A prospective randomized trial examining health care utilization in individuals using multiple smartphone-enabled biosensors

Smartphone based aptasensors as intelligent biodevice for food contamination detection in food and soil samples: Recent advances

Nowadays, the integration of conventional analytical approaches with smartphones has been developed novel, emerging and affordable devices for improving on-site detection platforms in the fields of food safety. Smartphone-based aptasensors as the next generation of portable aptasensing technique has attracted considerable attention as it offers a semi-automated user interface that can be exploited by inexpert characters. Wireless data transferability is an undeniable advantage that home-testing platforms have as well as it can suggest high computational power. In addition, these types of biodevices can provide real-time monitoring in terms of exchanging digital networks in real-time. To elaborate, the ability of smartphones to connect through the Internet is one of the most critical advantages of smartphone-based aptasensor that can be uploaded to Cloud databases and results can be disseminated as spatio-temporal maps across the globe. This review focused on the recent progress and technical breakthroughs of aptasensor on the smartphone as a groundbreaking enterprise in the field of biochemical analysis, importantly in the aspect of the combination of different types of biosensors including electrochemical, optical and colorimetric. In our opinion, this review can broaden our understanding of using smartphones as a portable sensing approach by addressing the current challenges and future perspectives.

A Mobile Health-Based Disease Management Program Improves Blood Pressure in People With Multiple Lifestyle-Related Diseases at Risk of Developing Vascular Disease ― A Retrospective Observational Study ―

Background: The overlap of multiple lifestyle-related diseases increases the risk of vascular diseases. This study investigated the effects of a mobile health (mHealth)-based disease management program on blood pressure and the safety of this program in people with multiple lifestyle-related diseases at risk of developing vascular disease.

Methods and Results: This retrospective observational study was conducted using secondary data collected by PREVENT Inc. People with a full history of hypertension, diabetes, and dyslipidemia and who participated in a 6-month mHealth-based disease management program were included in the study. The primary outcome was blood pressure. Adverse events during the program were investigated to evaluate safety. In total, 125 participants (mean [±SD] age 55.3±6.2 years) were examined. Systolic and diastolic blood pressure were significantly lower after the intervention than at baseline (systolic blood pressure, 128.0±12.3 vs. 131.9±12.7 mmHg [P<0.001]; diastolic blood pressure, 81.2±9.3 vs. 83.6±8.9 mmHg; P=0.003). No serious adverse events occurred during the program.

Conclusions: The present results indicate that the mHealth-based disease management program may reduce blood pressure in people with multiple lifestyle-related diseases at risk of developing vascular disease and that the program is safe. These findings will help shape future health instructions using mHealth-based interventions.

Singular Spectrum Analysis of Tremorograms for Human Neuromotor Reaction Estimation

Singular spectrum analysis (SSA) is a method of time series analysis and is used in various fields, including medicine. A tremorogram is a biological signal that allows evaluation of a person’s neuromotor reactions in order to infer the state of the motor parts of the central nervous system (CNS). A tremorogram has a complex structure, and its analysis requires the use of advanced methods of signal processing and intelligent analysis. The paper’s novelty lies in the application of the SSA method to extract diagnostically significant features from tremorograms with subsequent evaluation of the state of the motor parts of the CNS. The article presents the application of a method of singular spectrum decomposition, comparison of known variants of classification, and grouping of principal components for determining the components of the tremorogram corresponding to the trend, periodic components, and noise. After analyzing the results of the SSA of tremorograms, we proposed a new algorithm of grouping based on the analysis of singular values of the trajectory matrix. An example of applying the SSA method to the analysis of tremorograms is shown. Comparison of known clustering methods and the proposed algorithm showed that there is a reasonable correspondence between the proposed algorithm and the traditional methods of classification and pairing in the set of periodic components.

Consumer Devices for Patient-Generated Health Data Using Blood Pressure Monitors for Managing Hypertension: Systematic Review

Background

In the era of digital health information technology, there has been a proliferation of devices that collect patient-generated health data (PGHD), including consumer blood pressure (BP) monitors. Despite their widespread use, it remains unclear whether such devices can improve health outcomes.

Objective

We performed a systematic review of the literature on consumer BP monitors that collect PGHD for managing hypertension to summarize their clinical impact on health and surrogate outcomes. We focused particularly on studies designed to measure the specific effect of using a BP monitor independent of cointerventions. We have also summarized the process and consumer experience outcomes.

Methods

An information specialist searched PubMed, MEDLINE, and Embase for controlled studies on consumer BP monitors published up to May 12, 2020. We assessed the risk of bias using an adapted 9-item appraisal tool and performed a narrative synthesis of the results.

Results

We identified 41 different types of BP monitors used in 49 studies included for review. Device engineers judged that 38 (92%) of those devices were similar to the currently available consumer BP monitors. The median sample size was 222 (IQR 101-416) participants, and the median length of follow-up was 6 (IQR 3-12) months. Of the included studies, 18 (36%) were designed to isolate the clinical effects of BP monitors; 6 of the 18 (33%) studies evaluated health outcomes (eg, mortality, hospitalizations, and quality of life), and data on those outcomes were unclear. The lack of clarity was due to low event rates, short follow-up duration, and risk of bias. All 18 studies that isolated the effect of BP monitors measured both systolic and diastolic BP and generally demonstrated a decrease of 2 to 4 mm Hg in systolic BP and 1 to 3 mm Hg in diastolic BP compared with non–BP monitor groups. Adherence to using consumer BP monitors ranged from 38% to 89%, and ease of use and satisfaction ratings were generally high. Adverse events were infrequent, but there were a few technical problems with devices (eg, incorrect device alerts).

Conclusions

Overall, BP monitors offer small benefits in terms of BP reduction; however, the health impact of these devices continues to remain unclear. Future studies are needed to examine the effectiveness of BP monitors that transmit data to health care providers. Additional data from implementation studies may help determine which components are critical for sustained BP improvement, which in turn may improve prescription decisions by clinicians and coverage decisions by policy makers.

Patent Landscape of Automated Systems for Personalized Health Management (ASHM): Features, Shortcomings, and Implications for Developing an Optimal ASHM

The current struggle of national health care systems against global epidemic of non-communicable diseases (NCD) is both clinically ineffective and cost ineffective. On the other hand, rapid development of systems biology, P4 medicine and new digital and communication technologies are good prerequisites for creating an affordable and scalable automated system for personalized health management (ASHM). The current practice of ASHM is better represented in patent literature (36 relevant documents found in Google Patents and USPTO) than in scientific papers (17 documents found in PubMed and Google Scholar). However, only a small fraction of publications disclose a complete self-sufficient system. Problems that authors of ASHM aim to address, methodological approaches, and the most important technical solutions are reviewed and discussed along with shortcomings and limitations. Technical solutions for ASHM currently commercialized or described in literature generally fail to enable practicable, scalable and affordable automated and individualized screening, monitoring, prevention and correction of human health conditions. They also fail to provide a decision support system to patients that would help effectively prevent major NCD and their complications, be accessible and cost effective, consider individual lifestyle factors and involve patients in management of their individual health. Based on analysis of the literature, models of health and care, we propose conceptual framework for developing an ASHM that would be free from the mentioned problems.

Performing nutrition assessment remotely via telehealth

Performing nutrition assessment remotely via telehealth is a topic of significant interest given the global pandemic in 2020 that has necessitated physical distancing and virtual communications. This review presents an evidence-based approach to conducting nutrition assessments remotely. The authors present suggestions for adaptations that can be used to perform a remote nutrition-focused physical exam. Direct-to-consumer technologies that can be used in remote nutrition assessment are discussed and compared. Practice tips for conducting a telehealth visit are also presented. The aim of this publication is to provide interdisciplinary clinicians a set of guidelines and best practices for performing nutrition assessments in the era of telehealth.

