Modernizing Patient-Reported Outcome Measures: Are Patients Willing to Share Smartphone-Derived Health Data in Spine Clinics for Digital Phenotyping?

Modernizing Patient-Reported Outcome Measures: Are Patients Willing to Share Smartphone-Derived Health Data in Spine Clinics for Digital Phenotyping? | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Modernizing Patient-Reported Outcome Measures: Are Patients Willing to Share Smartphone-Derived Health Data in Spine Clinics for Digital Phenotyping? Mark R. Kraemer, Arnav Gambhir, Luke L. Jouppi, Julius Gerstmeyer, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9140129/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Background: Digital phenotyping involves the passive collection of behavioral and biometric data via smartphones and other personal devices. This emerging technology has the potential to transform outcome measurement in spine treatment by providing real-time objective data. Despite its promise, clinical adoption remains limited. This study aimed to assess patients’ comfort, willingness, and perceived barriers to digital phenotyping within a North American spine clinic. Methods : A single-center cross-sectional survey was administered to patients attending in-person appointments at the Swedish Neuroscience Institute between February and March 2025. The survey collected demographic data, familiarity with digital tools, comfort with data sharing, willingness to participate in digital phenotyping, and perceived barriers to participation. Results : A total of 183 patients completed the survey. Most respondents (61%) reported being comfortable sharing digital biometric data with their spine care team; however, 58% expressed concerns about data privacy. Preferences for outcome measurement methods were split: 38% were willing to undergo digital phenotyping, while 36% favored traditional survey strategies (e.g., paper-based or electronic surveys). Surprisingly, logistic regression to control for potential confounding demonstrated that age, gender, higher educational attainment, and current use of biometric technologies were not significantly associated with willingness to participate. Conclusion : Most patients appear open to integrating digital phenotyping into spine care, particularly those with higher education and prior experience using biometric tools. Privacy concerns remain the most common barrier to participation. Future implementation strategies should prioritize transparent data governance and flexible, patient-centered participation options to support broader and more equitable adoption. Digital phenotyping Patient Reported Outcome Measure PROM Cost Smartphone Data Biometrics Spine Outcome Figures Figure 1 Figure 2 Figure 3 Introduction The integration of digital health tools into clinical care has the potential to revolutionize modern medicine, creating new opportunities to both measure and improve patient outcomes.[1,2] Traditionally, patient-reported outcome measures (PROMs), including paper and electronic questionnaires, have been used to assess functional status and treatment response in spine care. Although originally designed for research in many cases, recent shifts in health policy have tied these tools directly to reimbursement, significantly increasing their influence in clinical decision-making and procedural authorization by payers. Among other well-documented problems, PROMs are hindered by recall bias, non-response bias, response shift, and the costly administrative burden of survey distribution, data incompletion, transcription, and analysis.[3-5] Digital phenotyping has emerged as a potential tool for real-time, passive collection through the automated capture of behavioral, social, and biometric data via sensors present on personal devices such as smartphones.[6,7] These passive metrics, including daily step counts, walking speed, distance traveled from home, time spent sedentary, and sociability provide objective functional assessments that complement traditional PROMs without requiring patient recall or effort. These tools have received some methodological validation.[7,8] In contrast to traditional methods, this new technology offers the potential to provide nonintrusive, continuous, objective, and comprehensive insights into patient well-being and biometrics with minimal reliance on dedicated human resources. Despite its potential, digital phenotyping has yet to achieve widespread implementation in clinical practice due to barriers such as concerns over data privacy, security, and patient comfort with technology. Additionally, willingness to adopt digital health tools may vary by demographic, geographic, and cultural factors. Understanding patient perspectives is a critical first step before attempting to integrate digital phenotyping into spine care. Our primary hypothesis predicts that most patients would be willing to participate in digital phenotyping within the context of spine care. Our secondary hypothesis posited that older adults (≥65 years old) are more skeptical and less willing to embrace this technology compared to younger patients, who are more likely to have experience with smartphones. The purpose of this study was to determine patient willingness to participate in digital phenotyping, to determine if willingness differs by specific demographic factors, and to inform providers regarding the potential implementation of these tools. Methods This was a cross-sectional, patient-reported survey study conducted at the spine outpatient clinic at the Swedish Neuroscience Institute (SNI). The survey was developed and refined through an iterative and consensus-driven process during spine division meetings at SNI including input from three neurosurgeons, five orthopedic spine surgeons, an epidemiologist, and the Providence Swedish Research Department to ensure the content was relevant and comprehensive. In addition to demographic information, the survey included questions to evaluate the following general categories: (1) familiarity with smartphone technology, (2) comfort with using smartphones for health-related purposes, (3) willingness to participate in digital biometric data collection and (4) concerns regarding data privacy and security. We performed psychometric validation of our survey instrument through internal trials prior to official distribution.[9] A copy of this survey instrument is available for readers in the supplementary material accompanying this article. Surveys were distributed to 210 consecutive eligible attending in-person appointments at the spine outpatient clinic at SNI between February 2025 and March 2025. Inclusion criteria were adults aged 18 years or older presenting for in-person evaluation at the spine clinic. Patients attending telehealth or virtual appointments were excluded. Patients unable to read or write in English and those with cognitive impairments preventing comprehension of the survey were also excluded. Surveys were distributed at the start of each clinic appointment. Medical assistants informed patients that the clinic was conducting a voluntary survey on the use of smartphones for collecting health data. To prevent duplicate responses, patients were asked if they had already completed the survey. Patients completed the surveys independently, without any guidance or discussion about either questions or answers, and returned them at the end of their visit. Surveys were anonymized and stored in compliance with institutional data protection protocols. All data generated and analyzed in this study are included and available within this article’s supplementary materials. Descriptive statistics were used to summarize survey response data. To evaluate associations between demographic variables and specific survey items while controlling for potential confounding, multivariable logistic regression was performed to evaluate the effect of age, self-reported gender, and educational status on patient willingness and current use of digital technology on comfort willingness to participate in digital phenotyping. Given the variety of educational categories, educational status was dichotomized as higher education (bachelor’s degree or above) versus less than a bachelor’s degree. Age was also dichotomized between participants under 65 years old and those at or above 65 years old. All analyses were conducted using Stata software, version 15.0 (College Station, Texas, USA). This study was conducted in accordance with the Declaration of Helsinki. The Institutional Review Board (IRB) of Providence Swedish Hospital approved the study protocol under the designation STUDY2025000039. No identifying images nor personal or clinical details of individuals