Current and future applications of mobile health technology for evaluating spine surgery patients: a review

Jacob K. GreenbergDepartments of Neurological Surgery,
Department of Neurological Surgery, Cleveland Clinic, Cleveland, Ohio;

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Saad JaveedDepartments of Neurological Surgery,

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Justin K. ZhangDepartments of Neurological Surgery,

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Braeden BenedictDepartments of Neurological Surgery,

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Madelyn R. FrumkinPsychology and Brain Sciences,

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Ziqi XuComputer Science and Engineering, and

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Jingwen ZhangComputer Science and Engineering, and

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Thomas L. RodebaughPsychology and Brain Sciences,

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Chenyang LuComputer Science and Engineering, and

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Jay F. PiccirilloOtolaryngology, Washington University, St. Louis, Missouri;

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Michael SteinmetzDepartment of Neurological Surgery, Cleveland Clinic, Cleveland, Ohio;

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Zoher GhogawalaDepartment of Neurological Surgery, Lahey Clinic, Burlington, Massachusetts; and

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Mohamad BydonDepartment of Neurological Surgery, Mayo Clinic, Rochester, Minnesota

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Wilson Z. RayDepartments of Neurological Surgery,

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Publication Date:
20 Jan 2023
Page Range:
1–10
Volume/Issue:
Publish Before Print
DOI link:
https://doi.org/10.3171/2022.12.SPINE221302
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Mobile health (mHealth) technology has assumed a pervasive role in healthcare and society. By capturing real-time features related to spine health, mHealth assessments have the potential to transform multiple aspects of spine care. Yet mHealth applications may not be familiar to many spine surgeons and other spine clinicians. Consequently, the objective of this narrative review is to provide an overview of the technology, analytical considerations, and applications of mHealth tools for evaluating spine surgery patients. Reflecting their near-ubiquitous role in society, smartphones are the most commonly available form of mHealth technology and can provide measures related to activity, sleep, and even social interaction. By comparison, wearable devices can provide more detailed mobility and physiological measures, although capabilities vary substantially by device. To date, mHealth evaluations in spine surgery patients have focused on the use of activity measures, particularly step counts, in an attempt to objectively quantify spine health. However, the correlation between step counts and patient-reported disease severity is inconsistent, and further work is needed to define the mobility metrics most relevant to spine surgery patients. mHealth assessments may also support a variety of other applications that have been studied less frequently, including those that prevent postoperative complications, predict surgical outcomes, and serve as motivational aids to patients. These areas represent key opportunities for future investigations. To maximize the potential of mHealth evaluations, several barriers must be overcome, including technical challenges, privacy and regulatory concerns, and questions related to reimbursement. Despite those obstacles, mHealth technology has the potential to transform many aspects of spine surgery research and practice, and its applications will only continue to grow in the years ahead.

ABBREVIATIONS

EHR = electronic health record; EMA = ecological momentary assessment; GPi = gait posture index; GPS = global positioning system; mHealth = mobile health; PROM = patient-reported outcome measure.
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Contributor Notes

Correspondence Jacob K. Greenberg: Washington University School of Medicine, St. Louis, MO. jacobgreenberg@wustl.edu.

INCLUDE WHEN CITING Published online January 20, 2023; DOI: 10.3171/2022.12.SPINE221302.

Disclosures Dr. Steinmetz reported royalties from Zimmer/Biomet, royalties from Globus, advisory fees from Cerepedics, and honoraria from Globus outside the submitted work. Dr. Ray reported personal fees from Globus and personal fees from DePuy Synthes outside the submitted work.

  • 1

    Strategy Analytics: Half the world owns a smartphone. Strategy Analytics. June 24, 2021. Accessed December 29, 2022. https://news.strategyanalytics.com/press-releases/press-release-details/2021/Strategy-Analytics-Half-the-World-Owns-a-Smartphone/default.aspx

    • Search Google Scholar
    • Export Citation
  • 2

    Sim I. Mobile devices and health. N Engl J Med. 2019;381(10):956–968.

  • 3

    Steinhubl SR, Muse ED, Topol EJ. The emerging field of mobile health. Sci Transl Med. 2015;7(283):283rv3.

