To determine whether an intervention to reduce eveningness chronotype improves sleep, circadian, and health (emotional, cognitive, behavioral, social, physical) outcomes.
Youth aged 10 to 18 years ...with an evening chronotype and who were "at risk" in 1 of 5 health domains were randomized to: (a) Transdiagnostic Sleep and Circadian Intervention for Youth (TranS-C; n = 89) or (b) Psychoeducation (PE; n = 87) at a university-based clinic. Treatments were 6 individual, weekly 50-minute sessions during the school year. TranS-C addresses sleep and circadian problems experienced by youth by integrating evidence-based treatments derived from basic research. PE provides education on the interrelationship between sleep, stress, diet, and health.
Relative to PE, TranS-C was not associated with greater pre-post change for total sleep time (TST) or bed time (BT) on weeknights but was associated with greater reduction in evening circadian preference (pre-post increase of 3.89 points, 95% CI = 2.94-4.85, for TranS-C, and 2.01 points, 95% CI = 1.05-2.97 for PE, p = 0.006), earlier endogenous circadian phase, less weeknight-weekend discrepancy in TST and wakeup time, less daytime sleepiness, and better self-reported sleep via youth and parent report. In terms of functioning in the five health domains, relative to PE, TranS-C was not associated with greater pre-post change on the primary outcome. However, there were significant interactions favoring TranS-C on the Parent-Reported Composite Risk Scores for cognitive health.
For at-risk youth, the evidence supports the use of TranS-C over PE for improving sleep and circadian functioning, and improving health on selected outcomes.
Triple Vulnerability? Circadian Tendency, Sleep Deprivation and Adolescence. https://clinicaltrials.gov; NCT01828320.
Purpose
Employers increasingly are asked to accommodate workers living with physical and mental health conditions that cause episodic disability, where periods of wellness are punctuated by ...intermittent and often unpredictable activity limitations (e.g., depression, anxiety, arthritis, colitis). Episodic disabilities may be challenging for workplaces which must comply with legislation protecting the privacy of health information while believing they would benefit from personal health details to meet a worker’s accommodation needs. This research aimed to understand organizational perspectives on disability communication-support processes.
Methods
Twenty-seven participants from diverse employment sectors and who had responsibilities for supporting workers living with episodic disabilities (e.g., supervisors, disability managers, union representatives, occupational health representatives, labour lawyers) were interviewed. Five participants also had lived experience of a physical or mental health episodic disability. Participants were recruited through organizational associations, community networks and advertising. Semi-structured interviews and qualitative content analysis framed data collection and analyses, and mapped communication-support processes.
Results
Seven themes underpinned communication-support process: (1) similarities and differences among physical and mental health episodic disabilities; (2) cultures of workplace support, including contrasting medical and biopsychosocial perspectives; (3) misgivings about others and their role in communication-support processes; (4) that subjective perceptions matter; (5) the inherent complexity of the response process; (6) challenges arising when a worker denies a disability; and (7) casting disability as a performance problem.
Conclusions
This study identifies a conceptual framework and areas where workplace disability support processes could be enhanced to improve inclusion and the sustainability of employment among workers living with episodic disabilities.
Constructing activity budgets for marine animals when they are at sea and cannot be directly observed is challenging, but recent advances in bio-logging technology offer solutions to this problem. ...Accelerometers can potentially identify a wide range of behaviours for animals based on unique patterns of acceleration. However, when analysing data derived from accelerometers, there are many statistical techniques available which when applied to different data sets produce different classification accuracies. We investigated a selection of supervised machine learning methods for interpreting behavioural data from captive otariids (fur seals and sea lions). We conducted controlled experiments with 12 seals, where their behaviours were filmed while they were wearing 3-axis accelerometers. From video we identified 26 behaviours that could be grouped into one of four categories (foraging, resting, travelling and grooming) representing key behaviour states for wild seals. We used data from 10 seals to train four predictive classification models: stochastic gradient boosting (GBM), random forests, support vector machine using four different kernels and a baseline model: penalised logistic regression. We then took the best parameters from each model and cross-validated the results on the two seals unseen so far. We also investigated the influence of feature statistics (describing some characteristic of the seal), testing the models both with and without these. Cross-validation accuracies were lower than training accuracy, but the SVM with a polynomial kernel was still able to classify seal behaviour with high accuracy (>70%). Adding feature statistics improved accuracies across all models tested. Most categories of behaviour -resting, grooming and feeding-were all predicted with reasonable accuracy (52-81%) by the SVM while travelling was poorly categorised (31-41%). These results show that model selection is important when classifying behaviour and that by using animal characteristics we can strengthen the overall accuracy.
