Apple HealthKit and Health App: Patient Uptake and Barriers in Primary Care

Feasibility of remote monitoring for fatal coronary heart disease using Apple Watch ECGs

Background

Fatal coronary heart disease (FCHD) is often described as sudden cardiac death (affects >4 million people/year), where coronary artery disease is the only identified condition. Electrocardiographic artificial intelligence (ECG-AI) models for FCHD risk prediction using ECG data from wearable devices could enable wider screening/monitoring efforts.

Objectives

To develop a single-lead ECG-based deep learning model for FCHD risk prediction and assess concordance between clinical and Apple Watch ECGs.

Methods

An FCHD single-lead ("lead I" from 12-lead ECGs) ECG-AI model was developed using 167,662 ECGs (50,132 patients) from the University of Tennessee Health Sciences Center. Eighty percent of the data (5-fold cross-validation) was used for training and 20% as a holdout. Cox proportional hazards (CPH) models incorporating ECG-AI predictions with age, sex, and race were also developed. The models were tested on paired clinical single-lead and Apple Watch ECGs from 243 St. Jude Lifetime Cohort Study participants. The correlation and concordance of the predictions were assessed using Pearson correlation (R), Spearman correlation (ρ), and Cohen's kappa.

Results

The ECG-AI and CPH models resulted in AUC = 0.76 and 0.79, respectively, on the 20% holdout and AUC = 0.85 and 0.87 on the Atrium Health Wake Forest Baptist external validation data. There was moderate-strong positive correlation between predictions (R = 0.74, ρ = 0.67, and κ = 0.58) when tested on the 243 paired ECGs. The clinical (lead I) and Apple Watch predictions led to the same low/high-risk FCHD classification for 99% of the participants. CPH prediction correlation resulted in an R = 0.81, ρ = 0.76, and κ = 0.78.

Conclusion

Risk of FCHD can be predicted from single-lead ECGs obtained from wearable devices and are statistically concordant with lead I of a 12-lead ECG.

Personalization strategies in digital mental health interventions: a systematic review and conceptual framework for depressive symptoms

Introduction

Personalization is a much-discussed approach to improve adherence and outcomes for Digital Mental Health interventions (DMHIs). Yet, major questions remain open, such as (1) what personalization is, (2) how prevalent it is in practice, and (3) what benefits it truly has.

Methods

We address this gap by performing a systematic literature review identifying all empirical studies on DMHIs targeting depressive symptoms in adults from 2015 to September 2022. The search in Pubmed, SCOPUS and Psycinfo led to the inclusion of 138 articles, describing 94 distinct DMHIs provided to an overall sample of approximately 24,300 individuals.

Results

Our investigation results in the conceptualization of personalization as purposefully designed variation between individuals in an intervention's therapeutic elements or its structure. We propose to further differentiate personalization by what is personalized (i.e., intervention content, content order, level of guidance or communication) and the underlying mechanism [i.e., user choice, provider choice, decision rules, and machine-learning (ML) based approaches]. Applying this concept, we identified personalization in 66% of the interventions for depressive symptoms, with personalized intervention content (32% of interventions) and communication with the user (30%) being particularly popular. Personalization via decision rules (48%) and user choice (36%) were the most used mechanisms, while the utilization of ML was rare (3%). Two-thirds of personalized interventions only tailored one dimension of the intervention.

Discussion

We conclude that future interventions could provide even more personalized experiences and especially benefit from using ML models. Finally, empirical evidence for personalization was scarce and inconclusive, making further evidence for the benefits of personalization highly needed.

Systematic Review Registration

Identifier: CRD42022357408.

Diabetes Self-management Apps: Systematic Review of Adoption Determinants and Future Research Agenda

BACKGROUND

Most diabetes management involves self-management. Effective self-management of the condition improves diabetes control, reduces the risk of complications, and improves patient outcomes. Mobile apps for diabetes self-management (DSM) can enhance patients' self-management activities. However, they are only effective if clinicians recommend them, and patients use them.

OBJECTIVE

This study aimed to explore the determinants of DSM apps' use by patients and their recommendations by health care professionals (HCPs). It also outlines the future research agenda for using DSM apps in diabetes care.

