Background Rapid advancements in eHealth and mobile health (mHealth) technologies have driven researchers to design and evaluate numerous technology-based interventions to promote smoking cessation. The evolving nature of cessation interventions emphasizes a strong need for knowledge synthesis. Objective This systematic review and meta-analysis aimed to summarize recent evidence from randomized controlled trials regarding the effectiveness of eHealth-based smoking cessation interventions in promoting abstinence and assess nonabstinence outcome indicators, such as cigarette consumption and user satisfaction, via narrative synthesis. Methods We searched for studies published in English between 2017 and June 30, 2022, in 4 databases: PubMed (including MEDLINE), PsycINFO, Embase, and Cochrane Library. Two independent reviewers performed study screening, data extraction, and quality assessment based on the GRADE (Grading of Recommendations, Assessment, Development, and Evaluations) framework. We pooled comparable studies based on the population, follow-up time, intervention, and control characteristics. Two researchers performed an independent meta-analysis on smoking abstinence using the Sidik-Jonkman random-effects model and log risk ratio (RR) as the effect measurement. For studies not included in the meta-analysis, the outcomes were narratively synthesized. Results A total of 464 studies were identified through an initial database search after removing duplicates. Following screening and full-text assessments, we deemed 39 studies (n=37,341 participants) eligible for this review. Of these, 28 studies were shortlisted for meta-analysis. According to the meta-analysis, SMS or app text messaging can significantly increase both short-term (3 months) abstinence (log RR=0.50, 95% CI 0.25-0.75; I2=0.72%) and long-term (6 months) abstinence (log RR=0.77, 95% CI 0.49-1.04; I2=8.65%), relative to minimal cessation support. The frequency of texting did not significantly influence treatment outcomes. mHealth apps may significantly increase abstinence in the short term (log RR=0.76, 95% CI 0.09-1.42; I2=88.02%) but not in the long term (log RR=0.15, 95% CI −0.18 to 0.48; I2=80.06%), in contrast to less intensive cessation support. In addition, personalized or interactive interventions showed a moderate increase in cessation for both the short term (log RR=0.62, 95% CI 0.30-0.94; I2=66.50%) and long term (log RR=0.28, 95% CI 0.04-0.53; I2=73.42%). In contrast, studies without any personalized or interactive features had no significant impact. Finally, the treatment effect was similar between trials that used biochemically verified or self-reported abstinence. Among studies reporting outcomes besides abstinence (n=20), a total of 11 studies reported significantly improved nonabstinence outcomes in cigarette consumption (3/14, 21%) or user satisfaction (8/19, 42%). Conclusions Our review of 39 randomized controlled trials found that recent eHealth interventions might promote smoking cessation, with mHealth being the dominant approach. Despite their success, the effectiveness of such interventions may diminish with time. The design of more personalized interventions could potentially benefit future studies. Trial Registration PROSPERO CRD42022347104; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=347104

This systematic review examined cancer care costs, the financial burden for patients, and their economic coping strategies in mainland China. We included 38 quantitative studies that reported out-of-pocket payment for cancer care and patients’ coping strategies in English or Chinese (PROSPERO: CRD42021273989). We searched PubMed, Embase, Ovid, Web of Science, Cochrane, CNKI, and Wanfang Data from 1 January 2009 to 10 August 2022. We referred to the standards for reporting observational studies to assess the methodological quality and transparent reporting of the included studies and reported the costs narratively. Annual mean medical costs (including inpatient and outpatient costs and fees for self-purchasing drugs) ranged from USD 7421 to USD 10,297 per patient. One study investigated medical costs for 5 years and indicated that inpatient costs accounted for 51.6% of the total medical costs, followed by self-purchasing drugs (43.9%). Annual medical costs as a percentage of annual household income ranged from 36.0% to 63.1% with a metaproportion of 51.0%. The common coping strategies included borrowing money and reduction of household expenses and expenses from basic health services. Costs of inpatient care and self-purchasing drugs are major drivers of medical costs for cancer care, and many affected households shoulder a very heavy financial burden.

Background: Cancer has been the leading cause of death in China and imposes heavy burdens on individuals and the health system. China’s cancer control plan includes efforts to mitigate financial hardship for the affected households in the context of ongoing health system reform. However, evidence is limited about how the affected families afford cancer care. Methods: This systematic review is to map medical, non-medical, and indirect costs incurred by cancer care, the financial burden for patients, and their economic coping strategies from 2009 onwards. We included original quantitative studies conducted in mainland China that reported out-of-pocket payment for cancer care and patients’ coping strategies in English or Chinese We searched PubMed, Embase, Ovid, Web of Science, Cochrane and two Chinese databases (CNKI and WanFang Data) from January 1st 2009 to 10th August 2022. We introduced ten quality criteria for quality assessment of the included studies according to the standards for reporting observational studies. We reported components of costs and coping strategies narratively and presented costs as a percentage of household income. Results: Annual mean medical costs (including inpatient and outpatient costs and fees for self-purchasing drugs) ranged from US$7421 to US$10297 (an unweighted average of US$8794) per patient. In a study that investigated medical costs for five years, inpatient costs accounted for 51.6% of total medical costs, followed by self-purchasing drugs (43.9%). The estimation of non-medical and indirect costs varied considerably. Annual medical costs as a percentage of annual household income ranged from 36.0% to 63.1% with a meta-proportion of 51.0%. The common coping strategies included borrowing money, reduction of household expenses and expenses from basic health services. Conclusions: Costs of inpatient care and self-purchasing drugs are major drivers of medical costs for cancer care, and many affected households shoulder a very heavy financial burden. This will require strengthening stewardship for cancer control and multi-sector cooperation to mitigate the risk of financial hardship.

