Iterative Design Process Research Articles

Developing Population Health Surveillance Using mHealth in Low-Resource Settings: Qualitative Assessment and Pilot Evaluation.

BackgroundPopulation surveillance data are essential for understanding population needs and evaluating health programs. Governmental and nongovernmental organizations in western Myanmar did not previous have means for conducting robust, electronic population health surveillance.ObjectiveThis study involved developing mobile health (mHealth)–based population health surveillance in a rural, low-resource setting with minimal cellular infrastructure in western Myanmar. This was an early formative study in which our goal was to establish the initial feasibility of conducting mHealth population health surveillance, optimizing procedures, and building capacity for future work.MethodsWe used an iterative design process to develop mHealth-based population health surveillance focused on general demographics (eg, total census, age category, sex, births, and deaths). Interviews were conducted with international consultants (nurse midwives) and local clinicians (nurses and physicians) in Myanmar. Our analytic approach was informed by the Systems Engineering Initiative for Patient Safety work systems model to capture the multilevel user needs for developing health interventions, which was used to create a prototype data collection tool. The prototype was then pilot-tested in 33 villages to establish an initial proof of concept.ResultsWe conducted 7 interviews with 5 participants who provided feedback regarding the domains of the work system, including environmental, organizational, sociocultural, technological, informational, and task- and people-based considerations, for adapting an mHealth tool. Environmental considerations included managing limited electricity and internet service. Organizational needs involved developing agreements to work within existing government infrastructure as well as leveraging the communal nature of societies to describe the importance of surveillance data collection and gain buy-in. Linguistic diversity and lack of experience with technology were both cited as people- and technology-based aspects to inform prototype design. The use of mobile tools was also viewed as a means to improve the quality of the data collected and as a feasible option for working in settings with limited internet access. Following the prototype design based on the findings of initial interviews, the mHealth tool was piloted in 33 villages, allowing our team to collect census data from 11,945 people for an initial proof of concept. We also detected areas of potentially missing data, which will need to be further investigated and mitigated in future studies.ConclusionsPrevious studies have not focused heavily on the early stages of developing population health surveillance capacity in low- and middle-income countries. Findings related to key design considerations using a work systems lens may be informative to others developing technology-based solutions in extremely low-resource settings. Future work will involve collecting additional health-related data and further evaluating the quality of the data collected. Our team established an initial proof of concept for using an mHealth tool to collect census-related information in a low-resource, extremely rural, and low-literacy environment.

The Emissions of Water Vapour and NOx from Modelled Hydrogen-Fuelled Aircraft and the Impact of NOx Reduction on Climate Compared with Kerosene-Fuelled Aircraft

A kerosene fuelled aircraft was modelled within a performance tool and fuel burn and the emissions of nitrogen oxides (NOx) and water vapour at different stages of flight throughout the mission were estimated. Adaptions were made to engine and aircraft parameters within the performance tool to accommodate a liquid hydrogen fuel over the same given mission. Once an iterative design process had been completed to ensure the aircraft could perform the given mission, the performance tool was again used to calculate total fuel burn. Fuel burn results alongside predicted emission indices were used to estimate the emissions of NOx, water vapour from hydrogen-fuelled aircraft. The use of hydrogen fuel over kerosene fuel in the modelled aircraft resulted in the removal of carbon-based emission species alongside 86% reduction in NOx and 4.3 times increase in water vapour emission. The climate impact of this switch with the reduction in NOx emission was assessed by a 3D global atmospheric chemistry and transport model, STOCHEM-CRI, which found a significant reduction in the concentration of a potent greenhouse gas, ozone, and an oxidizing agent, OH, by up to 6% and 25%, respectively. The reduction of OH levels could induce a positive radiative forcing effect as the lifetime of another important greenhouse gas, methane, is increased. However, the magnitude of this increase is very small (~0.3%), thus the overall impact of the reduction in NOx emissions is likely to have a net negative radiative forcing effect, improving aviation’s impact on the environment. However, further work is warranted on effects of other emission species, specifically water vapour, particulate matter and unburned hydrogen.

