The DOI matches its Crossref record; 121 of the 121 references it cites were found in a registry or an index.
A systematic review of IT for diabetes self-management: Are we there yet?
Abstract
Background Recent advances in information technology (IT) coupled with the increased ubiquitous nature of information technology (IT) present unique opportunities for improving diabetes self-management. The objective of this paper is to determine, in a systematic review, how IT has been used to improve self-management for adults with Type 1 and Type 2 diabetes.Methods The review covers articles extracted from relevant databases using search terms related information technology and diabetes self-management published after 1970 until August 2012. Additional articles were extracted using the citation map in Web of Science. Articles representing original research describing the use of IT as an enabler for self-management tasks performed by the patient are included in the final analysis.Results Overall, 74% of studies showed some form of added benefit, 13% articles showed no-significant value provided by IT, and 13% of articles did not clearly define the added benefit due to IT. Information technologies used included the Internet (47%), cellular phones (32%), telemedicine (12%), and decision support techniques (9%). Limitations and research gaps identified include usability, real-time feedback, integration with provider electronic medical record (EMR), as well as analytics and decision support capabilities.Conclusion There is a distinct need for more comprehensive interventions, in which several technologies are integrated in order to be able to manage chronic conditions such as diabetes. Such IT interventions should be theoretically founded and should rely on principles of user-centered and socio-technical design in its planning, design and implementation. Moreover, the effectiveness of self-management systems should be assessed along multiple dimensions: motivation for self-management, long-term adherence, cost, adoption, satisfaction and outcomes as a final result.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Tailored design and personalized contents for patients is a facilitator, and one-for-all unified contents and designs could be a barrier.”
From Digital health interventions for non-communicable disease management in primary health care in low-and middle-income countries (2023) · cites this paper as [34]
In the citing paper
“Technical constraints (e.g., cell phone unavailability, internet instability) in marginal and remote regions limit the coverage of digital health.”
From Digital health interventions for non-communicable disease management in primary health care in low-and middle-income countries (2023) · cites this paper as [34]
In the citing paper
“Designing incentives/motivations for primary healthcare providers and patients to use digital health facilitates the uptake, while lack of that could be a barrier.”
From Digital health interventions for non-communicable disease management in primary health care in low-and middle-income countries (2023) · cites this paper as [34]
In the citing paper
“Digital health tools that enable two-way human interactions are better accepted while replacing human communications with automatic responses is less favored.”
From Digital health interventions for non-communicable disease management in primary health care in low-and middle-income countries (2023) · cites this paper as [34]
In the citing paper
“Easily navigable interface is a facilitator, and poor/complicated interface is a barrier for the uptake of digital health tools.”
From Digital health interventions for non-communicable disease management in primary health care in low-and middle-income countries (2023) · cites this paper as [34]
Citations
Cites
Standards of Medical Care in Diabetes—2012
(2012) · Diabetes Care · doi:10.2337/dc12-s011 · Cited by 2,240
