BackgroundThis paper aims to synthesise the literature on machine learning (ML) and big data applications for mental health, highlighting current research and applications in practice.MethodsWe employed a scoping review methodology to rapidly map the field of ML in mental health. Eight health and information technology research databases were searched for papers covering this domain. Articles were assessed by two reviewers, and data were extracted on the article's mental health application, ML technique, data type, and study results. Articles were then synthesised via narrative review.ResultsThree hundred papers focusing on the application of ML to mental health were identified. Four main application domains emerged in the literature, including: (i) detection and diagnosis; (ii) prognosis, treatment and support; (iii) public health, and; (iv) research and clinical administration. The most common mental health conditions addressed included depression, schizophrenia, and Alzheimer's disease. ML techniques used included support vector machines, decision trees, neural networks, latent Dirichlet allocation, and clustering.ConclusionsOverall, the application of ML to mental health has demonstrated a range of benefits across the areas of diagnosis, treatment and support, research, and clinical administration. With the majority of studies identified focusing on the detection and diagnosis of mental health conditions, it is evident that there is significant room for the application of ML to other areas of psychology and mental health. The challenges of using ML techniques are discussed, as well as opportunities to improve and advance the field.

Australian Government National Health and Medical Research Council.

BackgroundParticipant retention strategies that minimise attrition in longitudinal cohort studies have evolved considerably in recent years. This study aimed to assess, via systematic review and meta-analysis, the effectiveness of both traditional strategies and contemporary innovations for retention adopted by longitudinal cohort studies in the past decade.MethodsHealth research databases were searched for retention strategies used within longitudinal cohort studies published in the 10-years prior, with 143 eligible longitudinal cohort studies identified (141 articles; sample size range: 30 to 61,895). Details on retention strategies and rates, research designs, and participant demographics were extracted. Meta-analyses of retained proportions were performed to examine the association between cohort retention rate and individual and thematically grouped retention strategies.ResultsResults identified 95 retention strategies, broadly classed as either: barrier-reduction, community-building, follow-up/reminder, or tracing strategies. Forty-four of these strategies had not been identified in previous reviews. Meta-regressions indicated that studies using barrier-reduction strategies retained 10% more of their sample (95%CI [0.13 to 1.08]; p = .01); however, studies using follow-up/reminder strategies lost an additional 10% of their sample (95%CI [− 1.19 to − 0.21]; p = .02). The overall number of strategies employed was not associated with retention.ConclusionsEmploying a larger number of retention strategies may not be associated with improved retention in longitudinal cohort studies, contrary to earlier narrative reviews. Results suggest that strategies that aim to reduce participant burden (e.g., flexibility in data collection methods) might be most effective in maximising cohort retention.Electronic supplementary materialThe online version of this article (10.1186/s12874-018-0586-7) contains supplementary material, which is available to authorized users.

Objective: To examine the impact of COVID-19 restrictions among children with attention-deficit/hyperactivity disorder (ADHD). Methods: Parents of 213 Australian children (5–17 years) with ADHD completed a survey in May 2020 when COVID-19 restrictions were in place (i.e., requiring citizens to stay at home except for essential reasons). Results: Compared to pre-pandemic, children had less exercise (Odds Ratio (OR) = 0.4; 95% CI 0.3–0.6), less outdoor time (OR = 0.4; 95% 0.3–0.6), and less enjoyment in activities (OR = 6.5; 95% CI 4.0–10.4), while television (OR = 4.0; 95% CI 2.5–6.5), social media (OR = 2.4; 95% CI 1.3–4.5), gaming (OR = 2.0; 95% CI 1.3–3.0), sad/depressed mood (OR = 1.8; 95% CI 1.2–2.8), and loneliness (OR = 3.6; 95% CI 2.3–5.5) were increased. Child stress about COVID-19 restrictions was associated with poorer functioning across most domains. Most parents (64%) reported positive changes for their child including more family time. Conclusions: COVID-19 restrictions were associated with both negative and positive impacts among children with ADHD.

Findings suggest the presence of a robust association between age of onset of cannabis use and subsequent educational achievement.

IMPORTANCEThere is widespread interest in associations between maternal perinatal depression and anxiety and offspring development; however, to date, there has been no systematic, meta-analytic review on the long-term developmental outcomes spanning infancy through adolescence. OBJECTIVETo provide a comprehensive systematic review and meta-analysis of the extant literature on associations between maternal perinatal depression and anxiety and social-emotional, cognitive, language, motor, and adaptability outcomes in offspring during the first 18 years of life.DATA SOURCES Six databases were searched (CINAHL Complete, Cochrane Library, Embase, Informit, MEDLINE Complete, and PsycInfo) for all extant studies reporting associations between perinatal maternal mental health problems and offspring development to March 1, 2020.STUDY SELECTION Studies were included if they were published in English; had a human sample, quantitative data, a longitudinal design, and measures of perinatal depression and/or anxiety and social-emotional, cognitive, language, motor, and/or adaptability development in offspring; and investigated an association between perinatal depression or anxiety and childhood development.DATA EXTRACTION AND SYNTHESIS Of 27 212 articles identified, 191 were eligible for meta-analysis. Data were extracted by multiple independent observers and pooled using a fixed-or a random-effects model. A series of meta-regressions were also conducted. Data were analyzed from January 1, 2019, to March 15, 2020. MAIN OUTCOMES AND MEASURESPrimary outcomes included social-emotional, cognitive, language, motor, and adaptability development in offspring during the first 18 years of life.RESULTS After screening, 191 unique studies were eligible for meta-analysis, with a combined sample of 195 751 unique mother-child dyads. Maternal perinatal depression and anxiety were associated with poorer offspring social-emotional (antenatal period, r = 0.21 [95% CI, 0.16-0.27]; postnatal period, r = 0.24 [95% CI, 0.19-0.28]), cognitive (antenatal period, r = −0.12 [95% CI, -0.19 to -0.05]; postnatal period, r = −0.25 [95% CI, -0.39 to -0.09]), language (antenatal period, r = −0.11 [95% CI, −0.20 to 0.02]; postnatal period, r = −0.22 [95% CI, −0.40 to 0.03]), motor (antenatal period, r = −0.07 [95% CI, −0.18 to 0.03]; postnatal period, r = −0.07 [95% CI, −0.16 to 0.03]), and adaptive behavior (antenatal period, r = −0.26 [95% CI, −0.39 to −0.12]) development. Findings extended beyond infancy, into childhood and adolescence. Meta-regressions confirmed the robustness of the results. CONCLUSIONS AND RELEVANCEEvidence suggests that perinatal depression and anxiety in mothers are adversely associated with offspring development and therefore are important targets for prevention and early intervention to support mothers transitioning into parenthood and the health and well-being of next-generation offspring.

No abstract

In this sample of relatively high SES women, most women ceased or reduced drinking once aware of their pregnancy. However, the rate of alcohol-exposed pregnancies was higher than previous estimates when the period prior to pregnancy recognition was taken into account.