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Mobile health apps and recovery after surgery: What are patients willing to do?
Accepted Manuscript Mobile health apps and recovery after surgery: What are patients willing to do? Jonathan S. Abelson, Matthew Symer, Alex Peters, Mary Charlson, Heather Yeo PII:
S0002-9610(17)30230-1
DOI:
10.1016/j.amjsurg.2017.06.009
Reference:
AJS 12405
To appear in:
The American Journal of Surgery
Received Date: 31 January 2017 Revised Date:
25 April 2017
Accepted Date: 18 June 2017
Please cite this article as: Abelson JS, Symer M, Peters A, Charlson M, Yeo H, Mobile health apps and recovery after surgery: What are patients willing to do?, The American Journal of Surgery (2017), doi: 10.1016/j.amjsurg.2017.06.009. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
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Mobile health apps and recovery after surgery: what are patients willing to do? Jonathan S. Abelson1, Matthew Symer1, Alex Peters1, Mary Charlson2, Heather Yeo1,3 1
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Correspondence and reprints should be directed to: Heather Yeo, MD, MHS Assistant Professor of Surgery Assistant Professor of Healthcare Policy and Research Department of Surgery NYP-Weill Cornell Medical Center 525 East 68th Street, Box 172 New York, NY 10065 [email protected] (646)962-2646 Fax (212)746-8262
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Department of Surgery, Weill Cornell Medicine, New York-Presbyterian Hospital, New York, New York. 2 Department of Integrative Medicine, Weill Cornell Medicine, New York-Presbyterian Hospital, New York, New York 3 Department of Public Health, Weill Medical College of Cornell University, New YorkPresbyterian Hospital, New York, New York.
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Abstract Background: Mobile health technologies (mHealth) may improve post-operative care but it is unknown if patients are willing to use this technology.
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Methods: We surveyed 800 NY State residents to determine their willingness to engage in mHealth after surgery and compared socioeconomic factors that may affect willingness to engage.
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Results: A majority of respondents reported willingness to wear a tracker on their wrist (80.6%), fill out a survey (74.3%), send pictures of their wound to their surgeon (66.3%),
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and share updates with friends/family (59.1%). Older age was associated with lower likelihood of having a smartphone, but not associated with willingness to engage with other features. Hispanic ethnicity was associated with lower likelihood of wearing a tracker while Black race was associated with lower willingness to send pictures.
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Conclusions: Overall, potential users of mHealth are interested and willing to use mHealth. Older respondents are as willing as younger respondents to engage with mHealth. Individuals with Hispanic ethnicity and Black race may be less willing to
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engage and therefore may require education regarding benefits of this technology.
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Keywords: telemedicine; medical informatics; postoperative period; age factors; surveys and questionnaires
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Introduction: Smartphone access and use has increased dramatically over the past five years1. In parallel, development of mobile health apps has also increased, predominantly for the
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management of chronic diseases2,3. Recently, a few small pilot studies have demonstrated the opportunity for mobile health technology4-7 and telehealth8-10 to monitor postoperative recovery to improve outcomes and potentially reduce patient costs.
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As part of the growing body of literature evaluating the utility of mobile health technology, several studies have evaluated the public’s interest11 and willingness to
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engage with mobile health applications12-15 and health information exchange16 to manage chronic medical disease. Only one other small, single center study evaluated patients’ perceptions regarding mobile health app use in the acute post-operative setting17. As a result, there is a paucity of data regarding which patients would be willing to participate
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and what they would be willing to do in the perioperative period. Our study objective was to ascertain the public's willingness to engage in mobile health technology in the post-operative setting, using a representative survey, and to
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identify patient-specific variables associated with a willingness to do so.
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Materials and Methods
Survey Development and Administration The Cornell University Survey Research Institute is a member of the Association
of Academic Survey Research Organization and is a well-established organization that has been conducting survey research since 199618. They have been conducting the Empire State Poll, a telephone survey of adult residents >18 years of age in New York
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State, every year since 2003. Each year there is a core set of questions addressing themes of community, government, and the economy. In addition to the core questions, Cornell investigators may submit questions to be included in the annual survey. Submitted
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questions are reviewed by the Survey Research Institute and a preliminary pilot of 25 individuals is conducted. Investigators are then given feedback regarding any issues encountered by surveyors when administering these questions and the pilot data is
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analyzed and questions refined. Based on this feedback, investigators then modify their
questions prior to conducting the survey. We submitted 5 closed-ended questions using a
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5-point Likert scale addressing the public’s access and willingness to use mobile health technologies in a hypothetical scenario assuming respondents had undergone surgery for any reason (Supplemental Table 1).
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Sampling
The 2016 Empire State Poll was administered from February 9, 2016 to April 19, 2016.
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3,230 individuals were contacted, 1,184 of whom were eligible. Of those eligible individuals contacted, 800 individuals completed the final survey (67.5% cooperation
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rate19 comparable to other research organizations and considered acceptable20). The average interview length was 18 minutes. Cornell Survey Research Institute used a dualframe random digit dial sampling of landlines and cell phones as previously described21. The state was divided into two regions, upstate and downstate, and sampling was conducted in proportion to population totals. The sample size of 800 produced a margin
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of error of 3.5% for questions with a 50/50 distribution and 95% confidence interval and is comparable with other research organizations22.
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Analysis
The primary outcome of our study was willingness to use a mobile health app
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after surgery. The secondary outcome of our study was patient specific variables
associated with willingness to use a mobile health app after surgery. Responses of ‘Very
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Willing’ and ‘Willing’ were categorized as “Willing” while responses of ‘Not sure’, ‘Unwilling’, and ‘Very Unwilling’ were categorized as “Not Willing”. Independent variables tested included age, gender, education level, race (White, Black, or ‘Other’ which included Asian, >2 races, or ‘other race’), ethnicity (Hispanic or Non-Hispanic),
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income, geographic location (according to Metropolitan Statistical Area), marital status (Married, Divorced/Separated/Widowed, or Single), social ideology, religion (Christian, Agnostic, or ‘Other’ which included Muslim, Jewish, and ‘other religion’), perceived
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Internet trustworthiness, and whether participants owned a smart phone that allowed them to download mobile apps. Social ideology responses were captured on a scale of 1-7 with
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the following response options: ‘Extremely Liberal’, ‘Liberal’, ‘Slightly Liberal’, ‘Moderate or middle of the road’, ‘Slightly Conservative’, ‘Conservative’, ‘Extremely Conservative’. These responses were then grouped into “Liberal”, “Middle of the road” and “Conservative”. Internet trustworthiness is a question including in every Empire State Poll and asks participants to rate on a scale of 1-10 how trustworthy they perceive the Internet to be, with 1 being “untrustworthy” and 10 being “trustworthy”. Based on
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the distribution of responses and for ease of interpretation, responses were categorized as “untrustworthy” (if response was 1-5) and “trustworthy” (if response was 6-10). 61 of the 800 respondents had one or more missing responses or refused to answer
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one or more questions and were dropped from analysis. Mean age was treated as both a continuous variable and a categorical variable based on prior studies evaluating this
topic13. When age was treated as a continuous variable, comparison was made using
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Wilcoxon Rank Sum Test; when age was treated as a categorical variable, comparison
was made using chi-square test. Other categorical variables were tested with chi-squared
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tests. Variables were added to a multivariable logistic model based on statistical significance and clinical assumptions. Forward and backward stepwise regression was used to arrive at a final model. All data were analyzed with STATA v13.1
Description of Cohort
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Results:
A total of 739 NY state residents had complete survey data. The average age of
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respondents was 47 years old (+17) with an equal distribution of men and women. Most respondents were White (67%), followed by Black (18%) and Other (15%). 13% of
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respondents identified as having Hispanic ethnicity. The median income was $70,000 (IQR = $42,000-$100,000) and 31% of the cohort did not complete any education beyond high school.
Primary Outcome: Willingness to engage with mHealth Overall, the majority of respondents reported a willingness to engage with mobile health technology. Respondents reported the highest willingness to wear a tracker on
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their wrist (80.6%), followed by willingness to fill out a daily survey (74.3%), send pictures (66.3%), and share updates (59.1%) (Figure 1). Secondary Outcome: Variables associated with willingness to engage with mHealth
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Although older age (>65 years) was independently associated with not having a smartphone compared to younger participants age 18-34 (OR=0.0, 95% CI=0.0-0.1),
older age was not associated with lower willingness to wear a tracker, complete a daily
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survey, send pictures to their surgeon, or share updates with family or friends on both univariable and multivariable analysis.
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Although Hispanic ethnicity was independently associated with having a smartphone (OR=2.40, 95% CI=1.06-5.43), Hispanic ethnicity was associated with lower likelihood to wear a tracker on their wrist (OR=0.43, 95% CI=0.24-0.75) on both univariable and multivariable analysis. Similarly, Black race was independently
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associated with lower willingness to send pictures (OR=0.6, 95% CI=0.4-0.9). Higher education, trust in Internet, and preexisting smartphone use were all independently associated with willingness to engage with various components of
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mHealth. For example, having a post-graduate education was independently associated with higher willingness to fill out a daily survey on multivariable analysis (OR=2.7, 95%
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CI=1.3-5.6). Internet trustworthiness was independently associated with higher willingness to share updates with friends and family (OR=0.4, 95% CI=1.0-2.0) and having a smartphone was independently associated with higher likelihood to send pictures (OR=1.8, 95% CI=1.2-2.6) (Tables 3-4).
Discussion:
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Use of mobile health apps and trackers after surgery offer the potential to shorten length of stay, and improve care by potentially identifying complications earlier thus decreasing 30-day readmission, improving recovery, and reducing healthcare costs4-7.
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Despite its possible benefits, previous studies have indicated that not all patients are
amenable to using these innovative technologies12,13,23. This is the first large-scale survey to address this important issue of patient willingness to use a mobile health app after
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surgery for monitoring recovery. Overall, we found that the majority of respondents were willing to engage with mobile health technology after surgery. They are willing to wear a
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tracker, fill out daily surveys, send pictures of their incisions to their surgeon, and share updates with select family and friends. Although older adults were less likely to have a smartphone, they were just as willing as younger respondents to engage with all features of the mobile health app described in our survey.
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Our finding that older adults are willing to participate in mobile health technologies runs counter to several recent reports assessing older adults’ willingness to use mobile health technology. For example, the most recent Pew Research Center report
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suggested that older adults were less likely to use their smartphone to get information about a health condition: whereas 77% of respondents age 18-29 reported using a
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smartphone to look up information about a health condition, only 39% of adults >50 years of age reported using smartphones for the same activity1. Although both their report and the Empire State Poll including approximately the same proportion of older adults >65 (15.7% vs 16.9%), their report was a nationally representative sample compared to our statewide sample. It is possible older residents of New York State are different from older residents outside of New York in their willingness to use mobile health technology.
