Possibilities, Problems, and Perspectives of Data Collection by Mobile Apps in Longitudinal Epidemiological Studies: Scoping Review

Evolutionary Trends in the Adoption, Adaptation, and Abandonment of Mobile Health Technologies: Viewpoint Based on 25 Years of Research

Over the past quarter-century, mobile health (mHealth) technologies have experienced significant changes in adoption rates, adaptation strategies, and instances of abandonment. Understanding the underlying factors driving these trends is essential for optimizing the design, implementation, and sustainability of interventions using these technologies. The evolution of mHealth adoption has followed a progressive trajectory, starting with cautious exploration and later accelerating due to technological advancements, increased smartphone penetration, and growing acceptance of digital health solutions by both health care providers and patients. However, alongside widespread adoption, challenges related to usability, interoperability, privacy concerns, and socioeconomic disparities have emerged, necessitating ongoing adaptation efforts. While many mHealth initiatives have successfully adapted to address these challenges, technology abandonment remains common, often due to unsustainable business models, inadequate user engagement, and insufficient evidence of effectiveness. This paper utilizes the Nonadoption, Abandonment, Scale-Up, Spread, and Sustainability (NASSS) framework to examine the interplay between the academic and industry sectors in patterns of adoption, adaptation, and abandonment, using 3 major mHealth innovations as examples: health-related SMS text messaging, mobile apps and wearables, and social media for health communication. Health SMS text messaging has demonstrated significant potential as a tool for health promotion, disease management, and patient engagement. The proliferation of mobile apps and devices has facilitated a shift from in-person and in-clinic practices to mobile- and wearable-centric solutions, encompassing everything from simple activity trackers to advanced health monitoring devices. Social media, initially characterized by basic text-based interactions in chat rooms and online forums, underwent a paradigm shift with the emergence of platforms such as MySpace and Facebook. This transition ushered in an era of mass communication through social media. The rise of microblogging and visually focused platforms such as Twitter(now X), Instagram, Snapchat, and TikTok, along with the integration of live streaming and augmented reality features, exemplifies the ongoing innovation within the social media landscape. Over the past 25 years, there have been remarkable strides in the adoption and adaptation of mHealth technologies, driven by technological innovation and a growing recognition of their potential to revolutionize health care delivery. Each mobile technology uniquely enhances public health and health care by catering to different user needs. SMS text messaging offers wide accessibility and proven effectiveness, while mobile apps and wearables provide comprehensive functionalities for more in-depth health management. Social media platforms amplify these efforts with their vast reach and community-building potential, making it essential to select the right tool for specific health interventions to maximize impact and engagement. Nevertheless, continued efforts are needed to address persistent challenges and mitigate instances of abandonment, ensuring that mHealth interventions reach their full potential in improving health outcomes and advancing equitable access to care.

Health workers' perspectives on the quality of maternal and newborn health care around the time of childbirth: Results of the Improving MAternal Newborn carE in the EURO Region (IMAgiNE EURO) project in 12 countries of the World Health Organization European Region

Background

Health workers' (HWs') perspectives on the quality of maternal and newborn care (QMNC) are not routinely collected. In this cross-sectional study, we aimed to document HWs' perspectives on QMNC around childbirth in 12 World Health Organization (WHO) European countries.

Methods

HWs involved in maternal/neonatal care for at least one year between March 2020 and March 2023 answered an online validated WHO standards-based questionnaire collecting 40 quality measures for improving QMNC. A QMNC index (score 0-400) was calculated as a synthetic measure.

Results

Data from 4143 respondents were analysed. For 39 out of 40 quality measures, at least 20% of HWs reported a 'need for improvement', with large variations across countries. Effective training on healthy women/newborns management (n = 2748, 66.3%), availability of informed consent job aids (n = 2770, 66.9%), and effective training on women/newborns rights (n = 2714, 65.5%) presented the highest proportion of HWs stating 'need for improvement'. Overall, 64.8% (n = 2684) of respondents declared that HWs' numbers were insufficient for appropriate care (66.3% in Portugal and 86.6% in Poland), and 22.4% described staff censorship (16.3% in Germany and 56.7% in Poland). The reported QMNC index was low in all countries (Poland median (MD) = 210.60, interquartile range (IQR) = 155.71, 273.57; Norway MD = 277.86; IQR = 244.32, 308.30). The 'experience of care' domain presented in eight countries had significantly lower scores than the other domains (P < 0.001). Over time, there was a significant monthly linear decrease in the QMNC index (P < 0.001), lacking correlation with the coronavirus disease 2019 (COVID-19) pandemic trends (P > 0.05). Multivariate analyses confirmed large QMNC variation by country. HWs with <10 years of experience, HWs from public facilities, and midwives rated QMNC with significantly lower scores (P < 0.001).