METHODS

We systematically reviewed the factors affecting the adoption of DSM apps by both patients and HCPs. Searches were performed using PubMed, Scopus, CINAHL, Cochrane Central, ACM, and Xplore digital libraries for articles published from 2008 to 2020. The search terms were diabetes, mobile apps, and self-management. Relevant data were extracted from the included studies and analyzed using a thematic synthesis approach.

RESULTS

A total of 28 studies met the inclusion criteria. We identified a range of determinants related to patients' and HCPs' characteristics, experiences, and preferences. Young female patients were more likely to adopt DSM apps. Patients' perceptions of the benefits of apps, ease of use, and recommendations by patients and other HCPs strongly affect their intention to use DSM apps. HCPs are less likely to recommend these apps if they do not perceive their benefits and may not recommend their use if they are unaware of their existence or credibility. Young and technology-savvy HCPs were more likely to recommend DSM apps.

CONCLUSIONS

Despite the potential of DSM apps to improve patients' self-care activities and diabetes outcomes, HCPs and patients remain hesitant to use them. However, the COVID-19 pandemic may hasten the integration of technology into diabetes care. The use of DSM apps may become a part of the new normal.

HealthTree Cure Hub: A Patient-Derived, Patient-Driven Clinical Cancer Information Platform Used to Overcome Hurdles and Accelerate Research in Multiple Myeloma

The study of rare diseases, such as multiple myeloma (MM), often experiences unique research hurdles that can delay or prevent lifesaving discoveries. HealthTree Cure Hub is a first-in-class software program designed to overcome these potential research hurdles.

Opportunities and Challenges of Integrating Food Practice into Clinical Decision-Making

BACKGROUND

Food practice plays an important role in health. Food practice data collected in daily living settings can inform clinical decisions. However, integrating such data into clinical decision-making is burdensome for both clinicians and patients, resulting in poor adherence and limited utilization. Automation offers benefits in this regard, minimizing this burden resulting in a better fit with a patient's daily living routines, and creating opportunities for better integration into clinical workflow. Although the literature on patient-generated health data (PGHD) can serve as a starting point for the automation of food practice data, more diverse characteristics of food practice data provide additional challenges.

OBJECTIVES

We describe a series of steps for integrating food practices into clinical decision-making. These steps include the following: (1) sensing food practice; (2) capturing food practice data; (3) representing food practice; (4) reflecting the information to the patient; (5) incorporating data into the EHR; (6) presenting contextualized food practice information to clinicians; and (7) integrating food practice into clinical decision-making.

METHODS

We elaborate on automation opportunities and challenges in each step, providing a summary visualization of the flow of food practice-related data from daily living settings to clinical settings.

RESULTS

We propose four implications of automating food practice hereinafter. First, there are multiple ways of automating workflow related to food practice. Second, steps may occur in daily living and others in clinical settings. Food practice data and the necessary contextual information should be integrated into clinical decision-making to enable action. Third, as accuracy becomes important for food practice data, macrolevel data may have advantages over microlevel data in some situations. Fourth, relevant systems should be designed to eliminate disparities in leveraging food practice data.

CONCLUSION

Our work confirms previously developed recommendations in the context of PGHD work and provides additional specificity on how these recommendations apply to food practice.

Digital health in stroke medicine: what are the opportunities for stroke patients?

PURPOSE OF REVIEW

The European Stroke Organisation published a European Stroke Action Plan (SAP-E) for the years 2018-2030. The SAP-E addresses the entire chain of care from primary prevention through to life after stroke. Within this document digital health tools are suggested for their potential to facilitate greater access to stroke care. In this review, we searched for digital health solutions for every domain of the SAP-E.

RECENT FINDINGS

Currently available digital health solutions for the cerebrovascular disease have been designed to support professionals and patients in healthcare settings at all stages. Telemedicine in acute settings has notably increased the access to tissue plasminogen activator and thrombectomy whereas in poststroke settings it has improved access to rehabilitation. Moreover, numerous applications aim to monitor vital signs and prescribed treatment adherence.

SUMMARY

SAP-E with its seven domains covers the whole continuum of stroke care, where digital health solutions have been considered to provide utility at a low cost. These technologies are progressively being used in all phases of stroke care, allowing them to overcome geographical and organizational barriers. The commercially available applications may also be used by patients and their careers in various context to facilitate accessibility to health improvement strategies.

