mHealth Tools Research Articles

Towards the development of reliable and economical mHealth solutions: A methodology for accurate detection of Buruli ulcer for hard-to-reach communities

mHealth interventions have the potential to increase access to healthcare for the most hard-to-reach communities. For rural communities suffering disproportionately from skin-related NTDs, and Buruli ulcer, there is a need for low-cost, non-invasive and mobile tools for the early detection and management of disease. Dermoscopy is a noninvasive in-vivo technique that has been useful in improving the diagnostic accuracy of pigmented skin lesions based on anatomical features and morphological structures of lesions.ObjectivesUsing dermoscopy, this study develops the automated tools necessary for developing an effective mHealth intervention towards identifying BU lesions in the early stages. Methods: This imaging methodology relies on an external attachment, a dermoscope, which uses polarized light to cancel out skin surface reflections. In our initial studies we used a dermoscope with only crosspolarized white-light (DL100, 3Gen) but later we adopted a more advanced multispectral dermoscope (DLIIm, 3Gen). The latter employed additional monochromatic light at different wavelengths of the visible spectral range, specifically blue (470 nm), yellow (580 nm), and red (660 nm) color, to visualize pigmented structures of skin layers at different depths.ResultsResults obtained using a subset of 58 white-light images with confirmed diagnosis (16 lesions BU and 42 lesions non-BU) resulting in sensitivity of 100 and specificity of 88.10, with an overall accuracy of 94.05 at 95% CI. Performance obtained using a second dataset of 197 dermoscopic multispectral images (16 lesions BU and 181 lesions non-BU) resulted in sensitivity of 90.00% and specificity of 93.39% with a balanced accuracy of 91.69% (86.95% to 95.12% at 95% CI).ConclusionsThis system will continue to perform even as the technology evolves and newer dermoscopes are available. Subsequent studies involve the DL4 which provides more uniform and brighter illumination, higher lesion magnification, and wider field of view which, combined with the superb resolution of modern smartphones, can result in faster and more accurate lesion assessment. This is an important step for the development of mHealth tools for use by non-specialists in community settings for the early detection of Buruli ulcer, skin-NTDs, and other dermatologic conditions associated with disease, including wound healing and management of disease progression.

Middle-aged and older adults' acceptance of mobile nutrition and fitness tools: A qualitative typology.

The utilization of mobile health (mHealth) devices such as nutrition and fitness tools seems to be promising in facilitating healthy lifestyle behaviors in middle-aged and older adults. As user acceptance plays a decisive role in the successful implementation of mHealth tools, it is vital to examine the target groups' acceptance, particularly their usage behavior and attitudes toward these tools. This qualitative study aimed to explore how far middle-aged as well as older adults accept mobile nutrition and fitness tools and to identify facilitators and barriers shaping their acceptance. Twenty-one qualitative semi-structured interviews were conducted with German adults aged 50 years and older. Data material was analyzed using Qualitative Content Analysis (Kuckartz). A comprehensive acceptance typology with three acceptance types could be reconstructed: The Rejection Type, The Selective Acceptance Type, and The Comprehensive Acceptance Type. The target group's acceptance of mobile nutrition and fitness tools appeared to differ considerably across the three acceptance types and between the two different types of mHealth tools - with mobile nutrition tools having been less accepted. Among others, high levels of usability were identified as a key facilitator, while a desire for autonomy and privacy concerns showed to be prominent barriers. The resulting typology indicates a pronounced heterogeneity among middle-aged and older adults regarding their acceptance of mobile nutrition and fitness tools. The findings highlight a need for more individualized mHealth tools along with respective promotion strategies that are specifically tailored to the needs and expectations of middle-aged and older adults.

Engagement and Resource Considerations in Developing and Implementing Mobile Health Technologies for COVID-19 Pandemic: Filipino Developers’ Perspectives

Objective. This paper aims to provide a better understanding of the different engagement, cost, and resourceconsiderations in developing and implementing mHealth solutions in the Philippines during the COVID-19 pandemic. Methods. First, six participants completed a form to document the estimated costs of developing a pseudo mobile application with features to mitigate the pandemic. Second, ten key informant interviews determined the facilitators, barriers, and resource requirements in developing mHealth tools. Results. The average cost estimate to develop and roll out a mobile application with public health and epidemiology features is Php 4,018,907 (US $78,650). The analysis of the interviews resulted in 12 themes organized in three domains: 1) facilitators and barriers in developing and sustaining mHealth solutions; 2) costs of sustaining mHealth technologies; and 3) factors affecting the costs of development and maintenance of mHealth technologies. Conclusion. While differences in the cost estimates are evident, it provides a ballpark figure and the different factors that implementers need to sustain and maintain an mHealth solution. This paper hopes to inform policies and practices in engaging technology solution partners and in scaling up mHealth technologies.

