The Wild Wild West: A Framework to Integrate mHealth Software Applications and Wearables to Support Physical Activity Assessment, Counseling and Interventions for Cardiovascular Disease Risk Reduction

Review and Comparative Evaluation of Mobile Apps for Cardiovascular Risk Estimation: Usability Evaluation Using mHealth App Usability Questionnaire

Background

Cardiovascular diseases (CVD) are the leading cause of death and disability worldwide, and their prevention is a major public health priority. Detecting health issues early and assessing risk levels can significantly improve the chances of reducing mortality. Mobile apps can help estimate and manage CVD risks by providing users with personalized feedback, education, and motivation. Incorporating visual analysis into apps is an effective method for educating society. However, the usability evaluation and inclusion of visualization of these apps are often unclear and variable.

Objective

The primary objective of this study is to review and compare the usability of existing apps designed to estimate CVD risk using the mHealth App Usability Questionnaire (MAUQ). This is not a traditional usability study involving user interaction design, but rather an assessment of how effectively these applications meet usability standards as defined by the MAUQ.

Methods

First, we used predefined criteria to review 16 out of 2238 apps to estimate CVD risk in the Google Play Store and the Apple App Store. Based on the apps' characteristics (ie, developed for health care professionals or patient use) and their functions (single or multiple CVD risk calculators), we conducted a descriptive analysis. Then we also compared the usability of existing apps using the MAUQ and calculated the agreement among 3 expert raters.

Results

Most apps used the Framingham Risk Score (8/16, 50%) and Atherosclerotic Cardiovascular Disease Risk (7/16, 44%) prognostic models to estimate CVD risk. The app with the highest overall MAUQ score was the MDCalc Medical Calculator (mean 6.76, SD 0.25), and the lowest overall MAUQ score was obtained for the CardioRisk Calculator (mean 3.96, SD 0.21). The app with the highest overall MAUQ score in the "ease-of-use" domain was the MDCalc Medical Calculator (mean 7, SD 0); in the domain "interface and satisfaction," it was the MDCalc Medical Calculator (mean 6.67, SD 0.33); and in the domain "usefulness," it was the ASCVD Risk Estimator Plus (mean 6.80, SD 0.32).

Conclusions

We found that the Framingham Risk Score is the most widely used prognostic model in apps for estimating CVD risk. The "ease-of-use" domain received the highest ratings. While more than half of the apps were suitable for both health care professionals and patients, only a few offered sophisticated visualizations for assessing CVD risk. Less than a quarter of the apps included visualizations, and those that did were single calculators. Our analysis of apps showed that they are an appropriate tool for estimating CVD risk.

Perspectives of key stakeholders on integrating wearable sensor technology into rehabilitation care: a mixed-methods analysis

Introduction

Rehabilitation is facing a critical practice gap: Patients seek out rehabilitation services to improve their activity in daily life, yet recent work demonstrates that rehabilitation may be having a limited impact on improving this outcome due to lack of objective data on patients' activity in daily life. Remote monitoring using wearable sensor technology is a promising solution to this address this gap. The purpose of this study was to understand patient and clinician awareness of the practice gap and preferences for integrating wearable sensor technology into rehabilitation care.

Methods

This study used a mixed-methods approach consisting of surveys and 1:1 interviews with clinicians (physical and occupational therapists or assistants) employed at an outpatient rehabilitation clinic within an academic medical center and patients seeking care at this clinic. Data were analyzed using descriptive statistics and thematic analysis.

Results

Data saturation was reached from nineteen clinicians and ten patients. Both clinicians and patients recognized the importance of measuring activity outside the clinic and viewed wearable sensor technology as an objective measurement tool. Most clinicians (63%) preferred continuous (vs. intermittent) monitoring within a care episode and most patients (60%) were willing to sync their sensor data as often as instructed by their provider. To maximize integration into clinical workflows, clinicians voiced a preference for availability of sensor data in the electronic health record.

Conclusions

Clinicians and patients value the use of wearable sensor technology to improve measurement of activity outside the clinic environment and expressed preferences for how this technology could best be integrated into routine rehabilitation care.

Jerks are useful: extracting pulse rate from wrist-placed accelerometry jerk during sleep in children

STUDY OBJECTIVES

Evaluate wrist-placed accelerometry predicted heartrate compared to electrocardiogram (ECG) heartrate in children during sleep.

METHODS

Children (n = 82, 61% male, 43.9% black) wore a wrist-placed Apple Watch Series 7 (AWS7) and ActiGraph GT9X during a polysomnogram. Three-Axis accelerometry data was extracted from AWS7 and the GT9X. Accelerometry heartrate estimates were derived from jerk (the rate of acceleration change), computed using the peak magnitude frequency in short time Fourier Transforms of Hilbert transformed jerk computed from acceleration magnitude. Heartrates from ECG traces were estimated from R-R intervals using R-pulse detection. Lin's concordance correlation coefficient (CCC), mean absolute error (MAE), and mean absolute percent error (MAPE) assessed agreement with ECG estimated heart rate. Secondary analyses explored agreement by polysomnography sleep stage and a signal quality metric.

RESULTS

The developed scripts are available on Github. For the GT9X, CCC was poor at -0.11 and MAE and MAPE were high at 16.8 (SD = 14.2) beats/minute and 20.4% (SD = 18.5%). For AWS7, CCC was moderate at 0.61 while MAE and MAPE were lower at 6.4 (SD = 9.9) beats/minute and 7.3% (SD = 10.3%). Accelerometry estimated heartrate for AWS7 was more closely related to ECG heartrate during N2, N3 and REM sleep than lights on, wake, and N1 and when signal quality was high. These patterns were not evident for the GT9X.

CONCLUSIONS

Raw accelerometry data extracted from AWS7, but not the GT9X, can be used to estimate heartrate in children while they sleep. Future work is needed to explore the sources (i.e. hardware, software, etc.) of the GT9X's poor performance.

Perspectives of Key Stakeholders on Integrating Wearable Sensor Technology into Rehabilitation Care: A Mixed-Methods Analysis

Introduction

Rehabilitation is facing a critical practice gap: Patients seek out rehabilitation services to improve their activity in daily life, yet recent work demonstrates that rehabilitation may be having a limited impact on improving this outcome due to lack of objective data on patients' activity in daily life. Remote monitoring using wearable sensor technology is a promising solution to this address this gap. The purpose of this study was to understand patient and clinician awareness of the practice gap and preferences for integrating wearable sensor technology into rehabilitation care.

Methods

This study used a mixed-methods approach consisting of surveys and 1:1 interviews with clinicians (physical and occupational therapists or assistants) employed at an outpatient rehabilitation clinic within an academic medical center and patients seeking care at this clinic. Data were analyzed using descriptive statistics and thematic analysis.

Results

Data saturation was reached from recruiting nineteen clinicians and ten patients. Both clinicians and patients recognized the importance of measuring activity outside the clinic and viewed wearable sensor technology as an objective measurement tool. Most clinicians (63%) preferred continuous (vs. intermittent) monitoring within a care episode and most patients (60%) were willing to sync their sensor data as often as instructed by their provider. To maximize integration into clinical workflows, clinicians voiced a preference for availability of sensor data in the electronic health record.

Conclusions

Clinicians and patients value the use of wearable sensor technology to improve measurement of activity outside the clinic environment and expressed preferences for how this technology could best be integrated into routine rehabilitation care.

Enhancing the implementation and integration of mHealth interventions in resource-limited settings: a scoping review

BACKGROUND

Although mobile health (mHealth) interventions have shown promise in improving health outcomes, most of them rarely translate to scale. Prevailing mHealth studies are largely small-sized, short-term and donor-funded pilot studies with limited evidence on their effectiveness. To facilitate scale-up, several frameworks have been proposed to enhance the generic implementation of health interventions. However, there is a lack of a specific focus on the implementation and integration of mHealth interventions in routine care in low-resource settings. Our scoping review aimed to synthesize and develop a framework that could guide the implementation and integration of mHealth interventions.

METHODS

We searched the PubMed, Google Scholar, and ScienceDirect databases for published theories, models, and frameworks related to the implementation and integration of clinical interventions from 1st January 2000 to 31st December 2023. The data processing was guided by a scoping review methodology proposed by Arksey and O'Malley. Studies were included if they were i) peer-reviewed and published between 2000 and 2023, ii) explicitly described a framework for clinical intervention implementation and integration, or iii) available in full text and published in English. We integrated different domains and constructs from the reviewed frameworks to develop a new framework for implementing and integrating mHealth interventions.

RESULTS

We identified eight eligible papers with eight frameworks composed of 102 implementation domains. None of the identified frameworks were specific to the integration of mHealth interventions in low-resource settings. Two constructs (skill impartation and intervention awareness) related to the training domain, four constructs (technical and logistical support, identifying committed staff, supervision, and redesigning) from the restructuring domain, two constructs (monetary incentives and nonmonetary incentives) from the incentivize domain, two constructs (organizational mandates and government mandates) from the mandate domain and two constructs (collaboration and routine workflows) from the integrate domain. Therefore, a new framework that outlines five main domains-train, restructure, incentivize, mandate, and integrate (TRIMI)-in relation to the integration and implementation of mHealth interventions in low-resource settings emerged.