Mobile apps for real-world evidence in health care

The use of real-world evidence for health care research and evaluation is growing. Mobile health apps have often-overlooked potential to contribute valuable real-world data that are not captured by other sources and could provide data that are more cost-effective and generalizable than can randomized controlled trials. However, there are several challenges that must be overcome to realize the potential value of patient-used mobile health app real-world data, including data quality, motivation for long-term use, privacy and security, methods of analysis, and standardization and integration. Addressing these challenges will increase the value of data from mobile health apps to inform real-world evidence and improve patient empowerment, clinical management, disease research, and treatment development.

Bridging the integration gap between patient-generated blood glucose data and electronic health records

Telemedicine can facilitate population health management by extending the reach of providers to efficiently care for high-risk, high-utilization populations. However, for telemedicine to be maximally useful, data collected using telemedicine technologies must be reliable and readily available to healthcare providers. To address current gaps in integration of patient-generated health data into the electronic health record (EHR), we examined 2 patient-facing platforms, Epic MyChart and Apple HealthKit, both of which facilitated the uploading of blood glucose data into the EHR as part of a diabetes telemedicine intervention. All patients were offered use of the MyChart platform; we subsequently invited a purposive sample of patients who used the MyChart platform effectively (n = 5) to also use the Apple HealthKit platform. Patients reported both platforms helped with diabetes self-management, and providers appreciated the convenience of the processes for obtaining patient data. Providers stated that the EHR data presentation format for Apple HealthKit was challenging to interpret; however, they also valued the greater perceived accuracy the Apple HealthKit data. Our findings indicate that patient-facing platforms can feasibly facilitate transmission of patient-generated health data into the EHR and support telemedicine-based care.

Wearable Activity Trackers in the Management of Rheumatic Diseases: Where Are We in 2020?

In healthcare, physical activity can be monitored in two ways: self-monitoring by the patient himself or external monitoring by health professionals. Regarding self-monitoring, wearable activity trackers allow automated passive data collection that educate and motivate patients. Wearing an activity tracker can improve walking time by around 1500 steps per day. However, there are concerns about measurement accuracy (e.g., lack of a common validation protocol or measurement discrepancies between different devices). For external monitoring, many innovative electronic tools are currently used in rheumatology to help support physician time management, to reduce the burden on clinic time, and to prioritize patients who may need further attention. In inflammatory arthritis, such as rheumatoid arthritis, regular monitoring of patients to detect disease flares improves outcomes. In a pilot study applying machine learning to activity tracker steps, we showed that physical activity was strongly linked to disease flares and that patterns of physical activity could be used to predict flares with great accuracy, with a sensitivity and specificity above 95%. Thus, automatic monitoring of steps may lead to improved disease control through potential early identification of disease flares. However, activity trackers have some limitations when applied to rheumatic patients, such as tracker adherence, lack of clarity on long-term effectiveness, or the potential multiplicity of trackers.

Big Data Everywhere: The Impact of Data Disjunction in the Direct-to-Consumer Testing Model

The recent increase in accessible medical and clinical laboratory "Big Data" has led to a corresponding increase in the use of machine-learning tools to develop integrative diagnostic models incorporating both existing and new test data. The rise of direct-to-consumer (DTC) testing paradigms raises the possibility of predictive models that use these new sources. This article discusses several distinct challenges raised by the DTC approach, including issues of centralized data collection, ascertainment bias, linkage to medical outcomes, and standardization/harmonization of results. Several solutions to maximize the promise of machine-learning data analytics for DTC data are suggested.

A nationwide observational study of locomotive syndrome in Japan using the ResearchKit: The Locomonitor study

BACKGROUND

We developed the Locomonitor application (app), the world's first iOS app to study locomotive syndrome, using the ResearchKit and examined the prevalence and risk factors for locomotive syndrome in Japanese general individuals 20-69 years old in a nationwide cross-sectional observational study.

METHODS

The participants were recruited from February to August 2016. The outcome measures for the locomotive function were evaluated by locomotive syndrome risk tests (LSRTs) using the Locomonitor app. The chi-squared test, a linear-by-linear association trend analysis, and Spearman's correlation test were performed as statistical analyses.