Effectiveness of ICT-based intimate partner violence interventions: a systematic review

BACKGROUND

Intimate Partner Violence is a "global pandemic". Meanwhile, information and communication technologies (ICT), such as the internet, mobile phones, and smartphones, are spreading worldwide, including in low- and middle-income countries. We reviewed the available evidence on the use of ICT-based interventions to address intimate partner violence (IPV), evaluating the effectiveness, acceptability, and suitability of ICT for addressing different aspects of the problem (e.g., awareness, screening, prevention, treatment, mental health).

METHODS

We conducted a systematic review, following PRISMA guidelines, using the following databases: PubMed, PsycINFO, and Web of Science. Key search terms included women, violence, domestic violence, intimate partner violence, information, communication technology, ICT, technology, email, mobile, phone, digital, ehealth, web, computer, online, and computerized. Only articles written in English were included.

RESULTS

Twenty-five studies addressing screening and disclosure, IPV prevention, ICT suitability, support and women's mental health were identified. The evidence reviewed suggests that ICT-based interventions were effective mainly in screening, disclosure, and prevention. However, there is a lack of homogeneity among the studies' outcome measurements and the sample sizes, the control groups used (if any), the type of interventions, and the study recruitment space. Questions addressing safety, equity, and the unintended consequences of the use of ICT in IPV programming are virtually non-existent.

CONCLUSIONS

There is a clear need to develop women-centered ICT design when programming for IPV. Our study showed only one study that formally addressed software usability. The need for more research to address safety, equity, and the unintended consequences of the use of ICT in IPV programming is paramount. Studies addressing long term effects are also needed.

Using digital health technology to evaluate the impact of chocolate on blood pressure: Results from the COCOA-BP study

Background

High blood pressure (BP) is a major risk factor for cardiovascular disease (CVD). Consumption of dark chocolate, which is high in flavonoids that may reduce CVD risk, is an attractive intervention to reduce to BP. Additionally, the use of mobile health (mHealth) technologies (eg, telehealth, smartphones, and wearable devices) can improve outcomes in patients with CVD.

Objective

The purpose of this study was to investigate the impact of dark chocolate intake on BP, subject use of mHealth, and integration of mHealth into a clinical trial.

Methods

The COCOA-BP (ChOcolate COnsumption And Blood Pressure) study was a prospective, single-center, pre-/postintervention study that enrolled 62 healthy volunteers. The study consisted of 3 phases: smartwatch/smart BP monitor familiarization and washout from chocolate (week 1); control (week 2); and intervention (weeks 3 and 4). During the intervention phase, subjects consumed 50 g of dark chocolate per day. The primary endpoint was change in resting systolic BP between the intervention and control phases. Additional endpoints included device accuracy and correlation with physical activity.

Results

Mean resting systolic BP was 116.4 mm Hg before chocolate intake among 62 participants (mean age 37 years; 61% female). After chocolate intake, mean resting systolic BP was 116.0 mm Hg (difference –0.4; P = .69). These findings suggest that 2 weeks of dark chocolate intake did not reduce resting systolic BP. There was poor agreement between mHealth device and standard (nurse-performed) measurements.

Conclusion

In this study, short-term dark chocolate intake did not seem to reduce BP. mHealth technology shows great potential for use in clinical studies, but challenges related to device accuracy and compliance need to be addressed.

Utilising digital health to improve medication-related quality of care for hypertensive patients: An integrative literature review

BACKGROUND

Hypertension or high blood pressure is considered as a significant contributor and risk factor to many serious conditions, approximately 1.13 billion people have hypertension globally. However, the integrated technologies can upscale health provisions and improve the effectiveness of the healthcare system. WHO has recommended that the digital health interventions (DHIs) and the Health System Challenges should be used in tandem in addressing health.

AIM

To summarise the outcomes from a range of research which investigated the use of DHI to improve the medication-related quality of care (MRQOC) for hypertensive patients.

METHODS

An integrative literature review was undertaken in October 2019 using the Medline, Cumulative Index of Nursing and Allied Health Literature, and Scopus databases for publications in English with no date limit.

RESULTS

In total, 18433 participants were included in this review from 28 studies meeting the eligibility criteria. There were 19 DHI identified within eight countries: Australia, Canada, India, South Korea, Lebanon, Pakistan, the United Kingdom, and the United States of America. The DHI were provided as community-based, clinical-based and home-based program through mobile phone, mobile health system, short message service, and telehealth, digital medicine, and online healthcare (web-based). The mean age of participants was 59 ranging from 42 to 81 years with an average mean systolic blood pressure of 143.3 mmHg at baseline, ranging from 129.0 mmHg to 159.0 mmHg. The proportion of male participants ranged from 13.9% to 92.0%. Eighteen interventions showed evidence of reduction in blood pressure and improvement of self-management in relation to medication adherence and blood pressure control. The reduction of systolic blood pressure ranged between 1.9 mmHg and 26.0 mmHg, with a mean of 10.8 mmHg. The digital health was found positively associated with the MRQOC for hypertensive patients such as improvement in medication adherence and medication management; better blood pressure control; maintaining follow-ups appointment and self-management; increasing access to healthcare particularly among patients living in rural area; and reducing adverse events. However, some interventions found no significant effect on hypertensive care. The follow up duration varied between 2 mo and 18 mo with an average attrition rate of 10.1%, ranging from 0.0% to 17.4%.

CONCLUSION

Utilising digital health innovation for hypertensive care in different settings with tailored interventions positively impacted on MRQOC leading to an improvement of patient outcomes and their quality of life. Nevertheless, inconclusive findings were found in some interventions, and inconsistent outcomes between DHI were noted. A future research and evidence-based DHI for hypertension or chronic diseases should be developed through the evidence-to-decision framework and guidelines.

Mobile-Application-Based Interventions for Patients With Hypertension and Ischemic Heart Disease: A Systematic Review

BACKGROUND

Hypertension and ischemic heart disease are major causes of adult mortality. Related interventions to manage these conditions are important to implement during long, symptom-free periods. The recent proliferation of smartphones has spawned numerous health interventions that rely on mobile applications.

PURPOSE

This systematic review was designed to summarize and analyze research on interventions using mobile applications for patients with hypertension and ischemic heart disease.

METHODS

We searched for related studies published from January 2006 to August 2017 on MEDLINE, EMBASE, CINAHL, and three Korean databases. Seventeen studies were identified and evaluated against eligibility criteria that included a focus on patients with hypertension or ischemic heart disease and a discussion of the detailed effects of a mobile-app-based intervention. All of the identified studies were evaluated qualitatively using a methodology checklist.

RESULTS

Twelve of the 17 studies were deemed as of acceptable quality according to the Scottish Intercollegiate Guideline Network quality assessment. According to the National Institutes of Health quality assessment tool, one article was of fair quality and four articles were of poor quality. Monitoring, education, and reminders were identified as application interventions. The effects of the app interventions were analyzed according to physiological factors, cognitive and behavioral factors, and psychological factors. Of the seven studies that measured blood pressure in patients with hypertension, five studies reported that the app-based interventions reduced blood pressure. Two of three studies showed a significant decrease of body mass index in patients with ischemic heart disease after the app-based interventions compared to the control group. Five of seven studies reported a significant change in medication adherence. Several studies showed different outcomes according to the disease, but the limited number of eligible studies was insufficient to demonstrate a conclusive effect.

CONCLUSIONS

To ensure the long-term effects of mobile-application-based interventions, healthcare professionals should consider the functions of mobile applications. Moreover, because the focus of these interventions may differ based on the nature of the disease, it is recommended that the composition of interventions be tailored to the specific disease.

Contribution of telemedicine and information technology to hypertension control

Due to fast-paced technological advancements, digital health and telemedicine represent a promising and complex reality, with the potential to change the current management of hypertension and improve its outcomes. New types of health-related strategies are available, ranging from telemonitoring of blood pressure (BP) values to counseling for patients and decisional tools for physicians, thanks to the development of new technology. Even though the strength of available evidence is currently low due to the high heterogeneity of studies and of the proposed interventions, available data suggest a beneficial effect of digital health strategies on BP control and, more generally, on cardiovascular risk reduction. In addition, well-designed randomized controlled trials are needed to further investigate the real impact of these new strategies on clinical outcomes. Furthermore, due to consistent commercial interests in this field, there is a strong need for strict regulations to ensure a safe and secure implementation of this new reality in clinical care.