Venkatesh, V., Brown, S. A., Maruping, L. M., & Bala, H. (2008) · MIS Quarterly · doi:10.2307/25148853 · Cited by 911
Tate, D. F., Jackvony, E. H., & Wing, R. R. (2003) · JAMA · doi:10.1001/jama.289.14.1833 · Cited by 666
A European Evidence-Based Guideline for the Prevention of Type 2 Diabetes
Paulweber, B., Valensi, P. E., Lindström, J., Lalic, N. M., Greaves, C. J., McKee, M., Kissimova‐Skarbek, K., Liatis, S., Cosson, E., Szendroedi, J., et al. (2010) · Hormone and Metabolic Research · doi:10.1055/s-0029-1240928 · Cited by 485
Chomutare, T., Fernandez-Luque, L., Årsand, E., & Hartvigsen, G. (2011) · Journal of Medical Internet Research · doi:10.2196/jmir.1874 · Cited by 451
Quinn, C. C., Clough, S. S., Minor, J. M., Lender, D., Okafor, M. C., & Gruber-Baldini, A. (2008) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2008.0283 · Cited by 406
Shea, S., Weinstock, R. S., Teresi, J. A., Palmas, W., Starren, J. B., Cimino, J. J., Lai, A. M., Field, L., Morin, P. C., Goland, R., et al. (2009) · Journal of the American Medical Informatics Association · doi:10.1197/jamia.m3157 · Cited by 383
Mobile Applications for Diabetes Self-Management: Status and Potential
El-Gayar, O., Timsina, P., Nawar, N., & Eid, W. (2013) · Journal of Diabetes Science and Technology · doi:10.1177/193229681300700130 · Cited by 370
Self-citationThe Diabetes Network Internet-Based Physical Activity Intervention
McKay, H. G., King, D., Eakin, E. G., Seeley, J. R., & Glasgow, R. E. (2001) · Diabetes Care · doi:10.2337/diacare.24.8.1328 · Cited by 363 · cited 2×
“5 Satisfaction Yes McKay (2001) [111] T2 Internet Two-way 78 2 Physical activity McKay (2002) [112] T2 Internet Two-way 160 3 Healthy behavior Yes McMohan (2005) [44] T1&2 Internet Two-way 104 12 HbA1c Yes >1% Meneghini (1998) [52] ND Phone Two-way 30 12 HbA1c Yes >0.”
Au, N., Ngai, E. W. T., & Cheng, T. C. E. (2008) · MIS Quarterly · doi:10.2307/25148828 · Cited by 360
Research works printed but not linked: 11
- Bond, 2008, Physical outcomes associated with a 12 month internet intervention among older adults with diabetes, Gerontologist, 48, 187
- Davis, 2008, The Diabetes TeleCare Study (DTC): a telemedicine-base diabetes self-management intervention in a rural community, Diabetes, 57, A37
- Grant, 2010, Diabetes connected health: a pilot randomized trial of a clinically integrated electronic home glucose monitoring system in primary care, Diabetes, 59, A638
- McCarrier, 2008, Effects of a web-based co-management program in younger adults with type 1 diabetes: a randomized controlled trial, Diabetes, 57, A348
- Cho, 2005, 30-month follow-up study of an internet based glucose monitoring system: a new era in diabetes management, Diabetes, 54, A98
- Glasgow, 2012, Twelve-month outcomes of an Internet-based diabetes self-management support program, Patient Educ. Couns., 87, 81, 10.1016/j.pec.2011.07.024 · DOI
- Goldberg, 2006, Internet co-management of type 2 diabetes: a randomized controlled trial (RCT), Diabetes, 55, A270
- Kim, 2012, A web-based pedometer programme in women with a recent history of gestational diabetes, Diabetic Med., 29, 278, 10.1111/j.1464-5491.2011.03415.x · DOI
- Landau, 2012, The effectiveness of Internet-based blood glucose monitoring system on improving diabetes control in adolescents with type 1 diabetes, Pediatr. Diabetes, 13, 203, 10.1111/j.1399-5448.2011.00800.x · DOI
- Simonson, 2009, Electronic diary and Internet-based clinical monitoring of hypoglycemia during intensive insulin titration in type 1 and type 2 diabetes, Diabetes, 58, A72
- Trief, 2010, Adherence to diabetes self care for White, African American and Hispanic American telemedicine participants: IDEATel project, Diabetes, 59, A68
Cited by
Sorted by citations
Whitehead, L., & Seaton, P. (2016) · Journal of Medical Internet Research · doi:10.2196/jmir.4883 · Cited by 743
“However, these areas have been raised in other studies as requiring regular monitoring and attention [34,35].”