were recorded or presented within this study; consent for publication was thus not required. Results A total of 183 out of 210 individuals completed the survey, reflecting an overall participation rate of 88%. The mean age was 66.2 years (range: 25–93), with 100 women (54.6%) and 83 men (45.4%, Table 1). Educational attainment was recorded, with most respondents having at least some college-level education. The distribution was as follows: some college (n = 57), bachelor’s or equivalent degree (n = 46), graduate or master’s degree (n = 44), high school diploma (n = 19), associates or technical degree (n = 16), and some high school (n = 1, Figure 1). Most participants were recruited at follow-up visits (62.3%), with others surveyed at new patient encounters (29.0%), post-operative visits (7.7%), and pre-operative appointments (1.1%). Younger patients demonstrate increased use of digital health tools Nearly all participants owned a smartphone, with only 9 respondents (4.9%) reporting otherwise. In contrast, familiarity with the term “digital phenotyping” was low, with only 7.7% reporting familiarity and an additional 10.4% indicating uncertainty. Overall, 35% of respondents reported current use of digital health or biometric tools. The most frequently reported technologies included Apple® products (28 mentions), Fitbit® devices (8 mentions), and various unspecified smartphone applications (10 mentions). The use of digital tools was significantly higher among younger participants: 46.2% of those under 65 years-old reported use, compared to 28.8% of those aged 65 or older (p = 0.02). Based on the results from the multivariable logistic regression, older patients were less than half as likely to use digital tools compared to younger patients, adjusted for education and gender (adjusted odds ratio [aOR] = 0.45, 95% confidence interval [CI]: 0.24 - 0.86, p = 0.02). Comfort With Digital Health Data Sharing Is Consistent Across Age Groups Most respondents reported being comfortable sharing digital health data, with 60% of respondents describing themselves as somewhat or very comfortable. In contrast, only 18% of respondents expressed discomfort (Figure 2). Furthermore, comfort with digital data sharing did not differ significantly between age groups (aOR = 0.84, 95% CI = 0.4 – 1.6, p=0.62), self-reported gender (aOR = 0.87, 95% CI = 0.5 – 1.6, p=0.65), or educational status (aOR = 1.50, 95% CI = 0.8 – 2.8, p=0.20). Respondents showed higher comfort levels when sharing pain levels, physical activity, heart rate, and sleep data (each > 80%), compared to lower comfort with diet, medication use, and mood or mental health data. (Table 2). Preferences for data sharing frequency varied: 31% of respondents would share data daily, 29% weekly, 9% monthly. Eighteen percent of respondents preferred that data was exchanged only during in-person clinic visits. Nine percent were unwilling to share any data. Overall, 38% of respondents expressed a willingness to participate in digital phenotyping as part of their spine care, while 36% preferred traditional outcome assessment tools (e.g., paper-based or electronic surveys). Nineteen percent indicated they would need more information before deciding, and 7% stated they would be unwilling to participate in such passive monitoring. Based on results from further multivariable logistic regression, there were no significant associations between willingness to participate and demographic variables, including age (aOR = 1.47, 95% CI = 0.8 – 2.9, p=0.24), self-reported gender (aOR = 0.87, 95% CI = 0.5 – 1.6, p=0.65), or educational status (aOR = 1.04, 95% CI = 0.6 – 1.9, p=0.32, Table 3). Additionally, current use of biometric devices was not associated with willingness to participate after adjusting for these demographic factors (aOR = 1.16, 95% CI = 0.6 – 2.2, p = 0.65). Data privacy concerns predominate limitations to participation The most frequently cited barriers to participation in digital data sharing were privacy concerns (27.3%), unfamiliarity with technology (23.0%), uncertainty about benefits (16.9%), perceived time burden (12.0%), and cost (11.5%, Figure 3). When asked specifically about privacy concerns, most respondents (58%) reported being somewhat or very concerned about privacy, while 21.9% were neutral (Figure 2). Privacy concerns were not significantly associated with self-reported gender (p=0.58) or educational status (p=0.47). However, there was a trend toward greater concern among older respondents, which approached statistical significance (p = 0.06). Discussion Though first pioneered in mental and behavioral health contexts, digital phenotyping has now been applied to monitor patients’ symptoms in several specialties.[1,6] In psychiatry, passive data collection on mobility patterns, phone use, heart rate, sleep, and social behavior has enabled clinicians to predict symptom exacerbation and/or relapse in mood disorders, anxiety disorders, and schizophrenia.[10,11] In heart failure patients, Stehlik et al. described how continuous data from wearable sensors could be analyzed via machine learning and used to predict rehospitalization with comparable efficacy to traditional implanted sensors.[12] Digital phenotyping shows similar promise in spine care. Cote et al. demonstrated the feasibility of monitoring mobility and quality of life in spine patients via digital phenotyping, while Boaro et al. validated GPS signatures against gold-standard outcome measures, finding that smartphone-derived mobility metrics correlated with traditional PROMs in post-surgical patients.[7,8] Amid increasing healthcare costs in the United States, a substantial focus has been decreed upon evaluating outcomes and the cost-effectiveness of treatment; considerable effort has been put into developing and validating numerous PROMs for this purpose.[13] Despite substantial improvements to these PROMs in recent years, concerns persist regarding selection biases, recall bias, and subjective evaluations.[3-5] In passively collecting objective biometric and behavioral data, digital phenotyping offers an objective and accessible option to supplement traditional PROMs. Our study is the first to systematically assess patient willingness and barriers to adoption of digital phenotyping amongst spine patients, providing insights for implementation feasibility. This survey highlights a general openness among patients to integrating digital tools into their spine care. While most respondents (61%) reported feeling somewhat or very comfortable sharing digital biometric data with their spine care team, only 38% identified as willing to participate in digital phenotyping, with another 36% reporting that they preferred traditional PROM techniques. This discrepancy between comfort and willingness likely reflects the unfamiliarity of the technology, with only 7.7% of patients reporting a prior awareness of the technology. Further, a substantial portion (58%) of respondents expressed moderate to significant concerns regarding data privacy. Taken together, these findings emphasize the need for clear, patient-centered policies that explain how data will be collected, stored, and used within clinical care. Transparent communication and strong data governance will be essential to maintaining and building further patient trust as digital phenotyping becomes more prevalent in clinical workflows. Respondents were nearly evenly divided in their preferences for data-sharing methods: 38% favored smartphone-based platforms, while 36% preferred traditional paper- or electronic-based approaches. This division underscores the benefit of a more flexible, inclusive digital health strategies that accommodate a variety of patient preferences and technological comfort levels. Surprisingly, we found that demographic factors like age, educational attainment, and even current technology use did not significantly influence patients’ willingness to participate in digital phenotyping. These findings might help shape patient counseling and outreach strategies. Contrary to our initial assumptions, older patients did not report increased skepticism or decreased willingness to participate in digital phenotyping, although current use of digital health technologies was lower in older patients 65 or older (28.8% vs 46.2%, p = 0.02) there was a trend toward elevated concerns over data privacy. Our findings challenge the frequent presumption in digital health design that age is a barrier to adoption; instead, our data supports the notion that with proper support, assurance about privacy concerns, and user-friendly interfaces, older adults may be just as willing as younger individuals to engage with digital health technologies. This is particularly pertinent in degenerative spine care, where a significant percentage of patients present in their sixth, seventh and eighth decades. There are several limitations in this study. First, the findings from our single-center cohort study may not be generalizable to all spine patient populations. Seattle, as a region, may represent a more technologically literate population, potentially inflating comfort and willingness levels compared to more rural or resource-limited areas. Second, while this study assessed attitudes toward digital phenotyping in general, it remains unclear how patients would respond to specific platforms. For example, the open-source application Beiwe developed by Onella et al. has emerged as a leading platform in digital phenotyping, capable of collecting diverse behavioral and biometric metrics.