Exploring app features with outcomes in mHealth studies involving chronic respiratory diseases, diabetes, and hypertension: a targeted exploration of the literature

Objectives

Limited data are available on the correlation of mHealth features and statistically significant outcomes. We sought to identify and analyze: types and categories of features; frequency and number of features; and relationship of statistically significant outcomes by type, frequency, and number of features.

Materials and Methods

This search included primary articles focused on app-based interventions in managing chronic respiratory diseases, diabetes, and hypertension. The initial search yielded 3622 studies with 70 studies meeting the inclusion criteria. We used thematic analysis to identify 9 features within the studies.

Results

Employing existing terminology, we classified the 9 features as passive or interactive. Passive features included: 1) one-way communication; 2) mobile diary; 3) Bluetooth technology; and 4) reminders. Interactive features included: 1) interactive prompts; 2) upload of biometric measurements; 3) action treatment plan/personalized health goals; 4) 2-way communication; and 5) clinical decision support system.

Discussion

Each feature was included in only one-third of the studies with a mean of 2.6 mHealth features per study. Studies with statistically significant outcomes used a higher combination of passive and interactive features (69%). In contrast, studies without statistically significant outcomes exclusively used a higher frequency of passive features (46%). Inclusion of behavior change features (ie, plan/goals and mobile diary) were correlated with a higher incident of statistically significant outcomes (100%, 77%).

Conclusion

This exploration is the first step in identifying how types and categories of features impact outcomes. While the findings are inconclusive due to lack of homogeneity, this provides a foundation for future feature analysis.

The virtual physical exam in the 21st century

The modern Western medical encounter follows a strict framework that weaves subjective and objective components into a unifying diagnosis. As health care changes to incorporate new technology, such as virtual health care, the components that lead to diagnosis must likewise evolve. The virtual physical exam has limitations compared with the traditional exam. Despite this limitation, every year more patients are seen virtually with high satisfaction. Data have shown that supplementary real-time patient-provider video telemedicine increases access and extends established patient-physician relationships which will likely fuel increased telemedicine adoption even further. However, to date, there are limited data regarding the validity of the virtual examination compared with the traditional physical exam. In this paper, we review the use of developing technology related to the virtual physical exam.

The Potential of mHealth Applications in Improving Resistant Hypertension Self-Assessment, Treatment and Control

PURPOSE OF REVIEW

To review the evidence supporting the use of mobile health (mHealth) apps to improve resistant hypertension self-assessment, treatment and control.

RECENT FINDINGS

mHealth apps have been used to directly measure blood pressure (BP) levels, either using the oscillometric method with automated inflatable cuffs or using pulse wave signals detected by smartphone technology without the need for cuffs. These app-based BP monitors tend to over or underestimate BP levels when compared to a gold standard aneroid sphygmomanometer. However, the differences in BP measurements are within the acceptable range of 5 mmHg pre-defined by the European Society of Hypertension International Protocol Revision 2010. mHealth apps are also used as tools to support physicians in improving hypertension treatment. App-based clinical decision support systems are innovative solutions, in which patient information is entered in the app and management algorithms provide recommendations for hypertension treatment. The use of these apps has been shown to be feasible and easily integrated into the workflow of healthcare professionals, and, therefore particularly useful in resource-limited settings. In addition, apps can be used to improve hypertension control by facilitating regular BP monitoring, communication between patients and health professionals, and patient education; as well as by reinforcing behaviours through reminders, including medication-taking and appointment reminders. Several studies provided evidence supporting the use of apps for hypertension control. Although some of the results are promising, there is still limited evidence on the benefits of using such mHealth tools, as these studies are relatively small and with a short-term duration. Recent research has shown that mHealth apps can be beneficial in terms of improving hypertension self-assessment, treatment and control, being especially useful to help differentiate and manage true and pseudo-resistant hypertension. However, future research, including large-scale randomised clinical trials with user-centred design, is crucial to further evaluate the potential scalability and effectiveness of such mHealth apps in the resistant hypertension context.

The Extent and Coverage of Current Knowledge of Connected Health: Systematic Mapping Study

Background

This study examines the development of the connected health (CH) research landscape with a view to providing an overview of the existing CH research. The research field of CH has experienced rapid growth coinciding with increasing pressure on health care systems to become more proactive and patient centered.

Objective

This study aimed to assess the extent and coverage of the current body of knowledge in CH. In doing so, we sought to identify specific topics that have drawn the attention of CH researchers and to identify research gaps, in particular those offering opportunities for further interdisciplinary research.

Methods

A systematic mapping study that combined scientific contributions from research in the disciplines of medicine, business, computer science, and engineering was used. Overall, seven classification criteria were used to analyze the papers, including publication source, publication year, research type, empirical type, contribution type, research topic, and the medical condition studied.

Results

The search resulted in 208 papers that were analyzed by a multidisciplinary group of researchers. The results indicated a slow start for CH research but showed a more recent steady upswing since 2013. The majority of papers proposed health care solutions (77/208, 37.0%) or evaluated CH approaches (49/208, 23.5%). Case studies (59/208, 28.3%) and experiments (55/208, 26.4%) were the most popular forms of scientific validation used. Diabetes, cancer, multiple sclerosis, and heart conditions were among the most prevalent medical conditions studied.

Conclusions

We conclude that CH research has become an established field of research that has grown over the last five years. The results of this study indicate a focus on technology-driven research with a strong contribution from medicine, whereas the business aspects of CH have received less research attention.

Safety and compatibility of smart device heart rhythm monitoring in patients with cardiovascular implantable electronic devices

INTRODUCTION

Emerging medical technology has allowed for monitoring of heart rhythm abnormalities using smartphone compatible devices. The safety and utility of such devices have not been established in patients with cardiac implantable electronic devices (CIEDs). We sought to assess the safety and compatibility of the Food and Drug Administration-approved AliveCor Kardia device in patients with CIEDs.

METHODS AND RESULTS

We prospectively recruited patients with CIED for a Kardia recording during their routine device interrogation. A recording was obtained in paced and nonpaced states. Adverse clinical events were noted at the time of recording. Electrograms (EGMs) from the cardiac device were obtained at the time of recording to assess for any electromagnetic interference (EMI) introduced by Kardia. Recordings were analyzed for quality and given a score of 3 (interpretable rhythm, no noise), 2 (interpretable rhythm, significant noise) or 1 (uninterpretable). A total of 251 patients were recruited (59% with a pacemaker and 41% with ICD). There were no adverse clinical events noted at the time of recording and no changes to CIED settings. Review of all EGMs revealed no EMI introduced by Kardia. Recordings were correctly interpreted in 90% of paced recordings (183 had a score of 3, 43 of 2, and 25 of 1) and 94.7% of nonpaced recordings (147 of 3, 15 of 2, and 9 of 1).

CONCLUSION

The AliveCor Kardia device has an excellent safety profile when used in conjunction with most CIEDs. The quality of recordings was preserved in this population. The device, therefore, can be considered for heart rhythm monitoring in patients with CIEDs.