ResultHou, C., Carter, B., Hewitt, J., Francisa, T., & Mayor, S. (2016) · Diabetes Care · doi:10.2337/dc16-0346 · Cited by 411
Kampmeijer, R., Pavlova, M., Tambor, M., Golinowska, S., & Groot, W. (2016) · BMC Health Services Research · doi:10.1186/s12913-016-1522-3 · Cited by 328
Reliability and validity of ten consumer activity trackers
Kooiman, T. J. M., Dontje, M. L., Sprenger, S. R., Krijnen, W. P., van der Schans, C. P., & de Groot, M. (2015) · BMC Sports Science, Medicine and Rehabilitation · doi:10.1186/s13102-015-0018-5 · Cited by 360
Diabetes Management via Mobile Phones: A Systematic Review
Holtz, B., & Lauckner, C. (2012) · Telemedicine and e-Health · doi:10.1089/tmj.2011.0119 · Cited by 318
Wu, Y., Yao, X., Vespasiani, G., Nicolucci, A., Dong, Y., Kwong, J., Li, L., Sun, X., Tian, H., & Li, S. (2017) · JMIR mHealth and uHealth · doi:10.2196/mhealth.6522 · Cited by 283
DFUNet: Convolutional Neural Networks for Diabetic Foot Ulcer Classification
Goyal, M., Reeves, N. D., Davison, A. K., Rajbhandari, S. M., Spragg, J., & Yap, M. H. (2018) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2018.2866254 · Cited by 293
Self-Care Technologies in HCI: Trends, Tensions, and Opportunities
Nunes, F., Verdezoto, N., Fitzpatrick, G., Kyng, M., Grönvall, E., & Storni, C. (2015) · ACM Transactions on Computer-Human Interaction · doi:10.1145/2803173 · Cited by 258
Sawesi, S., Rashrash, M., Phalakornkule, K., Carpenter, J. S., & Jones, J. (2016) · JMIR Medical Informatics · doi:10.2196/medinform.4514 · Cited by 265
Timpel, P., Oswald, S., Schwarz, P. E. H., & Harst, L. (2020) · Journal of Medical Internet Research · doi:10.2196/16791 · Cited by 243
DFU_QUTNet: diabetic foot ulcer classification using novel deep convolutional neural network
Alzubaidi, L., Fadhel, M. A., Oleiwi, S. R., Al-Shamma, O., & Zhang, J. (2019) · Multimedia Tools and Applications · doi:10.1007/s11042-019-07820-w · Cited by 233
Georgsson, M., & Staggers, N. (2016) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocv099 · Cited by 200
Jeffrey, B., Bagala, M., Creighton, A., Leavey, T., Nicholls, S., Wood, C., Longman, J., Barker, J., & Pit, S. (2019) · Diabetology & Metabolic Syndrome · doi:10.1186/s13098-019-0480-4 · Cited by 212
Wu, Y., Yao, X., Vespasiani, G., Nicolucci, A., Dong, Y., Kwong, J. S. W., Li, L., Sun, X., Tian, H., & Li, S. (2018) · JMIR mhealth and uhealth · doi:10.2196/mhealth.8789 · Cited by 176
Heitkemper, E. M., Mamykina, L., Travers, J., & Smaldone, A. (2017) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocx025 · Cited by 130
Caballero-Ruiz, E., García-Sáez, G., Rigla, M., Villaplana, M., Pons, B., & Hernando, M. E. (2017) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2017.02.014 · Cited by 136
Xiong, S., Lu, H., Peoples, N., Duman, E. K., Najarro, A., Ni, Z., Gong, E., Yin, R., Ostbye, T., Palileo-Villanueva, L. M., et al. (2023) · npj Digital Medicine · doi:10.1038/s41746-023-00764-4 · Cited by 126
Rollo, M. E., Aguiar, E. J., Williams, R. L., Wynne, K., Kriss, M., Callister, R., & Collins, C. E. (2016) · Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy · doi:10.2147/dmso.s95247 · Cited by 107
Technology-based interventions in the treatment of overweight and obesity: A systematic review
Raaijmakers, L. C. H., Pouwels, S., Berghuis, K. A., & Nienhuijs, S. W. (2015) · Appetite · doi:10.1016/j.appet.2015.07.008 · Cited by 114
Heintzman, N. D. (2016) · Journal of Diabetes Science and Technology · doi:10.1177/1932296815622453 · Cited by 98
