Personal health technology: A new era in cardiovascular disease prevention

Cardiovascular disease behavioural risk factors in rural interventions: cross-sectional study

This study aims to (1) assess the distribution of variables within the population and the prevalence of cardiovascular disease (CVD) behavioural risk factors in patients, (2) identify target risk factor(s) for behaviour modification intervention, and (3) develop an analytical model to define cluster(s) of risk factors which could help make any generic intervention more targeted to the local patient population. Study patients with at least one CVD behavioural risk factor living in a rural region of the Scottish Highlands. The study used the STROBE methodology for cross-sectional studies. Demographic and clinical data of patients (n = 2025) in NHS Highlands hospital were collected at the point of admission for PCI between 04.01.2016 and 31.12.2019. Collected data distributions were analysed by CVD behavioural risk factors for prevalence, associations, and direction of associations. Cluster definition was measured by assignment of a unit score each for the overall level of prevalence and significance of associations, and general logistics modelling for direction and significance of the risk. The mean (SD) age was 69.47(± 10.93) years [95% CI (68.99-69.94)]. The key risk factors were hyperlipidaemia, hypertension, and elevated body mass index (BMI). Approximately 40% of the population have multiple risk factor counts of two. Analytical measures revealed a population risk factor cluster with elevated BMI [77.5% (1570/2025)] that is mostly either hyperlipidaemic [9.43%, co-eff. (17), P = 0.007] or hypertensive [22.72%, co-eff. (17), P = 0.99] as key risk factor clusters. Carefully modelled analyses revealed clustered risk associated with elevated BMI. This information would support a strategy for targeting risk factor clusters in novel interventions to improve implementation efficiency. Exposure to and outcome of an elevated BMI is linked more to the population's socio-economic outcomes rather than to regional rurality or urbanity.

The Effectiveness of Digital Insomnia Treatment with Adjunctive Wearable Technology: A Pilot Randomized Controlled Trial

OBJECTIVE

This pilot trial aimed to provide evidence for whether the integration of a wearable device with digital behavioral therapy for insomnia (dBTi) improves treatment outcomes and engagement.

PARTICIPANTS AND METHODS

One hundred and twenty-eight participants with insomnia symptoms were randomized to a 3-week dBTi program (SleepFix®) with a wearable device enabling sleep data synchronization (dBTi+wearable group; n = 62) or dBTi alone (n = 66). Participants completed the Insomnia Severity Index (ISI) and modified Pittsburgh Sleep Quality Index (PSQI) parameters: wake-after-sleep-onset (WASO), sleep-onset-latency (SOL), and total sleep time (TST) at baseline and weeks 1, 2, 3, and primary endpoint of week 6 and follow-up at 12 weeks. Engagement was measured by the number of daily sleep diaries logged in the app.

RESULTS

There was no difference in ISI change scores between the groups from pre- to post-treatment (Cohen's d= 0.7, p= .061). The dBTi+wearable group showed greater improvements in WASO (d= 0.8, p = .005) and TST (d= 0.3, p= .049) compared to the dBTi group. Significantly greater engagement (sleep diary entries) was observed in the dBTi+wearable group (mean = 22.4, SD = 10.0) compared to the dBTi group (mean = 14.1, SD = 14.2) (p = .010).

CONCLUSIONS

This pilot trial found that integration of wearable device with a digital insomnia therapy enhanced user engagement and led to improvements in sleep parameters compared to dBTi alone. These findings suggest that adjunctive wearable technologies may improve digital insomnia therapy effectiveness.

Digital tools in allergy and respiratory care

Patient care in the allergy and respiratory fields is advancing rapidly, offering the possibility of the inclusion of a variety of digital tools that aim to improve outcomes of care. Impaired access to several health care facilities during the COVID-19 pandemic has considerably increased the appetite and need for the inclusion of e-health tools amongst end-users. Consequently, a multitude of different e-health tools have been launched worldwide with various registration and access options, and with a wide range of offered benefits. From the perspective of both patients and healthcare providers (HCPs), as well as from a legal and device-related perspective, several features are important for the acceptance, effectiveness,and long-term use of e-health tools. Patients and physicians have different needs and expectations of how digital tools might be of help in the care pathway. There is a need for standardization by defining quality assurance criteria. Therefore, the Upper Airway Diseases Committee of the World Allergy Organization (WAO) has taken the initiative to define and propose criteria for quality, appeal, and applicability of e-health tools in the allergy and respiratory care fields from a patient, clinician, and academic perspective with the ultimate aim to improve patient health and outcomes of care.