Impact of a Culturally Tailored mHealth Medication Regimen Self-Management Program upon Blood Pressure among Hypertensive Hispanic Adults

Background: Uncontrolled hypertension (HTN) and medication nonadherence are more prominent among Hispanics compared to non-Hispanic whites and African Americans. Advances in wireless health technology enable real-time monitoring of medication adherence (MA) and blood pressure (BP), facilitating timely patient–provider communication including tailored reinforcement/motivational feedback to patients and quicker titration changes by providers. The purpose of the current study was to conduct a 9-month smartphone-enabled efficacy trial addressing MA and BP control among Hispanic adults with uncontrolled HTN and poor MA. Methods: The research design was a 9-month, two-arm efficacy trial including an experimental (Smartphone Med Adherence Stops Hypertension, SMASH) group and an enhanced standard care (ESC) group. SMASH participants utilized a SMASH app which interfaced with a Bluetooth-enabled BP monitor for BP self-monitoring and an electronic medication tray. The ESC participants received text messages including links to PDFs and brief video clips containing healthy lifestyle tips for attention control. Results: Participants were 54 Hispanic adults (mean age: 46.5 years) with uncontrolled HTN. They were randomly assigned to either the SMASH (n = 26) or ESC group (n = 28). At baseline, no participants had controlled systolic BP (SBP). Baseline group averages for SBP between the SC and SMASH groups did not differ (150.7 and 152.3 mmHg, respectively; p = 0.53). At the 1, 3, 6, and 9-month time points, SBP averages were significantly lower in the SMASH versus SC groups (month 1: 125.3 vs. 140.6; month 3: 120.4 vs. 137.5, month 6: 121.2 vs. 145.7 mmHg; month 9: 121.8 vs. 145.7, respectively; all p-values <0.01). At months 3, 6, and 9 there was a significant difference between the percentage of participants meeting the 7th Joint National Committee cutoffs for SBP control in the SC and SMASH groups (month 3: 62.5 vs. 92.0%; month 6: 57.9 and 94.4%, month 9: 27.8 and 92.3%, respectively; all p-values ≤0.01). Average medical regimen adherence, as indicated by timestamped medication intake and BP monitoring for the SMASH group, ranged from 89.1 to 95.2% across the 9-month trial. Conclusion: Our findings indicate that our culturally tailored smartphone-enabled medical regimen self-management program may be an effective solution for the promotion of MA, resulting in statistically and clinically significant reductions in SBP among Hispanic adults with uncontrolled HTN.

Kardia Mobile applicability in clinical practice: A comparison of Kardia Mobile and standard 12-lead electrocardiogram records in 100 consecutive patients of a tertiary cardiovascular care center

BACKGROUND

Mobile devices are gaining a rising number of users in all countries around the globe. Novel solutions to diagnose patients with out-of-hospital onset of arrhythmic symptoms can be easily used to record such events, but the effectiveness of these devices remain unknown.

METHODS

In a group of 100 consecutive patients of an academic cardiology care center (mean age 68 ± 14.2 years, males: 66%) a standard 12-lead electrocardiogram (ECG) and a Kardia Mobile (KM) record were registered. Both versions were assessed by three independant groups of physicians.

RESULTS

The analysis of comparisons for standard ECG and KM records showed that the latter is of lower quality (p < 0.001). It was non-inferior for detection of atrial fibrillation and atrial flutter, showed weaker rhythm detection in pacemaker stimulation (p = 0.008), and was superior in sinus rhythm detection (p = 0.02), though. The sensitivity of KM to detect pathological Q-wave was low compared to specificity (20.6% vs. 93.7%, respectively, p < 0.001). Basic intervals measured by the KM device, namely PQ, RR, and QT were significantly different (shorter) than those observed in the standard ECG method (160 ms vs. 180 ms [p < 0.001], 853 ms vs. 880 ms [p = 0.03] and 393 ms vs. 400 ms [p < 0.001], respectively).

CONCLUSIONS

Initial and indicative value of atrial fibrillation and atrial flutter detection in KM is comparable to results achieved in standard ECG. KM was superior in detection of sinus rhythm than eye-ball evaluation of 12-lead ECG. Though, the PQ and QT intervals were shorter in KM as compared to 12-lead ECG. Clinical value needs to be verified in large studies, though.

The effectiveness of electronic health interventions on blood pressure control, self-care behavioural outcomes and psychosocial well-being in patients with hypertension: A systematic review and meta-analysis

OBJECTIVES

Hypertension is a global health issue. Electronic health (eHealth) is a potential alternative for managing hypertension and modifying hypertension-related self-care set of behaviour. This review aims to identify the delivery mode and strategies used by current eHealth interventions and examine the effectiveness of eHealth on blood pressure control, self-care behavioural outcomes and psychosocial well-being.

DESIGN

Systematic review and meta-analysis.

DATA SOURCE

Ten English databases (Cochrane Database of Systematic Reviews, Cochrane Central Register of Controlled Trials, Embase, Joanna Briggs Institute EBP Database, MEDLINE, CINAHL Plus, PsycINFO, SCOPUS, Web of Science and INSPEC) and two Chinese databases (China Journal Net and WanFang Data) were searched from January 2000 to November 2017.

REVIEW METHODS

This review was conducted based on the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Statement. Two reviewers independently selected potential articles and extracted the details of each eligible article. The Randomized Controlled Trial Checklist of Joanna Briggs Institute was used to assess the methodological quality of the included articles. Meta-analysis was conducted using Review Manager 5.3 for at least two studies reporting the same outcome. Otherwise, narrative synthesis was performed.

RESULTS

Fifteen articles from fourteen studies satisfied the inclusion criteria. The pooled result of 13 studies reported that eHealth intervention significantly affected the reduction of systolic blood pressure (mean difference [MD]: -5.96 mmHg, 95% confidence interval [CI]: -9.21 to -2.70, p < .001) and diastolic blood pressure (MD: -3.35 mmHg, 95% CI: -6.36 to -0.35, p < .05). eHealth interventions significantly decreased the proportion of patients with inadequate blood pressure control (risk ratio: 0.69, 95% CI: 0.57-0.84, p < .001) and their body weight (MD: -1.08 kg, 95% CI: -2.04 to -0.13, p < .05). Regarding self-care behavioural outcomes, the pooled results show that eHealth interventions significantly reduced the sodium intake.

CONCLUSIONS

This study reported that eHealth interventions positively affect blood pressure control and thus could be a promising alternative in the management of hypertension. However, their effectiveness on self-care behavioural change and psychosocial well-being is insufficient. Therefore, additional eHealth interventions with rigorous experimental design on hypertension self-care are needed to provide a robust evidence for a wide population and to address the increasing health care needs of patients with hypertension.

Mobile Apps to Support the Self-Management of Hypertension: Systematic Review of Effectiveness, Usability, and User Satisfaction

Background

Hypertension is a chronic disease that is considered to be a public health problem and requires efforts by patients to manage themselves. The global growth in the use of mobile phones and tablets has been accompanied by the increased use of health apps. Many of these apps support the self-management of hypertension and, therefore, they have the potential benefits of lowering blood pressure. Despite this, there is currently a lack of evidence for their effectiveness, usability, and patient satisfaction with their use.

Objective

A systematic review was conducted to assess the effectiveness of apps in lowering blood pressure, as well as their usability and patients’ satisfaction with their use.

Methods

We conducted searches in the following databases: MEDLINE (OVID), EMBASE (OVID), PsycINFO (OVID), CINAHL, the Cochrane Central Register of Controlled Trials (CENTRAL, The Cochrane Library), IEEE Xplore ASSIAN, Google Scholar and the main Arabic databases Al Manhal, AskZad, and Mandumah. We looked for studies that used apps in the self-management of hypertension from 2008-2016. We also checked the reference lists of the review papers and all the primary studies for additional references.

Results

A total of 21 studies with a total of 3112 participants were included in the review. Of the 14 studies that assessed the effectiveness of the apps in lowering blood pressure, 10 (71.4%) studies (6 RCTs and 4 nonrandomized studies) reported that using the apps led to significant decreases in blood pressure and seemed to be effective in the self-management of hypertension. Of these 10, only 2 (20%) RCTs and 3 (30%) nonrandomized studies had a low–moderate risk of bias. The results of this review are inconclusive regarding which combinations of functionalities would be most effective in lowering blood pressure because of variation in the studies’ quality, but the data suggest that apps incorporating more comprehensive functionalities are likely to be more effective. In all the studies that assessed the usability of the apps and users’ acceptance of them, all the apps seemed to be accepted and easy to use.

Conclusions

Most of the studies reported that apps might be effective in lowering blood pressure and are accepted by users. However, these findings should be interpreted with caution, as most of the studies had a high risk of bias. More well-designed, large-scale studies are required to evaluate the real effect of using apps in lowering blood pressure and to identify the most effective functionality combinations for lowering blood pressure.

