JMIR Publications

ABSTRACT

Background:

Globally, insomnia remains a highly prevalent public health problem. eHealth presents a novel opportunity to deliver effective, accessible, and affordable insomnia treatments on a population-wide scale. However, there is no quantitative integration of the evidence regarding the effectiveness of eHealth-based psychosocial interventions on insomnia.

Objective:

We aimed to evaluate the overall effectiveness and identify the effectiveness of specific factors of eHealth.

Methods:

We searched PubMed/Medline, EMBASE, Web of Science, PsycINFO, and the Cochrane Central Register of Controlled Trials from database inception to February 16, 2021, for publications investigating eHealth-based psychosocial interventions targeting insomnia, and updated the search of PubMed to December 6, 2021. We also screened grey literature for unpublished data. Eligible studies were randomized controlled trials of eHealth interventions targeting adults suffering from insomnia. Random-effects meta-analysis model was performed to assess the primary and secondary outcomes. Primary outcomes were insomnia severity and sleep quality. Planned subgroup analyses were performed by comparing eHealth interventions with in-person and inactive comparison conditions. We performed posthoc subgroup analyses and meta-regressions to explore specific factors that impacted the effectiveness. Secondary outcomes included sleep diary parameters and mental health-related outcomes.

Results:

Of the 19980 identified records, 37 RCTs (13227 participants, 9442 [71.4%] females and 3785 [28.6%] males) were included. eHealth interventions significantly reduced insomnia severity (Hedges’ g -0.87, [-1.01 to -0.74], P < .001) and improved sleep quality (g -0.48, [-0.69 to -0.27], P < .001) with no evidence of publication bias. eHealth interventions had a large effect in comparison to inactive control conditions on insomnia severity (g -1.01, [-1.13 to -0.89], P < .001) and sleep quality (g -0.62, [-0.80 to -0.45], P < .001). We found no significant difference compared to in-person treatment on improving insomnia severity (g 0.41, [-0.02 to 0.85], P = .06), and a significant advantage for in-person treatment on enhancing sleep quality (g 0.56, [0.24 to 0.88], P < .001). eHealth interventions had significant larger effects (P = .01) on alleviating insomnia severity in clinical samples than in subclinical samples. Tailored feedback significantly affected the effects of eHealth interventions on insomnia severity (P = .03). eHealth interventions that incorporated guidance from trained therapists had a significant greater effect (P = .02) on sleep quality than those with guidance from virtual therapists or no guidance. eHealth interventions significantly improved both sleep diary parameters and mental health-related outcomes (P < .01).

Conclusions:

eHealth interventions for insomnia are effective in improving sleep and mental health and can be considered a promising treatment for insomnia. Our findings support wider dissemination of eHealth interventions and further promotion in a stepped-care model. Offering blended care could improve treatment effectiveness. Future research needs to elucidate which specific intervention components are most important to reach intervention effectiveness. Blended eHealth interventions may be tailored to benefit people with low socioeconomic status, limited access to health care, or lack of eHealth literacy. Clinical Trial: This study was registered with PROSPERO, CRD42021233241.