  • 4

    Mualem W, Durrani S, Lakomkin N, Van Gompel J, Quiñones-Hinojosa A, Bydon M. Utilizing data from wearable technologies in the era of telemedicine to assess patient function and outcomes in neurosurgery: systematic review and time-trend analysis of the literature. World Neurosurg. 2022;166:90–119.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 5

    Panda N, Perez N, Tsangaris E, et al. Enhancing patient-centered surgical care with mobile health technology. J Surg Res. 2022;274:178–184.

  • 6

    Lee CK, Hansen HT, Weiss AB. Developmental lumbar spinal stenosis. Pathology and surgical treatment. Spine (Phila Pa 1976). 1978;3(3):246–255.

  • 7

    Fairbank JC, Couper J, Davies JB, O’Brien JP. The Oswestry low back pain disability questionnaire. Physiotherapy. 1980;66(8):271–273.

  • 8

    Asher M, Min Lai S, Burton D, Manna B. Scoliosis Research Society-22 patient questionnaire: responsiveness to change associated with surgical treatment. Spine (Phila Pa 1976). 2003;28(1):70–73.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 9

    Patel AA, Dodwad SM, Boody BS, et al. Validation of Patient Reported Outcomes Measurement Information System (PROMIS) computer adaptive tests (CATs) in the surgical treatment of lumbar spinal stenosis. Spine (Phila Pa 1976). 2018;43(21):1521–1528.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 10

    Eich E, Reeves JL, Jaeger B, Graff-Radford SB. Memory for pain: relation between past and present pain intensity. Pain. 1985;23(4):375–380.

  • 11

    Basil GW, Sprau AC, Eliahu K, Borowsky PA, Wang MY, Yoon JW. Using smartphone-based accelerometer data to objectively assess outcomes in spine surgery. Neurosurgery. 2021;88(4):763–772.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 12

    May M, Junghaenel DU, Ono M, Stone AA, Schneider S. Ecological momentary assessment methodology in chronic pain research: a systematic review. J Pain. 2018;19(7):699–716.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 13

    Greenberg JK, Frumkin MR, Javeed S, et al. Feasibility and acceptability of a preoperative multimodal mobile health assessment in spine surgery candidates. Neurosurgery. Published online May 17, 2022. doi: 10.1227/neu.0000000000002245

    • Search Google Scholar
    • Export Citation
  • 14

    Straczkiewicz M, James P, Onnela JP. A systematic review of smartphone-based human activity recognition methods for health research. NPJ Digit Med. 2021;4(1):148.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 15

    Ahmad HS, Yang AI, Basil GW, et al. Developing a prediction model for identification of distinct perioperative clinical stages in spine surgery with smartphone-based mobility data. Neurosurgery. 2022;90(5):588–596.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 16

    Straczkiewicz M, Glynn NW, Harezlak J. On placement, location and orientation of wrist-worn tri-axial accelerometers during free-living measurements. Sensors (Basel). 2019;19(9):2095.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 17

    Shoaib M, Bosch S, Incel OD, Scholten H, Havinga PJ. Fusion of smartphone motion sensors for physical activity recognition. Sensors (Basel). 2014;14(6):10146–10176.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 18

    Mooney SJ, Sheehan DM, Zulaika G, et al. Quantifying distance overestimation from global positioning system in urban spaces. Am J Public Health. 2016;106(4):651–653.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 19

    Cote DJ, Barnett I, Onnela JP, Smith TR. Digital phenotyping in patients with spine disease: a novel approach to quantifying mobility and quality of life. World Neurosurg. 2019;126:e241–e249.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 20

    Meyer N, Joyce DW, Karr C, et al. The temporal dynamics of sleep disturbance and psychopathology in psychosis: a digital sampling study. Psychol Med. 2022;52(13):2741–2750.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 21

    Alafeef M. Smartphone-based photoplethysmographic imaging for heart rate monitoring. J Med Eng Technol. 2017;41(5):387–395.

  • 22

    Stone AA, Broderick JE, Goldman RE, et al. I. Indices of pain intensity derived from ecological momentary assessments: rationale and stakeholder preferences. J Pain. 2021;22(4):359–370.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 23

    Huhn S, Axt M, Gunga HC, et al. The impact of wearable technologies in health research: scoping review. JMIR Mhealth Uhealth. 2022;10(1):e34384.