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Serrano et al conducted a survey assessing respondents’ willingness to exchange cancer-related health information using mobile health devices. They used the Health Information National Trends Survey, a cross-sectional mail-administered survey focused
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on cancer-related communication and did not specifically address patients recovering
from surgery. Similar to our study, they found a high willingness (>50% of respondents) to use mobile health technology in general, albeit lower willingness to send digital
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images. In contrast to our study, they found that those who were older (50-64 or >65)
were less willing to engage with mobile health information exchange compared to adults
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age 18-3413. It is possible that by using a more effort-intensive survey methodology selected for older adults who were more actively opposed to adopting a novel technology. Furthermore, in our survey, respondents were given cues about the usefulness of engaging with mobile health technology, suggesting that older adults would be just as
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willing to use this technology as long as they have specific information about why this will improve their recovery after surgery. For example, past work done by our colleagues in Geriatric Medicine confirmed that older adults are willing to engage with mobile
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health for specific reasons, such as the management of chronic pain24. Recent evidence suggests that older adults are particularly willing to engage with
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mobile health technology if it is for short-term purposes. McMahon et al23 studied the use of Fitbit OneTM on community-dwelling older adults (70-96 years of age). Not only did they find that older adults were receptive to using trackers, but participants rated the tracker more favorably for short periods of time (2.5 months vs. 8 months), making this ideal for post-operative monitoring.
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Another important finding of our study was that Hispanic ethnicity and Black race were independently associated with being less willing to engage with certain aspects of mobile health technology. Serrano et al13 did not find any difference in willingness to
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engage mobile health technology based on race or ethnicity. Another larger survey of
health app use among mobile phone users in the U.S. found that Hispanics were more likely to use health apps compared to Whites14. Although their survey had a higher
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representation of Hispanics compared to our survey (27.9% vs 13.1%), their aim was to generically characterize interaction with mobile health technology and not for a specific
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scenario as in our study. It is possible that our survey reflects an unwillingness of Hispanics to share personal health data through mobile health technology rather than simply an unwillingness to use mobile health apps. This represents an area for further research and should be used as guidance for surgeons interested in extending mobile
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health app usage to their Hispanic patients.
Other important variables associated with engaging in mobile health technology include higher income, education level, and trust in the Internet12,13,16. These findings
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should not be used to discourage surgeons from including patients who do not meet those criteria, but instead should give insight as to which patients may require more time to
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educate regarding the possible benefits of mobile health technology to help recover after surgery. In addition, although not having a smartphone seems to make patients less likely to use mobile health technology, there continue to be significant increases in smartphone use for all demographics1. This suggests that this potential barrier is continually shrinking.
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Interestingly, we found that the proportion of participants who were willing to engage with various components of a mobile health technology varied from 59-81%. It is possible that participants and future patients will be less likely to share sensitive
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information such as pictures of their incision or their stoma as compared to tracking their mobility with a tracker. It is also possible that they may not be as open to sharing
personal information with friends and family. These insights are important for researchers
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looking to develop a mobile health app that will have maximal participation.
Limitations of this study include those associated with telephone surveys
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including sampling error from systematic exclusion of households without telephones and interviewer-induced bias. For our specific questions, it is possible that participants did not fully understand the scenario described for them; we are therefore reliant on the trained interviewers to ensure adequate participant understanding. Responses were not based on
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documented engagement with mobile health technology, but instead on each respondent’s self-reported willingness to use mHealth, which may overestimate actual usage. Moreover, it is possible that patients who had undergone surgery may respond differently
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to the proposition of mobile health technology based on their physical condition, presence of comorbidities, and prior health experiences. Nevertheless, our findings
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represent an important initial understanding of which potential patients may be willing to engage with mobile health technology when recovering from surgery. Further research is necessary to determine the beliefs and practices of this patient population. Conclusions:
The majority of respondents in our large statewide survey indicated a willingness to engage with a variety of mobile health technology functions including wearing a
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tracker, filling out a daily survey, sending pictures of their wound to their surgeon, and sharing updates with select family and friends. Older respondents are just as willing as younger respondents to engage with mobile health technology. Individuals with Hispanic
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ethnicity and Black race may be less willing to engage and therefore may require education regarding benefits of this technology.
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Acknowledgements:
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Acknowledgements to Myung Hee Lee for statistical support.
Funding/Support:
Funding for this study came from two sources supporting Dr. Yeo: Career Development Award from the Society for Surgery of the Alimentary Tract and Center for Advanced
References:
3.
4.
5.
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2.
Pew Research Center. The Smartphone Difference. 2015; http://www.pewinternet.org/2015/04/01/us-smartphone-use-in-2015/. Accessed July 1, 2016. Gold J. FDA Seeks to Tame Exploding Medical App Market 2012; http://khn.org/news/fda-medical-app-market/. Accessed July 20, 2016. de Jongh T, Gurol-Urganci I, Vodopivec-Jamsek V, Car J, Atun R. Mobile phone messaging for facilitating self-management of long-term illnesses. Cochrane Database Syst Rev. 2012;12:CD007459. Martínez-Ramos C, Cerdán MT, López RS. Mobile phone-based telemedicine system for the home follow-up of patients undergoing ambulatory surgery. Telemed J E Health, 2009;15:531-537. Stomberg MW, Platon B, Widén A, Wallner I, Karlsson O. Health information: what can mobile phone assessments add? Perspect Health Inf Manag. 2012;9:110.
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1.
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Digestive Care at New York Presbyterian Hospital-Weill Cornell Medicine
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11.
12.
13.
14. 15. 16.
17.
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18.
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10.
SC
9.
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8.
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7.
Semple JL, Sharpe S, Murnaghan ML, et al. Using a mobile app for monitoring post-operative quality of recovery of patients at home: a feasibility study. JMIR Mhealth Uhealth, 2015;3:e18. Jaensson M, Dahlberg K, Eriksson M, et al. The Development of the Recovery Assessments by Phone Points (RAPP): A Mobile Phone App for Postoperative Recovery Monitoring and Assessment. JMIR Mhealth Uhealth, 2015;3:e86. Hwa K, Wren SM. Telehealth follow-up in lieu of postoperative clinic visit for ambulatory surgery: results of a pilot program. JAMA Surg, 2013;148:823-827. Vella MA, Kummerow Broman K, Tarpley JL, et al. Postoperative Telehealth Visits: Assessment of Quality and Preferences of Veterans. JAMA Surg, 2015;150:1197-1199. Kummerow Broman K, Roumie CL, Stewart MK, et al. Implementation of a Telephone Postoperative Clinic in an Integrated Health System. J Am Coll Surg, 2016;223:644-651. Sankaranarayanan J, Sallach RE. Rural patients' access to mobile phones and willingness to receive mobile phone-based pharmacy and other health technology services: a pilot study. Telemed J E Health, 2014;20:182-185. Atienza AA, Zarcadoolas C, Vaughon W, et al. Consumer Attitudes and Perceptions on mHealth Privacy and Security: Findings From a Mixed-Methods Study. J Health Commun, 2015;20:673-679. Serrano KJ, Yu M, Riley WT, et al. Willingness to Exchange Health Information via Mobile Devices: Findings From a Population-Based Survey. Ann Fam Med, 2016;14:34-40. Krebs P, Duncan DT. Health App Use Among US Mobile Phone Owners: A National Survey. JMIR Mhealth Uhealth, 2015;3:e101. Levine DM, Lipsitz SR, Linder JA. Trends in Seniors' Use of Digital Health Technology in the United States, 2011-2014. JAMA, 2016;316:538-540. O'Donnell HC, Patel V, Kern LM, et al. Healthcare consumers' attitudes towards physician and personal use of health information exchange. J Gen Intern Med, 2011;26:1019-1026. Wiseman JT, Fernandes-Taylor S, Barnes ML, et al. Conceptualizing smartphone use in outpatient wound assessment: patients' and caregivers' willingness to use technology. J Surg Res, 2015;198:245-251. Cornell University Survey Research Institute. https://www.sri.cornell.edu/sri/index.cfm. Accessed November 1, 2015. The American Association for Public Opinion Research. Standard definitions: final dispositions of case codes and outcome rates for surveys. 2016; http://www.aapor.org/AAPOR_Main/media/publications/StandardDefinitions20169theditionfinal.pdf. Accessed January 28, 2017. Assessing the Representativeness of Public Opinion Surveys. 2012; http://www.people-press.org/2012/05/15/assessing-the-representativeness-ofpublic-opinion-surveys/. Ancker JS, Edwards AM, Miller MC, et al. Consumer perceptions of electronic health information exchange. Am J Prev Med, 2012;43:76-80.
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6.
19.
20.
21.
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23.
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24.
The American Association for Public Opinion Research. http://www.aapor.org/AAPOR_Main/media/MainSiteFiles/Margin-of-SamplingError.pdf. Accessed January 28, 2017. McMahon SK, Lewis B, Oakes M, et al. Older Adults' Experiences Using a Commercially Available Monitor to Self-Track Their Physical Activity. JMIR Mhealth Uhealth, 2016;4:e35. Parker SJ, Jessel S, Richardson JE, et al. Older adults are mobile too! Identifying the barriers and facilitators to older adults' use of mHealth for pain management. BMC Geriatr, 2013;13:43.
