scholarly journals Heart Snapshot: a broadly validated smartphone measure of VO2max for collection of real world data

2020 ◽  
Author(s):  
Dan E. Webster ◽  
Meghasyam Tummalacherla ◽  
Michael Higgins ◽  
David Wing ◽  
Euan Ashley ◽  
...  
Keyword(s):  
Real World ◽  
Gold Standard ◽  
Large Scale ◽  
Digital Health ◽  
Clinical Care ◽  
Skin Pigmentation ◽  
Epidemiologic Studies ◽  
Step Test ◽  
Real World Data ◽  
Laboratory Equipment

AbstractExpanding access to precision medicine will increasingly require that patient biometrics can be measured in remote care settings. VO2max, the maximum volume of oxygen usable during intense exercise, is one of the most predictive biometric risk factors for cardiovascular disease, frailty, and overall mortality.1,2 However, VO2max measurements are rarely performed in clinical care or large-scale epidemiologic studies due to the high cost, participant burden, and need for specialized laboratory equipment and staff.3,4 To overcome these barriers, we developed two smartphone sensor-based protocols for estimating VO2max: a generalization of a 12-minute run test (12-MRT) and a submaximal 3-minute step test (3-MST). In laboratory settings, Lins concordance for these two tests relative to gold standard VO2max testing was pc=0.66 for 12-MRT and pc=0.61 for 3-MST. Relative to “silver standards”5 (Cooper/Tecumseh protocols), concordance was pc=0.96 and pc=0.94, respectively. However, in remote settings, 12-MRT was significantly less concordant with gold standard (pc=0.25) compared to 3-MST (pc=0.61), though both had high test-retest reliability (ICC=0.88 and 0.86, respectively). These results demonstrate the importance of real-world evidence for validation of digital health measurements. In order to validate 3-MST in a broadly representative population in accordance with the All of Us Research Program6 for which this measurement was developed, the camera-based heart rate measurement was investigated for potential bias. No systematic measurement error was observed that corresponded to skin pigmentation level, operating system, or cost of the phone used. The smartphone-based 3-MST protocol, here termed Heart Snapshot, maintained fidelity across demographic variation in age and sex, across diverse skin pigmentation, and between iOS and Android implementations of various smartphone models. The source code for these smartphone measurements, along with the data used to validate them,6 are openly available to the research community.

scholarly journals Smartphone-Based VO2max Measurement With Heart Snapshot in Clinical and Real-world Settings With a Diverse Population: Validation Study

10.2196/26006 ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. e26006
Author(s):  
Dan E Webster ◽  
Meghasyam Tummalacherla ◽  
Michael Higgins ◽  
David Wing ◽  
Euan Ashley ◽  
...  
Keyword(s):  
Heart Rate ◽  
Correlation Coefficient ◽  
Intraclass Correlation Coefficient ◽  
Real World ◽  
Gold Standard ◽  
Clinical Care ◽  
Skin Pigmentation ◽  
Intraclass Correlation ◽  
Step Test ◽  
Validation Data

Background Maximal oxygen consumption (VO2max) is one of the most predictive biometrics for cardiovascular health and overall mortality. However, VO2max is rarely measured in large-scale research studies or routine clinical care because of the high cost, participant burden, and requirement for specialized equipment and staff. Objective To overcome the limitations of clinical VO2max measurement, we aim to develop a digital VO2max estimation protocol that can be self-administered remotely using only the sensors within a smartphone. We also aim to validate this measure within a broadly representative population across a spectrum of smartphone devices. Methods Two smartphone-based VO2max estimation protocols were developed: a 12-minute run test (12-MRT) based on distance measured by GPS and a 3-minute step test (3-MST) based on heart rate recovery measured by a camera. In a 101-person cohort, balanced across age deciles and sex, participants completed a gold standard treadmill-based VO2max measurement, two silver standard clinical protocols, and the smartphone-based 12-MRT and 3-MST protocols in the clinic and at home. In a separate 120-participant cohort, the video-based heart rate measurement underlying the 3-MST was measured for accuracy in individuals across the spectrum skin tones while using 8 different smartphones ranging in cost from US $99 to US $999. Results When compared with gold standard VO2max testing, Lin concordance was pc=0.66 for 12-MRT and pc=0.61 for 3-MST. However, in remote settings, the 12-MRT was significantly less concordant with the gold standard (pc=0.25) compared with the 3-MST (pc=0.61), although both had high test-retest reliability (12-MRT intraclass correlation coefficient=0.88; 3-MST intraclass correlation coefficient=0.86). On the basis of the finding that 3-MST concordance was generalizable to remote settings whereas 12-MRT was not, the video-based heart rate measure within the 3-MST was selected for further investigation. Heart rate measurements in any of the combinations of the six Fitzpatrick skin tones and 8 smartphones resulted in a concordance of pc≥0.81. Performance did not correlate with device cost, with all phones selling under US $200 performing better than pc>0.92. Conclusions These findings demonstrate the importance of validating mobile health measures in the real world across a diverse cohort and spectrum of hardware. The 3-MST protocol, termed as heart snapshot, measured VO2max with similar accuracy to supervised in-clinic tests such as the Tecumseh (pc=0.94) protocol, while also generalizing to remote and unsupervised measurements. Heart snapshot measurements demonstrated fidelity across demographic variation in age and sex, across diverse skin pigmentation, and between various iOS and Android phone configurations. This software is freely available for all validation data and analysis code.

