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With a renewed focus on health equity in the United States driven by national crises and legislation to improve digital healthcare innovation, there is a need for the designers of digital health tools to take deliberate steps to design for equity in their work. A concrete toolkit of methods to design for health equity is needed to support digital health practitioners in this aim. This narrative review summarizes several health equity frameworks to help digital health practitioners conceptualize the equity dimensions of importance for their work, and then provides design approaches that accommodate an equity focus. Specifically, the Double Diamond Model, the IDEAS framework and toolkit, and community collaboration techniques such as participatory design are explored as mechanisms for practitioners to solicit input from members of underserved groups and better design digital health tools that serve their needs. Each of these design methods requires a deliberate effort by practitioners to infuse health equity into the approach. A series of case studies that use different methods to build in equity considerations are offered to provide examples of how this can be accomplished and demonstrate the range of applications available depending on resources, budget, product maturity, and other factors. We conclude with a call for shared rigor around designing digital health tools that deliver equitable outcomes for members of underserved populations.

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OBJECTIVE: Although previous research has established that students who perceive that their parents have lower drinking limits consume less alcohol, optimal approaches for effectively communicating these limits are less understood. To address this gap in the literature, the present study examined the effects of hypothetical limit-focused text messages on estimated drinking behavior. METHOD: Undergraduate college students (N=253) completed an online survey in which they were instructed to imagine a scenario where they were planning to go out at night to an event involving drinking. Participants were randomly assigned to a condition where they would receive one of four hypothetical text messages from their mother and/or father containing a range of drinking limits. They were then asked to report how much alcohol they would realistically consume on this night if they were and were not obligated to check in with their parent at the end of the night. RESULTS: Hypothetical text messages from mothers and fathers containing lower drinking limits resulted in lower estimated alcohol consumption. A drinking limit of zero resulted in the least amount of expected alcohol consumption but specifying 1-2 drinks as a limit might produce a similar effect if parents also required their student to check-in at the end of the night. CONCLUSION: Findings from this study provide initial support for future studies to investigate the effects of limit setting text messages sent from parents on college student drinking, as well as the effects of text messages sent from parents requiring that students check-in.

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Advances in biomedical engineering continue to produce innovative wearable health sensors capable of real-time ambulatory assessments (e.g., of physiology, the environment), holding great potential for advancing precision monitoring and interventions through the integration of such devices and data into eHealth systems. As with any novel device, however, user views on acceptability and concerns about the technology must be evaluated to facilitate widespread implementation and user adoption of such devices. One factor that may strongly influence user views is the potential relevance to, and need for, self-care for chronic disease management. We examined if acceptability and concerns regarding innovative wearable devices differed between individuals living with or without chronic disease. A U.S. adult sample (N = 448; 20-70 yrs.; 34 % Female; 60 % White, 35 % Hispanic) completed a web-based survey regarding their thoughts/opinions related to innovative wearable sensors. Two-thirds (67 %, N = 298) reported at least one chronic disease; one-third (33 %, N = 150) reported no chronic health conditions. Participants viewed learning modules about two innovative devices: a watch to detect environmental gases for respiratory health, and a chest-patch monitoring real-time ECG. For each device, participants rated acceptability across multiple dimensions, and then rated potential concerns (including general concerns and specific worries about negative health impacts). Respondents with and without chronic disease differed in education, race, and ethnicity. Controlling for these differences, individuals with chronic disease reported significantly higher acceptability for the watch and for the chest-patch. Healthy participants reported significantly higher general concerns about technology. However, when concern questions were asked specifically about the potential negative impacts of the two study devices on physical health and well-being, participants with chronic disease reported significantly higher concerns. Overall, results show that living with chronic disease influences acceptability and concerns associated with adoption of innovative sensors. These findings suggest it is essential to take potential users' health status into account when studying the design and implementation of innovative wearable sensors. Dissemination strategies may benefit from emphasizing the beneficial features of these devices, addressing hesitations, and customizing implementation approaches by user group.

