Background: The COVID-19 pandemic triggered vast governmental lockdowns. The impact of these lockdowns on mental health is inadequately understood. On the one hand such drastic changes in daily routines could be detrimental to mental health. On the other hand, it might not be experienced negatively, especially because the entire population was affected. Methods: The aim of this study was to determine mental health outcomes during pandemic induced lockdowns and to examine known predictors of mental health outcomes. We therefore surveyed n = 9,565 people from 78 countries and 18 languages. Outcomes assessed were stress, depression, affect, and wellbeing. Predictors included country, sociodemographic factors, lockdown characteristics, social factors, and psychological factors. Results: Results indicated that on average about 10% of the sample was languishing from low levels of mental health and about 50% had only moderate mental health. Importantly, three consistent predictors of mental health emerged: social support, education level, and psychologically flexible (vs. rigid) responding. Poorer outcomes were most strongly predicted by a worsening of finances and not having access to basic supplies. Conclusions: These results suggest that on whole, respondents were moderately mentally healthy at the time of a population-wide lockdown. The highest level of mental health difficulties were found in approximately 10% of the population. Findings suggest that public health initiatives should target people without social support and those whose finances worsen as a result of the lockdown. Interventions that promote psychological flexibility may mitigate the impact of the pandemic.

Introduction: The interrelationship between social support, depressive symptoms, stress and self-esteem in young adults remains unclear. This study aims to test the mediating role of self-esteem in the relationship between social support and depressive symptoms and the moderating role of perceived stress in the relationship between the two. This is important to inform components of future intervention development targeting youth depression. Methods: Three hundred forty-four (N = 344) young adults in Cyprus aged 17–26 (78% female) completed measures of self-esteem, social support, depressive symptoms, and perceived stress. Structural equation models were used to examine the interactions between social support and depressive psychopathology, whereas mediational analyses were run to examine the mediating role of self-esteem. Latent moderated mediation models were applied to examine the potentially moderating role of perceived stress. Results: Perceived social support from family and friends were significantly related to lower depressive symptoms. Self-esteem fully mediated the relationship between perceived family support and depressive symptoms. Perceived stress moderated the model, and perceived social support was found to be more protective against depressive symptoms when moderate levels of stress were presented. Conclusion: The study demonstrates that social support is protective against depressive symptoms. Self-esteem and perceived stress are important mechanisms that interact with this effect. Implications include the efforts to increase perceived family support during college years and management of stress levels before working with depressive symptoms.

Smartphone health applications (‘apps’) are widely available but experts remain cautious about their utility and safety. We reviewed currently available apps for the detection of melanoma (July 2014), aimed at general community, patient and generalist clinician users. A proforma was used to extract and assess each app that met the inclusion criteria, and we undertook content analysis to evaluate their content and the evidence applied in their development. Thirty‐nine apps were identified with the majority available only for Apple users. Over half (n = 22) provided information or education about melanoma, ultraviolet radiation exposure prevention advice, and skin self‐examination strategies, mainly using the ABCDE (A, Asymmetry; B, Border; C, Colour; D, Diameter; E, Evolving) method. Half (n = 19) helped users take and store images of their skin lesions either for review by a dermatologist or for self‐monitoring to identify change, an important predictor of melanoma; a similar number (n = 18) used reminders to help users monitor their skin lesions. A few (n = 9) offered expert review of images. Four apps provided a risk assessment to patients about the probability that a lesion was malignant or benign, and one app calculated users’ future risk of melanoma. None of the apps appeared to have been validated for diagnostic accuracy or utility using established research methods. Smartphone apps for detecting melanoma by nonspecialist users have a range of functions including information, education, classification, risk assessment and monitoring change. Despite their potential usefulness, and while clinicians may choose to use apps that provide information to educate their patients, apps for melanoma detection require further validation of their utility and safety.

Background: Social media is commonly used in public health interventions to promote cancer screening and early diagnosis, as it can rapidly deliver targeted public health messages to large numbers of people. However, there is currently little understanding of the breadth of social media interventions and evaluations, whether they are effective, and how they might improve outcomes. Objective: This scoping review aimed to map the evidence for social media interventions to improve cancer screening and early diagnosis, including their impact on behavior change and how they facilitate behavior change. Methods: Five databases and the grey literature were searched to identify qualitative and quantitative evaluations of social media interventions targeting cancer screening and early diagnosis. Two reviewers independently reviewed each Data extraction was carried out by one author and verified by a second author. Data on engagement was extracted using an adapted version of the key performance indicators and metrics related to social media use in health promotion. Insights, exposure, reach, and differing levels of engagement, including behavior change, were measured. The behavior change technique taxonomy was used to identify how interventions facilitated behavior change. Results: Of the 23 publications and reports included, the majority (16/23, 70%) evaluated national cancer awareness campaigns (eg, breast cancer awareness month). Most interventions delivered information via Twitter (13/23, 57%), targeted breast cancer (12/23, 52%), and measured exposure, reach, and low- to medium-level user engagement, such as number of likes (9/23, 39%). There were fewer articles about colorectal and lung cancer than about breast and prostate cancer campaigns. One study found that interventions had less reach and engagement from ethnic minority groups. A small number of articles (5/23, 22%) suggested that some types of social media interventions might improve high-level engagement, such as intended and actual uptake of screening. Behavior change techniques, such as providing social support and emphasizing the consequences of cancer, were used to engage users. Many national campaigns delivered fundraising messages rather than actionable health messages. Conclusions: The limited evidence suggests that social media interventions may improve cancer screening and early diagnosis. Use of evaluation frameworks for social media interventions could help researchers plan more robust evaluations that measure behavior change. We need a greater understanding of who engages with these interventions to know whether social media can be used to reduce some health inequalities in cancer screening and early diagnosis. International Registered Report Identifier (IRRID): RR2-10.1136/bmjopen-2019-033592

Melanoma incidence is increasing rapidly worldwide among white-skinned populations. Earlier diagnosis is the principal factor that can improve prognosis. Defining high-risk populations using risk prediction models may help targeted screening and early detection approaches. In this systematic review, we searched Medline, EMBASE, and the Cochrane Library for primary research studies reporting or validating models to predict risk of developing cutaneous melanoma. A total of 4,141 articles were identified from the literature search and six through citation searching. Twenty-five risk models were included. Between them, the models considered 144 possible risk factors, including 18 measures of number of nevi and 26 of sun/UV exposure. Those most frequently included in final risk models were number of nevi, presence of freckles, history of sunburn, hair color, and skin color. Despite the different factors included and different cutoff values for sensitivity and specificity, almost all models yielded sensitivities and specificities that fit along a summary ROC with area under the ROC (AUROC) of 0.755, suggesting that most models had similar discrimination. Only two models have been validated in separate populations and both also showed good discrimination with AUROC values of 0.79 (0.70–0.86) and 0.70 (0.64–0.77). Further research should focus on validating existing models rather than developing new ones. Cancer Epidemiol Biomarkers Prev; 23(8); 1450–63. ©2014 AACR.