Insomnia as a mediating therapeutic target for depressive symptoms: a sub‐analysis of participant data from two large randomized controlled trials of a digital sleep intervention

Insomnia predicts the onset of depression, commonly co‐presents with depression and often persists following depression remission. However, these conditions can be challenging to treat concurrently using depression‐specific therapies. Cognitive behavioural therapy for insomnia may be an appropriate treatment to improve both insomnia and depressive symptoms. We examined the effects of a fully‐automated digital cognitive behavioural therapy intervention for insomnia (Sleepio) on insomnia and depressive symptoms, and the mediating role of sleep improvement on depressive symptoms in participants from two randomized controlled trials of digital cognitive behavioural therapy for insomnia. We also explored potential moderators of intervention effects. All participants met criteria for probable insomnia disorder and had clinically significant depressive symptomatology (PHQ‐9 ≥ 10; n = 3,352). Individuals allocated to treatment in both trials were provided access to digital cognitive behavioural therapy. Digital cognitive behavioural therapy significantly improved insomnia (p < .001; g = 0.76) and depressive symptoms (p < .001; g = 0.48) at post‐intervention (weeks 8–10), and increased the odds (OR = 2.9; 95% CI = 2.34, 3.65) of clinically significant improvement in depressive symptoms (PHQ‐9 < 10). Improvements in insomnia symptoms at mid‐intervention mediated 87% of the effects on depressive symptoms at post‐intervention. No variables moderated effectiveness outcomes, suggesting generalizability of these findings. Our results suggest that effects of digital cognitive behavioural therapy for insomnia extend to depressive symptoms in those with clinically significant depressive symptomatology. Insomnia may, therefore, be an important therapeutic target to assist management of depressive symptoms

Insomnia as a mediating therapeutic target for depressive symptoms: A sub-analysis of participant data from two large randomized controlled trials of a digital sleep intervention

Insomnia predicts the onset of depression, commonly co-presents with depression and often persists following depression remission. However, these conditions can be challenging to treat concurrently using depression-specific therapies. Cognitive behavioural therapy for insomnia may be an appropriate treatment to improve both insomnia and depressive symptoms. We examined the effects of a fully-automated digital cognitive behavioural therapy intervention for insomnia (Sleepio) on insomnia and depressive symptoms, and the mediating role of sleep improvement on depressive symptoms in participants from two randomized controlled trials of digital cognitive behavioural therapy for insomnia. We also explored potential moderators of intervention effects. All participants met criteria for probable insomnia disorder and had clinically significant depressive symptomatology (PHQ-9 ≥ 10; n = 3,352). Individuals allocated to treatment in both trials were provided access to digital cognitive behavioural therapy. Digital cognitive behavioural therapy significantly improved insomnia (p <.001; g = 0.76) and depressive symptoms (p <.001; g = 0.48) at post-intervention (weeks 8–10), and increased the odds (OR = 2.9; 95% CI = 2.34, 3.65) of clinically significant improvement in depressive symptoms (PHQ-9 < 10). Improvements in insomnia symptoms at mid-intervention mediated 87% of the effects on depressive symptoms at post-intervention. No variables moderated effectiveness outcomes, suggesting generalizability of these findings. Our results suggest that effects of digital cognitive behavioural therapy for insomnia extend to depressive symptoms in those with clinically significant depressive symptomatology. Insomnia may, therefore, be an important therapeutic target to assist management of depressive symptoms

Alternatives to polysomnography (PSG): A validation of wrist actigraphy and a partial-PSG system

The objective of this study was to assess the validity of a sleep/wake activity monitor, an energy expenditure activity monitor, and a partial-polysomnography system at measuring sleep and wake under identical conditions. Secondary aims were to evaluate the sleep/wake thresholds for each activity monitor and to compare the three devices. To achieve these aims, two nights of sleep were recorded simultaneously with polysomnography (PSG), two activity monitors, and a partial-PSG system in a sleep laboratory. Agreement with PSG was evaluated epoch by epoch and with summary measures including total sleep time (TST) and wake after sleep onset (WASO). All of the devices had high agreement rates for identifying sleep and wake, but the partial-PSG system was the best, with an agreement of 91.6 % +/- 5.1 %. Attheir best thresholds, the sleep/wake monitor (medium threshold, 87.7 % +/- 7.6 %) and the energy expenditure monitor (very low threshold, 86.8 % +/- 8.6 %) had similarly high rates of agreement. The summary measures were similar to those determined by PSG, but thepartial-PSG system provided the most consistent estimates. Although the partial-PSG system was the most accurate device, both activity monitors were also valid for sleep estimation, provided that appropriate thresholds were selected. Each device has advantages, so the primary consideration for researchers will be to determine which best suits a given research design