Methods Inf Med 2014; 53(03): 225-234
DOI: 10.3414/ME13-01-0032
Original Articles
Schattauer GmbH

Reliable Blood Pressure Self-measurement in the Obstetric Waiting Room

S. Wagner
1   Department of Engineering, Aarhus University, Aarhus, Denmark
,
C. H. Kamper
2   Department of Obstetrics, Aarhus University Hospital, Skejby, Aarhus, Denmark
,
N. H. Rasmussen
1   Department of Engineering, Aarhus University, Aarhus, Denmark
,
P. Ahrendt
1   Department of Engineering, Aarhus University, Aarhus, Denmark
,
T. S. Toftegaard
1   Department of Engineering, Aarhus University, Aarhus, Denmark
,
O. W. Bertelsen
3   Department of Computer Science, Aarhus University, Aarhus, Denmark
› Author Affiliations
Further Information

Publication History

received: 16 March 2013

accepted: 27 January 2014

Publication Date:
20 January 2018 (online)

Summary

Background: Patients often fail to adhere to clinical recommendations when using current blood pressure self-measurement (BPSM) methods and equipment. As existing BPSM equipment is not able to detect non-adherent behavior, this could result in mis-diagnosis and treatment error. To overcome this problem, we suggest introducing an alternative method for achieving reliable BPSM by measuring additional context meta-data for validating patient adherence. To facilitate this, we have developed ValidAid, a context-aware system for determining patient adherence levels during BPSM.

Objectives: The aim of this study was to validate this new reliable BPSM method based on ValidAid in the clinical setting. Specifically, we wanted to evaluate ValidAid’s ability to accurately detect and model patient adherence levels during BPSM in the clinic.

Methods: The validation was done by asking 41 pregnant diabetic patients scheduled for self-measuring their blood pressure (BP) in the waiting room at an obstetrics department’s outpatient clinic to perform an additional BPSM using ValidAid. We then compared the automatically measured and classified values from ValidAid with our manual observations.

Results: We found that a) the pregnant diabetics did not adhere to given instructions when performing BPSM in the waiting room, and that b) the ValidAid system was able to accurately classify patient adherence to the modeled recommendations.

Conclusions: A new method for ensuring reliable BPSM based on the ValidAid system was validated. Results indicate that context-aware technology is useful for accurately modeling important aspects of non-adherent patient behavior. This may be used to identify patients in need of additional training, or to design better aids to actively assist the patients during measurements. ValidAid is also applicable to other self-measurement environments including the home setting and outpatient clinics in remote or underserved areas as it is built using telemedicine technology and thus well-suited for remote monitoring and diagnosis.

