“CATCH-IT Reports” are Critically Appraised Topics in Communication, Health Informatics, and Technology, discussing recently published ehealth research. We hope these reports will draw attention to important work published in journals, provide a platform for discussion around results and methodological issues in eHealth research, and help to develop a framework for evidence-based eHealth. CATCH-IT Reports arise from “journal club” - like sessions founded in February 2003 by Gunther Eysenbach.

Friday, October 23, 2009

CATCH-IT Final Report: Cellular phone and Internet-based individual intervention on blood pressure and obesity in obese patients with hypertension

Links: Abstract . Comments . Draft Report . Presentation

Park MJ, Kim HS, Kim KS. Cellular phone and Internet-based individual intervention on blood pressure and obesity in obese patients with hypertension. International journal of medical informatics. 2009 Oct;78(10):704-10.


Globally overweight and obesity, representing at least 300 million clinically obese persons, poses a major risk for chronic diseases, including type 2 diabetes, cardiovascular disease, hypertension and stroke (1). Increasingly, health care planners are looking for affordable strategies (2) inclusive of leveraging the use of information and communication technology (ICT) as a viable tool to support patients’ self-management of these diseases. This is a critical appraisal review of one of the most recent studies, which evaluates the use of both cellular phone and internet as an intervention in the self-management of clinical measures for obese hypertension.

One of the authors of this paper, Hee-Sung Kim, has experience with previous research studies using ICT for chronic disease management dated back to 2003. No further work in this area is established for the other authors.

Objectives of Study

The aim of the study is to evaluate whether an intervention using short message service (SMS) by cellular phone and Internet would improve blood pressure (BP), weight control, and serum lipids of obese patients with hypertension during 8 weeks. The authors cite the rationale for this as “no study has been done to test the direct efficacy of the cellular phone or internet-based system” on improving of these measures for hypertension”. Logan etal, 2007 presented a paper on use of both inventions, which is referenced by authors making this not a novel research.

Methodological Issues

Firstly, the intervention is not clearly defined, “.. intervention group were requested to record their blood pressure and body weight in a weekly web based diary through the Internet or by cellular phones.” No justification is given for the choosing SMS or descriptive given of how the patient would input information, if any, from the cellular phone. No clear indication is stated as to how the input of information on BP, weight and drug information would allow the system to give advice about fast food intake and exercise duration. In addition, given that the data is self-reported, there is no indication of any objective way to confirm the data reported on which the decisions are made for the SMS alerts. In the research by Logan etal (2007), a Bluetooth-enabled home BP monitor is used for greater validity of information.

Secondly, omitted is the actual population size of the data collected from which the participants are drawn as well as how the sample is selected. Associated with this is the potential for selection bias, as it is unclear who selected the control group and what other factors may have been considered in addition to matching the age, sex, systolic BP, diastolic BP and body weight to the intervention group at the same department. Amongst other internal validities that are observed, this appears to pose the single most important threat, as it is possible to pull patient records with no change in the clinical outcome variables, unless this was a blinded process.

Thirdly, usage data of the intervention is omitted. Review done on related articles presenting studies with the use of ICT intervention such as by Patrick etal (2009), Raab etal (2009), Cocosila etal (2009), Morak etal (2008), Logan etal, (2007), and Kwon etal (2004), report results in addition to the clinical outcome. Data expected are those such as mean number of logon times per patient per day, alerts sent from both SMS and internet, entries for clinical measures such as blood pressure, weight, drug entries and most frequent comments over the period. In addition, how did the researchers analyze this data when not all patients had access to a computer of phone?

Fourthly, given that the study incorporates the behavioural pattern of patients, the theoretical approach used is not stated explicitly for self-efficacy (4). This would enrich the research for persons interested in cognitive and behavioural research.


This study is contributing to the body of literature on behavioural change through online intervention, still a relatively new area of research and will prompt development of more in-depth research. However, the results must be taken with caution base on the fundamentally flawed methodological issues that is associated with the research.

Hee-Seung Kim has authored one publication (5) and co-authored eleven publications. In one of the earlier researches co-authored in 2004, “Establishment of Blood Glucose Monitoring System Using the Internet” (6), the ICT intervention is clearly defined with inclusion screenshots and reporting of the results as seen in related researches (7,8,9,10). It is quite noticeable that subsequent studies have used the same web-based diary intervention from the same institution, which has been extended into variations of research papers on diabetes management in conjunction with SMS cellular phone. This study has portions of the writing that are verbatim with blind application of parts of study with no evidence of results or relevance. How much of this study is original work and could this have lead to the decrease in the quality of the reporting and exclusion of the pertinent information alluded to in the methodological issues above? The authors state that some patients may not have had access to a computer or able to use a cell phone, despite the inclusion criterion that they "should be able to input data into the website and have their own cellular phone". Clearly, this is conflicting and poses additional difficulty on the validity of assessing “direct efficacy”.

