Another step (count) towards leveraging mobile health data for clinical prediction
Authors: Andrew R Murphy, Carissa A Low
Journal: The Lancet Digital Health (2021)

Advances in wearable device and home blood pressure monitoring technology offer promising opportunities to characterise population health and to improve understanding of cardiovascular disease risk. These devices also allow for remote longitudinal patient monitoring, increased engagement and health literacy, and delivery of personalised behavioural interventions all while reducing overall health-care costs.1Michie S Yardley L West R Patrick K Greaves F Developing and evaluating digital interventions to promote behavior change in health and health care: recommendations resulting from an international workshop.J Med Internet Res. 2017; 19: e232Crossref PubMed Scopus (381) Google Scholar Various health systems and start-ups seek to combine these data with clinical information from the electronic medical record, and several insurance companies are already trying to incentivise the uptake of wearable devices.2Dinh-Le C Chuang R Chokshi S Mann D Wearable health technology and electronic health record integration: scoping review and future directions.JMIR Mhealth Uhealth. 2019; 7e12861Crossref PubMed Scopus (97) Google Scholar Novel applications include monitoring of atrial fibrillation, heart rate monitoring for influenza screening, and assessing correlation between activity level and mortality.3Jaakkola J Jaakkola S Lahdenoja O et al.mobile phone detection of atrial fibrillation with mechanocardiography: the MODE-AF study (mobile phone detection of atrial fibrillation).Circulation. 2018; 137: 1524-1527Crossref PubMed Scopus (35) Google Scholar, 4Perez MV Mahaffey KW Hedlin H et al.Large-scale assessment of a smartwatch to identify atrial fibrillation.N Engl J Med. 2019; 381: 1909-1917Crossref PubMed Scopus (630) Google Scholar, 5Lee IM Shiroma EJ Kamada M Bassett DR Matthews CE Buring JE Association of step volume and intensity with all-cause mortality in older women.JAMA Intern Med. 2019; 179: 1105-1112Crossref PubMed Scopus (229) Google Scholar However, important questions remain, including how patients will respond to this data collection, whether data can be reliably obtained, and the validity of these data, particularly when faced with the challenges of selection bias, mismeasurement, and missing data.6Hicks JL Althoff T Sosic R et al.Best practices for analyzing large-scale health data from wearables and smartphone apps.NPJ Digit Med. 2019; 2: 45Crossref PubMed Scopus (72) Google Scholar In The Lancet Digital Health, Golbus and colleagues7Golbus JR Pescatore NA Nallamothu BK Shah N Kheterpal S Wearable device signals and home blood pressure data across age, sex, race, ethnicity, and clinical phenotypes in the Michigan Predictive Activity & Clinical Trajectories in Health (MIPACT) study: a prospective, community-based observational study.Lancet Digit Health. 2021; 3: e707-e715Summary Full Text Full Text PDF PubMed Scopus (6) Google Scholar describe initial findings from their attempt to answer such questions through the Michigan predictive activity and clinical trajectories in health (MIPACT) study.7Golbus JR Pescatore NA Nallamothu BK Shah N Kheterpal S Wearable device signals and home blood pressure data across age, sex, race, ethnicity, and clinical phenotypes in the Michigan Predictive Activity & Clinical Trajectories in Health (MIPACT) study: a prospective, community-based observational study.Lancet Digit Health. 2021; 3: e707-e715Summary Full Text Full Text PDF PubMed Scopus (6) Google Scholar The study is a large, prospective, observational study seeking to enroll a heterogenous patient population and integrate wearable and home device data with electronic medical record data. The study was divided into two phases: an intensive 45-day collection phase (phase 1); and a 3-year longitudinal monitoring phase (phase 2). The authors report the first 90 days of data for all participants, which includes all of phase 1 and the first 45 days of phase 2. The authors provided study participants with an Apple Watch (Apple) and a Omron Evolv (Omron HealthCare) home blood pressure monitor. Participants were required to own an iPhone that had regular access to the internet. To recruit participants, an extensive online and physical mail campaign was done, as well as in-person recruitment at clinical sites with 123 448 people invited to participate. Ultimately 6765 patients