 
  • References

  • 1 Campbell NRC, McKay DW. Accurate blood pressure measurement: Why does it matter?. Can Med Assoc J 1999; 161 (03) 277-278.
  • 2 Pickering TG. Ambulatory monitoring and blood pressure variability. Science Press; 1991
  • 3 Pierdomenico SD, Di Nicola M, Esposito AL, Di Mascio R, Ballone E, Lapenna D. et al Prognostic value of different indices of blood pressure variability in hypertensive patients. Am J Hypertens 2009; 22 (08) 842-847.
  • 4 O’Brien E, Asmar R, Beilin L, Imai Y, Mallion JM, Mancia G. et al European Society of Hypertension recommendations for conventional, ambulatory and home blood pressure measurement. J Hypertens 2003; 21 (05) 821-848.
  • 5 Pickering TG, White WB, Giles TD, Black HR, Izzo JL, Materson BJ. et al When and how to use self (home) and ambulatory blood pressure monitoring. J Am Soc Hypertens 2010; 4 (02) 56-61.
  • 6 Wagner S, Toftegaard TS, Bertelsen OW. Challenges in Blood Pressure Self-Measurement. International Journal of Telemedicine and Applications. 2012
  • 7 Pickering TG, Miller NH, Ogedegbe G, Krakoff LR, Artinian NT, Goff D. et al Call to action on use and reimbursement for home blood pressure monitoring: executive summary: a joint scientific statement from the American Heart Association, American Society Of Hypertension, and Preventive Cardiovascular Nurses Association. Hypertension 2008; 52 (01) 1-9.
  • 8 AbuDagga A, Resnick HE, Alwan M. Impact of blood pressure telemonitoring on hypertension outcomes: a literature review. Telemed J E Health 2010; 16 (07) 830-838.
  • 9 Wagner S, Rasmussen NH, Ahrendt P, Toftegaard TS, Bertelsen OW. Context classification during blood pressure self-measurement using the sensor seat and the audio classification device. Proceedings of the 6th International Conference on Pervasive Computing Technologies for Healthcare. 2012
  • 10 Wagner S, Toftegaard TS, Bertelsen OW. Novel approach for ensuring increased validity in home blood pressure monitoring. Proceedings of the 4th International Conference on Pervasive Computing Technologies for Healthcare. 2010
  • 11 Wagner S, Toftegaard TS, Bertelsen OW. Increased Data Quality in Home Blood Pressure Monitoring through Context Awareness. Proceedings of the 5th International Conference on Pervasive Computing Technologies for Healthcare. 2011
  • 12 Wagner S, Toftegaard T, Bertelsen O. Context Assessment during Blood Pressure Self-measurement Utilizing the Sensor Chair. Ambient Intelligence. 2011: 295-299.
  • 13 Weiser M. Some computer science issues in ubiquitous computing. Communications of the ACM 1993; July 7: 036
  • 14 Dey AK. Ubiquitous Computing Fundamentals. Krumm J. editor CRC Press; 2010
  • 15 Wagner S, Ahrendt P, Toftegaard TS, Bertelsen OW. Audio Context Classification for Determining Blood Pressure Self-Measurement Adherence. Proceedings of the IADIS International Conference on e-Health, July. 2012: 17-19.
  • 16 Wood AJJ, Sibai BM. Treatment of hypertension in pregnant women. N Engl J Med 1996; 335 (04) 257-265.
  • 17 Catalano PM, Sacks DA. Timing of indicated late preterm and early-term birth in chronic medical complications: diabetes. Semin Perinatol 2011; 35 (05) 297-301.
  • 18 Demiris G, Skubic M, Keller J. Nurse participation in the design of user interfaces for a smart home system. Proceedings of the International Conference on Smart Homes and Health Telematics. 2006
  • 19 Clemensen J, Larsen SB, Kyng M, Kirkevold M. Participatory design in health sciences: Using cooperative experimental methods in developing health services and computer technology. Qual Health Res 2007; 17 (01) 122-130.
  • 20 Wagner S, Toftegaard TS, Bertelsen OW. Introducing the Adherence Strategy Engineering Framework (ASEF). Support for Developing Technology-based Self-care Solutions. Methods Inf Med 2013; 52 (03) 220-230.