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The prevalence of non-communicable diseases, such as diabetes and cardiovascular disease, is rising in low- and middle-income countries (LMICs). Health behavior change (HBC) interventions such as the widely used Diabetes Prevention Program (DPP) are effective at reducing chronic disease risk, but have not been adapted for LMICs. Leveraging mobile health (mHealth) technology such as text messaging (SMS) to enhance reach and participant engagement with these interventions has great promise, yet we lack evidence-informed approaches to guide the integration of SMS specifically to support HBC interventions in LMIC contexts. To address this gap, we integrated guidance from the mHealth literature with expertise and first-hand experience to establish specific development steps for building and implementing SMS systems to support HBC programming in LMICs. Specifically, we provide real-world examples of each development step by describing our experience in designing and delivering an SMS system to support a culturally-adapted DPP designed for delivery in South Africa. We outline eight key SMS development steps, including: 1) determining if SMS is appropriate; 2) developing system architecture and programming; 3) developing theory-based messages; 4) developing SMS technology; 5) addressing international SMS delivery; 6) testing; 7) system training and technical support; and 8) cost considerations. We discuss lessons learned and extractable principles that may be of use to other mHealth and HBC researchers working in similar LMIC contexts.Trial registration Clinicaltrials.gov, NCT03342274 . Registered 10 November 2017.

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BACKGROUND: Lifestyle Africa is an adapted version of the Diabetes Prevention Program designed for delivery by community health workers to socioeconomically disadvantaged populations in low- and middle-income countries (LMICs). Results from the Lifestyle Africa trial conducted in an under-resourced community in South Africa indicated that the programme had a significant effect on reducing haemoglobin A1c (HbA1c). OBJECTIVE: To estimate the cost of implementation and the cost-effectiveness (in cost per point reduction in HbA1c) of the Lifestyle Africa programme to inform decision-makers of the resources required and the value of this intervention. METHODS: Interviews were held with project administrators to identify the activities and resources required to implement the intervention. A direct-measure micro-costing approach was used to determine the number of units and unit cost for each resource. The incremental cost per one point improvement in HbA1c was calculated. RESULTS: The intervention equated to 71 United States dollars (USD) in implementation costs per participant and a 0.26 improvement in HbA1c per participant. CONCLUSIONS: Lifestyle Africa reduced HbA1c for relatively little cost and holds promise for addressing chronic disease in LMIC. Decision-makers should consider the comparative clinical effectiveness and cost-effectiveness of this intervention when making resource allocation decisions. TRIAL REGISTRATION: Trial registration is at ClinicalTrials.gov (NCT03342274).