CONCLUSION

The TRIMI framework presents a realistic and realizable solution for the implementation and integration deficits of mHealth interventions in low-resource settings.

ICF classification of therapeutic goals for outpatient episodes of neurorehabilitation in post-stroke and Parkinson disease

PURPOSE

To understand therapeutic priorities, a secondary data analysis on a retrospective cohort was conducted to classify rehabilitation goals according to the International Classification of Functioning, Disability, and Health (ICF).

MATERIALS AND METHODS

Therapeutic goals from an initial outpatient physical or occupational therapy evaluation for patients post-stroke or with Parkinson disease, were classified into Level 1 of the ICF. Goals in the Activity and Participation component were further sub-classified as activity capacity or activity performance (self-report or direct) in daily life.

RESULTS

776 goals across 104 participants were classified into Level 1 of the ICF. The majority, 73% (563/776) were classified as Activity and Participation, 20% (155/776) as Body Function and 2% (17/776) as Environmental Factors. Fifty-two percent (400/776) of all goals were classified as activity capacity and 21% (163/776) as activity performance in daily life, with 21% (160/776) of goals measuring self-report activity performance in daily life and less than 1% (3/776) of goals measuring direct activity performance in daily life.

CONCLUSIONS

While the majority of therapeutic goals were classified into the Activity and Participation component, less than 1% of goals measured direct activity performance in daily life. If people seek outpatient rehabilitation to improve functioning in their real-world environment, therapeutic goal setting should reflect this.

Enhancing the Implementation and Integration of mHealth Interventions in Resource-Limited Settings: A Scoping Review

Background

Although mobile health (mHealth) interventions have shown promise in improving health outcomes, most of them rarely translate to scale. Prevailing mHealth studies are largely small-sized, short-term and donor-funded pilot studies with limited evidence on their effectiveness. To facilitate scale-up, several frameworks have been proposed to enhance the generic implementation of health interventions. However, there is a lack of a specific focus on the implementation and integration of mHealth interventions in routine care in low-resource settings. Our scoping review aimed to synthesize and develop a framework that could guide the implementation and integration of mHealth interventions.

Methods

We searched the PubMed, Google Scholar, and ScienceDirect databases for published theories, models, and frameworks related to the implementation and integration of clinical interventions from 1st January 2000 to 31st December 2023. The data processing was guided by a scoping review methodology proposed by Arksey and O'Malley. Studies were included if they were i) peer-reviewed and published between 2000 and 2023, ii) explicitly described a framework for clinical intervention implementation and integration, or iii) available in full text and published in English. We integrated different domains and constructs from the reviewed frameworks to develop a new framework for implementing and integrating mHealth interventions.

Results

We identified eight eligible papers with eight frameworks composed of 102 implementation domains. None of the identified frameworks were specific to the integration of mHealth interventions in low-resource settings. Two constructs (skill impartation and intervention awareness) related to the training domain, four constructs (technical and logistical support, identifying committed staff, supervision, and redesigning) from the restructuring domain, two constructs (monetary incentives and nonmonetary incentives) from the incentivize domain, two constructs (organizational mandates and government mandates) from the mandate domain and two constructs (collaboration and routine workflows) from the integrate domain. Therefore, a new framework that outlines five main domains-train, restructure, incentivize, mandate, and integrate (TRIMI)-in relation to the integration and implementation of mHealth interventions in low-resource settings emerged.

Conclusion

The TRIMI framework presents a realistic and realizable solution for the implementation and integration deficits of mHealth interventions in low-resource settings.

Quantifying the effects of sleep on sensor-derived variables from upper limb accelerometry in people with and without upper limb impairment

BACKGROUND

Despite the promise of wearable sensors for both rehabilitation research and clinical care, these technologies pose significant burden on data collectors and analysts. Investigations of factors that may influence the wearable sensor data processing pipeline are needed to support continued use of these technologies in rehabilitation research and integration into clinical care settings. The purpose of this study was to investigate the effect of one such factor, sleep, on sensor-derived variables from upper limb accelerometry in people with and without upper limb impairment and across a two-day wearing period.

METHODS

This was a secondary analysis of data collected during a prospective, longitudinal cohort study (n = 127 individuals, 62 with upper limb impairment and 65 without). Participants wore a wearable sensor on each wrist for 48 h. Five upper limb sensor variables were calculated over the full wear period (sleep included) and with sleep time removed (sleep excluded): preferred time, non-preferred time, use ratio, non-preferred magnitude and its standard deviation. Linear mixed effects regression was used to quantify the effect of sleep on each sensor variable and determine if the effect differed between people with and without upper limb impairment and across a two-day wearing period.

RESULTS

There were significant differences between sleep included and excluded for the variables preferred time (p < 0.001), non-preferred time (p < 0.001), and non-preferred magnitude standard deviation (p = 0.001). The effect of sleep was significantly different between people with and without upper limb impairment for one variable, non-preferred magnitude (p = 0.02). The effect of sleep was not substantially different across wearing days for any of the variables.

CONCLUSIONS

Overall, the effects of sleep on sensor-derived variables of upper limb accelerometry are small, similar between people with and without upper limb impairment and across a two-day wearing period, and can likely be ignored in most contexts. Ignoring the effect of sleep would simplify the data processing pipeline, facilitating the use of wearable sensors in both research and clinical practice.

Physicians' and Patients' Expectations From Digital Agents for Consultations: Interview Study Among Physicians and Patients

BACKGROUND

Physicians are currently overwhelmed by administrative tasks and spend very little time in consultations with patients, which hampers health literacy, shared decision-making, and treatment adherence.

OBJECTIVE

This study aims to examine whether digital agents constructed using fast-evolving generative artificial intelligence, such as ChatGPT, have the potential to improve consultations, adherence to treatment, and health literacy. We interviewed patients and physicians to obtain their opinions about 3 digital agents-a silent digital expert, a communicative digital expert, and a digital companion (DC).

METHODS

We conducted in-depth interviews with 25 patients and 22 physicians from a purposeful sample, with the patients having a wide age range and coming from different educational backgrounds and the physicians having different medical specialties. Transcripts of the interviews were deductively coded using MAXQDA (VERBI Software GmbH) and then summarized according to code and interview before being clustered for interpretation.

RESULTS

Statements from patients and physicians were categorized according to three consultation phases: (1) silent and communicative digital experts that are part of the consultation, (2) digital experts that hand over to a DC, and (3) DCs that support patients in the period between consultations. Overall, patients and physicians were open to these forms of digital support but had reservations about all 3 agents.

CONCLUSIONS

Ultimately, we derived 9 requirements for designing digital agents to support consultations, treatment adherence, and health literacy based on the literature and our qualitative findings.

A Device Agnostic Approach to Predict Children's Activity from Consumer Wearable Accelerometer Data: A Proof-of-Concept Study

INTRODUCTION

This study examined the potential of a device agnostic approach for predicting physical activity from consumer wearable accelerometry compared with a research-grade accelerometry.

METHODS

Seventy-five 5- to 12-year-olds (58% male, 63% White) participated in a 60-min protocol. Children wore wrist-placed consumer wearables (Apple Watch Series 7 and Garmin Vivoactive 4) and a research-grade device (ActiGraph GT9X) concurrently with an indirect calorimeter (COSMED K5). Activity intensities (i.e., inactive, light, moderate-to-vigorous physical activity) were estimated via indirect calorimetry (criterion), and the Hildebrand thresholds were applied to the raw accelerometer data from the consumer wearables and research-grade device. Epoch-by-epoch (e.g., weighted sensitivity, specificity) and discrepancy (e.g., mean bias, absolute error) analyses evaluated agreement between accelerometry-derived and criterion estimates. Equivalence testing evaluated the equivalence of estimates produced by the consumer wearables and ActiGraph.

RESULTS

Estimates produced by the raw accelerometry data from ActiGraph, Apple, and Garmin produced similar criterion agreement with weighted sensitivity = 68.2% (95% confidence interval (CI), 67.1%-69.3%), 73.0% (95% CI, 71.8%-74.3%), and 66.6% (95% CI, 65.7%-67.5%), respectively, and weighted specificity = 84.4% (95% CI, 83.6%-85.2%), 82.0% (95% CI, 80.6%-83.4%), and 75.3% (95% CI, 74.7%-75.9%), respectively. Apple Watch produced the lowest mean bias (inactive, -4.0 ± 4.5; light activity, 2.1 ± 4.0) and absolute error (inactive, 4.9 ± 3.4; light activity, 3.6 ± 2.7) for inactive and light physical activity minutes. For moderate-to-vigorous physical activity, ActiGraph produced the lowest mean bias (1.0 ± 2.9) and absolute error (2.8 ± 2.4). No ActiGraph and consumer wearable device estimates were statistically significantly equivalent.