  • 24

    Lee TJ, Galetta MS, Nicholson KJ, et al. Wearable technology in spine surgery. Clin Spine Surg. 2020;33(6):218–221.

  • 25

    Pantelopoulos A, Bourbakis NG. A survey on wearable sensor-based systems for health monitoring and prognosis. IEEE Trans Syst Man Cybern C. 2010;40(1):1–12.

    • Search Google Scholar
    • Export Citation
  • 26

    Stienen MN, Rezaii PG, Ho AL, et al. Objective activity tracking in spine surgery: a prospective feasibility study with a low-cost consumer grade wearable accelerometer. Sci Rep. 2020;10(1):4939.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 27

    Chakravorty A, Mobbs RJ, Anderson DB, et al. The role of wearable devices and objective gait analysis for the assessment and monitoring of patients with lumbar spinal stenosis: systematic review. BMC Musculoskelet Disord. 2019;20(1):288.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 28

    Mobbs RJ, Mobbs RR, Choy WJ. Proposed objective scoring algorithm for assessment and intervention recovery following surgery for lumbar spinal stenosis based on relevant gait metrics from wearable devices: the Gait Posture index (GPi). J Spine Surg. 2019;5(3):300–309.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 29

    Mobbs RJ, Phan K, Maharaj M, Rao PJ. Physical activity measured with accelerometer and self-rated disability in lumbar spine surgery: a prospective study. Global Spine J. 2016;6(5):459–464.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 30

    Henriksen A, Haugen Mikalsen M, Woldaregay AZ, et al. Using fitness trackers and smartwatches to measure physical activity in research: analysis of consumer wrist-worn wearables. J Med Internet Res. 2018;20(3):e110.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 31

    Chandrasekaran R, Katthula V, Moustakas E. Patterns of use and key predictors for the use of wearable health care devices by US adults: insights from a national survey. J Med Internet Res. 2020;22(10):e22443.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 32

    Tedesco S, Sica M, Ancillao A, Timmons S, Barton J, O’Flynn B. Accuracy of consumer-level and research-grade activity trackers in ambulatory settings in older adults. PLoS One. 2019;14(5):e0216891.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 33

    Fuller D, Colwell E, Low J, et al. Reliability and validity of commercially available wearable devices for measuring steps, energy expenditure, and heart rate: systematic review. JMIR Mhealth Uhealth. 2020;8(9):e18694.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 34

    Zhang J, Li D, Dai R, et al. Predicting post-operative complications with wearables: a case study with patients undergoing pancreatic surgery. Proc ACM Interact Mob Wearable Ubiquitous Technol. 2022;6(2):1–27.

    • Search Google Scholar
    • Export Citation
  • 35

    Mun CJ, Suk HW, Davis MC, et al. Investigating intraindividual pain variability: methods, applications, issues, and directions. Pain. 2019;160(11):2415–2429.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 36

    Asparouhov T, Hamaker EL, Muthén B. Dynamic structural equation models. Struct Equ Modeling. 2018;25(3):359–388.

  • 37

    Singh JA, Lemay CA, Nobel L, et al. Association of early postoperative pain trajectories with longer-term pain outcome after primary total knee arthroplasty. JAMA Netw Open. 2019;2(11):e1915105.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 38

    Doryab A, Dey AK, Kao G, Low C. Modeling biobehavioral rhythms with passive sensing in the wild: a case study to predict readmission risk after pancreatic surgery. Proc ACM Interact Mob Wearable Ubiquitous Technol. 2019;3(1):1–21.

    • Search Google Scholar
    • Export Citation
  • 39

    Cos H, Li D, Williams G, et al. Predicting outcomes in patients undergoing pancreatectomy using wearable technology and machine learning: prospective cohort study. J Med Internet Res. 2021;23(3):e23595.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 40

    Xue B, Jiao Y, Kannampallil T, et al. Perioperative predictions with interpretable latent representation. ACM SIGKDD Conference on Knowledge Discovery & Data Mining (KDD’22), August 2022; pp 4268–4278. Accessed December 30, 2022. https://dl.acm.org/doi/10.1145/3534678.3539190

    • Search Google Scholar
    • Export Citation
  • 41

    Greenberg JK, Otun A, Ghogawala Z, et al. Translating data analytics into improved spine surgery outcomes: a roadmap for biomedical informatics research in 2021. Global Spine J. 2022;12(5):952–963.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 42

    Bydon M, Schirmer CM, Oermann EK, et al. Big data defined: a practical review for neurosurgeons. World Neurosurg. 2020;133:e842–e849.

