Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: Cancers pose a serious threat to health of Chinese people, resulting in a immense challenge to public health work. Nowadays, people could get related information not only from medical workers in hospitals, but also retrieve on Internet everywhere in real-time. Online search behaviors can reflect population’s awareness on cancers from a completely new perspective, which could be driven by underlying cancer epidemiology. Objective: The objective of our study was mainly to explore whether there was a correlation among incidence and mortaliy of cancers and online search behaviors. A secondary objective was to predict future incidence and mortality of theses cancers if the correlation was existed. Methods: We used standard Boolean operators to choose search terms of each cancer. We used Spearman’s rank correlation coefficient to validate the correlation among monthly search index values of each cancer type and their monthly incidence and mortality rates. ARIMA model and linear regression model were simultaneously used to predict incidence and mortality of these cancers in future. Results: Monthly search index values statistically significantly correlated with cancer incidence rates in 26 of 28 cancers in China, ( lung cancer: r=.80, P<.001; liver cancer: r=.28, P=.016; stomach cancer: r=.50, P<.001; esophageal cancer: r=.50, P<.001; colorectal cancer: r=.81, P<.001; pancreatic cancer: r=.86, P<.001; breast cancer: r=.56, P<.001; brain and nervous system cancer: r=.63, P<.001; leukemia: r=.75, P<.001; Non-Hodgkin lymphoma: r=.88, P<.001; Hodgkin lymphoma: r=.91, P<.001; cervical cancer: r=.64, P<.001; prostate cancer: r=.67, P<.001; bladder cancer: r=.62, P<.001; gallbladder and biliary tract cancer: r=.88, P<.001; lip and oral cavity cancer: r=.88, P<.001; ovarian cancer: r=.58, P<.001; larynx cancer: r=.82, P<.001; kidney cancer: r=.73, P<.001; squamous-cell carcinoma: r=.94, P<.001; multiple myeloma: r=.84, P<.001; thyroid cancer: r=.77, P<.001; malignant skin melanoma: r=.55, P<.001; mesothelioma: r=.79, P<.001; testicular cancer: r=.57, P<.001; basal-cell carcinoma: r=.83, P<.001). Meanwhile, monthly Baidu search index values statistically significantly correlated with cancer mortality rates in 24 of 27 cancers but not for stomach cancer, esophageal cancer and testicular cancer. Conclusions: Online search behaviors indeed reflect public awareness from a different angle. Research on Internet search behaviors may be an innovative and advance-time tool to monitor and estimate cancer incidence and mortality rates, especially for cancers not included in national registries.

Background: Internet search data on health-related terms can reflect people’s concerns about their health status in near real time, and hence serve as a supplementary metric of disease characteristics. However, studies using internet search data to monitor and predict chronic diseases at a geographically finer state-level scale are sparse. Objective: The aim of this study was to explore the associations of internet search volumes for lung cancer with published cancer incidence and mortality data in the United States. Methods: We used Google relative search volumes, which represent the search frequency of specific search terms in Google. We performed cross-sectional analyses of the original and disease metrics at both national and state levels. A smoothed time series of relative search volumes was created to eliminate the effects of irregular changes on the search frequencies and obtain the long-term trends of search volumes for lung cancer at both the national and state levels. We also performed analyses of decomposed Google relative search volume data and disease metrics at the national and state levels. Results: The monthly trends of lung cancer-related internet hits were consistent with the trends of reported lung cancer rates at the national level. Ohio had the highest frequency for lung cancer-related search terms. At the state level, the relative search volume was significantly correlated with lung cancer incidence rates in 42 states, with correlation coefficients ranging from 0.58 in Virginia to 0.94 in Oregon. Relative search volume was also significantly correlated with mortality in 47 states, with correlation coefficients ranging from 0.58 in Oklahoma to 0.94 in North Carolina. Both the incidence and mortality rates of lung cancer were correlated with decomposed relative search volumes in all states excluding Vermont. Conclusions: Internet search behaviors could reflect public awareness of lung cancer. Research on internet search behaviors could be a novel and timely approach to monitor and estimate the prevalence, incidence, and mortality rates of a broader range of cancers and even more health issues.