Conclusions

HWs from 12 European countries reported significant gaps in QMNC, lacking association with COVID-19 pandemic trends. Routine monitoring of QMNC and tailored actions are needed to improve health services for the benefit of both users and providers.

Registration

ClinicalTrials.gov NCT04847336.

Mitigating data quality challenges in ambulatory wrist-worn wearable monitoring through analytical and practical approaches

Chronic disease management and follow-up are vital for realizing sustained patient well-being and optimal health outcomes. Recent advancements in wearable technologies, particularly wrist-worn devices, offer promising solutions for longitudinal patient monitoring, replacing subjective, intermittent self-reporting with objective, continuous monitoring. However, collecting and analyzing data from wearables presents several challenges, such as data entry errors, non-wear periods, missing data, and wearable artifacts. In this work, we explore these data analysis challenges using two real-world datasets (mBrain21 and ETRI lifelog2020). We introduce practical countermeasures, including participant compliance visualizations, interaction-triggered questionnaires to assess personal bias, and an optimized pipeline for detecting non-wear periods. Additionally, we propose a visualization-oriented approach to validate processing pipelines using scalable tools such as tsflex and Plotly-Resampler. Lastly, we present a bootstrapping methodology to evaluate the variability of wearable-derived features in the presence of partially missing data segments. Prioritizing transparency and reproducibility, we provide open access to our detailed code examples, facilitating adaptation in future wearable research. In conclusion, our contributions provide actionable approaches for improving wearable data collection and analysis.

Using Monte Carlo Simulation to Forecast the Scientific Utility of Psychological App Studies: A Tutorial

Mobile applications offer a wide range of opportunities for psychological data collection, such as increased ecological validity and greater acceptance by participants compared to traditional laboratory studies. However, app-based psychological data also pose data-analytic challenges because of the complexities introduced by missingness and interdependence of observations. Consequently, researchers must weigh the advantages and disadvantages of app-based data collection to decide on the scientific utility of their proposed app study. For instance, some studies might only be worthwhile if they provide adequate statistical power. However, the complexity of app data forestalls the use of simple analytic formulas to estimate properties such as power. In this paper, we demonstrate how Monte Carlo simulations can be used to investigate the impact of app usage behavior on the utility of app-based psychological data. We introduce a set of questions to guide simulation implementation and showcase how we answered them for the simulation in the context of the guessing game app Who Knows (Rau et al., 2023). Finally, we give a brief overview of the simulation results and the conclusions we have drawn from them for real-world data generation. Our results can serve as an example of how to use a simulation approach for planning real-world app-based data collection.

Using Principles of Digital Development for a Smartphone App to Support Data Collection in Patients With Acute Myocardial Infarction and Physical Activity Intolerance: Case Study

BACKGROUND

Advances in health have highlighted the need to implement technologies as a fundamental part of the diagnosis, treatment, and recovery of patients at risk of or with health alterations. For this purpose, digital platforms have demonstrated their applicability in the identification of care needs. Nursing is a fundamental component in the care of patients with cardiovascular disorders and plays a crucial role in diagnosing human responses to these health conditions. Consequently, the validation of nursing diagnoses through ongoing research processes has become a necessity that can significantly impact both patients and health care professionals.

OBJECTIVE

We aimed to describe the process of developing a mobile app to validate the nursing diagnosis "intolerance to physical activity" in patients with acute myocardial infarction.

METHODS

We describe the development and pilot-testing of a mobile system to support data collection for validating the nursing diagnosis of activity intolerance. This was a descriptive study conducted with 11 adults (aged ≥18 years) who attended a health institution for highly complex needs with a suspected diagnosis of coronary syndrome between August and September 2019 in Floridablanca, Colombia. An app for the clinical validation of activity intolerance (North American Nursing Diagnosis Association [NANDA] code 00092) in patients with acute coronary syndrome was developed in two steps: (1) operationalization of the nursing diagnosis and (2) the app development process, which included an evaluation of the initial requirements, development and digitization of the forms, and a pilot test. The agreement level between the 2 evaluating nurses was evaluated with the κ index.