  • 43

    Ahmad HS, Singh S, Jiao K, et al. Data-driven phenotyping of preoperative functional decline patterns in patients undergoing lumbar decompression and lumbar fusion using smartphone accelerometry. Neurosurg Focus. 2022;52(4):E4.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 44

    Voglis S, Ziga M, Zeitlberger AM, et al. Smartphone-based real-life activity data for physical performance outcome in comparison to conventional subjective and objective outcome measures after degenerative lumbar spine surgery. Brain Spine. 2022;2:100881.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 45

    Smuck M, Muaremi A, Zheng P, et al. Objective measurement of function following lumbar spinal stenosis decompression reveals improved functional capacity with stagnant real-life physical activity. Spine J. 2018;18(1):15–21.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 46

    Maldaner N, Sosnova M, Zeitlberger AM, et al. Evaluation of the 6-minute walking test as a smartphone app-based self-measurement of objective functional impairment in patients with lumbar degenerative disc disease. J Neurosurg Spine. 2020;33(6):779–788.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 47

    Stienen MN, Maldaner N, Sosnova M, et al. External validation of the Timed Up and Go test as measure of objective functional impairment in patients with lumbar degenerative disc disease. Neurosurgery. 2021;88(2):E142–E149.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 48

    Lee IM, Shiroma EJ, Kamada M, Bassett DR, Matthews CE, Buring JE. Association of step volume and intensity with all-cause mortality in older women. JAMA Intern Med. 2019;179(8):1105–1112.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 49

    Boaro A, Leung J, Reeder HT, et al. Smartphone GPS signatures of patients undergoing spine surgery correlate with mobility and current gold standard outcome measures. J Neurosurg Spine. 2021;35(6):796–806.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 50

    Jakob V, Küderle A, Kluge F, et al. Validation of a sensor-based gait analysis system with a gold-standard motion capture system in patients with Parkinson’s Disease. Sensors (Basel). 2021;21(22):7680.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 51

    Alsaadi SM, McAuley JH, Hush JM, et al. The bidirectional relationship between pain intensity and sleep disturbance/quality in patients with low back pain. Clin J Pain. 2014;30(9):755–765.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 52

    Alsaadi SM, McAuley JH, Hush JM, et al. Assessing sleep disturbance in low back pain: the validity of portable instruments. PLoS One. 2014;9(4):e95824.

  • 53

    Bootsman R, Markopoulos P, Qi Q, Wang Q, Timmermans AAA. Wearable technology for posture monitoring at the workplace. Int J Hum Comput Stud. 2019;132:99–111.

    • Search Google Scholar
    • Export Citation
  • 54

    Simpson L, Maharaj MM, Mobbs RJ. The role of wearables in spinal posture analysis: a systematic review. BMC Musculoskelet Disord. 2019;20(1):55.

  • 55

    Cho CH, Lee T, Kim MG, In HP, Kim L, Lee HJ. Mood prediction of patients with mood disorders by machine learning using passive digital phenotypes based on the circadian rhythm: prospective observational cohort study. J Med Internet Res. 2019;21(4):e11029.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 56

    Jhanji S, Thomas B, Ely A, Watson D, Hinds CJ, Pearse RM. Mortality and utilisation of critical care resources amongst high-risk surgical patients in a large NHS trust. Anaesthesia. 2008;63(7):695–700.

    • Search Google Scholar
    • Export Citation
  • 57

    Ghaferi AA, Birkmeyer JD, Dimick JB. Hospital volume and failure to rescue with high-risk surgery. Med Care. 2011;49(12):1076–1081.

  • 58

    Aalami O, Ingraham A, Arya S. Applications of mobile health technology in surgical innovation. JAMA Surg. 2021;156(5):414–415.