IntroductionSecondary prevention of stroke is a leading challenge globally and only a few strategies have been tested to be effective in supporting stroke survivors. The system-integrated and technology-enabled model of care (SINEMA) intervention, a primary care-based and technology-enabled model of care, has been proven effective in strengthening the secondary prevention of stroke in rural China. The aim of this protocol is to outline the methods for the cost-effectiveness evaluation of the SINEMA intervention to better understand its potential economic benefits.MethodsThe economic evaluation will be a nested study based on the SINEMA trial; a cluster-randomized controlled trial implemented in 50 villages in rural China. The effectiveness of the intervention will be estimated using quality-adjusted life years for the cost-utility analysis and reduction in systolic blood pressure for the cost-effectiveness analysis. Health resource and service use and program costs will be identified, measured, and valued at the individual level based on medication use, hospital visits, and inpatients' records. The economic evaluation will be conducted from the perspective of the healthcare system.ConclusionThe economic evaluation will be used to establish the value of the SINEMA intervention in the Chinese rural setting, which has great potential to be adapted and implemented in other resource-limited settings.

BACKGROUND With the rapid development of electronic health (eHealth) and mobile health (mHealth) technologies, researchers have created and tested a variety of technology-assisted smoking cessation interventions. Given the ever-changing landscape of cessation interventions, there is a strong need for regular knowledge syntheses. OBJECTIVE This systematic review aims to (1) summarize recent evidence on the effectiveness of randomized controlled trials (RCTs) regarding eHealth-based smoking cessation on abstinence; and (2) assess important secondary outcome indicators such as cigarette consumption, quit attempts, and user satisfaction via narrative synthesis. METHODS We searched for studies published in English between 2017 to June 30, 2022 in four databases: PubMed (including MEDLINE), PsycINFO, Embase, and Cochrane Library. Two independent reviewers performed study screening, data extraction, and quality assessment based on the GRADE framework. We pooled studies based on population, follow-up time, intervention, and control characteristics and two researchers performed independent meta-analysis on smoking abstinence using the Mantel-Haenszel fixed effect model and log Risk Ratio (logRR) as effect measurement. For studies not included in the meta-analysis, we synthesized the outcomes narratively. RESULTS We identified a total of 464 studies through the initial database search after removing duplicates. After screening and full-text assessments, 39 studies covering 37,341 participants were deemed eligible for this review, and 28 of these studies were included in meta-analysis. According to the meta-analysis, SMS/App text messaging can significantly increase both 3-month (short-term) abstinence (log RR=-0.50, 95% CI 0.25–0.75, I2=0.00%) and 6-month (long-term) abstinence (log RR=0.79, 95% CI 0.52–1.05, I2=0.00%) when compared to minimal cessation support. The frequency of texting makes no difference on treatment outcome. mHealth apps may significantly increase abstinence in short term (logRR=0.73, 95% CI 0.54–0.93, I2=60.73%) but not in long term (logRR=0.00, 95% CI -0.11–0.11, I2=66.95%) when compared to less intensive cessation support. Psycho/pharmacological therapy yielded similar abstinence results with or without the accompany of mHealth apps (logRR=0.26, 95% CI -0.11–0.63, I2=0.00%). Finally, trials using biochemical verification for abstinence found a moderate increase in cessation in both short-term (logRR=0.46, 95% CI 0.23–0.70, I2=1.39%) and long-term (logRR=0.28, 95% CI 0.06–0.50, I2=13.72%) whereas studies only used self-reported outcomes found no such significant impact. Among studies reporting outcomes besides abstinence (n=28), a total of 12 studies reported significantly improved secondary outcomes in cigarette consumption (3/14, 21.43%), quit attempts (1/11, 9.09%), and user satisfaction (8/19, 42.11%). CONCLUSIONS Based on 39 RCTs, we found current eHealth interventions, with mHealth being the dominant approach, could be effective in smoking cessation though the effectiveness may diminish over time. Future studies could potentially benefit from more personalized intervention designs to improve user adherence, thus achieving better long-term abstinence outcomes. CLINICALTRIAL PROSPERO International Prospective Register of Systematic Reviews CRD42022347104; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=347104