Improving Health-Related Quality of Life of Patients With an Ostomy Using a Novel Digital Wearable Device: Protocol for a Pilot Study

BACKGROUND

Ostomy surgeries involving the placement of an ostomy bag (eg, colostomy, ileostomy, urostomy, etc) have been shown to have a negative impact on health-related quality of life. To date, no studies have been conducted examining what impact, if any, wearable biosensors have on the health-related quality of life of ostomy patients.

OBJECTIVE

In the present study, we plan to assess the quality of life of ostomy patients using the Ostom-i alert sensor, a portable, wearable, Bluetooth-linked biosensor that facilitates easier ostomy bag output measurements. We hypothesize that using the Ostom-i alert sensor will result in an improved, ostomy-specific, health-related quality of life as compared to baseline measurement before the use of the sensor.

METHODS

A total of 20 ostomy patients will be screened and recruited to participate in this prospective, observational, cross-over pilot study using an Ostom-i alert sensor for one month. The primary outcome of this study will compare ostomy-specific, health-related quality of life at baseline (prior to Ostom-i alert sensor use) to ostomy-specific, health-related quality of life after 2 and 4 weeks of Ostom-i use by utilizing the City of Hope Quality of Life Questionnaire for Patients with an Ostomy. Secondary outcomes of general health-related quality of life and adjustment to ostomy will be evaluated using the Medical Outcomes Study 36-item short form health survey and the Olbrisch Ostomy Adjustment Scale Short Form 2.

RESULTS

The project was funded by the Department of Anesthesiology, Perioperative and Pain Medicine at Stanford University School of Medicine. Enrollment is currently underway and data analysis is expected to be completed in 2018.

CONCLUSIONS

Proposed benefits of mobile, internet-linked personal health monitors, such as the Ostom-i, include a reduction in the cost of care by reducing resource utilization and infection rates, improving patient-provider communication, reducing time spent as an inpatient as well as improved quality of life. Prior studies have demonstrated decreased health-related quality of life in patients with an ostomy bag. We aim to examine the extent to which the Ostom-i alert sensor affects the health-related quality of life of its users. The Ostom-i alert sensor has the potential to improve quality of life of users by giving them the freedom and confidence to partake in daily activities with the knowledge that they can check how full their ostomy bag is in a private, discrete manner.

TRIAL REGISTRATION

ClinicalTrials.gov NCT02319434; https://clinicaltrials.gov/ct2/show/NCT02319434 (Archived at WebCite at http://www.webcitation.org/6xhFDThmq).

Are Nomothetic or Ideographic Approaches Superior in Predicting Daily Exercise Behaviors?

OBJECTIVES

The understanding of how stress influences health behavior can provide insights into developing healthy lifestyle interventions. This understanding is traditionally attained through observational studies that examine associations at a population level. This nomothetic approach, however, is fundamentally limited by the fact that the environment- person milieu that constitutes stress exposure and experience can vary substantially between individuals, and the modifiable elements of these exposures and experiences are individual-specific. With recent advances in smartphone and sensing technologies, it is now possible to conduct idiographic assessment in users' own environment, leveraging the full-range observations of actions and experiences that result in differential response to naturally occurring events. The aim of this paper is to explore the hypothesis that an ideographic N-of-1 model can better capture an individual's stress- behavior pathway (or the lack thereof) and provide useful person-specific predictors of exercise behavior.

METHODS

This paper used the data collected in an observational study in 79 participants who were followed for up to a 1-year period, wherein their physical activity was continuously and objectively monitored by actigraphy and their stress experience was recorded via ecological momentary assessment on a mobile app. In addition, our analyses considered exogenous and environmental variables retrieved from public archive such as day in a week, daylight time, temperature and precipitation. Leveraging the multiple data sources, we developed prediction algorithms for exercise behavior using random forest and classification tree techniques using a nomothetic approach and an N-of-1 approach. The two approaches were compared based on classification errors in predicting personalized exercise behavior.

RESULTS

Eight factors were selected by random forest for the nomothetic decision model, which was used to predict whether a participant would exercise on a particular day. The predictors included previous exercise behavior, emotional factors (e.g., midday stress), external factors such as weather (e.g., temperature), and self-determination factors (e.g., expectation of exercise). The nomothetic model yielded an average classification error of 36%. The ideographic N-of-1 models used on average about two predictors for each individual, and had an average classification error of 25%, which represented an improvement of 11 percentage points.

CONCLUSIONS

Compared to the traditional one-size-fits-all, nomothetic model that generalizes population-evidence for individuals, the proposed N-of-1 model can better capture the individual difference in their stressbehavior pathways. In this paper, we demonstrate it is feasible to perform personalized exercise behavior prediction, mainly made possible by mobile health technology and machine learning analytics.

Impact of remote patient monitoring on clinical outcomes: an updated meta-analysis of randomized controlled trials

Despite growing interest in remote patient monitoring, limited evidence exists to substantiate claims of its ability to improve outcomes. Our aim was to evaluate randomized controlled trials (RCTs) that assess the effects of using wearable biosensors (e.g. activity trackers) for remote patient monitoring on clinical outcomes. We expanded upon prior reviews by assessing effectiveness across indications and presenting quantitative summary data. We searched for articles from January 2000 to October 2016 in PubMed, reviewed 4,348 titles, selected 777 for abstract review, and 64 for full text review. A total of 27 RCTs from 13 different countries focused on a range of clinical outcomes and were retained for final analysis; of these, we identified 16 high-quality studies. We estimated a difference-in-differences random effects meta-analysis on select outcomes. We weighted the studies by sample size and used 95% confidence intervals (CI) around point estimates. Difference-in-difference point estimation revealed no statistically significant impact of remote patient monitoring on any of six reported clinical outcomes, including body mass index (-0.73; 95% CI: -1.84, 0.38), weight (-1.29; -3.06, 0.48), waist circumference (-2.41; -5.16, 0.34), body fat percentage (0.11; -1.56, 1.34), systolic blood pressure (-2.62; -5.31, 0.06), and diastolic blood pressure (-0.99; -2.73, 0.74). Studies were highly heterogeneous in their design, device type, and outcomes. Interventions based on health behavior models and personalized coaching were most successful. We found substantial gaps in the evidence base that should be considered before implementation of remote patient monitoring in the clinical setting.

An interpretable health behavioral intervention policy for mobile device users

An increasing number of people use mobile devices to monitor their behavior, such as exercise, and record their health status, such as psychological stress. However, these devices rarely provide ongoing support to help users understand how their behavior contributes to changes in their health status. To address this challenge, we aim to develop an interpretable policy for physical activity recommendations that reduce a user's perceived psychological stress, over a given time horizon. We formulate this problem as a sequential decision-making problem and solve it using a new method that we refer to as threshold Q-learning (TQL). The advantage of the TQL method over traditional Q-learning is that it is "doubly robust" and interpretable. This interpretability is achieved by making model assumptions and incorporating threshold selection into the learning process. Our simulation results indicate that the TQL method performs better than the Q-learning method given model misspecification. Our analyses are performed on data collected from 79 healthy adults over a 7 week period, where the data comprise physical activity patterns collected from mobile devices and self-assessed stress levels of the users. This work serves as a first step toward a computational health coaching solution for mobile device users.

Digitally Driven Integrated Primary Care and Behavioral Health: How Technology Can Expand Access to Effective Treatment

PURPOSE OF REVIEW

Widespread implementation of integrated primary care and behavioral health is possible, but workforce shortages, competencies to deliver evidence-based approaches, and sufficient reimbursement are lacking. There are numerous telehealth solutions that could be utilized to assist with integration efforts that have the potential to be successfully used alone or in combination. This will require that the developers of such technologies understand the current evidence base for effective integration efforts and apply this knowledge to new solutions.