Another critical element missing from this and past reports of similar study by the authors is the lack of information regarding the patients’ perspective on the ease of use, acceptance and effectiveness of the interventions. It would be valuable to know the extent to which patients find the ICT interventions to be helpful in disease self-management, increased self-efficacy, and treatment adherence, as the technology becomes an integral part of people's everyday life. This information would also help to inform future research and long-term planning.


The authors conclude, “the intervention using SMS of cellular phone and Internet improved blood pressure, body weight, waist circumference, and HDL-C at 8 weeks in obese hypertensive patients.” However, given the number of concerns regarding the methodological issues, limited timeline of this intervention, and lack of generalization due to low sample size; these will greatly limit the level of confidence in all inferences that might be drawn from this study deeming the results not valid. Overall, the poor quality of reporting has detracted from the goal of the study.

Questions to the Authors

1. What is the usage data of the cellular phone and internet intervention such as daily frequency response rate per patient per measure, number of alerts sent, and number of entries for clinical measures over the 8 weeks period?

2. How was the data analyzed to determine alerts to be sent if not all patients had access to a computer of phone?

3. What is the actual population size of the data collected from which the participants are drawn?

4. Who did the selection of the control group and how exactly was this done? What were the variables used for matching? What is the potential for a selection bias?

5. What is the rationale for exclusion of patients that changed medication during the period of the interventions and how many persons were excluded due to this in the intervention and control group?

6. What specific differences are identified using the paired t-test with Bonferroni correction and why is ANOVA used rather than t-test when comparing the groups?

7. What exactly was the paired t-test with Bonferroni correction used for?

8. Are the findings presented in the results of statistical significance only or were these also verified for clinical significance?

9. What measurement is used to determine self-efficacy in the adherence to control of hypertension?

10. Why is the patient’s perspective not included on the usability and effectiveness of the intervention?

11. Do you think doing a qualitative study of patients' perspectives might have altered the results or help to inform future research and long-term planning.


Thank you to the Professor Eysenbach and fellow graduate students of the 2009 CATCH-IT Journal Club at the University of Toronto, for their helpful and insightful discussion and comments that contributed to this report.


1. World Health Organization. Obesity and overweight. [Online].; 2003 [cited 2009 October Available from: http://www.who.int/dietphysicalactivity/publications/facts/obesity/en/.

2. Prentice A. The emerging epidemic of obesity in developing countries. Int. J. Epidemiol. 2006; 35: p. 93–99.

3. Park M, Kim H, Kim K. Cellular phone and Internet-based individual intervention on blood pressure and obesity in obese patients with hypertension. Int J Med Inform. 2009 Oct; 78(10): p. 704-10.

4. Kim H. A randomized controlled trial of a nurse short-message service by cellular phone for people with diabetes. Int J Nurs Stud. 2007 July; 44(5): p. 687-692.

5. Kwon H, Cho J, Kim H, Song B, Ko S, Lee J, et al. Establishment of Blood Glucose Monitoring System Using the Internet. Diabetes Care. 2004 February; 27(2): p. 478-483.

6. Logan AG, McIsaac WJ, Tisler A, Irvine MJ, Saunders A, Dunai A, et al. Mobile Phone-Based Remote Patient Monitoring System for Management of Hypertension in Diabetic Patients. Am J Hypertens. 2007 September; 20(9): p. 942-948.

7. Morak J, Schindler K, Goerzer E, Kastner P, Toplak H, Ludvik B, et al. A pilot study of mobile phone-based therapy for obese patients. J Telemed Telecare. 2008; 14(3): p. 147-9.

8. Cocosila M, Archer N, Haynes RB, Yuan Y. Can wireless text messaging improve adherence to preventive activities? Results of a randomised controlled trial. Int J Med Inform. 2009 April; 78(4): p. 230-238.

9. Patrick K, Raab F, Adams M, Dillon L, Zabinski M, Rock C, et al. A Text Message–Based Intervention for Weight Loss: Randomized Controlled Trial. J Med Internet Res. 2009; 11(1 ): e1.

10. Anhoj J, Moldrup C. Feasibility of Collecting Diary Data From Asthma Patients Through Mobile Phones and SMS (Short Message Service): Response Rate Analysis and Focus Group Evaluation From a Pilot Study. J Med Internet Res. 2004 Oct–Dec; 6(4): e42.

1 comment:

  1. Thanks for the final CATCH-IT report (archived at http://www.webcitation.org/5kn0EOBM6). Although there were also some other criticisms raised in our journal club session and at http://ehealth-catchit.blogspot.com/2009/10/cellular-phone-and-internet-based.html , which did not make it into your discussion, it is a good summary of some of the important problems with this study. While your statement regarding "fundamentally flawed methodological issues" may be a bit strong, there was consensus among the group that the study was poorly planned, poorly written up, poorly peer-reviewed, and poorly edited, leaving many questions open and introducing many possibilities for bias, so that at the end of the day it is impossible to say whether the improvement of clinical variables observed in the intervention group was actually a result of the intervention, or just a regression toward the mean. Unfortunately, we see this level of rigor and sloppy reporting all to often in the medical informatics literature.