Hirst, J. E., Mackillop, L. H., Loerup, L., Kevat, D., Bartlett, K., Gibson, O., Kenworthy, Y., Levy, J. C., Tarassenko, L., & Farmer, A. A. (2014) · Journal of Diabetes Science and Technology · doi:10.1177/1932296814556506 · Cited by 105
Alvarado, M. M., Kum, H.-C., Gonzalez Coronado, K., Foster, M. J., Ortega, P., & Lawley, M. A. (2017) · Journal of Medical Internet Research · doi:10.2196/jmir.6382 · Cited by 95
Or, C. K. L., & Tao, D. (2014) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2014.01.009 · Cited by 76
Sensor Monitoring of Physical Activity to Improve Glucose Management in Diabetic Patients: A Review
Ding, S., & Schumacher, M. (2016) · Sensors · doi:10.3390/s16040589 · Cited by 82
Self-tracking of Physical Activity in People With Type 2 Diabetes: A Randomized Controlled Trial
Kooiman, T. J. M., de Groot, M., Hoogenberg, K., Krijnen, W. P., van der Schans, C. P., & Kooy, A. (2018) · CIN: Computers, Informatics, Nursing · doi:10.1097/cin.0000000000000443 · Cited by 78
Mobile Apps for Individuals With Rheumatoid Arthritis
Luo, D., Wang, P., Lu, F., Elias, J. S., Sparks, J. A., & Lee, Y. (2018) · JCR Journal of Clinical Rheumatology · doi:10.1097/rhu.0000000000000800 · Cited by 76
Systematic map and review of predictive techniques in diabetes self-management
Idrissi, T. E., Idri, A., & Bakkoury, Z. (2018) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2018.09.011 · Cited by 75
Neinstein, A., Wong, J., Look, H., Arbiter, B., Quirk, K., McCanne, S., Sun, Y., Blum, M., & Adi, S. (2016) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocv104 · Cited by 66
Evaluating Diabetes Mobile Applications for Health Literate Designs and Functionality, 2014
Caburnay, C. A., Graff, K. A., Harris, J. K., McQueen, A., Smith, M., Fairchild, M., & Kreuter, M. W. (2015) · Preventing Chronic Disease · doi:10.5888/pcd12.140433 · Cited by 66
Teleconsultation in type 1 diabetes mellitus (TELEDIABE)
Bertuzzi, F., Stefani, I., Rivolta, B., Pintaudi, B., Meneghini, E., Luzi, L., & Mazzone, A. (2018) · Acta Diabetologica · doi:10.1007/s00592-017-1084-9 · Cited by 58
Harvey, J., Dopson, S., McManus, R. J., & powell, J. A. (2015) · Implementation Science · doi:10.1186/s13012-015-0350-x · Cited by 65
Horner, G., Agboola, S. O., Jethwani, K., Tan‐McGrory, A., & López, L. (2017) · JMIR mhealth and uhealth · doi:10.2196/mhealth.6666 · Cited by 59
Quinn, C. C., Butler, E. C., Swasey, K. K., Shardell, M. D., Terrin, M. L., Barr, E. A., & Gruber‐Baldini, A. L. (2018) · JMIR mhealth and uhealth · doi:10.2196/mhealth.9265 · Cited by 67
the members of the Guidelines International Network Implementation Working Group, Vernooij, R. W. M., Willson, M. L., & Gagliardi, A. R. (2015) · Implementation Science · doi:10.1186/s13012-016-0419-1 · Cited by 58
Alharbi, N., Alsubki, N., Jones, S., Khunti, K., Munro, N. M., & de Lusignan, S. (2016) · Journal of Medical Internet Research · doi:10.2196/jmir.5778 · Cited by 57
Adherence of Subjects With Cystic Fibrosis to Their Home Program: A Systematic Review
O'Donohoe, R., & Fullen, B. M. (2014) · Respiratory Care · doi:10.4187/respcare.02990 · Cited by 54
Macdonald, E. M., Perrin, B. M., & Kingsley, M. I. (2018) · Journal of Telemedicine and Telecare · doi:10.1177/1357633x17699990 · Cited by 53
Sittig, S., Wang, J., Iyengar, S., Myneni, S., & Franklin, A. (2020) · JMIR mhealth and uhealth · doi:10.2196/15927 · Cited by 54
A systematic review on incentive-driven mobile health technology: As used in diabetes management