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BACKGROUND: Low- and middle-income countries (LMICs) are experiencing major increases in diabetes and cardiovascular conditions linked to overweight and obesity. Lifestyle interventions such as the United States National Diabetes Prevention Program (DPP) developed in high-income countries require adaptation and cultural tailoring for LMICs. The objective of this study was to evaluate the efficacy of "Lifestyle Africa," an adapted version of the DPP tailored for an underresourced community in South Africa compared to usual care. METHODS AND FINDINGS: Participants were residents of a predominantly Xhosa-speaking urban township of Cape Town, South Africa characterized by high rates of poverty. Participants with body mass index (BMI) ≥ 25 kg/m2 who were members of existing social support groups or "clubs" receiving health services from local nongovernmental organizations (NGOs) were enrolled in a cluster randomized controlled trial that compared Lifestyle Africa (the intervention condition) to usual care (the control condition). The Lifestyle Africa intervention consisted of 17 video-based group sessions delivered by trained community health workers (CHWs). Clusters were randomized using a numbered list of the CHWs and their assigned clubs based on a computer-based random allocation scheme. CHWs, participants, and research team members could not be blinded to condition. Percentage weight loss (primary outcome), hemoglobin A1c (HbA1c), blood pressure, triglycerides, and low-density lipoprotein (LDL) cholesterol were assessed 7 to 9 months after enrollment. An individual-level intention-to-treat analysis was conducted adjusting for clustering within clubs and baseline values. Trial registration is at ClinicalTrials.gov (NCT03342274). Between February 2018 and May 2019, 782 individuals were screened, and 494 were enrolled. Participants were predominantly retired (57% were receiving a pension) and female (89%) with a mean age of 68 years. Participants from 28 clusters were allocated to Lifestyle Africa (15, n = 240) or usual care (13, n = 254). Fidelity assessments indicated that the intervention was generally delivered as intended. The modal number of sessions held across all clubs was 17, and the mean attendance of participants across all sessions was 61%. Outcome assessment was completed by 215 (90%) intervention and 223 (88%) control participants. Intent-to-treat analyses utilizing multilevel modeling included all randomized participants. Mean weight change (primary outcome) was -0.61% (95% confidence interval (CI) = -1.22, -0.01) in Lifestyle Africa and -0.44% (95% CI = -1.06, 0.18) in control with no significant difference (group difference = -0.17%; 95% CI = -1.04, 0.71; p = 0.71). However, HbA1c was significantly lower at follow-up in Lifestyle Africa compared to the usual care group (mean difference = -0.24, 95% CI = -0.39, -0.09, p = 0.001). None of the other secondary outcomes differed at follow-up: systolic blood pressure (group difference = -1.36; 95% CI = -6.92, 4.21; p = 0.63), diastolic blood pressure (group difference = -0.39; 95% CI = -3.25, 2.30; p = 0.78), LDL (group difference = -0.07; 95% CI = -0.19, 0.05; p = 0.26), triglycerides (group difference = -0.02; 95% CI = -0.20, 0.16; p = 0.80). There were no unanticipated problems and serious adverse events were rare, unrelated to the intervention, and similar across groups (11 in Lifestyle Africa versus 13 in usual care). Limitations of the study include the lack of a rigorous dietary intake measure and the high representation of older women. CONCLUSIONS: In this study, we found that Lifestyle Africa was feasible for CHWs to deliver and, although it had no effect on the primary outcome of weight loss or secondary outcomes of blood pressure or triglycerides, it had an apparent small significant effect on HbA1c. The study demonstrates the potential feasibility of CHWs to deliver a program without expert involvement by utilizing video-based sessions. The intervention may hold promise for addressing cardiovascular disease (CVD) and diabetes at scale in LMICs. TRIAL REGISTRATION: ClinicalTrials.gov NCT03342274.

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BACKGROUND: With the growing interest in mobile health (mHealth), behavioral medicine researchers are increasingly conducting intervention studies that use mobile technology (eg, to support healthy behavior change). Such studies' scientific premises are often sound, yet there is a dearth of implementational data on which to base mHealth research methodologies. Notably, mHealth approaches must be designed to be acceptable to research participants to support meaningful engagement, but little empirical data about design factors influencing acceptability in such studies exist. OBJECTIVE: This study aims to evaluate the impact of two common design factors in mHealth intervention research-requiring multiple devices (eg, a study smartphone and wrist sensor) relative to requiring a single device and providing individually tailored feedback as opposed to generic content-on reported participant acceptability. METHODS: A diverse US adult convenience sample (female: 104/255, 40.8%; White: 208/255, 81.6%; aged 18-74 years) was recruited to complete a web-based experiment. A 2×2 factorial design (number of devices×nature of feedback) was used. A learning module explaining the necessary concepts (eg, behavior change interventions, acceptability, and tailored content) was presented, followed by four vignettes (representing each factorial cell) that were presented to participants in a random order. The vignettes each described a hypothetical mHealth intervention study featuring different combinations of the two design factors (requiring a single device vs multiple devices and providing tailored vs generic content). Participants rated acceptability dimensions (interest, benefit, enjoyment, utility, confidence, difficulty, and overall likelihood of participating) for each study presented. RESULTS: Reported interest, benefit, enjoyment, confidence in completing study requirements, and perceived utility were each significantly higher for studies featuring tailored (vs generic) content, and the overall estimate of the likelihood of participation was significantly higher. Ratings of interest, benefit, and perceived utility were significantly higher for studies requiring multiple devices (vs a single device); however, multiple device studies also had significantly lower ratings of confidence in completing study requirements, and participation was seen as more difficult and was associated with a lower estimated likelihood of participation. The two factors did not exhibit any evidence of statistical interactions in any of the outcomes tested. CONCLUSIONS: The results suggest that potential research participants are sensitive to mHealth design factors. These mHealth intervention design factors may be important for initial perceptions of acceptability (in research or clinical settings). This, in turn, may be associated with participant (eg, self) selection processes, differential compliance with study or treatment processes, or retention over time.