scholarly journals Real-world longitudinal data collected from the SleepHealth mobile app study

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Sean Deering ◽  
Abhishek Pratap ◽  
Christine Suver ◽  
A. Joseph Borelli ◽  
Adam Amdur ◽  
...  
Keyword(s):  
Real World ◽  
Large Scale ◽  
Digital Health ◽  
The United States ◽  
Mobile App ◽  
Environmental Data ◽  
Sleep Habits ◽  
Real World Data ◽  
Disease Manifestation ◽  
Novel Approach

AbstractConducting biomedical research using smartphones is a novel approach to studying health and disease that is only beginning to be meaningfully explored. Gathering large-scale, real-world data to track disease manifestation and long-term trajectory in this manner is quite practical and largely untapped. Researchers can assess large study cohorts using surveys and sensor-based activities that can be interspersed with participants’ daily routines. In addition, this approach offers a medium for researchers to collect contextual and environmental data via device-based sensors, data aggregator frameworks, and connected wearable devices. The main aim of the SleepHealth Mobile App Study (SHMAS) was to gain a better understanding of the relationship between sleep habits and daytime functioning utilizing a novel digital health approach. Secondary goals included assessing the feasibility of a fully-remote approach to obtaining clinical characteristics of participants, evaluating data validity, and examining user retention patterns and data-sharing preferences. Here, we provide a description of data collected from 7,250 participants living in the United States who chose to share their data broadly with the study team and qualified researchers worldwide.

scholarly journals Can pragmatic research, real-world data and digital technologies aid the development of psychedelic medicine?

2021 ◽  
pp. 026988112110085
Author(s):  
Robin L Carhart-Harris ◽  
Anne C Wagner ◽  
Manish Agrawal ◽  
Hannes Kettner ◽  
Jerold F Rosenbaum ◽  
...  
Keyword(s):  
Real World ◽  
Best Practice ◽  
Digital Health ◽  
Real World Data ◽  
Treatment Protocols ◽  
World Data ◽  
Cost Efficient ◽  
Personalised Treatment ◽  
New Research ◽  
Confirmatory Trials

Favourable regulatory assessments, liberal policy changes, new research centres and substantial commercial investment signal that psychedelic therapy is making a major comeback. Positive findings from modern trials are catalysing developments, but it is questionable whether current confirmatory trials are sufficient for advancing our understanding of safety and best practice. Here we suggest supplementing traditional confirmatory trials with pragmatic trials, real-world data initiatives and digital health solutions to better support the discovery of optimal and personalised treatment protocols and parameters. These recommendations are intended to help support the development of safe, effective and cost-efficient psychedelic therapy, which, given its history, is vulnerable to excesses of hype and regulation.

scholarly journals The Value of Real-World Data in Understanding Prostate Cancer Risk and Improving Clinical Care: Examples from Swedish Registries

Cancers ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 875
Author(s):  
Kerri Beckmann ◽  
Hans Garmo ◽  
Ingela Franck Lissbrant ◽  
Pär Stattin
Keyword(s):  
Prostate Cancer ◽  
Real World ◽  
Clinical Care ◽  
Prostate Cancer Risk ◽  
Real World Data ◽  
Level Of Evidence ◽  
World Data ◽  
Cancer Data ◽  
Using Data ◽  
Randomised Controlled

Real-world data (RWD), that is, data from sources other than controlled clinical trials, play an increasingly important role in medical research. The development of quality clinical registers, increasing access to administrative data sources, growing computing power and data linkage capacities have contributed to greater availability of RWD. Evidence derived from RWD increases our understanding of prostate cancer (PCa) aetiology, natural history and effective management. While randomised controlled trials offer the best level of evidence for establishing the efficacy of medical interventions and making causal inferences, studies using RWD offer complementary evidence about the effectiveness, long-term outcomes and safety of interventions in real-world settings. RWD provide the only means of addressing questions about risk factors and exposures that cannot be “controlled”, or when assessing rare outcomes. This review provides examples of the value of RWD for generating evidence about PCa, focusing on studies using data from a quality clinical register, namely the National Prostate Cancer Register (NPCR) Sweden, with longitudinal data on advanced PCa in Patient-overview Prostate Cancer (PPC) and data linkages to other sources in Prostate Cancer data Base Sweden (PCBaSe).