Design of virtual laneway infrastructure: community activation through augmented reality in underutilized spaces in cities pressured to densify

ABSTRACT This study undertook literature studies, site and precinct analyses, and observations of 34 laneways in Perth, Western Australia. Ownership, proximity to residential, and mass transit public transport, critical node linkages, proportions, surface ‘canvas’ area, and development restrictions were assessed eliciting a suitable location to develop an Augmented Reality architectural placemaking ‘projection’ proposition. The ideation presents iterative design process visualizations within a vernacular ‘place’ setting with localized spatial linkages and functionalities, and does not draw to a definitive solution, but a novel, cost-effective, adaptable, and ‘agile’ method of activating ‘lost spaces’ without permanent structural alteration while retaining their original utility.

Developing an Immersive Virtual Reality Training System for Novel Pediatric Power Wheelchair Users: Protocol for a Feasibility Study.

BackgroundPower wheelchairs can empower children with physical limitations to gain independence in their everyday lives; however, traditional methods of power wheelchair training are often limited by poor accessibility and safety concerns. Immersive virtual reality technology (IVRT) uses advanced display technology to place users in a fully immersive web-based environment that can support real-time skills training, often requiring less resources and fewer safety concerns than real-world methods. IVRT interventions have shown to be a feasible training option among adult power wheelchair users; however, there is still a need to understand the technical and clinical feasibility of developing an IVRT power wheelchair training tool for the pediatric population.ObjectiveThis proposed study aims to use expert feedback and an iterative design process to develop an IVRT training intervention for pediatric power wheelchair skill development.MethodsThis 3-phase feasibility study will be conducted within the assistive technology unit of a public pediatric hospital. Separate participant groups will be recruited for each phase, consisting of approximately 10 to 15 clinicians (phase 1), 10 pediatric power wheelchair users (phase 2), and 15 to 20 additional pediatric power wheelchair users (phase 3). Phase 1 will be conducted to gather feedback on the baseline IVRT training intervention. Clinicians will test the intervention and assess its usability and acceptability using qualitative and quantitative methods. Phase 1 participants will also be invited back for a subsequent session to reassess a revised version of the training intervention that has been updated based on their previous feedback. Phase 2 and phase 3 will also use mixed methods to gather feedback on the usability, acceptability, and user experience of the IVRT training intervention from current pediatric power wheelchair users. In addition, phase 3 participants will perform a skills transfer assessment to compare power mobility skill performance between the virtual reality and real-life environments. Data gathered in phase 2 will be used to further refine the IVRT intervention, whereas phase 3 data will be used to statistically evaluate the final version.ResultsThis study was approved by the Izaak Walton Killam Health Centre research ethics board in August 2021. Phase 1 testing began in February 2022. The entire study is expected to be completed by 2023.ConclusionsThe results of this study will be used to create an IVRT training intervention for pediatric power wheelchair skill development through an iterative and collaborative design process. Results may also assist in directing future studies in this area.International Registered Report Identifier (IRRID)DERR1-10.2196/39140

Machine-learning-aided design optimization of internal flow channel cross-sections

• Artificial neural network-based models are developed for prediction of nusselt number and friction factor. • The predictions apply for fully developed laminar flow in constant cross-section ducts of different cross section shapes. • The machine learning (ML) model predictions are validated and compared against numerical simulations. • Use of ML models in shape optimization of channel cross section for different objective functions is demonstrated. • Guidelines for usage of the developed ML models are discussed. The design optimization of various thermal management components such as cold plates, heat sinks, and heat exchangers relies on accurate prediction of flow heat transfer and pressure drop. During the iterative design process, the heat transfer and pressure drop is typically either computed numerically or obtained using geometry-specific correlations for Nusselt number and friction factor. Numerical approaches are accurate for evaluation of a single design but become computationally expensive if many design iterations are required (such as during formal optimization processes). Correlation-based approaches restrict the design space to a specific set of geometries for which correlations are available. Surrogate models for the Nusselt number and friction factor, which are more universally applicable to all geometries than traditional correlations, would enable flexible and computationally inexpensive design optimization. The current work develops machine-learning-based surrogate models for predicting the Nusselt number and friction factor under fully developed internal flow in channels of arbitrary cross section and demonstrates use of these models for optimization of the cross-sectional channel shape. The predictive performance and generality of the machine learning surrogate models is first verified on various shapes outside the training dataset, and then the models are used in the design optimization of flow cross sections based on performance metrics that weigh both heat transfer and pressure drop. The optimization process leads to novel shapes outside the training data, and so numerical simulations are carried out on these optimized shapes to compare with the surrogate model predictions and show their performance is at least as good as that of shapes with known correlations available. A three-lobed shape was found to reduce friction factor, whereas a pentagon with rounded corners and an ice cream cone-shaped duct, both found using different performance metrics. Although the ML model predictions lose accuracy outside the training set for these novel shapes, the predictions follow the correct trends with parametric variations of the shape and therefore successfully direct the search toward optimized shapes.

