Suzanne Suggs's research while affiliated with University of Lugano and other places

Background: There is a public health need to tackle high levels of undiagnosed (or late diagnosed) HIV amongst the UK's African communities. This research aimed to assess the feasibility and acceptability of using a text messaging intervention to encourage uptake of HIV testing amongst this population in a UK city. Methods: Participatory research adopting a mixed-methods design. Four distinct stages included: [1] Formative Research: Six focus group discussions (FGDs) were conducted with diverse sections of the African community to assess perceptions about HIV and to inform message development (n=48). The Health Belief Model (HBM) was used as an organising framework for data analysis and interpretation. [2] Message Development: SMS messages were developed based on HBM constructs, existing HIV campaigns and FGD findings, and tailored according to language, gender and religion. 12 HIV-related and 12 generic health-related text messages were developed and piloted using elicitation interview processes. [3] Intervention and outcomes: 172 participants were recruited. They received 2 messages per week for 12 weeks. Data was collected in pre and post questionnaire surveys assessing uptake of HIV testing, HIV-related attitudes and knowledge and perceived general health. [4] Evaluation: Acceptability and meaningfulness of the intervention were explored via semi-structured telephone interviews (n=21). Data were analysed using thematic content analysis.

In this paper we present an experimental study that investigates the effects of a Virtual Representation of Health (VRH) – an online virtual character that personifies an individual’s health. Testing four different variations of the VRH, we aimed to understand which variation yields the strongest overall positive effect on triggering health behavior change. The results from data collected from 512 participants in three countries indicate that all tested variations can have a positive impact on health behavior change, and show that the ‘richest’ VRH variation, a virtual character that models health behavior using animations, juxtaposed by an animated personification of a possible future health, has the strongest overall positive effect, compared to the other tested variations.

http://digitalcommons.wayne.edu/jmasm/vol13/iss1/6/ The advantages of modeling the unreliability of outcomes when evaluating the comparative effectiveness of health interventions is illustrated. Adding an action-research intervention component to a regular summer job program for youth was expected to help in preventing risk behaviors. A series of simple two-group alternative structural equation models are compared to test the effect of the intervention on one key attitudinal outcome in terms of model fit and statistical power with Monte Carlo simulations. Some models presuming parameters equal across the intervention and comparison groups were under-powered to detect the intervention effect, yet modeling the unreliability of the outcome measure increased their statistical power and helped in the detection of the hypothesized effect. Comparative Effectiveness Research (CER) could benefit from flexible multi-group alternative structural models organized in decision trees, and modeling unreliability of measures can be of tremendous help for both the fit of statistical models to the data and their statistical power.

This study illustrates the modeling of mutual dynamical changes to assess the comparative effectiveness of intervention effects of substance use prevention programs. Youth Action Research for Prevention aimed to improve self-efficacy and strengthen drug prevention skills among African American, Caribbean and Latino youth from high-risk US neighborhoods. Applying the Latent Change Scores dynamical modeling to a four-wave longitudinal data set, we demonstrate that the intervention has spurred a strong dynamic coupling effect between Internal Locus of Control (ILC) and Intent to Use Substances (IUS) in the intervention group (nI = 113), as well as new mutual dynamic links – i.e. changes in ILC causing subsequent IUS changes, and vice versa; these were not manifested in the comparison group (nC = 202). The dynamic models explain well the wave-like pattern of mutual changes uncovered in the intervention group. Our findings carry strong methodological implications for policy makers, practitioners and addiction researchers alike: dynamic modeling can be critical in uncovering the mechanisms involved in derailing the development of addictions. http://informahealthcare.com/doi/abs/10.3109/16066359.2014.892932 Read More: http://informahealthcare.com/doi/abs/10.3109/16066359.2014.892932