scholarly journals Realities of conducting digital health research: Challenges to consider

2019 ◽  
Vol 5 ◽  
pp. 205520761986946
Author(s):  
Emily de Redon ◽  
Amanda Centi
Keyword(s):  
Large Scale ◽  
Digital Health ◽  
Clinical Care ◽  
Health Sector ◽  
Healthcare Systems ◽  
Research Challenges ◽  
Technological Advances ◽  
Barriers To Implementation ◽  
Smart Health ◽  
Health Applications

The health sector has been slow to adopt and integrate new technological advances into antiquated workflows and processes. The onset of smart health applications and devices has initiated a push for healthcare systems as well as physicians to incorporate and utilize such technology and the big data it collects. However, without considering the challenges experienced in large-scale trials, physicians and their clinics will run into similar barriers to implementation and uptake. Thoughtful implementation and preparation will make the use of such technological advances possible, palatable and effective in improving clinical care.

scholarly journals Developing a machine learning environmental allergy prediction model from real world data through a novel decentralized mobile study platform.

2020 ◽  
Author(s):  
Chethan Sarabu ◽  
Sandra Steyaert ◽  
Nirav Shah
Keyword(s):  
Machine Learning ◽  
Real World ◽  
Predictive Models ◽  
Clinical Care ◽  
Sensor Data ◽  
Real World Data ◽  
World Data ◽  
Demographic Groups ◽  
Prediction And Prevention ◽  
Wide Range

Environmental allergies cause significant morbidity across a wide range of demographic groups. This morbidity could be mitigated through individualized predictive models capable of guiding personalized preventive measures. We developed a predictive model by integrating smartphone sensor data with symptom diaries maintained by patients. The machine learning model was found to be highly predictive, with an accuracy of 0.801. Such models based on real-world data can guide clinical care for patients and providers, reduce the economic burden of uncontrolled allergies, and set the stage for subsequent research pursuing allergy prediction and prevention. Moreover, this study offers proof-of-principle regarding the feasibility of building clinically useful predictive models from 'messy,' participant derived real-world data.

scholarly journals Proton Pump Inhibitors and Risk of Dementia: A Hypothesis Generated but Not Adequately Tested

2021 ◽  
Vol 36 ◽  
pp. 153331752110624
Author(s):  
Mishah Azhar ◽  
Lawrence Fiedler ◽  
Patricio S. Espinosa ◽  
Charles H. Hennekens
Keyword(s):  
Proton Pump Inhibitors ◽  
Proton Pump ◽  
Real World ◽  
Large Scale ◽  
Basic Research ◽  
The United States ◽  
Epidemiological Studies ◽  
Real World Data ◽  
Health Authorities ◽  
Public Health Authorities

We reviewed the evidence on proton pump inhibitors (PPIs) and dementia. PPIs are among the most widely utilized drugs in the world. Dementia affects roughly 5% of the population of the United States (US) and world aged 60 years and older. With respect to PPIs and dementia, basic research has suggested plausible mechanisms but descriptive and analytic epidemiological studies are not inconsistent. In addition, a single large-scale randomized trial showed no association. When the evidence is incomplete, it is appropriate for clinicians and researchers to remain uncertain. Regulatory or public health authorities sometimes need to make real-world decisions based on real-world data. When the evidence is complete, then the most rational judgments for individual patients the health of the general public are possible At present, the evidence on PPIs and dementia suggests more reassurance than alarm. Further large-scale randomized evidence is necessary to do so.

scholarly journals A Joint Learning Approach to Intelligent Job Interview Assessment

2018 ◽  
Author(s):  
Dazhong Shen ◽  
Hengshu Zhu ◽  
Chen Zhu ◽  
Tong Xu ◽  
Chao Ma ◽  
...  
Keyword(s):  
Real World ◽  
Latent Variable ◽  
Large Scale ◽  
Real World Data ◽  
Job Interviews ◽  
Variable Model ◽  
Job Interview ◽  
Joint Learning ◽  
Interview Process ◽  
The Right

The job interview is considered as one of the most essential tasks in talent recruitment, which forms a bridge between candidates and employers in fitting the right person for the right job. While substantial efforts have been made on improving the job interview process, it is inevitable to have biased or inconsistent interview assessment due to the subjective nature of the traditional interview process. To this end, in this paper, we propose a novel approach to intelligent job interview assessment by learning the large-scale real-world interview data. Specifically, we develop a latent variable model named Joint Learning Model on Interview Assessment (JLMIA) to jointly model job description, candidate resume and interview assessment. JLMIA can effectively learn the representative perspectives of different job interview processes from the successful job application records in history. Therefore, a variety of applications in job interviews can be enabled, such as person-job fit and interview question recommendation. Extensive experiments conducted on real-world data clearly validate the effectiveness of JLMIA, which can lead to substantially less bias in job interviews and provide a valuable understanding of job interview assessment.

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