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OBJECTIVE: Intensive ambulatory assessment, such as ecological momentary assessment (EMA), is increasingly used to capture naturalistic patient-reported outcomes. EMA design features (eg, study duration, prompt frequency) vary widely between studies, but it is not known if such design decisions influence potential subjects' willingness to participate in a study. We hypothesise that intentions to participate will be higher in studies that are less burdensome and have higher reward (eg, compensation). DESIGN: This experimental study examined if four EMA study design features (study duration, prompt frequency, prompt length, compensation) affected intentions to participate in a hypothetical EMA study and participation appraisals (eg, participation effort). Participants were randomly assigned to conditions (reflecting a fully crossed design of the four features, each with two levels). Each condition presented a vignette describing a study (each a unique combination of design features) and asked them to report on likelihood of participating and study appraisals. PARTICIPANTS: A convenience sample of participants (n=600; 46% female, Mage=40.39) were recruited using an online service. PRIMARY AND SECONDARY OUTCOME MEASURES: Primary outcomes were willingness to participate (No/Yes) and reported participation likelihood (0-100 scale). Secondary outcomes included appraisals of interest, enjoyment, effort, and if the study makes a valuable contribution to science. RESULTS: We examined main effects, and two-way interactions for participation likelihood, across study design features. Overall, reported willingness to participate and participation likelihood were high (89%, M=83.90, respectively). Shorter study duration, fewer prompts, shorter prompts and higher compensation increased willingness to participate and elicited higher participation likelihood (each associated with ~6%-8% increases). Findings suggested that more intensive studies were judged as somewhat less interesting and enjoyable, and requiring more effort. CONCLUSION: Hypotheses were generally supported. Design features influence behavioural intentions to participate in, and appraisals of, EMA studies. Implications for participant recruitment and generalisability, and remaining research questions, are discussed.

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New technologies and innovations have often improved population well-being and societal function; however, these are also often initially accompanied by worry and fear. In some cases, such worries can impede, or even prevent entirely, the adoption of the technology. Mobile health (mHealth), a discipline broadly focused on employing ambulatory technologies to improve the affordability, reach, and effectiveness of health promotion and clinical intervention approaches, offers new innovations and opportunities. Despite emerging evidence supporting mHealth efficacy (eg, for improving health outcomes), some individuals have concerns about mHealth technology that may impede scalability, efficacy, and, ultimately, the public health benefits of mHealth. We present a review and conceptual framework to examine these issues, focusing on three overarching themes: biophysiological, psychological, and societal concerns. There are features of mHealth that lead to worries about the potential negative effects on an individual's health (eg, due to exposure to electromagnetic or radio waves), despite evidence supporting the safety of these technologies. When present, such beliefs can lead to worry that gives rise to the experience of unpleasant and concerning physical symptoms-the nocebo effect. This may represent an important implementational barrier because of apprehension toward beneficial mHealth products (or features thereof, such as wireless charging, wearable or implantable sensors, etc) and may also have broader ramifications (eg, leading to economic, governmental, and legislative actions). In addition to reviewing evidence on these points, we provide a broad three-step model of implementation research in mHealth that focuses on understanding and preventing health concerns to facilitate the safe and effective scalability of mHealth (and that may be generalizable and applied to similar technologies): (1) evaluating and better discerning public perceptions and misperceptions (and how these may differ between populations), (2) developing theory-based public health communication strategies regarding the safety of mHealth, and (3) disseminating this messaging using evidence-based methods. Collectively, these steps converge on reviewing evidence regarding the potential role of worry and nocebo in mHealth and providing a model for understanding and changing attitudes and preventing unfounded negative perceptions related to mHealth technology.

