In many regions globally, including low-resource settings, there is a growing trend towards using mHealth technology, such as wearable sensors, to enhance health behaviors and outcomes. However, adoption of such devices in research conducted in low-resource settings lags behind use in high-resource areas. Moreover, there is a scarcity of research that specifically examines the user experience, readiness for and challenges of integrating wearable sensors into health research and community interventions in low-resource settings specifically. This study summarizes the reactions and experiences of young women (N = 57), ages 18 to 24 years, living in poverty in Kampala, Uganda, who wore Garmin vívoactive 3 smartwatches for five days for a research project. Data collected from the Garmins included participant location, sleep, and heart rate. Through six focus group discussions, we gathered insights about the participants’ experiences and perceptions of the wearable devices. Overall, the wearable devices were met with great interest and enthusiasm by participants. The findings were organized across 10 domains to highlight reactions and experiences pertaining to device settings, challenges encountered with the device, reports of discomfort/comfort, satisfaction, changes in daily activities, changes to sleep, speculative device usage, community reactions, community dynamics and curiosity, and general device comfort. The study sheds light on the introduction of new technology in a low-resource setting and also on the complex interplay between technology and culture in Kampala’s slums. We also learned some insights into how wearable devices and perceptions may influence behaviors and social dynamics. These practical insights are shared to benefit future research and applications by health practitioners and clinicians to advance and enhance the implementation and effectiveness of wearable devices in similar contexts and populations. These insights and user experiences, if incorporated, may enhance device acceptance and data quality for those conducting research in similar settings or seeking to address population-specific needs and health issues.

Young women in informal settlements in sub-Saharan Africa (SSA) experience significant health disparities. Despite growing understanding that population health outcomes are socially determined, the social determinants of health among young women in the informal settlements of SSA are not well understood. Furthermore, young women’s emic perspectives on the pathways that contribute to health risk are rarely reported in scientific research. This research brief describes findings from a qualitative study and specifically addresses young women’s insights into the social drivers of infectious disease transmission and treatment. We conducted focus groups including 60 young women participants aged 18 to 24 at three sites across Kampala, Uganda. Using thematic analysis, we identified four thematic clusters: (1) intersectional social injustices drive sexual exploitation and HIV transmission; (2) inadequate housing and sanitation contribute to the spread of infectious disease; (3) existing health care resources have limited capacity to address high infectious disease incidence; and (4) participants recommended building health promotion efforts on existing resources and developing community infrastructure to improve outcomes. This study highlights the social influences that contribute to the burden of infectious disease among young women living in Kampala’s informal settlements. Young women named several ecological factors and resources which, if engaged, could promote health and well-being in their communities. Future research should engage young women in SSA’s informal settlements as partners and peers in the development of community-owned research and intervention efforts to better understand community perspectives, address areas of most urgent need, and investigate barriers and facilitators to successful program implementation.

Background: Wearable fitness trackers have become a valuable tool in public health research due to their ability to collect large-scale, individual-level data at a low cost. Because of this, wearables have the potential to mitigate the urgent need for health research in low-resource settings. However, their use has been largely limited to high-income settings, and a major challenge remains: high rates of missing data. This problem may be exacerbated in low-resource environments where logistical and operational barriers further complicate data collection. Wearable sleep data collected in an urban informal settlement in Uganda during The Onward Project on Well-being and Adversity project revealed substantial challenges related to missingness in low-resource research settings. Objective: The study aimed to characterize the patterns and frequency of missing data and to demonstrate the use of analytical methods to determine the underlying missingness mechanism. Methods: For this study, 300 women in slum communities in Kampala were equipped with Garmin smartwatches that collected data over 5 days. The nature of missingness was assessed through four methods: pattern analysis, the Little test, a random forest classification model, and a logistic regression classification model. Pattern analysis is an established method of identifying missing data patterns, while the Little test is used specifically to identify missing completely at random data. Random forest and logistic regression models are more recent methods proposed to identify the mechanism of missingness; both were used to examine agreement between the 2 methods and to ensure a thorough examination of missingness patterns. Results: Approximately 30% of nighttime data were missing. Three patterns were identified that occurred in over 10% of participants’ data: no data missing, fifth night of data missing, and all nights of data missing. Pattern analysis and the Little test ( P <.001) indicated that the data were not missing completely at random. Both the random forest (area under the curve=0.7) and logistic regression models suggested that the data were missing at random (MAR). Conclusions: Evidence of missingness in this dataset was consistent with MAR. Potential causes of missingness include device removal, battery failure, and technical malfunctions. These findings have important implications for both wearable device users and future research. Understanding the mechanisms behind missingness can inform strategies to improve data quality, particularly in low-resource settings, and allow researchers to manage missingness using appropriate strategies designed to handle MAR data without introducing bias into the results.

Justice-involved veterans are a unique subpopulation with different mental health needs in pre-trial diversion than civilians. Veteran diversion can be challenging, creating evaluation issues at the program (i.e., development, implementation, outcomes), and individual (i.e., positive outcomes) levels. A case study of VetStar, a unified approach diversion for veterans spanning both the criminal justice and healthcare systems, is presented as a model for diversion of justice-involved veterans. The researchers obtained data from five publicly available sources: recorded radio interviews, VetStar published stakeholder reports, the VetStar Texas Veterans & Family Alliance 2018 grant submission, a photovoice project from the Lubbock Art Walk November 2019, and the Texas Veterans Commission Quarterly Reports and JMHCP Data Review. This case study found 10 major themes. VetStar is a non-profit organization using a modified version of assertive community treatment serving justice-involved veterans with mental health issues. We describe the progression of justice-involved veterans in the VetStar diversion program; 92% of the 571 veterans completing the VetStar program in five years did not re-offend for the same crime. VetStar’s approach could be performed in other subpopulations with distinct cultural identities. Modifications can be constructed and aligned within the construct of the subpopulation identity. Further research is needed to investigate VetStar’s FASTRR model, to help document a more detailed theory of the approach.

Pay-for data collection platforms are increasingly popular in health sciences, due to the ease of use, accessibility, and speed of data collection. However, data quality among platforms may differ. The purpose of this study was to compare data quality from three commonly used, pay-for data collection crowdsourcing platforms, using predefined quality control measures. Data were collected using three major platforms, Qualtrics (N = 705), SurveyMonkey (N = 576), and Amazon Mechanical Turk (N = 1,034), for the methodological purpose of assessing data quality for a scale used by public health practitioners to assess ankle activity level. Data were collected using attempted quota sampling for gender, age, ethnicity and region, for representativeness of the general US population. Twenty quality checks in five categories were utilized to identify poor-quality data: attention verification (e.g. ‘please select response B’); demographic verification (e.g. state and zip code match); illogical responses (e.g. respondents separately reporting higher difficulty walking up one flight of stairs than walking up four flights); honesty/reliability verification (e.g. male reported to be pregnant); and assessment of open-ended questions (rejected based on similarity of content or nonsensical responses). Of the 2,315 completed responses received from the three different platforms, 947 (40.91%) passed our quality checks and were considered good-quality responses. SurveyMonkey yielded the highest proportion of good-quality data (70.5%), followed by Qualtrics (61.8%) and Amazon Mechanical Turk (10.2%); however, open-ended responses could not be included in SurveyMonkey due to their 80-item limit, which may have inflated its proportion of good-quality responses. This study demonstrates that data quality issues may differ across platforms and a variety of quality control measures should be implemented for researchers utilizing pay-for data collection services from crowdsourcing platforms.