de Ridder, M., Kim, J., Jing, Y., Khadra, M., & Nanan, R. (2017) · Journal of Telemedicine and Telecare · doi:10.1177/1357633x15625539 · Cited by 48
Influences on Technology Use and Efficacy in Type 1 Diabetes
Franklin, V. L. (2016) · Journal of Diabetes Science and Technology · doi:10.1177/1932296816639315 · Cited by 49
Lamprinos, I., Demski, H., Mantwill, S., Kabak, Y., Hildebrand, C., & Ploessnig, M. (2016) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2016.04.006 · Cited by 46
Chen, Q., & Carbone, E. T. (2017) · JMIR Diabetes · doi:10.2196/diabetes.8045 · Cited by 46
Dobson, R., Whittaker, R., Dale, L. P., & Maddison, R. (2017) · Digital Health · doi:10.1177/2055207617740315 · Cited by 44
Dadgar, M., Joshi, K. D., & Univeristy, W. S. (2018) · Journal of the Association for Information Systems · doi:10.17705/1jais.00485 · Cited by 87
Lau, Y., Htun, T. P., Wong, S. N., Tam, W., & Klainin‐Yobas, P. (2016) · Journal of Medical Internet Research · doi:10.2196/jmir.6153 · Cited by 40
Arens‐Volland, A., Spassova, L., & Bohn, T. (2015) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2015.08.006 · Cited by 45
Macdonald, E. M., Perrin, B., Hyett, N., & Kingsley, M. (2019) · Journal of Foot and Ankle Research · doi:10.1186/s13047-019-0340-3 · Cited by 39
Bults, M., van Leersum, C. M., Olthuis, T. J. J., Bekhuis, R. E. M., & den Ouden, M. E. M. (2022) · JMIR Diabetes · doi:10.2196/41076 · Cited by 48
Yan, Q., Stevens, P. E., Wang, Y., Weinhardt, L. S., Holt, C. L., O'Connor, C., Feller, T., Xie, H. “., & Luelloff, S. (2015) · American Journal of Health Behavior · doi:10.5993/ajhb.39.3.12 · Cited by 38
Evaluation of clinical decision support systems for diabetes care: An overview of current evidence
Jia, P., Zhao, P., Chen, J., & Zhang, M. (2018) · Journal of Evaluation in Clinical Practice · doi:10.1111/jep.12968 · Cited by 36
Christensen, J., Valentiner, L. S., Petersen, R. J., & Langberg, H. (2016) · Telemedicine Journal and e-Health · doi:10.1089/tmj.2015.0165 · Cited by 36
Dobson, K. G., & Hall, P. A. (2014) · Journal of Diabetes Science and Technology · doi:10.1177/1932296814560395 · Cited by 33
Lauritsen, L., Andersen, L., Olsson, E. C., Søndergaard, S. R., Nørregaard, L. B., Løventoft, P. K., Svendsen, S. D., Frøkjær, E., Jensen, H. M., Hageman, I., et al. (2017) · Journal of Medical Internet Research · doi:10.2196/jmir.6673 · Cited by 36
Competitive neural layer-based method to identify people with high risk for diabetic foot
Ferreira, A. C. B. H., Ferreira, D. D., Oliveira, H. C., de Resende, I. C., Anjos, A., & Lopes, M. H. B. D. M. (2020) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2020.103744 · Cited by 32
Challenges for the adoption of ICT for diabetes self‐management in South Africa
Petersen, F., Brown, A., Pather, S., & Tucker, W. D. (2020) · THE ELECTRONIC JOURNAL OF INFORMATION SYSTEMS IN DEVELOPING COUNTRIES · doi:10.1002/isd2.12113 · Cited by 31
Telehealth interventions to reduce management complications in type 1 diabetes: A review
Balkhi, A. M. (2015) · World Journal of Diabetes · doi:10.4239/wjd.v6.i3.371 · Cited by 31
Bromuri, S., Puricel, S., Schumann, R., Krampf, J., Ruiz, J. C., & Schumacher, M. (2016) · Journal of Ambient Intelligence and Smart Environments · doi:10.3233/ais-160365 · Cited by 30
Lawton, J., Kirkham, J., Rankin, D., Barnard, K. D., Cooper, C. L., Taylor, C., Heller, S. R., & Elliott, D. (2014) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2014.09.011 · Cited by 30
Understanding patients' health and technology attitudes for tailoring self-management interventions.
