With over 75% of people using mobile phones worldwide, text messaging might be a simple, cost-effective platform to encourage lifestyle changes.1 mHealth involves the use of mobile and wireless devices (e.g., wearable sensor technology) to provide health services and information.2,3 Such technologies have the potential to fundamentally change health practices. Indeed, several healthcare-related applications and mobile phone text messaging systems have already been designed; yet, very few have undergone rigorous testing to confirm clinical benefit.4
Despite major advancements in treatment, coronary heart disease (CHD) remains the leading cause of morbidity and mortality due to lack of adherence not just to medication, but to lifestyle changes recommended by providers, including smoking cessation, physical activity, and diet.5,6 Use of mHealth technologies has the potential to engage patients and support behavioral health changes that can reduce cardiovascular risk.
A recent systematic review assessed the effect of mobile phone-based interventions on smoking cessation. In two short-term studies included in the meta-analysis, self-reported quit rates were significantly increased in the intervention groups who received text messages (relative risk [RR] 2.18, 95% CI 1.80 to 2.65).7 However, a major limitation of these trials included the use of self-reports instead of objective data such as carbon monoxide meter breath analyzer testing. In a recent AHA scientific statement on mobile health, it was noted that very few mHealth applications have undergone rigorous evaluations. Most studies have not been randomized, lacked a usual care comparator, failed to use objective clinical measures, and/or enrolled narrowly defined patient populations from specialty settings.4
The investigators of the Tobacco, Exercise, and Diet Messages (TEXT ME) trial designed a text message-based intervention to encourage lifestyle modifications and evaluated its impact on cardiovascular risk in patients with established CHD.8 This was a parallel-design, single-blind, randomized clinical trial enrolling 710 patients with CHD from a large tertiary hospital in Australia. Patients were enrolled if they were older than 18 years and able to provide informed consent. Patients were excluded if they did not have active mobile phone or sufficient English language proficiency to read text messages. Documented CHD was defined as prior myocardial infarction, coronary artery bypass graft surgery, percutaneous coronary intervention, or ≥ 50% in at least one major coronary artery on angiography.
The investigators used a computerized randomization program to enroll patients in such a way that the study personnel were blinded. Patients were randomized to receive (n = 352) or not receive (n = 358) a text messages that were semipersonalized (some messages personally addressed with patient’s preferred name) at regular intervals. This automated, interactive, tailored text message system was developed with input from investigators, clinicians, academics, and patients through a multi-stage iterative process. The text messages provided advice, motivation, and information targeted to improve diet, increase physical activity, and encourage smoking cessation (if applicable). An example of a text message regarding diet was “Try avoiding adding salt to your foods by using other spices or herbs.” The program randomly selected text messages from a bank of messages using key baseline data entered into the system. For example, nonsmokers did not receive messages regarding smoking cessation. Participants randomized to the intervention group received 4 messages per week for 24 weeks.
The primary outcome was level of plasma low-density lipoprotein cholesterol (LDL-C) at 6 months. Secondary outcomes included total cholesterol level, systolic blood pressure (SBP), body mass index (BMI), total physical activity, smoking status, and the proportion achieving guideline targets for modifiable risk factors (See Table 1). Use of cardioprotective medications were assessed prior to randomization and at follow-up visits. Smoking status was determined with a carbon monoxide meter breath analyzer.
Table 1. Modifiable Risk Factor Targets
|
LDL-C, low-density lipoprotein cholesterol; BMI, body mass index
The baseline demographics were comparable between the two groups. The mean age was 57.6 years, 82% were men. At baseline the mean LDL-C was 101 mg/dL, mean proportion of patients with SBP > 140 mmHg was 11.9%, and mean proportion of patients with BMI > 25 kg/m2 was 77.6%. Very few patients in either group were achieving 4 of 5 key risk factor targets at baseline (5.3%). Five hundred and ninety-one patients were excluded from the study. The main reasons for excluding these patients were either because they did not own a mobile phone (265 patients) or were not proficient in English (205 patients). Only 70 patients declined to participate in the study. The most common cardioprotective medications that patients were taking prior to randomization included angiotensin-converting enzyme inhibitors/angiotensin receptor blockers (66.2%), aspirin (93.2%), beta blockers (71%), and statins (88.5%).
At 6 months, there were statistically significant but modest clinical reductions in LDL-C level, SBP, and BMI in the intervention group compared to the control group (See Table 2). Participants in the intervention group were more likely to control their blood pressure, exercise regularly, and achieve nonsmoking status (Table 3). The number needed to treat for smoking cessation was 6 patients. Also, patients in the intervention group were significantly more likely to achieve target goals for 4 or more key risk factors. When soliciting feedback from patients regarding the text-message system, the majority reported the system to be useful (91%), easy to understand (97%), and motivating with respect to diet (81%) and physical activity change (73%).