RESULTS

We developed a form that included sociodemographic data, hospital admission data, medical history, current pharmacological treatment, and thrombolysis in myocardial infarction risk score (TIMI-RS) and GRACE (Global Registry of Acute Coronary Events) scores. To identify the defining characteristics, we included official guidelines, physiological measurements, and scales such as the Piper fatigue scale and Borg scale. Participants in the pilot test (n=11) had an average age of 63.2 (SD 4.0) years and were 82% (9/11) men; 18% (2/11) had incomplete primary schooling. The agreement between the evaluators was approximately 80% for most of the defining characteristics. The most prevalent characteristics were exercise discomfort (10/11, 91%), weakness (7/11, 64%), dyspnea (3/11, 27%), abnormal heart rate in response to exercise (2/10, 20%), electrocardiogram abnormalities (1/10, 9%), and abnormal blood pressure in response to activity (1/10, 10%).

CONCLUSIONS

We developed a mobile app for validating the diagnosis of "activity intolerance." Its use will guarantee not only optimal data collection, minimizing errors to perform validation, but will also allow the identification of individual care needs.

Users' views on the use of a smartwatch app to collect daily symptom data in individuals with multiple long-term conditions (Multimorbidity): A qualitative study

Introduction

Long-term conditions are a major burden on health systems. One way to facilitate more research and better clinical care among patients with long-term conditions is to collect accurate data on their daily symptoms (patient-generated health data) using wearable technologies. Whilst evidence is growing for the use of wearable technologies in single conditions, there is less evidence of the utility of frequent symptom tracking in those who have more than one condition.

Aims

To explore patient views of the acceptability of collecting daily patient-generated health data for three months using a smartwatch app.

Methods

Watch Your Steps was a longitudinal study which recruited 53 patients to track over 20 symptoms per day for a 90-day period using a study app on smartwatches. Semi-structured interviews were conducted with a sub-sample of 20 participants to explore their experience of engaging with the app.

Results

In a population of older people with multimorbidity, patients were willing and able to engage with a patient-generated health data app on a smartwatch. It was suggested that to maintain engagement over a longer period, more 'real-time' feedback from the app should be available. Participants did not seem to consider the management of more than one condition to be a factor in either engagement or use of the app, but the presence of severe or chronic pain was at times a barrier.

Conclusion

This study has provided preliminary evidence that multimorbidity was not a major barrier to engagement with patient-generated health data via a smartwatch symptom tracking app.

Using a smartphone application to capture daily work activities: a longitudinal pilot study in a farming population

OBJECTIVES

Smartphones are increasingly used to collect real-time information on time-varying exposures. We developed and deployed an application (app) to evaluate the feasibility of using smartphones to collect real-time information on intermittent agricultural activities and to characterize agricultural task variability in a longitudinal study of farmers.

METHODS

We recruited 19 male farmers, aged 50-60 years, to report their farming activities on 24 randomly selected days over 6 months using the Life in a Day app. Eligibility criteria include personal use of an iOS or Android smartphone and >4 h of farming activities at least two days per week. We developed a study-specific database of 350 farming tasks that were provided in the app; 152 were linked to questions that were asked when the activity ended. We report eligibility, study compliance, number of activities, duration of activities by day and task, and responses to the follow-up questions.

RESULTS

Of the 143 farmers we reached out to for this study, 16 were not reached by phone or refused to answer eligibility questions, 69 were ineligible (limited smartphone use and/or farming time), 58 met study criteria, and 19 agreed to participate. Refusals were mostly related to uneasiness with the app and/or time commitment (32 of 39). Participation declined gradually over time, with 11 farmers reporting activities through the 24-week study period. We obtained data on 279 days (median 554 min/day; median 18 days per farmer) and 1,321 activities (median 61 min/activity; median 3 activities per day per farmer). The activities were predominantly related to animals (36%), transportation (12%), and equipment (10%). Planting crops and yard work had the longest median durations; short-duration tasks included fueling trucks, collecting/storing eggs, and tree work. Time period-specific variability was observed; for example, crop-related activities were reported for an average of 204 min/day during planting but only 28 min/day during pre-planting and 110 min/day during the growing period. We obtained additional information for 485 (37%) activities; the most frequently asked questions were related to "feed animals" (231 activities) and "operate fuel-powered vehicle (transportation)" (120 activities).