Current and future applications of mobile health technology for evaluating spine surgery patients: a review.

Mobile health (mHealth) technology has assumed a pervasive role in healthcare and society. By capturing real-time features related to spine health, mHealth assessments have the potential to transform multiple aspects of spine care. Yet mHealth applications may not be familiar to many spine surgeons and other spine clinicians. Consequently, the objective of this narrative review is to provide an overview of the technology, analytical considerations, and applications of mHealth tools for evaluating spine surgery patients. Reflecting their near-ubiquitous role in society, smartphones are the most commonly available form of mHealth technology and can provide measures related to activity, sleep, and even social interaction. By comparison, wearable devices can provide more detailed mobility and physiological measures, although capabilities vary substantially by device. To date, mHealth evaluations in spine surgery patients have focused on the use of activity measures, particularly step counts, in an attempt to objectively quantify spine health. However, the correlation between step counts and patient-reported disease severity is inconsistent, and further work is needed to define the mobility metrics most relevant to spine surgery patients. mHealth assessments may also support a variety of other applications that have been studied less frequently, including those that prevent postoperative complications, predict surgical outcomes, and serve as motivational aids to patients. These areas represent key opportunities for future investigations. To maximize the potential of mHealth evaluations, several barriers must be overcome, including technical challenges, privacy and regulatory concerns, and questions related to reimbursement. Despite those obstacles, mHealth technology has the potential to transform many aspects of spine surgery research and practice, and its applications will only continue to grow in the years ahead.

SmartMoms Canada: An evaluation of a mobile app intervention to support a healthy pregnancy

BackgroundThere is a lack of cost-effective and readily available access to evidence-based information to manage healthy behaviours for pregnant individuals. Mobile health (mHealth) tools offer a cost-effective, interactive, personalized option that can be delivered anywhere at a time most convenient for the user. This study protocol was primarily developed to, i) assess the feasibility of the SmartMoms Canada intervention in supporting participants to achieve gestational weight gain (GWG) guidelines. The secondary objectives are to, ii) assess user experience with the app, measured by adherence to the program via app software metrics and frequency of use, iii) determine the impact of SmartMoms Canada app usage on the adoption of healthful behaviours related to nutrition, physical activity and sleep habits, improvements in health-related quality of life, pregnancy-related complications, and symptoms of depression, and iv) investigate the potential extended effects of the app on postpartum health-related outcomes. MethodsThis is a feasibility trial. Pregnant individuals aged 18–40 years with pre-gravid body mass index between 18.5 and 39.9 kg/m2, carrying a singleton fetus, having Wi-Fi access, and at ≤20 weeks' gestation will be recruited. Eligible people will be followed from recruitment until 12 months postpartum. DiscussionSmartMoms Canada is the first bilingual Canadian-centric app designed for pregnant people. This mHealth intervention, with its ability to supply frequent interactions, provides pregnancy- related health knowledge to users, potentially leading to an improvement in pregnancy-related outcomes and behaviours, and, ultimately a reduction in the present economic burden related to in-person interventions.Trial registration: ISRCTN, ISRCTN16254958. Registered 20 December 2019, http://www.isrctn.com/ ISRCTN16254958.

A new mobile smartphone application, AF-EduApp, for atrial fibrillation patients: what do they use most?