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Figure 1. Willingness to engage with mobile health technology
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Red bubble reflects median value for each questions; percentage reflects proportion of respondents who stated either “very willing” or “willing”
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Table 1. Participant characteristics and association with willingness to engage with wearing a wrist tracker
32 40 42 28
168 (28) 161 (27) 171 (29) 97 (16)
(22) (28) (30) (20)
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P value
0.13 0.52
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Willing (N=497) n (%) 46 (16)
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0.54
68 (48) 74 (52)
303 (51) 294 (49)
84 (59) 24 (17) 34 (24)
414 (69) 107 (18) 76 (13)
33 (23) 109 (77)
62 (10) 535 (90)
<0.01
62 35 21 9 15
(44) (25) (15) (6) (10)
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Mean age, years (std) Age-group, years 18-34 35-49 50-64 >65 Gender Male Female Race White Black Othera Ethnicity Hispanic/Latino Non Hispanic/Latino Metropolitan resident Center city of MSA Outside center city of MSA Inside suburban county of MSA In MSA with no center city Not in MSA Marital status* Married Divorced/Separated/Widow Single Education Less than High School Grad High School Grad Some post-college training College grad Post-grad Income group <$30,000 $30,000-$49,999 $50,00-$74,999 $75,000-$99,999 $100,000-$149,999 >$150,000 Internet trustworthiness
Not willing (N =142) n (%) 49 (17)
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Characteristic
<0.01
0.02
224 (37) 173 (29) 56 (9) 87 (15) 57 (10)
0.95
69 (49) 22 (15) 51 (36)
283 (48) 88 (15) 221 (37)
23 44 35 20 20
39 (6) 121 (20) 159 (27) 159 (27) 119 (20)
<0.01 (16) (31) (25) (14) (14)
0.02 26 32 44 9 19 12
(18) (23) (31) (6) (13) (9)
81 (14) 121 (20) 133 (22) 88 (15) 101 (17) 73 (12) 0.01
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412 (69) 185 (31) <0.01 209 (35) 229 (38) 159 (27)
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0.04
376 (63) 147 (25) 72 (12)
<0.01
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98 (16) 499 (84)
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Untrustworthyb 114 (80) Trustworthyc 28 (20) Social Ideology Liberal 31 (22) Middle of the road 42 (30) Conservative 69 (48) Religion** Christian 104 (74) Agnostic 26 (19) Otherd 10 (7) Smartphone Use Does not have smartphone 48 (34) Has smartphone 94 (66) a Other races = Asian, >2 Races, Other b Below the 50th percentile of responses c Above the 50th percentile of responses d Other religion = Muslim, Jewish, other *Excludes 5 responses of “Other” **Excludes 4 responses of “Refused to answer”
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Table 2. Participant characteristics and association with willingness to engage with completing a daily survey
44 43 57 39
156 (28) 158 (28) 156 (28) 86 (16)
(24) (24) (31) (21)
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P value
0.04 0.15
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Willing (N=556) n (%) 46 (17)
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0.12
101 (55) 82 (45)
270 (49) 286 (51)
115 (63) 30 (16) 38 (21)
383 (69) 101 (18) 72 (13)
33 (18) 150 (82)
62 (11) 494 (89)
0.04
72 50 18 24 19
(39) (27) (10) (13) (11)
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Mean age, years (std) Age-group, years 18-34 35-49 50-64 >65 Gender Male Female Race White Black Othera Ethnicity Hispanic/Latino Non Hispanic/Latino Metropolitan resident Center city of MSA Outside center city of MSA Inside suburban county of MSA In MSA with no center city Not in MSA Marital status* Married Divorced/Separated/Widow Single Education Less than High School Grad High School Grad Some post-college training College grad Post-grad Income group <$30,000 $30,000-$49,999 $50,00-$74,999 $75,000-$99,999 $100,000-$149,999 >$150,000 Internet trustworthiness
Not willing (N =183) n (%) 49 (17)
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Characteristic
0.02
0.99
214 (38) 158 (28) 59 (11) 72 (13) 53 (10)
0.15
95 (52) 30 (17) 56 (31)
257 (46) 80 (14) 216 (39)
25 56 46 35 21
37 (7) 109 (19) 148 (27) 144 (26) 118 (21)
<0.01 (14) (31) (25) (19) (11)
0.17 29 44 50 23 21 16
(16) (24) (27) (13) (11) (9)
78 (14) 109 (20) 127 (23) 74 (13) 99 (18) 69 (12) 0.03
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384 (69) 172 (31) <0.01 199 (36) 207 (37) 150 (27)
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0.13
350 (63) 139 (25) 64 (12)
<0.01
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88 (16) 468 (84)
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Untrustworthyb 142 (78) Trustworthyc 41 (22) Social Ideology Liberal 41 (22) Middle of the road 64 (35) Conservative 78 (43) Religion** Christian 130 (71) Agnostic 34 (19) Otherd 18 (10) Smartphone Use Does not have smartphone 58 (32) Has smartphone 125 (68) a Other races = Asian, >2 Races, Other b Below the 50th percentile of responses c Above the 50th percentile of responses d Other religion = Muslim, Jewish, other *Excludes 5 responses of “Other” **Excludes 4 responses of “Refused to answer”
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Table 3. Univariable analysis evaluating participants’ likelihood of engaging with mobile health technology Fill out survey Unadjusted OR (95% CI)
Send pictures Unadjusted OR (95% CI)
Share Updates Unadjusted OR (95% CI)
Ref 0.4 (0.2-0.9) 0.1 (0.1-0.3) 0.0 (0.0-0.1)
Ref 0.8 (0.5-1.3) 0.8 (0.5-1.3) 0.7 (0.4-1.2)
Ref 1.0 (0.6-1.7) 0.8 (0.5-1.2) 0.6 (0.4-1.0)
Ref 0.9 (0.6-1.4) 0.9 (0.6-1.4) 0.7 (0.5-1.2)
Ref 1.1 (0.7-1.6) 1.3 (0.9-1.9) 1.1 (0.7-1.7)
Ref 1.2 (0.8-1.7)
Ref 0.9 (0.6-1.3)
Ref 1.3 (0.9-1.8)
Ref 0.9 (0.7-1.2)
Ref 0.9 (0.7-1.2)
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Ref 0.7 (0.5-1.2) 0.9 (0.5-1.5)
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Wear Tracker Unadjusted OR (95% CI)
Ref 0.9 (0.5-1.5) 0.5 (0.3-0.7)
Ref 1.0 (0.6-1.6) 0.6 (0.4-0.9)
Ref 0.6 (0.4-0.8) 0.7 (0.4-1.0)
Ref 1.1 (0.7-1.6) 1.0 (0.7-1.6)
Ref 0.4 (0.2-0.6)
Ref 0.6 (0.4-0.9)
Ref 0.8 (0.5-1.2)
Ref 0.7 (0.4-1.0)
Ref 1.4 (0.9-2.2)
Ref 1.1 (0.7-1.6)
Ref 1.4 (0.9-2.0)
Ref 1.2 (0.8-1.7)
1.2 (0.6-1.3)
0.7 (0.4-1.3)
1.1 (0.6-2.0)
1.1 (0.7-1.9)
0.8 (0.5-1.3)
1.3 (0.7-2.4)
2.7 (1.3-5.6)
1.0 (0.6-1.7)
2.0 (1.2-3.5)
1.1 (0.7-1.8)
0.7 (0.4-1.3)
1.1 (0.6-2.0)
0.9 (0.5-1.7)
1.5 (0.8-2.6)
1.2 (0.7-2.0)
Ref 0.5 (0.3-0.8)
Ref 1.0 (0.6-1.7)
Ref 1.0 (0.6-1.6)
Ref 0.8 (0.5-1.3)
Ref 0.9 (0.6-1.4)
1.3 (0.9-2.1)
1.1 (0.7-1.6)
1.4 (1.0-2.1)
1.0 (0.7-1.5)
1.0 (0.7-1.3)
Ref 1.5 (0.8-2.8) 3.2 (1.7-5.9)
Ref 1.6 (0.9-3.0) 2.7 (1.4-5.0)
Ref 1.3 (0.7-2.4) 2.2 (1.2-4.0)
Ref 1.0 (0.6-1.8) 1.3 (0.7-2.4)
Ref 0.8 (0.4-1.4) 1.2 (0.7-2.1)
6.1 (3.0-12.2) 7.6 (3.5-16.6)
4.7 (2.3-9.4) 3.5 (1.7-7.1)
2.8 (1.5-5.2) 3.8 (1.9-7.6)
1.6 (0.9-2.8) 2.3 (1.2-4.3)
1.2 (0.6-2.1) 1.3 (0.7-2.4)
Ref 1.1 (0.7-2.0) Ref 1.2 (0.8-1.9)
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Age-group, y 18-34 35-49 50-64 >65 Gender Male Female Race White Black Othera Ethnicity Non Hispanic/Latino Hispanic/Latino Metropolitan resident Center city of MSA Outside center city of MSA Inside suburban co. of MSA In MSA without center city Not in MSA Marital status Married Divorced/ Separated/ Widow Single Education Less than HS Grad HS Grad Some post-college training College grad Post-grad Internet Trustworthiness
Have smartphone Unadjusted OR (95% CI)
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Characteristic
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Ref 1.8 (1.2-2.9)
Ref 1.6 (1.0-2.3)
Ref 1.3 (1.0-1.9)
Ref 1.5 (1.0-2.0)
Ref 1.3 (0.8-2.2) 2.6 (1.5-4.5) 3.4 (1.7-6.8) 4.3 (2.2-8.6) 48.2 (6.5359.7)
Ref 1.2 (0.7-2.2) 1.0 (0.6-1.7) 3.1 (1.4-7.1) 1.7 (0.9-3.3) 2.0 (0.9-4.1)
Ref 0.9 (0.5-1.6) 0.9 (0.6-1.6) 1.2 (0.6-2.3) 1.8 (0.9-3.3) 1.6 (0.8-3.2)
Ref 0.9 (0.5-1.5) 1.1 (0.7-1.8) 1.5 (0.8-2.6) 1.6 (0.9-2.8) 2.4 (1.2-4.6)
Ref 0.9 (0.5-1.4) 0.9 (0.5-1.4) 1.6 (0.9-2.8) 1.7 (1.0-2.9) 1.4 (0.8-2.5)
Ref 0.7 (0.4-1.0) 0.4 (0.3-0.6)
Ref 1.0 (0.7-1.5) 0.7 (0.4-1.0)
Ref 1.3 (0.9-1.9) 0.9 (0.6-1.3)
Ref 1.5 (1.0-2.3) 1.3 (0.8-2.3)
Ref 1.2 (0.9-1.8) 1.2 (0.7-2.0)
Ref 0.9 (0.6-1.2) 0.8 (0.5-1.3)
Ref
Ref
Ref
2.5 (1.7-3.6)
1.8 (1.3-2.6)
1.3 (0.9-1.9)
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Social Ideology Liberal Ref Ref Middle of the road 0.5 (0.3-0.8) 0.8 (0.5-1.3) Conservative 0.4 (0.2-0.6) 0.3 (0.2-0.5) Religion Christian Ref Ref Agnostic 2.2 (1.3-3.5) 1.6 (1.0-2.5) Otherd 2.6 (1.2-5.3) 2.0 (1.0-4.0) Smartphone User N/A Does not have Ref smartphone Has smartphone 2.6 (1.7-3.9) a Other races = Asian, >2 Races, Other b Below the 50th percentile of responses c Above the 50th percentile of responses d Other religion = Muslim, Jewish, other
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Ref 1.2 (0.8-1.9)
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Untrustworthyb Trustworthyc Income group <$30,000 $30,000-$49,999 $50,00-$74,999 $75,000-$99,999 $100,000-$149,999 >$150,000
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Table 4. Multivariable analysis* evaluating participants’ likelihood of engaging with mobile health technology
--
--
--
--
--
--
Ref 2.4 (1.1-5.4) --
Ref 0.4 (0.2-0.7) Ref 1.3 (0.8-2.1) 0.5 (0.3-1.0) 2.6 (1.2-5.8) 1.0 (0.5-2.0)
Share Updates Adjusted OR (95% CI) --
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Ref 0.2 (0.1-0.6) 0.1 (0.0-0.2) 0.0 (0.0-0.1) --
Send pictures Adjusted OR (95% CI) --
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Fill out survey Adjusted OR (95% CI) --
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Age-group, y 18-34 35-49 50-64 >65 Gender Male Female Race White Black Othera Ethnicity Non Hispanic/Latino Hispanic/Latino Metropolitan resident Center city of MSA Outside center city of MSA Inside suburban co. of MSA In MSA with no center city Not in MSA Marital status Married Divorced/ Separated/ Widow Single Education Less than HS Grad HS Grad Some post-college training College grad Post-grad
Wear Tracker Adjusted OR (95% CI) --
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Have smartphone Adjusted OR (95% CI)
--
--
--
Ref 0.6 (0.4-0.9) 0.7 (0.4-1.0) --
--
--
Ref 0.7 (0.4-1.1) --
--
--
--
--
--
--
Ref 1.3 (0.7-2.6) 1.9 (0.9-3.8) 3.0 (1.4-6.5) 1.8 (0.8-4.0)
Ref 1.2 (0.6-2.2) 1.8 (0.9-3.3) 2.2 (1.1-4.3) 2.7 (1.3-5.6)
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Characteristic
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--
Ref 1.8 (0.8-3.9) 4.1 (1.8-9.2) 6.2 (2.515.2) 8.4 (3.122.5) Internet Trustworthiness --
--
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--
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Ref 1.4 (1.0-2.0) --
-Ref 0.8 (0.5-1.3) 0.8 (0.5-1.3) 1.4 (0.8-2.6) 1.5 (0.9-2.7) 1.3 (0.7-2.3)
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Ref 1.6 (0.8-3.1) 2.4 (1.2-5.0) 3.4 (1.4-7.9) 3.6 (1.5-8.6) 38.0 (4.6314.1)
--
Ref 0.7 (0.4-1.2) 0.4 (0.2-0.8) --
N/A
Ref 1.0 (0.6-1.7) 0.4 (0.2-0.6) --
Ref 0.8 (0.5-1.2) 0.5 (0.3-0.7) --