Digital Technology Interventions for Risk Factor Modification in Patients With Cardiovascular Disease: Systematic Review and Meta-analysis

BACKGROUND

Approximately 50% of cardiovascular disease (CVD) cases are attributable to lifestyle risk factors. Despite widespread education, personal knowledge, and efficacy, many individuals fail to adequately modify these risk factors, even after a cardiovascular event. Digital technology interventions have been suggested as a viable equivalent and potential alternative to conventional cardiac rehabilitation care centers. However, little is known about the clinical effectiveness of these technologies in bringing about behavioral changes in patients with CVD at an individual level.

OBJECTIVE

The aim of this study is to identify and measure the effectiveness of digital technology (eg, mobile phones, the internet, software applications, wearables, etc) interventions in randomized controlled trials (RCTs) and determine which behavior change constructs are effective at achieving risk factor modification in patients with CVD.

METHODS

This study is a systematic review and meta-analysis of RCTs designed according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analysis) statement standard. Mixed data from studies extracted from selected research databases and filtered for RCTs only were analyzed using quantitative methods. Outcome hypothesis testing was set at 95% CI and P=.05 for statistical significance.

RESULTS

Digital interventions were delivered using devices such as cell phones, smartphones, personal computers, and wearables coupled with technologies such as the internet, SMS, software applications, and mobile sensors. Behavioral change constructs such as cognition, follow-up, goal setting, record keeping, perceived benefit, persuasion, socialization, personalization, rewards and incentives, support, and self-management were used. The meta-analyzed effect estimates (mean difference [MD]; standard mean difference [SMD]; and risk ratio [RR]) calculated for outcomes showed benefits in total cholesterol SMD at -0.29 [-0.44, -0.15], P<.001; high-density lipoprotein SMD at -0.09 [-0.19, 0.00], P=.05; low-density lipoprotein SMD at -0.18 [-0.33, -0.04], P=.01; physical activity (PA) SMD at 0.23 [0.11, 0.36], P<.001; physical inactivity (sedentary) RR at 0.54 [0.39, 0.75], P<.001; and diet (food intake) RR at 0.79 [0.66, 0.94], P=.007. Initial effect estimates showed no significant benefit in body mass index (BMI) MD at -0.37 [-1.20, 0.46], P=.38; diastolic blood pressure (BP) SMD at -0.06 [-0.20, 0.08], P=.43; systolic BP SMD at -0.03 [-0.18, 0.13], P=.74; Hemoglobin A1C blood sugar (HbA1c) RR at 1.04 [0.40, 2.70], P=.94; alcohol intake SMD at -0.16 [-1.43, 1.10], P=.80; smoking RR at 0.87 [0.67, 1.13], P=.30; and medication adherence RR at 1.10 [1.00, 1.22], P=.06.

CONCLUSIONS

Digital interventions may improve healthy behavioral factors (PA, healthy diet, and medication adherence) and are even more potent when used to treat multiple behavioral outcomes (eg, medication adherence plus). However, they did not appear to reduce unhealthy behavioral factors (smoking, alcohol intake, and unhealthy diet) and clinical outcomes (BMI, triglycerides, diastolic and systolic BP, and HbA1c).

Effect of Electronic Activity Monitors and Pedometers on Health: Results from the TAME Health Pilot Randomized Pragmatic Trial

Background: Brief counseling and self-monitoring with a pedometer are common practice within primary care for physical activity promotion. It is unknown how high-tech electronic activity monitors compare to pedometers within this setting. This study aimed to investigate the outcomes, through effect size estimation, of an electronic activity monitor-based intervention to increase physical activity and decrease cardiovascular disease risk. Method: The pilot randomized controlled trial was pre-registered online at clinicaltrials.gov (NCT02554435). Forty overweight, sedentary participants 55–74 years of age were randomized to wear a pedometer or an electronic activity monitor for 12 weeks. Physical activity was measured objectively for 7 days at baseline and follow-up by a SenseWear monitor and cardiovascular disease risk was estimated by the Framingham risk calculator. Results: Effect sizes for behavioral and health outcomes ranged from small to medium. While these effect sizes were favorable to the intervention group for physical activity (PA) (d = 0.78) and general health (d = 0.39), they were not favorable for measures. Conclusion: The results of this pilot trial show promise for this low-intensity intervention strategy, but large-scale trials are needed to test its efficacy.