CONCLUSIONS

Raw accelerometry estimated inactive and light activity from wrist-placed consumer wearables performed similarly to, if not better than, a research-grade device, when compared with indirect calorimetry. This proof-of-concept study highlights the potential of device-agnostic methods for quantifying physical activity intensity via consumer wearables.

Promoting physical activity in clinical practice through wearable technology

ABSTRACT

The negative health consequences of physical inactivity continue to be a global problem that must be addressed from the highest levels of government down to local primary care providers. Physical activity has been identified as a useful patient vital sign in health care. Advanced practice nurses should aggressively prescribe physical activity as an evidence-based intervention to help mitigate the increased mortality and morbidity associated with a sedentary lifestyle. A focused literature review was conducted using PubMed, CINAHL, and the Cochrane online databases. Fitness wearables and mobile health trackers are a catalyst for lifestyle behavior change and cultivate a health care partnership between the patient and their provider. The evolution of fitness wearables into mainstream health care hinges on the ability of devices to integrate into electronic health records, uniformity of manufacturer standards, intuitiveness, and the assurance of user privacy and security. It is incumbent on nurse practitioners to educate themselves about the reliability and practicality of fitness trackers for their patient population. Future research should focus on adopting quality standards for all consumer devices, the seamless integration of device data into electronic health records and ensuring personal privacy and security.

Smart Technologies used as Smart Tools in the Management of Cardiovascular Disease and their Future Perspective

Cardiovascular disease (CVD) is a leading cause of morbidity and mortality worldwide. The advent of smart technologies has significantly impacted the management of CVD, offering innovative tools and solutions to improve patient outcomes. Smart technologies have revolutionized and transformed the management of CVD, providing innovative tools to improve patient care, enhance diagnostics, and enable more personalized treatment approaches. These smart tools encompass a wide range of technologies, including wearable devices, mobile applications,3D printing technologies, artificial intelligence (AI), remote monitoring systems, and electronic health records (EHR). They offer numerous advantages, such as real-time monitoring, early detection of abnormalities, remote patient management, and data-driven decision-making. However, they also come with certain limitations and challenges, including data privacy concerns, technical issues, and the need for regulatory frameworks. In this review, despite these challenges, the future of smart technologies in CVD management looks promising, with advancements in AI algorithms, telemedicine platforms, and bio fabrication techniques opening new possibilities for personalized and efficient care. In this article, we also explore the role of smart technologies in CVD management, their advantages and disadvantages, limitations, current applications, and their smart future.

Improving Predictability and Effectiveness in Preventive Digital Health Interventions: Scoping Review

BACKGROUND

Lifestyle-related diseases caused by inadequate diet and physical activity cause premature death, loss of healthy life years, and increased health care costs. Randomized controlled trial (RCT) studies indicate that preventive digital health interventions (P-DHIs) can be effective in preventing these health problems, but the results of these studies are mixed. Adoption studies have identified multiple factors related to individuals and the context in which they live that complicate the transfer of positive results from RCT studies to practical use. Implementation studies have revealed barriers to the large-scale implementation of mobile health (mHealth) solutions in general. Consequently, there is no clear path to delivering predictable outcomes from P-DHIs and achieving effectiveness when scaling up interventions to reduce health problems in society.

OBJECTIVE

This research aimed to expand our understanding of how to increase the outcome predictability of P-DHIs by focusing on physical activity and diet behaviors and amplify our understanding of how to improve effectiveness in large-scale implementations.

METHODS

The research objective was pursued through a multidisciplinary scoping review. This scoping review used the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) as a guide. A comprehensive search of Web of Science and PubMed limited to English-language journal articles published before January 2022 was conducted. Google Scholar was used for hand searches. Information systems theory was used to identify key constructs influencing outcomes of IT in general. Public health and mHealth literature were used to identify factors influencing the adoption of, outcomes from, and implementation of P-DHIs. Finally, the P-DHI investment model was developed based on information systems constructs and factors from the public health and mHealth literature.

RESULTS

In total, 203 articles met the eligibility criteria. The included studies used a variety of methodologies, including literature reviews, interviews, surveys, and RCT studies. The P-DHI investment model suggests which constructs and related factors should be emphasized to increase the predictability of P-DHI outcomes and improve the effectiveness of large-scale implementations.

CONCLUSIONS

The research suggests that outcome predictability could be improved by including descriptions of the constructs and factors in the P-DHI investment model when reporting from empirical studies. Doing so would increase our understanding of when and why P-DHIs succeed or fail. The effectiveness of large-scale implementations may be improved by using the P-DHI investment model to evaluate potential difficulties and possibilities in implementing P-DHIs to create better environments for their use before investing in them and when designing and implementing them. The cost-effectiveness of large-scale implementations is unknown; implementations are far more complicated than just downloading and using apps, and there is uncertainty accompanying implementations given the lack of coordinated control over the constructs and factors that influence the outcome.

Use of Wearable Activity-Monitoring Technologies to Promote Physical Activity in Cancer Survivors: Challenges and Opportunities for Improved Cancer Care

The aim of this review was to explore the acceptability, opportunities, and challenges associated with wearable activity-monitoring technology to increase physical activity (PA) behavior in cancer survivors. A search of Medline, Embase, CINAHL, and SportDiscus was conducted from 1 January 2011 through 3 October 2022. The search was limited to English language, and peer-reviewed original research. Studies were included if they reported the use of an activity monitor in adults (+18 years) with a history of cancer with the intent to motivate PA behavior. Our search identified 1832 published articles, of which 28 met inclusion/exclusion criteria. Eighteen of these studies included post-treatment cancer survivors, eight were on active cancer treatment, and two were long-term cancer survivor studies. ActiGraph accelerometers were the primary technology used to monitor PA behaviors, with Fitbit as the most commonly utilized self-monitoring wearable technology. Overall, wearable activity monitors were found to be an acceptable and useful tool in improving self-awareness, motivating behavioral change, and increasing PA levels. Self-monitoring wearable activity devices have a positive impact on short-term PA behaviors in cancer survivors, but the increase in PA gradually attenuated through the maintenance phase. Further study is needed to evaluate and increase the sustainability of the use of wearable technologies to support PA in cancer survivors.

Hurdles to developing and scaling remote patients' health management tools and systems: a scoping review

BACKGROUND

Despite all the excitement and hype generated regarding the expected transformative impact of digital technology on the healthcare industry, traditional healthcare systems around the world have largely remained unchanged and resultant improvements in developed countries are slower than anticipated. One area which was expected to significantly improve the quality of and access to primary healthcare services in particular is remote patient monitoring and management. Based on a combination of rapid advances in body sensors and information and communication technologies (ICT), it was hoped that remote patient management tools and systems (RPMTSs) would significantly reduce the care burden on traditional healthcare systems as well as health-related costs. However, the uptake or adoption of above systems has been extremely slow and their roll out has not yet properly taken off especially in developing countries where they ought to have made the greatest positive impact.

AIM

The aim of the study was to assess whether or not recent, relevant literature would support the development of in-community, design, deployment and implementation framework based on three factors thought to be important drivers and levers of RPMTS's adoption and scalability.

METHODS

A rapid, scoping review conducted on relevant articles obtained from PubMed, MEDLINE, PMC and Cochrane databases and grey literature on Google and published between 2012 and May 2020, by combining a number of relevant search terms and phrases.

RESULTS

Most RPMTSs are targeted at and focused on a single disease, do not extensively involve patients and clinicians in their early planning and design phases, are not designed to best serve a specific catchment area and are mainly directed at post-hospital, disease management settings. This may be leading to a situation where patients, potential patients and clinicians simply do not make use of these tools, leading to low adoption and scalability thereof.

CONCLUSION

The development of a user-centred, context-dependent, customizable design and deployment framework could potentially increase the adoption and scalability of RPMTSs, if such framework addressed a combination of diseases, prevalent in a given specific catchment area, especially in developing countries with limited financial resources.

A randomized trial to promote physical activity in adult pre-hypertensive and hypertensive patients

The purpose of the pilot study was to determine the effectiveness of wearable activity trackers alone or in combination with behaviour change strategies for promoting physical activity (PA) among individuals with pre-hypertension or hypertension. A sample of 44 adults (68% female and mean age 55) were randomized to receive either a Fitbit Charge HR 3 alone (FB) or the Fitbit in combination with behaviour change strategies (i.e., goal setting, behaviour goal review, adaptive feedback) delivered by a trained health coach (FB+) for 12 weeks. Moderate and vigorous PA (MVPA), steps, and sedentary time (ST) were assessed using ActiGraph wGT3X-BT. The FB+ group significantly increased PA [+1854 (2518) steps/day, p < .01] and MVPA [+26 (34) mins/day, p < .05], and decreased their ST [-63 (73) mins/day, p < .01]. The FB group significantly increased  MVPA [+11 (16) mins/day, p < .05], and decreased their ST [-87 (117) mins/day, p < .01]. Participants in FB+ had a significantly greater increase in MVPA/day compared to FB only with a between-group effect size of 0.6 (p < .05). Using Fitbit for self-monitoring is effective in increasing PA and reducing ST among pre-hypertensive and hypertensive participants. Additional behaviour change support amplified the intervention effectiveness for promoting MVPA.

