Globally, smoking is a leading cause of cancer. There are nearly 500 smartphone applications for smoking cessation which have been downloaded over 33 million times. However, the January 2020 US Surgeon Report concluded that the "evidence is inadequate" on whether smartphone apps are efficacious for stopping smoking. In this talk, I will provide the nine years of history of the design, development, and testing of three iterative versions of a smartphone app developed in my Health and Behavioral Innovations in Technology (HABIT) lab. I will then present the recent efficacy results from a large (N = 2415; 35% racial/ethnic minority) NCI-funded national randomized controlled trial with 12-month follow-up (87% retention) that tested the latest version of the app, called "iCanQuit." I will conclude by discussing the implications of the iCanQuit trial for mhealth research and public health.
This session will illustrate the power of individual-level data through a case example of the BID Initiative. The BID Initiative worked in Tanzania and Zambia to enhance immunization and health service delivery through improved data collection, quality, and use. BID partnered with countries to design, test, and rollout a package of interventions that included an electronic immunization registry (EIR). EIRs are confidential, computerized, population-based systems that capture individual-level data on a patient’s vaccine history. Access to individual-level data, linked over time to capture all touchpoints with the immunization program, allows for new analyses and ways of exploring data that can benefit immunization programs, national and regional ministry staff, healthcare providers and administrators, funders, and other stakeholders. This session will provide an overview of how individual-level data has added value for the immunization programs in Tanzania and Zambia and will highlight some of the innovative analyses that it has made possible.
I will cover the results of two studies using short, daily, cell phone-based questionnaires to assess episodes of sexual activity without condoms and HIV exposures. In a cohort of 50 South African women, we validated the accuracy of the mobile responses by examining the presence of semen in vaginal secretions and comparing with CASI questionnaires delivered at the clinic. We demonstrated the mobile phone-based responses were more accurate than clinic questionnaires, as they have less recall time. We also identified 2 episodes of HIV exposures confirmed by RNA in vaginal secretions.
In the second study, we followed 30 MSM and TGW from New York City at high risk of HIV infection. Participants reported their sexual activity daily via a mobile app, and collected rectal swabs either daily or after a sex event. We demonstrated that the mobile app documented more episodes of anal sex than the clinic questionnaires, and had less relevance bias in reporting anal sex with condoms than the clinic assessments. In the cohort, we identified 6 episodes of rectal HIV exposures in two participants on PrEP. Together these studies highlight the benefits of daily mobile-based behavioral assessments in those at risk of HIV infection, as well as the use of mucosal biomarkers as a validation tool.
Understanding and managing one's health often requires tracking one's behaviors, context, and outcomes. People increasingly turn to digital tools to help record and make sense of these data. Despite some successes, many people still struggle with misalignments between the goals they have and the goals tracking tools are designed to support. People also turn to others, such as peers and experts, for help understanding and acting on their data. These collaborations, however, bring additional challenges and opportunities for goal misalignment. In this talk, I will describe common goal mis-alignments and demonstrate how designs that focus on people's goals can help narrow and focus the tracking process, reducing burdens and improving people's understanding.
Abstract: PATH has been working for over a decade to advance digital health for health equity through direct country capacity and systems strengthening and through global leadership and advocacy. This presentation will overview the multi-sectoral global community of digital health for health equity, including global normative guidance from WHO and other multilateral agencies, global and regional collaborative networks, and regular digital health convenings around the world. Participants will get a high-level orientation to the ongoing global digital health conversation, including recent developments in response to the COVID-19 pandemic and the ongoing mandate for global vaccine deployment.
The International Digital Health & AI Research Collaborative (I-DAIR) is a global platform to enable inclusive, impactful, and responsible research into digital health and Artificial Intelligence (AI) for health. It is being co-created with a diverse range of stakeholders from the public, private, and academic sectors in readiness for its launch in 2022. Ahead of the launch, it is deploying an innovative framework for building a substantive agenda, which includes a small number of ‘Pathfinders’ pursued in collaboration with research partners from regional hubs to support the platform building. In the context of a Pathfinder on Benchmarking digital health, I-DAIR will be working on the electronic Patient-reported Outcome Measures or ePROMS. By leveraging on learning from previous clinical trials (done amongst cohorts of non-communicating patients with the use of medical wearables for the design and validation of a set of related digital biomarkers), we aim to move these learnings from the clinical trials settings to a validated ePROM approach based on digital biomarkers.
In high HIV prevalence regions in subSaharan Africa, it has been noted that clinic retention and adherence declines over time among women living with HIV attending prevention of mother-to-child transmission (PMTCT) programs and among women using PrEP. SMS messaging is an attractive approach to bolster retention and adherence given wide usage of cellphones; however, evidence is mixed regarding effectiveness. While mHealth technology is widely deployed, few studies have rigorously demonstrated generalizable effects and numerous questions linger. What makes a message persuasive? How do messages influence behaviors? How does signal outweigh noise in a world with ever increasing messages? Members of our research group (including Drs. John Kinuthia, Jenn Unger, Keshet Ronen, and Jillian Pintye) have led studies to examine how to use SMS to support women, including during the critical windows of pregnancy and postpartum. I will be discussing what we are learning from these studies and what questions remain.
Mobile technologies expand opportunities for both management of participants and data collection in randomized trials of HIV prevention interventions. In some cases, mobile technologies can also deliver key parts of the intervention content. This talk will review the different roles that mobile technologies can play in the conduct of randomized trials and the delivery of interventions, identify study management tools available through the Emory Center for AIDS Research, and give examples of the range of utilities for mobile resources in randomized trials. The emphasis will be not on specific apps or interventions, but on the types of mobile functionalities that can provide prevention components, improve the completeness and quality of study data, and move intervention content to self-service formats.
Maintaining high medication adherence is essential for achieving desired efficacy in clinical trials, especially prevention trials. However, adherence is traditionally measured by self-reports that are subject to reporting biases and measurement error. Recently, electronic medication dispenser devices have been adopted in several HIV pre-exposure prophylaxis prevention studies. These devices are capable of collecting objective, frequent, and timely drug adherence data. The device opening signals generated by such devices are often represented as regularly or irregularly spaced discrete functional data, which are challenging for statistical analysis. We focus on clustering the adherence monitoring data from such devices. We first pre-process the raw discrete functional data into smoothed functional data. Parametric mixture models with change-points, as well as several non-parametric and semi-parametric functional clustering approaches, are adapted and applied to the smoothed adherence data. Simulation studies were conducted to evaluate finite sample performances, on the choices of tuning parameters in the pre-processing step as well as the relative performance of different clustering algorithms. We applied these methods to the HIV Prevention Trials Network 069 study for identifying subgroups with distinct adherence behavior over the study period.