Mobile Commerce App Adoption: Consumer Behavior Differences between Europe and Asia

Title: ‘Mobile Commerce App Adoption: Consumer Behavior Differences between Europe and Asia’ Research Question: 1. What are the differences between European and Asian consumer behavior of adopting m-commerce apps? 2. Why is there a different level of m-commerce app adoption between European and Asian consumers? Research Purpose: The purpose of this research is to investigate the reasons why consumer adoption behavior of m-commerce apps in the European market differs from those in the Asian market. Our findings will contribute to helping to raise their level of m-commerce app adoption in the European market. Method: This research is a qualitative study and utilizes survey as a research design. Interviews designed according to a theoretical framework were used to collect data for analysis. The interviews were conducted in four countries: UK, Sweden, China and South Korea. Conclusion: Our results showed that European consumers lacked knowledge and were unable to perceive the full conveniences of using apps, compared to Asian respondents. What’s more European consumers placed a lot of importance on risks and anxieties when adopting apps. Findings showed that these differences could be explained through Hofstede’s cultural dimensions

Development and Validation of a Risk Score for Chronic Kidney Disease in HIV Infection Using Prospective Cohort Data from the D:A:D Study

Ristola M. on työryhmien DAD Study Grp ; Royal Free Hosp Clin Cohort ; INSIGHT Study Grp ; SMART Study Grp ; ESPRIT Study Grp jäsen.Background Chronic kidney disease (CKD) is a major health issue for HIV-positive individuals, associated with increased morbidity and mortality. Development and implementation of a risk score model for CKD would allow comparison of the risks and benefits of adding potentially nephrotoxic antiretrovirals to a treatment regimen and would identify those at greatest risk of CKD. The aims of this study were to develop a simple, externally validated, and widely applicable long-term risk score model for CKD in HIV-positive individuals that can guide decision making in clinical practice. Methods and Findings A total of 17,954 HIV-positive individuals from the Data Collection on Adverse Events of Anti-HIV Drugs (D:A:D) study with >= 3 estimated glomerular filtration rate (eGFR) values after 1 January 2004 were included. Baseline was defined as the first eGFR > 60 ml/min/1.73 m2 after 1 January 2004; individuals with exposure to tenofovir, atazanavir, atazanavir/ritonavir, lopinavir/ritonavir, other boosted protease inhibitors before baseline were excluded. CKD was defined as confirmed (>3 mo apart) eGFR In the D:A:D study, 641 individuals developed CKD during 103,185 person-years of follow-up (PYFU; incidence 6.2/1,000 PYFU, 95% CI 5.7-6.7; median follow-up 6.1 y, range 0.3-9.1 y). Older age, intravenous drug use, hepatitis C coinfection, lower baseline eGFR, female gender, lower CD4 count nadir, hypertension, diabetes, and cardiovascular disease (CVD) predicted CKD. The adjusted incidence rate ratios of these nine categorical variables were scaled and summed to create the risk score. The median risk score at baseline was -2 (interquartile range -4 to 2). There was a 1: 393 chance of developing CKD in the next 5 y in the low risk group (risk score = 5, 505 events), respectively. Number needed to harm (NNTH) at 5 y when starting unboosted atazanavir or lopinavir/ritonavir among those with a low risk score was 1,702 (95% CI 1,166-3,367); NNTH was 202 (95% CI 159-278) and 21 (95% CI 19-23), respectively, for those with a medium and high risk score. NNTH was 739 (95% CI 506-1462), 88 (95% CI 69-121), and 9 (95% CI 8-10) for those with a low, medium, and high risk score, respectively, starting tenofovir, atazanavir/ritonavir, or another boosted protease inhibitor. The Royal Free Hospital Clinic Cohort included 2,548 individuals, of whom 94 individuals developed CKD (3.7%) during 18,376 PYFU (median follow-up 7.4 y, range 0.3-12.7 y). Of 2,013 individuals included from the SMART/ESPRIT control arms, 32 individuals developed CKD (1.6%) during 8,452 PYFU (median follow-up 4.1 y, range 0.6-8.1 y). External validation showed that the risk score predicted well in these cohorts. Limitations of this study included limited data on race and no information on proteinuria. Conclusions Both traditional and HIV-related risk factors were predictive of CKD. These factors were used to develop a risk score for CKD in HIV infection, externally validated, that has direct clinical relevance for patients and clinicians to weigh the benefits of certain antiretrovirals against the risk of CKD and to identify those at greatest risk of CKD.Peer reviewe

Detection of drug resistance mutations at low plasma HIV-1 RNA load in a European multicentre cohort study

Guidelines indicate a plasma HIV-1 RNA load of 500-1000 copies/mL as the minimal threshold for antiretroviral drug resistance testing. Resistance testing at lower viral load levels may be useful to guide timely treatment switches, although data on the clinical utility of this remain limited. We report here the influence of viral load levels on the probability of detecting drug resistance mutations (DRMs) and other mutations by routine genotypic testing in a large multicentre European cohort, with a focus on tests performed at a viral load <1000 copies/mL