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Figure.
Duration of Sustained Remote Monitoring of Physical Activity Data After Hospital Discharge to Home
Duration of Sustained Remote Monitoring of Physical Activity Data After Hospital Discharge to Home

The graph shows the percentage of patients who transmitted step count data to the study during the 180-day period. Patients in both groups received a reminder to synchronize their device if data had not been received for 4 consecutive days. Duration is defined as the last day a step value was received. Patients were censored on death (5 patients in the smartphone group and 7 in the wearable-device group). Patients who dropped out of the study because they were no longer interested were classified as no longer transmitting data but were not censored (2 patients in the smartphone group and 5 in the wearable-device group).

Table.  
Sample Characteristics
Sample Characteristics
1.
Pew Research Center. Mobile fact sheet. https://www.pewinternet.org/fact-sheet/mobile/. Published June 12, 2019. Accessed July 22, 2019.
2.
Case  MA, Burwick  HA, Volpp  KG, Patel  MS.  Accuracy of smartphone applications and wearable devices for tracking physical activity data.  JAMA. 2015;313(6):625-626. doi:10.1001/jama.2014.17841PubMedGoogle ScholarCrossref
3.
Patel  MS, Asch  DA, Volpp  KG.  Wearable devices as facilitators, not drivers, of health behavior change.  JAMA. 2015;313(5):459-460. doi:10.1001/jama.2014.14781PubMedGoogle ScholarCrossref
4.
Perez  MV, Mahaffey  KW, Hedlin  H,  et al; Apple Heart Study Investigators.  Large-scale assessment of a smartwatch to identify atrial fibrillation.  N Engl J Med. 2019;381(20):1909-1917. doi:10.1056/NEJMoa1901183PubMedGoogle ScholarCrossref
5.
Evans  CN, Volpp  KG, Polsky  D,  et al.  Prediction using a randomized evaluation of data collection integrated through connected technologies (PREDICT): design and rationale of a randomized trial of patients discharged from the hospital to home.  Contemp Clin Trials. 2019;83:53-56. doi:10.1016/j.cct.2019.06.018PubMedGoogle ScholarCrossref
6.
Patel  MS, Small  DS, Harrison  JD,  et al.  Effectiveness of behaviorally designed gamification interventions with social incentives for increasing physical activity among overweight and obese adults across the United States: the STEP UP randomized clinical trial.  JAMA Intern Med. 2019;179(12):1624–1632. doi:10.1001/jamainternmed.2019.3505PubMedGoogle ScholarCrossref
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    Research Letter
    Health Policy
    February 7, 2020

    Smartphones vs Wearable Devices for Remotely Monitoring Physical Activity After Hospital Discharge: A Secondary Analysis of a Randomized Clinical Trial

    Author Affiliations
    • 1Perelman School of Medicine, University of Pennsylvania, Philadelphia
    • 2Wharton School, University of Pennsylvania, Philadelphia
    • 3Penn Medicine Nudge Unit, University of Pennsylvania, Philadelphia
    • 4Leonard Davis Institute for Health Economics, University of Pennsylvania, Philadelphia
    • 5LDI Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia
    • 6Department of Medicine, Crescenz Veterans Affairs Medical Center, Philadelphia, Pennsylvania
    • 7Department of Health Policy and Management, Johns Hopkins University, Baltimore, Maryland
    • 8Department of Statistics and Data Science, Carnegie Mellon University, Pittsburgh, Pennsylvania
    JAMA Netw Open. 2020;3(2):e1920677. doi:10.1001/jamanetworkopen.2019.20677
    Introduction

    Nearly 80% of US adults own a smartphone,1 which accurately tracks physical activity.2 Wearable devices are growing in adoption and can track other biometrics.2-4 However, it is unknown whether smartphones or wearables are more sustainable for remotely monitoring patients over longer-term periods. The objective of this study was to compare the duration of remotely monitoring physical activity from smartphones vs wearables in the 6 months after hospital discharge.