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Social Ideology Liberal Middle of the road Conservative Religion Christian Agnostic Otherd Smartphone User Does not have smartphone Has smartphone
Ref 1.9 (1.2-3.1) --
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Untrustworthyb Trustworthyc Income group <$30,000 $30,000-$49,999 $50,00-$74,999 $75,000-$99,999 $100,000-$149,999 >$150,000
--
--
--
--
Ref
Ref
Ref
2.0 (1.3-3.2)
1.8 (1.2-2.8)
1.8 (1.2-2.6)
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*Variables adjusted based on clinical significance and clinical assumptions to arrive at final model. a Other races = Asian, >2 Races, Other b Below the 50th percentile of responses c Above the 50th percentile of responses
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Supplemental Table 1: Mobile Health Apps Questions - 2016 Empire State Poll Yes
No
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Q1. Do you have a smartphone that allows you to download mobile apps? The following 4 questions ask you about your willingness to use mobile health technology after surgery. For the purposes of these questions please imagine that you have undergone surgery for any reason. Please also imagine that the products that are mentioned are provided to you for free by your surgeon, are confidential and secure, and that they have been proven to help you recover after surgery. Very Unwilling Not sure Willing Very Unwilling Willing Q2. Would you be willing to wear something on your wrist that tracks your heart rate and the number of steps you take each day (e.g. Fitbit, Apple Watch)? Q3. Would you be willing to use an interactive mobile health app that is secure and confidential to fill out a survey for a few minutes each day to help your surgeon take care of you? Q4. Would you be willing to use an interactive mobile health app to send confidential and secure pictures of your surgical wound to your surgeon to help your surgeon take care of you? Q5. Would you be willing to use an interactive mobile health app to share selected secure updates about your health and recovery with friends or family that you designate?
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Supplemental Table 2. Participant characteristics and association with willingness to engage with sending pictures to surgeon
61 64 70 47
139 (28) 137 (27) 143 (29) 78 (16)
(25) (27) (29) (19)
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P value
0.15 0.60
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Willing (N=497) n (%) 46 (17)
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0.58
118 (49) 124 (51)
253 (51) 244 (49)
145 (60) 55 (23) 42 (17)
353 (71) 76 (15) 68 (14)
36 (15) 206 (85)
59 (12) 438 (88)
0.01
108 (45) 64 (26) 27 (11) 22 (9) 21 (9)
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Mean age, years (std) Age-group, years 18-34 35-49 50-64 >65 Gender Male Female Race White Black Othera Ethnicity Hispanic/Latino Non Hispanic/Latino Metropolitan resident Center city of MSA Outside center city of MSA Inside suburban county of MSA In MSA with no center city Not in MSA Marital status* Married Divorced/Separated/Widow Single Education Less than High School Grad High School Grad Some post-college training College grad Post-grad Income group <$30,000 $30,000-$49,999 $50,00-$74,999 $75,000-$99,999 $100,000-$149,999 >$150,000 Internet trustworthiness
Not willing (N =242) n (%) 48 (17)
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Characteristic
0.25
0.08
178 (36) 144 (29) 50 (10) 74 (15) 51 (10)
0.62
113 (48) 40 (16) 85 (36)
239 (48) 70 (14) 187 (38)
25 66 65 54 32
37 (7) 99 (20) 129 (26) 125 (25) 107 (22)
0.02 (10) (27) (27) (22) (13)
0.02 40 62 62 28 33 17
(17) (26) (26) (12) (14) (7)
67 (13) 91 (18) 115 (23) 69 (14) 87 (18) 68 (14) 0.09
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344 (69) 153 (31) 0.03 168 (34) 191 (38) 138 (28)
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0.48
316 (64) 122 (25) 57 (11)
<0.01
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82 (16) 415 (84)
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Untrustworthyb 182 (75) Trustworthyc 60 (25) Social Ideology Liberal 72 (30) Middle of the road 80 (33) Conservative 90 (37) Religion** Christian 164 (68) Agnostic 51 (21) Otherd 25 (10) Smartphone Use Does not have smartphone 64 (26) Has smartphone 178 (74) a Other races = Asian, >2 Races, Other b Below the 50th percentile of responses c Above the 50th percentile of responses d Other religion = Muslim, Jewish, other *Excludes 5 responses of “Other” **Excludes 4 responses of “Refused to answer”
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Supplemental Table 3. Participant characteristics and association with willingness to engage with sharing updates with family and friends
85 83 78 51
115 (26) 118 (27) 135 (30) 74 (17)
(29) (28) (26) (17)
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P value
0.70 0.64
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Willing (N=442) n (%) 47 (17)
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0.44
144 (48) 153 (52)
227 (51) 215 (49)
202 (68) 51 (17) 44 (15)
296 (67) 80 (18) 66 (15)
46 (15) 251 (85)
49 (11) 393 (89)
0.94
119 (40) 78 (26) 36 (12) 37 (13) 27 (9)
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Mean age, years (std) Age-group, years 18-34 35-49 50-64 >65 Gender Male Female Race White Black Othera Ethnicity Hispanic/Latino Non Hispanic/Latino Metropolitan resident Center city of MSA Outside center city of MSA Inside suburban county of MSA In MSA with no center city Not in MSA Marital status* Married Divorced/Separated/Widow Single Education Less than High School Grad High School Grad Some post-college training College grad Post-grad Income group <$30,000 $30,000-$49,999 $50,00-$74,999 $75,000-$99,999 $100,000-$149,999 >$150,000 Internet trustworthiness
Not willing (N =297) n (%) 47 (17)
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Characteristic
0.08
0.63
167 (38) 130 (30) 41 (9) 59 (13) 45 (10)
0.91
139 (47) 46 (16) 110 (37)
213 (48) 64 (15) 162 (37)
26 79 74 69 49
36 (8) 86 (20) 120 (27) 110 (25) 90 (20)
0.19 (9) (27) (25) (23) (16)
0.02 46 71 82 31 37 30
(16) (24) (28) (10) (12) (10)
61 82 95 66 83 55
(14) (19) (21) (15) (19) (12) 0.02
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301 (68) 141 (32) 0.11 140 (32) 175 (39) 127 (29)
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0.59
292 (67) 100 (23) 46 (10)
0.12
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79 (18) 363 (82)
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EP
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Untrustworthyb 225 (76) Trustworthyc 72 (24) Social Ideology Liberal 100 (34) Middle of the road 96 (32) Conservative 101 (34) Religion** Christian 187 (63) Agnostic 73 (25) Otherd 36 (12) Smartphone Use Does not have smartphone 67 (23) Has smartphone 230 (77) a Other races = Asian, >2 Races, Other b Below the 50th percentile of responses c Above the 50th percentile of responses d Other religion = Muslim, Jewish, other *Excludes 5 responses of “Other” **Excludes 4 responses of “Refused to answer”
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Supplemental Table 4. Participant characteristics and association with having a smartphone.
9 21 55 61
191 (32) 180 (20) 158 (27) 64 (11)
(6) (14) (38) (42)
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P value
<0.01 <0.01
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Smartphone (N=593) n (%) 44 (16)
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0.39
78 (53) 68 (47)
293 (49) 300 (51)
93 (64) 31 (21) 22 (15)
405 (68) 100 (17) 88 (15)
17 (12) 129 (88)
78 (13) 515 (87)
0.44
60 37 14 16 19
(41) (25) (10) (11) (13)
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Mean age, years (std) Age-group, years 18-34 35-49 50-64 >65 Gender Male Female Race White Black Othera Ethnicity Hispanic/Latino Non Hispanic/Latino Metropolitan resident Center city of MSA Outside center city of MSA Inside suburban county of MSA In MSA with no center city Not in MSA Marital status* Married Divorced/Separated/Widow Single Education Less than High School Grad High School Grad Some post-college training College grad Post-grad Income group <$30,000 $30,000-$49,999 $50,00-$74,999 $75,000-$99,999 $100,000-$149,999 >$150,000 Internet trustworthiness
No smartphone (N = 146) n (%) 60 (15)
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Characteristic
0.62 0.48
226 (38) 171 (29) 63 (11) 80 (13) 53 (9)
<0.01
68 (47) 36 (25) 41 (28)
284 (48) 74 (14) 231 (39)
26 53 36 19 12
36 (6) 112 (19) 158 (27) 160 (27) 127 (21)
<0.01 (18) (36) (25) (13) (8)
<0.01 39 46 32 14 14 1
(26) (31) (22) (10) (10) (1)
68 (12) 107 (18) 145 (24) 83 (14) 106 (18) 84 (14) 0.30
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417 (70) 176 (30) <0.01 212 (36) 213 (36) 168 (28)
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<0.01
365 (62) 151 (26) 73 (12)
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EP
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Untrustworthyb 109 (75) Trustworthyc 37 (25) Social Ideology Liberal 28 (19) Middle of the road 58 (40) Conservative 60 (41) Religion** Christian 115 (79) Agnostic 22 (15) Otherd 9 (6) a Other races = Asian, >2 Races, Other b Below the 50th percentile of responses c Above the 50th percentile of responses d Other religion = Muslim, Jewish, other *Excludes 5 responses of “Other” **Excludes 4 responses of “Refused to answer”
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S0002-9610(17)30230-1
DOI:
10.1016/j.amjsurg.2017.06.009
Reference:
AJS 12405
To appear in:
The American Journal of Surgery
Received Date: 31 January 2017 Revised Date:
25 April 2017
Accepted Date: 18 June 2017
Please cite this article as: Abelson JS, Symer M, Peters A, Charlson M, Yeo H, Mobile health apps and recovery after surgery: What are patients willing to do?, The American Journal of Surgery (2017), doi: 10.1016/j.amjsurg.2017.06.009. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
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Mobile health apps and recovery after surgery: what are patients willing to do? Jonathan S. Abelson1, Matthew Symer1, Alex Peters1, Mary Charlson2, Heather Yeo1,3 1
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Correspondence and reprints should be directed to: Heather Yeo, MD, MHS Assistant Professor of Surgery Assistant Professor of Healthcare Policy and Research Department of Surgery NYP-Weill Cornell Medical Center 525 East 68th Street, Box 172 New York, NY 10065 [email protected] (646)962-2646 Fax (212)746-8262
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Department of Surgery, Weill Cornell Medicine, New York-Presbyterian Hospital, New York, New York. 2 Department of Integrative Medicine, Weill Cornell Medicine, New York-Presbyterian Hospital, New York, New York 3 Department of Public Health, Weill Medical College of Cornell University, New YorkPresbyterian Hospital, New York, New York.