Changing the Health Behavior of Patients With Cardiovascular Disease Through an Electronic Health Intervention in Three Different Countries: Cost-Effectiveness Study in the Do Cardiac Health: Advanced New Generation Ecosystem (Do CHANGE) 2 Randomized Controlled Trial

BACKGROUND

During the last few decades, preventing the development of cardiovascular disease has become a mainstay for reducing cardiovascular morbidity and mortality. It has been suggested that interventions should focus more on committed approaches of self-care, such as electronic health techniques.

OBJECTIVE

This study aimed to provide evidence to understand the financial consequences of implementing the "Do Cardiac Health: Advanced New Generation Ecosystem" (Do CHANGE 2) intervention, which was evaluated in a multisite randomized controlled trial to change the health behavior of patients with cardiovascular disease.

METHODS

The cost-effectiveness analysis of the Do CHANGE 2 intervention was performed with the Monitoring and Assessment Framework for the European Innovation Partnership on Active and Healthy Ageing tool, based on a Markov model of five health states. The following two types of costs were considered for both study groups: (1) health care costs (ie, costs associated with the time spent by health care professionals on service provision, including consultations, and associated unplanned hospitalizations, etc) and (2) societal costs (ie, costs attributed to the time spent by patients and informal caregivers on care activities).

RESULTS

The Do CHANGE 2 intervention was less costly in Spain (incremental cost was -€2514.90) and more costly in the Netherlands and Taiwan (incremental costs were €1373.59 and €1062.54, respectively). Compared with treatment as usual, the effectiveness of the Do CHANGE 2 program in terms of an increase in quality-adjusted life-year gains was slightly higher in the Netherlands and lower in Spain and Taiwan.

CONCLUSIONS

In general, we found that the incremental cost-effectiveness ratio strongly varied depending on the country where the intervention was applied. The Do CHANGE 2 intervention showed a positive cost-effectiveness ratio only when implemented in Spain, indicating that it saved financial costs in relation to the effect of the intervention.

TRIAL REGISTRATION

ClinicalTrials.gov NCT03178305; https://clinicaltrials.gov/ct2/show/NCT03178305.

A Machine Learning Approach to Classifying Self-Reported Health Status in a Cohort of Patients With Heart Disease Using Activity Tracker Data

Constructing statistical models using personal sensor data could allow for tracking health status over time, thereby enabling the possibility of early intervention. The goal of this study was to use machine learning algorithms to classify patient-reported outcomes (PROs) using activity tracker data in a cohort of patients with stable ischemic heart disease (SIHD). A population of 182 patients with SIHD were monitored over a period of 12 weeks. Each subject received a Fitbit Charge 2 device to record daily activity data, and each subject completed eight Patient-Reported Outcomes Measurement Information Systems short form at the end of each week as a self-assessment of their health status. Two models were built to classify PRO scores using activity tracker data. The first model treated each week independently, whereas the second used a hidden Markov model (HMM) to take advantage of correlations between successive weeks. Retrospective analysis compared the classification accuracy of the two models and the importance of each feature. In the independent model, a random forest classifier achieved a mean area under curve (AUC) of 0.76 for classifying the physical function PRO. The HMM model achieved significantly better AUCs for all PROs (p < 0.05) other than Fatigue and Sleep Disturbance, with a highest mean AUC of 0.79 for the physical function-short form 10a. Our study demonstrates the ability of activity tracker data to classify health status over time. These results suggest that patient outcomes can be monitored in real time using activity trackers.