[2,6] While Beiwe provides structured and validated data streams, the flexibility to tailor the type and frequency of data collection may be limited. It is also unclear whether patients surveyed here would engage with such a platform if customization and control over data parameters were not available. Conclusion This survey offers helpful early insights into patient attitudes toward the new phenomenon of ‘digital phenotyping,’ its potential applications in spine care, and highlights key targets for patient education and opportunities for technology personalization. These insights may guide future implementation efforts aimed at ensuring equitable, transparent, and effective adoption of digital phenotyping tools in clinical practice. Declarations Ethics approval and consent to participate: This study was conducted in accordance with the Declaration of Helsinki. The Institutional Review Board (IRB) of Providence Swedish Hospital approved the study protocol under the designation STUDY2025000039. Consent for publication: Patients were informed of this study and invited to complete an anonymous, paper-based survey regarding their feelings on digital phenotyping for patient-reported outcome measures. Completion of the survey implied informed consent. No identifiable information was collected. No identifying images nor personal or clinical details of individuals were recorded or presented within this study; consent for publication was thus not required. Availability of data and materials: All data generated and analyzed in this study are available and included within this article’s supplementary materials. Funding: This study received funding from the Swedish Neuroscience Institute: Complex and Minimally Invasive Spine to cover its article processing charge. This study received no other specific funding. Competing interests: J.R. Chapman reports a relationship with Globus Medical Inc. that includes consulting or advisory. R.J. Oskouian reports a relationship with Globus Medical Inc., DePuy Synthes, NuVasive, and Stryker that includes consulting or advisory. The remaining authors have no conflicts to report. Author’s Contributions: All authors were involved in the methodology, creation, and validation of the survey instrument. M.K., C.G., C.H., and N.M were involved in data collection. D.N. was responsible for formal analysis. M.K., A.G., L.J., and D.N. were involved in the preparation of the original draft of the manuscript. M.K. and L.J. were involved in visualization and project administration. All authors were involved in critical revision and editing of the final draft of the manuscript. Acknowledgements: Not applicable. References Jain S, Powers B, Hawkins J et al (2015) The digital phenotype - PubMed. Nat Biotechnol 33. 10.1038/nbt.3223 Torous J, Kiang M, Lorme J et al New Tools for New Research in Psychiatry: A Scalable and Customizable Platform to Empower Data Driven Smartphone Research - PubMed. JMIR mental health 05/05/2016; 3: 10.2196/mental.5165 Zhang H, Glassman SD, Bisson EF et al Patient expectations impact patient-reported outcomes and satisfaction after lumbar fusion. Spine J 2024/02 /01; 24: 10.1016/j.spinee.2023.09.023 Zakaria HM, Mansour T, Telemi E et al Patient Demographic and Surgical Factors that Affect Completion of Patient-Reported Outcomes 90 Days and 1 Year After Spine Surgery: Analysis from the Michigan Spine Surgery Improvement Collaborative (MSSIC). World Neurosurgery 2019/10/01; 130: 10.1016/j.wneu.2019.06.058 Finkelstein J, Schwartz C Patient-reported outcomes in spine surgery: past, current, and future directions - PubMed. J Neurosurg Spine 08/01/2019; 31: 10.3171/2019.1.SPINE18770 Onnela J, Rauch S (2016) Harnessing Smartphone-Based Digital Phenotyping to Enhance Behavioral and Mental Health - PubMed. Neuropsychopharmacology: official publication Am Coll Neuropsychopharmacol 41. 10.1038/npp.2016.7 Cote D, Barnett I, Onnela J et al (2019) Digital Phenotyping in Patients with Spine Disease: A Novel Approach to Quantifying Mobility and Quality of Life - PubMed. World Neurosurg 126. 10.1016/j.wneu.2019.01.297 Boaro A, Leung J, Reeder H et al Smartphone GPS signatures of patients undergoing spine surgery correlate with mobility and current gold standard outcome measures - PubMed. J Neurosurg Spine 08/27/2021; 35: 10.3171/2021.2.SPINE202181 [Anonymous] (2018) Psychometric validity: Establishing the accuracy and appropriateness of psychometric measures. Wiley Handb Psychometric Test. 10.1002/9781118489772.ch24 Bufano P, Laurino M, Said S et al (2023) Digital Phenotyping for Monitoring Mental Disorders: Systematic Review. J Med Internet Res 25. 10.2196/46778 Barnett I, Torous J, Staples P et al (2018) Relapse prediction in schizophrenia through digital phenotyping: a pilot study - PubMed. Neuropsychopharmacology: official publication Am Coll Neuropsychopharmacol 43. 10.1038/s41386-018-0030-z Stehlik J, Schmalfuss C, Bozkurt B et al (2020) Continuous Wearable Monitoring Analytics Predict Heart Failure Hospitalization: The LINK-HF Multicenter Study - PubMed. Circulation Heart Fail 13. 10.1161/CIRCHEARTFAILURE.119.006513 Lee T, Thomas A, Grandhi N et al (2020) Cost-effectiveness Applications of Patient-reported Outcome Measures (PROMs) in Spine Surgery - PubMed. Clin spine Surg 33. 10.1097/BSD.0000000000000982 Tables Characteristics Participants, (mean ± SD or n, %) Age 66.16 (13.4) Self-reported gender Male 84 (45.4%) Female 100 (54.6%) Educational attainment (Completed Bachelor’s degree or higher) 90 (49.2%) Smartphone ownership 174 (95.1%) Familiarity with digital phenotyping 14 (7.7%) Current use of digital health technologies 64 (35.0%) Comfortability with data sharing General comfort with data sharing[1] 1.30 (1.27) Physical activity 157 (85.7%) Sleep 149 (81.4%) Pain 161 (88.0%) Heart 152 (83.1%) Stress 136 (74.3%) Diet 134 (73.2%) Medication-related data 140 (76.5%) Mood 131 (71.6%) Willingness to engage with digital phenotyping technologies 69 (37.7%) Concerns Privacy concerns 50 (27.3%) Uncertainty about benefits 31 (16.9%) Too time consuming 22 (12.0%) Unfamiliar technology 42 (23.0%) Cost-related concerns 21 (11.5%) Table 1. Characteristics and responses of survey participants separated by categories. [1] This value was not dichotomized for reporting in this table, and is instead reported as an average and standard deviation. Please see our results section or survey instrument in this article’s supplemental files for further detail. Percentage of respondents willing to share data <65 years-old 65+ years-old Physical Activity 82 87 Sleep 83 81 Pain/pain episodes 88 88 Heart rate 85 82 Stress 77 72 Diet 75 72 Medication usage 75 77 Mood/mental health 74 70 Table 2: Respondents reported their willingness to share specific digital biometric data, which are sorted by younger (<65) and older (65+) respondents. There are no statistical differences. Factor Adjusted Odds Ratio (aOR) 95% Confidence Interval (95% CI) p Value Comfort sharing data with spine team Age 0.85 0.44 – 1.62 0.62 Gender 0.87 0.47 – 1.60 0.65 Education 1.50 0.81 – 2.77 0.20 Willingness to use digital phenotyping technologies Age 1.44 0.76 – 2.74 0.27 Gender 0.87 0.48 – 1.60 0.65 Education 1.04 0.57 – 1.92 0.89 Current Use 1.16 0.61 – 2.20 0.65 Current use of digital health tools Age .45 0.24 – 0.86 0.02 Gender .85 0.46 – 1.60 0.62 Education 1.43 0.76 – 2.70 0.26 Table 3. Logistic regression analyses on factors influencing patients’ sentiments on data sharing, digital phenotyping technologies, and current use of digital health tools. Additional Declarations Competing interest reported. The article processing charge of this research was supported by the Swedish Neuroscience Institute: Complex and Minimally Invasive Spine section. J.R. Chapman reports a relationship with Globus Medical Inc. that includes consulting or advisory. R.J. Oskouian reports a relationship with Globus Medical Inc., DePuy Synthes, and NuVasive that includes consulting or advisory as well a relationship to Stryker that includes royalties. The remaining authors have no conflicts to report. Supplementary Files Digitalphenotypingsurvey2.18.docx digitalphenotypingdataBMC.xlsx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 07 May, 2026 Reviews received at journal 01 Apr, 2026 Reviewers agreed at journal 01 Apr, 2026 Reviewers agreed at journal 29 Mar, 2026 Reviewers invited by journal 29 Mar, 2026 Editor assigned by journal 29 Mar, 2026 Editor invited by journal 24 Mar, 2026 Submission checks completed at journal 23 Mar, 2026 First submitted to journal 23 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9140129","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":614173931,"identity":"b57b6502-ae2b-4c8a-a3a1-dd33a16daf6d","order_by":0,"name":"Mark R. 