Improving Tobacco Cessation Rates Using Inline Clinical Decision Support.

Tobacco use is a significant cause of morbidity and mortality in the United States. Even brief advice from a clinician can significantly influence cessation rates among tobacco users, but clinicians often miss opportunities to provide this simple intervention. The intent of this quality improvement project was to increase tobacco cessation among tobacco users by nudging clinicians using a clinical decision support (CDS) tool. We developed a CDS tool using principles of user-centered design and the CDS Five Rights to dynamically insert actionable information about current tobacco users into the Assessment and Plan section of clinicians' notes. We conducted a retrospective analysis of patients at four primary care practices in the Denver Metro area evaluating the impact of the CDS tool on time to tobacco cessation. A multivariable Cox proportional-hazards model was used in this determination. Kaplan-Meier curves were used to estimate tobacco cessation probabilities at 90, 180, and 365 days. We analyzed 5,644 patients with a median age of 45 years, most of whom lived in an urban location (99.5%) and the majority of whom were males (60%). The median follow-up time for patients was 16 months. After adjustment for age, gender, practice site, and patient location (rural, urban), the intervention group had significantly greater risk of tobacco cessation compared to those in the control group (hazard ratio: 1.22, 95% confidence interval: 1.08-1.36; p = 0.001). This study suggests a CDS intervention which respects the CDS Five Rights and incorporates user-centered design can affect tobacco use rates. Future work should expand the target population of this CDS tool and continue a user-centered, iterative design process.

Pure Gamification: An Energy Case

Dutch energy suppliers are obliged to cooperate in reducing consumption by stimulating energy saving behaviours among their customers. One important strategy for this context is personalized gamification. We investigate how a personalized gamified energy saving application can be designed based on individual preferences within the context of an energy supplier. To design a personalized gamification application the proposed method of Knutas et al. was adapted by leaving out the parts about machine learning and adding an evaluation phase. We show that customization by users can be a valuable alternative for using machine learning personalization strategies. We used this iterative design process to focus on personalizing, in this use case for the three main user types of the customer base of a Dutch energy supplier. The design of the gamified application was adapted using feedback of both stakeholders and customers using interviews and focus groups. This resulted in two final designs designed to stimulate energy saving behaviour among customers. The design had a dashboard which allowed for personalization within gamified elements, and an energy editor in which users can change characteristics in their households to learn about the effects of actions on their energy consumption. These two final designs were validated using an interactive prototype along with interviews. The added value of this study is that it shows that the Hexad framework with its proposed gamification user types is suitable to understand the main motivations of a target group. Results suggest that designing within gamified mechanics based on a user type’s main motivation is an effective strategy for personalization. Motivation-based design is not the only successful personalization design strategy but adapting designs to personal situation and existing energy saving behaviour might also motivate users.

User-Centered Design of a Clinical Tool for Shared Decision-making About Diet in Primary Care.

Health information technology is a leading cause of clinician burnout and career dissatisfaction, often because it is poorly designed by nonclinicians who have limited knowledge of clinicians' information needs and health care workflow. Describe how we engaged primary care clinicians and their patients in an iterative design process for a software application to enhance clinician-patient diet discussions. Descriptive study of the steps followed when involving clinicians and their at-risk patients in the design of the content, layout, and flow of an application for collaborative dietary goal setting. This began with individual clinician and patient interviews to detail the desired informational content of the screens displayed followed by iterative reviews of intermediate and final versions of the program and its outputs. Primary care clinicians practicing in an urban federally qualified health center and two academic primary care clinics, and their patients who were overweight or obese with diet-sensitive conditions. Descriptions of the content, format, and flow of information from pre-visit dietary history to the display of evidence-based, guideline-driven suggested goals to final display of dietary goals selected, with information on how the patient might reach them and patients' confidence in achieving them. Through three iterations of design and review, there was substantial evolution of the program's content, format, and flow of information. This involved "tuning" of the information desired: from too little, to too much, to the right amount displayed that both clinicians and patients believed would facilitate shared dietary goal setting. Clinicians' well-founded criticisms of the design of health information technology can be mitigated by involving them and their patients in the design of such tools that clinicians may find useful, and use, in their everyday medical practice.