Role of Digital Games in Self-Management of Cardiovascular Diseases: A Scoping Review

OBJECTIVE

Examine research on the use of digital games to improve self-management (SM) behaviors in patients diagnosed with cardiovascular diagnoses of hypertension, coronary artery disease, heart failure, or myocardial infarction.

MATERIALS AND METHODS

For this scoping review, the CINAHL, PubMed, and Web of Science databases were searched for studies published from January 1, 2008 to December 20, 2017 using terms relevant to digital games and cardiovascular diseases (CVDs).

RESULTS

Eight articles met the inclusion/exclusion criteria, seven of which presented studies with participants 50 years or older. Five of the eight studies assessed physical activity. Only two studies included a control group. Digital games significantly improved exercise capacity and energy expenditure but did not affect quality of life, self-efficacy, anxiety, or depression. Digital games were found enjoyable by 79%-93% of participants, including those with lower education or age; however, barriers to game use included being tired or bored, lack of interest in digital games, poor perception of fitness through games, sensor limitations, conflicts with daily life routine, and preferences for group exercise. Average adherence ranged from 70% to 100% over 2 weeks to 6 months of study duration, with higher adherence rates in studies that included human contact through supervision or social support.

CONCLUSION

Paucity of studies about digital games for CVD SM behaviors precludes the need to undertake a full systematic review. Future studies examining digital games should include larger sample sizes, longer durations, game-design guided by behavioral change theoretical frameworks, and CVD SM behaviors in addition to physical activity behaviors.

Acceptability and Feasibility of Implementing Accelorometry-Based Activity Monitors and a Linked Web Portal in an Exercise Referral Scheme: Feasibility Randomized Controlled Trial

Background

Exercise referral schemes (ERSs) are recommended for patients with health conditions or risk factors. Evidence points to the initial effectiveness and cost-effectiveness of such schemes for increasing physical activity, but effects often diminish over time. Techniques such as goal setting, self-monitoring, and personalized feedback may support motivation for physical activity and maintenance of effects. Wearable technologies could provide an opportunity to integrate motivational techniques into exercise schemes. However, little is known about acceptability to exercise referral populations or implementation feasibility within exercise referral services.

Objective

To determine the feasibility and acceptability of implementing an activity-monitoring device within the Welsh National ERS to inform a decision on whether and how to proceed to an effectiveness trial.

Methods

We conducted a feasability randomized controlled trial with embedded mixed-methods process evaluation and an exploratory economic analysis. Adults (N=156) were randomized to intervention (plus usual practice; n=88) or usual practice only (n=68). Usual practice was a 16-week structured exercise program. The intervention group additionally received an accelerometry-based activity monitor (MyWellnessKey) and associated Web platform (MyWellnessCloud). The primary outcomes were predefined progression criteria assessing acceptability and feasibility of the intervention and proposed evaluation. Postal questionnaires were completed at baseline (time 0:T0), 16 weeks (T1), and 12 months after T0 (T2). Routine data were accessed at the same time-points. A subsample of intervention participants and scheme staff were interviewed following the initiation of intervention delivery and at T2.

Results

Participants were on average aged 56.6 (SD 16.3) years and mostly female (101/156, 64.7%) and white (150/156, 96.2%). Only 2 of 5 progression criteria were met; recruitment and randomization methods were acceptable to participants, and contamination was low. However, recruitment and retention rates (11.3% and 67.3%, respectively) fell substantially short of target criteria (20% and 80%, respectively), and disproportionally recruited from the least deprived quintile. Only 57.4% of intervention participants reported receipt of the intervention (below the 80% progression threshold). Less than half reported the intervention to be acceptable at T2. Participant and staff interviews revealed barriers to intervention delivery and engagement related to the device design as well as context-specific technological challenges, all of which made it difficult to integrate the technology into the service. Routinely collected health economic measures had substantial missing data, suggesting that other methods for collecting these should be used in future.

Conclusions

To our knowledge, this is the first study to evaluate short- and long-term feasibility and acceptability of integrating wearable technologies into community-based ERSs. The findings highlight device- and context-specific barriers to doing this in routine practice, with typical exercise referral populations. Key criteria for progression to a full-scale evaluation were not met.