    Methods

    This secondary analysis of a randomized clinical trial was approved by the University of Pennsylvania institutional review board and followed the Consolidated Standards of Reporting Trials (CONSORT) reporting guideline. Data were obtained from an ongoing trial that enrolled patients from January 23, 2017, to January 7, 2019.5 Patients provided written informed consent to participate in the trial.

    Adults admitted to medicine services at 2 hospitals in Philadelphia, Pennsylvania, were eligible if they could ambulate, had a smartphone compatible with the Withings HealthMate application, and planned to be discharged to home. Patients were randomly assigned to use the smartphone alone or with a wearable (Withings Steel) for 6 months (eFigure in the Supplement). Physical activity data from devices were obtained using Way to Health as described in a previous study.6 All patients received $50 to enroll and $50 to complete the trial. Patients in the smartphone group were given the wearable after trial completion. All patients selected communication preferences (text message, email, or telephone voice recording) and were sent a notification to synchronize their device if data had not been transmitted for 4 consecutive days.

    For each patient, the duration of data transmission was estimated using the last day a step value was received and compared at 30, 90, and 180 days using Pearson χ2 tests. A Cox proportional hazard model was fit and adjusted for age, gender, race/ethnicity, insurance, education, marital status, annual household income, body mass index, and Charlson Comorbidity Index score; censoring took place on patient death. Because patients may not have synchronized data for all days, the proportion of days of data transmission was also compared using a χ2 test.

    The intention-to-treat analysis was conducted in SAS software version 9.4 (SAS Institute) and used 2-sided hypothesis tests (level of significance, P < .05). Investigators and analysts were blinded to group assignment.

    Results

    The sample comprised 500 patients (250 using smartphones and 250 using wearables) with a mean (SD) age of 46.6 (13.7) years; 320 (64%) were women, 219 (44%) white, 231 (46%) black, 141 (28%) enrolled in Medicare, and 128 (26%) enrolled in Medicaid (Table). Rates of patient death (smartphones, 5 patients; wearables, 7 patients) and overall dropout including death (smartphones, 7 patients; wearables, 12 patients) were similar.

    The proportion of patients transmitting data among the smartphone group was not different than among the wearable group after 30 days (86.7% vs 81.9%; difference, 4.9 percentage points; 95% CI, −1.5 to 11.3 percentage points; P = .13) but was significant at 90 days (77.6% vs 67.6%; difference, 9.9 percentage points; 95% CI, 2.1 to 17.8 percentage points; P = .01) and 180 days (61.2% vs 46.5%; difference, 14.7 percentage points; 95% CI, 6.0 to 23.5 percentage points; P = .001) (Figure). Patients in the smartphone group transmitted data for a significantly greater proportion of days during the 180-day period than patients in the wearable group (69.4% vs 58.9%; difference, 10.5 percentage points; 95% CI, 4.4 to 17.8 percentage points; P = .001).

    In the multivariate model, differences in duration remained significant, with lower discontinuation among the smartphone group (hazard ratio, 0.66; 95% CI, 0.50-0.86; P = .002). Being male was associated with less likelihood of discontinuation (hazard ratio, 0.71; 95% CI, 0.53-0.95; P = .02), and Medicare insurance was associated with greater likelihood of discontinuation (hazard ratio, 2.05; 95% CI, 1.41-2.96; P < .001).

    Discussion

    Patients discharged from the hospital using smartphones transmitted data for a greater duration and proportion of time, with a 32% relative increase in patients completing the 180-day period compared with those using wearables. This study is limited to physical activity data from patients at 1 health system. Wearables track behaviors that smartphones do not (eg, sleep), and future research will evaluate the usefulness of these data.5 Because smartphones are ubiquitious,1 our findings indicate that these devices could be a scalable approach for remotely monitoring patient health behaviors.

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    Article Information

    Accepted for Publication: December 10, 2019.