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BACKGROUND: Despite increased interest in developing mobile technology-based interventions, little research has examined preferences and beliefs about using smartphones for psychosocial or health behavior change interventions, particularly among women with overweight/obesity residing in rural communities. PURPOSE: The aims of this study were to examine the beliefs of pre- and interconceptional women about using smartphones and to examine the extent to which women's preferences for using smartphones changed as a result of participating in study interviews. METHODS: Forty women (M age = 28.2 years; M BMI = 31.4; 50% obese) participated in one-time 90 minute interviews and completed questionnaires before and after the interviews. Descriptive statistics were used to examine the frequency of women's preferences for using smartphones and applications. Interviews were downloaded and transcribed; principles of thematic analysis were used to code the interviews and identify themes. RESULTS: Women identified advantages of using smartphones for behavioral interventions, including being convenient, useful, and able to provide social support. Primary disadvantages were annoyances and needing technology support for phone problems. Participating in interviews also resulted in significant improvements in participant willingness to use smartphones in health behavior change interventions. DISCUSSION: The study findings highlight the importance of understanding beliefs about using smartphones before designing effective smartphone-based interventions, especially for use with pre- and interconceptional women with overweight/obesity who may have unique challenges with study adherence. These findings also suggest beliefs about smartphone utility can be improved over the course of a brief interview that taps into technology-related preferences. CONCLUSION: Identifying advantages/disadvantages of smartphone use can inform intervention design. Future research should explore how to capitalize on strategies that enable the benefits of technology (e.g., convenience, social support) while minimizing participant barriers (e.g., distractions) to promote behavior change and facilitate intervention compliance.

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BACKGROUND: The prevalence of maternal perinatal obesity is rising, and in turn, increases health risks and morbidity for both mother and child. Past evidence suggests the preconceptional Strong Healthy Women (SHW) intervention can reduce multiple biobehavioral risk factors for adverse perinatal health. The SHW intervention, however, was time- and resource-intensive to deliver. Mobile health (mHealth) technologies provide an opportunity to expand intervention reach while reducing implementation cost and burden. Previous research suggests that preconceptional women are broadly supportive of using smartphones for behavior change, yet few studies have elicited their specific preferences for a targeted mHealth intervention. The objective of this study was to evaluate women's preferences for receiving SHW content via smartphone to supplement the design of SMART SHW, a redeveloped version of the intervention that utilizes smartphones to enhance delivery. METHODS: Overweight/obese (mean BMI =31.4) preconceptional community women (N=40) participated in semi-structured focus group interviews. SHW components across four content areas (physical activity, nutrition, stress, weight management) were presented to participants; women provided preferences for program elements viewed as acceptable to convert to smartphone. Thematic analysis was used to analyze interview data. After the interviews were completed, an iterative review of the data to determine which aspects of SHW were feasible to modify for mobile delivery was conducted. RESULTS: Women preferred to receive SHW communications, surveys, and educational materials on their smartphones via texting, mobile websites, and a SMART SHW app; MyFitnessPal and a wearable pedometer were preferred methods for tracking nutrition and activity. Salient mHealth design themes included providing pop-ups as reminders, using web-based videos to supplement the curriculum, and presenting on-screen information in a concise format. In designing the final prototype, 87% of participant preferences were able to be incorporated. CONCLUSIONS: Smartphone devices can enhance the reach of face-to-face behavioral interventions by reducing implementation burden. Engaging end-users (in this case, preconceptional women with overweight/obesity) in the mHealth design process through semi-structured focus groups is a feasible and useful approach. Eliciting and leveraging user preferences guides the development of an intervention framework that is highly acceptable to the target participants.

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Program evaluation has become increasingly important, and information on program performance often drives funding decisions. Technology use and integration can help ease the burdens associated with program evaluation by reducing the resources needed (e.g., time, money, staff) and increasing evaluation efficiency. This paper reviews how program evaluators, across disciplines, can apply internet and mobile technologies to key aspects of program evaluation, which consist of participant registration, participant tracking and retention, process evaluation (e.g., fidelity, assignment completion), and outcome evaluation (e.g., behavior change, knowledge gain). In addition, the paper focuses on the ease of use, relative cost, and fit with populations. An examination on how these tools can be integrated to enhance data collection and program evaluation is discussed. Important limitations of and considerations for technology integration, including the level of technical skill, cost needed to integrate various technologies, data management strategies, and ethical considerations, are highlighted. Lastly, a case study of technology use in an evaluation conducted by the Clearinghouse for Military Family Readiness at Penn State is presented and illustrates how technology integration can enhance program evaluation.

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