Purpose
Workplace support needs for women and men living with mental health conditions are not well understood. This study examined workplace accommodation and support needs among women and men with ...and without mental health or cognitive conditions and individual and workplace factors associated with having unmet needs.
Methods
A cross-sectional survey of 3068 Canadian workers collected information on disability, gender, gendered occupations, job conditions, work contexts, and workplace accommodations. Multivariable logistic regression analyses examined gender- and disability-based differences in unmet needs for workplace flexibility, work modifications, and health benefits, and the association of work context (i.e., work schedule, job sector) and job conditions (i.e., precarious work) on the likelihood of unmet accommodation needs. The additive (i.e., super- or sub-additive) and multiplicative effects of disability, gender, and occupational gender distribution on the probability of unmet accommodation needs were also assessed.
Results
The most common unmet workplace accommodation was work modifications reported by 35.9% of respondents with mental/cognitive disability and workplace flexibility reported by 19.6% of individuals without a mental/cognitive disability. Women, employees in female dominant occupations, and participants with mental/cognitive disabilities were more likely to report unmet needs compared with men, employees in non-female dominant occupations, and participants without disabilities but these findings were largely explained by differences in job conditions and work contexts. No interacting effects on the likelihood of reporting unmet needs for workplace accommodations were observed.
Conclusions
To support employee mental health, attention is needed to address work contexts and job conditions, especially for people working with mental/cognitive disabilities, women, and workers in female-dominated occupations where unmet accommodation needs are greatest.
Purpose
Sensibility refers to a tool’s comprehensiveness, understandability, relevance, feasibility, and length. It is used in the early development phase to begin assessing a new tool or ...intervention. This study examined the sensibility of the job demands and accommodation planning tool (JDAPT). The JDAPT identifies job demands related to physical, cognitive, interpersonal, and working conditions to better target strategies for workplace supports and accommodations aimed at assisting individuals with chronic health conditions.
Methods
Workers with a chronic health condition and workplace representatives were recruited from health charities, workplaces, and newsletters using convenience sampling. Cognitive interviews assessed the JDAPT’s sensibility. A 70% endorsement rate was the minimum level of acceptability for sensibility concepts. A short screening tool also was administered, and answers compared to the complete JDAPT.
Results
Participants were 46 workers and 23 organizational representatives (n = 69). Endorsements highly exceeded the 70% cut-off for understandability, relevance, and length. Congruence between screening questions and the complete JDAPT suggested both workers and organizational representatives overlooked job demands when completing the screener. Participants provided additional examples and three new items to improve comprehensiveness. The JDAPT was rated highly relevant and useful, although not always easy to complete for someone with an episodic condition.
Conclusions
This study highlights the need for tools that facilitate accommodations for workers with episodic disabilities and provides early evidence for the sensibility of the JDAPT.