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Abstract Background: Mobile health technologies (mHealth) may improve post-operative care but it is unknown if patients are willing to use this technology.
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Methods: We surveyed 800 NY State residents to determine their willingness to engage in mHealth after surgery and compared socioeconomic factors that may affect willingness to engage.
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Results: A majority of respondents reported willingness to wear a tracker on their wrist (80.6%), fill out a survey (74.3%), send pictures of their wound to their surgeon (66.3%),
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and share updates with friends/family (59.1%). Older age was associated with lower likelihood of having a smartphone, but not associated with willingness to engage with other features. Hispanic ethnicity was associated with lower likelihood of wearing a tracker while Black race was associated with lower willingness to send pictures.
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Conclusions: Overall, potential users of mHealth are interested and willing to use mHealth. Older respondents are as willing as younger respondents to engage with mHealth. Individuals with Hispanic ethnicity and Black race may be less willing to
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engage and therefore may require education regarding benefits of this technology.
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Keywords: telemedicine; medical informatics; postoperative period; age factors; surveys and questionnaires
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Introduction: Smartphone access and use has increased dramatically over the past five years1. In parallel, development of mobile health apps has also increased, predominantly for the
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management of chronic diseases2,3. Recently, a few small pilot studies have demonstrated the opportunity for mobile health technology4-7 and telehealth8-10 to monitor postoperative recovery to improve outcomes and potentially reduce patient costs.
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As part of the growing body of literature evaluating the utility of mobile health technology, several studies have evaluated the public’s interest11 and willingness to
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engage with mobile health applications12-15 and health information exchange16 to manage chronic medical disease. Only one other small, single center study evaluated patients’ perceptions regarding mobile health app use in the acute post-operative setting17. As a result, there is a paucity of data regarding which patients would be willing to participate
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and what they would be willing to do in the perioperative period. Our study objective was to ascertain the public's willingness to engage in mobile health technology in the post-operative setting, using a representative survey, and to
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identify patient-specific variables associated with a willingness to do so.
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Materials and Methods
Survey Development and Administration The Cornell University Survey Research Institute is a member of the Association
of Academic Survey Research Organization and is a well-established organization that has been conducting survey research since 199618. They have been conducting the Empire State Poll, a telephone survey of adult residents >18 years of age in New York
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State, every year since 2003. Each year there is a core set of questions addressing themes of community, government, and the economy. In addition to the core questions, Cornell investigators may submit questions to be included in the annual survey. Submitted
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questions are reviewed by the Survey Research Institute and a preliminary pilot of 25 individuals is conducted. Investigators are then given feedback regarding any issues encountered by surveyors when administering these questions and the pilot data is
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analyzed and questions refined. Based on this feedback, investigators then modify their
questions prior to conducting the survey. We submitted 5 closed-ended questions using a
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5-point Likert scale addressing the public’s access and willingness to use mobile health technologies in a hypothetical scenario assuming respondents had undergone surgery for any reason (Supplemental Table 1).
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Sampling
The 2016 Empire State Poll was administered from February 9, 2016 to April 19, 2016.
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3,230 individuals were contacted, 1,184 of whom were eligible. Of those eligible individuals contacted, 800 individuals completed the final survey (67.5% cooperation
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rate19 comparable to other research organizations and considered acceptable20). The average interview length was 18 minutes. Cornell Survey Research Institute used a dualframe random digit dial sampling of landlines and cell phones as previously described21. The state was divided into two regions, upstate and downstate, and sampling was conducted in proportion to population totals. The sample size of 800 produced a margin
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of error of 3.5% for questions with a 50/50 distribution and 95% confidence interval and is comparable with other research organizations22.
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Analysis
The primary outcome of our study was willingness to use a mobile health app
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after surgery. The secondary outcome of our study was patient specific variables
associated with willingness to use a mobile health app after surgery. Responses of ‘Very
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Willing’ and ‘Willing’ were categorized as “Willing” while responses of ‘Not sure’, ‘Unwilling’, and ‘Very Unwilling’ were categorized as “Not Willing”. Independent variables tested included age, gender, education level, race (White, Black, or ‘Other’ which included Asian, >2 races, or ‘other race’), ethnicity (Hispanic or Non-Hispanic),
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income, geographic location (according to Metropolitan Statistical Area), marital status (Married, Divorced/Separated/Widowed, or Single), social ideology, religion (Christian, Agnostic, or ‘Other’ which included Muslim, Jewish, and ‘other religion’), perceived
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Internet trustworthiness, and whether participants owned a smart phone that allowed them to download mobile apps. Social ideology responses were captured on a scale of 1-7 with
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the following response options: ‘Extremely Liberal’, ‘Liberal’, ‘Slightly Liberal’, ‘Moderate or middle of the road’, ‘Slightly Conservative’, ‘Conservative’, ‘Extremely Conservative’. These responses were then grouped into “Liberal”, “Middle of the road” and “Conservative”. Internet trustworthiness is a question including in every Empire State Poll and asks participants to rate on a scale of 1-10 how trustworthy they perceive the Internet to be, with 1 being “untrustworthy” and 10 being “trustworthy”. Based on
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the distribution of responses and for ease of interpretation, responses were categorized as “untrustworthy” (if response was 1-5) and “trustworthy” (if response was 6-10). 61 of the 800 respondents had one or more missing responses or refused to answer
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one or more questions and were dropped from analysis. Mean age was treated as both a continuous variable and a categorical variable based on prior studies evaluating this
topic13. When age was treated as a continuous variable, comparison was made using
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Wilcoxon Rank Sum Test; when age was treated as a categorical variable, comparison
was made using chi-square test. Other categorical variables were tested with chi-squared
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tests. Variables were added to a multivariable logistic model based on statistical significance and clinical assumptions. Forward and backward stepwise regression was used to arrive at a final model. All data were analyzed with STATA v13.1
Description of Cohort
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Results:
A total of 739 NY state residents had complete survey data. The average age of
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respondents was 47 years old (+17) with an equal distribution of men and women. Most respondents were White (67%), followed by Black (18%) and Other (15%). 13% of
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respondents identified as having Hispanic ethnicity. The median income was $70,000 (IQR = $42,000-$100,000) and 31% of the cohort did not complete any education beyond high school.
Primary Outcome: Willingness to engage with mHealth Overall, the majority of respondents reported a willingness to engage with mobile health technology. Respondents reported the highest willingness to wear a tracker on
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their wrist (80.6%), followed by willingness to fill out a daily survey (74.3%), send pictures (66.3%), and share updates (59.1%) (Figure 1). Secondary Outcome: Variables associated with willingness to engage with mHealth
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Although older age (>65 years) was independently associated with not having a smartphone compared to younger participants age 18-34 (OR=0.0, 95% CI=0.0-0.1),
older age was not associated with lower willingness to wear a tracker, complete a daily
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survey, send pictures to their surgeon, or share updates with family or friends on both univariable and multivariable analysis.
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Although Hispanic ethnicity was independently associated with having a smartphone (OR=2.40, 95% CI=1.06-5.43), Hispanic ethnicity was associated with lower likelihood to wear a tracker on their wrist (OR=0.43, 95% CI=0.24-0.75) on both univariable and multivariable analysis. Similarly, Black race was independently
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associated with lower willingness to send pictures (OR=0.6, 95% CI=0.4-0.9). Higher education, trust in Internet, and preexisting smartphone use were all independently associated with willingness to engage with various components of
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mHealth. For example, having a post-graduate education was independently associated with higher willingness to fill out a daily survey on multivariable analysis (OR=2.7, 95%
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CI=1.3-5.6). Internet trustworthiness was independently associated with higher willingness to share updates with friends and family (OR=0.4, 95% CI=1.0-2.0) and having a smartphone was independently associated with higher likelihood to send pictures (OR=1.8, 95% CI=1.2-2.6) (Tables 3-4).
Discussion:
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Use of mobile health apps and trackers after surgery offer the potential to shorten length of stay, and improve care by potentially identifying complications earlier thus decreasing 30-day readmission, improving recovery, and reducing healthcare costs4-7.
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Despite its possible benefits, previous studies have indicated that not all patients are
amenable to using these innovative technologies12,13,23. This is the first large-scale survey to address this important issue of patient willingness to use a mobile health app after
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surgery for monitoring recovery. Overall, we found that the majority of respondents were willing to engage with mobile health technology after surgery. They are willing to wear a
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tracker, fill out daily surveys, send pictures of their incisions to their surgeon, and share updates with select family and friends. Although older adults were less likely to have a smartphone, they were just as willing as younger respondents to engage with all features of the mobile health app described in our survey.
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Our finding that older adults are willing to participate in mobile health technologies runs counter to several recent reports assessing older adults’ willingness to use mobile health technology. For example, the most recent Pew Research Center report
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suggested that older adults were less likely to use their smartphone to get information about a health condition: whereas 77% of respondents age 18-29 reported using a
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smartphone to look up information about a health condition, only 39% of adults >50 years of age reported using smartphones for the same activity1. Although both their report and the Empire State Poll including approximately the same proportion of older adults >65 (15.7% vs 16.9%), their report was a nationally representative sample compared to our statewide sample. It is possible older residents of New York State are different from older residents outside of New York in their willingness to use mobile health technology.