Trial Registration

ISRCTN Registry ISRCTN85785652; http://www.isrctn.com/ISRCTN85785652

Health Economic Evaluations of Digital Health Interventions for Secondary Prevention in Stroke Patients: A Systematic Review

BACKGROUND

In the first 5 years after their stroke, about a quarter of patients will suffer from a recurrent stroke. Digital health interventions facilitating interactions between a caregiver and a patient from a distance are a promising approach to improve patient adherence to lifestyle changes proposed by secondary prevention guidelines. Many of these interventions are not implemented in daily practice, even though efficacy has been shown. One of the reasons can be the lack of clear economic incentives for implementation. We propose to map all health economic evidence regarding digital health interventions for secondary stroke prevention.

SUMMARY

We performed a systematic search according to PRISMA-P guidelines and searched on PubMed, Web of Science, Cochrane, and National Institute for Health Research Economic Evaluation Database. Only digital health interventions for secondary prevention in stroke patients were included and all study designs and health economic outcomes were accepted. We combined the terms "Stroke OR Cardiovascular," "Secondary prevention," "Digital health interventions," and "Cost" in one search string using the AND operator. The search performed on April 20, 2017 yielded 163 records of which 26 duplicates were removed. After abstract screening, 20 articles were retained for full-text analysis, of which none reported any health economic evidence that could be included for analysis or discussion. Key Messages: There is a lack of evidence on health economic outcomes on digital health interventions for secondary stroke prevention. Future research in this area should take health economics into consideration when designing a trial and there is a clear need for health economic evidence and models.

Advances in Health Technology Use and Implementation in the Era of Healthy Living: Implications for Precision Medicine

Much of the focus of precision medicine has been directed toward genomics, despite the fact that "lifestyle and behavioral factors" are included in the description of precision medicine. Numerous structured diet and PA interventions have demonstrated success in preventing and/or reducing chronic-disease risk. The use of personal health technologies has expanded exponentially in the health care arena; there are a number of consumer-based technologies yielding health information to individual users. The explosion in technology use provides an opportunity for broader dissemination of health care services and products. In addition, tracking cardiovascular disease risk and lifestyle and behavioral aspects of healthy living (HL) profiles in those products may be an important leveraging interface for precision medicine. This review will discuss and present an overview of current health technologies, their use in promotion of HL metrics and how this data may be integrated into venues that support HL and precision medicine.

Evaluating utility and compliance in a patient-based eHealth study using continuous-time heart rate and activity trackers

Telemedicine has been used to remotely diagnose and treat patients, yet previously applied telemonitoring approaches have been fraught with adherence issues. The primary goal of this study was to evaluate the adherence rates using a consumer-grade continuous-time heart rate and activity tracker in a mid-risk cardiovascular patient population. As a secondary analysis, we show the ability to utilize the information provided by this device to identify information about a patient's state by correlating tracker information with patient-reported outcome survey scores. We showed that using continuous-time activity trackers with heart rate monitors can be effective in a telemonitoring application, as patients had a high level of adherence (90.0% median usage) and low attrition (0.09% decrease per day) over a 90-day period. Furthermore, data collected correlated significantly with clinically relevant patient surveys (r2=0.15 for PROMIS global health scores, p < .00001), and therefore might provide an effective signal for identifying patients in need of intervention.

A randomized controlled trial to evaluate utilization of physical activity recommendations among patients of cardiovascular healthcare centres in Eastern Slovakia: study design and rationale of the AWATAR study

Background

Guidelines on modifiable risk factors regarding cardiological patients are poorly implemented in clinical practice perhaps due to low health literacy. Several digital tools for improving lifestyle and behavioural intervention were developed. Our primary aim is to evaluate the effectiveness of a digital exercise prescription tool on the adherence to physical activity recommendations among patients with cardiovascular diseases.

Methods

A randomized controlled trial will be realized in cooperation with Cardiovascular Health Centres in Eastern Slovakia. Patients recruited through their cardiologists, will be randomised at 1:1 ratio to the three-months’ experimental condition or control condition. The experimental group will receive standard lifestyle consultation leading to individually optimized prescription of physical activity. The control group will receive standard, usual-cardio-care lifestyle counselling, also in the domain of physical activity. The digital system will be used for optimized exercise prescription. The primary outcome is a change in the patient’s adherence to exercise recommendations. Data will be collected in both groups prior to consultation and after 3 months.

