Participant Engagement in Microrandomized Trials of mHealth Interventions: Scoping Review

Population-Based Cancer Prevention Education Intervention Through mHealth: A Randomized Controlled Trial

Despite the high potential of mHealth-related educational interventions to reach large segments of the population, implementation and adoption of such interventions may be challenging. The objective of this study was to gather knowledge on the feasibility of a future cancer prevention education intervention based on the European Code Against Cancer (ECAC), using a population-based mHealth implementation strategy. A type-2 hybrid effectiveness-implementation study was conducted in a sample of the Spanish general population to assess adoption, fidelity, appropriateness, and acceptability of an intervention to disseminate cancer prevention messages, and willingness to consult further digital information. Participation rates, sociodemographic data, mHealth use patterns and implementation outcomes were calculated. Receiving cancer prevention messages through mHealth is acceptable, appropriate (frequency, timing, understandability and perceived usefulness) and feasible. mHealth users reported high access to the Internet through different devices, high ability and confidence to browse a website, and high willingness to receive cancer prevention messages in the phone, despite low participation rates in comparison to the initial positive response rates. Although adoption of the intervention was high, post-intervention fidelity was seriously hampered by the disruptions caused by the Covid-19 pandemic, which may have affected recall bias. In the context of the Europe's Beating Cancer Plan to increase knowledge about cancer prevention across the European Union, this study contributes to inform the design of future interventions using mHealth at large scale, to ensure a broad coverage and adoption of cancer prevention messages as those promoted by the ECAC.Trial Registration: ClinicalTrials.gov from the U.S. National Library of Medicine, NCT05992792. Registered 15 August 2023 - Retrospectively registered https://clinicaltrials.gov/study/NCT05992792?cond=Cancer&term=NCT05992792&rank=1 .

Engagement with mHealth Alcohol Interventions: User Perspectives on an App or Chatbot-Delivered Program to Reduce Drinking

Research suggests participant engagement is a key mediator of mHealth alcohol interventions' effectiveness in reducing alcohol consumption among users. Understanding the features that promote engagement is critical to maximizing the effectiveness of mHealth-delivered alcohol interventions. The purpose of this study was to identify facilitators and barriers to mHealth alcohol intervention utilization among hazardous-drinking participants who were randomized to use either an app (Step Away) or Artificial Intelligence (AI) chatbot-based intervention for reducing drinking (the Step Away chatbot). We conducted semi-structured interviews from December 2019 to January 2020 with 20 participants who used the app or chatbot for three months, identifying common facilitators and barriers to use. Participants of both interventions reported that tracking their drinking, receiving feedback about their drinking, feeling held accountable, notifications about high-risk drinking times, and reminders to track their drinking promoted continued engagement. Positivity, personalization, gaining insight into their drinking, and daily tips were stronger facilitator themes among bot users, indicating these may be strengths of the AI chatbot-based intervention when compared to a user-directed app. While tracking drinking was a theme among both groups, it was more salient among app users, potentially due to the option to quickly track drinks in the app that was not present with the conversational chatbot. Notification glitches, technology glitches, and difficulty with tracking drinking data were usage barriers for both groups. Lengthy setup processes were a stronger barrier for app users. Repetitiveness of the bot conversation, receipt of non-tailored daily tips, and inability to self-navigate to desired content were reported as barriers by bot users. To maximize engagement with AI interventions, future developers should include tracking to reinforce behavior change self-monitoring and be mindful of repetitive conversations, lengthy setup, and pathways that limit self-directed navigation.

Digital Adaptive Behavioral Interventions to Improve HIV Prevention and Care: Innovations in Intervention Approach and Experimental Design

PURPOSE OF REVIEW

Recent advances in digital technologies can be leveraged to adapt HIV prevention and treatment services to the rapidly changing needs of individuals in everyday life. However, to fully take advantage of these technologies, it is critical to effectively integrate them with human-delivered components. Here, we introduce a new experimental approach for optimizing the integration and adaptation of digital and human-delivered behavioral intervention components for HIV prevention and treatment.

RECENT FINDINGS

Typically, human-delivered components can be adapted on a relatively slow timescale (e.g., every few months or weeks), while digital components can be adapted much faster (e.g., every few days or hours). Thus, the systematic integration of these components requires an experimental approach that involves sequential randomizations on multiple timescales. Selecting an experimental approach should be motivated by the type of adaptive intervention investigators would like to develop, and the scientific questions they have about its construction.