Cell phoneâ€œbased text messaging may be used to feasibly support chronic disease management and engagement in diabetes self-care behaviors for some patients.
Objectives: To assess the feasibility of engaging adults with diabetes in self management behaviors between clinic visits by using cell phone text messaging to provide blood sugar measurement prompts and appointment reminders.
Study Design: Quasi-experimental pilot among adult diabetic patients with cell phones who receive regular care at a federally qualified community health center in Denver, Colorado, which serves a population that is predominantly either uninsured (41%) or on Medicaid or Medicare (56%).
Methods: Patients (N = 47) received text message prompts over a 3-month period. Blood sugar readings were requested 3 times per week (Monday, Wednesday, and Friday). Reminders were sent 7, 3, and 1 day(s) before each scheduled appointment. Acknowledgments were returned for all patient-sent messages. Focus groups were conducted in English and Spanish with selected patients (n = 8).
Results: Patients of all ages were active participants. Correctly formatted responses were received for 67.3% of 1585 prompts. More than three-fourths (79%) of the cohort responded to more than 50% of their prompts. The appointment analysis was underpowered to detect significant changes in attendance. Participants reported increased social support, feelings that the program “made them accountable,” and increased awareness of health information. Two-thirds (66%) of patients provided glucose readings when prompted during the study, compared with 12% at 2 preceding clinic visits.
Conclusions: For certain patients, cell phone—based text messaging may enhance chronic disease management support and patient-provider communications beyond the clinic setting.
(Am J Manag Care. 2012;18(2):e42-e47)Healthcare systems can better support chronic disease management by offering patients customized communication that identifies and supports patients outside the clinic visit setting.
The impact of chronic disease is substantial. An estimated 60% of deaths worldwide are thought to be caused by chronic disease,1 with 4 chronic diseases—cardiovascular disease, cancer, chronic respiratory disease, and diabetes—responsible for 29 million deaths in 2002 alone.2 The situation is no better in the United States, where 70% of deaths annually are attributable to chronic disease3 and almost 50% of the population has at least 1 chronic illness.4
Patients need to manage chronic diseases on a daily basis, yet US healthcare systems are not well structured to support self management across large patient populations.5 Both patients and providers express frustration with the standard approach to managing chronic disease through the 20-minute provider-driven clinic visit.5-7 In addition, traditionally vulnerable and medically underserved groups, which are more likely to have poorly controlled chronic illness,8 are also more likely to experience barriers in accessing healthcare, which means that chronic disease management strategies focused on only during the clinic visit present further challenges for these patients.6,7
Health information technology (HIT) has been shown to be useful in helping patients to manage chronic conditions outside the clinic setting.9-11 The combination of Web-based health informatics with case management strategies has been reported to improve blood pressure control among patients with hypertension12 and glycemic control among patients with diabetes.13,14 Cell phone—based text messaging has also been associated with improved glycemic control when used to assist with diabetes case management.15 The latter is of particular interest given that high rates of cell phone access are reported among groups in the United States that exhibit low rates of computer and Internet use,16,17 including 71% of blacks and 59% of all Latinos, both English-dominant and Spanish-dominant.18,19 Cell phone access has also been associated with knowledge of health information.20
This article describes the results of a pilot study conducted to evaluate the feasibility of utilizing low-cost, mobile HIT to support chronic disease self management between clinic visits among patients in an urban safety net setting. Cell phone text messaging was used to provide blood sugar measurement prompts and appointment reminders to adult patients with diabetes in order to promote engagement in self-care behaviors.
The quasi-experimental pilot study was conducted among English-speaking and Spanish-speaking adult patients with diabetes who have access to cell phones and who regularly receive primary healthcare at Sam Sandos Westside Family Health Center (Westside), 1 of 8 federally qualified community health centers in the Denver Health integrated healthcare system in Denver, Colorado. Westside serves a predominantly Latino population (81%) that is largely either uninsured (41%) or on Medicaid or Medicare (56%).
The Colorado Multiple Institutional Review Board approved this study prior to enrollment and implementation activities. All English-dominant and Spanish-dominant adult patients included in the Denver Health diabetes registry were identified as potential study participants. Westside providers and clinic staff confirmed potential participants’ access to cell phones at clinic visits and referred patients to the study nurse for recruitment. All patients signed a written consent prior to participation.