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Serrano et al conducted a survey assessing respondents’ willingness to exchange cancer-related health information using mobile health devices. They used the Health Information National Trends Survey, a cross-sectional mail-administered survey focused
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on cancer-related communication and did not specifically address patients recovering
from surgery. Similar to our study, they found a high willingness (>50% of respondents) to use mobile health technology in general, albeit lower willingness to send digital
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images. In contrast to our study, they found that those who were older (50-64 or >65)
were less willing to engage with mobile health information exchange compared to adults
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age 18-3413. It is possible that by using a more effort-intensive survey methodology selected for older adults who were more actively opposed to adopting a novel technology. Furthermore, in our survey, respondents were given cues about the usefulness of engaging with mobile health technology, suggesting that older adults would be just as
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willing to use this technology as long as they have specific information about why this will improve their recovery after surgery. For example, past work done by our colleagues in Geriatric Medicine confirmed that older adults are willing to engage with mobile
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health for specific reasons, such as the management of chronic pain24. Recent evidence suggests that older adults are particularly willing to engage with
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mobile health technology if it is for short-term purposes. McMahon et al23 studied the use of Fitbit OneTM on community-dwelling older adults (70-96 years of age). Not only did they find that older adults were receptive to using trackers, but participants rated the tracker more favorably for short periods of time (2.5 months vs. 8 months), making this ideal for post-operative monitoring.
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Another important finding of our study was that Hispanic ethnicity and Black race were independently associated with being less willing to engage with certain aspects of mobile health technology. Serrano et al13 did not find any difference in willingness to
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engage mobile health technology based on race or ethnicity. Another larger survey of
health app use among mobile phone users in the U.S. found that Hispanics were more likely to use health apps compared to Whites14. Although their survey had a higher
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representation of Hispanics compared to our survey (27.9% vs 13.1%), their aim was to generically characterize interaction with mobile health technology and not for a specific
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scenario as in our study. It is possible that our survey reflects an unwillingness of Hispanics to share personal health data through mobile health technology rather than simply an unwillingness to use mobile health apps. This represents an area for further research and should be used as guidance for surgeons interested in extending mobile
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health app usage to their Hispanic patients.
Other important variables associated with engaging in mobile health technology include higher income, education level, and trust in the Internet12,13,16. These findings
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should not be used to discourage surgeons from including patients who do not meet those criteria, but instead should give insight as to which patients may require more time to
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educate regarding the possible benefits of mobile health technology to help recover after surgery. In addition, although not having a smartphone seems to make patients less likely to use mobile health technology, there continue to be significant increases in smartphone use for all demographics1. This suggests that this potential barrier is continually shrinking.
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Interestingly, we found that the proportion of participants who were willing to engage with various components of a mobile health technology varied from 59-81%. It is possible that participants and future patients will be less likely to share sensitive
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information such as pictures of their incision or their stoma as compared to tracking their mobility with a tracker. It is also possible that they may not be as open to sharing
personal information with friends and family. These insights are important for researchers
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looking to develop a mobile health app that will have maximal participation.
Limitations of this study include those associated with telephone surveys
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including sampling error from systematic exclusion of households without telephones and interviewer-induced bias. For our specific questions, it is possible that participants did not fully understand the scenario described for them; we are therefore reliant on the trained interviewers to ensure adequate participant understanding. Responses were not based on
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documented engagement with mobile health technology, but instead on each respondent’s self-reported willingness to use mHealth, which may overestimate actual usage. Moreover, it is possible that patients who had undergone surgery may respond differently
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to the proposition of mobile health technology based on their physical condition, presence of comorbidities, and prior health experiences. Nevertheless, our findings
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represent an important initial understanding of which potential patients may be willing to engage with mobile health technology when recovering from surgery. Further research is necessary to determine the beliefs and practices of this patient population. Conclusions:
The majority of respondents in our large statewide survey indicated a willingness to engage with a variety of mobile health technology functions including wearing a
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tracker, filling out a daily survey, sending pictures of their wound to their surgeon, and sharing updates with select family and friends. Older respondents are just as willing as younger respondents to engage with mobile health technology. Individuals with Hispanic
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ethnicity and Black race may be less willing to engage and therefore may require education regarding benefits of this technology.
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Acknowledgements:
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Acknowledgements to Myung Hee Lee for statistical support.
Funding/Support:
Funding for this study came from two sources supporting Dr. Yeo: Career Development Award from the Society for Surgery of the Alimentary Tract and Center for Advanced
References:
3.
4.
5.
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Pew Research Center. The Smartphone Difference. 2015; http://www.pewinternet.org/2015/04/01/us-smartphone-use-in-2015/. Accessed July 1, 2016. Gold J. FDA Seeks to Tame Exploding Medical App Market 2012; http://khn.org/news/fda-medical-app-market/. Accessed July 20, 2016. de Jongh T, Gurol-Urganci I, Vodopivec-Jamsek V, Car J, Atun R. Mobile phone messaging for facilitating self-management of long-term illnesses. Cochrane Database Syst Rev. 2012;12:CD007459. Martínez-Ramos C, Cerdán MT, López RS. Mobile phone-based telemedicine system for the home follow-up of patients undergoing ambulatory surgery. Telemed J E Health, 2009;15:531-537. Stomberg MW, Platon B, Widén A, Wallner I, Karlsson O. Health information: what can mobile phone assessments add? Perspect Health Inf Manag. 2012;9:110.
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1.
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Digestive Care at New York Presbyterian Hospital-Weill Cornell Medicine
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11.
12.
13.
14. 15. 16.
17.
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18.
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10.
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9.
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8.
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7.
Semple JL, Sharpe S, Murnaghan ML, et al. Using a mobile app for monitoring post-operative quality of recovery of patients at home: a feasibility study. JMIR Mhealth Uhealth, 2015;3:e18. Jaensson M, Dahlberg K, Eriksson M, et al. The Development of the Recovery Assessments by Phone Points (RAPP): A Mobile Phone App for Postoperative Recovery Monitoring and Assessment. JMIR Mhealth Uhealth, 2015;3:e86. Hwa K, Wren SM. Telehealth follow-up in lieu of postoperative clinic visit for ambulatory surgery: results of a pilot program. JAMA Surg, 2013;148:823-827. Vella MA, Kummerow Broman K, Tarpley JL, et al. Postoperative Telehealth Visits: Assessment of Quality and Preferences of Veterans. JAMA Surg, 2015;150:1197-1199. Kummerow Broman K, Roumie CL, Stewart MK, et al. Implementation of a Telephone Postoperative Clinic in an Integrated Health System. J Am Coll Surg, 2016;223:644-651. Sankaranarayanan J, Sallach RE. Rural patients' access to mobile phones and willingness to receive mobile phone-based pharmacy and other health technology services: a pilot study. Telemed J E Health, 2014;20:182-185. Atienza AA, Zarcadoolas C, Vaughon W, et al. Consumer Attitudes and Perceptions on mHealth Privacy and Security: Findings From a Mixed-Methods Study. J Health Commun, 2015;20:673-679. Serrano KJ, Yu M, Riley WT, et al. Willingness to Exchange Health Information via Mobile Devices: Findings From a Population-Based Survey. Ann Fam Med, 2016;14:34-40. Krebs P, Duncan DT. Health App Use Among US Mobile Phone Owners: A National Survey. JMIR Mhealth Uhealth, 2015;3:e101. Levine DM, Lipsitz SR, Linder JA. Trends in Seniors' Use of Digital Health Technology in the United States, 2011-2014. JAMA, 2016;316:538-540. O'Donnell HC, Patel V, Kern LM, et al. Healthcare consumers' attitudes towards physician and personal use of health information exchange. J Gen Intern Med, 2011;26:1019-1026. Wiseman JT, Fernandes-Taylor S, Barnes ML, et al. Conceptualizing smartphone use in outpatient wound assessment: patients' and caregivers' willingness to use technology. J Surg Res, 2015;198:245-251. Cornell University Survey Research Institute. https://www.sri.cornell.edu/sri/index.cfm. Accessed November 1, 2015. The American Association for Public Opinion Research. Standard definitions: final dispositions of case codes and outcome rates for surveys. 2016; http://www.aapor.org/AAPOR_Main/media/publications/StandardDefinitions20169theditionfinal.pdf. Accessed January 28, 2017. Assessing the Representativeness of Public Opinion Surveys. 2012; http://www.people-press.org/2012/05/15/assessing-the-representativeness-ofpublic-opinion-surveys/. Ancker JS, Edwards AM, Miller MC, et al. Consumer perceptions of electronic health information exchange. Am J Prev Med, 2012;43:76-80.
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24.
The American Association for Public Opinion Research. http://www.aapor.org/AAPOR_Main/media/MainSiteFiles/Margin-of-SamplingError.pdf. Accessed January 28, 2017. McMahon SK, Lewis B, Oakes M, et al. Older Adults' Experiences Using a Commercially Available Monitor to Self-Track Their Physical Activity. JMIR Mhealth Uhealth, 2016;4:e35. Parker SJ, Jessel S, Richardson JE, et al. Older adults are mobile too! Identifying the barriers and facilitators to older adults' use of mHealth for pain management. BMC Geriatr, 2013;13:43.