Discussion

This study protocol presents background and design of a randomized control trial to investigate the effectiveness of a digital system-provide exercise prescription tool on the adherence to physical activity recommendations. An optimized exercise prescription that better reflects patient’s diagnosis, comorbidities and medication can have a significant impact on secondary prevention of cardiovascular disease. This trial can provide important evidence about the effectiveness of digital exercise guidance in everyday practice of cardiovascular healthcare.

Trial registration

The study was registered on 1st November, 2017 and is available online at ClinicalTrials.gov (ID:NCT03329053).

PATHway I: design and rationale for the investigation of the feasibility, clinical effectiveness and cost-effectiveness of a technology-enabled cardiac rehabilitation platform

INTRODUCTION

Exercise-based cardiac rehabilitation (CR) independently alters the clinical course of cardiovascular diseases resulting in a significant reduction in all-cause and cardiac mortality. However, only 15%-30% of all eligible patients participate in a phase 2 ambulatory programme. The uptake rate of community-based programmes following phase 2 CR and adherence to long-term exercise is extremely poor. Newer care models, involving telerehabilitation programmes that are delivered remotely, show considerable promise for increasing adherence. In this view, the PATHway (Physical Activity Towards Health) platform was developed and now needs to be evaluated in terms of its feasibility and clinical efficacy.

METHODS AND ANALYSIS

In a multicentre randomised controlled pilot trial, 120 participants (m/f, age 40-80 years) completing a phase 2 ambulatory CR programme will be randomised on a 1:1 basis to PATHway or usual care. PATHway involves a comprehensive, internet-enabled, sensor-based home CR platform and provides individualised heart rate monitored exercise programmes (exerclasses and exergames) as the basis on which to provide a personalised lifestyle intervention programme. The control group will receive usual care. Study outcomes will be assessed at baseline, 3 months and 6 months after completion of phase 2 of the CR programme. The primary outcome is the change in active energy expenditure. Secondary outcomes include cardiopulmonary endurance capacity, muscle strength, body composition, cardiovascular risk factors, peripheral endothelial vascular function, patient satisfaction, health-related quality of life (HRQoL), well-being, mediators of behaviour change and safety. HRQoL and healthcare costs will be taken into account in cost-effectiveness evaluation.

ETHICS AND DISSEMINATION

The study will be conducted in accordance with the Declaration of Helsinki. This protocol has been approved by the director and clinical director of the PATHway study and by the ethical committee of each participating site. Results will be disseminated via peer-reviewed scientific journals and presentations at congresses and events.

TRIAL REGISTRATION NUMBER

NCT02717806. This trial is currently in the pre-results stage.

Mobile health as a viable strategy to enhance stroke risk factor control: A systematic review and meta-analysis

BACKGROUND

With the rapid growth worldwide in cell-phone use, Internet connectivity, and digital health technology, mobile health (mHealth) technology may offer a promising approach to bridge evidence-treatment gaps in stroke prevention. We aimed to evaluate the effectiveness of mHealth for stroke risk factor control through a systematic review and meta-analysis.

METHODS

We searched PubMed from January 1, 2000 to May 17, 2016 using the following keywords: mobile health, mHealth, short message, cellular phone, mobile phone, stroke prevention and control, diabetes mellitus, hypertension, hyperlipidemia and smoking cessation. We performed a meta-analysis of all eligible randomized control clinical trials that assessed a sustained (at least 6months) effect of mHealth.

RESULTS

Of 78 articles identified, 13 met eligibility criteria (6 for glycemic control and 7 for smoking cessation) and were included for the final meta-analysis. There were no eligible studies for dyslipidemia or hypertension. mHealth resulted in greater Hemoglobin A1c reduction at 6months (6 studies; 663 subjects; SMD: -0.44; 95% CI: [-0.82, -0.06], P=0.02; Mean difference of decrease in HbA1c: -0.39%; 95% CI: [-0.74, -0.04], P=0.03). mHealth also lead to relatively higher smoking abstinence rates at 6months (7 studies; 9514 subjects; OR: 1.54; 95% CI: [1.24, 1.90], P<0.0001).