CONCLUSIONS

Our study demonstrated feasibility and good compliance in collecting longitudinal activity data over 6 months using smartphones in a relatively homogeneous population of farmers. We captured most of the farming day and observed substantial heterogeneity in activities, highlighting the need for individual activity data when characterizing exposure in farmers. We also identified several areas for improvement. In addition, future evaluations should include more diverse populations.

Immune Checkpoint Inhibitors and the Exposome: Host-Extrinsic Factors Determine Response, Survival, and Toxicity

Cancer immunotherapy, largely represented by immune checkpoint inhibitors (ICI), has led to substantial changes in preclinical cancer research and clinical oncology practice over the past decade. However, the efficacy and toxicity profiles of ICIs remain highly variable among patients, with only a fraction achieving a significant benefit. New combination therapeutic strategies are being investigated, and the search for novel predictive biomarkers is ongoing, mainly focusing on tumor- and host-intrinsic components. Less attention has been directed to all the external, potentially modifiable factors that compose the exposome, including diet and lifestyle, infections, vaccinations, and concomitant medications, that could affect the immune system response and its activity against cancer cells. We hereby provide a review of the available clinical evidence elucidating the impact of host-extrinsic factors on ICI response and toxicity.

A Technology-Enriched Approach to Studying Microlongitudinal Aging Among Adults Aged 18 to 85 Years: Protocol for the Labs Without Walls Study

BACKGROUND

Traditional longitudinal aging research involves studying the same individuals over a long period, with measurement intervals typically several years apart. App-based studies have the potential to provide new insights into life-course aging by improving the accessibility, temporal specificity, and real-world integration of data collection. We developed a new research app for iOS named Labs Without Walls to facilitate the study of life-course aging. Combined with data collected using paired smartwatches, the app collects complex data including data from one-time surveys, daily diary surveys, repeated game-like cognitive and sensory tasks, and passive health and environmental data.

OBJECTIVE

The aim of this protocol is to describe the research design and methods of the Labs Without Walls study conducted between 2021 and 2023 in Australia.

METHODS

Overall, 240 Australian adults will be recruited, stratified by age group (18-25, 26-35, 36-45, 46-55, 56-65, 66-75, and 76-85 years) and sex at birth (male and female). Recruitment procedures include emails to university and community networks, as well as paid and unpaid social media advertisements. Participants will be invited to complete the study onboarding either in person or remotely. Participants who select face-to-face onboarding (n=approximately 40) will be invited to complete traditional in-person cognitive and sensory assessments to be cross-validated against their app-based counterparts. Participants will be sent an Apple Watch and headphones for use during the study period. Participants will provide informed consent within the app and then begin an 8-week study protocol, which includes scheduled surveys, cognitive and sensory tasks, and passive data collection using the app and a paired watch. At the conclusion of the study period, participants will be invited to rate the acceptability and usability of the study app and watch. We hypothesize that participants will be able to successfully provide e-consent, input survey data through the Labs Without Walls app, and have passive data collected over 8 weeks; participants will rate the app and watch as user-friendly and acceptable; the app will allow for the study of daily variability in self-perceptions of age and gender; and data will allow for the cross-validation of app- and laboratory-based cognitive and sensory tasks.

RESULTS

Recruitment began in May 2021, and data collection was completed in February 2023. The publication of preliminary results is anticipated in 2023.

CONCLUSIONS

This study will provide evidence regarding the acceptability and usability of the research app and paired watch for studying life-course aging processes on multiple timescales. The feedback obtained will be used to improve future iterations of the app, explore preliminary evidence for intraindividual variability in self-perceptions of aging and gender expression across the life span, and explore the associations between performance on app-based cognitive and sensory tests and that on similar traditional cognitive and sensory tests.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/47053.

Perceptions of Mobile Health Technology in Elective Surgery: A Qualitative Study of North American Surgeons

OBJECTIVES

To explore the surgeon-perceived added value of mobile health technologies (mHealth), and determine facilitators of and barriers to implementing mHealth.

BACKGROUND

Despite the growing popularity of mHealth and evidence of meaningful use of patient-generated health data in surgery, implementation remains limited.