Abstract Background The management of atrial fibrillation (AF) is complex and based on three main pillars: avoid stroke, better symptom control and cardiovascular risk factor management. Therefore, a holistic, multidisciplinary approach is needed in which the patient has a central role. Smartphone ownership increases strongly in the elderly population (in Belgian 65+ years old: 52% in 2018 to 82% in 2020). This digital growth creates opportunities for a closer patient follow-up. An in-house developed application, AF-EduApp, focused on delivering targeted education and guiding self-care, has been validated and is currently being studied in an ongoing clinical trial. Purpose Intermediate analysis of the user data of AF-EduApp. Methods At two Belgian hospitals, an open, prospective, randomized trial is currently performed. A total of 153 AF patients hospitalized or seen at an out-patient visit were included. Patients could use the application during a follow-up of 12 months. The AF-EduApp consists of six different modules: education, questionnaires with immediate patient feedback, medication overview with reminders, measurements (e.g. blood pressure, heart rate), appointments, and the possibility to ask questions to the caregivers. Knowledge about AF and its treatment was tested through the Jessa Atrial fibrillation Knowledge Questionnaire (JAKQ) with feedback on incorrectly answered questions. The main aim of the AF-EduApp is to improve patients' medication adherence through improved education and medication reminders. Results Currently, a total of 132 patients have completed a follow-up of 12 months (follow-up days: mean 357.3±60.7 and median: 365.5 [350.3–382.0]). The app was used on average 122.5±126.6 days (median: 55.0 [23.3–241.0]), or 34.3% of the available days. As shown in Fig. 1, the measurements and medication modules were the most used module (on 66.1% resp. 55.2% of the days). The education module was the least used module (3.5% of the days); the average education time was 17.0±27.7 min (median: 6.1 [1.4–20.6]). Within the measurement module (mean: 80.9±109.4 days used), the most frequently entered parameter was blood pressure, with on average 208.3±351.3 entries (median: 53.5 [7.0–296.3]) (Fig. 2). AF episodes was the least entered data (average 37.0±185.0 times; median 8.0 [4.0–19.0.3]). Conclusion Patients actively engaged with an educational smartphone AF application on 1/3th of the available days. The measurement module was the most used (to enter health data) together with the medication module (to confirm intake after reminder). It shows that many patients appreciate the mHealth tool to “connect” with their condition. The clinical trial tries to answer whether such increasing interaction leads to improved self-management and outcomes. Funding Acknowledgement Type of funding sources: Private company. Main funding source(s): The AF-EduApp study is supported by an BMS/Pfizer European Thrombosis Investigator Initiated Research Program (ERISTA) grant.

OPPORTUNITIES FOR MHEALTH IN DEMENTIA CARE/CARE MANAGEMENT: LESSONS LEARNED FROM OTHER CLINICAL DISCIPLINES

Abstract The use of mHealth applications in dementia care/care management has recently become of increasing interest to patient stakeholders, clinicians, and researchers working in the field. However, there exists a rich literature of successful examples of the development, implementation, and efficacy of mHealth applications across a variety of health care disciplines. Thus there is high potential to take these lessons learned from other clinical fields and apply such successes to dementia care/care management. We will walk through several examples of mHealth tools used for managing hypertension, diabetes, migraines, and weight loss, while highlighting specific opportunities for dementia care/care management. Points of discussion will include optimizing the user-centered design process during the development phase of mHealth applications, increasing patient engagement with mHealth applications – especially among underrepresented individuals in research, promoting adherence to mHealth interventions via principles from behavioral economics, and scaling up mHealth interventions to test for efficacy at a population-level.

The Development of a Novel mHealth Tool for Obstructive Sleep Apnea: Tracking Continuous Positive Airway Pressure Adherence as a Percentage of Time in Bed.

Continuous positive airway pressure (CPAP) is the mainstay obstructive sleep apnea (OSA) treatment; however, poor adherence to CPAP is common. Current guidelines specify 4 hours of CPAP use per night as a target to define adequate treatment adherence. However, effective OSA treatment requires CPAP use during the entire time spent in bed to optimally treat respiratory events and prevent adverse health effects associated with the time spent sleeping without wearing a CPAP device. Nightly sleep patterns vary considerably, making it necessary to measure CPAP adherence relative to the time spent in bed. Weight loss is an important goal for patients with OSA. Tools are required to address these clinical challenges in patients with OSA. This study aimed to develop a mobile health tool that combined weight loss features with novel CPAP adherence tracking (ie, percentage of CPAP wear time relative to objectively assessed time spent in bed) for patients with OSA. We used an iterative, user-centered process to design a new CPAP adherence tracking module that integrated with an existing weight loss app. A total of 37 patients with OSA aged 20 to 65 years were recruited. In phase 1, patients with OSA who were receiving CPAP treatment (n=7) tested the weight loss app to track nutrition, activity, and weight for 10 days. Participants completed a usability and acceptability survey. In phase 2, patients with OSA who were receiving CPAP treatment (n=21) completed a web-based survey about their interpretations and preferences for wireframes of the CPAP tracking module. In phase 3, patients with recently diagnosed OSA who were CPAP naive (n=9) were prescribed a CPAP device (ResMed AirSense10 AutoSet) and tested the integrated app for 3 to 4 weeks. Participants completed a usability survey and provided feedback. During phase 1, participants found the app to be mostly easy to use, except for some difficulty searching for specific foods. All participants found the connected devices (Fitbit activity tracker and Fitbit Aria scale) easy to use and helpful. During phase 2, participants correctly interpreted CPAP adherence success, expressed as percentage of wear time relative to time spent in bed, and preferred seeing a clearly stated percentage goal ("Goal: 100%"). In phase 3, participants found the integrated app easy to use and requested push notification reminders to wear CPAP before bedtime and to sync Fitbit in the morning. We developed a mobile health tool that integrated a new CPAP adherence tracking module into an existing weight loss app. Novel features included addressing OSA-obesity comorbidity, CPAP adherence tracking via percentage of CPAP wear time relative to objectively assessed time spent in bed, and push notifications to foster adherence. Future research on the effectiveness of this tool in improving OSA treatment adherence is warranted.