The acoustic impedance of three-dimensional turbines

Thermoacoustic stability assessment of gas turbine combustors requires an acoustic impedance boundary condition for the downstream turbomachinery. Actuator disk type analytical methods offer rapid predictions of acoustic impedance in an iterative design process; previous studies show that this class of models works well for cascades consistent with a two-dimensional assumption. Real turbines, however, are three-dimensional with multiple stages, coolant flows, and leakage flows. The first part of this paper validates a cambered semi-actuator disk model for the acoustic impedance of a realistic multi-stage turbine using non-linear time-marching computations: the two methods agree to within 9% for incident pressure waves and 14% for incident entropy waves. Simulations of the multi-stage turbine with different inlet conditions confirm that, to a close approximation, inlet corrected flow and hence Mach numbers and acoustic impedance are constant during off-design operation. The second part of the paper then applies both analytical and computational approaches to families of parametrically generated turbine stages to quantify three-dimensional design effects. The results show that the influence of hub-to-tip ratio on acoustic impedance is weak, and the two-dimensional analytical model is accurate even for high aspect ratio stages. Front-loaded camber lines increase axial Mach number within blade passages, raising acoustic impedance by up to 51% compared to a datum quadratic camber line. Varying the stator–rotor axial gap changes the relative phase of reflections from vanes and blades, causing the total impedance to either increase or decrease, at different frequencies, by up to 11%. The cambered semi-actuator disk method consistently captures the correct trends, showing that the physical basis of the model is sufficient to produce a broadly applicable design tool for rapid assessment of turbine impedance boundary conditions.

Development of a cost of illness inventory questionnaire for children with autism spectrum disorder in South Asia

BackgroundThe economic burden of autism is substantial and includes a range of costs, including healthcare, education, productivity losses, informal care and respite care, among others. In India, approximately, 2 million children aged 2–9 years have autism. Given the likely substantial burden of illness and the need to identify effective and cost-effective interventions, this research aimed to produce a comprehensive cost of illness inventory (COII) suitable for children with autism in South Asia (India) to support future research.MethodsA structured and iterative design process was followed to create the COII, including literature reviews, interviews with caregivers, pilot testing and translation. Across the development of the COII, thirty-two families were involved in the design and piloting of the tool. The COII was forward translated (from English to Hindi) and back translated. Each stage of the process of development of the COII resulted in the further refinement of the tool.ResultsDomains covered in the final COII include education, childcare, relocation, healthcare contacts (outpatient, inpatient, medical emergencies, investigations and medication), religious retreats and rituals, specialist equipment, workshops and training, special diet, support and care, certification, occupational adjustments and government rebates/schemes. Administration and completion of the COII determined it to be feasible to complete in 35 minutes by qualified and trained researchers. The final COII is hosted by REDCap Cloud and is a bilingual instrument (Hindi and English).ConclusionsThe COII was developed using experiences gathered from an iterative process in a metropolitan area within the context of one low- and middle-income country (LMIC) setting, India. Compared to COII tools used for children with autism in high-income country settings, additional domains were required, such as complimentary medication (e.g. religious retreats and homeopathy). The COII will allow future research to quantify the cost of illness of autism in India from a broad perspective and will support relevant economic evaluations. Understanding the process of developing the questionnaire will help researchers working in LMICs needing to adapt the current COII or developing similar questionnaires.