RECENT FINDINGS

Evidence-based models of integrated care such as the collaborative care model have a robust evidence base including studies that demonstrate effective delivery from remote locations. Technology solutions that can serve as practice extenders by performing some of the tasks, and can expand the competency of primary care providers to treat mild to moderate mental illness, have an emerging literature in the behavioral health arena that shows promise for integrating care. More widespread implementation of effective integrated primary care and behavioral health can be accomplished with the help of technology solutions that can address the problems of workforce shortages and competencies. Use of these technologies alone or in combination is a growing area of research and development and an untapped frontier that warrants further investigation.

Added Value from Secondary Use of Person Generated Health Data in Consumer Health Informatics

Introduction: Various health-related data, subsequently called Person Generated Health Data (PGHD), is being collected by patients or presumably healthy individuals as well as about them as much as they become available as measurable properties in their work, home, and other environments. Despite that such data was originally just collected and used for dedicated predefined purposes, more recently it is regarded as untapped resources that call for secondary use. Method: Since the secondary use of PGHD is still at its early evolving stage, we have chosen, in this paper, to produce an outline of best practices, as opposed to a systematic review. To this end, we identified key directions of secondary use and invited protagonists of each of these directions to present their takes on the primary and secondary use of PGHD in their sub-fields. We then put secondary use in a wider perspective of overarching themes such as privacy, interpretability, interoperability, utility, and ethics. Results: We present the primary and secondary use of PGHD in four focus areas: (1) making sense of PGHD in augmented Shared Care Plans for care coordination across multiple conditions; (2) making sense of PGHD from patient-held sensors to inform cancer care; (3) fitting situational use of PGHD to evaluate personal informatics tools in adaptive concurrent trials; (4) making sense of environment risk exposure data in an integrated context with clinical and omics-data for biomedical research. Discussion: Fast technological progress in all the four focus areas calls for a societal debate and decision-making process on a multitude of challenges: how emerging or foreseeable results transform privacy; how new data modalities can be interpreted in light of clinical data and vice versa; how the sheer mass and partially abstract mathematical properties of the achieved insights can be interpreted to a broad public and can consequently facilitate the development of patient-centered services; and how the remaining risks and uncertainties can be evaluated against new benefits. This paper is an initial summary of the status quo of the challenges and proposals that address these issues. The opportunities and barriers identified can serve as action items individuals can bring to their organizations when facing challenges to add value from the secondary use of patient-generated health data.

Effectiveness of Digital Medicines to Improve Clinical Outcomes in Patients with Uncontrolled Hypertension and Type 2 Diabetes: Prospective, Open-Label, Cluster-Randomized Pilot Clinical Trial

Background

Hypertension and type 2 diabetes mellitus are major modifiable risk factors for cardiac, cerebrovascular, and kidney diseases. Reasons for poor disease control include nonadherence, lack of patient engagement, and therapeutic inertia.

Objective

The aim of this study was to assess the impact on clinic-measured blood pressure (BP) and glycated hemoglobin (HbA1c) using a digital medicine offering (DMO) that measures medication ingestion adherence, physical activity, and rest using digital medicines (medication taken with ingestible sensor), wearable sensor patches, and a mobile device app.

Methods

Participants with elevated systolic BP (SBP ≥140 mm Hg) and HbA1c (≥7%) failing antihypertensive (≥2 medications) and oral diabetes therapy were enrolled in this three-arm, 12-week, cluster-randomized study. Participants used DMO (includes digital medicines, the wearable sensor patch, and the mobile device app) for 4 or 12 weeks or received usual care based on site randomization. Providers in the DMO arms could review the DMO data via a Web portal. In all three arms, providers were instructed to make medical decisions (medication titration, adherence counseling, education, and lifestyle coaching) on all available clinical information at each visit. Primary outcome was change in SBP at week 4. Other outcomes included change in SBP and HbA1c at week 12, and low-density lipoprotein cholesterol (LDL-C) and diastolic blood pressure (DBP) at weeks 4 and 12, as well as proportion of patients at BP goal (<140/90 mm Hg) at weeks 4 and 12, medical decisions, and medication adherence patterns.

Results

Final analysis included 109 participants (12 sites; age: mean 58.7, SD years; female: 49.5%, 54/109; Hispanic: 45.9%, 50/109; income ≤ US $20,000: 56.9%, 62/109; and ≤ high school education: 52.3%, 57/109). The DMO groups had 80 participants (7 sites) and usual care had 29 participants (5 sites). At week 4, DMO resulted in a statistically greater SBP reduction than usual care (mean –21.8, SE 1.5 mm Hg vs mean –12.7, SE 2.8 mmHg; mean difference –9.1, 95% CI –14.0 to –3.3 mm Hg) and maintained a greater reduction at week 12. The DMO groups had greater reductions in HbA1c, DBP, and LDL-C, and a greater proportion of participants at BP goal at weeks 4 and 12 compared with usual care. The DMO groups also received more therapeutic interventions than usual care. Medication adherence was ≥80% while using the DMO. The most common adverse event was a self-limited rash at the wearable sensor site (12%, 10/82).

Conclusions

For patients failing hypertension and diabetes oral therapy, this DMO, which provides dose-by-dose feedback on medication ingestion adherence, can help lower BP, HbA1c, and LDL-C, and promote patient engagement and provider decision making.

Trial Registration

Clinicaltrials.gov NCT02827630; https://clinicaltrials.gov/show/NCT02827630 (Archived by WebCite at http://www.webcitation.org/6rL8dW2VF)

iOS Appstore-Based Phone Apps for Diabetes Management: Potential for Use in Medication Adherence

Background

Currently, various phone apps have been developed to assist patients. Many of these apps are developed to assist patients in the self-management of chronic diseases such as diabetes. It is essential to analyze these various apps to understand the key features that would potentially be instrumental in helping patients successfully achieve goals in disease self-management.

Objective

The objective of this study was to conduct a review of all the available diabetes-related apps in the iOS App Store to evaluate which diabetic app is more interactive and offers a wide variety of operations such as monitoring glucose, water, carbohydrate intake, weight, body mass index (BMI), medication, blood pressure (BP) levels, reminders or push notifications, food database, charts, exercise management, email, sync between devices, syncing data directly to the prescribers, and other miscellaneous functions such as (Twitter integration, password protection, retina display, barcode scanner, apple watch functionality, and cloud syncing).

Methods

Data was gathered using the iOS App Store on an iPad. The search term “diabetes” resulted in 1209 results. Many of the results obtained were remotely related to diabetes and focused mainly on diet, exercise, emergency services, refill reminders, providing general diabetes information, and other nontherapeutic options. We reviewed each app description and only included apps that were meant for tracking blood glucose levels. All data were obtained in one sitting by one person on the same device, as we found that carrying out the search at different times or on different devices (iPhones) resulted in varying results. Apps that did not have a feature for tracking glucose levels were excluded from the study.

Results

The search resulted in 1209 results; 85 apps were retained based on the inclusion criteria mentioned above. All the apps were reviewed for average customer ratings, number of reviews, price, and functions. Of all the apps surveyed, 18 apps with the highest number of user ratings were used for in-depth analysis. Of these 18 apps, 50% (9/18) also had a medication adherence function. Our analysis revealed that the Diabetes logbook used by the mySugr app was one of the best; it differentiated itself by introducing fun as a method of increasing adherence.

Conclusions

A large variation was seen in patient ratings of app features. Many patient reviewers desired simplicity of app functions. Glucose level tracking and email features potentially helped patients and health care providers manage the disease more efficiently. However, none of the apps could sync data directly to the prescribers. Additional features such as graph customization, availability of data backup, and recording previous entries were also requested by many users. Thus, the use of apps in disease management and patient and health-care provider involvement in future app refinement and development should be encouraged.

Increasing the Capacity of Primary Care Through Enabling Technology

Primary care is the foundation of effective and high-quality health care. The role of primary care clinicians has expanded to encompass coordination of care across multiple providers and management of more patients with complex conditions. Enabling technology has the potential to expand the capacity for primary care clinicians to provide integrated, accessible care that channels expertise to the patient and brings specialty consultations into the primary care clinic. Furthermore, technology offers opportunities to engage patients in advancing their health through improved communication and enhanced self-management of chronic conditions. This paper describes enabling technologies in four domains (the body, the home, the community, and the primary care clinic) that can support the critical role primary care clinicians play in the health care system. It also identifies challenges to incorporating these technologies into primary care clinics, care processes, and workflow.