A software platform, the Patient Relationship Manager (PRM), was created in partnership between Denver Health, EMC Consulting, and Microsoft Corporation to handle text messaging activities. The PRM system was designed to automatically send text messages to patients according to an established schedule, and to process text message responses received from patients for appropriate action based on established threshold values.
Participants (N = 47) received text message prompts in their choice of English or Spanish over a 3-month period. Messages requesting fasting blood sugar readings were sent to each patient at 7:15 am on Monday, Wednesday, and Friday of each week during the study period, and appointment reminder messages were sent to each patient 3 times in advance of each scheduled appointment that patient had during the study period, at 7 days, 3 days, and 1 day prior to the appointment date. Blood sugar requests included the day of the week and calendar date that the message was sent and a request for the patient to respond with his or her fasting blood sugar that day in numeric values. Appointment reminders included the time, date, and location of the upcoming appointment along with a request for patients to respond with “Y” or “N” to indicate whether or not they planned to attend the appointment. The system automatically returned an acknowledgment of receipt in response to all patient-sent messages. Examples of all message types are shown in Table 1.
Misformatted responses and fasting blood sugar values reported by patients as either above or below an established clinical range of 70 to 400 mg/dL were automatically flagged by PRM and routed to a work queue for further action. A registered nurse dedicated to the study at 0.2 full-time equivalents reviewed all flagged messages and contacted patients by telephone for follow-up assessment. The study nurse problem-solved message formatting issues with patients and presented out-of-range values to a physician champion, who helped formulate a targeted response plan to improve the patient’s glucose control (eg, by changing eating patterns or adjusting medications). The nurse also ensured that both telephone encounters and patient-reported blood sugar measurements were appropriately documented in the medical record.
Outcome measures included appointment attendance rates and patient response rates to text message prompts. A power analysis was performed using Power Analysis and Sample Size (PASS 2008) software (NCSS, Kaysville, Utah), with the significance level (alpha) targeted at .05 with greater than 90% certainty to identify the number of appointments needed to detect a significant absolute difference of 10% in preintervention and postintervention attendance rates. Clinical chart review for all study patients was undertaken for the last 2 clinic visits prior to study enrollment to assess how often patients discussed home glucose readings with their providers.
Focus groups were conducted with patients in order to assess satisfaction with the HIT intervention program by soliciting feedback on the experience and recommendations for future improvement. Two focus groups were held, 1 conducted in English and 1 in Spanish with the aid of an experienced medical interpreter. Both group sessions were audio recorded. Recordings were supplemented with written notes taken by the group facilitator. Focus group data were obtained through inductive analysis of the audio recordings and written notes. Emergent themes and patterns among patients’ remarks were identified and synthesized into a summary report.
Demographics of both study participants and the overall population of Westside patients with diabetes are presented in Table 2. Patients of all ages were active participants in the program,with the majority of participants aged 40 to 49 years (n = 14), 50 to 59 years (n = 17), and 60 to 69 years (n = 9). The intervention group was significantly younger than the clinic population, but well matched for sex and racial/ethnic background.
A total of 1585 text messages requesting a response were automatically sent to patients by the PRM system. Patients responded to prompts with a total of 1080 text messages (68.14%), of which almost all were correctly formatted (1066 messages; 98.7%). More than three-fourths (79%) of the cohort responded regularly to more than 50% of their message prompts. Home glucometer result availability was significantly improved among study participants. Two-thirds (66.4%) of study patients provided correctly formatted blood sugar values in response to medical measurement text message requests received during the study period. By contrast, chart review data for each study participant indicated that providers were able to review home glucometer readings with patients only 12% of the time during the last 2 clinic visits prior to the intervention.
Despite reminders for 69 appointments during the intervention, cancellation rates and no-show rates were 21% and 14%, respectively, and did not differ from preintervention rates. However, the power analysis indicated that a sample size of 349 appointments was needed to achieve adequate power to detect a significant difference in appointment attendance rates.