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Norvell","email":"","orcid":"","institution":"Spectrum Research Consulting, LLC","correspondingAuthor":false,"prefix":"","firstName":"Daniel","middleName":"C.","lastName":"Norvell","suffix":""},{"id":614173944,"identity":"f12a54fa-000d-45e7-84ef-4793eae71fa9","order_by":9,"name":"Amir Abdul-Jabbar","email":"","orcid":"","institution":"Seattle Science Foundation","correspondingAuthor":false,"prefix":"","firstName":"Amir","middleName":"","lastName":"Abdul-Jabbar","suffix":""},{"id":614173945,"identity":"74a91b12-06cf-454e-87c5-dbd38dc017da","order_by":10,"name":"Rod J. Oskouian","email":"","orcid":"","institution":"Seattle Science Foundation","correspondingAuthor":false,"prefix":"","firstName":"Rod","middleName":"J.","lastName":"Oskouian","suffix":""},{"id":614173946,"identity":"75231172-e1df-4041-913a-df3905a68359","order_by":11,"name":"Jens R. Chapman","email":"","orcid":"","institution":"Seattle Science Foundation","correspondingAuthor":false,"prefix":"","firstName":"Jens","middleName":"R.","lastName":"Chapman","suffix":""}],"badges":[],"createdAt":"2026-03-16 16:13:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9140129/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9140129/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106094218,"identity":"70196dbd-cfa6-4fb2-acdc-4653bbf500ce","added_by":"auto","created_at":"2026-04-03 11:41:49","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":92484,"visible":true,"origin":"","legend":"\u003cp\u003eRespondent demographics, demonstrating a clustering of patients aged 60-70 years-old, with a higher percentage of advanced education among older adults (65+) as compared to younger (\u0026lt;65) respondents.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9140129/v1/b6cf8c45b0bf9c8c65594043.jpeg"},{"id":105982683,"identity":"324e308d-2260-4edf-9923-fedcf8f07719","added_by":"auto","created_at":"2026-04-02 07:04:14","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":145748,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Comfort with sharing digital biometric data with spine team. There is no significant difference between younger (\u0026lt;65) and older (65+) respondents (P=0.92). (B) Concerns over data privacy. There is no statistical difference in comparison of younger and older respondents.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9140129/v1/778fb505997829bed1c314fb.jpeg"},{"id":105982682,"identity":"9b014b13-04d8-49e9-b7f5-d6b17f8f1acc","added_by":"auto","created_at":"2026-04-02 07:04:14","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":82921,"visible":true,"origin":"","legend":"\u003cp\u003eReasons respondents are unwilling to undergo digital biometric analysis. There were no statistically significant differences between younger (\u0026lt;65) and older (65+) respondents.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9140129/v1/4191f84723cf776f056275da.jpeg"},{"id":106723942,"identity":"a608ffc8-0279-4a78-be5b-bf1d323d7447","added_by":"auto","created_at":"2026-04-12 18:21:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":864033,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9140129/v1/f79de55b-4bb2-49ac-a698-7042a6f8dfec.pdf"},{"id":105982679,"identity":"1a9387ac-d3b8-4998-945b-0ec174992488","added_by":"auto","created_at":"2026-04-02 07:04:14","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":23145,"visible":true,"origin":"","legend":"","description":"","filename":"Digitalphenotypingsurvey2.18.docx","url":"https://assets-eu.researchsquare.com/files/rs-9140129/v1/cd0b83cd7665fe082309745a.docx"},{"id":106094170,"identity":"5067c37b-5862-4ab7-80f7-60b70a1f95b4","added_by":"auto","created_at":"2026-04-03 11:41:23","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":28098,"visible":true,"origin":"","legend":"","description":"","filename":"digitalphenotypingdataBMC.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9140129/v1/e8063c1aced47c5681195315.xlsx"}],"financialInterests":"Competing interest reported. The article processing charge of this research was supported by the Swedish Neuroscience Institute: Complex and Minimally Invasive Spine section. J.R. Chapman reports a relationship with Globus Medical Inc. that includes consulting or advisory. R.J. Oskouian reports a relationship with Globus Medical Inc., DePuy Synthes, and NuVasive that includes consulting or advisory as well a relationship to Stryker that includes royalties. The remaining authors have no conflicts to report.","formattedTitle":"Modernizing Patient-Reported Outcome Measures: Are Patients Willing to Share Smartphone-Derived Health Data in Spine Clinics for Digital Phenotyping?","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe integration of digital health tools into clinical care has the potential to revolutionize modern medicine, creating new opportunities to both measure and improve patient outcomes.[1,2] Traditionally, patient-reported outcome measures (PROMs), including paper and electronic questionnaires, have been used to assess functional status and treatment response in spine care. Although originally designed for research in many cases, recent shifts in health policy have tied these tools directly to reimbursement, significantly increasing their influence in clinical decision-making and procedural authorization by payers. Among other well-documented problems, PROMs are hindered by recall bias, non-response bias, response shift, and the costly administrative burden of survey distribution, data incompletion, transcription, and analysis.[3-5] Digital phenotyping has emerged as a potential tool for real-time, passive collection through the automated capture of behavioral, social, and biometric data via sensors present on personal devices such as smartphones.[6,7] These passive metrics, including daily step counts, walking speed, distance traveled from home, time spent sedentary, and sociability provide objective functional assessments that complement traditional PROMs without requiring patient recall or effort. These tools have received some methodological validation.[7,8] In contrast to traditional methods, this new technology offers the potential to provide nonintrusive, continuous, objective, and comprehensive insights into patient well-being and biometrics with minimal reliance on dedicated human resources.\u003c/p\u003e\n\u003cp\u003eDespite its potential, digital phenotyping has yet to achieve widespread implementation in clinical practice due to barriers such as concerns over data privacy, security, and patient comfort with technology. Additionally, willingness to adopt digital health tools may vary by demographic, geographic, and cultural factors. Understanding patient perspectives is a critical first step before attempting to integrate digital phenotyping into spine care. Our primary hypothesis predicts that most patients would be willing to participate in digital phenotyping within the context of spine care. Our secondary hypothesis posited that older adults (≥65 years old) are more skeptical and less willing to embrace this technology compared to younger patients, who are more likely to have experience with smartphones. The purpose of this study was to determine patient willingness to participate in digital phenotyping, to determine if willingness differs by specific demographic factors, and to inform providers regarding the potential implementation of these tools.