Open mHealth Architecture: A Primer for Tomorrow's Orthopedic Surgeon and Introduction to Its Use in Lower Extremity Arthroplasty

BACKGROUND

The recent private-public partnership to unlock and utilize all available health data has large-scale implications for public health and personalized medicine, especially within orthopedics. Today, consumer based technologies such as smartphones and "wearables" store tremendous amounts of personal health data (known as "mHealth") that, when processed and contextualized, have the potential to open new windows of insight for the orthopedic surgeon about their patients.

METHODS

In the present report, the landscape, role, and future technical considerations of mHealth and open architecture are defined with particular examples in lower extremity arthroplasty.

RESULTS

A limitation of the current mHealth landscape is the fragmentation and lack of interconnectivity between the myriad of available apps. The importance behind the currently lacking open mHealth architecture is underscored by the offer of improved research, increased workflow efficiency, and value capture for the orthopedic surgeon.

CONCLUSION

There exists an opportunity to leverage existing mobile health data for orthopaedic surgeons, particularly those specializing in lower extremity arthroplasty, by transforming patient small data into insightful big data through the implementation of "open" architecture that affords universal data standards and a global interconnected network.

The Asthma Mobile Health Study, a large-scale clinical observational study using ResearchKit

The feasibility of using mobile health applications to conduct observational clinical studies requires rigorous validation. Here, we report initial findings from the Asthma Mobile Health Study, a research study, including recruitment, consent, and enrollment, conducted entirely remotely by smartphone. We achieved secure bidirectional data flow between investigators and 7,593 participants from across the United States, including many with severe asthma. Our platform enabled prospective collection of longitudinal, multidimensional data (e.g., surveys, devices, geolocation, and air quality) in a subset of users over the 6-month study period. Consistent trending and correlation of interrelated variables support the quality of data obtained via this method. We detected increased reporting of asthma symptoms in regions affected by heat, pollen, and wildfires. Potential challenges with this technology include selection bias, low retention rates, reporting bias, and data security. These issues require attention to realize the full potential of mobile platforms in research and patient care.

Digital Health: Tracking Physiomes and Activity Using Wearable Biosensors Reveals Useful Health-Related Information

A new wave of portable biosensors allows frequent measurement of health-related physiology. We investigated the use of these devices to monitor human physiological changes during various activities and their role in managing health and diagnosing and analyzing disease. By recording over 250,000 daily measurements for up to 43 individuals, we found personalized circadian differences in physiological parameters, replicating previous physiological findings. Interestingly, we found striking changes in particular environments, such as airline flights (decreased peripheral capillary oxygen saturation [SpO2] and increased radiation exposure). These events are associated with physiological macro-phenotypes such as fatigue, providing a strong association between reduced pressure/oxygen and fatigue on high-altitude flights. Importantly, we combined biosensor information with frequent medical measurements and made two important observations: First, wearable devices were useful in identification of early signs of Lyme disease and inflammatory responses; we used this information to develop a personalized, activity-based normalization framework to identify abnormal physiological signals from longitudinal data for facile disease detection. Second, wearables distinguish physiological differences between insulin-sensitive and -resistant individuals. Overall, these results indicate that portable biosensors provide useful information for monitoring personal activities and physiology and are likely to play an important role in managing health and enabling affordable health care access to groups traditionally limited by socioeconomic class or remote geography.

"What Goes Around Comes Around": Lessons Learned from Economic Evaluations of Personalized Medicine Applied to Digital Medicine

BACKGROUND

The growth of "big data" and the emphasis on patient-centered health care have led to the increasing use of two key technologies: personalized medicine and digital medicine. For these technologies to move into mainstream health care and be reimbursed by insurers, it will be essential to have evidence that their benefits provide reasonable value relative to their costs. These technologies, however, have complex characteristics that present challenges to the assessment of their economic value. Previous studies have identified the challenges for personalized medicine and thus this work informs the more nascent topic of digital medicine.

OBJECTIVES

To examine the methodological challenges and future opportunities for assessing the economic value of digital medicine, using personalized medicine as a comparison.

METHODS

We focused specifically on digital biomarker technologies and multigene tests. We identified similarities in these technologies that can present challenges to economic evaluation: multiple results, results with different types of utilities, secondary findings, downstream impact (including on family members), and interactive effects.

RESULTS

Using a structured review, we found that there are few economic evaluations of digital biomarker technologies, with limited results.

CONCLUSIONS

We conclude that more evidence on the effectiveness of digital medicine will be needed but that the experiences with personalized medicine can inform what data will be needed and how such analyses can be conducted. Our study points out the critical need for typologies and terminology for digital medicine technologies that would enable them to be classified in ways that will facilitate research on their effectiveness and value.

Comprehensive Cardiovascular Risk Reduction and Cardiac Rehabilitation in Diabetes and the Metabolic Syndrome

The epidemic of obesity has contributed to a growing burden of metabolic syndrome (MetS) and diabetes mellitus (DM) worldwide. MetS is defined as central obesity along with associated factors such as hypertriglyceridemia, low high-density lipoprotein cholesterol, hyperglycemia, and hypertension. MetS and DM are associated with significant cardiovascular morbidity and mortality. Healthy behavioural modification is the cornerstone for reducing the atherosclerotic cardiovascular disease burden in this population. Comprehensive, multidisciplinary cardiac rehabilitation (CR) programs reduce mortality and hospitalizations in patients with MetS and DM. Despite this benefit, patients with MetS and DM are less likely to attend and complete CR because of numerous barriers. Implementation of innovative CR delivery models might improve utilization of CR and cardiovascular outcomes in this high-risk population.

A framework for smartphone-enabled, patient-generated health data analysis

Background: Digital medicine and smartphone-enabled health technologies provide a novel source of human health and human biology data. However, in part due to its intricacies, few methods have been established to analyze and interpret data in this domain. We previously conducted a six-month interventional trial examining the efficacy of a comprehensive smartphone-based health monitoring program for individuals with chronic disease. This included 38 individuals with hypertension who recorded 6,290 blood pressure readings over the trial.

Methods: In the present study, we provide a hypothesis testing framework for unstructured time series data, typical of patient-generated mobile device data. We used a mixed model approach for unequally spaced repeated measures using autoregressive and generalized autoregressive models, and applied this to the blood pressure data generated in this trial.

Results: We were able to detect, roughly, a 2 mmHg decrease in both systolic and diastolic blood pressure over the course of the trial despite considerable intra- and inter-individual variation. Furthermore, by supplementing this finding by using a sequential analysis approach, we observed this result over three months prior to the official study end—highlighting the effectiveness of leveraging the digital nature of this data source to form timely conclusions.

Conclusions: Health data generated through the use of smartphones and other mobile devices allow individuals the opportunity to make informed health decisions, and provide researchers the opportunity to address innovative health and biology questions. The hypothesis testing framework we present can be applied in future studies utilizing digital medicine technology or implemented in the technology itself to support the quantified self.

A single-center randomized, controlled trial investigating the efficacy of a mHealth ECG technology intervention to improve the detection of atrial fibrillation: the iHEART study protocol

Background

Atrial fibrillation is a major public health problem and is the most common cardiac arrhythmia, affecting an estimated 2.7 million Americans. The true prevalence of atrial fibrillation is likely underestimated because episodes are often sporadic; therefore, it is challenging to detect and record an occurrence in a “real world” setting. To date, mobile health tools that promote earlier detection and treatment of atrial fibrillation and improvement in self-management behaviors and knowledge have not been evaluated. This study will be the first to address the epidemic problem of atrial fibrillation with a novel approach utilizing advancements in mobile health electrocardiogram technology to empower patients to actively engage in their healthcare and to evaluate impact on quality of life and quality-adjusted life years. Furthermore, sending a daily electrocardiogram transmission, coupled with receiving educational and motivational text messages aimed at promoting self-management and a healthy lifestyle may improve the management of chronic cardiovascular conditions (e.g., hypertension, diabetes, heart failure, etc.). Therefore, we are currently conducting a randomized controlled trial to assess the efficacy of a mobile health intervention, iPhone® Helping Evaluate Atrial fibrillation Rhythm through Technology (iHEART) versus usual cardiac care.