Use of a mHealth Mobile Application to Reduce Stress in Adults with Autism: a Pre-Post Pilot Study of the Stress Autism Mate (SAM)

ObjectivesAdults with autism often need support to detect their stress and to apply adequate coping strategies for dealing with daily stress. The personalized mobile application Stress Autism Mate (SAM) is developed for and by adults with autism to detect and cope with daily life stress. SAM measures stress four times daily, generates an overview of the patients’ stress level and gives personalized advice to reduce stress.MethodsWith a pre-to post-treatment design, the level of perceived stress, coping self-efficacy and self-rated quality of life (QoL) was assessed at baseline (pre-test), after the four-week intervention (post-test) and after eight-week follow-up. Data was analysed using multilevel analysis taking within subject variance into account.ResultsAt post-test measurement, there was a significant decrease in perceived stress. At post-test as well as follow-up, a significant improvement in coping self-efficacy and improvement in self-rated QoL was seen.ConclusionsThe results of this pilot study suggest that the personalized mHealth tool SAM can support adults with autism in detecting stress, improving their stress coping skills and improving their self-rated quality of life. In practice, SAM can be seen as an external stress monitor that can easily be integrated in the lives of adults with autism, to detect and cope with stress.

Identification by cluster analysis of patients with asthma and nasal symptoms using the MASK-air® mHealth app

BackgroundThe self-reporting of asthma frequently leads to patient misidentification in epidemiological studies. Strategies combining the triangulation of data sources may help to improve the identification of people with asthma. We aimed to combine information from the self-reporting of asthma, medication use and symptoms to identify asthma patterns in the users of an mHealth app. MethodsWe studied MASK-air® users who reported their daily asthma symptoms (assessed by a 0-100 visual analogue scale – “VAS Asthma”) at least three times (either in three different months or in any period). K-means cluster analysis methods were applied to identify asthma patterns based on: (i) whether the user self-reported asthma; (ii) whether the user reported asthma medication use and (iii) VAS asthma. Clusters were compared by the number of medications used, VAS asthma levels and Control of Asthma and Allergic Rhinitis Test (CARAT) levels. FindingsWe assessed a total of 8,075 MASK-air® users. The main clustering approach resulted in the identification of seven groups. These groups were interpreted as probable: (i) severe/uncontrolled asthma despite treatment (11.9-16.1% of MASK-air® users); (ii) treated and partly-controlled asthma (6.3-9.7%); (iii) treated and controlled asthma (4.6-5.5%); (iv) untreated uncontrolled asthma (18.2-20.5%); (v) untreated partly-controlled asthma (10.1-10.7%); (vi) untreated controlled asthma (6.7-8.5%) and (vii) no evidence of asthma (33.0-40.2%). This classification was validated in a study of 192 patients enrolled by physicians. InterpretationWe identified seven profiles based on the probability of having asthma and on its level of control. mHealth tools are hypothesis-generating and complement classical epidemiological approaches in identifying patients with asthma.

Patients' and Health Care Providers' Perceptions on mHealth Use After High-Altitude Climate Therapy for Severe Asthma: Mixed Methods Study.