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThis was a cross-sectional, patient-reported survey study conducted at the spine outpatient clinic at the Swedish Neuroscience Institute (SNI). The survey was developed and refined through an iterative and consensus-driven process during spine division meetings at SNI including input from three neurosurgeons, five orthopedic spine surgeons, an epidemiologist, and the Providence Swedish Research Department to ensure the content was relevant and comprehensive. In addition to demographic information, the survey included questions to evaluate the following general categories: (1) familiarity with smartphone technology, (2) comfort with using smartphones for health-related purposes, (3) willingness to participate in digital biometric data collection and (4) concerns regarding data privacy and security. We performed psychometric validation of our survey instrument through internal trials prior to official distribution.[9] A copy of this survey instrument is available for readers in the supplementary material accompanying this article.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSurveys were distributed to 210 consecutive eligible attending in-person appointments at the spine outpatient clinic at SNI between February 2025 and March 2025. Inclusion criteria were adults aged 18 years or older presenting for in-person evaluation at the spine clinic. Patients attending telehealth or virtual appointments were excluded. Patients unable to read or write in English and those with cognitive impairments preventing comprehension of the survey were also excluded. Surveys were distributed at the start of each clinic appointment. Medical assistants informed patients that the clinic was conducting a voluntary survey on the use of smartphones for collecting health data. To prevent duplicate responses, patients were asked if they had already completed the survey. Patients completed the surveys independently, without any guidance or discussion about either questions or answers, and returned them at the end of their visit. Surveys were anonymized and stored in compliance with institutional data protection protocols. All data generated and analyzed in this study are included and available within this article’s supplementary materials.\u003c/p\u003e\n\u003cp\u003eDescriptive statistics were used to summarize survey response data. To evaluate associations between demographic variables and specific survey items while controlling for potential confounding, multivariable logistic regression was performed to evaluate the effect of age, self-reported gender, and educational status on patient willingness and current use of digital technology on comfort willingness to participate in digital phenotyping. Given the variety of educational categories, educational status was dichotomized as higher education (bachelor’s degree or above) versus less than a bachelor’s degree. Age was also dichotomized between participants under 65 years old and those at or above 65 years old. All analyses were conducted using Stata software, version 15.0 (College Station, Texas, USA).\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the Declaration of Helsinki. The Institutional Review Board (IRB) of Providence Swedish Hospital approved the study protocol under the designation STUDY2025000039. No identifying images nor personal or clinical details of individuals were recorded or presented within this study; consent for publication was thus not required.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 183 out of 210 individuals completed the survey, reflecting an overall participation rate of 88%. The mean age was 66.2 years (range: 25–93), with 100 women (54.6%) and 83 men (45.4%, Table 1). Educational attainment was recorded, with most respondents having at least some college-level education. The distribution was as follows: some college (n = 57), bachelor’s or equivalent degree (n = 46), graduate or master’s degree (n = 44), high school diploma (n = 19), associates or technical degree (n = 16), and some high school (n = 1, Figure 1). Most participants were recruited at follow-up visits (62.3%), with others surveyed at new patient encounters (29.0%), post-operative visits (7.7%), and pre-operative appointments (1.1%).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eYounger patients demonstrate increased use of digital health tools\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNearly all participants owned a smartphone, with only 9 respondents (4.9%) reporting otherwise. In contrast, familiarity with the term “digital phenotyping” was low, with only 7.7% reporting familiarity and an additional 10.4% indicating uncertainty. Overall, 35% of respondents reported current use of digital health or biometric tools. The most frequently reported technologies included Apple® products (28 mentions), Fitbit® devices (8 mentions), and various unspecified smartphone applications (10 mentions). The use of digital tools was significantly higher among younger participants: 46.2% of those under 65 years-old reported use, compared to 28.8% of those aged 65 or older (p = 0.02). Based on the results from the multivariable logistic regression, older patients were less than half as likely to use digital tools compared to younger patients, adjusted for education and gender (adjusted odds ratio [aOR] = 0.45, 95% confidence interval [CI]: 0.24 - 0.86, p = 0.02).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eComfort With Digital Health Data Sharing Is Consistent Across Age Groups\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eMost respondents reported being comfortable sharing digital health data, with 60% of respondents describing themselves as somewhat or very comfortable. In contrast, only 18% of respondents expressed discomfort (Figure 2). Furthermore, comfort with digital data sharing did not differ significantly between age groups (aOR = 0.84, 95% CI = 0.4 – 1.6, p=0.62), self-reported gender (aOR = 0.87, 95% CI = 0.5 – 1.6, p=0.65), or educational status (aOR = 1.50, 95% CI = 0.8 – 2.8, p=0.20). Respondents showed higher comfort levels when sharing pain levels, physical activity, heart rate, and sleep data (each \u0026gt; 80%), compared to lower comfort with diet, medication use, and mood or mental health data. (Table 2). Preferences for data sharing frequency varied: 31% of respondents would share data daily, 29% weekly, 9% monthly. Eighteen percent of respondents preferred that data was exchanged only during in-person clinic visits. Nine percent were unwilling to share any data.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOverall, 38% of respondents expressed a willingness to participate in digital phenotyping as part of their spine care, while 36% preferred traditional outcome assessment tools (e.g., paper-based or electronic surveys). Nineteen percent indicated they would need more information before deciding, and 7% stated they would be unwilling to participate in such passive monitoring. Based on results from further multivariable logistic regression, there were no significant associations between willingness to participate and demographic variables, including age (aOR = 1.47, 95% CI = 0.8 – 2.9, p=0.24), self-reported gender (aOR = 0.87, 95% CI = 0.5 – 1.6, p=0.65), or educational status (aOR = 1.04, 95% CI = 0.6 – 1.9, p=0.32, Table 3). Additionally, current use of biometric devices was not associated with willingness to participate after adjusting for these demographic factors (aOR = 1.16, 95% CI = 0.6 – 2.2, p = 0.65).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eData privacy concerns predominate limitations to participation\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe most frequently cited barriers to participation in digital data sharing were privacy concerns (27.3%), unfamiliarity with technology (23.0%), uncertainty about benefits (16.9%), perceived time burden (12.0%), and cost (11.5%, Figure 3). When asked specifically about privacy concerns, most respondents (58%) reported being somewhat or very concerned about privacy, while 21.9% were neutral (Figure 2). Privacy concerns were not significantly associated with self-reported gender (p=0.58) or educational status (p=0.47). However, there was a trend toward greater concern among older respondents, which approached statistical significance (p = 0.06).\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThough first pioneered in mental and behavioral health contexts, digital phenotyping has now been applied to monitor patients’ symptoms in several specialties.[1,6] In psychiatry, passive data collection on mobility patterns, phone use, heart rate, sleep, and social behavior has enabled clinicians to predict symptom exacerbation and/or relapse in mood disorders, anxiety disorders, and schizophrenia.[10,11] In heart failure patients, Stehlik et al. described how continuous data from wearable sensors could be analyzed via machine learning and used to predict rehospitalization with comparable efficacy to traditional implanted sensors.