O’Leary, K., Vizer, L. M., Eschler, J., Ralston, J. D., & Pratt, W. (2015) · PubMed Central · Cited by 26
Agarwal, G., Gaber, J., Richardson, J. A., Mangin, D. A., Ploeg, J., Valaitis, R. K., Reid, G. J., Lamarche, L., Parascandalo, F., Javadi, D., et al. (2019) · Pilot and Feasibility Studies · doi:10.1186/s40814-019-0504-8 · Cited by 26
Peiris, D., Sun, L., Patel, A., Tian, M., Essue, B., Jan, S., & Zhang, P. (2015) · Implementation Science · doi:10.1186/s13012-016-0481-8 · Cited by 25
Usability testing of existing type 2 diabetes mellitus websites
Davis, D., & Jiang, S. X. (2016) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2016.04.012 · Cited by 23
Discovering Design Principles for Health Behavioral Change Support Systems
Al-Ramahi, M. A., Liu, J., & El-Gayar, O. (2017) · ACM Transactions on Management Information Systems · doi:10.1145/3055534 · Cited by 23
Self-citationMorcos, M. W., Teeter, M. G., Somerville, L. E., & Lanting, B. A. (2019) · Journal of Orthopaedics · doi:10.1016/j.jor.2019.11.049 · Cited by 22
Providing Medical Information to Older Adults in a Web-Based Environment: Systematic Review
McLean, B., Hossain, N., Donison, V., Gray, M., Durbano, S., Haase, K. R., Alibhai, S. M. H., & Puts, M. T. E. (2021) · JMIR Aging · doi:10.2196/24092 · Cited by 22
Boogerd, E. A., Schaaijk, N. M.-V., Noordam, C., Marks, H., & Verhaak, C. M. (2015) · Journal for Specialists in Pediatric Nursing · doi:10.1111/jspn.12118 · Cited by 39
Poppe, L., Crombez, G., Bourdeaudhuij, I. D., der Mispel, C. V., Shadid, S., & Verloigne, M. (2018) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph15050954 · Cited by 20
Gardsten, C., Blomqvist, K., Rask, M., Larsson, Å., Lindberg, A., & Olsson, G. (2018) · Journal of Clinical Nursing · doi:10.1111/jocn.14330 · Cited by 19
An integrated mHealth model for type 2 diabetes patients using mobile tablet devices
Park, S., Burford, S. J., Hanlen, L., Dawda, P., Dugdale, P., Nolan, C. J., & Burns, J. (2016) · Journal of Mobile Technology in Medicine · doi:10.7309/jmtm.5.2.4 · Cited by 20
Using Interactive 3D Game Play to Make Complex Medical Knowledge More Accessible
Patterson, D. (2014) · Procedia Computer Science · doi:10.1016/j.procs.2014.05.032 · Cited by 19
Höfer, T., Schumacher, M., & Bromuri, S. (2015) · EAI Endorsed Transactions on Pervasive Health and Technology · doi:10.4108/icst.pervasivehealth.2015.259186 · Cited by 19
Self-care programmes for people living with leprosy: a scoping review
Ilozumba, O., & Lilford, R. J. (2021) · Leprosy Review · doi:10.47276/lr.92.4.317 · Cited by 19
Remote Care Technology: A Systematic Review of Reviews and Meta-Analyses
Queirós, A., Alvarelhão, J., Cerqueira, M., Silva, A. G., Santos, M., & Rocha, N. P. (2018) · Technologies · doi:10.3390/technologies6010022 · Cited by 18
Usability factors of mobile health application for chronic diseases
Zahra, F., Hussain, A., & Mohd, H. (2016) · AIP Conference Proceedings · doi:10.1063/1.4960948 · Cited by 18