Monitoring of Cardiovascular Diseases: An Analysis of the Mobile Applications Available in the Google Play Store

Cardiovascular diseases have always been here, but there has been an increase in their numbers over time. Even though there are in the digital world a few applications to help with this kind of problem, there are not enough to fulfill the needs of the patients. This study reviews mobile applications that allow patients to monitor and report cardiovascular diseases. It presents a review of 14 mobile applications that were free to download in Portugal and classified and compared according to their characteristics. The selection criteria combined the following keywords: “patient”, “cardiac/or heart”, “report”, and (“tracking” or “monitoring”). Based on the analysis, we point out the errors of the applications and present some solutions. To finish, we investigated how mobile applications can help patients track and self-report cardiovascular diseases.

"Listen to Your Immune System When It's Calling for You": Monitoring Autoimmune Diseases Using the iShU App

The immune system plays a key role in protecting living beings against bacteria, viruses, and fungi, among other pathogens, which may be harmful and represent a threat to our own health. However, for reasons that are not fully understood, in some people this protective mechanism accidentally attacks the organs and tissues, thus causing inflammation and leads to the development of autoimmune diseases. Remote monitoring of human health involves the use of sensor network technology as a means of capturing patient data, and wearable devices, such as smartwatches, have lately been considered good collectors of biofeedback data, owing to their easy connectivity with a mHealth system. Moreover, the use of gamification may encourage the frequent usage of such devices and behavior changes to improve self-care for autoimmune diseases. This study reports on the use of wearable sensors for inflammation surveillance and autoimmune disease management based on a literature search and evaluation of an app prototype with fifteen stakeholders, in which eight participants were diagnosed with autoimmune or inflammatory diseases and four were healthcare professionals. Of these, six were experts in human–computer interaction to assess critical aspects of user experience. The developed prototype allows the monitoring of autoimmune diseases in pre-, during-, and post-inflammatory crises, meeting the personal needs of people with this health condition. The findings suggest that the proposed prototype—iShU—achieves its purpose and the overall experience may serve as a foundation for designing inflammation surveillance and autoimmune disease management monitoring solutions.

Wearable Devices for Physical Monitoring of Heart: A Review

Cardiovascular diseases (CVDs) are the leading cause of death globally. An effective strategy to mitigate the burden of CVDs has been to monitor patients' biomedical variables during daily activities with wearable technology. Nowadays, technological advance has contributed to wearables technology by reducing the size of the devices, improving the accuracy of sensing biomedical variables to be devices with relatively low energy consumption that can manage security and privacy of the patient's medical information, have adaptability to any data storage system, and have reasonable costs with regard to the traditional scheme where the patient must go to a hospital for an electrocardiogram, thus contributing a serious option in diagnosis and treatment of CVDs. In this work, we review commercial and noncommercial wearable devices used to monitor CVD biomedical variables. Our main findings revealed that commercial wearables usually include smart wristbands, patches, and smartwatches, and they generally monitor variables such as heart rate, blood oxygen saturation, and electrocardiogram data. Noncommercial wearables focus on monitoring electrocardiogram and photoplethysmography data, and they mostly include accelerometers and smartwatches for detecting atrial fibrillation and heart failure. However, using wearable devices without healthy personal habits will cause disappointing results in the patient's health.

Adopting wearables to customize health insurance contributions: a ranking-type Delphi

Background

Wearables, as small portable computer systems worn on the body, can track user fitness and health data, which can be used to customize health insurance contributions individually. In particular, insured individuals with a healthy lifestyle can receive a reduction of their contributions to be paid. However, this potential is hardly used in practice.

Objective

This study aims to identify which barrier factors impede the usage of wearables for assessing individual risk scores for health insurances, despite its technological feasibility, and to rank these barriers according to their relevance.

Methods

To reach these goals, we conduct a ranking-type Delphi study with the following three stages. First, we collected possible barrier factors from a panel of 16 experts and consolidated them to a list of 11 barrier categories. Second, the panel was asked to rank them regarding their relevance. Third, to enhance the panel consensus, the ranking was revealed to the experts, who were then asked to re-rank the barriers.

Results

The results suggest that regulation is the most important barrier. Other relevant barriers are false or inaccurate measurements and application errors caused by the users. Additionally, insurers could lack the required technological competence to use the wearable data appropriately.

Conclusion

A wider use of wearables and health apps could be achieved through regulatory modifications, especially regarding privacy issues. Even after assuring stricter regulations, users’ privacy concerns could partly remain, if the data exchange between wearables manufacturers, health app providers, and health insurers does not become more transparent.

Perspective: Opportunities and Challenges of Technology Tools in Dietary and Activity Assessment: Bridging Stakeholder Viewpoints

The science and tools of measuring energy intake and output in humans have rapidly advanced in the last decade. Engineered devices such as wearables and sensors, software applications, and Web-based tools are now ubiquitous in both research and consumer environments. The assessment of energy expenditure in particular has progressed from reliance on self-report instruments to advanced technologies requiring collaboration across multiple disciplines, from optics to accelerometry. In contrast, assessing energy intake still heavily relies on self-report mechanisms. Although these tools have improved, moving from paper-based to online reporting, considerable room for refinement remains in existing tools, and great opportunities exist for novel, transformational tools, including those using spectroscopy and chemo-sensing. This report reviews the state of the science, and the opportunities and challenges in existing and emerging technologies, from the perspectives of 3 key stakeholders: researchers, users, and developers. Each stakeholder approaches these tools with unique requirements: researchers are concerned with validity, accuracy, data detail and abundance, and ethical use; users with ease of use and privacy; and developers with high adherence and utilization, intellectual property, licensing rights, and monetization. Cross-cutting concerns include frequent updating and integration of the food and nutrient databases on which assessments rely, improving accessibility and reducing disparities in use, and maintaining reliable technical assistance. These contextual challenges are discussed in terms of opportunities and further steps in the direction of personalized health.

Group-Based Exercise in CKD Stage 3b to 4: A Randomized Clinical Trial

Rationale & Objective

We aimed to test interventions to improve physical activity in persons with advanced chronic kidney disease not yet receiving dialysis.

Study Design

Randomized controlled trial with parallel-group design.

Setting & Participants

We embedded a pragmatic referral to exercise programming in high-volume kidney clinics servicing diverse populations in San Jose, CA, and Atlanta, GA. We recruited 56 participants with estimated glomerular filtration rates < 45 mL/min/1.73 m².

Interventions

We randomly assigned participants to a mobile health (mHealth) group—wearable activity trackers and fitness professional counseling, or an Exercise is Medicine intervention framework (EIM) group—mHealth components plus twice-weekly small-group directed exercise sessions customized to persons with kidney disease. We performed assessments at baseline, 8 weeks at the end of active intervention, and 16 weeks after passive follow-up and used multilevel mixed models to assess between-group differences.

Outcomes

Activity tracker total daily step count.

Results

Of 56 participants, 86% belonged to a racial/ethnic minority group; randomly assigned groups were well balanced on baseline step count. In intention-to-treat analyses, the EIM and mHealth groups both experienced declines in daily step counts, but there was an attenuated reduction in light intensity physical activity (standard error 0.2 [5.8] vs −8.5 [5.4] min/d; P = 0.08) in the EIM compared with the mHealth group at 8 weeks. In as-treated analyses, total daily step count, distance covered, and light and moderate-vigorous activity minutes per day improved in the EIM group and declined in the mHealth group at 8 weeks (standard error +335 [506] vs −884 [340] steps per day; P = 0.05; P < 0.05 for secondary measures), but group differences faded at 16 weeks. There were no differences in quality-of-life and mental health measures during the study.

Limitations

Small sample size, limited duration of study, assessment of intermediate outcomes (steps per day).

Conclusions

A clinic-integrated referral to small-group exercise sessions is feasible, safe, and moderately effective in improving physical activity in an underserved population with high comorbid conditions.

Funding

Normon S Coplon Applied Pragmatic Clinical Research program.

Trial Registration

NCT03311763

Perceptions of physical activity and technology enabled exercise interventions among people with advanced chronic kidney disease: a qualitative study

BACKGROUND

Exercise improves health outcomes and quality of life in persons with chronic kidney disease (CKD). The numbers of persons with advanced CKD meeting physical activity guidelines however is low. We undertook a qualitative study of men and women aged 36-74 from various race/ethnic populations with advanced CKD not requiring dialysis to describe their experiences and opinions around prior physical activity, motivating factors for and barriers to exercise, and perceptions of exercise-promoting technology and group-based programming designed to improve physical activity levels.

METHODS

Nineteen persons with advanced CKD not requiring dialysis were interviewed at two high volume nephrology clinics enriched with racial/ethnic minority patients (Emory University and Santa Clara Valley Medical Center). We used thematic analysis to identify dominant themes (n = 4) and subthemes (n = 19) around exercise experience, barriers, motivators, views, and preferences.

