders (n = 2), evaluate response to interventions (n = 3) or deliver interventions (n = 5).”

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Internet interventions Sensors Apps Other devices Telehealth

Technologies for Behavioral Sleep Medicine Mobile CBT-I

Empirical Evidence

Rate My Sleep: Examining the Information, Function, and Basis in Empirical Evidence Within Sleep Applications for Mobile Devices Lee‐Tobin, P. A., Ogeil, R. P., Savic, M., & Lubman, D. I. (2017). Journal of Clinical Sleep Medicine, 13(11), 1349‐1354. http://jcsm.aasm.org/ViewAbstra ct.aspx?pid=31126

“…to examine the information and functions found within sleep apps, determine if the information is based on empirical evidence…” N = 76 Sleep apps found in the Google Play store

• Only 32.9% of sleep apps contained

empirical evidence supporting their claims,

• 15.8% contained clinical input, and
• 13.2% contained links to sleep

literature Mobile CBT-I

Apps Review

Mobile Phone Interventions for Sleep Disorders and Sleep Quality: Systematic Review Shin JC, Kim J, Grigsby‐ Toussaint D JMIR Mhealth Uhealth 2017;5(9):e131 http://mhealth.jmir.org /2017/9/e131

Sample size based on the intervention methods (N=16) Mobile CBT-I

CBT‐I Coach

Kuhn, E., Weiss, B. J., Taylor, K. L., Hoffman, J. E., Ramsey, K. M., Manber, R., … Trockel, M. (2016). CBT‐ I Coach: A Description and Clinician Perceptions of a Mobile App for Cognitive Behavioral Therapy for

• Insomnia. Journal of Clinical Sleep

Medicine, 12(4), 597–606. http://doi.org/10.5664/jcsm.5700 Koffel, E., Kuhn, E., Petsoulis, N., Erbes, C. R., Anders, S., Hoffman, J. E., ... & Polusny, M. A. (2016). A randomized controlled pilot study of CBT‐I Coach: feasibility, acceptability, and potential impact of a mobile phone application for patients in cognitive behavioral therapy for

• insomnia. Health informatics journal.

https://doi.org/10.1177/ 1460458216656472

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Mobile CBT-I

The Sleepcare App

Mobile Phone‐Delivered Cognitive Behavioral Therapy for Insomnia: A Randomized Waitlist Controlled Trial. Horsch, C. H., Lancee, J., Griffioen‐ Both, F., Spruit, S., Fitrianie, S., Neerincx, M. A., … Brinkman, W.‐P. (2017). Journal of Medical Internet Research, 19(4), e70. http://doi.org/10.2196/jmir.6524

“Win‐Win aSleep” [WWaS]

Mobile Application–Assisted Cognitive Behavioral Therapy for Insomnia in an Older Adult Chen Yong‐Xiang, Hung Yi‐Ping, and Chen Hsi‐Chung. Telemedicine and e‐Health. March 2016, 22(4): 332‐334. https://doi.org/10.1089/tmj.2015.0064

Mobile CBT-I

Sleep Ninja

A Smartphone App for Adolescents With Sleep Disturbance: Development

• f the Sleep Ninja

Werner‐Seidler A, O'Dea B, Shand F, Johnston L, Frayne A, Fogarty AS, Christensen H JMIR Ment Health 2017;4(3):e28 http://mental.jmir.org/2017/3/e28

Mobile CBT-I

Internet interventions Sensors Apps Other devices Telehealth

Technologies for Behavioral Sleep Medicine

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Internet interventions Sensors Apps Other devices Telehealth

Technologies for Behavioral Sleep Medicine

INTERNET INTERVENTIONS:

• behavioral treatments
• highly structured
• semi‐self guided
• based on effective face‐to‐face treatment
• personalized and tailored to user
• interactive
• makes extensive use of graphics, animations, audio, and possibly video
• provides follow‐up and feedback

BENEFITS: Internet Interventions Defined

Ritterband, L. M., Gonder‐Frederick, L. A., Cox, D. C., Clifton, A. D., West, R. W., & Borowitz, S. M. (2003). Internet interventions: In review, in use, and into the future. Professional Psychology: Research and Practice, 34(5), 527‐534.