Methods

The iHEART study is a single center, prospective, randomized controlled trial. A total of 300 participants with a recent history of atrial fibrillation will be enrolled. Participants will be randomized 1:1 to receive the iHEART intervention, receiving an iPhone® equipped with an AliveCor® Mobile ECG and accompanying Kardia application and behavioral altering motivational text messages or usual cardiac care for 6 months.

Discussion

This will be the first study to investigate the utility of a mobile health intervention in a “real world” setting. We will evaluate the ability of the iHEART intervention to improve the detection and treatment of recurrent atrial fibrillation and assess the intervention's impact on improving clinical outcomes, quality of life, quality-adjusted life-years and disease-specific knowledge.

Trial registration

NCT02731326; Verified April 2016

The Influence of Wireless Self-Monitoring Program on the Relationship Between Patient Activation and Health Behaviors, Medication Adherence, and Blood Pressure Levels in Hypertensive Patients: A Substudy of a Randomized Controlled Trial

Background

Active engagement in the management of hypertension is important in improving self-management behaviors and clinical outcomes. Mobile phone technology using wireless monitoring tools are now widely available to help individuals monitor their blood pressure, but little is known about the conditions under which such technology can effect positive behavior changes or clinical outcomes.

Objective

To study the influence of wireless self-monitoring program and patient activation measures on health behaviors, medication adherence, and blood pressure levels as well as control of blood pressure in hypertensive patients.

Methods

We examined a subset of 95 hypertensive participants from a 6-month randomized controlled trial designed to determine the utility of a wireless self-monitoring program (n=52 monitoring program, n=43 control), which consisted of a blood pressure monitoring device connected with a mobile phone, reminders for self-monitoring, a Web-based disease management program, and a mobile app for monitoring and education, compared with the control group receiving a standard disease management program. Study participants provided measures of patient activation, health behaviors including smoking, drinking, and exercise, medication adherence, and blood pressure levels. We assessed the influence of wireless self-monitoring as a moderator of the relationship between patient activation and health behaviors, medication adherence, and control of blood pressure.

Results

Improvements in patient activation were associated with improvements in cigarette smoking (beta=−0.46, P<.001) and blood pressure control (beta=0.04, P=.02). This relationship was further strengthened in reducing cigarettes (beta=−0.60, P<.001), alcohol drinking (beta=−0.26, P=.01), and systolic (beta=−0.27, P=.02) and diastolic blood pressure (beta=−0.34, P=.007) at 6 months among individuals participating in the wireless self-monitoring program. No differences were observed with respect to medication adherence.

Conclusions

Participation in a wireless self-monitoring program provides individuals motivated to improve their health management with an added benefit above and beyond that of motivation alone. Hypertensive individuals eager to change health behaviors are excellent candidates for mobile health self-monitoring..

Trial Registration

ClinicalTrials.gov NCT01975428, https://clinicaltrials.gov/ct2/show/NCT01975428 (Archived by WebCite at http://www.webcitation.org/6iSO5OgOG)

New Approaches to Evaluating and Monitoring Blood Pressure

Digital health innovations for hypertension include cuffless blood pressure sensors, wireless smartphone-enabled upper arm blood pressure monitors, mobile applications, and remote monitoring technologies. Wearable trackers have drawn interest from medical professionals and patients alike. They have the potential to improve hypertension control and medication adherence through easier logging of repeated blood pressure measurements, better connectivity with health-care providers, and medication reminder alerts. With increasing emphasis on home and ambulatory blood pressure monitoring to confirm hypertension prior to treatment, such devices also can help improve the diagnostic landscape. However, privacy, accuracy, and cost concerns have prevented widespread clinical uptake. To foster implementation, device designers and clinical researchers should collaborate on development of rigorous clinical trials that test cardiovascular outcomes associated with emerging technologies. We review the current literature on mobile health technologies and novel diagnostic and management protocols and make recommendations on how to incorporate these innovations into practice.

A Multimodal mHealth Intervention (FeatForward) to Improve Physical Activity Behavior in Patients with High Cardiometabolic Risk Factors: Rationale and Protocol for a Randomized Controlled Trial

Background

Physical inactivity is one of the leading risk factors contributing to the rising rates of chronic diseases and has been associated with deleterious health outcomes in patients with chronic disease conditions. We developed a mobile phone app, FeatForward, to increase the level of physical activity in patients with cardiometabolic risk (CMR) factors. This intervention is expected to result in an overall improvement in patient health outcomes.

Objective

The objective of this study is to evaluate the effect of a mobile phone–based app, FeatForward, on physical activity levels and other CMR factors in patients with chronic conditions.

Methods

The study will be implemented as a 2-arm randomized controlled trial with 300 adult patients with chronic conditions over a 6-month follow-up period. Participants will be assigned to either the intervention group receiving the FeatForward app and standard care versus a control group who will receive only usual care. The difference in physical activity levels between the control group and intervention group will be measured as the primary outcome. We will also evaluate the effect of this intervention on secondary measures including clinical outcome changes in global CMR factors (glycated hemoglobin, fasting blood glucose, blood pressure, waist circumference, Serum lipids, C-reactive protein), health-related quality of life, health care usage, including attendance of scheduled clinic visits and hospitalizations, usability, and satisfaction, participant engagement with the FeatForward app, physician engagement with physician portal, and willingness to engage in physical activity. Instruments that will be used in evaluating secondary outcomes include the Short-Form (SF)-12, app usability and satisfaction questionnaires, physician satisfaction questionnaire. The intention-to-treat approach will be used to evaluate outcomes. All outcomes will be measured longitudinally at baseline, midpoint (3 months), and 6 months. Our primary outcome, physical activity, will be assessed by mixed-model analysis of variance with intervention assignment as between-group factor and time as within-subject factor. A similar approach will be used to analyze continuous secondary outcomes while categorical outcomes will be analyzed by chi-square test.

Results

The study is still in progress and we hope to have the results by the end of 2016.

Conclusions

The mobile phone–based app, FeatForward, could lead to significant improvements in physical activity and other CMR factors in patients.

Remote Patient Monitoring via Non-Invasive Digital Technologies: A Systematic Review

BACKGROUND

We conducted a systematic literature review to identify key trends associated with remote patient monitoring (RPM) via noninvasive digital technologies over the last decade.

MATERIALS AND METHODS

A search was conducted in EMBASE and Ovid MEDLINE. Citations were screened for relevance against predefined selection criteria based on the PICOTS (Population, Intervention, Comparator, Outcomes, Timeframe, and Study Design) format. We included studies published between January 1, 2005 and September 15, 2015 that used RPM via noninvasive digital technology (smartphones/personal digital assistants [PDAs], wearables, biosensors, computerized systems, or multiple components of the formerly mentioned) in evaluating health outcomes compared to standard of care or another technology. Studies were quality appraised according to Critical Appraisal Skills Programme.

RESULTS

Of 347 articles identified, 62 met the selection criteria. Most studies were randomized control trials with older adult populations, small sample sizes, and limited follow-up. There was a trend toward multicomponent interventions (n = 26), followed by smartphones/PDAs (n = 12), wearables (n = 11), biosensor devices (n = 7), and computerized systems (n = 6). Another key trend was the monitoring of chronic conditions, including respiratory (23%), weight management (17%), metabolic (18%), and cardiovascular diseases (16%). Although substantial diversity in health-related outcomes was noted, studies predominantly reported positive findings.

CONCLUSIONS

This review will help decision makers develop a better understanding of the current landscape of peer-reviewed literature, demonstrating the utility of noninvasive RPM in various patient populations. Future research is needed to determine the effectiveness of RPM via noninvasive digital technologies in delivering patient healthcare benefits and the feasibility of large-scale implementation.