Asthma is a common chronic disease with various clinical presentations. Although most patients are able to reach good asthma control, some patients are not able to reach sufficient asthma control following the regular treatment guidelines and could be referred to high-altitude climate therapy (HACT). HACT includes environmental trigger avoidance in the alpine climate with multidisciplinary clinical treatment. Patients with severe and difficult-to-control asthma, who are unable to reach asthma control at sea level, can follow a 12-week lung rehabilitation program at 1600 m above sea level. Mobile health (mHealth) tools can be used to enhance self-management in these patients when they return home. For an mHealth system to be effective, it must meet the expectations of the end users. In this Davos@home study, we explored the attitudes toward mHealth aimed at supporting the self-management of patients with severe, difficult-to-control asthma who underwent HACT and asthma health care providers. In the first stage, interviews with referrers to HACT and focus groups with patients with asthma who participated in or completed HACT were conducted. The data were then analyzed thematically. On the basis of these results, a questionnaire was developed. In the second stage of the study, this questionnaire, combined with the Asthma Control Questionnaire and the Individual Innovativeness Questionnaire, was provided to patients who completed HACT. In total, 11 interviews and 3 focus groups (n=18, age 47.6, SD 12.1 years, Asthma Control Questionnaire score 2.6, SD 1.0) were conducted. A total of 3 themes were identified: potential goals, useful measurements, and perceived barriers and facilitators. The questionnaire developed in stage 2 included items based on these results. The most agreed-upon goal among the 52 patients who completed the questionnaire was to increase their asthma control (45/52, 86% of the patients). Different patients reported that they would benefit the most from different functionalities. Therefore, it is important to tailor functionalities to individual (treatment) goals. When developing an mHealth intervention, it is important to allow personalization to avoid overwhelming the users.

Association of Noninvasive Peripheral Blood Hemoglobin Assessments with Venous Blood Draws Among Sickle Cell Patients

Introduction: Sickle cell disease (SCD) is the most common genetic blood disorder, affecting an estimated 100,000 people in the US and over 20 million worldwide. SCD costs one and a half billion dollars of economic loss annually in the US, resulting in sustained burden to the families and the healthcare systems (Value in Health 2021;24:1095). Many patients living with SCD require frequent blood draws and may have to have a central line for routine blood draws. A blood hemoglobin (Hgb) level is the central information about the pathophysiology of SCD including anemia. Low blood Hgb levels have shown as a potential indicator of painful vaso-occlusion crises (Blood 2021;137:2010). Blood Hgb levels are checked for evaluation of new symptoms, medication monitoring, and routine follow up. Real-time and noninvasive monitoring of blood Hgb levels would be clinically beneficial, but less feasible through frequent invasive blood draws. Developing a reliable, self-educated, and completely non-invasive mobile health (mHealth) tool for real time blood Hgb assessments can directly benefit many patients living with SCD, minimizing the number of iatrogenic blood loss from frequent blood draws and improving the quality of care. Methods: Our team has recently developed an mHealth algorithm that can reliably estimate a blood Hgb level from a photo of a peripheral tissue site at individual's inner eyelid (palpebral conjunctiva) (Figure 1a). We conducted a pilot study to determine the exact relationship between peripherally detected blood Hgb levels (accessed from the inner eyelid) and blood Hgb tests (venous blood draws) performed at a clinical laboratory. Fifteen patients with SCD aged from 14 to 73 years were recruited at Hematology Clinic at University Hospital of the Indiana University School of Medicine. Acquisitions of the inner eyelid photos in RAW format were conducted by Samsung Galaxy S21 immediately before or after a blood draw. Blood Hgb levels from the inner eyelid's photo were computed using the previously developed statistical learning algorithm (Optica 2020;7:563); the first step was to recovery a spectrum from an RGB image (spectral learning) and the second step was to compute blood Hgb content using a spectral analysis (Figure 1a). Results: The linear correlation between the peripheral mHealth blood Hgb levels and the venous blood Hgb levels in the blood (i.e., the gold standard) shows a value of 0.82 with a p-value of 0.0002 (Figure 1b). A Bland-Altman analysis was also conducted as a non-parametric statistical method. The limits of agreement (LOA) were defined by a 95% prediction interval of the standard deviation of the differences between the mHealth and conventional blood Hgb levels. 95% Bland-Altman LOA was +/-2.3 g/dL (Figure 1b) and within LOA, the data points were uniformly scattered, indicating no significant bias. This level of performance is comparable to point-of-care blood sampling (finger prick) testing and noninvasive medical devices (Respiratory Care 2019;64:1343). Conclusion: This preliminary study supports the feasibility of peripheral and noninvasive mHealth blood Hgb assessments among patients with SCD. Importantly, peripheral mHealth blood Hgb levels are significantly associated with conventional venous blood Hgb tests among SCD patients. Thus, the mHealth technology can lay the groundwork for empowering individual patients by offering an easily accessible tool for quickly estimating blood Hgb levels by a smartphone. This mHealth technology has potential advantages including mobility, simplicity, and affordability for rapid and real-time blood Hgb quantification among patients living with SCD. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Analyzing User Engagement Within a Patient-Reported Outcomes Texting Tool for Diabetes Management: Engagement Phenotype Study.