Objective. The aim of this study was to evaluate the cost effectiveness of a mobile-based intervention for patients with type 2 diabetes mellitus (T2DM) and compare it with the usual management mode. Method. A total of 215 patients with T2DM in a tertiary-care hospital specific to diabetes were selected as the study population. This study was conducted from January 1, 2019 to January 1, 2020. Of the 215 patients, 130 were randomly assigned to the mHealth group and 85 were assigned to the usual care group. IBM SPSS 25.0 software was used for descriptive statistics, t tests, chi-square tests, and correlation analyses. Haemoglobin A1c (HbA1c) was the effectiveness parameter adopted. Cost-effectiveness analyses were performed, and incremental cost-effectiveness ratios (ICERs) were calculated. Results. Of the 215 patients with T2DM, the proportion of male patients was 66.0%. The mean age of the patients was 47.2 (SD 9.95). Differences in baseline information were not statistically significant between the two groups ( ). At the 3-, 6-, and 12-month follow-ups, the mHealth group reported higher control rates of HbA1c than the usual care group, 67.9% versus 46.2% ( ), 72.4% versus 45.4% ( ), and 74.6% versus 47.1% ( ), respectively. The value of HbA1c was positively related to total patient cost, material fee, Western medicine fee, and hospitalization expenses ( ), with correlation coefficients of 0.202, 0.200, 0.172, and 0.183, respectively. The costs of the mHealth group and usual care group were CNY¥ 1169.76 and CNY¥ 1775.44 per patient/year, respectively. The incremental cost of the mHealth intervention was CNY¥ −605.68 per patient/year. The ICER was CNY¥ −22.02 per patient/year. Conclusion. Compared with the usual care mode, the mHealth management model for patients with T2DM improved the control rate of HbA1c, and the mHealth management mode had better cost effectiveness.

Background: Mobile phone use has brought convenience, but the long or improper use of mobile phones can cause harm to the human body. Objective: We aimed to assess the impact of improper mobile phone use on the risks of accidents and chronic disorders. Methods: We systematically searched in PubMed, EMBASE, Cochrane, and Web of Science databases for studies published prior to April 5, 2019; relevant reviews were also searched to identify additional studies. A random-effects model was used to calculate the overall pooled estimates. Results: Mobile phone users had a higher risk of accidents (relative risk [RR] 1.37, 95% CI 1.22 to 1.55). Long-term use of mobile phones increased accident risk relative to nonuse or short-term use (RR 2.10, 95% CI 1.63 to 2.70). Compared with nonuse, mobile phone use resulted in a higher risk for neoplasms (RR 1.07, 95% CI 1.01 to 1.14), eye diseases (RR 2.03, 95% CI 1.27 to 3.23), mental health disorders (RR 1.16, 95% CI 1.02 to 1.32), and headaches (RR 1.25, 95% CI 1.18 to 1.32); the pooled risk of other chronic disorders was 1.20 (95% CI 0.90 to 1.59). Subgroup analyses also confirmed the increased risk of accidents and chronic disorders. Conclusions: Improper use of mobile phones can harm the human body. While enjoying the convenience brought by mobile phones, people have to use mobile phones properly and reasonably.

BACKGROUND: Cumulative evidence suggests a correlation between physical or mental activity and the risk of stroke. However, the combined impact of these activities on stroke onset remains unexplored. This study identified physical and mental activity patterns using principal component analysis and investigated their associations with risk of incident stroke in the general population. METHODS: Our study was sourced from the UK Biobank cohort between 2006 and 2010. Information on physical and mental-related activities were obtained through a touch-screen questionnaire. The incident stroke was diagnosed by physicians and subsequently verified through linkage to Hospital Episode Statistics. Principal component analysis was used to identify potential physical and mental activity patterns. Cox proportional hazard regression models were performed to calculate hazard ratios (HRs) and 95% CIs of incident stroke, adjusting for potential confounders. RESULTS: The initial UK Biobank cohort originally consisted of 502 411 individuals, of whom a total of 386 902 participants (aged 38–79 years) without any history of stroke at baseline were included in our study. During a median follow-up of 7.7 years, 6983 (1.8%) cases of stroke were documented. The mean age of the included participants was 55.9 years, and the proportion of women was 55.1%. We found that multiple individual items related to physical and mental activity showed significant associations with risk of stroke. We identified 4 patterns of physical activity and 3 patterns of mental activity using principal component analysis. The adherence to activity patterns of vigorous exercise, housework, and walking predominant patterns were associated with a lower risk of stroke by 17% (HR, 0.83 [95% CI, 0.78–0.89]; 20% (HR, 0.80 [95% CI, 0.75–0.85]; and 20% (HR, 0.80 [95% CI, 0.75–0.86), respectively. Additionally, the transportation predominant pattern (HR, 1.36 [95% CI, 1.28–1.45) and watching TV pattern (HR, 1.43 [95% CI, 1.33–1.53) were found to be significantly associated with a higher risk of stroke. These associations remained consistent across all subtypes of stroke. CONCLUSIONS: Activity patterns mainly related to frequent vigorous exercise, housework, and walking were associated with lower risks of stroke and all its subtypes. Our findings provide new insights for promoting suitable patterns of physical and mental activity for primary prevention of stroke.