Internet Intervention Outcomes

• 92 Studies
• Overall mean weighted ES = .53 (medium effect)
• Similar to avg ES of traditional F2F therapy

Barak, A., Hen, L., Boniel‐Nissim, M., & Shapira, N. (2008). A comprehensive review and a meta‐analysis of the effectiveness of internet‐based psychotherapeutic interventions. Journal of Technology and Human Services, 26, 109–160. Andersson, G., Cuijpers, P., Carlbring, P., Riper, H., & Hedman, E. (2014). Guided Internet‐based vs. face‐to‐face cognitive behavior therapy for psychiatric and somatic disorders: a systematic review and meta‐analysis. World Psychiatry, 13(3), 288‐295.

Effect Size by Type of Problem

Type of Problem ES N PTSD 0.88 148 Panic & Anxiety 0.80 498 Smoking Cessation 0.62 5460 Drinking 0.48 351 Body Image 0.45 221 Depression 0.32 2500 Physiological 0.27 212 Weight Loss 0.17 1604 Other 0.55 1427

Note ES = effect size, N=number of participants

Guided Internet‐based vs. face‐to‐face cognitive behavior therapy for psychiatric and somatic disorders:

• Systematic searches resulted in 13

studies (total N=1053) that were included in the review.

• Results showed a pooled effect size

(Hedges’ g) at post‐treatment of 20.01 (95% CI: 20.13 to 0.12), indicating that guided ICBT and face‐ to‐face treatment produce equivalent overall effects.

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Internet Intervention Outcomes

A systematic review of meta‐analyses:

• 71 meta‐analyses met inclusion

criteria.

• Within the 71 meta‐analyses, there

were 1733 studies that contained 268 unique RCTs which tested self‐ help interventions.

• On review of the 268 studies, 21.3%

(57/268) had functional websites.

Rogers, M. A., Lemmen, K., Kramer, R., Mann, J., & Chopra, V. (2017). Internet‐Delivered Health Interventions That Work: Systematic Review of Meta‐Analyses and Evaluation of Website Availability. Journal

• f

Medical Internet Research, 19(3), e90. http://www.jmir.org/2017/3/e90/

Self-Help CBT-I Meta-Analysis

Meta‐Analysis of Self‐Help CBT‐I RCTs

• Literature search through May 2013
• 20 RCTs with 2411 participants
• Sleep Efficiency
• Sleep Onset Latency
• Wake After Sleep Onset
• Sleep Quality
• Total Sleep Time
• # of Awakenings

‐‐‐‐‐‐

• Anxiety
• Depression
• DBAS

g = 0.80, 95% CI: 0.4 to 1.2 g = ‐0.66, 95% CI: ‐1.0 to ‐0.04 g = ‐0.55, 95% CI: ‐0.9 to ‐0.2 g = 0.35, 95% CI: 0.2 to 0.6 g = 0.24, 95% CI: 0.0 to 0.5 g = ‐0.41, 95% CI: ‐0.7 to ‐0.2 ‐‐‐‐‐‐ g = ‐0.42, 95% CI: ‐0.6 to ‐0.2 g = ‐0.50, 95% CI: ‐0.7 to ‐0.3 g = ‐0.84, 95% CI: ‐1.6 to ‐0.04

Findings:

• Sleep improvements

maintained over time.

• Clinical significance was

also demonstrated.

• No publication bias was

found.

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 therapy for insomnia: a meta‐analysis of randomized controlled trials. Sleep medicine reviews, 19, 17‐28.

Self‐Help CBT vs. Waiting‐List Control, Routine Care or No Treatment on Sleep Questionnaire Score at Immediate Posttreatment

Internet Interventions for Insomnia eCBT-I Meta-Analysis

Zachariae, R., Lyby, M. S., Ritterband, L., & O’Toole, M. S. (2017). Efficacy of Internet‐delivered cognitive‐behavioral therapy for insomnia – a systematic review and meta‐analysis of randomized controlled trials. Sleep Medicine Reviews.

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eCBT-I Meta-Analysis

• 1. Strom L, Pettersson R, Andersson G. Internet-based treatment for

insomnia: a controlled evaluation. J Consult Clin Psychol 2004 Feb;72:113-20.

• 2. Suzuki E, Tsuchiya M, Hirokawa K, Taniguchi T, Mitsuhashi T,

Kawakami N. Evaluation of an internet-based self-help program for better quality of sleep among Japanese workers: a randomized controlled trial. J Occup Health 2008;50:387-99.

• 3. Ritterband LM, Thorndike FP, Gonder-Frederick LA, Magee JC,

Bailey ET, Saylor DK, et al. Efficacy of an Internet-based behavioral intervention for adults with insomnia. Arch Gen Psychiatry 2009 Jul;66:692-8.

• 4. Vincent N, Lewycky S. Logging on for better sleep: RCT of the

effectiveness of online treatment for insomnia. Sleep 2009 Jun;32:807-15.

