Digital Cognitive Behavioural Therapy for insomnia in the real world: Does using a wearable device help?
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1
University of Oxford, Nuffield Department of Clinical Neurosciences, United Kingdom
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2
Big Health Ltd, United Kingdom
Rationale: Cognitive Behavioural Therapy is the recommended treatment for persons struggling with insomnia; insomnia is defined as trouble falling asleep, staying asleep or waking up too early for at least 3 nights per week for at least 3 months. Advanced digital technology (web and mobile) has the potential to scale up the dissemination of CBT, particularly since the field is struggling with a shortage of therapists. Digital CBT (dCBT) has been found to be effective for insomnia in randomised controlled trials but so far ‘real-world’ data are limited. In addition, little is known about the integration of wearable devices that estimate sleep into dCBT.
Aims: Assessing the effectiveness of dCBT in the real-world and the use of wearable devices within dCBT.
Methodology: A cohort of 3500 users (63% female, mean age 44.43 ± 14.78 years) who graduated from a dCBT program (Sleepio, Big Health Ltd, London, UK) and who completed an integrated post-therapy evaluation was selected for this study. This included 376 (10.7%) users who connected a wearable device to the program. The Sleep Condition Indicator (SCI, 7 Items), Patient Health Questionnaire (PHQ-2, 2 items), Generalized Anxiety Disorder (GAD-2, 2 item), and 1 item measures about perceived stress, life satisfaction and work productivity were collected at baseline and post-treatment within the program. In addition, we collected information about demographics, lifestyle and interaction with the program. All users consent to the anonymised use of their data when they access the program (www.sleepio.com/privacy). The abstract presents an evaluation of an ongoing service; therefore no approval of a research ethics committee was obtained.
Analysis: Group comparisons were made for those who connected a device to automatically generate daily diary data and those who did not connect a device, in addition to baseline to post-treatment comparisons. Data were analysed using Chi-square tests, t-tests (paired and unpaired), Mann-Whitney U-test (unpaired) and the Wilcoxon signed rank test depending on the measurement and distribution of the data.
Results: Sleep quality (SCI-7) significantly improved from 4.24 (SD:1.93) to 7.01 (SD:1.92) after dCBT for insomnia (t(3453)=82.58, p<0.001; Cohen’s d=1.44), as did depressive symptoms (PHQ-2, Z=-26.59, p<0.001), anxiety (GAD-2, Z=-29.41, p<0.001), perceived stress (Z=-28.37, p<0.001), life satisfaction (Z=-18.98, p<0.001) and work productivity (Z=-25.43, p<0.001). Treatment effects were similar for those who connected a device and those who did not. Those who did connect a wearable device were interacting more with the program; users with a device connected were more likely to view the library (85.1% vs. 75.9%, Chi-square(1)=16.12, p<0.001), and although both groups were equally likely to view the community (80.1% vs. 80.2%, Chi-square(1)=0.01, p=0.928), the users who connected a device were more likely to post in the community (18.4% vs. 12.6%, Chi-square(1)=9.505, p=0.002).
Conclusions: dCBT is a suitable treatment for insomnia in the real-world and improves several well-being factors, next to sleep. Although wearable users are more likely to interact with additional components of the program, wearable devices did not affect treatment effectiveness. dCBT and wearables may therefore be useful to facilitate large scale treatment for insomnia.
Keywords:
insomnia,
Sleep,
cbt,
Online,
sleep tracker,
fitness band
Conference:
3rd UCL Centre for Behaviour Change Digital Health Conference 2017: Harnessing digital technology for behaviour change, London, United Kingdom, 22 Feb - 23 Feb, 2017.
Presentation Type:
Research abstract
Topic:
Digital Health
Citation:
Luik
AI,
Farias-Machado
P and
Espie
CA
(2017). Digital Cognitive Behavioural Therapy for insomnia in the real world: Does using a wearable device help?.
Front. Public Health.
Conference Abstract:
3rd UCL Centre for Behaviour Change Digital Health Conference 2017: Harnessing digital technology for behaviour change.
doi: 10.3389/conf.FPUBH.2017.03.00060
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Received:
22 Feb 2017;
Published Online:
22 Feb 2017.
*
Correspondence:
Dr. Annemarie I Luik, University of Oxford, Nuffield Department of Clinical Neurosciences, Oxford, United Kingdom, annemarie.luik@sleepio.com