ProHealth eCoach: user-centered design and development of an eCoach app to promote healthy lifestyle with personalized activity recommendations

BackgroundRegular physical activity (PA), healthy habits, and an appropriate diet are recommended guidelines to maintain a healthy lifestyle. A healthy lifestyle can help to avoid chronic diseases and long-term illnesses. A monitoring and automatic personalized lifestyle recommendation system (i.e., automatic electronic coach or eCoach) with considering clinical and ethical guidelines, individual health status, condition, and preferences may successfully help participants to follow recommendations to maintain a healthy lifestyle. As a prerequisite for the prototype design of such a helpful eCoach system, it is essential to involve the end-users and subject-matter experts throughout the iterative design process.MethodsWe used an iterative user-centered design (UCD) approach to understend context of use and to collect qualitative data to develop a roadmap for self-management with eCoaching. We involved researchers, non-technical and technical, health professionals, subject-matter experts, and potential end-users in design process. We designed and developed the eCoach prototype in two stages, adopting different phases of the iterative design process. In design workshop 1, we focused on identifying end-users, understanding the user’s context, specifying user requirements, designing and developing an initial low-fidelity eCoach prototype. In design workshop 2, we focused on maturing the low-fidelity solution design and development for the visualization of continuous and discrete data, artificial intelligence (AI)-based interval forecasting, personalized recommendations, and activity goals.ResultsThe iterative design process helped to develop a working prototype of eCoach system that meets end-user’s requirements and expectations towards an effective recommendation visualization, considering diversity in culture, quality of life, and human values. The design provides an early version of the solution, consisting of wearable technology, a mobile app following the “Google Material Design” guidelines, and web content for self-monitoring, goal setting, and lifestyle recommendations in an engaging manner between the eCoach app and end-users.ConclusionsThe adopted iterative design process brings in a design focus on the user and their needs at each phase. Throughout the design process, users have been involved at the heart of the design to create a working research prototype to improve the fit between technology, end-user, and researchers. Furthermore, we performed a technological readiness study of ProHealth eCoach against standard levels set by European Union (EU).

Developing Representations to Support Clinical Decision Making: Insights from Work Analysis

Principles of Work Domain Analysis (WDA) and Ecological Interface Design (EID) are illustrated using the example of a graphical interface designed to support clinical decision making with respect to cardiovascular health. Multiple iterations of the graphical interface will be presented that show the iterative analysis and design process and the logic of mapping meaningful dimensions into a representation that helps healthcare professionals to understand a complex biological system.

Modified Winding Function Analysis of Synchronous Reluctance Motor for Design Iteration Purposes

This article proposes a hybrid analytical modeling procedure for the performance parameter estimation of synchronous reluctance machine, including its operation in the region of magnetic saturation. The proposed model better estimated the torque profile through improved translation and stepped transition of rotor flux barriers. The non-linear operation is simulated by considering the global saturation and estimating the machine’s operating point on the magnetization curve of the core material. The procedure formulation focuses on improving the accuracy and rapidness of the iterative design process. The obtained results are validated against an already established design analysis technique, finite-element analysis, and the frequency spectrum analysis of practical machine results. With the improved accuracy and the least requirement of simulation runtime and computational resources, the modeling procedure can be utilized as an initial design iteration tool.

A User-Centered Approach to Designing Secondary Anesthesia Medication Labels

Poorly designed and implemented medication labels have been identified as a source of medication errors within anesthesia delivery. Previous studies noted that simplified text and icons are useful in warning labels used for prescription drugs, especially for people with low literacy levels. In addition, Tallman lettering can reduce errors due to the custom capitalization of text. However, icons, color, and Tallman lettering have not been explored for improving the readability of anesthesia medication labels. This study utilizes a user- centered approach to design and evaluate icons and other graphical features to be included on secondary medication labels placed on infusion bags within anesthesia point-of-care. The study utilizes an iterative design process to examine the potential efficacy of these design characteristics by evaluating them with the anesthesia providers/clinicians through an online survey. Findings suggest that introducing graphical components like icons and color may be useful and accepted by clinicians to improve medication recognition.