A total of 8 patients (6 female and 2 male) participated in 2 focus groups. Among these participants, 2 were aged 30 to 39 years, 2 were aged 50 to 59 years, and 4 were aged 60 to 69 years. Two participants were Spanish dominant and the remaining 6 participants were English dominant. Half of the focus group participants were white, and 37.5% were Latino. Participants’ comfort level with program technology, perception of and satisfaction with the intervention program (including message type and frequency), and the perceived impact of the program on diabetes self management were explored as major domains in group discussions.
Participants’ Comfort With Technology
All 8 participants reported comfort with the process of text messaging, both in sending and receiving messages. Three participants identified text messaging as a means they already used for communicating with their children. One participant with vision impairment involved other family members to assist with reading and replying to messages. Three participants expressed a preference for text messaging over other computer-based communications such as e-mail.
Participants’ Satisfaction With the Program
All 8 participants viewed the text message program favorably, expressing both a liking for it and a desire for it to continue, along with a willingness to recommend it to others. The program was perceived as having expanded participants’ communication and engagement with the healthcare system; half of the participants remarked on feeling that their providers were “more connected” or otherwise aware of what was going on with them. Financial costs associated with text messaging were perceived by participants to be negligible; all participants reported having unlimited text messaging plans. Five participants indicated that they would be willing to pay for program messages even if they did not have unlimited plans.
All participants found the frequency of text messaging during the study period to be acceptable. Half of the participants expressed interest in receiving daily messages, while the other half were content with receiving messages according to the current schedule. No one wanted to receive messages less often. Four participants expressed specific appreciation for being able to respond to messages throughout the day on which they were requested, according to the patient’s own time frame and personal schedule. Six participants noted that they also liked the acknowledgment from the system that their responses had been received. Although all participants received self-management support messages, not all received appointment reminders. Two who did expressed appreciation for them, and the others indicated interest in receiving them.
Program Impact on Diabetes Self Management
In addition to a general overall perception of usefulness, the program was specifically considered to be helpful in establishing regular morning routines (4 participants) and in improving recollection of and adherence to self-management activities (4 participants). Participants reported increased awareness of their health information (2 participants) and feelings of being made accountable for their choices (4 participants), along with better perceived social support either from family (2 participants) or from the system itself (3 participants) in the absence of other established social groups. Three participants observed that they would like additional, personalized feedback about their performance on self-management activities.
In this quasi-experimental pilot study, we assessed the feasibility of engaging both English-speaking and Spanishspeaking adults with diabetes in self-management behaviors between clinic visits by using cell phone text messaging to provide blood sugar measurement prompts and appointment reminders. Over the course of the study, there was a sustained high response rate of 68% to all text message prompts received, with 66% of patients providing home glucose data upon request and 79% of patients responding to more than half of their glucose prompts. Focus group participants categorically embraced the text message—based outreach.
This pilot intervention differed from usual care by engaging patients between clinic visits. Like many safety net health centers, Denver Health does not have the capability to support downloading home glucose reading data into the clinical system during clinic visits. Depending solely on clinic visits to support discussion of self-monitoring data is challenging as well: chart review of 2 clinic visits prior to the intervention for each study participant indicated that providers reviewed home glucometer readings only 12% of the time. Although in its infancy, text messaging has been used to improve the management of asthma, hypertension, and diabetes,3,21-24 but we are not aware of studies conducted in an indigent, predominantly Latino population.
The results of this pilot study demonstrate that there is potential for using cell phone text messaging to support chronic disease management and engagement in an underserved population. Additionally, the sustained response rate and consistent response patterns demonstrated among study participants enhances the ability of the patient and provider to engage in shared decision making at clinic visits by improving access to self-monitoring glucose data collected in sufficient volume to guide well-informed clinical decisions.
The endeavor additionally highlighted future directions and challenges as we enhance our ability to engage patients with chronic disease between clinic visits. Improved integration of text message—based interactions into the electronic health record with alerts to identify participating patients to the healthcare team will be essential as we strive to create a coordinated approach within a patient-centered medical home. Another key enhancement is the establishment of a technology standards–based interface between text message platforms like PRM and clinical data sets to enable automated outreach to at-risk patients such as those overdue for laboratory tests or medication fills.