Table 2. Primary and Secondary Outcomes (mean values) at 6-Month Follow-up
Parameter |
Intervention |
Control |
Mean Difference (95% CI) |
p Value |
LDL-C, mg/dL |
79 |
84 |
-5 (-9 to 0) |
0.04 |
SBP, mm Hg |
128.2 |
135.8 |
-7.6 (-9.8 – -5.4) |
< 0.001 |
BMI |
29 |
30.3 |
-1.3 (-1.6 – -0.9) |
< 0.001 |
Physical activity, MET min/week |
936.1 |
642.7 |
239.4 (102.0-484.8) |
0.003 |
Smoking, N/total (%) |
88/339 |
152/354 |
RR 0.61 (0.48-0.76) |
< 0.001 |
LDL-C, low-density lipoprotein cholesterol; SBP, systolic blood pressure; BMI, body mass index; RR, relative risk; MET, metabolic equivalent task
Table 3. Secondary Outcomes – Risk Factor Targets Achieved at 6-Month Follow-up
Parameter |
No/Total (%) Intervention |
Control |
Relative Risk (95% CI) |
p Value |
LDL-C < 77 mg/dL |
168/332 (50.6) |
158/342 (46.2) |
1.10 (0.94-1.28) |
0.25 |
Blood pressure < 140/90 mmHg |
262/331 (79.2) |
189/344 (54.9) |
1.44 (1.29-1.61) |
< 0.001 |
Exercising regularly |
182/338 (53.8) |
79/351 (22.5) |
2.39 (1.92-2.97) |
< 0.001 |
Nonsmoker |
253/339 (74.6) |
198/354 (55.9) |
1.33 (1.19-1.49) |
< 0.001 |
Guideline targets achieved |
||||
Achieving all 5 |
15/322 (4.7) |
6/330 (1.8) |
2.56 (1.01-6.52) |
0.05 |
Achieving ≥ 4 |
93/322 (28.9) |
34/330 (10.3) |
2.80 (1.95-4.02) |
< 0.001 |
Achieving ≥ 3 |
202/322 (62.7) |
111/330 (33.6) |
1.87 (1.57-2.22) |
< 0.001 |
LDL-C, low-density lipoprotein cholesterol
The results of this study show significant improvements in several CHD risk parameters and are strengthened by the randomized design as well as the use of objective measures. Using LDL-C level as the primary outcome was a rational decision since LDL-C elevation is a well-validated marker of CHD risk. Another strength of the study was the use of a questionnaire to assess patient satisfaction. The investigators also calculated the cost of the intervention. Using an average cost of $0.10 per message, the investigators determined the cost would be less than $10 per patient for the 6-month program. Most notably, the trial also assessed the effect of text-message system on achieving combined risk factor targets in addition to each individually. The fact that this single intervention produced improvements in multiple risk factors is impressive and may have a substantial downstream impact on CHD events.
Although the trial was well designed, several limitations warrant discussion. First – it was conducted at a single center in Australia with mostly white male participants. Many patients were excluded because of language barriers and lack of access to a mobile phone, limiting generalizability. Also, the definition of LDL-C < 77 mg/dL as a target is not employed universally, limiting the extrapolation of results to other countries that may use different targets. The LDL-C reduction (5 mg/dL) may not be clinically important. Another limitation was the short duration of 6-month follow-up. Whether the benefits of this intervention would last for several years remains unclear. In addition, the authors used some self-reported outcomes such as physical activity. Patients were not blinded to intervention status – so reporting bias may have been a factor. Such bias could be avoided by using wearable wireless devices to quantify physical activity in future studies. Finally, the investigators did not validate the content of text messages or assess the impact of specific text messages that may have had the greatest influence on behavioral change.
The TEXT ME study provides robust findings to support a simple, inexpensive intervention to modify cardiovascular risk … at least over the short term. The findings are consistent with other evaluations of similar text messaging programs.7 Given the magnitude of the benefit and their relatively low cost, text message delivery systems should be a routine part of heath care delivery. Pharmacists can use text message systems to encourage medication adherence and lifestyle modifications. In any healthcare setting, pharmacists could use text messages to communicate critical information such as INR test results to improve the efficient delivery of care.9 Additional studies in more diverse patient populations and different practice settings are still needed to determine the best practices for text messaging as part of an integrated multimodal effort to reduce cardiovascular disease. Are you sending text messaging to your patients in your practice? What are some other creative ways that text messages can be used to promote healthy behaviors?
- Meeker M. Internet Trends 2015. Kleiner Perkins Caufield & Byers website. http://www.kpcb.com/internet-trends. Accesseed 2015 December 11.
- Akter S, D’Ambra J, Ray P. Trustworthiness in mHealth information services: an assessment of a hierarchical model with mediating and moderating effects using partial least squares (PLS). J Am Soc Inf Sci Technol. 2011;62:100-116.
- Ranney ML, Choo EK, Wang Y, Baum A, Clark MA, Mello MJ. Emergency department patients’ preference for technology-based behavioral interventions. Ann Emerg Med. 2012;60:218-27.
- Burke LE, Ma J, Azar KMJ, et al; American Heart Association Publications Committee of the Council on Epidemiology and Prevention, Behavior Change Committee of the Council on Cardiometabolic Health, Council on Cardiovascular and Stroke Nursing, Council on Functional Genomics and Translational Biology, Council on Quality of Care and Outcomes Research, and Stroke Council. Current science on consumer use of mobile health for cardiovascular disease prevention: a scientific statement from the American Heart Association. Circulation. 2015;132:1157-1213.
- World Health Organization (WHO). The top 10 causes of death. Available: http://www.who.int/mediacentre/factsheets/fs310/en/index/html. Accessed 2015 December 11.
- Wald NJ, Law MR. A strategy to reduce cardiovascular disease by more than 80%. BMJ. 326:1419-23.
- Whittaker R, Borland R, Bullen C, Lin RB, McRobbie H, Rodgers A. Mobile phone-based interventions for smoking cessation. Cochrane Database Syst Rev. 2009; 4:CD006611.
- Chow CK, Redfern J, Hillis GS, et al. Effect of lifestyle-focused text messaging on risk factor modification in patients with coronary heart disease: A randomized clinical trial. JAMA. 2015;314:1255-1263.
- Lin SM, Kang WY, Lin DT, et al. Comparison of warfarin therapy clinical outcomes following implementation of an automated mobile phone-based critical laboratory value text alert system. BMC Med Genomics. 2014; 7:S13.