Reducing the Digital Divide: Why Culturally Relevant eHealth Interventions Can Reduce Latino Health Disparities

Objectives: This paper systematically reviews the recent literature on incorporating culturally relevant material in electronic health (eHealth) tools for Latinos. Latinos are a fast-growing ethnic population set to reach 119 million individuals by 2060 (Velasco-Mondragon et al., 2016). Latinos are also disproportionately affected by comorbidities and other poor health outcomes. Developing a culturally sensitive eHealth tool can lead to positive health outcomes among Latinos. Methods: Peer-reviewed articles and analyses were extracted to identify whether eHealth was associated with positive health outcomes among Latino adults. Four literature databases were used to extract English-language articles published from 2001 to 2022. Furthermore, data from the CDC and WHO were extracted for statistical data regarding Latinos in the US. Recommendations: Improvements in eHealth are needed to increase Latino engagement. Possible factors to consider when developing a culturally relevant eHealth intervention are peer support, technical training, and language done by community messengers like CHWs and trusted members of the community. Conclusion: eHealth use is increasing throughout the country, but the service is not tailored to Latino communities. With necessary improvements, eHealth can increase engagement in healthcare services and improve health outcomes among the target population

Evaluating the impact of physical activity apps and wearables: interdisciplinary review

Background: Although many smartphone apps and wearables have been designed to improve physical activity, their rapidly evolving nature and complexity present challenges for evaluating their impact. Traditional methodologies, such as randomized controlled trials (RCTs), can be slow. To keep pace with rapid technological development, evaluations of mobile health technologies must be efficient. Rapid alternative research designs have been proposed, and efficient in-app data collection methods, including in-device sensors and device-generated logs, are available. Along with effectiveness, it is important to measure engagement (ie, users’ interaction and usage behavior) and acceptability (ie, users’ subjective perceptions and experiences) to help explain how and why apps and wearables work. Objectives: This study aimed to (1) explore the extent to which evaluations of physical activity apps and wearables: employ rapid research designs; assess engagement, acceptability, as well as effectiveness; use efficient data collection methods; and (2) describe which dimensions of engagement and acceptability are assessed. Method: An interdisciplinary scoping review using 8 databases from health and computing sciences. Included studies measured physical activity, and evaluated physical activity apps or wearables that provided sensor-based feedback. Results were analyzed using descriptive numerical summaries, chi-square testing, and qualitative thematic analysis. Results: A total of 1829 abstracts were screened, and 858 articles read in full. Of 111 included studies, 61 (55.0%) were published between 2015 and 2017. Most (55.0%, 61/111) were RCTs, and only 2 studies (1.8%) used rapid research designs: 1 single-case design and 1 multiphase optimization strategy. Other research designs included 23 (22.5%) repeated measures designs, 11 (9.9%) nonrandomized group designs, 10 (9.0%) case studies, and 4 (3.6%) observational studies. Less than one-third of the studies (32.0%, 35/111) investigated effectiveness, engagement, and acceptability together. To measure physical activity, most studies (90.1%, 101/111) employed sensors (either in-device [67.6%, 75/111] or external [23.4%, 26/111]). RCTs were more likely to employ external sensors (accelerometers: P=.005). Studies that assessed engagement (52.3%, 58/111) mostly used device-generated logs (91%, 53/58) to measure the frequency, depth, and length of engagement. Studies that assessed acceptability (57.7%, 64/111) most often used questionnaires (64%, 42/64) and/or qualitative methods (53%, 34/64) to explore appreciation, perceived effectiveness and usefulness, satisfaction, intention to continue use, and social acceptability. Some studies (14.4%, 16/111) assessed dimensions more closely related to usability (ie, burden of sensor wear and use, interface complexity, and perceived technical performance). Conclusions: The rapid increase of research into the impact of physical activity apps and wearables means that evaluation guidelines are urgently needed to promote efficiency through the use of rapid research designs, in-device sensors and user-logs to assess effectiveness, engagement, and acceptability. Screening articles was time-consuming because reporting across health and computing sciences lacked standardization. Reporting guidelines are therefore needed to facilitate the synthesis of evidence across disciplines

Mobile Delivery of Treatment for Alcohol Use Disorders: A Review of the Literature

Several systems for treating alcohol-use disorders (AUDs) exist that operate on mobile phones. These systems are categorized into four groups: text-messaging monitoring and reminder systems, text-messaging intervention systems, comprehensive recovery management systems, and game-based systems. Text-messaging monitoring and reminder systems deliver reminders and prompt reporting of alcohol consumption, enabling continuous monitoring of alcohol use. Text-messaging intervention systems additionally deliver text messages designed to promote abstinence and recovery. Comprehensive recovery management systems use the capabilities of smart-phones to provide a variety of tools and services that can be tailored to individuals, including in-the-moment assessments and access to peer discussion groups. Game-based systems engage the user using video games. Although many commercial applications for treatment of AUDs exist, few (if any) have empirical evidence of effectiveness. The available evidence suggests that although texting-based applications may have beneficial effects, they are probably insufficient as interventions for AUDs. Comprehensive recovery management systems have the strongest theoretical base and have yielded the strongest and longest-lasting effects, but challenges remain, including cost, understanding which features account for effects, and keeping up with technological advances

Toward Data-Driven Digital Therapeutics Analytics: Literature Review and Research Directions