A Rapid Solver for the Prediction of Flow-Field of High-Speed Vehicle Moving in a Tube

High-speed vehicles traveling in a tube with pressures similar to those experienced by aircraft at their maximum altitude are presented. Although the concept resembles Hyperloop, the pressure level investigated here is much higher and safer than that suggested by Hyperloop, and, therefore, the system design is markedly different. Calculating a vehicle’s aerodynamic performance in the initial design stages requires low-budget computational tools to enable iterative design processes. This study presents an algorithm for rapid flow-field prediction based on a one-dimensional Reimann solution, including viscosity and heat transfer effects. The flow-field is divided into near- and far-fields, where the near-field represents the solution directly around the vehicle. The far-field demonstrates the impact of the vehicle’s motion on the vehicle’s flow-field upstream and downstream. Two-dimensional URANS models are compared to the current numerical scheme. The developed algorithm analyzes the flow-field and the propagation of pressure waves along the tube to simulate the vehicle’s movement. The one-dimensional model shows the robustness and predictability of the near and far flow-fields. The results from the developed scheme provide good agreement, with less than a few percent deviations, compared to CFD simulations but with significantly lower computational resources.

Machine Learning-Aided Microdroplets Breakup Characteristic Prediction in Flow-Focusing Microdevices by Incorporating Variations of Cross-Flow Tilt Angles

Controlling droplet breakup characteristics such as size, frequency, regime, and droplet quality within flow-focusing microfluidic devices is critical for different biomedical applications of droplet microfluidics such as drug delivery, biosensing, and nanomaterial preparation. The development of a prediction platform capable of forecasting droplet breakup characteristics can significantly improve the iterative design and fabrication processes required for achieving desired performance. The present study aims to develop a multipurpose platform capable of predicting the working conditions of user-specific droplet size and frequency and reporting the quality of the generated droplets, regime, and hydrodynamical breakup characteristics in flow-focusing microdevices with different cross-junction tilt angles. Four different neural network-based prediction platforms were compared to accurately estimate capsule size, generation rate, uniformity, and circle metric. The trained capsule size and frequency networks were optimized using the heuristic optimization approach for establishing the Pareto optimal solution plot. To investigate the transition of the droplet generation regime (i.e., squeezing, dripping, and jetting), two different classification models (LDA and MLP) were developed and compared in terms of their prediction accuracy. The MLP model outperformed the LDA model with a cross-validation measure evaluated as 97.85%, demonstrating that the droplet quality and regime prediction models can provide an engineering judgment for the decision maker to choose between the suggested solutions on the Pareto front. The study followed a comprehensive hydrodynamical analysis of the junction angle effect on the dispersed thread formation, pressure, and velocity domains in the orifice.

Review of automated time series forecasting pipelines

AbstractTime series forecasting is fundamental for various use cases in different domains such as energy systems and economics. Creating a forecasting model for a specific use case requires an iterative and complex design process. The typical design process includes five sections (1) data preprocessing, (2) feature engineering, (3) hyperparameter optimization, (4) forecasting method selection, and (5) forecast ensembling, which are commonly organized in a pipeline structure. One promising approach to handle the ever‐growing demand for time series forecasts is automating this design process. The article, thus, reviews existing literature on automated time series forecasting pipelines and analyzes how the design process of forecasting models is currently automated. Thereby, we consider both automated machine learning (AutoML) and automated statistical forecasting methods in a single forecasting pipeline. For this purpose, we first present and compare the identified automation methods for each pipeline section. Second, we analyze these automation methods regarding their interaction, combination, and coverage of the five pipeline sections. For both, we discuss the reviewed literature that contributes toward automating the design process, identify problems, give recommendations, and suggest future research. This review reveals that the majority of the reviewed literature only covers two or three of the five pipeline sections. We conclude that future research has to holistically consider the automation of the forecasting pipeline to enable the large‐scale application of time series forecasting.This article is categorized under: Technologies > Machine Learning Technologies > Prediction Algorithmic Development > Spatial and Temporal Data Mining

Translation of AADL model to security attack tree (TAMSAT) to SMART evaluation of monetary security risk

ABSTRACT Designing secure architectures for IT infrastructure is a difficult process that needs mechanisms to provide security risk metrics that can help guide the system design process. It is through this evaluation process that a designer can ensure that implementations of a model meet the necessary security-based requirements. This work presents a scheme called TAMSAT for translating early-stage system architecture design models into security-based attack trees, which are evaluated for security risk. These attack trees can be evaluated around a set of assets of importance, whose security risk is classified by a monetary value. This security risk value can inform the system designer and provide input into an iterative design process, as well as illuminate unexpected sources of potential future security issues.

