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Good treatment uptake is essential for clinically effective interventions to be fully utilised. Numerous studies have examined barriers to help-seeking for mental health treatment and to a lesser extent, facilitators. However, much of the current research focuses on changing help-seeking attitudes, which often do not lead to changes in behaviour. There is a clear gap in the literature for interventions that successfully change help-seeking behaviour among the general public. This gap is particularly relevant for early intervention. Here we describe the development of a new model which combines facilitators to treatment and an engaging, acceptable intervention for the general public. It is called the ‘PLACES’ (Publicity, Lay, Acceptable, Convenient, Effective, Self-referral) model of treatment engagement. It is based on theoretical work, as well as empirical research on a low intensity psychoeducational cognitive behavioural therapy (CBT) intervention: one-day workshops for stress and depression. In this paper, we describe the development of the model and the results of its use among four different clinical groups (adults experiencing stress, adults experiencing depression, adolescents (age 16–18) experiencing stress, and mothers with postnatal depression). We recorded high rates of uptake by people who have previously not sought help and by racial and ethnic minority groups across all four of these clinical groups. The clinical and research implications and applications of this model are discussed.

During the COVID-19 pandemic, pregnant women have been at high risk for psychological distress. Lifestyle factors may be modifiable elements to help reduce and promote resilience to prenatal stress. We used Machine-Learning (ML) algorithms applied to questionnaire data obtained from an international cohort of 804 pregnant women to determine whether physical activity and diet were resilience factors against prenatal stress, and whether stress levels were in turn predictive of sleep classes. A support vector machine accurately classified perceived stress levels in pregnant women based on physical activity behaviours and dietary behaviours. In turn, we classified hours of sleep based on perceived stress levels. This research adds to a developing consensus concerning physical activity and diet, and the association with prenatal stress and sleep in pregnant women. Predictive modeling using ML approaches may be used as a screening tool and to promote positive health behaviours for pregnant women.

BACKGROUND Postpartum depression (PPD) is linked to long-standing negative consequences for women and families. Virtual care applications present a solution to the challenge of recruiting large samples for genetic PPD research. OBJECTIVE This study evaluated the feasibility of using an iOS mobile application (PPD-ACT app) to collect genetic samples from Canadian women with current or past PPD. METHODS Women downloaded the PPD-ACT app to provide consent, phenotypic data, and if they met inclusion criteria, mailing information for a genetic sample. PPD was defined as: (1) Edinburgh Postnatal Depression Scale (EPDS) score of >12 at most severe episode, (2) onset in pregnancy or 0-3 months postpartum, and (3) no preterm birth or severe maternal or child illness. Latent class analysis (LCA) described PPD case phenotypes. Women self-reporting lifetime postpartum psychosis (PPP) were also asked to provide a DNA sample. RESULTS Of 797 women meeting PPD case criteria, 75.0% (n=598) agreed to be sent a DNA collection kit; 67.6% of whom (n=404) submitted a sample. Women (86.7% White) came from 11/13 Canadian provinces/territories, with a mean of 4.7 years (SD=7.0) since the PPD episode. LCA identified two PPD classes, clinically distinct by illness severity: (1) moderate severity (mean EPDS=18.5, SD=2.5; 8.6% with suicidality), and (2) severe (mean EDPS=24.5, SD=2.1; 52.8% with suicidality). 109 women self-reported a history of PPP (39 without PPD; 60.7% submitted DNA). CONCLUSIONS A mobile application rapidly collected data from women with relatively severe forms of PPD, an advantage for genetics where specificity is optimal, and may be feasible for collecting data from women with a history of the even more rare condition of PPP. Expanding the mobile platform options might increase accessibility as well as the diversity of the sample.