[12] Digital phenotyping shows similar promise in spine care. Cote et al. demonstrated the feasibility of monitoring mobility and quality of life in spine patients via digital phenotyping, while Boaro et al. validated GPS signatures against gold-standard outcome measures, finding that smartphone-derived mobility metrics correlated with traditional PROMs in post-surgical patients.[7,8] Amid increasing healthcare costs in the United States, a substantial focus has been decreed upon evaluating outcomes and the cost-effectiveness of treatment; considerable effort has been put into developing and validating numerous PROMs for this purpose.[13] Despite substantial improvements to these PROMs in recent years, concerns persist regarding selection biases, recall bias, and subjective evaluations.[3-5] In passively collecting objective biometric and behavioral data, digital phenotyping offers an objective and accessible option to supplement traditional PROMs. Our study is the first to systematically assess patient willingness and barriers to adoption of digital phenotyping amongst spine patients, providing insights for implementation feasibility.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis survey highlights a general openness among patients to integrating digital tools into their spine care. While most respondents (61%) reported feeling somewhat or very comfortable sharing digital biometric data with their spine care team, only 38% identified as willing to participate in digital phenotyping, with another 36% reporting that they preferred traditional PROM techniques. This discrepancy between comfort and willingness likely reflects the unfamiliarity of the technology, with only 7.7% of patients reporting a prior awareness of the technology. Further, a substantial portion (58%) of respondents expressed moderate to significant concerns regarding data privacy. Taken together, these findings emphasize the need for clear, patient-centered policies that explain how data will be collected, stored, and used within clinical care. Transparent communication and strong data governance will be essential to maintaining and building further patient trust as digital phenotyping becomes more prevalent in clinical workflows.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRespondents were nearly evenly divided in their preferences for data-sharing methods: 38% favored smartphone-based platforms, while 36% preferred traditional paper- or electronic-based approaches. This division underscores the benefit of a more flexible, inclusive digital health strategies that accommodate a variety of patient preferences and technological comfort levels. Surprisingly, we found that demographic factors like age, educational attainment, and even current technology use did not significantly influence patients’ willingness to participate in digital phenotyping. These findings might help shape patient counseling and outreach strategies.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eContrary to our initial assumptions, older patients did not report increased skepticism or decreased willingness to participate in digital phenotyping, although current use of digital health technologies was lower in older patients 65 or older (28.8% vs 46.2%, p = 0.02) there was a trend toward elevated concerns over data privacy. Our findings challenge the frequent presumption in digital health design that age is a barrier to adoption; instead, our data supports the notion that with proper support, assurance about privacy concerns, and user-friendly interfaces, older adults may be just as willing as younger individuals to engage with digital health technologies. This is particularly pertinent in degenerative spine care, where a significant percentage of patients present in their sixth, seventh and eighth decades.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThere are several limitations in this study. First, the findings from our single-center cohort study may not be generalizable to all spine patient populations. Seattle, as a region, may represent a more technologically literate population, potentially inflating comfort and willingness levels compared to more rural or resource-limited areas. Second, while this study assessed attitudes toward digital phenotyping in general, it remains unclear how patients would respond to specific platforms. For example, the open-source application Beiwe developed by Onella et al. has emerged as a leading platform in digital phenotyping, capable of collecting diverse behavioral and biometric metrics.[2,6] While Beiwe provides structured and validated data streams, the flexibility to tailor the type and frequency of data collection may be limited. It is also unclear whether patients surveyed here would engage with such a platform if customization and control over data parameters were not available.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis survey offers helpful early insights into patient attitudes toward the new phenomenon of \u0026lsquo;digital phenotyping,\u0026rsquo; its potential applications in spine care, and highlights key targets for patient education and opportunities for technology personalization. These insights may guide future implementation efforts aimed at ensuring equitable, transparent, and effective adoption of digital phenotyping tools in clinical practice.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the Declaration of Helsinki. The Institutional Review Board (IRB) of Providence Swedish Hospital approved the study protocol under the designation STUDY2025000039.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePatients were informed of this study and invited to complete an anonymous, paper-based survey regarding their feelings on digital phenotyping for patient-reported outcome measures. Completion of the survey implied informed consent. No identifiable information was collected. No identifying images nor personal or clinical details of individuals were recorded or presented within this study; consent for publication was thus not required.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated and analyzed in this study are available and included within this article’s supplementary materials.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study received funding from the Swedish Neuroscience Institute: Complex and Minimally Invasive Spine to cover its article processing charge. This study received no other specific funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJ.R. Chapman reports a relationship with Globus Medical Inc. that includes consulting or advisory. R.J. Oskouian reports a relationship with Globus Medical Inc., DePuy Synthes, NuVasive, and Stryker that includes consulting or advisory. The remaining authors have no conflicts to report.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor’s Contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors were involved in the methodology, creation, and validation of the survey instrument. M.K., C.G., C.H., and N.M were involved in data collection. D.N. was responsible for formal analysis. M.K., A.G., L.J., and D.N. were involved in the preparation of the original draft of the manuscript. M.K. and L.J. were involved in visualization and project administration. All authors were involved in critical revision and editing of the final draft of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eJain S, Powers B, Hawkins J et al (2015) The digital phenotype - PubMed. Nat Biotechnol 33. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/nbt.3223\u003c/span\u003e\u003cspan address=\"10.1038/nbt.3223\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTorous J, Kiang M, Lorme J et al New Tools for New Research in Psychiatry: A Scalable and Customizable Platform to Empower Data Driven Smartphone Research - PubMed. JMIR mental health 05/05/2016; 3: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2196/mental.5165\u003c/span\u003e\u003cspan address=\"10.2196/mental.5165\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang H, Glassman SD, Bisson EF et al Patient expectations impact patient-reported outcomes and satisfaction after lumbar fusion. Spine J 2024/02 /01; 24: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.spinee.2023.09.023\u003c/span\u003e\u003cspan address=\"10.1016/j.spinee.2023.09.023\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZakaria HM, Mansour T, Telemi E et al Patient Demographic and Surgical Factors that Affect Completion of Patient-Reported Outcomes 90 Days and 1 Year After Spine Surgery: Analysis from the Michigan Spine Surgery Improvement Collaborative (MSSIC). World Neurosurgery 2019/10/01; 130: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.wneu.2019.06.058\u003c/span\u003e\u003cspan address=\"10.1016/j.wneu.2019.06.058\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFinkelstein J, Schwartz C Patient-reported outcomes in spine surgery: past, current, and future directions - PubMed. J Neurosurg Spine 08/01/2019; 31: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3171/2019.1.SPINE18770\u003c/span\u003e\u003cspan address=\"10.3171/2019.1.SPINE18770\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOnnela J, Rauch S (2016) Harnessing Smartphone-Based Digital Phenotyping to Enhance Behavioral and Mental Health - PubMed. Neuropsychopharmacology: official publication Am Coll Neuropsychopharmacol 41. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/npp.2016.7\u003c/span\u003e\u003cspan address=\"10.1038/npp.2016.7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCote D, Barnett I, Onnela J et al (2019) Digital Phenotyping in Patients with Spine Disease: A Novel Approach to Quantifying Mobility and Quality of Life - PubMed. World Neurosurg 126. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.wneu.2019.01.297\u003c/span\u003e\u003cspan address=\"10.1016/j.wneu.2019.01.297\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBoaro A, Leung J, Reeder H et al Smartphone GPS signatures of patients undergoing spine surgery correlate with mobility and current gold standard outcome measures - PubMed. J Neurosurg Spine 08/27/2021; 35: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3171/2021.2.SPINE202181\u003c/span\u003e\u003cspan address=\"10.3171/2021.2.SPINE202181\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e[Anonymous] (2018) Psychometric validity: Establishing the accuracy and appropriateness of psychometric measures. Wiley Handb Psychometric Test. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/9781118489772.ch24\u003c/span\u003e\u003cspan address=\"10.1002/9781118489772.ch24\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBufano P, Laurino M, Said S et al (2023) Digital Phenotyping for Monitoring Mental Disorders: Systematic Review. J Med Internet Res 25. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2196/46778\u003c/span\u003e\u003cspan address=\"10.2196/46778\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarnett I, Torous J, Staples P et al (2018) Relapse prediction in schizophrenia through digital phenotyping: a pilot study - PubMed. Neuropsychopharmacology: official publication Am Coll Neuropsychopharmacol 43. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41386-018-0030-z\u003c/span\u003e\u003cspan address=\"10.1038/s41386-018-0030-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStehlik J, Schmalfuss C, Bozkurt B et al (2020) Continuous Wearable Monitoring Analytics Predict Heart Failure Hospitalization: The LINK-HF Multicenter Study - PubMed. Circulation Heart Fail 13. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1161/CIRCHEARTFAILURE.119.006513\u003c/span\u003e\u003cspan address=\"10.1161/CIRCHEARTFAILURE.119.006513\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee T, Thomas A, Grandhi N et al (2020) Cost-effectiveness Applications of Patient-reported Outcome Measures (PROMs) in Spine Surgery - PubMed. Clin spine Surg 33. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/BSD.0000000000000982\u003c/span\u003e\u003cspan address=\"10.1097/BSD.0000000000000982\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 384px;\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 240px;\"\u003e\n \u003cp\u003eParticipants, (mean \u0026plusmn; SD or n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 384px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 240px;\"\u003e\n \u003cp\u003e66.16 (13.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 384px;\"\u003e\n \u003cp\u003eSelf-reported gender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 240px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 384px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 240px;\"\u003e\n \u003cp\u003e84 (45.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 384px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 240px;\"\u003e\n \u003cp\u003e100 (54.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 384px;\"\u003e\n \u003cp\u003eEducational attainment (Completed Bachelor\u0026rsquo;s degree or higher)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 240px;\"\u003e\n \u003cp\u003e90 (49.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 384px;\"\u003e\n \u003cp\u003eSmartphone ownership\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 240px;\"\u003e\n \u003cp\u003e174 (95.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 384px;\"\u003e\n \u003cp\u003eFamiliarity with digital phenotyping\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 240px;\"\u003e\n \u003cp\u003e14 (7.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 384px;\"\u003e\n \u003cp\u003eCurrent use of digital health technologies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 240px;\"\u003e\n \u003cp\u003e64 (35.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 384px;\"\u003e\n \u003cp\u003eComfortability with data sharing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 240px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 384px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; General comfort with data sharing[1]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 240px;\"\u003e\n \u003cp\u003e1.30 (1.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 384px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Physical activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 240px;\"\u003e\n \u003cp\u003e157 (85.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 384px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Sleep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 240px;\"\u003e\n \u003cp\u003e149 (81.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 384px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Pain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 240px;\"\u003e\n \u003cp\u003e161 (88.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 384px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Heart\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 240px;\"\u003e\n \u003cp\u003e152 (83.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 384px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Stress\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 240px;\"\u003e\n \u003cp\u003e136 (74.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 384px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Diet\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 240px;\"\u003e\n \u003cp\u003e134 (73.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 384px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Medication-related data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 240px;\"\u003e\n \u003cp\u003e140 (76.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 384px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Mood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 240px;\"\u003e\n \u003cp\u003e131 (71.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 384px;\"\u003e\n \u003cp\u003eWillingness to engage with digital phenotyping technologies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 240px;\"\u003e\n \u003cp\u003e69 (37.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 384px;\"\u003e\n \u003cp\u003eConcerns\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 240px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 384px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Privacy concerns\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 240px;\"\u003e\n \u003cp\u003e50 (27.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 384px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Uncertainty about benefits\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 240px;\"\u003e\n \u003cp\u003e31 (16.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 384px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Too time consuming\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 240px;\"\u003e\n \u003cp\u003e22 (12.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 384px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Unfamiliar technology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 240px;\"\u003e\n \u003cp\u003e42 (23.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 384px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Cost-related concerns\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 240px;\"\u003e\n \u003cp\u003e21 (11.