were enrolled in the study. Key findings from the first 90 days of the study included data on participant compliance with the study protocol and average heart rate, step count, and blood pressure data. The authors highlighted differences among demographic groups, notably that males and older adults (aged >65 years) had higher blood pressure and lower heart rates, and females and older adults had a lower step count and distance walked. These initial descriptive results are exciting, suggesting that real-world acquisition of wearable and home blood pressure data is feasible. Adherence was high, with participants wearing the Apple Watch on 80 (89%) of the 90 days (SD 16) and averaging over 15 h per day wearing the device on days it was worn. We also applaud the authors for making their aggregated data available online with an easily navigable website allowing comparison of groups by age, race and ethnicity, gender, body-mass index, use of a β blocker, and up to three medical conditions. With data collection planned to continue for 3 years, MIPACT's novel integration of wearable device data with clinical information in a large sample will enable future analyses linking longitudinal patterns of activity, heart rate, and home blood pressure data with clinical phenotypes within and across groups. The authors also recruited a diverse patient base with more than 15% representation of both Asian and African American participants and substantial clinical comorbidities. However, we urge caution when drawing generalisations from these data. Of note, of the more than 100 000 patients that were contacted either in person or through physical mail or digital means, 6765 (5·5%) were ultimately enrolled. In addition, 1283 out of 2036 eligible in-person participants (63%) chose not to enroll and 1201 out of the 3237 approached in-person participants (37%) were ineligible because they did not own an iPhone. Ideally a comparison of participant characteristics between those enrolled and those who opted not to enroll or who were not eligible would help to establish the representativeness of their sample. This issue is essential to consider especially as the authors seek to work towards establishing a baseline or normative dataset for different populations. Questions about the generalisability of these data are highlighted when considering that the average step count of 7511 steps per day was substantially higher than that reported by studies with larger sample sizes (3491–4961 steps per day).5Lee IM Shiroma EJ Kamada M Bassett DR Matthews CE Buring JE Association of step volume and intensity with all-cause mortality in older women.JAMA Intern Med. 2019; 179: 1105-1112Crossref PubMed Scopus (229) Google Scholar, 8Avram R Tison GH Aschbacher K et al.Real-world heart rate norms in the Health eHeart study.NPJ Digit Med. 2019; 2: 58Crossref PubMed Scopus (47) Google Scholar, 9Althoff T Sosič R Hicks JL King AC Delp SL Leskovec J Large-scale physical activity data reveal worldwide activity inequality.Nature. 2017; 547: 336-339Crossref PubMed Scopus (490) Google Scholar The authors offer a promising contribution to the literature by providing a descriptive look at initial data from a longitudinal study of a diverse population that is likely to yield valuable insights. Among the studied participants, these data appear robust and reliable with a high level of participant adherence, framed by an easily navigable web portal to compare clinical groups. We think that this study and similar work leveraging the immense amount of continuous health data available from consumer wearable devices supports the feasibility and value of integrating smartwatch data with clinical outcomes and has the potential to transform understanding, prevention, and management of cardiovascular and other chronic medical illnesses. This work was supported in part by the National Cancer Institute (grant K07CA204380, R37CA242545). CAL reports grants from the US National Institute of Health and honoraria from the Cancer Center Survivorship Research Forum. ARM declares no competing interests. Wearable device signals and home blood pressure data across age, sex, race, ethnicity, and clinical phenotypes in the Michigan Predictive Activity & Clinical Trajectories in Health (MIPACT) study: a prospective, community-based observational studyThese data could inform clinical trial design, interpretation of wearable data in clinical practice, and health-care interventions. Full-Text PDF Open Access