Patients exhibit poor memory for treatment. A novel Memory Support Intervention, derived from basic science in cognitive psychology and education, is tested with the goal of improving patient memory ...for treatment and treatment outcome. Adults with major depressive disorder (MDD) were randomized to 14 sessions of cognitive therapy (CT)+Memory Support (n = 25) or CT-as-usual (n = 23). Outcomes were assessed at baseline, post-treatment and 6 months later. Memory support was greater in CT+Memory Support compared to the CT-as-usual. Compared to CT-as-usual, small to medium effect sizes were observed for recall of treatment points at post-treatment. There was no difference between the treatment arms on depression severity (primary outcome). However, the odds of meeting criteria for ‘response’ and ‘remission’ were higher in CT+Memory Support compared with CT-as-usual. CT+Memory Support also showed an advantage on functional impairment. While some decline was observed, the advantage of CT+Memory Support was evident through 6-month follow-up. Patients with less than 16 years of education experience greater benefits from memory support than those with 16 or more years of education.
Memory support can be manipulated, may improve patient memory for treatment and may be associated with an improved outcome.
•Mental disorders are highly prevalent and accessing adequate treatment is difficult.•Evidence-based psychological treatments (EBPTs) are highly effective.•Barriers and solutions to accessing EBTs are discussed at five levels of analysis.•There is a need to continue to work toward innovation in treatment development.•There is a need to help patients identify EBPT providers and train more providers.
Mental disorders are prevalent and can lead to significant impairment. Some progress has been made toward establishing treatments; however, effect sizes are small to moderate, gains may not persist, ...and many patients derive no benefit. Our goal is to highlight the potential for empirically supported psychosocial treatments to be improved by incorporating insights from cognitive psychology and research on education. Our central question is: If it were possible to improve memory for the content of sessions of psychosocial treatments, would outcome substantially improve? We leverage insights from scientific knowledge on learning and memory to derive strategies for transdiagnostic and transtreatment cognitive support interventions. These strategies can be applied within and between sessions and to interventions delivered via computer, the Internet, and text message. Additional novel pathways to improving memory include improving sleep, engaging in exercise, and using imagery. Given that memory processes change across the lifespan, services to children and older adults may benefit from different types and amounts of cognitive support.
Background Semi-automating the analyses of accelerometry data makes it possible to synthesize large data sets. However, when constructing activity budgets from accelerometry data, there are many ...methods to extract, analyse and report data and results. For instance, machine learning is a robust approach to classifying data. We used a new method, super learning, that combines base learners (different machine learning methods) in an optimal manner to achieve overall improved accuracy. Other facets of super learning include the number of behavioural categories to predict, the number of epochs (sample window size) used to split data for training and testing and the parameters on which to train the models. Results The super learner accurately classified behaviour categories with higher accuracy and lower variance than comparative models. For all models tested, using four behaviours, in comparison with six, achieved higher rates of accuracy. The number of epochs chosen also affected the accuracy with smaller epochs (7 and 13) performing better than longer epochs (25 and 75). Conclusions Correct model selection, training and testing are imperative to creating reliable and valid classification models. To do so means model fitting must use a wide array of selection criteria. We evaluated a number of these including model, number of behaviours to classify and epoch length and then used a parameter grid search to implement the models. We found that all criteria tested contributed to the models’ overall accuracies. Fewer behaviour categories and shorter epoch length improved the performance of all models tested. The super learner classified behaviours with higher accuracy and lower variance than other models tested. However, when using this model, users need to consider the additional human and computational time required for implementation. Machine learning is a powerful method for classifying the behaviour of animals from accelerometers. Care and consideration of the modelling parameters evaluated in this study are essential when using this type of statistical analysis.
Accurate time-energy budgets summarise an animal's energy expenditure in a given environment, and are potentially a sensitive indicator of how an animal responds to changing resources. Deriving ...accurate time-energy budgets requires an estimate of time spent in different activities and of the energetic cost of that activity. Bio-loggers (e.g., accelerometers) may provide a solution for monitoring animals such as fur seals that make long-duration foraging trips. Using low resolution to record behaviour may aid in the transmission of data, negating the need to recover the device.