METHODS

This was an exploratory qualitative study following the Consolidated Criteria for Reporting Qualitative Research. Purposive sampling was used to identify surgeons across the United States and Canada. The Consolidated Framework for Implementation Research informed development of a semistructured interview guide. Video-based interviews were conducted (September-November 2020) and interview transcripts were thematically analyzed.

RESULTS

Thirty surgeons from 8 specialties and 6 North American regions were interviewed. Surgeons identified opportunities to integrate mHealth data pre- operatively (eg, expectation-setting, decision-making) and during recovery (eg, remote monitoring, earlier detection of adverse events) among higher risk patients. Perceived advantages of mHealth data compared with surgical and patient-reported outcomes included easier data collection, higher interpretability and objectivity of mHealth data, and the potential to develop more patientcentered and functional measures of health. Surgeons identified a variety of implementation facilitators and barriers around surgeon- and patient buy-in, integration with electronic medical records, regulatory/reimbursement concerns, and personnel responsible for mHealth data. Surgeons described similar considerations regarding perceptions of mHealth among patients, including the potential to address or worsen existing disparities in surgical care.

CONCLUSIONS

These findings have the potential to inform the effective and equitable implementation of mHealth for the purposes of supporting patients and surgical care teams throughout the delivery of surgical care.

MindKind: A mixed-methods protocol for the feasibility of global digital mental health studies in young people

Background: While an estimated 14-20% of young adults experience mental health conditions worldwide, the best strategies for prevention and management are not fully understood. The ubiquity of smartphone use among young people makes them excellent candidates for collecting data about lived experiences and their relationships to mental health. However, not much is known about the factors affecting young peoples’ willingness to share information about their mental health.

Objective: We aim to understand the data governance and engagement strategies influencing young peoples’ (aged 16-24) participation in app-based studies of mental health. We hypothesize that willingness to participate in research is influenced by involvement  in how their data is collected, shared, and used.

Methods: Here, we describe the MindKind Study, which employs mixed methods to understand the feasibility of global, smartphone-based studies of youth mental health. A pilot 12-week app-based substudy will query participants’ willingness to engage with remote mental health studies. Participants will be randomized into one of four different data governance models designed to understand their preferences, as well as the acceptability of models that allow them more or less control over how their data are accessed and used. Enrolees will receive one of two different engagement strategies. A companion qualitative study will employ a deliberative democracy approach to examine the preferences, concerns and expectations of young people, with respect to remote mental health research. We also detail our engagement with young people as co-researchers in this study. This pilot study is being conducted in India, South Africa and the United Kingdom.

Conclusions: This study is expected to generate new insights into the feasibility of, and best practices for, remote smartphone-based studies of mental health in youth and represents an important step toward understanding which approaches could help people better manage their mental health.

Smartphone keyboard interaction monitoring as an unobtrusive method to approximate rest-activity patterns: Inter-individual and metric-specific variations (Preprint)

BACKGROUND

Sleep is an important determinant of individuals' health and behavior during the wake phase. Novel research methods for field assessments are required to enable the monitoring of sleep over a prolonged period and across a large number of people. The ubiquity of smartphones offers new avenues for detecting rest-activity patterns in everyday life in a noninvasive an inexpensive manner and on a large scale. Recent studies provided evidence for the potential of smartphone interaction monitoring as a novel tracking method to approximate rest-activity patterns based on the timing of smartphone activity and inactivity throughout the 24-hour day. These findings require further replication and more detailed insights into interindividual variations in the associations and deviations with commonly used metrics for monitoring rest-activity patterns in everyday life.

OBJECTIVE

This study aimed to replicate and expand on earlier findings regarding the associations and deviations between smartphone keyboard-derived and self-reported estimates of the timing of the onset of the rest and active periods and the duration of the rest period. Moreover, we aimed to quantify interindividual variations in the associations and time differences between the 2 assessment modalities and to investigate to what extent general sleep quality, chronotype, and trait self-control moderate these associations and deviations.

METHODS

Students were recruited to participate in a 7-day experience sampling study with parallel smartphone keyboard interaction monitoring. Multilevel modeling was used to analyze the data.