RESULTS

Four dominant themes and 19 subthemes were identified. The most common motivators to exercise included physical and mental health benefits, appearance, improvement in energy levels, and potential social interaction in group-based programs. Common barriers included health concerns, particularly complications related to other co-morbidities, as well as time and transportation constraints. Participants were skeptical of exercise programs solely reliant on technology.

CONCLUSIONS

The use of group-based exercise programs may motivate persons with CKD to increase exercise levels, while programs entirely based on technology may be less effective.

Irrelevant Gadgets or a Source of Worry

Wearable activity trackers are routinely applied in physical activity (PA) interventions in late life, but there is little research that focuses on older adults' perspectives on the technology. We conducted a qualitative study with 24 older persons to explore their perspective on wearables and PA. First, we discussed their relationship with PA and wearable trackers during focus groups. Next, nine participants crafted prototypes for wearables during co-design sessions. Through Thematic Analysis, we identified two main themes: (1) PA is personal in terms of preferred activities and reasons for PA, and (2) wearables are an emotional technology, causing negative emotions when resembling medical trackers or pressurizing to perform. We followed upon these results through a survey with 41 participants, which further highlighted individual differences in the perception of wearables. We conclude with questions to guide the design of wearables and reflect on their role to support PA in late life.

Multi Agent Architecture for Automated Health Coaching

For software applications in health coaching domains to be effective, it is vital that they address issues of privacy, modularity, scalability, individualization, data integration, transferability, coordination and flexibility. In this paper, we propose a novel generic multi-agent architecture which serves as a template for health coaching applications involving wearable sensors. Analyzer and communication modules allow different functionalities like goal formation, planning, scheduling, event detection, learning, inter-agent + human communication and long-term data collection, based on the capabilities of the underlying sensor platforms. To show the flexibility of our proposed architecture, we have successfully built two different health coaching systems with the proposed architecture: (1) a static system based on the Fitbit platform where the coaching is done at specific preset times to encourage increased physical activity, and (2) a dynamic system based on the Apple Watch platform where the smart coach adapts and learns when to intervene to encourage physical activity and reduce sedentary behavior.

Telemonitoring of Real-World Health Data in Cardiology: A Systematic Review

Background: New sensor technologies in wearables and other consumer health devices open up promising opportunities to collect real-world data. As cardiovascular diseases remain the number one reason for disease and mortality worldwide, cardiology offers potent monitoring use cases with patients in their out-of-hospital daily routines. Therefore, the aim of this systematic review is to investigate the status quo of studies monitoring patients with cardiovascular risks and patients suffering from cardiovascular diseases in a telemedical setting using not only a smartphone-based app, but also consumer health devices such as wearables and other sensor-based devices. Methods: A literature search was conducted across five databases, and the results were examined according to the study protocols, technical approaches, and qualitative and quantitative parameters measured. Results: Out of 166 articles, 8 studies were included in this systematic review; these cover interventional and observational monitoring approaches in the area of cardiovascular diseases, heart failure, and atrial fibrillation using various app, wearable, and health device combinations. Conclusions: Depending on the researcher’s motivation, a fusion of apps, patient-reported outcome measures, and non-invasive sensors can be orchestrated in a meaningful way, adding major contributions to monitoring concepts for both individual patients and larger cohorts.

Pricing through health apps generated data-Digital dividend as a game changer: Discrete choice experiment

Objectives

The objective of this paper is to study under which circumstances wearable and health app users would accept a compensation payment, namely a digital dividend, to share their self-tracked health data.

Methods

We conducted a discrete choice experiment alternative, a separated adaptive dual response. We chose this approach to reduce extreme response behavior, considering the emotionally-charged topic of health data sales, and to measure willingness to accept. Previous experiments in lab settings led to demands for high monetary compensation. After a first online survey and two pre-studies, we validated four attributes for the final online study: monthly bonus payment, stakeholder handling the data (e.g., health insurer, pharmaceutical or medical device companies, universities), type of data, and data sales to third parties. We used a random utility framework to evaluate individual choice preferences. To test the expected prices of the main study for robustness, we assigned respondents randomly to one of two identical questionnaires with varying price ranges.

Results

Over a period of three weeks, 842 respondents participated in the main survey, and 272 respondents participated in the second survey. The participants considered transparency about data processing and no further data sales to third parties as very important to the decision to share data with different stakeholders, as well as adequate monetary compensation. Price expectations resulting from the experiment were high; pharmaceutical and medical device companies would have to pay an average digital dividend of 237.30€/month for patient generated health data of all types. We also observed an anchor effect, which means that people formed price expectations during the process and not ex ante. We found a bimodal distribution between relatively low price expectations and relatively high price expectations, which shows that personal data selling is a divisive societal issue. However, the results indicate that a digital dividend could be an accepted economic incentive system to gather large-scale, self-tracked data for research and development purposes. After the COVID-19 crisis, price expectations might change due to public sensitization to the need for big data research on patient generated health data.

Conclusion

A continuing success of existing data donation models is highly unlikely. The health care sector needs to develop transparency and trust in data processing. An adequate digital dividend could be an effective long-term measure to convince a diverse and large group of people to share high-quality, continuous data for research purposes.

Smartphone-Based Interventions for Physical Activity Promotion: Scoping Review of the Evidence Over the Last 10 Years

BACKGROUND

Several reviews of mobile health (mHealth) physical activity (PA) interventions suggest their beneficial effects on behavior change in adolescents and adults. Owing to the ubiquitous presence of smartphones, their use in mHealth PA interventions seems obvious; nevertheless, there are gaps in the literature on the evaluation reporting processes and best practices of such interventions.

OBJECTIVE

The primary objective of this review is to analyze the development and evaluation trajectory of smartphone-based mHealth PA interventions and to review systematic theory- and evidence-based practices and methods that are implemented along this trajectory. The secondary objective is to identify the range of evidence (both quantitative and qualitative) available on smartphone-based mHealth PA interventions to provide a comprehensive tabular and narrative review of the available literature in terms of its nature, features, and volume.

METHODS

We conducted a scoping review of qualitative and quantitative studies examining smartphone-based PA interventions published between 2008 and 2018. In line with scoping review guidelines, studies were not rejected based on their research design or quality. This review, therefore, includes experimental and descriptive studies, as well as reviews addressing smartphone-based mHealth interventions aimed at promoting PA in all age groups (with a subanalysis conducted for adolescents). Two groups of studies were additionally included: reviews or content analyses of PA trackers and meta-analyses exploring behavior change techniques and their efficacy.

RESULTS

Included articles (N=148) were categorized into 10 groups: commercial smartphone app content analyses, smartphone-based intervention review studies, activity tracker content analyses, activity tracker review studies, meta-analyses of PA intervention studies, smartphone-based intervention studies, qualitative formative studies, app development descriptive studies, qualitative follow-up studies, and other related articles. Only 24 articles targeted children or adolescents (age range: 5-19 years). There is no agreed evaluation framework or taxonomy to code or report smartphone-based PA interventions. Researchers did not state the coding method, used various evaluation frameworks, or used different versions of behavior change technique taxonomies. In addition, there is no consensus on the best behavior change theory or model that should be used in smartphone-based interventions for PA promotion. Commonly reported systematic practices and methods have been successfully identified. They include PA recommendations, trial designs (randomized controlled trials, experimental trials, and rapid design trials), mixed methods data collection (surveys, questionnaires, interviews, and focus group discussions), scales to assess app quality, and industry-recognized reporting guidelines.

CONCLUSIONS

Smartphone-based mHealth interventions aimed at promoting PA showed promising results for behavior change. Although there is a plethora of published studies on the adult target group, the number of studies and consequently the evidence base for adolescents is limited. Overall, the efficacy of smartphone-based mHealth PA interventions can be considerably improved through a more systematic approach of developing, reporting, and coding of the interventions.

Returning Cardiac Rhythm Data to Patients: Opportunities and Challenges

Spurred by federal legislation, professional organizations, and patients themselves, patient access to data from electronic cardiac devices is increasingly transparent. Patients can collect data through consumer devices and access data traditionally shared only with health care providers. These data may improve screening, self-management, and shared decision-making for cardiac arrhythmias, but challenges remain, including patient comprehension, communication with providers, and sustained engagement. Ways to address these challenges include leveraging visualizations that support comprehension, involving patients in designing and developing patient-facing digital tools, and establishing clear practices and goals for data exchange with health care providers.

Wearable Monitors Facilitate Exercise in Adult and Pediatric Stem Cell Transplant

Hematopoietic stem cell transplant (HSCT) is a potentially curative treatment for hematopoietic malignancies, complicated by decreased performance status and quality of life. Exercise therapy improves outcomes in HSCT, but several barriers have prevented exercise from becoming routine clinical practice. Based on existing data that wearable technologies facilitate exercise participation in other sedentary and chronic illness populations, we propose the novel hypothesis that wearable technologies are a valuable tool in transcending barriers and developing exercise therapy programs for HSCT patients.