RESULTS

A total of 2177 subjects from all prefectures in Japan were included (average 42.2 years old). The Locomo25 and Stand-Up test scores in female participants and the Two-Step test scores in male participants showed age-dependent deterioration. In the overall population, the incidence of Locomo stage 1 and 2, as evaluated by the Locomo25, Stand-Up test or Two-Step test, was 30.2% and 29.2%, respectively. In subjects without locomotive syndrome (40.5%), LSRT scores showed age-dependent deterioration in both sexes. Locomotive syndrome in participants with a body mass index (BMI) of ≥25 kg/m² was more frequent than in those with a BMI of <25 kg/m² (age- and gender-adjusted odds ratio [OR] 1.344 [95% confidence interval {CI} 1.03-1.75, p = 0.027]). Locomotive syndrome in participants with an exercise habit was less frequent than in those without an exercise habit (age- and gender-adjusted OR 0.499 [95% CI 0.33-0.755, p < 0.0001]).

CONCLUSIONS

The Locomonitor app, a newly developed remote platform, revealed that approximately 20%-30% of Japanese individuals 20-69 years old in the general population met the definition of locomotive syndrome. Locomotive syndrome in participants with obesity was more frequent than those without obesity, while locomotive syndrome in participants with an exercise habit was less frequent than those without an exercise habit.

Design and testing of Medivate, a mobile app to achieve medication list portability via Fast Healthcare Interoperability Resources

OBJECTIVES

To describe the architecture, design, and testing of an innovative mobile application (Medivate) that facilities accurate sharing of medication lists with linked education.

SETTING

The deployment and testing of this app occurred in both the community and hospital settings in Pittsburgh, PA.

PRACTICE INNOVATION

Medivate is an iOS smartphone application and cloud architecture for patients and providers to keep medication and vaccine lists accurate by providing a method and tool to easily exchange these data. Medications are added directly to the app from the electronic health record (EHR) or by the patient manually. Quick response (QR) code technology is used to trigger the secure transfer and sharing of medications on demand via HL-7 Fast Healthcare Interoperability Resources-based data transfer. An iterative user-centered design process involving patients and student pharmacists practicing in community settings was used to develop and refine functionality.

PRACTICE DESCRIPTION

Adults with an iPhone were approached for participation in the design and evaluation of Medivate. Its functionality and integration into clinical workflow at hospital discharge or vaccine administration in the community were determined.

EVALUATION

In the community setting, interviews of pharmacists were conducted. In the hospital, metrics of study participation and experience with app deployment were determined.

RESULTS

The app was deployed in the community for patients that received vaccinations. Interviews provided insight into barriers and logistics for successful engagement. The app was integrated into hospital workflow and demonstrated interoperability with the inpatient EHR. Thirteen patients were provided the app before discharge. Engagement with the app was evident through medication list shares, education access, and changes to medication lists. Patients noted strong agreement with usefulness of the app to learn more about the purposes and adverse effects of medications prescribed.

CONCLUSION

A mobile app to achieve medication and vaccine list portability was successfully designed and integrated into the inpatient and community settings.

The Internet of Things in Health Care in Oxford: Protocol for Proof-of-Concept Projects

Background

Demands on health services across are increasing because of the combined challenges of an expanding and aging population, alongside complex comorbidities that transcend the classical boundaries of modern health care. Continuing to provide and coordinate care in the current manner is not a viable route to sustain the improvements in health outcomes observed in recent history. To ensure that there continues to be improvement in patient care, prevention of disease, and reduced burden on health systems, it is essential that we adapt our models of delivery. Providers of health and social care are evolving to face these pressures by changing the way they think about the care system and, importantly, how to involve patients in the planning and delivery of services.

Objective

The objective of this paper is to provide (1) an overview of the current state of Internet of Things (IoT) and key implementation considerations, (2) key use cases demonstrating technology capabilities, (3) an overview of the landscape for health care IoT use in Oxford, and (4) recommendations for promoting the IoT via collaborations between higher education institutions and industry proof-of-concept (PoC) projects.

Methods

This study describes the PoC projects that will be created to explore cost-effectiveness, clinical efficacy, and user adoption of Internet of Medical Things systems. The projects will focus on 3 areas: (1) bring your own device integration, (2) chronic disease management, and (3) personal health records.

Results

This study is funded by Research England’s Connecting Capability Fund. The study started in March 2018, and results are expected by the end of 2019.