Patient-reported outcomes (PROs) capture patients' views on their health conditions and its management, and are increasingly used in clinical trials, including those targeting type 2 diabetes (T2D). Mobile health (mHealth) tools offer novel solutions for collecting PRO data in real time. Although patients are at the center of any PRO-based intervention, few studies have examined user engagement with PRO mHealth tools. This study aimed to evaluate user engagement with a PRO mHealth tool for T2D management, identify patterns of user engagement and similarities and differences between the patients, and identify the characteristics of patients who are likely to drop out or be less engaged with a PRO mHealth tool. We extracted user engagement data from an ongoing clinical trial that tested the efficacy of a PRO mHealth tool designed to improve hemoglobin A1c levels in patients with uncontrolled T2D. To date, 61 patients have been randomized to the intervention, where they are sent 6 PRO text messages a day that are relevant to T2D self-management (healthy eating and medication adherence) over the 12-month study. To analyze user engagement, we first compared the response rate (RR) and response time between patients who completed the 12-month intervention and those who dropped out early (noncompleters). Next, we leveraged latent class trajectory modeling to classify patients from the completer group into 3 subgroups based on similarity in the longitudinal engagement data. Finally, we investigated the differences between the subgroups of completers from various cross-sections (time of the day and day of the week) and PRO types. We also explored the patient demographics and their distribution among the subgroups. Overall, 19 noncompleters had a lower RR to PRO questions and took longer to respond to PRO questions than 42 completers. Among completers, the longitudinal RRs demonstrated differences in engagement patterns over time. The completers with the lowest engagement showed peak engagement during month 5, almost at the midstage of the program. The remaining subgroups showed peak engagement at the beginning of the intervention, followed by either a steady decline or sustained high engagement. Comparisons of the demographic characteristics showed significant differences between the high engaged and low engaged subgroups. The high engaged completers were predominantly older, of Hispanic descent, bilingual, and had a graduate degree. In comparison, the low engaged subgroup was composed mostly of African American patients who reported the lowest annual income, with one of every 3 patients earning less than US $20,000 annually. There are discernible engagement phenotypes based on individual PRO responses, and their patterns vary in the timing of peak engagement and demographics. Future studies could use these findings to predict engagement categories and tailor interventions to promote longitudinal engagement. Clinicaltrials.gov NCT03652389; https://clinicaltrials.gov/ct2/show/NCT03652389. RR2-10.2196/18554.

Abstract 13358: A Platform for the Personalized Management of Diabetes and Cardiovascular Disease at Population Scale With Data From Multiple Sensors

Introduction: The American Heart Association has identified the design of mobile health (mHealth) tools to improve the management of diabetes and cardiovascular disease and to prevent unplanned hospital readmissions as a priority. Methods: We developed HEART, an open source platform for personalized telehealth at population scale based on data from internet-connected health sensors and data extracted from the electronic medical record (EMR). Health sensors include scales, blood pressure monitors, activity monitors, continuous glucose monitors, and insulin pumps. EMR data include gender, age, race, reason for admission, all surgical procedures performed, and all medications prescribed upon discharge. We collected admission and readmission data for all cardiology patients at an academic pediatric medical center over the last 7 years. Results: HEART has five independent modules (Figure): 1) a data processing module that pulls data in from a variety of devices and EHR feeds, 2) an algorithm module that identifies reasons why a provider may want to contact a patient and ranks patients for contact, 3) a visual interface module that summarizes population data and provides additional details for patients selected by the provider, 4) an intervention module that facilitates messages being sent to providers and documented in the EMR, and 5) a user tracking module that monitors providers use. HEART ranks the entire population cared for by a clinic and allows providers to identify easily those patients who may benefit most from a secure message, telemedicine contact, or in-person visit. There were 5,011 admissions to cardiology among 2,916 patients, with 1000 urgent readmissions for 512 patients and 35,300 outpatient clinic visits. Conclusions: HEART is a population-level framework to facilitate personalized mHealth with data from a variety of sensors and EMR data feeds. It may facilitate outpatient monitoring as part of an effort to prevent unplanned hospital readmission.

