The impact of a digital sleep-improvement program on health care costs

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OBJECTIVES Access to non-pharmacological interventions for insomnia is limited, despite evidence for their effectiveness. This is due in part to a shortage of trained providers. Digital cognitive behavioural therapy for insomnia (dCBT-I), which requires less input per session, is there-fore an attractive complement or substitute to face-to-face cognitive behavioural therapy. Sleepio is a self-help online sleep improvement programme and app, which has been shown to improve sleep quality. In this study, we sought to identify whether providing access to Sleepio results in a change in the trend of patients' primary care costs. METHODS The study adopted a before and after quasi-experimental design alongside population-wide rollout of Sleepio in the Thames Valley region of England. A generalised linear segmented regression model of the interrupted time series data was estimated to identify the change in trend of the average primary care costs per patient per week. Costs included GP attendanc-es and prescription costs. Data were collected from two sources: i) primary care providers and ii) Sleepio user data collected through the app. Interim results are presented, based on data from 12 months pre-intervention (October 2017) to 6 months post-intervention (May 2019). We additionally explore the possible impact on resource use under alternative refer-ral mechanisms. RESULTS More than 6,000 individuals used the Sleepio service, with around a third converting to dCBT-I and gaining 30 minutes extra sleep per night, on average. Of dCBT-I users within the clinical range on the Sleep Condition Indicator, 42% moved to a non-clinical range. In the full primary care sample (n=9,582), the introduction of Sleepio was associated with a tem-porary increase in costs followed by a downward trend. For the subgroup of people with a diagnosis of depression or anxiety (n=4,471), Sleepio was associated with cost savings at 28-weeks post-rollout. For the full sample, we project cost-savings at 58 weeks. CONCLUSIONS Interim findings show that population rollout of Sleepio is effective in improving sleep for people with clinically significant sleep problems. Access to Sleepio reduces health care re-source use. If trends are maintained in the full-term analysis, provision of Sleepio is likely to be cost saving.

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