CONCLUSIONS

Our meta-analysis supports that use of mHealth improves glycemic control and smoking abstinence rates.

The Role of Technology in Healthy Living Medicine

Health care consumers are taking control of their health information and desire a greater role in managing their health. Approximately 77% of Americans now own a smartphone and the use of health apps have doubled over the past two years. These effects are particularly notable in patients with chronic disease, now representing half the adult population and responsible for 86% of United States health care (HC) costs and 70% of deaths. New opportunities exist as a result of recent advances in home-based wireless devices, apps, wearables, and interactive systems enabling health delivery systems to monitor, advise and treat disease near real time and engage patients in healthy living medicine. These technologies will provide a new framework for patient engagement and care delivery that will enhance clinical outcomes and generate precision interventions that ultimately reduce HC costs.

Validation study of Polar V800 accelerometer

BACKGROUND

The correct quantification of physical activity (PA) and energy expenditure (EE) in daily life is an important target for researchers and professionals. The objective of this paper is to study the validity of the Polar V800 for the quantification of PA and the estimation of EE against the ActiGraph (ActiTrainer) in healthy young adults.

METHODS

Eighteen Caucasian active people (50% women) aged between 19-23 years wore an ActiTrainer on the right hip and a Polar V800 on the preferred wrist during 7 days. Paired samples t-tests were used to analyze differences in outcomes between devices, and Pearson's correlation coefficients to examine the correlation between outcomes. The agreement was studied using the Bland-Altman method. Also, the association between the difference and the magnitude of the measurement (heteroscedasticity) was examined. Sensitivity, specificity and area under the receiver operating characteristic curve (ROC-AUC value) were calculated to evaluate the ability of the devices to accurately define a person who fulfills the recommendation of 10,000 daily steps.

RESULTS

The devices significantly differed from each other on all outcomes (P<0.05), except for Polar V800's alerts vs. ActiTrainer's 1 hour sedentary bouts (P=0.595) and Polar V800's walking time vs. ActiTrainer's lifestyle time (P=0.484). Heteroscedasticity analyses were significant for all outcomes, except for Kcal and sitting time. The ROC-AUC value was fair (0.781±0.048) and the sensitivity and specificity was 98% and 58%, respectively.

CONCLUSIONS

The Polar V800 accelerometer has a comparable validity to the accelerometer in free-living conditions, regarding "1 hour sedentary bouts" and "V800's walking time vs. ActiTrainer's lifestyle time" in young adults.

The P4 Health Spectrum - A Predictive, Preventive, Personalized and Participatory Continuum for Promoting Healthspan

Chronic diseases (i.e., noncommunicable diseases), mainly cardiovascular disease, cancer, respiratory diseases and type-2-diabetes, are now the leading cause of death, disability and diminished quality of life on the planet. Moreover, these diseases are also a major financial burden worldwide, significantly impacting the economy of many countries. Healthcare systems and medicine have progressively improved upon the ability to address infectious diseases and react to adverse health events through both surgical interventions and pharmacology; we have become efficient in delivering reactive care (i.e., initiating interventions once an individual is on the verge of or has actually suffered a negative health event). However, with slowly progressing and often 'silent' chronic diseases now being the main cause of illness, healthcare and medicine must evolve into a proactive system, moving away from a merely reactive approach to care. Minimal interactions among the specialists and limited information to the general practitioner and to the individual receiving care lead to a fragmented health approach, non-concerted prescriptions, a scattered follow-up and a suboptimal cost-effectiveness ratio. A new approach in medicine that is predictive, preventive, personalized and participatory, which we label here as "P4" holds great promise to reduce the burden of chronic diseases by harnessing technology and an increasingly better understanding of environment-biology interactions, evidence-based interventions and the underlying mechanisms of chronic diseases. In this concept paper, we propose a 'P4 Health Continuum' model as a framework to promote and facilitate multi-stakeholder collaboration with an orchestrated common language and an integrated care model to increase the healthspan.

