Despite the emergence of curated app libraries for mental health apps, personal searches by consumers remain a common method for discovering apps. App store descriptions therefore represent a key channel to inform consumer choice. This study examined the claims invoked through these app store descriptions, the extent to which scientific language is used to support such claims, and the corresponding evidence in the literature. Google Play and iTunes were searched for apps related to depression, self-harm, substance use, anxiety, and schizophrenia. The descriptions of the top-ranking, consumer-focused apps were coded to identify claims of acceptability and effectiveness, and forms of supporting statement. For apps which invoked ostensibly scientific principles, a literature search was conducted to assess their credibility. Seventy-three apps were coded, and the majority (64%) claimed effectiveness at diagnosing a mental health condition, or improving symptoms, mood or self-management. Scientific language was most frequently used to support these effectiveness claims (44%), although this included techniques not validated by literature searches (8/24 = 33%). Two apps described low-quality, primary evidence to support the use of the app. Only one app included a citation to published literature. A minority of apps (14%) described design or development involving lived experience, and none referenced certification or accreditation processes such as app libraries. Scientific language was the most frequently invoked form of support for use of mental health apps; however, high-quality evidence is not commonly described. Improved knowledge translation strategies may improve the adoption of other strategies, such as certification or lived experience co-design.

Background Lifestyle risk behaviours typically emerge during adolescence, track into adulthood, and commonly co-occur. Interventions targeting multiple risk behaviours in adolescents have the potential to efficiently improve health outcomes, yet further evidence is required to determine their effect. We reviewed the effectiveness of eHealth school-based interventions targeting multiple lifestyle risk behaviours. Methods In this systematic review and meta-analysis, we searched Ovid MEDLINE, Embase, PsycINFO, and the Cochrane Library databases between Jan 1, 2000, and March 14, 2019, with no language restrictions, for publications on school-based eHealth multiple health behaviour interventions in humans. We also screened the grey literature for unpublished data. Eligible studies were randomised controlled trials of eHealth (internet, computers, tablets, mobile technology, or tele-health) interventions targeting two or more of six behaviours of interest: alcohol use, smoking, diet, physical activity, sedentary behaviour, and sleep. Primary outcomes of interest were the prevention or reduction of unhealthy behaviours, or improvement in healthy behaviours of the six behaviours. Outcomes were summarised in a narrative synthesis and combined using random-effects meta-analysis. This systematic review is registered with PROSPERO, identifier CRD42017072163. Findings Of 10 571 identified records, 22 publications assessing 16 interventions were included, comprising 18 873 students, of whom on average 56•2% were female, with a mean age of 13•41 years (SD 1•52). eHealth schoolbased multiple health behaviour change interventions significantly increased fruit and vegetable intake (standard mean difference 0•11, 95% CI 0•03 to 0•19; p=0•007) and both accelerometer-measured (0•33, 0•05 to 0•61; p=0•02) and self-reported (0•14, 0•05 to 0•23; p=0•003) physical activity, and reduced screen time (-0•09,-0•17 to-0•01; p=0•03) immediately after the intervention; however, these effects were not sustained at follow-up when data were available. No effect was seen for alcohol or smoking, fat or sugar-sweetened beverage or snack consumption. No studies examined sleep or used mobile health interventions. The risk of bias in masking of final outcome assessors and selective outcome reporting was high or unclear across studies and overall we deemd the quality of evidence to be low to very low. Interpretation eHealth school-based interventions addressing multiple lifestyle risk behaviours can be effective in improving physical activity, screen time, and fruit and vegetable intake. However, effects were small and only evident immediately after the intervention. Further high quality, adolescent-informed research is needed to develop eHealth interventions that can modify multiple behaviours and sustain long-term effects.

The 6-item Kessler Psychological Distress Scale (K6; Kessler et al., 2002) is a screener for psychological distress that has robust psychometric properties among adults. Given that a significant proportion of adolescents experience mental illness, there is a need for measures that accurately and reliably screen for mental disorders in this age group. This study examined the psychometric properties of the K6 in a large general population sample of adolescents (N = 4,434; mean age = 13.5 years; 44.6% male). Factor analyses were conducted to examine the dimensionality of the K6 in adolescents and to investigate sex-based measurement invariance. This study also evaluated the K6 as a predictor of scores on the Strengths and Difficulties Questionnaire (SDQ; Goodman, 1997). The K6 demonstrated high levels of internal consistency, with the 6 items loading primarily on 1 factor. Consistent with previous research, females reported higher mean levels of psychological distress when compared with males. The identification of sex-based measurement noninvariance in the item thresholds indicated that these mean differences most likely represented reporting bias in the K6 items rather than true differences in the underlying psychological distress construct. The K6 was a fair to good predictor of abnormal scores on the SDQ, but predictive utility was relatively low among males. Future research needs to focus on refining and augmenting the K6 scale to maximize its utility in adolescents. (PsycINFO Database Record

Objectives: This review aimed to identify free, high-quality, smoking cessation mobile applications (apps) that adhere to Australian smoking cessation treatment guidelines. Methods:A systematic search of smoking cessation apps was conducted using Google. The technical quality of relevant apps was rated using the Mobile Application Rating Scale. The content of apps identified as high quality was assessed for adherence to smoking cessation treatment guidelines.Results: 112 relevant apps were identified. The majority were of poor technical quality and only six 'high-quality' apps were identified. These apps adhered to Australian treatment guidelines in part. The efficacy of two apps had been previously evaluated. Conclusions:In lieu of more substantial research in this area, it is suggested that the highquality apps identified in this review may be more likely than other available apps to encourage smoking cessation. Implications for public health:Smoking cessation apps have the potential to address many barriers that prevent smoking cessation support being provided; however few high-quality smoking cessation apps are currently available in Australia, very few have been evaluated and the app market is extremely volatile. More research to evaluate smoking cessation apps, and sustained funding for evidence-based apps, is needed.