  • 21 Bray T, Paoli J, Sperberg-McQueen CM, Maler E, Yergeau F. Extensible markup language (XML) 1.0. 2000
  • 22 Brochhausen M, Burgun A, Ceusters W, Hasman A, Leong T, Musen M. et al Discussion of “Biomedical Ontologies: Toward Scientific Debate”. Methods Inf Med 2011; 50 (03) 217
  • 23 Nagel C, Evjen B, Glynn J, Watson K, Skinner M. Professional C# 2012 and. NET 4.5 Wrox. 2012
  • 24 Smith J. Inside microsoft windows communication foundation. Microsoft Press; 2007
  • 25 Bang LE, Christensen KL, Hansen KW, Skov K, Wiinberg N. Diagnostisk blodtryksmåling - på døgnbasis, hjemme og i konsultationen. Available at. http://www.dahs.dk/fileadmin/BTmaaling_version-17.pdf Accessed 02/16/2012
  • 26 Microsoft Inc. Multiple Channel Audio Data and WAVE Files. Available at. http://msdn.microsoft.com/en-us/windows/hardware/gg463006.aspx Accessed 01/01/2013
  • 27 Wagner S, Toftegaard TS, Bertelsen OW. Requirements for an Evaluation Infrastructure for Reliable Pervasive Healthcare Research. Proceedings of the 6th International Conference on Pervasive Computing Technologies for Healthcare. 2012
  • 28 ASUSTek Computer Inc. Eee Slate EP121. Available at. http://www.asus.com/Eee/Eee_Pad/Eee_Slate_EP121 Accessed 1/1/2012
  • 29 Bishop CM. Pattern recognition and machine learning. Springer New York: 2006
  • 30 UA-767 Semi Automatic Blood Pressure Monitor - A&D Medical Pty Ltd Available at. http://www.andmedical.com.au/web.php?p=1201&pp=&pcat=aibpm Accessed 1/17/2012
  • 31 Huniche L, Dinesen B, Grann O, Toft E, Nielsen C. Empowering patients with COPD using Tele-homecare technology. Stud Health Technol Inform 2010; 155: 48-54.
  • 32 Shimmer Reseearch. Shimmer - Wireless Sensor Platform for Wearable Applications. Available at. http://www.shimmer-research.com . Accessed: 1/2/2012
  • 33 Tekscn Inc. FlexiForce. Load/Force Sensors and ELF: Economical Load and Force Measurement Systems. Available at. http://www.tekscan.com/flexiforce.html. Accessed: 1/3/ 2012
  • 34 Premier Farnell UK Limited. DEFENDER - PM1/ PK - PRESSURE MAT. Available at:. http://cpc.farnell.com/defender-security/pm1-pk . Accessed: 1/2/ 2012
  • 35 Rabiner LR, Juang B. Fundamental of Speech Recognition. Prentice Hall. 1993
  • 36 Davis SB, Mermelstein P. Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences. IEEE Transactions on Acoustics, Speech and Signal Processing 1980; ASSP (028) 357-366.
  • 37 Brokes M. VOICEBOX: Speech Processing Toolbox for MATLAB. Available at. http://www.ee.ic.ac.uk/hp/staff/dmb/voicebox/voicebox.html . Accessed 02/16/2012
  • 38 Ashton University. Netlab Neural Network Software (Netlab Matlab Package). Available at. http://www1.aston.ac.uk/eas/research/groups/ncrg/resources/netlab/. Accessed 02/16/2012
  • 39 Pickering TG, Hall JE, Appel LJ, Falkner BE, Graves J, Hill MN. et al Recommendations for blood pressure measurement in humans and experimental animals. Part 1: blood pressure measurement in humans: a statement for professionals from the Subcommittee of Professional and Public Education of the American Heart Association Council on High Blood Pressure Research. Circulation 2005; 111 (05) 697-716.
  • 40 Wessel SE, van der Hoeven NV, Cammenga M, van Montfrans GA, van den Born BH. ‘Diag- nostic mode’ improves adherence to the home blood pressure measurement schedule. Blood Press Monit 2012; 17 (05) 214-219. 10.1097/MBP. 0b013e328357352a
  • 41 Johnson KA, Partsch DJ, Rippole LL, McVey DM. Reliability of self-reported blood pressure measurements. Arch Intern Med 1999; 159 (022) 2689-2693.
  • 42 Mengden T, Hernandez Medina RM, Beltran B, Alvarez E, Kraft K, Vetter H. Reliability of reporting self-measured blood pressure values by hypertensive patients. Am J Hypertens 1998; 11 (012) 1413-1417.
  • 43 Myers MG. Self-measurement of blood pressure at home: The potential for reporting bias. Blood Press Monit 1998; 3 (Suppl. 01) Suppl S19-S22.