• 5. Ritterband LM, Bailey ET, Thorndike FP, Lord HR, Farrell-

Carnahan L, Baum LD. Initial evaluation of an Internet intervention to improve the sleep of cancer survivors with

• insomnia. Psychooncology 2012;21:695-705.
• 6. Lancee J, van den Bout J, van SA, Spoormaker VI. Internet-

delivered or mailed self-help treatment for insomnia?: a randomized waiting-list controlled trial. Behav Res Ther 2012 Jan;50:22-9.

• 7. Espie CA, Kyle SD, Williams C, Ong JC, Douglas NJ, Hames P, et
• al. A randomized, placebo-controlled trial of online cognitive

behavioral therapy for chronic insomnia disorder delivered via an automated media-rich web application. Sleep 2012 Jun;35:769-81.

• 8. van SA, Emmelkamp J, de WJ, Lancee J, Andersson G, Van

Someren EJ, et al. Guided Internet-delivered cognitive behavioural treatment for insomnia: a randomized trial. Psychol Med 2013 Sep 4;1-12.

• 9. Ho FY, Chung KF, Yeung WF, Ng TH, Cheng SK. Weekly brief

phone support in self-help cognitive behavioral therapy for insomnia disorder: Relevance to adherence and efficacy. Behav Res Ther 2014 Oct 23;63C:147-56.

• 10. Thiart H, Lehr D, Ebert DD, Berking M, Riper H. Log in and

breathe out: internet-based recovery training for sleepless employees with work-related strain-results of a RCT. Scand J Work Environ Health 2015 Mar;41:164-74.

• 11. Pillai V, Anderson JR, Cheng P, Bazan L, Bostock S, Espie CA, et
• al. The anxiolytic effects of cognitive behavior therapy for

insomnia: preliminary results from a web-delivered protocol. J Sleep Med Disord 2015;2:1017.

1. Based on face‐to‐face Cognitive Behavioral Therapy for Insomnia 2. Program is fully automated; does not require clinician interaction 3. Intervention is tailored to individual user’s symptoms as entered in daily online sleep diaries 4. Components to foster engagement

• Extensive use of graphics, animations, audio, and video
• Emails used to prompt adherence

5. The metering of content

• Cores made available out over time
• Next Core available 1 week after previous Core complete

6. All (six) treatment Cores cover:

• Sleep Restriction, Stimulus Control, Cognitive Restructuring, Sleep

Hygiene, Relapse Prevention

SHUTi Outcomes: ISI

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Perilous Predictions from the Past

• 1876: "The Americans have need of the telephone, but we do not. We have plenty of

messenger boys." — William Preece, British Post Office.

• 1889: “Fooling around with alternating current is just a waste of time. Nobody will use

it, ever.” — Thomas Edison

• 1946: "Television won't be able to hold on to any market it captures after the first six
• months. People will soon get tired of staring at a plywood box every night." — Darryl

Zanuck, 20th Century Fox.

• 1966: "Remote shopping, while entirely feasible, will flop.” — Time Magazine.
• 1995: "I predict the Internet will soon go spectacularly supernova and in 1996

catastrophically collapse." — Robert Metcalfe, founder of 3Com.

• 2005: "There's just not that many videos I want to watch." — Steve Chen, CTO and co‐

founder of YouTube expressing concerns about his company’s long term viability.

• 2006: "Everyone's always asking me when Apple will come out with a cell phone. My

answer is, 'Probably never.'" — David Pogue, The New York Times.

• 2007: “There’s no chance that the iPhone is going to get any significant market

share.” — Steve Ballmer, Microsoft CEO.

“Web‐based treatment interventions… will

likely flourish as increased and improved Internet access becomes more

• prevalent. Wireless technology is also rapidly improving, and consumer

products with wireless capabilities are becoming more available. Some users already have cellular

phones and hand‐held computers with wireless Internet access. These products will become even more widely available and easier to use over the coming years. These

wireless products will allow individuals to track and share information with their health care professions in real time, ease data input, reduce data‐entry errors, and, we hope, improve compliance.”

Ritterband et al., Professional Psychology Research and Practice, 2003

“…some critical issues still need to be addressed and resolved, including problems of self‐assessment and diagnosis, dissemination of information and treatments, establishment of a financial model, and compliance.”

Rise of eHealth research and publication in last two decades

Q & A

Lee Ritterband LEER@virginia.edu @LeeRitterband

University of Virginia The Center for Behavioral Health & Technology