Testing Activity Monitors' Effect on Health: Study Protocol for a Randomized Controlled Trial Among Older Primary Care Patients

BACKGROUND

Cardiovascular disease is the leading cause of mortality in the United States. Maintaining healthy levels of physical activity is critical to cardiovascular health, but many older adults are inactive. There is a growing body of evidence linking low motivation and inactivity. Standard behavioral counseling techniques used within the primary care setting strive to increase motivation, but often do not emphasize the key component of self-control. The addition of electronic activity monitors (EAMs) to counseling protocols may provide more effective behavior change and increase overall motivation for exercise through interactive self-monitoring, feedback, and social support from other users.

OBJECTIVE

The objective of the study is to conduct a three month intervention trial that will test the feasibility of adding an EAM system to brief counseling within a primary care setting. Participants (n=40) will be randomized to receive evidence-based brief counseling plus either an EAM or a pedometer.

METHODS

Throughout the intervention, we will test its feasibility and acceptability, the change in primary outcomes (cardiovascular risk and physical activity), and the change in secondary outcomes (adherence, weight and body composition, health status, motivation, physical function, psychological feelings, and self-regulation). Upon completion of the intervention, we will also conduct focus groups with the participants and with primary care stakeholders.

RESULTS

The study started recruitment in October 2015 and is scheduled to be completed by October 2016.

CONCLUSIONS

This project will lay the groundwork and establish the infrastructure for intervention refinement and ultimately translation within the primary care setting in order to prevent cardiovascular disease on a population level.

TRIAL REGISTRATION

ClinicalTrials.gov NCT02554435; https://clinicaltrials.gov/ct2/show/NCT02554435 (Archived by WebCite at http://www.webcitation/6fUlW5tdT).

Utility of Consumer Physical Activity Trackers as an Intervention Tool in Cardiovascular Disease Prevention and Treatment

Consumer activity trackers have grown in popularity over the last few years. These devices are typically worn on the hip or wrist and provide the user with information about physical activity measures such as steps taken, energy expenditure, and time spent in moderate to vigorous physical activity. The consumer may also use the computer interface (e.g. device websites, smartphone applications) to monitor and track achievement of PA goals and compete with other users. This review will describe some of the most popular consumer devices and discuss the user feedback tools. We will also present the limited evidence available about the accuracy of these devices and highlight how they have been used in cardiovascular disease management. We conclude with some recommendations for future research, focusing on how consumer devices might be used to assess effectiveness of various cardiovascular treatments.

Personalized Weight Management Interventions for Cardiovascular Risk Reduction: A Viable Option for African-American Women

Obesity is an independent contributor to cardiovascular disease (CVD) and a major driving force behind racial/ethnic and gender disparities in risk. Due to a multitude of interrelating factors (i.e., personal, social, cultural, economic and environmental), African-American (AA) women are disproportionately obese and twice as likely to succumb to CVD, yet they are significantly underrepresented in behavioral weight management interventions. In this selective review we highlight components of the limited interventions shown to enhance weight loss outcomes in this population and make a case for leveraging Web-based technology and artificial intelligence techniques to deliver personalized programs aimed at obesity treatment and CVD risk reduction. Although many of the approaches discussed are generally applicable across populations burdened by disparate rates of obesity and CVD, we specifically focus on AA women due to the disproportionate impact of these non-communicable diseases and the general paucity of interventions targeted to this high-risk group.

Understanding and Improving Cardiovascular Health: An Update on the American Heart Association's Concept of Cardiovascular Health

The American Heart Association's 2020 Strategic Impact Goal is "By 2020, to improve the cardiovascular health of all Americans by 20% while reducing deaths from cardiovascular diseases and stroke by 20%." To monitor progress towards this goal, a new construct "ideal cardiovascular health" (iCVH) was defined that includes the simultaneous presence of optimal levels of seven health behaviors (physical activity, smoking, dietary intake, and body mass index) and factors (total cholesterol, blood pressure and fasting blood glucose). In this review, we present a summary of major concepts related to the concept of iCVH and an update of the literature in this area since publication of the 2020 Strategic Impact Goal, including trends in iCVH prevalence, new determinants and outcomes related to iCVH, strategies for maintaining or improving iCVH, policy implications of the iCVH model, and the remaining challenges to reaching the 2020 Strategic Impact Goal.