Kabeza, C. B., Harst, L., Schwarz, P. E. H., & Timpel, P. (2019) · Therapeutic Advances in Endocrinology and Metabolism · doi:10.1177/2042018819845318 · Cited by 19
Reidy, C., Foster, C. L., & Rogers, A. E. (2019) · Journal of Medical Internet Research · doi:10.2196/13980 · Cited by 17
Poppe, L., Bourdeaudhuij, I. D., Verloigne, M., Degroote, L., Shadid, S., & Crombez, G. (2019) · JMIR Research Protocols · doi:10.2196/12413 · Cited by 17
Diabetic patients' willingness to use tele-technology to manage their disease
Saddik, B. A., & Al-Dulaijan, N. (2015) · Online Journal of Public Health Informatics · doi:10.5210/ojphi.v7i2.6011 · Cited by 18
Kirkovits, T., Schinkoethe, T., Drewes, C., Gehring, C., Bauerfeind, I., Harbeck, N., & Wuerstlein, R. (2016) · JMIR Cancer · doi:10.2196/cancer.5132 · Cited by 17
Computerised decision support in physical activity interventions: A systematic literature review
Triantafyllidis, A. K., Filos, D., Claes, J., Buys, R., Cornelissen, V., Kouidi, E. J., Chouvarda, I., & Maglaveras, N. (2017) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2017.12.012 · Cited by 18
Mateo, A. C., López‐López, L., Ciuró, A. H., Núñez, J. M., Rodríguez‐Torres, J., Ortíz‐Rubio, A., & Valenza, M. C. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph182312692 · Cited by 16
Discovering Design Principles for Persuasive Systems: A Grounded Theory and Text Mining Approach
Al-Ramahi, M. A., El-Gayar, O., & Liu, J. (2016) · Hawaii International Conference on System Sciences (HICSS) · doi:10.1109/hicss.2016.387 · Cited by 17
Self-citationHutting, N., Engels, J. A., Staal, J. B., Heerkens, Y. F., & der Sanden, M. W. G. N. (2015) · Journal of Occupational Medicine and Toxicology · doi:10.1186/s12995-015-0051-z · Cited by 15
Agrawal, L., Ndabu, T., Mulgund, P., & Sharman, R. (2021) · Journal of Medical Internet Research · doi:10.2196/30637 · Cited by 13
Ráthonyi, G., Takács, V. L., Szilágyi, R., Bába, É. B., Müller, A., Bács, Z., Harangi–Rákos, M., Balogh, L., & Ráthonyi-Ódor, K. (2021) · Frontiers in Public Health · doi:10.3389/fpubh.2021.661471 · Cited by 18
Patients’ attitudes to the use of modern technologies in the treatment of diabetes
Marešová, P., & Cerna, L. (2016) · Patient Preference and Adherence · doi:10.2147/ppa.s118040 · Cited by 14
Designing an ICT self-management service: suggestions from persons with type 2 diabetes
Gardsten, C., Mörtberg, C., & Blomqvist, K. (2017) · Health and Technology · doi:10.1007/s12553-016-0176-9 · Cited by 14
Dehnavi, Z., Ayatollahi, H., Hemmat, M., & Abbasi, R. (2020) · Current Diabetes Reviews · doi:10.2174/1573399816666200719012849 · Cited by 13
Experiences of People with Diabetes by Payer Type: An Analysis of the Roper Diabetes Data Set
Garfield, S. S., Xenakis, J. J., Bastian, A., & McBride, M. (2015) · Diabetes Therapy · doi:10.1007/s13300-015-0109-z · Cited by 13
Miller, J. J., Pozehl, B., Alonso, W., Schmaderer, M. S., & Eisenhauer, C. M. (2020) · Western Journal of Nursing Research · doi:10.1177/0193945920902146 · Cited by 12
