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BACKGROUND: Digital cognitive behavioral therapy for insomnia (CBT-i) in people with low back pain (LBP) may be efficacious in improving both sleep and pain; and twin trial designs provide greater precision of treatment effects by accounting for genetic and early environmental factors. We aimed to determine the feasibility of a trial investigating the efficacy of a digital CBT-i program in people with comorbid symptoms of insomnia and LBP, in twins and people from the general community (singletons). METHODS: Thirty-two twins (16 pairs) and 66 singletons with comorbid symptoms of insomnia and LBP (> 6 weeks duration) were randomized to digital CBT-i (intervention) or educational program (control) for 6 weeks. The digital CBT-I, Sleepio (developed by Big Health Inc.), was an online interactive, automated, personalized course comprising of six sessions, once a week. The education program was six emails with general sleep information, once a week. Participants were blinded to their group allocation and offered the alternative intervention at the completion of the study. Feasibility outcomes included recruitment and follow-up rates, data collection and outcome measure completion, contamination (communication about trial interventions), acceptability (adherence), credibility, and participants' experience of the intervention. RESULTS: Sixteen out of 722 contacted twin pairs were recruited (recruitment rate = 2.2%). Twins were recruited between September 2015 and August 2018 (35 months) and singletons between October 2017 and Aug 2018 (10 months). Follow-up rates for post-intervention and 3-month follow-up were 81% and 72% for twins and 82% and 78% for singletons respectively. Adherence rates (percentage of sessions completed out of six) for the digital CBT-i were 63% for twins and 55% for singletons. Contamination (speaking about the study to each other) was present in two twin pairs (13%). Written or verbal feedback (n = 21) regarding the digital CBT-i intervention from p

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.

Objectives: To evaluate brain metabolites in objective insomnia subtypes defined from polysomnography (PSG): Insomnia with short sleep duration (I-SSD) and insomnia with normal sleep duration (I-NSD), relative to good sleeping controls (GSCs). Methods: PSG empirically grouped insomnia patients into I-SSD (n = 12: Mean [SD] total sleep time [TST] = 294.7 minutes [30.5]) or I-NSD (n = 19: TST = 394.4 minutes [34.9]). 1H magnetic resonance spectroscopy (MRS) acquired in the left occipital cortex (LOCC), left prefrontal cortex, and anterior cingulate cortex was used to determine levels of creatine, aspartate, glutamate, and glutamine (referenced to water). Glutathione, glycerophosphocholine, lactate, myoinositol, and N-acetylaspartate measurements were also obtained. Sixteen GSCs were included for comparison. Multivariate analysis of variance was used to evaluate differences in creatine, aspartate, glutamate, and glutamine. Results: Aspartate and glutamine concentrations were reduced in the LOCC in I-SSD compared with I-NSD (both p < .05, d = .80-.99). Creatine displayed a nonsignificant mean reduction in I-SSD compared with I-NSD (p = .05, d = .58). Glutamine was reduced in I-SSD compared with controls (p < .05, d = .93). There were no differences in metabolites between all (I-SSD and I-NSD) insomnia patients and controls. In patients with insomnia, LOCC glutamine concentrations were found to be positively correlated with TST (r = .43, p < .05) and negatively correlated with wake-time after sleep onset (r = -.40, p < .05). Conclusions: Results indicate that I-SSD is associated with reduced brain metabolites in the LOCC compared with I-NSD and control concentrations of aspartate, glutamine, and creatine.

The literature on zooplankton population dynamics provides more estimation techniques than reliable estimates of population parameters. In this review we show how different techniques relate to each other in terms of underlying models and assumptions. There are two main routes to parameter estimates. The vertical approaches utilize the stage-structure of samples taken at the same point in time. They require assumptions about the constancy in the parameters, but relax assumptions concerning advective influence. The horizontal approaches utilize information provided by the stage structure within samples, as well as information on temporal changes in abundance. They relax assumptions about the constancy of parameters, but advective influences may be introduced. Of the horizontal methods, variants of what are commonly termed cohort methods have been widely used. These provide mathematical simplicity, but are based on more restrictive assumptions than methods fitting predescribed models (delay differential, partial differential equation, and matrix models of population dynamics). Because the quality of estimates, such as of mortality rates, depends upon the quality of the data, some sampling considerations in regard to advection and biases in estimating stage composition are discussed. It is important to reduce the number of parameters to be estimated from census data, thus we review some techniques for independent measurement of stage durations and birth rates. Finally, we provide some general recommendations for future studies. © 1997 Taylor & Francis Group, LLC.

The literature on zooplankton population dynamics provides more estimation techniques than reliable estimates of population parameters. In this review we show how different techniques relate to each other in terms of underlying models and assumptions. There are two main routes to parameter estimates. The vertical approaches utilize the stage-structure of samples taken at the same point in time. They require assumptions about the constancy in the parameters, but relax assumptions concerning advective influence. The horizontal approaches utilize information provided by the stage structure within samples, as well as information on temporal changes in abundance. They relax assumptions about the constancy of parameters, but advective influences may be introduced. Of the horizontal methods, variants of what are commonly termed cohort methods have been widely used. These provide mathematical simplicity, but are based on more restrictive assumptions than methods fitting predescribed models (delay differential, partial differential equation, and matrix models of population dynamics). Because the quality of estimates, such as of mortality rates, depends upon the quality of the data, some sampling considerations in regard to advection and biases in estimating stage composition are discussed. It is important to reduce the number of parameters to be estimated from census data, thus we review some techniques for independent measurement of stage durations and birth rates. Finally, we provide some general recommendations for future studies. © 1997 Taylor & Francis Group, LLC.

The quality of water in watersheds is expected to be altered due to climate change. However, there is insufficient scientific knowledge to predict how these changes are likely to be manifested. The assessment of long-term trends in historical data over time and space (e.g. watershed and regional variability) may offer insight into the potential impacts of future climate change scenarios on water quality. The compilation of 3 to 4 decades of national, provincial, and territorial water quality monitoring data across Canada was initiated in 2018. Using this data, this project aims to answer the following questions: (1) Are there specific metals or water quality parameters that show consistent responses to climate change that could be used as ‘indicators’ for future projections? (2) Are there specific areas in Canada that are more sensitive to changes in climate (i.e. hot spots)? (3) Does underlying geology influence the extent and magnitude of climate-driven changes in water quality? This project provides an opportunity to establish a national database to track and monitor water quality in a changing climate which would be of use to research organizations, governments as well as offering potential for citizen science applications.