    Published: February 7, 2020. doi:10.1001/jamanetworkopen.2019.20677

    Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2020 Patel MS et al. JAMA Network Open.

    Corresponding Author: Mitesh S. Patel, MD, MBA, MS, University of Pennsylvania, 3400 Civic Center Blvd, 14-176 South Pavilion, Philadelphia, PA 19104 (mpatel@pennmedicine.upenn.edu).

    Author Contributions: Dr Patel had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

    Concept and design: Patel, Polsky, Volpp.

    Acquisition, analysis, or interpretation of data: Patel, Kennedy, Small, Evans, Rareshide, Volpp.

    Drafting of the manuscript: Patel, Evans.

    Critical revision of the manuscript for important intellectual content: Polsky, Kennedy, Small, Rareshide, Volpp.

    Statistical analysis: Kennedy, Small, Rareshide.

    Obtained funding: Patel, Volpp.

    Administrative, technical, or material support: Evans, Volpp.

    Supervision: Patel, Polsky.

    Conflict of Interest Disclosures: Dr Patel reported receiving personal fees from Catalyst Health LLC, owning stock options for and serving on the advisory board of LifeVest Health, receiving personal fees from, owning stock options for, and serving on the advisory board of HealthMine Services, and receiving personal fees from Holistic Industries outside the submitted work. Dr Volpp reported receiving personal fees from and performing consulting work for VAL Health, and receiving grants from Humana, grants from Oscar, grants from Discovery/Vitality, grants and personal fees from WW (Weight Watchers), grants from Hawaii Medical Services Association, and personal fees from Center for Corporate Innovation outside the submitted work. No other disclosures were reported.

    Funding/Support: This study was funded, in part, under a grant with the Pennsylvania Department of Health through the Commonwealth Universal Research Enhancement (CURE) Program. This trial was also supported by the University of Pennsylvania Health System through the Penn Medicine Nudge Unit.

    Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

    Trial Registration: This study was registered at ClinicalTrials.gov (Identifier: NCT02983812).

    Disclaimer: The Pennsylvania Department of Health specifically disclaims responsibility for any analyses, interpretations, or conclusions.

    References
    1.
    Pew Research Center. Mobile fact sheet. https://www.pewinternet.org/fact-sheet/mobile/. Published June 12, 2019. Accessed July 22, 2019.
    2.
    Case  MA, Burwick  HA, Volpp  KG, Patel  MS.  Accuracy of smartphone applications and wearable devices for tracking physical activity data.  JAMA. 2015;313(6):625-626. doi:10.1001/jama.2014.17841PubMedGoogle ScholarCrossref
    3.
    Patel  MS, Asch  DA, Volpp  KG.  Wearable devices as facilitators, not drivers, of health behavior change.  JAMA. 2015;313(5):459-460. doi:10.1001/jama.2014.14781PubMedGoogle ScholarCrossref
    4.
    Perez  MV, Mahaffey  KW, Hedlin  H,  et al; Apple Heart Study Investigators.  Large-scale assessment of a smartwatch to identify atrial fibrillation.  N Engl J Med. 2019;381(20):1909-1917. doi:10.1056/NEJMoa1901183PubMedGoogle ScholarCrossref
    5.
    Evans  CN, Volpp  KG, Polsky  D,  et al.  Prediction using a randomized evaluation of data collection integrated through connected technologies (PREDICT): design and rationale of a randomized trial of patients discharged from the hospital to home.  Contemp Clin Trials. 2019;83:53-56. doi:10.1016/j.cct.2019.06.018PubMedGoogle ScholarCrossref
    6.
    Patel  MS, Small  DS, Harrison  JD,  et al.  Effectiveness of behaviorally designed gamification interventions with social incentives for increasing physical activity among overweight and obese adults across the United States: the STEP UP randomized clinical trial.  JAMA Intern Med. 2019;179(12):1624–1632. doi:10.1001/jamainternmed.2019.3505PubMedGoogle ScholarCrossref
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