RESULTS

In total, 157 students participated in the study, with an overall response rate of 88.9% for the diaries. The results revealed moderate to strong relationships between the keyboard-derived and self-reported estimates, with stronger associations for the timing-related estimates (β ranging from .61 to .78) than for the duration-related estimates (β=.51 and β=.52). The relational strength between the time-related estimates was lower, but did not substantially differ for the duration-related estimates, among students experiencing more disturbances in their general sleep quality. Time differences between the keyboard-derived and self-reported estimates were, on average, small (<0.5 hours); however, large discrepancies were also registered for quite some nights. The time differences between the 2 assessment modalities were larger for both timing-related and rest duration-related estimates among students who reported more disturbances in their general sleep quality. Chronotype and trait self-control did not significantly moderate the associations and deviations between the 2 assessment modalities.

CONCLUSIONS

We replicated the positive potential of smartphone keyboard interaction monitoring for estimating rest-activity patterns among populations of regular smartphone users. Chronotype and trait self-control did not significantly influence the metrics' accuracy, whereas general sleep quality did: the behavioral proxies obtained from smartphone interactions appeared to be less powerful among students who experienced lower general sleep quality. The generalization and underlying process of these findings require further investigation.

The development and acceptability testing of an app-based smart survey system to record smoking behaviour, use of nicotine replacement therapy (NRT) and e-cigarettes

Objective

Nicotine replacement therapy (NRT) helps people stop smoking. Monitoring treatment adherence is important as poor adherence to NRT limits its effectiveness. As e-cigarettes contain nicotine, their use (‘vaping’) is likely to affect both NRT use and smoking. We wished to measure adherence to NRT, and to investigate relationships between NRT, vaping and smoking so we developed ‘NicUse’, a smartphone App linked to a cloud database for collecting data relevant to NRT adherence. We report user-acceptability and investigate data validity among pregnant people by comparing heaviness of smoking reported to NicUse surveys with contemporaneous exhaled carbon monoxide readings.

Results

Thirty five pregnant women participating in a pilot study were asked to install and use NicUse on their smartphones. 32/35 (91%) logged into NicUse, 31 (89%) completed one or more surveys, and 22 (63%) completed these on ≥ 20 of 28 study days. Twenty-four gave end-of-study user acceptability ratings; 23 (96%) agreed or strongly agreed NicUse was ‘Easy to use’ and ‘Instructions were clear’. There was a strong correlation between the number of daily cigarettes reported on NicUse and exhaled CO readings taken on study Day 7 (Pearson’s r = 0.95, p < 0.001). NicUse appears highly acceptable, and smoking data reported to it shows validity.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13104-022-05983-8.

Patient and provider perspectives on self-administered electronic substance use and mental health screening in HIV primary care

Background

Substance use disorders, depression and anxiety disproportionately affect people with HIV (PWH) and lead to increased morbidity and mortality. Routine screening can help address these problems but is underutilized. This study sought to describe patient and provider perspectives on the acceptability and usefulness of systematic electronic, self-administered screening for tobacco, alcohol, other substance use, and mental health symptoms among patients in HIV primary care.

Methods

Screening used validated instruments delivered pre-appointment by both secure messaging and clinic-based tablets, with results integrated into the electronic health record (EHR). Qualitative analysis of semi-structured interviews with 9 HIV primary care providers and 12 patients in the 3 largest HIV primary care clinics in the Kaiser Permanente Northern California health system who participated in a clinical trial evaluating computerized screening and behavioral interventions was conducted. Interviews were audio-recorded and transcribed. A thematic approach was utilized for coding and analysis of interview data using a combination of deductive and inductive methods.

Results

Four key themes were identified: (1) perceived clinical benefit of systematic, electronic screening and EHR integration for providers and patients; (2) usefulness of having multiple methods of questionnaire completion; (3) importance of the patient–provider relationship to facilitate completion and accurate reporting; and (4) barriers, include privacy and confidentiality concerns about reporting sensitive information, particularly about substance use, and potential burden from repeated screenings.

Conclusions

Findings suggest that electronic, self-administered substance use and mental health screening is acceptable to patients and may have clinical utility to providers. While offering different methods of screening completion can capture a wider range of patients, a strong patient–provider relationship is a key factor in overcoming barriers and ensuring accurate patient responses. Further investigation into facilitators, barriers, and utility of electronic screening for PWH and other high-priority patient populations is indicated.

Trial registration ClinicalTrials.gov, NCT03217058. Registered 13 July 2017, https://clinicaltrials.gov/ct2/show/NCT03217058

Supplementary Information

The online version contains supplementary material available at 10.1186/s13722-022-00293-7.