With the advent of Digital Therapeutics (DTx), the development of software as a medical device (SaMD) for mobile and wearable devices has gained significant attention in recent years. Existing DTx evaluations, such as randomized clinical trials, mostly focus on verifying the effectiveness of DTx products. To acquire a deeper understanding of DTx engagement and behavioral adherence, beyond efficacy, a large amount of contextual and interaction data from mobile and wearable devices during field deployment would be required for analysis. In this work, the overall flow of the data-driven DTx analytics is reviewed to help researchers and practitioners to explore DTx datasets, to investigate contextual patterns associated with DTx usage, and to establish the (causal) relationship of DTx engagement and behavioral adherence. This review of the key components of data-driven analytics provides novel research directions in the analysis of mobile sensor and interaction datasets, which helps to iteratively improve the receptivity of existing DTx.Comment: This paper has been accepted by the IEEE/CAA Journal of Automatica Sinic

From Personalized Medicine to Population Health: A Survey of mHealth Sensing Techniques

Mobile Sensing Apps have been widely used as a practical approach to collect behavioral and health-related information from individuals and provide timely intervention to promote health and well-beings, such as mental health and chronic cares. As the objectives of mobile sensing could be either \emph{(a) personalized medicine for individuals} or \emph{(b) public health for populations}, in this work we review the design of these mobile sensing apps, and propose to categorize the design of these apps/systems in two paradigms -- \emph{(i) Personal Sensing} and \emph{(ii) Crowd Sensing} paradigms. While both sensing paradigms might incorporate with common ubiquitous sensing technologies, such as wearable sensors, mobility monitoring, mobile data offloading, and/or cloud-based data analytics to collect and process sensing data from individuals, we present a novel taxonomy system with two major components that can specify and classify apps/systems from aspects of the life-cycle of mHealth Sensing: \emph{(1) Sensing Task Creation \& Participation}, \emph{(2) Health Surveillance \& Data Collection}, and \emph{(3) Data Analysis \& Knowledge Discovery}. With respect to different goals of the two paradigms, this work systematically reviews this field, and summarizes the design of typical apps/systems in the view of the configurations and interactions between these two components. In addition to summarization, the proposed taxonomy system also helps figure out the potential directions of mobile sensing for health from both personalized medicines and population health perspectives.Comment: Submitted to a journal for revie

Health Capability Maturity Model: Person-centered Approach in Personal Health Record system

Personal health record (PHR) system is considered an important component in implementing continuity of care and evidence based treatment in modern healthcare. However, the adoption rates for PHR system by general public still remained low due to lack of interest and low health literacy level. In this paper, we propose a health capability maturity model (HCMM) and corresponding improvement paths to improve an individual’s capability to manage one’s health systematically by using PHRs. The HCMM allows an individual to collect, monitor, and control one’s health information. To this end, we attempt to integrate some of the key processes and concepts from Capability Maturity Model Integration (CMMI) and Trans-theoretical Model (TTM) into HCMM that assesses an individual’s capability and awareness on managing health and well-being and suggests customized improvement goals

Leveraging Digital Technologies for Management of Peripartum Depression to Mitigate Health Disparities

Health disparities are adverse, preventable differences in health outcomes that affect disadvantaged populations. Examples of health disparities can be seen in the condition of peripartum depression (PPD), a mood disorder affecting approximately 10-15% of peripartum women. For example, Hispanic and African-American women are less likely to start or continue PPD treatment. Digital health technologies have emerged as practical solutions for PPD care and self-management. However, existing digital solutions lack an incorporation of behavior theory and distinctive information needs based on women’s personal, social, and clinical profiles. Bridging this gap, I adapt Digilego, an integrative digital health development framework consisting of: a) mixed-methods user needs analysis, (b) behavior and health literacy theory mapping, and (c) content and feature engineering specifications for future programmatic development, to address health disparities. This enhanced framework is then used to design and develop a digital platform (MomMind) for PPD prevention among women in their peripartum period. This platform contains a digital journal, social forum, a library repository of PPD patient education materials, and a repository of PPD self-monitoring surveys. In line with the existing Digilego digital health framework, throughout my iterative process of design and development, I gather design insights from my target population (n=19) and their health providers (n=9) using qualitative research methods (e.g., interviews) and secondary analysis of peer interactions in two PPD online forums (n=55,301 posts from 9,364 users spanning years 2008-2022). These multimodal needs gathering efforts allowed me to a) compile women’s information and technology needs, and b) utilize them as a guide for MomMind intervention development and evaluation. One key MomMind strength is its grounding in theory-driven behavior change techniques (e.g., shaping knowledge) and patient engagement features (e.g., electronic questionnaires) as facilitated by Digilego. Also, I extend Digilego by incorporating literacy domains (e.g., health literacy) and cognitive processes (e.g., understanding) from the eHealth literacy framework into my content engineering approach. After an in-house usability assessment, I conducted a pilot acceptability evaluation of MomMind using cross-sectional acceptability surveys and PPD health literacy surveys administered pre-and-post use of MomMind. Interviews were also conducted to assess participant’s personal opinions and feedback. The study sample included n=30 peripartum women, of whom 16 (53.3%) were Hispanic and 17 (56.7%) were in low-income ranges. A total of 29/30 (96.6%) participants approved of MomMind, 28/30 (93.3%) deemed it a good fit, and 29/30 (96.67%) deemed it easy to use. Participants showed statistically significant improvement (