Mobile Health Applications, Cancer Survivors, and Lifestyle Modification: An Integrative Review

Cancer survivors' well-being is threatened by the risk of cancer recurrence and the increased risk of chronic diseases resulting from cancer treatments. Improving lifestyle behaviors attenuates these risks. Traditional approaches to lifestyle modification (ie, counseling) are expensive, require significant human resources, and are difficult to scale. Mobile health interventions offer a novel alternative to traditional approaches. However, to date, systematic reviews have yet to examine the use of mobile health interventions for lifestyle behavior improvement among cancer survivors. The objectives of this integrative review were to synthesize research findings, critically appraise the scientific literature, examine the use of theory in intervention design, and identify survivors' preferences in using mobile health interventions for lifestyle improvement. Nineteen articles met eligibility requirements. Only two studies used quantitative methods. Study quality was low, and only one study reported the use of theory in app design. Unfortunately, the evidence has not yet sufficiently matured, in quality or in rigor, to make recommendations on how to improve health behaviors or outcomes. However, six themes emerged as important considerations for intervention development for cancer survivors (app features/functionality, social relationships/support, provider relationships/support, app content, app acceptability, and barriers to use). These findings underscored the need for rigorous, efficacy studies before the use of mobile health interventions can be safely recommended for cancer survivors.

Comparing Methods to Identify Wear-Time Intervals for Physical Activity With the Fitbit Charge 2

There is no established method for processing data from commercially available physical activity trackers. This study aims to develop a standardized approach to defining valid wear time for use in future interventions and analyses. Sixteen African American women (mean age = 62.1 years and mean body mass index = 35.5 kg/m2) wore the Fitbit Charge 2 for 20 days. Method 1 defined a valid day as ≥10-hr wear time with heart rate data. Method 2 removed minutes without heart rate data, minutes with heart rate ≤ mean - 2 SDs below mean and ≤2 steps, and nighttime. Linear regression modeled steps per day per week change. Using Method 1 (n = 292 person-days), participants had 20.5 (SD = 4.3) hr wear time per day compared with 16.3 (SD = 2.2) hr using Method 2 (n = 282) (p < .0001). With Method 1, participants took 7,436 (SD = 3,543) steps per day compared with 7,298 (SD = 3,501) steps per day with Method 2 (p = .64). The proposed algorithm represents a novel approach to standardizing data generated by physical activity trackers. Future studies are needed to improve the accuracy of physical activity data sets.

Understanding Gaps Between Daily Living and Clinical Settings in Chronic Disease Management: Qualitative Study

BACKGROUND

Management of chronic conditions entails numerous activities in both clinical and daily living settings. Activities across these settings interact, creating a high potential for a gap to occur if there is an inconsistency or disconnect between controlled clinical settings and complex daily living environments.

OBJECTIVE

The aim of this study is to characterize gaps (from the patient's perspective) between health-related activities across home-based and clinical settings using anticoagulation treatment as an example. The causes, consequences, and mitigation strategies (reported by patients) were identified to understand these gaps. We conceptualized gaps as latent phenomena (ie, a break in continuity).

METHODS

Patients (n=39) and providers (n=4) from the anticoagulation clinic of an urban, western mountain health care system were recruited. Data were collected through primary interviews with patients, patient journaling with tablet computers, exit interviews with patients, and provider interviews. Data were analyzed qualitatively using a theory-driven approach and framework method of analysis.

RESULTS

The causes of gaps included clinician recommendations not fitting into patients' daily routines, recommendations not fitting into patients' living contexts, and information not transferred across settings. The consequences of these gaps included increased cognitive and physical workload on the patient, poor patient satisfaction, and compromised adherence to the therapy plan. We identified resources and strategies used to overcome these consequences as patient-generated strategies, routines, collaborative management, social environment, and tools and technologies.

CONCLUSIONS

Understanding gaps, their consequences, and mitigating strategies can lead to the development of interventions that help narrow these gaps. Such interventions could take the form of collaborative health information technologies, novel patient and clinician education initiatives, and programs that strongly integrate health systems and community resources. Current technologies are insufficient to narrow the gaps between clinical and daily living settings due to the limited number and types of routines that are tracked.

Patient Use of Cardiovascular Devices and Apps: Are We Getting Our Money's Worth?

The evolution of technology makes it likely that a large number of people will invest in and use health-related mobile applications and wearable devices. Yet the question remains: Do these technology-based interventions modify health behavior and improve health…and are we getting our money's worth? The vast majority of studies concerning health-related apps and wearable devices have small sample sizes and short time spans of 6 months or less, so it is not clear if these durations were determined by lack of consistent use over time. Furthermore, many of the most popular applications have not been subjected to randomized trials. Overall, the small demonstrated improvements in outcomes are often associated with professional involvement from clinicians, coaches, or diabetes educators provided in conjunction with use of mobile health (mHealth) platforms. This paper explores the use of mHealth technologies that address cardiovascular disease/prevention (eg, diabetes, diet, physical activity, and associated weight loss) and discusses the lack of adequate evidence to support even minimal patient investment in mobile applications or wearable devices at this time.

Sensor, Wearable, and Remote Patient Monitoring Competencies for Clinical Care and Training: Scoping Review

Sensor, wearable, and remote patient monitoring technologies are typically used in conjunction with video and/or in-person care for a variety of interventions and care outcomes. This scoping review identifies clinical skills (i.e., competencies) needed to ensure quality care and approaches for organizations to implement and evaluate these technologies. The literature search focused on four concept areas: (1) competencies; (2) sensors, wearables, and remote patient monitoring; (3) mobile, asynchronous, and synchronous technologies; and (4) behavioral health. From 2846 potential references, two authors assessed abstracts for 2828 and, full text for 521, with 111 papers directly relevant to the concept areas. These new technologies integrate health, lifestyle, and clinical care, and they contextually change the culture of care and training-with more time for engagement, continuity of experience, and dynamic data for decision-making for both patients and clinicians. This poses challenges for users (e.g., keeping up, education/training, skills) and healthcare organizations. Based on the clinical studies and informed by clinical informatics, video, social media, and mobile health, a framework of competencies is proposed with three learner levels (novice/advanced beginner, competent/proficient, advanced/expert). Examples are provided to apply the competencies to care, and suggestions are offered on curricular methodologies, faculty development, and institutional practices (e-culture, professionalism, change). Some academic health centers and health systems may naturally assume that clinicians and systems are adapting, but clinical, technological, and administrative workflow-much less skill development-lags. Competencies need to be discrete, measurable, implemented, and evaluated to ensure the quality of care and integrate missions.

Wrist-Worn Activity Trackers in Laboratory and Free-Living Settings for Patients With Chronic Pain: Criterion Validity Study

BACKGROUND

Physical activity is evidently a crucial part of the rehabilitation process for patients with chronic pain. Modern wrist-worn activity tracking devices seemingly have a great potential to provide objective feedback and assist in the adoption of healthy physical activity behavior by supplying data of energy expenditure expressed as metabolic equivalent of task units (MET). However, no studies of any wrist-worn activity tracking devices' have examined criterion validity in estimating energy expenditure, heart rate, or step count in patients with chronic pain.

OBJECTIVE

The aim was to determine the criterion validity of wrist-worn activity tracking devices for estimations of energy expenditure, heart rate, and step count in a controlled laboratory setting and free-living settings for patients with chronic pain.

METHODS

In this combined laboratory and field validation study, energy expenditure, heart rate, and step count were simultaneously estimated by a wrist-worn activity tracker (Fitbit Versa), indirect calorimetry (Jaeger Oxycon Pro), and a research-grade hip-worn accelerometer (ActiGraph GT3X) during treadmill walking at 3 speeds (3.0 km/h, 4.5 km/h, and 6.0 km/h) in the laboratory setting. Energy expenditure and step count were also estimated by the wrist-worn activity tracker in free-living settings for 72 hours. The criterion validity of each measure was determined using intraclass and Spearman correlation, Bland-Altman plots, and mean absolute percentage error. An analysis of variance was used to determine whether there were any significant systematic differences between estimations.

RESULTS

A total of 42 patients (age: 25-66 years; male: 10/42, 24%; female: 32/42, 76%), living with chronic pain (duration, in years: mean 9, SD 6.72) were included. At baseline, their mean pain intensity was 3.5 (SD 1.1) out of 6 (Multidimensional Pain Inventory, Swedish version). Results showed that the wrist-worn activity tracking device (Fitbit Versa) systematically overestimated energy expenditure when compared to the criterion standard (Jaeger Oxycon Pro) and the relative criterion standard (ActiGraph GT3X). Poor agreement and poor correlation were shown between Fitbit Versa and both Jaeger Oxycon Pro and ActiGraph GT3X for estimated energy expenditure at all treadmill speeds. Estimations of heart rate demonstrated poor to fair agreement during laboratory-based treadmill walks. For step count, the wrist-worn devices showed fair agreement and fair correlation at most treadmill speeds. In free-living settings; however, the agreement for step count between the wrist-worn device and waist-worn accelerometer was good, and the correlation was excellent.