Emerging Technology: Preparing Tomorrow's MCH Workforce to Innovate for Equity

Purpose

This commentary proposes a new direction to train the MCH workforce by leveraging today’s rapidly changing innovation and technology to address persistent health inequities.

Description

We outline the creation of an MCH technology and innovation training pipeline developed by harnessing creative funding opportunities, diversifying training modalities, and expanding partnerships beyond traditional academic-practice partners, that be replicated and adapted by other academic programs.

Assessment

Technology and innovation will continue to be a growing intersection between health and equity, and we must create a robust pipeline of MCH leaders prepared to collaborate with entrepreneurial and innovation leaders.

Conclusion

Technology offers an important opportunity to improve MCH outcomes and reduce disparities, but only if we train the MCH workforce to seize these opportunities.

Engagement with consumer smartwatches for tracking symptoms of individuals living with multiple long-term conditions (multimorbidity): A longitudinal observational study

Introduction

People living with multiple long-term conditions (multimorbidity) (MLTC-M) experience an accumulating combination of different symptoms. It has been suggested that these symptoms can be tracked longitudinally using consumer technology, such as smartphones and wearable devices.

Aim

The aim of this study was to investigate longitudinal user engagement with a smartwatch application, collecting survey questions and active tasks over 90 days, in people living with MLTC-M.

Methods

‘Watch Your Steps’ was a prospective observational study, administering multiple questions and active tasks over 90 days. Adults with more than one clinician-diagnosed long-term conditions were loaned Fossil® Sport smartwatches, pre-loaded with the study app. Around 20 questions were prompted per day.

Daily completion rates were calculated to describe engagement patterns over time, and to explore how these varied by patient characteristics and question type.

Results

Fifty three people with MLTC-M took part in the study. Around half were male ( = 26; 49%) and the majority had a white ethnic background (n = 45; 85%). About a third of participants engaged with the smartwatch app nearly every day. The overall completion rate of symptom questions was 45% inter-quartile range (IQR 23–67%) across all study participants. Older patients and those with greater MLTC-M were more engaged, although engagement was not significantly different between genders.

Conclusion

It was feasible for people living with MLTC-M to report multiple symptoms per day over 3 months. User engagement appeared as good as other mobile health studies that recruited people with single health conditions, despite the higher daily data entry burden.

MindKind: A mixed-methods protocol for the feasibility of global digital mental health studies in young people

Background: While an estimated 14-20% of young adults experience mental health conditions worldwide, the best strategies for prevention and management are not fully understood. The ubiquity of smartphone use among young people makes them excellent candidates for collecting data about lived experiences and their relationships to mental health. However, not much is known about the factors affecting young peoples’ willingness to share information about their mental health. Objective: We aim to understand the data governance and engagement strategies influencing young peoples’ (aged 16-24) participation in app-based studies of mental health. We hypothesize that the willingness to participate in research is impacted by their ability to be involved in how their data is collected, shared, and used. Methods: Here, we describe the MindKind Study, which employs mixed methods to understand the feasibility of global, smartphone-based studies of youth mental health. A pilot 12-week app-based substudy will query participants’ willingness to engage with remote mental health studies. Participants will be randomized into one of four different data governance models designed to understand their preferences, as well as the acceptability of models that allow them more or less control over how their data are accessed and used. Enrolees will receive one of two different engagement strategies. A companion qualitative study will employ a deliberative democracy approach to examine the preferences, concerns and expectations of young people, with respect to remote mental health research. We also detail our engagement with young people as co-researchers in this study. This pilot study is being conducted in India, South Africa and the United Kingdom. Conclusions: This study is expected to generate new insights into the feasibility of, and best practices for, remote smartphone-based studies of mental health in youth and represents an important step toward understanding which approaches could help people better manage their mental health.

Effective German and English Language mHealth Apps for Self-management of Bronchial Asthma in Children and Adolescents: Comparison Study

Background

Mobile health (mHealth) apps hold great potential for asthma self-management. Data on the suitability of asthma apps intended for children are insufficient, and the availability of German language apps is still inadequate compared with English language apps.

Objective

This study aims to identify functional asthma apps for children in German and to compare them with English language apps. In line with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, the Google Play Store and Apple App Store are systematically searched to preselect the most efficient apps, which are then compared according to a self-compiled criteria catalog.