Improving pediatric COVID-19 vaccine uptake using an mHealth tool (MoVeUp): study protocol for a randomized, controlled trial

BackgroundCoronavirus disease 2019 (COVID-19) vaccines demonstrate excellent effectiveness against infection, severe disease, and death. However, pediatric COVID-19 vaccination rates lag among individuals from rural and other medically underserved communities. The research objective of the current protocol is to determine the effectiveness of a vaccine communication mobile health (mHealth) application (app) on parental decisions to vaccinate their children against COVID-19.MethodsCustodial parents/caregivers with ≥ 1 child eligible for COVID-19 vaccination who have not yet received the vaccine will be randomized to download one of two mHealth apps. The intervention app will address logistical and motivational barriers to pediatric COVID-19 vaccination. Participants will receive eight weekly push notifications followed by two monthly push notifications (cues to action) regarding vaccinating their child. Through branching logic, users will access customized content based on their locality, degree of rurality-urbanicity, primary language (English/Spanish), race/ethnicity, and child’s age to address COVID-19 vaccine knowledge and confidence gaps. The control app will provide push notifications and information on general pediatric health and infection prevention and mitigation strategies based on recommendations from the American Academy of Pediatrics (AAP) and the Centers for Disease Control and Prevention (CDC). The primary outcome is the proportion of children who complete COVID-19 vaccination series. Secondary outcomes include the proportion of children who receive ≥ 1 dose of COVID-19 vaccine and changes in parent/caregiver scores from baseline to immediately post-intervention on the modified WHO SAGE Vaccine Hesitancy Scale adapted for the COVID-19 vaccine.DiscussionThe COVID-19 pandemic inflicts disproportionate harm on individuals from underserved communities, including those in rural settings. Maximizing vaccine uptake in these communities will decrease infection rates, severe illness, and death. Given that most US families from these communities use smart phones, mHealth interventions hold the promise of broad uptake. Bundling multiple mHealth vaccine uptake interventions into a single app may maximize the impact of deploying such a tool to increase COVID-19 vaccination. The new knowledge to be gained from this study will directly inform future efforts to increase COVID-19 vaccination rates across diverse settings and provide an evidentiary base for app-based vaccine communication tools that can be adapted to future vaccine-deployment efforts.Clinical trials registrationClinicalTrials.gov NCT05386355. Registered on May 23, 2022.

Lessons and Reflections From an Extended Co-design Process Developing an mHealth App With and for Older Adults: Multiphase, Mixed Methods Study.

There are many mobile health (mHealth) apps for older adult patients, but research has found that broadly speaking, mHealth still fails to meet the specific needs of older adult users. Others have highlighted the need to embed users in the mHealth design process in a fulsome and meaningful way. Co-design has been widely used in the development of mHealth apps and involves stakeholders in each phase of the design and development process. The involvement of older adults in the co-design processes is variable. To date, co-design approaches have tended toward embedding the stakeholders in early phases (eg, predesign and generative) but not throughout. The aim of this study was to reflect on the processes and lessons learned from engaging in an extended co-design process to develop an mHealth app for older adults, with older users contributing at each phase. This study aimed to design an mHealth tool to assist older adults in coordinating their care with health care professionals and caregivers. Our work to conceptualize, develop, and test the mHealth app consisted of 4 phases: phase 1, consulting stakeholders; phase 2, app development and co-designing with older adults; phase 3, field-testing with a smaller sample of older adult volunteer testers; and phase 4, reflecting, internally, on lessons learned from this process. In each phase, we drew on qualitative methods, including in-depth interviews and focus groups, all of which were analyzed in NVivo 11, using team-based thematic analysis. In phase 1, we identified key features that older adults and primary care providers wanted in an app, and each user group identified different priority features (older adults principally sought support to use the mHealth app, whereas primary care providers prioritized recoding illnesses, immunizations, and appointments). Phases 2 and 3 revealed significant mismatches between what the older adult users wanted and what our developers were able and willing to deliver. We were unable to craft the app that our consultations recommended, which the older adult field testers asked for. In phase 4, we reflected on our abilities to embed the voices and perspectives of older adults throughout the project when working with a developer not familiar with or committed to the core principles of co-design. We draw on this challenging experience to highlight several recommendations for those embarking on a co-design process that includes developers and IT vendors, researchers, and older adult users. Although our final mHealth app did not reflect all the needs and wishes of our older adult testers, our consultation process identified key features and contextual information essential for those developing apps to support older adults in managing their health and health care.

Digital Health in Schools: A Systematic Review

Worldwide, the growing digitalization process and increase in smartphone usage have contributed to promoting mobile health (mHealth) services. This study provides an overview of the research targeting the effectiveness of mHealth interventions among children and adolescents in the school environment. A systematic literature review was performed following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) in the PubMed, Web of Science, and Scopus databases. The results show that physical activity and nutrition are the main intervention topics. Health literacy, mental health, productive health, vaccination rates, and social interaction were also considered in mHealth interventions. Of the 13 studies that remained for analysis, 12 described positive outcomes in at least one health variable after using an mHealth tool. Overall, interventions ranged between four and 24 weeks. Only seven studies managed to have at least 80% of the participants from the baseline until completion. Adding personal information, user interaction, and self-reference comparisons of performance seems crucial for designing successful health digital tools for school-aged children and adolescents.