CONCLUSIONS

The wrist-worn device systematically overestimated energy expenditure and showed poor agreement and correlation compared to the criterion standard (Jaeger Oxycon Pro) and the relative criterion standard (ActiGraph GT3X), which needs to be considered when used clinically. Step count measured with a wrist-worn device, however, seemed to be a valid estimation, suggesting that future guidelines could include such variables in this group with chronic pain.

PatientDataChain: A Blockchain-Based Approach to Integrate Personal Health Records

Currently there is not a single trusted infrastructure used for the exchange and storage of medical data along the healthcare value chain and, thus, there is no platform used for monitoring patients' traceability within the entire healthcare chain. This situation leads to difficult communication and increased procedural costs, and thus it limits healthcare players from developing a better understanding and know-how of patients' traceability that could further boost innovation and development of the best-fitted health services. PatientDataChain blockchain-based technology is a novel approach, based on a decentralized healthcare infrastructure that incorporates a trust layer in the healthcare value chain. Our aim was to provide an integrated vision based on interoperability principles, that relies on the usage of specific sensors from various wearable devices, allowing us to collect specific data from patients' medical records. Interconnecting different healthcare providers, the collected data is integrated into a unitary personal health records (PHR) system, where the patient is the owner of his/her data. The decentralized nature of PatientDataChain, based on blockchain technology, leveraged the proper context to create a novel and improved data-sharing and exchange system, which is secure, flexible, and reliable. This approach brings increased benefits to data confidentiality and privacy, while providing secure access to patient medical records. This paper presents the design, implementation, and experimental validation of our proposed system, called PatientDataChain. The original contributions of our paper include the definition of the concept of unifying the entire healthcare value chain, the design of the architectural model of the system, the development of the system components, as well as the validation through a proof of concept (PoC) conducted with a medical clinic from Bucharest, using a dataset of 100 patients and over 1000 transactions. The proof of concept demonstrated the feasibility of the model in integrating the personal health records from heterogeneous sources (healthcare systems and sensors) in a unified, decentralized PHR system, with enhanced data exchange among healthcare players.

Potential Benefits and Risks Resulting From the Introduction of Health Apps and Wearables Into the German Statutory Health Care System: Scoping Review

BACKGROUND

Germany is the first country worldwide that has introduced a digital care act as an incentive system to enhance the use of digital health devices, namely health apps and wearables, among its population. The act allows physicians to prescribe statutory financed and previously certified health apps and wearables to patients. This initiative has the potential to improve treatment quality through better disease management and monitoring.

OBJECTIVE

The aim of this paper was to outline the key concepts related to the potential risks and benefits discussed in the current literature about health apps and wearables. Furthermore, this study aimed to answer the research question: Which risks and benefits may result from the implementation of the digital care act in Germany?

METHODS

We conducted the scoping study by searching the databases PubMed, Google Scholar, and JMIR using the keywords health apps and wearables. We discussed 55 of 136 identified articles published in the English language from 2015 to March 2019 in this paper using a qualitative thematic analysis approach.

RESULTS

We identified four key themes within the articles: Effectivity of health apps and wearables to improve health; users of health apps and wearables; the potential of bring-your-own, self-tracked data; and concerns and data privacy risks. Within these themes, we identified three main stages of benefits for the German health care system: Usage of health apps and wearables; continuing to use health apps and wearables; and sharing bring-your-own; self-tracked data with different agents in the health care sector.

CONCLUSIONS

The digital care act could lead to an improvement in treatment quality through better patient monitoring, disease management, personalized therapy, and better health education. However, physicians should play an active role in recommending and supervising health app use to reach digital-illiterate or health-illiterate people. Age must not be an exclusion criterion. Yet, concerns about data privacy and security are very strong in Germany. Transparency about data processing should be provided at all times for continuing success of the digital care act in Germany.

Prescribing Physical Activity in Parks and Nature: Health Care Provider Insights on Park Prescription Programs

BACKGROUND

Health care providers (HCPs) promoting physical activity (PA) through programs such as Park Prescriptions (ParkRx) are gaining momentum. However, it is difficult to realize provider PA practices and program interest, and differences in program success exist by provider type (eg, primary vs secondary). This study explored HCPs' (1) PA counseling practices, (2) knowledge/interest in ParkRx, (3) barriers and resources needed to implement PA counseling and ParkRx programs, and (4) differences in primary versus secondary HCPs.

METHODS

An e-survey administered in Spring/Summer 2018 to HCPs in 3 states examined study objectives.

RESULTS

Respondents (n = 278) were mostly primary (58.3%) HCPs. The majority asked about patient PA habits and offered PA counseling (mean = 5.0, SD = 1.5; mean = 4.8, SD = 1.5), but few provided written prescriptions (mean = 2.5, SD = 1.6). Providers were satisfied with their PA counseling knowledge (mean = 3.8, SD = 1.0) but not with prescribing practices (mean = 3.2, SD = 1.1). Secondary HCPs placed higher importance (P = .012) and provided significantly more written PA prescriptions (P = .005). Time was a common barrier to prescribing PA (mean = 3.4, SD = 1.2), though more so for primary HCPs (P = .000). Although few HCPs knew about ParkRx programs, 81.6% expressed interest. Access to park information and community partnerships was an important resource for program implementation.

CONCLUSIONS

HCPs underutilize PA prescriptions. Despite little awareness, HCPs were interested in ParkRx programs.

Wearable device adoption among older adults: A mixed-methods study

•

Using a mixed-methods approach, we analyzed the adoption of wearable devices among older adults.

•

Perceived complexity of devices (specifically interpreting the outputs) is the most salient deterrent of adoption.

•

The effect of cognitive age on adoption is moderated by subjective well-being (SWB).

•

Cognitive age negatively (vs. positively) impacts the older adults’ adoption intention when their SWB is high (vs. low).

Recently, the popularity of smart wearable technologies, such as Fitbit, has significantly increased. There are numerous potential benefits in using these devices, especially among seniors. Yet, little is known about seniors’ adoption behavior. Through a mixed-methods approach, this study investigates the factors that impact seniors’ intention to use wearable devices. Results from an online survey and interviews showed that seniors’ perception of the complexity of working with these devices is a barrier to their adoption decisions. Looking more deeply into the role of complexity revealed that seniors’ concern about the complexity of reading and interpreting the output of wearable devices is the main deterring element. Furthermore, we explored the role of two important elements: seniors’ cognitive age, and the influence of their subjective well-being on their adoption behavior. Results demonstrated that cognitive age does not significantly impact use intention by itself; nonetheless, subjective well-being moderates its effect. This result revealed an interesting finding, which is that the influence of cognitive age on seniors’ use intention depends on seniors’ level of subjective well-being. When seniors’ subjective well-being is low, surprisingly, cognitive age increases seniors’ intention to use the device. These findings provide interesting implications for practice and future research.

Metrology and sensors as dermo-cosmetic technology opportunities for a change of paradigm

OBJECTIVE

Metrology and measures are changing the way patients and consumers behave and help find new, more effective solutions.

METHODS

This Review and Prospective Paper identifies applications in the field of dermatology and beauty tech.

RESULTS

The review of skincare as well as dermatological applications and analysis provides a comprehensive picture of the dynamics in the process of impacting the complete value chain in the field of dermo-cosmetics, as well as the opportunities offered by a strict approach around new and innovative measures, especially in the field of better patient/consumer knowledge, understanding, and personalized solution offering. It identifies the new business models or opportunities for the cosmetic industry.

CONCLUSION

Adapting metrology and measures to skincare is a significant opportunity to change the way things are done today.

Technology Acceptance in Mobile Health: Scoping Review of Definitions, Models, and Measurement

BACKGROUND

Designing technologies that users will be interested in, start using, and keep using has long been a challenge. In the health domain, the question of technology acceptance is even more important, as the possible intrusiveness of technologies could lead to patients refusing to even try them. Developers and researchers must address this question not only in the design and evaluation of new health care technologies but also across the different stages of the user's journey. Although a range of definitions for these stages exists, many researchers conflate related terms, and the field would benefit from a coherent set of definitions and associated measurement approaches.

OBJECTIVE

This review aims to explore how technology acceptance is interpreted and measured in mobile health (mHealth) literature. We seek to compare the treatment of acceptance in mHealth research with existing definitions and models, identify potential gaps, and contribute to the clarification of the process of technology acceptance.

METHODS

We searched the PubMed database for publications indexed under the Medical Subject Headings terms "Patient Acceptance of Health Care" and "Mobile Applications." We included publications that (1) contained at least one of the terms "acceptability," "acceptance," "adoption," "accept," or "adopt"; and (2) defined the term. The final corpus included 68 relevant studies.

RESULTS

Several interpretations are associated with technology acceptance, few consistent with existing definitions. Although the literature has influenced the interpretation of the concept, usage is not homogeneous, and models are not adapted to populations with particular needs. The prevalence of measurement by custom surveys suggests a lack of standardized measurement tools.