This study used controlled captive experiments and previous energetic research to derive time-energy budgets of juvenile Australian fur seals (
equipped with tri-axial accelerometers. First, captive fur seals and sea lions were equipped with accelerometers recording at high (20 Hz) and low (1 Hz) resolutions, and their behaviour recorded. Using this data, machine learning models were trained to recognise four states-foraging, grooming, travelling and resting. Next, the energetic cost of each behaviour, as a function of location (land or water), season and digestive state (pre- or post-prandial) was estimated. Then, diving and movement data were collected from nine wild juvenile fur seals wearing accelerometers recording at high- and low- resolutions. Models developed from captive seals were applied to accelerometry data from wild juvenile Australian fur seals and, finally, their time-energy budgets were reconstructed.
Behaviour classification models built with low resolution (1 Hz) data correctly classified captive seal behaviours with very high accuracy (up to 90%) and recorded without interruption. Therefore, time-energy budgets of wild fur seals were constructed with these data. The reconstructed time-energy budgets revealed that juvenile fur seals expended the same amount of energy as adults of similar species. No significant differences in daily energy expenditure (DEE) were found across sex or season (winter or summer), but fur seals rested more when their energy expenditure was expected to be higher. Juvenile fur seals used behavioural compensatory techniques to conserve energy during activities that were expected to have high energetic outputs (such as diving).
As low resolution accelerometry (1 Hz) was able to classify behaviour with very high accuracy, future studies may be able to transmit more data at a lower rate, reducing the need for tag recovery. Reconstructed time-energy budgets demonstrated that juvenile fur seals appear to expend the same amount of energy as their adult counterparts. Through pairing estimates of energy expenditure with behaviour this study demonstrates the potential to understand how fur seals expend energy, and where and how behavioural compensations are made to retain constant energy expenditure over a short (dive) and long (season) period.
Background
The reported incidence of acute hepatitis C virus (HCV) infection is increasing among persons of childbearing age in the United States. Infants born to pregnant persons with HCV infection ...are at risk for perinatal HCV acquisition. In 2020, the United States Preventive Services Task Force and Centers for Disease Control and Prevention recommended that all pregnant persons be screened during each pregnancy for hepatitis C. However, there are limited data on trends in hepatitis C testing during pregnancy.
Objective
We estimated hepatitis C testing rates in a large cohort of patients with Medicaid and commercial insurance who gave birth during 2015-2019 and described demographic and risk-based factors associated with testing.
Methods
Medicaid and commercial insurance claims for patients aged 15-44 years and who gave birth between 2015 and 2019 were included. Birth claims were identified using procedure and diagnosis codes for vaginal or cesarean delivery. Hepatitis C testing was defined as an insurance claim during the 42 weeks before delivery. Testing rates were calculated among patients who delivered and among the subset of patients who were continuously enrolled for 42 weeks before delivery. We also compared the timing of testing relative to delivery among patients with commercial or Medicaid insurance. Multivariable logistic regression was used to identify factors associated with testing.
Results
Among 1,142,770 Medicaid patients and 1,207,132 commercially insured patients, 175,223 (15.3%) and 221,436 (18.3%) were tested for hepatitis C during pregnancy, respectively. Testing rates were 89,730 (21.8%) and 187,819 (21.9%) among continuously enrolled Medicaid and commercially insured patients, respectively. Rates increased from 2015 through 2019 among Medicaid (from 20,758/108,332, 19.2% to 13,971/52,330, 26.8%) and commercially insured patients (from 38,308/211,555, 18.1% to 39,152/139,972, 28%), respectively. Among Medicaid patients, non-Hispanic Black (odds ratio 0.73, 95% CI 0.71-0.74) and Hispanic (odds ratio 0.53, 95% CI 0.51-0.56) race or ethnicity were associated with lower odds of testing. Opioid use disorder, HIV infection, and high-risk pregnancy were associated with higher odds of testing in both Medicaid and commercially insured patients.
Conclusions
Hepatitis C testing during pregnancy increased from 2015 through 2019 among patients with Medicaid and commercial insurance, although tremendous opportunity for improvement remains. Interventions to increase testing among pregnant persons are needed.
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