Developing an Unstructured Supplementary Service Data-based mobile phone app to provide adolescents with sexual reproductive health information: a human-centered design approach

BackgroundAdolescent pregnancies and sexually-transmitted infections continue to impact 15 – 19-year-olds across the globe. The lack of sexual reproductive health information (SRH) in resource-limited settings due to cultural and societal attitudes towards adolescent SRH could be contributing to the negative outcomes. Innovative approaches, including mobile phone technologies, are needed to address the need for reliable adolescent SRH information.ObjectiveThe study aimed to co-design a Unstructured Supplementary Service Data (USSD) based mobile app prototype to provide confidential adolescent SRH information on-demand and evaluate the mobile app’s usability and user experience.MethodsA human-centered design methodology was applied. This practice framework allowed the perspectives and feedback of adolescent users to be included in the iterative design process. To participate, an adolescent must have been 15 to 19 years old, resided in Kibra and would be able to access a mobile phone. Adolescents were enrolled for the alpha and field testing of the app prototype at different time-points. The Mobile Application Rating Scale (MARS) a multidimensional mobile phone evaluation tool was used to access the functionality, engagement, aesthetics and quality of information in the app. Responses from the MARS were reported as mean scores for each category and a mean of the aggregate scores making the app’s quality score. The MARS data was also evaluated as categorical data, A Chi square test of independence was carried out to show significance of any observed differences using cumulative and inverse cumulative distribution functions.ResultsDuring the usability test, 62/109 (54.9%) of the adolescents that were followed-up had used the app at least once, 30/62 (48.4%) of these were male participants and 32/62 (51.6%) female. On engagement, the app had a mean score of 4.3/5 (SD 0.44), 4.6/5 (SD 0.38) on functionality, 4.3/5 (SD 0.57) on aesthetics and 4.4/5 (SD 0.60) on the quality of information. The overall app quality mean score was 4.4/5 (SD 0.31). The app was described as ‘very interesting’ to use by 44/62 (70.9%) of the participants, 20/44 males and 24/44 females. The content was deemed to be either ‘perfectly’ or ‘well targeted’ on sexual reproductive health by 60/62 (96.7%) adolescents, and the app was rated ‘best app’ by 45/62 (72.6%) adolescents, 27/45 females and 18/45 males, with a p-value = 0.011.ConclusionsAdolescents need on-demand, accurate and trusted SRH information. A mobile phone app is a feasible and acceptable way to deliver adolescent SRH information in resource-limited settings. The USSD mobile phone technology shows promise in the delivery of much needed adolescent SRH information on-demand..

Applying User-Centered Design in the Development of a Supportive mHealth App for Women in Substance Use Recovery.

Women in recovery describe stigma, negative treatment, and limited support as barriers to achieving their health and parenting goals. Mobile health technologies carefully tailored to support the unique needs of recovery communities can provide less burdensome alternatives to in-person services for women transitioning out of substance use treatment. An iterative design process integrated women's interests into the structure, content, and interaction flow of a mobile health (mHealth) app. Participants included women in recovery from opioid, alcohol, and polysubstance use disorders in a comprehensive housing program in urban Arizona. Five focus groups with 3-7 participants each (n = 27 total) informed creation of the mHealth app. Informed by theoretical models of usability and person-centered design, development involved an iterative series of focus groups in which we asked women to comment on interest in using each feature. This provided a qualitative priority framework for feature development. We then modified the app and repeated the process to gauge consensus and continually refine our prototype. Women were interested in access to resources, such as housing, counseling, and parenting advice in settings known to treat women in recovery with respect. They also asked for positive messages, chatting with peers, and access to expert answers. They were less interested in points-based learning modules and "scored" activities, leading us to develop a "daily challenges" concept that builds good habits, but does not feel like "classwork". Women's recommendations shaped an mHealth app tailored to maximize utility, access, and safety for this at-risk population. Integration of user-centered design with applied ethnographic techniques guided the development of a custom-tailored mHealth app responsive to lived experiences and needs of women in recovery. Future research should evaluate the potential for user-centered apps to increase self-efficacy, perceived social support, and to reduce risk of relapse.