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.

Successful Aging for Community-Dwelling Older Adults: An Experimental Study with a Tablet App.

Mobile health (mHealth) technologies offer an opportunity to enable the care and support of community-dwelling older adults, however, research examining the use of mHealth in delivering quality of life (QoL) improvements in the older population is limited. We developed a tablet application (eSeniorCare) based on the Successful Aging framework and investigated its feasibility among older adults with low socioeconomic status. Twenty five participants (females = 14, mean age = 65 years) used the app to set and track medication intake reminders and health goals, and to play selected casual mobile games for 24 weeks. The Older person QoL and Short Health (SF12v2) surveys were administered before and after the study. The Wilcoxon rank tests were used to determine differences from baseline, and thematic analysis was used to analyze post-study interview data. The improvements in health-related QoL (HRQoL) scores were statistically significant (, ) across all participants. The frequent eSeniorCare users experienced statistically significant improvements in their physical health (, ) and HRQoL (, ) scores. Participants reported that the eSeniorCare app motivated timely medication intake and health goals achievement, whereas tablet games promoted mental stimulation. Participants were willing to use mobile apps to self-manage their medications (70%) and adopt healthy activities (72%), while 92% wanted to recommend eSeniorCare to a friend. This study shows the feasibility and possible impact of an mHealth tool on the health-related QoL in older adults with a low socioeconomic status. mHealth support tools and future research to determine their effects are warranted for this population.

A new mobile smartphone application, AF-EduApp, for atrial fibrillation patients: what do they use most?

Abstract Background The management of atrial fibrillation (AF) is complex and based on three main pillars: avoid stroke, better symptom control and cardiovascular risk factor management. Therefore, a holistic, multidisciplinary approach is needed in which the patient has a central role. Smartphone ownership increases strongly in the elderly population (in Belgian 65+ years old: 52% in 2018 to 82% in 2020). This digital growth creates opportunities for a closer patient follow-up. An in-house developed application, AF-EduApp, focused on delivering targeted education and guiding self-care, has been validated and is currently being studied in an ongoing clinical trial. Purpose Intermediate analysis of the user data of AF-EduApp. Methods At two Belgian hospitals, an open, prospective, randomized trial is currently performed. A total of 153 AF patients hospitalized or seen at an out-patient visit were included. Patients could use the application during a follow-up of 12 months. The AF-EduApp consists of six different modules: education, questionnaires with immediate patient feedback, medication overview with reminders, measurements (e.g. blood pressure, heart rate), appointments, and the possibility to ask questions to the caregivers. Knowledge about AF and its treatment was tested through the Jessa Atrial fibrillation Knowledge Questionnaire (JAKQ) with feedback on incorrectly answered questions. The main aim of the AF-EduApp is to improve patients' medication adherence through improved education and medication reminders. Results Currently, a total of 132 patients have completed a follow-up of 12 months (follow-up days: mean 357.3±60.7 and median: 365.5 [350.3–382.0]). The app was used on average 122.5±126.6 days (median: 55.0 [23.3–241.0]), or 34.3% of the available days. As shown in Fig. 1, the measurements and medication modules were the most used module (on 66.1% resp. 55.2% of the days). The education module was the least used module (3.5% of the days); the average education time was 17.0±27.7 min (median: 6.1 [1.4–20.6]). Within the measurement module (mean: 80.9±109.4 days used), the most frequently entered parameter was blood pressure, with on average 208.3±351.3 entries (median: 53.5 [7.0–296.3]) (Fig. 2). AF episodes was the least entered data (average 37.0±185.0 times; median 8.0 [4.0–19.0.3]). Conclusion Patients actively engaged with an educational smartphone AF application on 1/3th of the available days. The measurement module was the most used (to enter health data) together with the medication module (to confirm intake after reminder). It shows that many patients appreciate the mHealth tool to “connect” with their condition. The clinical trial tries to answer whether such increasing interaction leads to improved self-management and outcomes. Funding Acknowledgement Type of funding sources: Private company. Main funding source(s): The AF-EduApp study is supported by an BMS/Pfizer European Thrombosis Investigator Initiated Research Program (ERISTA) grant.