Additional patient-centered development will expand the available communication modalities from text messaging alone to include additional options such as support for interactive voice response systems, e-mail, or a Web-based patient portal, based on a patient’s individual preference. Also key to improving patient engagement will be providing support for tailored interactions. Examples of such tailoring include the ability to select a desired time of day for message receipt, customizing the frequency of message prompts, and allowing the patient to choose a particular area of self-management focus such as exercise, diet, or taking medications appropriately. Ideally, threshold values will be individualized, and remote, algorithmic assistance will enable patients to make behavioral and medication changes to achieve glycemic, blood pressure, and other clinical goals.
While this program was offered to all diabetic patients at clinic visits, we anticipate that selection bias likely occurred. Although 9 of our participants were more than 59 years old, study participants in general were found to be significantly younger than the overall clinic population. Focus group conclusions were limited by the small number of participants. Moreover, although a high response rate to text message prompts was sustained over the study period, “message fatigue” may develop among patients over time. A study with more patients and a longer duration will be needed to permit evaluation of potential message fatigue as well as to conduct initial assessment of any impact on appointment attendance rates, health outcomes, and demographic predictors for participation. Long-term sustainability will be dependent on containing costs and/or improving reimbursement for care provided between clinic visits.
We believe that healthcare systems can better support chronic disease management by offering patients customized extra-visit communication that identifies and supports at-risk patients and integrates well into the patient-centered medical home.25 To that end, HIT such as that used in this study to enhance regular patient-provider communications and promote engagement with the healthcare system outside the traditional clinic visit setting holds great promise.Acknowledgments
The results of this pilot study were presented as posters at the Society of General Internal Medicine 34th Annual Meeting; May 2011; Phoenix, AZ; and at the AcademyHealth Annual Research Meeting; June 2011; Seattle, WA.
Author Affiliations: From Denver Health and Hospital Authority (HHF, SLM, DG, AJD, CMR-P, MJD, TDM, ROE, AWS), Denver, CO; University of Colorado Denver School of Medicine (HHF, DG, AJD, TDM, ROE, AWS), Denver, CO; Colorado School of Public Health (AJD), Aurora, CO.
Funding Source: This project was supported through a collaborative partnership between Denver Health, EMC Consulting, and Microsoft Corporation. EMC Consulting and Microsoft Corporation contributed resources to assist with the development of the PRM platform. Neither EMC nor Microsoft personnel were involved in data analysis or manuscript development.
Author Disclosures: All authors (HHF, SLM, CMR-P, MJD, TDM, ROE, AWS) report employment with Denver Health, cofunder of this study.
Authorship Information: Concept and design (HHF, SLM, AJD, TDM, ROE, AWS); acquisition of data (AJD, CMR-P, MJD, AWS); analysis and interpretation of data (HHF, SLM, MJD, AWS); drafting of the manuscript (HHF, SLM); critical revision of the manuscript for important intellectual content (HHF, SLM, ROE, AWS); statistical analysis (MJD); provision of study materials or patients (AWS); obtaining funding (TDM); administrative, technical, or logistic support (SLM, AJD, TDM, AWS); supervision (HHF, AWS); and technical design (AJD).
Address correspondence to: Henry H. Fischer, MD, Denver Health, 777 Bannock St, MC 3000, Denver, CO 80204-4507. E-mail: firstname.lastname@example.org. World Health Organization. The World Health Report 2002: Reducing Risks, Promoting Healthy Life. Geneva, Switzerland: World Health Organization; 2002.
2. Yach D, Hawkes C, Gould CL, Hofman KJ. The global burden of chronic diseases: overcoming impediments to prevention and control. JAMA. 2004;291(21):2616-2222.
3. Kung HC, Hoyert DL, Xu JQ, Murphy SL. Deaths: final data for 2005. National Vital Stat Rep. 2008;56(10):1-120.
4. Wu SY, Green A. Projection of Chronic Illness Prevalence and Cost Inflation. Santa Monica, CA: RAND Health; 2000.
5. Rothman AA, Wagner EH. Chronic illness management: what is the role of primary care? Ann Intern Med. 2003;138(3):256-261.