Methods

Both app stores were screened for the term asthma. Following a PRISMA preselection process, the apps that met the inclusion criteria (ie, available free of charge, German or English language, and suitable for children) were rated by 3 independent persons following a criteria catalog consisting of 9 categories, some conceived for this purpose (availability, child-friendly, learning factor, and range of functions) and some adopted from existing validated catalogs (functionality and design, ease of use, potential for improving asthma self-management, fun factor and incentives, and information management and medical accuracy). The highest rated apps in German and English were compared.

Results

A total of 403 apps were identified on the Google Play Store and the Apple App Store. Finally, 24 apps that met the inclusion criteria were analyzed. In the first step of the quality assessment, only 4 available German language asthma apps were compared with 20 English language asthma apps. The 4 German language apps were then compared with the 4 highest rated English language apps. All selected apps, independent of the language, were comparable in the following categories: availability, functionality and design, ease of use, and information management and medical accuracy. The English language apps scored significantly higher in the following categories: potential for improving self-management, child-friendly, fun factor, learning factor, and range of function. English language apps (mean total points 34.164, SD 1.09) performed significantly better than German language asthma apps (mean total points 22.91, SD 2.898; P=.003). The best rated English language app was Kiss my asthma (36/42 points), whereas the best rated German language app Kata achieved only 27.33 points.

Conclusions

The recommended English language apps are Kiss my asthma, AsthmaXcel, AsthmaAustralia, and Ask Me, AsthMe!, whereas the only recommended German language app is Kata. The use of apps plays an increasingly important role in patients’ lives and in the medical field, making mHealth a staple in the future of asthma treatment plans. Although validated recommendations on rating mHealth apps have been published, it remains a challenging task for physicians and patients to choose a suitable app for each case, especially in non–English-speaking countries.

Mobile App-Reported Use of Traditional Medicine for Maintenance of Health in India During the COVID-19 Pandemic: Cross-sectional Questionnaire Study

Background

India follows a pluralistic system for strategic and focused health care delivery in which traditional systems of medicine such as Ayurveda, yoga and naturopathy, Unani, Siddha, Sowa Rigpa, and homoeopathy (AYUSH) coexist with contemporary medicine, and this system functions under the Ministry of AYUSH (MoA). The MoA developed a mobile app, called AYUSH Sanjivani, to document the trends of the use of AYUSH-based traditional and holistic measures by the public across India. Analysis of the data generated through this app can help monitor the extent of the use of AYUSH measures for maintenance of health during the COVID-19 pandemic and aid effective health promotion and communication efforts focused on targeted health care delivery during the pandemic.

Objective

The purpose of the study was to determine the extent of use of AYUSH measures by the public in India for maintenance of health during the COVID-19 pandemic as reported through the AYUSH Sanjivani mobile app.

Methods

Cross-sectional analysis of the data generated through the Ayush Sanjivani app from May 4 to July 31, 2020, was performed to study the pattern and extent of the use of AYUSH-based measures by the Indian population. The responses of the respondents in terms of demographic profile, use pattern, and benefits obtained; the association between the use of AYUSH-based measures and symptomatic status; and the association between the duration of use of AYUSH-based measures and the outcome of COVID-19 testing were evaluated based on bivariate and multivariate logistic regression analysis.

Results

Data from 723,459 respondents were used for the analysis, among whom 616,295 (85.2%) reported that they had been using AYUSH measures for maintenance of health during the COVID-19 pandemic. Among these 616,295 users, 553,801 (89.8%) either strongly or moderately agreed to have benefitted from AYUSH measures. Ayurveda and homeopathic measures and interventions were the most preferred by the respondents across India. Among the 359,785 AYUSH users who described their overall improvement in general health, 144,927 (40.3%) rated it as good, 30,848 (8.6%) as moderate, and 133,046 (40.3%) as slight. Respondents who had been using AYUSH measures for less than 30 days were more likely to be COVID-19–positive among those who were tested (odds ratio 1.52, 95% CI 1.44-1.60). The odds of nonusers of AYUSH measures being symptomatic if they tested positive were greater than those of AYUSH users (odds ratio 4.01, 95% CI 3.61-4.59).

Conclusions

The findings of this cross-sectional analysis assert that a large proportion of the representative population practiced AYUSH measures across different geographic locations of the country during the COVID-19 pandemic and benefitted considerably in terms of general well-being, with a possible impact on their quality of life and specific domains of health.