Fisher, L., Glasgow, R. E., & Huebschmann, A. G. (2020) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2020.0383 · Cited by 10
Ruiz, E. C., García-Sáez, G., Rigla, M., Villaplana, M., Pons, B., & Hernando, M. E. (2016) · Expert Systems with Applications · doi:10.1016/j.eswa.2016.07.019 · Cited by 12
INFLUENCE OF THE DIGITAL DIVIDE AND SOCIO-ECONOMIC FACTORS ON PREVALENCE OF DIABETES
(2020) · Issues in Information Systems · doi:10.48009/4_iis_2020_103-113 · Cited by 12
Alhodaib, H. I., Antza, C., Chandan, J. S., Hanif, W., Sankaranarayanan, S., Paul, S., Sutcliffe, P. A., & Nirantharakumar, K. (2020) · JMIR Diabetes · doi:10.2196/19650 · Cited by 12
Emerging Research and Development in Technology-based Self-monitoring
Bruhn, A. L., & Wills, H. P. (2018) · Advances in learning and behavioral disabilities · doi:10.1108/s0735-004x20180000030005 · Cited by 12
Jacobs-Basadien, M., Pather, S., & Petersen, F. (2022) · Universal Access in the Information Society · doi:10.1007/s10209-022-00951-2 · Cited by 9
Sabo, R., Robins, J. L. W., Lutz, S., Kashiri, P. L., Day, T., Webel, B., & Krist, A. H. (2021) · JMIR Diabetes · doi:10.2196/26621 · Cited by 8
Gray, K. M., Clarke, K., Alzougool, B., Hines, C., Tidhar, G. A., & Frukhtman, F. (2014) · JMIR Research Protocols · doi:10.2196/resprot.3201 · Cited by 8
Dadgar, M., & Joshi, K. D. (2015) · Hawaii International Conference on System Sciences (HICSS) · doi:10.1109/hicss.2015.388 · Cited by 8
A non-invasive blood glucose monitoring system
Al-Jammas, M. H., Iobaid, A. S., Al-Deen, M. M. N., & Aziz, Y. W. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.110133 · Cited by 10
Metadata
- Authors
- El-Gayar, O., Timsina, P., Nawar, N., & Eid, W.
- Affiliations
- Dakota State University (US)
- St. Elizabeth Healthcare (US)
- Corresponding author
- El-Gayar, Omar
- Venue
- International Journal of Medical Informatics
- Publisher
- Elsevier BV
- Published
- 2013-08
- Volume
- 82
- Issue
- 8
- Pages
- 637-652
- ISSN
- 1386-5056 (ISSN-L)
- OpenAlex
- W2050896776
- Cited by
- 148
- References
- 121
- Citations by year
- 2025: 4 · 2024: 2 · 2023: 5 · 2022: 3 · 2021: 12 · 2020: 11 · 2019: 13 · 2018: 19 · 2017: 22 · 2016: 25 · 2015: 22 · 2014: 8 · 2013: 1
- FWCI
- 2.26
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- International Journal of Medical Informatics (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Education
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
- Keywords
- diabetes self-management, information technology, type 1 diabetes, type 2 diabetes, telemedicine, mobile health, online intervention, clinical decision support, electronic health records, usability, real-time feedback, user-centered design, socio-technical design, chronic disease management
- MeSH terms
- Adult, Diabetes Mellitus, Humans, Medical Informatics, Self Care
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review