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Characteristics and responses of survey participants separated by categories.\u003c/p\u003e\n\u003cp\u003e[1] This value was not dichotomized for reporting in this table, and is instead reported as an average and standard deviation. Please see our results section or survey instrument in this article\u0026rsquo;s supplemental files for further detail.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"569\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 199px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 371px;\"\u003e\n \u003cp\u003ePercentage of respondents willing to share data\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;65 years-old\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u003cem\u003e65+ years-old\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003ePhysical Activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003e82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003eSleep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003ePain/pain episodes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003e88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e88\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003eHeart rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003e85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003eStress\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003eDiet\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003eMedication usage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003eMood/mental health\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2:\u003c/strong\u003e Respondents reported their willingness to share specific digital biometric data, which are sorted by younger (\u0026lt;65) and older (65+) respondents. There are no statistical differences.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003eFactor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003eAdjusted Odds Ratio (aOR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e95% Confidence Interval (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003ep Value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003eComfort sharing data with spine team\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Age\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e0.44 \u0026ndash; 1.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Gender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e0.47 \u0026ndash; 1.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e1.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e0.81 \u0026ndash; 2.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003eWillingness to use digital phenotyping technologies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Age\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e1.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e0.76 \u0026ndash; 2.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Gender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e0.48 \u0026ndash; 1.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e0.57 \u0026ndash; 1.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Current Use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e0.61 \u0026ndash; 2.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003eCurrent use of digital health tools\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Age\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e0.24 \u0026ndash; 0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.02\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Gender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e0.46 \u0026ndash; 1.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e1.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e0.76 \u0026ndash; 2.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u003c/strong\u003e Logistic regression analyses on factors influencing patients\u0026rsquo; sentiments on data sharing, digital phenotyping technologies, and current use of digital health tools.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-digital-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [BMC Digital Health](https://bmcdigitalhealth.biomedcentral.com/)","snPcode":"44247","submissionUrl":"https://submission.nature.com/new-submission/44247/3","title":"BMC Digital Health","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Digital phenotyping, Patient Reported Outcome Measure, PROM, Cost, Smartphone, Data, Biometrics, Spine, Outcome","lastPublishedDoi":"10.21203/rs.3.rs-9140129/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9140129/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003eBackground: \u003c/em\u003eDigital phenotyping involves the passive collection of behavioral and biometric data via smartphones and other personal devices. This emerging technology has the potential to transform outcome measurement in spine treatment by providing real-time objective data. Despite its promise, clinical adoption remains limited. This study aimed to assess patients’ comfort, willingness, and perceived barriers to digital phenotyping within a North American spine clinic.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eMethods\u003c/em\u003e: A single-center cross-sectional survey was administered to patients attending in-person appointments at the Swedish Neuroscience Institute between February and March 2025. The survey collected demographic data, familiarity with digital tools, comfort with data sharing, willingness to participate in digital phenotyping, and perceived barriers to participation.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eResults\u003c/em\u003e: A total of 183 patients completed the survey. Most respondents (61%) reported being comfortable sharing digital biometric data with their spine care team; however, 58% expressed concerns about data privacy. Preferences for outcome measurement methods were split: 38% were willing to undergo digital phenotyping, while 36% favored traditional survey strategies (e.g., paper-based or electronic surveys). Surprisingly, logistic regression to control for potential confounding demonstrated that age, gender, higher educational attainment, and current use of biometric technologies were not significantly associated with willingness to participate.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConclusion\u003c/em\u003e: Most patients appear open to integrating digital phenotyping into spine care, particularly those with higher education and prior experience using biometric tools. Privacy concerns remain the most common barrier to participation. Future implementation strategies should prioritize transparent data governance and flexible, patient-centered participation options to support broader and more equitable adoption.\u003c/p\u003e","manuscriptTitle":"Modernizing Patient-Reported Outcome Measures: Are Patients Willing to Share Smartphone-Derived Health Data in Spine Clinics for Digital Phenotyping?","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-02 07:04:10","doi":"10.21203/rs.3.rs-9140129/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"127394011890279517090653907771633640083","date":"2026-05-07T13:33:27+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-01T16:55:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"170340377105736684199142461237028533411","date":"2026-04-01T16:31:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"201041292555927259038042912864138828588","date":"2026-03-29T23:34:11+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-29T23:01:30+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-29T22:55:35+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-24T08:08:17+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-23T17:48:39+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Digital Health","date":"2026-03-23T17:18:32+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-digital-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [BMC Digital Health](https://bmcdigitalhealth.biomedcentral.com/)","snPcode":"44247","submissionUrl":"https://submission.nature.com/new-submission/44247/3","title":"BMC Digital Health","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e9bd2c25-c505-4ec0-af40-70b10f9ecd5b","owner":[],"postedDate":"April 2nd, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"127394011890279517090653907771633640083","date":"2026-05-07T13:33:27+00:00","index":86,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-02T07:04:10+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-02 07:04:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9140129","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9140129","identity":"rs-9140129","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}