6. Lee TH. The future of primary care: the need for re-invention. N Engl J Med. 2008;359(20):2085-2086.
7. Bodenheimer T. The future of primary care: transforming practice. N Engl J Med. 2008;359(20):2086, 2089.
8. Pandey DK, Labarthe DR, Goff DC, Chan W, Nichaman MZ. Community-wide coronary heart disease mortality in Mexican Americans equals or exceeds that in non-Hispanic whites: the Corpus Christi Heart Project. Am J Med. 2001;110(2):81-87.
9. Gibbons MC, Wilson RF, Samal L, et al. Impact of Consumer Health Informatics Applications. Evidence Report/Technology Assessment No. 188. Rockville, MD: Agency for Healthcare Research and Quality; October 2009. AHRQ publication 09(10)-E019. http://www.ahrq.gov/clinic/tp/chiapptp.htm#Report. Accessed January 18, 2012.
10. Wyne K. Information technology for the treatment of diabetes: improving outcomes and controlling costs. J Manag Care Pharm. 2008;14(2)(suppl):S12-S17.
11. Jackson CL, Bolen S, Brancati FL, Batts-Turner ML, Gary TL. A systematic review of interactive computer-assisted technology in diabetes care: interactive information technology in diabetes care. J Gen Intern Med. 2006;21(2):105-110.
12. Green BB, Cook AJ, Ralston JD, et al. Effectiveness of home blood pressure monitoring, Web communication, and pharmacist care on hypertension control: a randomized controlled trial [published correction appears in JAMA. 2009;302(18):1972]. JAMA. 2008;299(24):2857-2867.
13. Azar M, Gabbay R. Web-based management of diabetes through glucose uploads: has the time come for telemedicine? Diabetes Res Clin Pract. 2009;83(1):9-17.
14. Ralston JD, Hirsch IB, Hoath J, Mullen M, Cheadle A, Goldberg HI. Web-based collaborative care for type 2 diabetes. Diabetes Care. 2009;32(2):234-239.
15. Kim HS, Jeong HS. A nurse short message service by cellular phone in type-2 diabetic patients for six months. J Clin Nursing. 2007;16(6):1082-1087.
16. Giménez-Pérez G, Gallach M, Acera E, et al. Evaluation of accessibility and use of new communication technologies in patients with type 1 diabetes mellitus. J Med Internet Res. 2002;4(3):E16.
17. Blake H. Mobile phone technology in chronic disease management. Nursing Standard. 2008;23(12):43-46.
18. Horrigan J. Mobile access to data and information. Pew Internet & American Life Project. http://www.pewinternet.org/Reports/2008/Mobile-Access-to-Data-and-Information.aspx. Published March 5, 2008. Accessed August 15, 2011.
19. Fox S, Livingston G. Latinos online. Pew Internet & American Life Project. http://www.pewinternet.org/Reports/2007/Latinos-Online.aspx. Published March 14, 2007. Accessed August 15, 2011.
20. Haughton LT, Kreuter MW, Hall J, Holt CL, Wheetley E. Digital divide and stability of access in African American women visiting urban public health centers. J Health Care Poor Underserved. 2005;16(2):362-374.
21. Lloyd-Jones DM, Hong Y, Labarthe D, et al; American Heart Association Strategic Planning Task Force and Statistics Committee. Defining and setting national goals for cardiovascular health promotion and disease reduction: the American Heart Association’s strategic Impact Goal through 2020 and beyond. Circulation. 2010;121(4):586-613.
22. US Census Bureau. Population estimates. http://www.census.gov/popest/. Accessed September 25, 2009.
23. Krishna S, Boren SA, Balas EA. Healthcare via cell phones: a systematic review. Telemed J E Health. 2009;15(3):231-240.
24. Cole-Lewis H, Kershaw T. Text messaging as a tool for behavior change in disease prevention and management. Epidemiol Rev. 2010;32(1):56-69.
25. Qualis Health. Change concepts. Safety Net Medical Home Initiative. http://qhmedicalhome.org/safety-net/empanelment.cfm. Accessed August 15, 2011.