Conclusions

Embracing digital solutions to support the evolution and transformation of health services is essential. Importantly, this should not simply be undertaken by providers in isolation. It must embrace and exploit the advances being seen in the consumer devices, national rollout of high-speed broadband services, and the rapidly expanding medical device industry centered on mobile and wearable technologies. Oxford University Hospitals and its partner providers, patients, and stakeholders are building on their leading position as an exemplar site for digital maturity in the National Health Service to implement and evaluate technologies and solutions that will capitalize on the IoT. Although early in the application to health, the IoT and the potential it provides to make the patient a partner at the center of decisions about care represent an exciting opportunity. If achieved, a fully connected and interoperable health care environment will enable continuous acquisition and real-time analysis of patient data, offering unprecedented ability to monitor patients, manage disease, and potentially deliver early diagnosis. The clinical benefit of this is clear, but additional patient benefit and value will be gained from being able to provide expert care at home or close to home.

International Registered Report Identifier (IRRID)

DERR1-10.2196/12077

Physiotherapists' and Physiotherapy Students' Perspectives on the Use of Mobile or Wearable Technology in Their Practice

Purpose: Although extensive research has been carried out on the determinants of mobile or wearable health care technology (mHealth), as well as on its acceptance by patients and other health care providers, very little research has been done on physiotherapists' perspectives on the use of mHealth in their current or future practice. The aims of this study were to (1) explore the attitudes of physiotherapists toward mHealth using a modified technology acceptance model questionnaire, (2) understand the applications and delivery paradigms that are most desirable, and (3) assess the content validity of the questionnaire. Method: The questionnaire was administered online. Participants (n=76) were recruited using snowball and convenience sampling. Data were analyzed using factor analysis and partial least-squares path modelling. Results: Results indicate that perceived usefulness and perceived ease of use were related to early adoptive behaviour among participants. We found no evidence that age, gender, experience, or practice setting influenced early adoptive behaviour. Participants demonstrated favourable attitudes toward mHealth tools in clinical practice. Conclusions: This article provides initial insights into factors that are likely to be significant determinants of adoption of mHealth among physiotherapists. Further work, including qualitative research, will help to identify personal and institutional factors that will improve the acceptance of mHealth.

From smartphone to EHR: a case report on integrating patient-generated health data

Patient-generated health data (PGHD), collected from mobile apps and devices, represents an opportunity for remote patient monitoring and timely interventions to prevent acute exacerbations of chronic illness—if data are seen and shared by care teams. This case report describes the technical aspects of integrating data from a popular smartphone platform to a commonly used EHR vendor and explores the challenges and potential of this approach for disease management. Consented subjects using the Asthma Health app (built on Apple’s ResearchKit platform) were able to share data on inhaler usage and peak expiratory flow rate (PEFR) with a local pulmonologist who ordered this data on Epic’s EHR. For users who had installed and activated Epic’s patient portal (MyChart) on their iPhone and enabled sharing of health data between apps via HealthKit, the pulmonologist could review PGHD and, if necessary, make recommendations. Four patients agreed to share data with their pulmonologist, though only two patients submitted more than one data point across the 4.5-month trial period. One of these patients submitted 101 PEFR readings across 65 days; another submitted 24 PEFR and inhaler usage readings across 66 days. PEFR for both patients fell within predefined physiologic parameters, except once where a low threshold notification was sent to the pulmonologist, who responded with a telephone discussion and new e-prescription to address symptoms. This research describes the technical considerations and implementation challenges of using commonly available frameworks for sharing PGHD, for the purpose of remote monitoring to support timely care interventions.

Patients with asthma who record inhaler usage and lung function scores with a smartphone app and transmit the data to an electronic health record (EHR) can get timelier care and prescription adjustments from their doctors. A team led by Yvonne Chan and Nicholas Genes from the Icahn School of Medicine at Mount Sinai in New York, NY, USA explored the feasibility of having patients self-report health data on an iPhone app called Asthma Health and then share the information with their pulmonologists via an EHR patient portal app. Four patients took part in the study, but only two really engaged in the platform. Those patients submitted multiple measures of peak expiratory flow rate per week. In one instance, the measure triggered a pulmonologist to call the patient and prescribe new allergy medications.