  • 59

    Gunter RL, Chouinard S, Fernandes-Taylor S, et al. Current use of telemedicine for post-discharge surgical care: a systematic review. J Am Coll Surg. 2016;222(5):915–927.

    • Search Google Scholar
    • Export Citation
  • 60

    Felbaum DR, Stewart JJ, Anaizi AN, Sandhu FA, Nair MN, Voyadzis JM. Implementation and evaluation of a smartphone application for the perioperative care of neurosurgery patients at an academic medical center: implications for patient satisfaction, surgery cancelations, and readmissions. Oper Neurosurg (Hagerstown). 2018;14(3):303–311.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 61

    Debono B, Bousquet P, Sabatier P, Plas JY, Lescure JP, Hamel O. Postoperative monitoring with a mobile application after ambulatory lumbar discectomy: an effective tool for spine surgeons. Eur Spine J. 2016;25(11):3536–3542.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 62

    Armstrong KA, Coyte PC, Brown M, Beber B, Semple JL. Effect of home monitoring via mobile app on the number of in-person visits following ambulatory surgery: a randomized clinical trial. JAMA Surg. 2017;152(7):622–627.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 63

    Simon BA, Assel MJ, Tin AL, et al. Association between electronic patient symptom reporting with alerts and potentially avoidable urgent care visits after ambulatory cancer surgery. JAMA Surg. 2021;156(8):740–746.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 64

    Dawes AJ, Lin AY, Varghese C, Russell MM, Lin AY. Mobile health technology for remote home monitoring after surgery: a meta-analysis. Br J Surg. 2021;108(11):1304–1314.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 65

    Wang SB, Coppersmith DDL, Kleiman EM, et al. A pilot study using frequent inpatient assessments of suicidal thinking to predict short-term postdischarge suicidal behavior. JAMA Netw Open. 2021;4(3):e210591.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 66

    Buchman AS, Dawe RJ, Leurgans SE, et al. Different combinations of mobility metrics derived from a wearable sensor are associated with distinct health outcomes in older adults. J Gerontol A Biol Sci Med Sci. 2020;75(6):1176–1183.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 67

    Dai R, Kannampallil T, Zhang J, Lv N, Ma J, Lu C. Multi-task learning for randomized controlled trials: a case study on predicting depression with wearable data. Proc ACM Interact Mob Wearable Ubiquitous Technol. 2022;6(2):1–23.

    • Search Google Scholar
    • Export Citation
  • 68

    Liu LH, Garrett SB, Li J, et al. Patient and clinician perspectives on a patient-facing dashboard that visualizes patient reported outcomes in rheumatoid arthritis. Health Expect. 2020;23(4):846–859.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 69

    Cronin RM, Conway D, Condon D, Jerome RN, Byrne DW, Harris PA. Patient and healthcare provider views on a patient-reported outcomes portal. J Am Med Inform Assoc. 2018;25(11):1470–1480.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 70

    Tedesco S, Sica M, Ancillao A, Timmons S, Barton J, O’Flynn B. Validity evaluation of the Fitbit Charge2 and the Garmin vivosmart HR+ in free-living environments in an older adult cohort. JMIR Mhealth Uhealth. 2019;7(6):e13084.

    • Search Google Scholar
    • Export Citation
  • 71

    Coravos A, Khozin S, Mandl KD. Developing and adopting safe and effective digital biomarkers to improve patient outcomes. NPJ Digit Med. 2019;2(1):14.

  • 72

    Office of the National Coordinator for Health Information Technology. Hospitals Participating in the CMS EHR Incentive Programs, Health IT Quick-Stat #45. HealthIT.gov. Accessed March 18, 2020. https://www.healthit.gov/data/quickstats/hospitals-participating-cms-ehr-incentive-programs

    • Search Google Scholar
    • Export Citation
  • 73

    Eapen ZJ, Turakhia MP, McConnell MV, et al. Defining a mobile health roadmap for cardiovascular health and disease. J Am Heart Assoc. 2016;5(7):e003119.

  • 74

    Abelson JS, Kaufman E, Symer M, Peters A, Charlson M, Yeo H. Barriers and benefits to using mobile health technology after operation: a qualitative study. Surgery. 2017;162(3):605–611.

    • PubMed
    • Search Google Scholar
    • Export Citation

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