  • 44 Santamore WP, Homko CJ, Kashem A, McConnell TR, Menapace FJ, Bove AA. Accuracy of blood pressure measurements transmitted through a telemedicine system in underserved populations. Telemed J E Health 2008; 14 (04) 333-338.
  • 45 Copetti A, Loques O, Leite JCB, Barbosa TPC, da Nobrega ACL. Intelligent context-aware monitoring of hypertensive patients. Pervasive Computing Technologies for Healthcare, 2009. 3rd International Conference on PervasiveHealth 2009. IEEE. 2009
  • 46 D’Angelo LT, Lohmann M, Lueth TC. A new device for motion-aware ambulatory blood pressure measurement. Proceedings of the 5th International Conference on Pervasive Computing Technologies for Healthcare). 2011
  • 47 Intel Corporation. Intel Health Guide PHS6000 Available at. http://www.intel.com/corporate/healthcare/emea/eng/healthguide/pdfs/Health_Guide_Product_Brief.pdf Accessed 1/1/2011
  • 48 Takahashi PY, Pecina JL, Upatising B, Chaudhry R, Shah ND, Van Houten H. et al A randomized controlled trial of telemonitoring in older adults with multiple health issues to prevent hospitalizations and emergency department visits. Arch Intern Med. 2012 archinternmed. 2012.256 v1
  • 49 Carroll R, Cnossen R, Schnell M, Simons D. Continua: An interoperable personal healthcare ecosystem. IEEE Pervasive Computing. 2007: 90-94.
  • 50 Tunstall Limited. Telehealth solutions. Avail- able at: Online. http://www.tunstall.co.uk/our-products/telehealth-solutions Accessed01/01/ 2011
  • 51 Koff P, Jones RH, Cashman JM, Voelkel NF, Vandivier R. Proactive integrated care improves quality of life in patients with COPD. European Respiratory Journal 2009; 33 (05) 1031-1038.
  • 52 Osterberg L, Blaschke T. Adherence to medication. N Engl J Med 2005; 353 (05) 487-497.
  • 53 Rodrigo de Oliveira, Mauro Cherubini and Nuria Oliver. MoviPill: improving medication compliance for elders using a mobile persuasive social game. Proceedings of the 12th ACM international conference on Ubiquitous computing. New York, NY, USA: ACM; 2010
  • 54 Hayes TL, Hunt JM, Adami A, Kaye JA. An Electronic Pillbox for Continuous Monitoring of Medication Adherence. Engineering in Medicine and Biology Society, 2006. EMBS ’06. 28th Annual International Conference of the IEEE. 2006
  • 55 Arlt S, Lindner R, Rosler A, von Renteln-Kruse W. Adherence to medication in patients with dementia: predictors and strategies for improvement. Drugs Aging 2008; 25 (012) 1033-1047.
  • 56 Bennett JW, Glasziou PP. Computerised reminders and feedback in medication management: a systematic review of randomised controlled trials. Med J Aust 2003; 178 (05) 217-222.
  • 57 Bardram JE. Pervasive healthcare as a scien- tific discipline. Methods Inf Med 2008; 47 (03) 178-185.
  • 58 Grönvall E, Aarhus R, Larsen SB. Vestibular rehabilitation in the home: design challenges. Proceedings of the Ninth Danish HCI Research Symposium; December. 2009
  • 59 Aarhus R, Grönvall E, Larsen SB. Interactive healthcare systems in the home: vestibular rehabilitation. Proceedings of the Workshop on Interactive Systems in Healthcare; April. 2010: 10-11.
  • 60 Tang J, Mandrusiak A, Russell T. The Feasibility and Validity of a Remote Pulse Oximetry System for Pulmonary Rehabilitation: A Pilot Study. International Journal of Telemedicine and Applications. 2012
  • 61 Wagner S, Kamper CH, Toftegaard TS, Rasmussen NH, Ahrendt P, Bertelsen OW. Blood pressure self-measurement in the obstetric waiting room. Telemed J E Health 2013; 19 (11) 872-874.
  • 62 Demiris G. Smart homes and ambient assisted living in an aging society. New opportunities and challenges for biomedical informatics. Methods Inf Med 2008; 47 (01) 56-57.
  • 63 Tabak M, Vollenbroek-Hutten MM, van der Valk, Paul DLPM, van der Palen J, Tönis TM, Hermens HJ. Telemonitoring of daily activity and symptom behavior in patients with COPD. International journal of telemedicine and applications. 2012