Purpose Unhealthy lifestyle behaviors are associated with inferior health outcomes among cancer survivors, including increased mortality. It is crucial to identify vulnerable subgroups, yet investigations have been limited. Thus, this study aimed to examine sociodemographic and clinical characteristics associated with risky health behaviors among cancer survivors. Methods We used national, cross-sectional survey data (Health Information National Trends Survey, HINTS 2017–2020) for 2579 cancer survivors. We calculated the prevalence of risky alcohol use, current cigarette smoking, e-cigarette use, and not meeting physical activity guidelines. We performed weighted logistic regression to obtain multivariable-adjusted odds ratios (OR) for the association between each unhealthy behavior with sociodemographic and clinical characteristics. Results Overall, 25% showed risky alcohol use, 12% were current cigarette smokers, 3% were current e-cigarette users, and 68% did not meet physical activity guidelines. Cancer survivors who were males, non-Hispanic Whites or African Americans, without a college education, not married and with comorbidities or psychological distress were more likely to have unhealthy behaviors. Those with lung disease or depression were 2 times as likely to smoke cigarette or e-cigarettes and those with psychological distress were 1.6 times as likely to be physically inactive. Moreover, risky drinkers (OR = 1.75, 95% CI = 1.22–2.52) and e-cigarette smokers (OR = 16.40, 95% CI 3.29–81.89) were more likely to be current cigarette smokers. Conclusions We identified vulnerable subpopulations of cancer survivors with multiple unhealthy lifestyle behaviors. Implications for Cancer Survivors Our findings inform clinicians and program and policy makers of the subgroups of cancer survivors to target for multiple health behavior interventions.

Background Depression and anxiety contribute to an estimated 74.6 million years of life with disability, and 80% of this burden occurs in low- and middle-income countries (LMICs), where there is a large gap in care. Objective We aimed to systematically synthesize available evidence and quantify the effectiveness of digital mental health interventions in reducing depression and anxiety in LMICs. Methods In this systematic review and meta-analysis, we searched PubMed, Embase, and Cochrane databases from the inception date to February 2022. We included randomized controlled trials conducted in LMICs that compared groups that received digital health interventions with controls (active control, treatment as usual, or no intervention) on depression or anxiety symptoms. Two reviewers independently extracted summary data reported in the papers and performed study quality assessments. The outcomes were postintervention measures of depression or anxiety symptoms (Hedges g). We calculated the pooled effect size weighted by inverse variance. Results Among 11,196 retrieved records, we included 80 studies in the meta-analysis (12,070 participants n=6052, 50.14% in the intervention group and n=6018, 49.85% in the control group) and 96 studies in the systematic review. The pooled effect sizes were −0.61 (95% CI −0.78 to −0.44; n=67 comparisons) for depression and −0.73 (95% CI −0.93 to −0.53; n=65 comparisons) for anxiety, indicating that digital health intervention groups had lower postintervention depression and anxiety symptoms compared with controls. Although heterogeneity was considerable (I2=0.94 for depression and 0.95 for anxiety), we found notable sources of variability between the studies, including intervention content, depression or anxiety symptom severity, control type, and age. Grading of Recommendations, Assessments, Development, and Evaluation showed that the evidence quality was overall high. Conclusions Digital mental health tools are moderately to highly effective in reducing depression and anxiety symptoms in LMICs. Thus, they could be effective options to close the gap in depression and anxiety care in LMICs, where the usual mental health care is minimal. Trial Registration PROSPERO CRD42021289709; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=289709

BACKGROUND Depression and anxiety contribute to an estimated 74.6 million years lived with disability, and 80% of this burden occurs in low- and middle-income countries (LMICs), where there is a large gap in care. OBJECTIVE We aimed to systematically synthesize the available evidence and quantify the effectiveness of digital mental health interventions in reducing depression and anxiety in LMICs. METHODS In this systematic review and meta-analysis, we searched PubMed, Embase, and Cochrane databases from the inception date to February 2022. We included Randomized Controlled Trials (RCTs) conducted in LMICs that compared groups who received digital health interventions to controls (active control, treat as usual, or no intervention) on depression or anxiety symptoms. Two reviewers independently extracted summary data reported in the articles and performed study quality assessments. The outcomes were post-intervention measures of depression or anxiety symptoms (Hedges’ g). We calculated pooled effect size weighted by inverse variance. PROSPERO (CRD42021289709). RESULTS Among 11,196 retrieved records, we included 80 studies in the meta-analysis (12,070 participants: 6,052 in intervention and 6,018 in control) and 96 studies in the systematic review. Pooled effect sizes were -0.61 (-0.78 to -0.44; n = 67 comparisons) for depression and -0.73 (-0.93 to -0.53; n = 65 comparisons) for anxiety, indicating that digital health intervention groups had lower post-intervention depression and anxiety symptoms compared to controls. Although heterogeneity was considerable (I2=0.94 for depression and 0.95 for anxiety), we found significant sources of variability between studies, including intervention content, depression/anxiety symptom severity, control type, and age. Grading of Recommendations Assessments, Development, and Evaluation (GRADE) showed the evidence quality was overall high. CONCLUSIONS Digital mental health tools are moderately to highly effective in reducing depression and anxiety symptoms in LMICs. Thus, they could be effective options to close the gap in depression/anxiety care in LMICs, where the usual mental health care is minimal. CLINICALTRIAL N/A

No abstract