CONCLUSIONS

Definitions from the literature were published separately, which may contribute to inconsistent usage. A definition framework would bring coherence to the reporting of results, facilitating the replication and comparison of studies. We propose the Technology Acceptance Lifecycle, consolidating existing definitions, articulating the different stages of technology acceptance, and providing an explicit terminology. Our findings illustrate the need for a common definition and measurement framework and the importance of viewing technology acceptance as a staged process, with adapted measurement methods for each stage.

Human-Centered Design Strategies for Device Selection in mHealth Programs: Development of a Novel Framework and Case Study

BACKGROUND

Despite the increasing use of remote measurement technologies (RMT) such as wearables or biosensors in health care programs, challenges associated with selecting and implementing these technologies persist. Many health care programs that use RMT rely on commercially available, "off-the-shelf" devices to collect patient data. However, validation of these devices is sparse, the technology landscape is constantly changing, relative benefits between device options are often unclear, and research on patient and health care provider preferences is often lacking.

OBJECTIVE

To address these common challenges, we propose a novel device selection framework extrapolated from human-centered design principles, which are commonly used in de novo digital health product design. We then present a case study in which we used the framework to identify, test, select, and implement off-the-shelf devices for the Remote Assessment of Disease and Relapse-Central Nervous System (RADAR-CNS) consortium, a research program using RMT to study central nervous system disease progression.

METHODS

The RADAR-CNS device selection framework describes a human-centered approach to device selection for mobile health programs. The framework guides study designers through stakeholder engagement, technology landscaping, rapid proof of concept testing, and creative problem solving to develop device selection criteria and a robust implementation strategy. It also describes a method for considering compromises when tensions between stakeholder needs occur.

RESULTS

The framework successfully guided device selection for the RADAR-CNS study on relapse in multiple sclerosis. In the initial stage, we engaged a multidisciplinary team of patients, health care professionals, researchers, and technologists to identify our primary device-related goals. We desired regular home-based measurements of gait, balance, fatigue, heart rate, and sleep over the course of the study. However, devices and measurement methods had to be user friendly, secure, and able to produce high quality data. In the second stage, we iteratively refined our strategy and selected devices based on technological and regulatory constraints, user feedback, and research goals. At several points, we used this method to devise compromises that addressed conflicting stakeholder needs. We then implemented a feedback mechanism into the study to gather lessons about devices to improve future versions of the RADAR-CNS program.

CONCLUSIONS

The RADAR device selection framework provides a structured yet flexible approach to device selection for health care programs and can be used to systematically approach complex decisions that require teams to consider patient experiences alongside scientific priorities and logistical, technical, or regulatory constraints.

Physical Activity Assessment and Counseling in Pediatric Clinical Settings

Physical activity plays an important role in children's cardiovascular health, musculoskeletal health, mental and behavioral health, and physical, social, and cognitive development. Despite the importance in children's lives, pediatricians are unfamiliar with assessment and guidance regarding physical activity in children. With the release of the 2018 Physical Activity Guidelines by the US Department of Health and Human Services, pediatricians play a critical role in encouraging physical activity in children through assessing physical activity and physical literacy; providing guidance toward meeting recommendations by children and their families; advocating for opportunities for physical activity for all children in schools, communities, and hospitals; setting an example and remaining physically active personally; advocating for the use of assessment tools and insurance coverage of physical activity and physical literacy screening; and incorporating physical activity assessment and prescription in medical school curricula.

Liver transplant recipients' experiences and perspectives of a telehealth-delivered lifestyle programme: A qualitative study

INTRODUCTION

Dietary modification and exercise are encouraged to address cardiometabolic risk factors after solid organ transplantation. However, the lived experience of attempting positive lifestyle changes for liver transplant recipients is not known. The aim of this study was to explore the experiences of liver transplant recipients and their perspectives of a 12-week telehealth lifestyle programme and assess the feasibility of this innovative health service.

METHODS

Focus groups and one-on-one interviews were conducted with participants who had completed a 12-week, group-based, telehealth-delivered diet and exercise programme and thematic qualitative analysis was used to code and theme the data.

RESULTS

In total, 19 liver transplant recipients participated in the study (25-68 years, median time since transplant 4.4 years, 63% male). Overarching themes included: (a) 'broad telehealth advantages' which highlighted that telehealth reduced the perceived burdens of face-to-face care; (b) 'impact of employment' which identified employment as a competing priority and appeared to effect involvement with the programme; (c) 'adapting Mediterranean eating pattern to meet individual needs' which identified the adaptability of the Mediterranean diet supported by sessions with the dietitian; (d) 'increasing exercise confidence' which recognised that a tailored approach facilitated confidence and acceptability of the exercise component of the programme.

DISCUSSION

A telehealth lifestyle programme delivered by dietitians and exercise physiologists is an acceptable alternative to face-to-face care that can meet the needs of liver transplant recipients. There is a need to further innovate and broaden the scope of routine service delivery beyond face-to-face consultations.

Standardizing Analytic Methods and Reporting in Activity Monitor Validation Studies

INTRODUCTION

A lack of standardization with accelerometry-based monitors has made it hard to advance applications for both research and practice. Resolving these challenges is essential for developing methods for consistent, agnostic reporting of physical activity outcomes from wearable monitors in clinical applications.

METHODS

This article reviewed the literature on the methods used to evaluate the validity of contemporary consumer activity monitors. A rationale for focusing on energy expenditure as a key outcome measure in validation studies was provided followed by a summary of the strengths and limitations of different analytical methods. The primary review included 23 recent validation studies that collectively reported energy expenditure estimates from 58 monitors relative to values from appropriate criterion measures.

RESULTS

The majority of studies reported weak indicators such as correlation coefficients (87%), but only half (52%) reported the recommended summary statistic of mean absolute percent error needed to evaluate actual individual error. Fewer used appropriate tests of agreement such as equivalence testing (22%).

CONCLUSIONS

The use of inappropriate analytic methods and incomplete reporting of outcomes is a major limitation for systematically advancing research with both research grade and consumer-grade activity monitors. Guidelines are provided to standardize analytic methods and reporting in these types of studies to enhance the utility of the devices for clinical mHealth applications.

Evaluation of mHealth Applications Related to Cardiovascular Diseases: a Systematic Review

Introduction:

Currently, with the widespread penetration of mobile devices with Internet access, including smartphones, they can allow specific and/or complementary activities in the health field as well as in other commercial sectors.

Aim:

This systematic review examined the impact of mHealth-based for cardiovascular research. The specific aims of the systematic review are to (1) classification of the studies according to the type of research (scientific articles and master’s and doctoral theses) and (2) relationship of studies with topics associated with cardiovascular diseases.

Results:

This review corresponds to information in scientific journals of high impact. This review intends to respond to the following question: How these research works have evaluated the performance of health mobile applications, with a special interest in cardiac issues? This review of these searches corresponds to an analysis by 14 categories, being these: 1) Scientific paper; 2) Doctoral Thesis; 3) Master thesis; 4) Telemedicine; 5) m-sssshealth, e-health; 6) cardiovascular, coronary diseases, heart failures, cardiopulmonary, cardiac rehabilitation; 7) rural health; 8) prevention and control, protection; 9) wearables; 10) mobile, web applications, app, smartphone, software, platform; 11) mhealth education, training, promotion, formative process; 12) self-management; 13) Multiple Vital Sign Monitoring, medical device, heart rate measurement, health care information systems; 14) health regulatory. It provides evidence of how some apps have been evaluated, and in some cases, the effectiveness of the estimated accuracy is not in line with the real situation.

Conclusion:

The analysis of these studies allows us to locate the sources of the development of mobile health projects. It also guides us to discover some needs that require new technology implementations.

Stressing the accuracy: Wrist-worn wearable sensor validation over different conditions

Wearable sensors are promising instruments for conducting both laboratory and ambulatory research in psychophysiology. However, scholars should be aware of their measurement error and the conditions in which accuracy is achieved. This study aimed to assess the accuracy of a wearable sensor designed for research purposes, the E4 wristband (Empatica, Milan, Italy), in measuring heart rate (HR), heart rate variability (HRV), and skin conductance (SC) over five laboratory conditions widely used in stress reactivity research (seated rest, paced breathing, orthostatic, Stroop, speech task) and two ecological conditions (slow walking, keyboard typing). Forty healthy participants concurrently wore the wristband and two gold standard measurement systems (i.e., electrocardiography and finger SC sensor). The wristband accuracy was determined by evaluating the signal quality and the correlations with and the Bland-Altman plots against gold standard-derived measurements. Moreover, exploratory analyses were performed to assess predictors of measurement error. Mean HR measures showed the best accuracy over all conditions. HRV measures showed satisfactory accuracy in seated rest, paced breathing, and recovery conditions but not in dynamic conditions, including speaking. Accuracy was diminished by wrist movements, cognitive and emotional stress, nonstationarity, and larger wrist circumferences. Wrist SC measures showed neither correlation nor visual resemblance with finger SC signal, suggesting that the two sites may reflect different phenomena. Future studies are needed to assess the responsivity of wrist SC to emotional and cognitive stress. Limitations and implications for laboratory and ambulatory research are discussed.