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Figure 1. Willingness to engage with mobile health technology
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Red bubble reflects median value for each questions; percentage reflects proportion of respondents who stated either “very willing” or “willing”
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Table 1. Participant characteristics and association with willingness to engage with wearing a wrist tracker
32 40 42 28
168 (28) 161 (27) 171 (29) 97 (16)
(22) (28) (30) (20)
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P value
0.13 0.52
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Willing (N=497) n (%) 46 (16)
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0.54
68 (48) 74 (52)
303 (51) 294 (49)
84 (59) 24 (17) 34 (24)
414 (69) 107 (18) 76 (13)
33 (23) 109 (77)
62 (10) 535 (90)
<0.01
62 35 21 9 15
(44) (25) (15) (6) (10)
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Mean age, years (std) Age-group, years 18-34 35-49 50-64 >65 Gender Male Female Race White Black Othera Ethnicity Hispanic/Latino Non Hispanic/Latino Metropolitan resident Center city of MSA Outside center city of MSA Inside suburban county of MSA In MSA with no center city Not in MSA Marital status* Married Divorced/Separated/Widow Single Education Less than High School Grad High School Grad Some post-college training College grad Post-grad Income group <$30,000 $30,000-$49,999 $50,00-$74,999 $75,000-$99,999 $100,000-$149,999 >$150,000 Internet trustworthiness
Not willing (N =142) n (%) 49 (17)
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Characteristic
<0.01
0.02
224 (37) 173 (29) 56 (9) 87 (15) 57 (10)
0.95
69 (49) 22 (15) 51 (36)
283 (48) 88 (15) 221 (37)
23 44 35 20 20
39 (6) 121 (20) 159 (27) 159 (27) 119 (20)
<0.01 (16) (31) (25) (14) (14)
0.02 26 32 44 9 19 12
(18) (23) (31) (6) (13) (9)
81 (14) 121 (20) 133 (22) 88 (15) 101 (17) 73 (12) 0.01
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412 (69) 185 (31) <0.01 209 (35) 229 (38) 159 (27)
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0.04
376 (63) 147 (25) 72 (12)
<0.01
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98 (16) 499 (84)
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Untrustworthyb 114 (80) Trustworthyc 28 (20) Social Ideology Liberal 31 (22) Middle of the road 42 (30) Conservative 69 (48) Religion** Christian 104 (74) Agnostic 26 (19) Otherd 10 (7) Smartphone Use Does not have smartphone 48 (34) Has smartphone 94 (66) a Other races = Asian, >2 Races, Other b Below the 50th percentile of responses c Above the 50th percentile of responses d Other religion = Muslim, Jewish, other *Excludes 5 responses of “Other” **Excludes 4 responses of “Refused to answer”
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Table 2. Participant characteristics and association with willingness to engage with completing a daily survey
44 43 57 39
156 (28) 158 (28) 156 (28) 86 (16)
(24) (24) (31) (21)
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P value
0.04 0.15
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Willing (N=556) n (%) 46 (17)
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0.12
101 (55) 82 (45)
270 (49) 286 (51)
115 (63) 30 (16) 38 (21)
383 (69) 101 (18) 72 (13)
33 (18) 150 (82)
62 (11) 494 (89)
0.04
72 50 18 24 19
(39) (27) (10) (13) (11)
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Mean age, years (std) Age-group, years 18-34 35-49 50-64 >65 Gender Male Female Race White Black Othera Ethnicity Hispanic/Latino Non Hispanic/Latino Metropolitan resident Center city of MSA Outside center city of MSA Inside suburban county of MSA In MSA with no center city Not in MSA Marital status* Married Divorced/Separated/Widow Single Education Less than High School Grad High School Grad Some post-college training College grad Post-grad Income group <$30,000 $30,000-$49,999 $50,00-$74,999 $75,000-$99,999 $100,000-$149,999 >$150,000 Internet trustworthiness
Not willing (N =183) n (%) 49 (17)
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Characteristic
0.02
0.99
214 (38) 158 (28) 59 (11) 72 (13) 53 (10)
0.15
95 (52) 30 (17) 56 (31)
257 (46) 80 (14) 216 (39)
25 56 46 35 21
37 (7) 109 (19) 148 (27) 144 (26) 118 (21)
<0.01 (14) (31) (25) (19) (11)
0.17 29 44 50 23 21 16
(16) (24) (27) (13) (11) (9)
78 (14) 109 (20) 127 (23) 74 (13) 99 (18) 69 (12) 0.03
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384 (69) 172 (31) <0.01 199 (36) 207 (37) 150 (27)
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0.13
350 (63) 139 (25) 64 (12)
<0.01
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88 (16) 468 (84)
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Untrustworthyb 142 (78) Trustworthyc 41 (22) Social Ideology Liberal 41 (22) Middle of the road 64 (35) Conservative 78 (43) Religion** Christian 130 (71) Agnostic 34 (19) Otherd 18 (10) Smartphone Use Does not have smartphone 58 (32) Has smartphone 125 (68) a Other races = Asian, >2 Races, Other b Below the 50th percentile of responses c Above the 50th percentile of responses d Other religion = Muslim, Jewish, other *Excludes 5 responses of “Other” **Excludes 4 responses of “Refused to answer”
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Table 3. Univariable analysis evaluating participants’ likelihood of engaging with mobile health technology Fill out survey Unadjusted OR (95% CI)
Send pictures Unadjusted OR (95% CI)
Share Updates Unadjusted OR (95% CI)
Ref 0.4 (0.2-0.9) 0.1 (0.1-0.3) 0.0 (0.0-0.1)
Ref 0.8 (0.5-1.3) 0.8 (0.5-1.3) 0.7 (0.4-1.2)
Ref 1.0 (0.6-1.7) 0.8 (0.5-1.2) 0.6 (0.4-1.0)
Ref 0.9 (0.6-1.4) 0.9 (0.6-1.4) 0.7 (0.5-1.2)
Ref 1.1 (0.7-1.6) 1.3 (0.9-1.9) 1.1 (0.7-1.7)
Ref 1.2 (0.8-1.7)
Ref 0.9 (0.6-1.3)
Ref 1.3 (0.9-1.8)
Ref 0.9 (0.7-1.2)
Ref 0.9 (0.7-1.2)
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Ref 0.7 (0.5-1.2) 0.9 (0.5-1.5)
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Wear Tracker Unadjusted OR (95% CI)
Ref 0.9 (0.5-1.5) 0.5 (0.3-0.7)
Ref 1.0 (0.6-1.6) 0.6 (0.4-0.9)
Ref 0.6 (0.4-0.8) 0.7 (0.4-1.0)
Ref 1.1 (0.7-1.6) 1.0 (0.7-1.6)
Ref 0.4 (0.2-0.6)
Ref 0.6 (0.4-0.9)
Ref 0.8 (0.5-1.2)
Ref 0.7 (0.4-1.0)
Ref 1.4 (0.9-2.2)
Ref 1.1 (0.7-1.6)
Ref 1.4 (0.9-2.0)
Ref 1.2 (0.8-1.7)
1.2 (0.6-1.3)
0.7 (0.4-1.3)
1.1 (0.6-2.0)
1.1 (0.7-1.9)
0.8 (0.5-1.3)
1.3 (0.7-2.4)
2.7 (1.3-5.6)
1.0 (0.6-1.7)
2.0 (1.2-3.5)
1.1 (0.7-1.8)
0.7 (0.4-1.3)
1.1 (0.6-2.0)
0.9 (0.5-1.7)
1.5 (0.8-2.6)
1.2 (0.7-2.0)
Ref 0.5 (0.3-0.8)
Ref 1.0 (0.6-1.7)
Ref 1.0 (0.6-1.6)
Ref 0.8 (0.5-1.3)
Ref 0.9 (0.6-1.4)
1.3 (0.9-2.1)
1.1 (0.7-1.6)
1.4 (1.0-2.1)
1.0 (0.7-1.5)
1.0 (0.7-1.3)
Ref 1.5 (0.8-2.8) 3.2 (1.7-5.9)
Ref 1.6 (0.9-3.0) 2.7 (1.4-5.0)
Ref 1.3 (0.7-2.4) 2.2 (1.2-4.0)
Ref 1.0 (0.6-1.8) 1.3 (0.7-2.4)
Ref 0.8 (0.4-1.4) 1.2 (0.7-2.1)
6.1 (3.0-12.2) 7.6 (3.5-16.6)
4.7 (2.3-9.4) 3.5 (1.7-7.1)
2.8 (1.5-5.2) 3.8 (1.9-7.6)
1.6 (0.9-2.8) 2.3 (1.2-4.3)
1.2 (0.6-2.1) 1.3 (0.7-2.4)
Ref 1.1 (0.7-2.0) Ref 1.2 (0.8-1.9)
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Age-group, y 18-34 35-49 50-64 >65 Gender Male Female Race White Black Othera Ethnicity Non Hispanic/Latino Hispanic/Latino Metropolitan resident Center city of MSA Outside center city of MSA Inside suburban co. of MSA In MSA without center city Not in MSA Marital status Married Divorced/ Separated/ Widow Single Education Less than HS Grad HS Grad Some post-college training College grad Post-grad Internet Trustworthiness
Have smartphone Unadjusted OR (95% CI)
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Characteristic
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Ref 1.8 (1.2-2.9)
Ref 1.6 (1.0-2.3)
Ref 1.3 (1.0-1.9)
Ref 1.5 (1.0-2.0)
Ref 1.3 (0.8-2.2) 2.6 (1.5-4.5) 3.4 (1.7-6.8) 4.3 (2.2-8.6) 48.2 (6.5359.7)
Ref 1.2 (0.7-2.2) 1.0 (0.6-1.7) 3.1 (1.4-7.1) 1.7 (0.9-3.3) 2.0 (0.9-4.1)
Ref 0.9 (0.5-1.6) 0.9 (0.6-1.6) 1.2 (0.6-2.3) 1.8 (0.9-3.3) 1.6 (0.8-3.2)
Ref 0.9 (0.5-1.5) 1.1 (0.7-1.8) 1.5 (0.8-2.6) 1.6 (0.9-2.8) 2.4 (1.2-4.6)
Ref 0.9 (0.5-1.4) 0.9 (0.5-1.4) 1.6 (0.9-2.8) 1.7 (1.0-2.9) 1.4 (0.8-2.5)
Ref 0.7 (0.4-1.0) 0.4 (0.3-0.6)
Ref 1.0 (0.7-1.5) 0.7 (0.4-1.0)
Ref 1.3 (0.9-1.9) 0.9 (0.6-1.3)
Ref 1.5 (1.0-2.3) 1.3 (0.8-2.3)
Ref 1.2 (0.9-1.8) 1.2 (0.7-2.0)
Ref 0.9 (0.6-1.2) 0.8 (0.5-1.3)
Ref
Ref
Ref
2.5 (1.7-3.6)
1.8 (1.3-2.6)
1.3 (0.9-1.9)
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Social Ideology Liberal Ref Ref Middle of the road 0.5 (0.3-0.8) 0.8 (0.5-1.3) Conservative 0.4 (0.2-0.6) 0.3 (0.2-0.5) Religion Christian Ref Ref Agnostic 2.2 (1.3-3.5) 1.6 (1.0-2.5) Otherd 2.6 (1.2-5.3) 2.0 (1.0-4.0) Smartphone User N/A Does not have Ref smartphone Has smartphone 2.6 (1.7-3.9) a Other races = Asian, >2 Races, Other b Below the 50th percentile of responses c Above the 50th percentile of responses d Other religion = Muslim, Jewish, other
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Ref 1.2 (0.8-1.9)
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Untrustworthyb Trustworthyc Income group <$30,000 $30,000-$49,999 $50,00-$74,999 $75,000-$99,999 $100,000-$149,999 >$150,000
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Table 4. Multivariable analysis* evaluating participants’ likelihood of engaging with mobile health technology
--
--
--
--
--
--
Ref 2.4 (1.1-5.4) --
Ref 0.4 (0.2-0.7) Ref 1.3 (0.8-2.1) 0.5 (0.3-1.0) 2.6 (1.2-5.8) 1.0 (0.5-2.0)
Share Updates Adjusted OR (95% CI) --
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Ref 0.2 (0.1-0.6) 0.1 (0.0-0.2) 0.0 (0.0-0.1) --
Send pictures Adjusted OR (95% CI) --
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Fill out survey Adjusted OR (95% CI) --
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Age-group, y 18-34 35-49 50-64 >65 Gender Male Female Race White Black Othera Ethnicity Non Hispanic/Latino Hispanic/Latino Metropolitan resident Center city of MSA Outside center city of MSA Inside suburban co. of MSA In MSA with no center city Not in MSA Marital status Married Divorced/ Separated/ Widow Single Education Less than HS Grad HS Grad Some post-college training College grad Post-grad
Wear Tracker Adjusted OR (95% CI) --
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Have smartphone Adjusted OR (95% CI)
--
--
--
Ref 0.6 (0.4-0.9) 0.7 (0.4-1.0) --
--
--
Ref 0.7 (0.4-1.1) --
--
--
--
--
--
--
Ref 1.3 (0.7-2.6) 1.9 (0.9-3.8) 3.0 (1.4-6.5) 1.8 (0.8-4.0)
Ref 1.2 (0.6-2.2) 1.8 (0.9-3.3) 2.2 (1.1-4.3) 2.7 (1.3-5.6)
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Characteristic
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--
Ref 1.8 (0.8-3.9) 4.1 (1.8-9.2) 6.2 (2.515.2) 8.4 (3.122.5) Internet Trustworthiness --
--
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Ref 1.4 (1.0-2.0) --
-Ref 0.8 (0.5-1.3) 0.8 (0.5-1.3) 1.4 (0.8-2.6) 1.5 (0.9-2.7) 1.3 (0.7-2.3)
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Ref 1.6 (0.8-3.1) 2.4 (1.2-5.0) 3.4 (1.4-7.9) 3.6 (1.5-8.6) 38.0 (4.6314.1)
--
Ref 0.7 (0.4-1.2) 0.4 (0.2-0.8) --
N/A
Ref 1.0 (0.6-1.7) 0.4 (0.2-0.6) --
Ref 0.8 (0.5-1.2) 0.5 (0.3-0.7) --
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Social Ideology Liberal Middle of the road Conservative Religion Christian Agnostic Otherd Smartphone User Does not have smartphone Has smartphone
Ref 1.9 (1.2-3.1) --
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Untrustworthyb Trustworthyc Income group <$30,000 $30,000-$49,999 $50,00-$74,999 $75,000-$99,999 $100,000-$149,999 >$150,000
--
--
--
--
Ref
Ref
Ref
2.0 (1.3-3.2)
1.8 (1.2-2.8)
1.8 (1.2-2.6)
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*Variables adjusted based on clinical significance and clinical assumptions to arrive at final model. a Other races = Asian, >2 Races, Other b Below the 50th percentile of responses c Above the 50th percentile of responses
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Supplemental Table 1: Mobile Health Apps Questions - 2016 Empire State Poll Yes
No
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SC
RI PT
Q1. Do you have a smartphone that allows you to download mobile apps? The following 4 questions ask you about your willingness to use mobile health technology after surgery. For the purposes of these questions please imagine that you have undergone surgery for any reason. Please also imagine that the products that are mentioned are provided to you for free by your surgeon, are confidential and secure, and that they have been proven to help you recover after surgery. Very Unwilling Not sure Willing Very Unwilling Willing Q2. Would you be willing to wear something on your wrist that tracks your heart rate and the number of steps you take each day (e.g. Fitbit, Apple Watch)? Q3. Would you be willing to use an interactive mobile health app that is secure and confidential to fill out a survey for a few minutes each day to help your surgeon take care of you? Q4. Would you be willing to use an interactive mobile health app to send confidential and secure pictures of your surgical wound to your surgeon to help your surgeon take care of you? Q5. Would you be willing to use an interactive mobile health app to share selected secure updates about your health and recovery with friends or family that you designate?
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ACCEPTED MANUSCRIPT
Supplemental Table 2. Participant characteristics and association with willingness to engage with sending pictures to surgeon
61 64 70 47
139 (28) 137 (27) 143 (29) 78 (16)
(25) (27) (29) (19)
EP
AC C
P value
0.15 0.60
RI PT
Willing (N=497) n (%) 46 (17)
SC
0.58
118 (49) 124 (51)
253 (51) 244 (49)
145 (60) 55 (23) 42 (17)
353 (71) 76 (15) 68 (14)
36 (15) 206 (85)
59 (12) 438 (88)
0.01
108 (45) 64 (26) 27 (11) 22 (9) 21 (9)
TE D
Mean age, years (std) Age-group, years 18-34 35-49 50-64 >65 Gender Male Female Race White Black Othera Ethnicity Hispanic/Latino Non Hispanic/Latino Metropolitan resident Center city of MSA Outside center city of MSA Inside suburban county of MSA In MSA with no center city Not in MSA Marital status* Married Divorced/Separated/Widow Single Education Less than High School Grad High School Grad Some post-college training College grad Post-grad Income group <$30,000 $30,000-$49,999 $50,00-$74,999 $75,000-$99,999 $100,000-$149,999 >$150,000 Internet trustworthiness
Not willing (N =242) n (%) 48 (17)
M AN U
Characteristic
0.25
0.08
178 (36) 144 (29) 50 (10) 74 (15) 51 (10)
0.62
113 (48) 40 (16) 85 (36)
239 (48) 70 (14) 187 (38)
25 66 65 54 32
37 (7) 99 (20) 129 (26) 125 (25) 107 (22)
0.02 (10) (27) (27) (22) (13)
0.02 40 62 62 28 33 17
(17) (26) (26) (12) (14) (7)
67 (13) 91 (18) 115 (23) 69 (14) 87 (18) 68 (14) 0.09
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ACCEPTED MANUSCRIPT
344 (69) 153 (31) 0.03 168 (34) 191 (38) 138 (28)
RI PT
0.48
316 (64) 122 (25) 57 (11)
<0.01
SC
82 (16) 415 (84)
AC C
EP
TE D
M AN U
Untrustworthyb 182 (75) Trustworthyc 60 (25) Social Ideology Liberal 72 (30) Middle of the road 80 (33) Conservative 90 (37) Religion** Christian 164 (68) Agnostic 51 (21) Otherd 25 (10) Smartphone Use Does not have smartphone 64 (26) Has smartphone 178 (74) a Other races = Asian, >2 Races, Other b Below the 50th percentile of responses c Above the 50th percentile of responses d Other religion = Muslim, Jewish, other *Excludes 5 responses of “Other” **Excludes 4 responses of “Refused to answer”
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ACCEPTED MANUSCRIPT
Supplemental Table 3. Participant characteristics and association with willingness to engage with sharing updates with family and friends
85 83 78 51
115 (26) 118 (27) 135 (30) 74 (17)
(29) (28) (26) (17)
EP
AC C
P value
0.70 0.64
RI PT
Willing (N=442) n (%) 47 (17)
SC
0.44
144 (48) 153 (52)
227 (51) 215 (49)
202 (68) 51 (17) 44 (15)
296 (67) 80 (18) 66 (15)
46 (15) 251 (85)
49 (11) 393 (89)
0.94
119 (40) 78 (26) 36 (12) 37 (13) 27 (9)
TE D
Mean age, years (std) Age-group, years 18-34 35-49 50-64 >65 Gender Male Female Race White Black Othera Ethnicity Hispanic/Latino Non Hispanic/Latino Metropolitan resident Center city of MSA Outside center city of MSA Inside suburban county of MSA In MSA with no center city Not in MSA Marital status* Married Divorced/Separated/Widow Single Education Less than High School Grad High School Grad Some post-college training College grad Post-grad Income group <$30,000 $30,000-$49,999 $50,00-$74,999 $75,000-$99,999 $100,000-$149,999 >$150,000 Internet trustworthiness
Not willing (N =297) n (%) 47 (17)
M AN U
Characteristic
0.08
0.63
167 (38) 130 (30) 41 (9) 59 (13) 45 (10)
0.91
139 (47) 46 (16) 110 (37)
213 (48) 64 (15) 162 (37)
26 79 74 69 49
36 (8) 86 (20) 120 (27) 110 (25) 90 (20)
0.19 (9) (27) (25) (23) (16)
0.02 46 71 82 31 37 30
(16) (24) (28) (10) (12) (10)
61 82 95 66 83 55
(14) (19) (21) (15) (19) (12) 0.02
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ACCEPTED MANUSCRIPT
301 (68) 141 (32) 0.11 140 (32) 175 (39) 127 (29)
RI PT
0.59
292 (67) 100 (23) 46 (10)
0.12
SC
79 (18) 363 (82)
AC C
EP
TE D
M AN U
Untrustworthyb 225 (76) Trustworthyc 72 (24) Social Ideology Liberal 100 (34) Middle of the road 96 (32) Conservative 101 (34) Religion** Christian 187 (63) Agnostic 73 (25) Otherd 36 (12) Smartphone Use Does not have smartphone 67 (23) Has smartphone 230 (77) a Other races = Asian, >2 Races, Other b Below the 50th percentile of responses c Above the 50th percentile of responses d Other religion = Muslim, Jewish, other *Excludes 5 responses of “Other” **Excludes 4 responses of “Refused to answer”
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ACCEPTED MANUSCRIPT
Supplemental Table 4. Participant characteristics and association with having a smartphone.
9 21 55 61
191 (32) 180 (20) 158 (27) 64 (11)
(6) (14) (38) (42)
EP
AC C
P value
<0.01 <0.01
RI PT
Smartphone (N=593) n (%) 44 (16)
SC
0.39
78 (53) 68 (47)
293 (49) 300 (51)
93 (64) 31 (21) 22 (15)
405 (68) 100 (17) 88 (15)
17 (12) 129 (88)
78 (13) 515 (87)
0.44
60 37 14 16 19
(41) (25) (10) (11) (13)
TE D
Mean age, years (std) Age-group, years 18-34 35-49 50-64 >65 Gender Male Female Race White Black Othera Ethnicity Hispanic/Latino Non Hispanic/Latino Metropolitan resident Center city of MSA Outside center city of MSA Inside suburban county of MSA In MSA with no center city Not in MSA Marital status* Married Divorced/Separated/Widow Single Education Less than High School Grad High School Grad Some post-college training College grad Post-grad Income group <$30,000 $30,000-$49,999 $50,00-$74,999 $75,000-$99,999 $100,000-$149,999 >$150,000 Internet trustworthiness
No smartphone (N = 146) n (%) 60 (15)
M AN U
Characteristic
0.62 0.48
226 (38) 171 (29) 63 (11) 80 (13) 53 (9)
<0.01
68 (47) 36 (25) 41 (28)
284 (48) 74 (14) 231 (39)
26 53 36 19 12
36 (6) 112 (19) 158 (27) 160 (27) 127 (21)
<0.01 (18) (36) (25) (13) (8)
<0.01 39 46 32 14 14 1
(26) (31) (22) (10) (10) (1)
68 (12) 107 (18) 145 (24) 83 (14) 106 (18) 84 (14) 0.30
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ACCEPTED MANUSCRIPT
417 (70) 176 (30) <0.01 212 (36) 213 (36) 168 (28)
SC
RI PT
<0.01
365 (62) 151 (26) 73 (12)
AC C
EP
TE D
M AN U
Untrustworthyb 109 (75) Trustworthyc 37 (25) Social Ideology Liberal 28 (19) Middle of the road 58 (40) Conservative 60 (41) Religion** Christian 115 (79) Agnostic 22 (15) Otherd 9 (6) a Other races = Asian, >2 Races, Other b Below the 50th percentile of responses c Above the 50th percentile of responses d Other religion = Muslim, Jewish, other *Excludes 5 responses of “Other” **Excludes 4 responses of “Refused to answer”
30
AC C
EP
TE D
M AN U
SC
RI PT
ACCEPTED MANUSCRIPT