CoChat
Sign Up
Cited by 134Healthcare Operations and Scheduling OptimizationMobile Health and mHealth ApplicationsMedication Adherence and Compliance

Text messaging reminders to reduce non-attendance in chronic disease follow-up: a clinical trial

Open access
Liew, Su-May, Tong, Seng Fah, Mun Lee, Verna Kar, Ng, Chirk Jenn, Leong, Kwok Chi, Teng, Cheong Lieng·2009·British Journal of General Practice
DOI 10.3399/bjgp09x472250PMC PMC2784529
Open this paper in CoChat — free accountOpen PDF

Abstract

BACKGROUND: Non-attendance results in administrative problems and disruption in patient care. Several interventions have been used to reduce non-attendance, with varying degree of success. A relatively new intervention, text messaging, has been shown to be as effective as telephone reminders in reducing non-attendance. However, no study has looked specifically at using text messaging reminders to reduce non-attendance in chronic disease care. AIM: To determine if text messaging would be effective in reducing non-attendance in patients on long-term followup, compared with telephone reminders and no reminder. DESIGN OF STUDY: A randomised controlled trial with three arms: text messaging reminder, telephone reminder, and control. SETTING: Two primary care clinics in Malaysia. METHOD: A total of 931 subjects who had been on at least 6 months of follow-up were randomised into the three groups. Demographic variables were recorded at the first visit. In the intervention arms, a reminder was sent 24–48 hours prior to the appointment. Non-attendance rate was documented at the second visit. Non-attenders were defined as those who did not attend, attended early, or attended late without rescheduling their appointment. Attenders were defined as participants who had turned up for their scheduled appointment and those who had changed or cancelled their appointment with notification. RESULTS: The non-attendance rates in the text messaging group (odds ratio [OR] = 0.62, 95% confidence interval [CI] = 0.41 to 0.93, P = 0.020) and the telephone reminder group (OR = 0.53, 95% CI = 0.35 to 0.81), P = 0.003) were significantly lower than the control group. The absolute non-attendance rate for telephone reminders was lower by 2% compared to the text messaging group. This difference was not found to be statistically significant (P = 0.505). CONCLUSION: Text messaging was found to be as effective as telephone reminder in reducing non-attendance in patients who required long-term follow-up for their chronic illnesses in this study. It could be used as an alternative to conventional reminder systems.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.3399/bjgp09x472250

Cites

20 of 20 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

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 a Malaysian RCT (n=931), nonattendance rates were 13.7% for the text messaging group, 15.6% for the telephone group, and 23.0% for the control group.

    “The nonattendance rates for the three groups were 13.7% for the text messaging group, 15.6% for the telephone group, and 23.0% for the control group.”

    From The Empirical Foundations of Telemedicine Interventions in Primary Care (2016) · cites this paper as [95]

    Table 2 (p. 919) assigns 13.7% non-attendance to telephone reminders and 15.6% to text messaging—the reverse of the claim. The control rate of 23.0% and total sample of 931 are correct. (LLM, conf=0.90)

    Checked by a reasoning model — may be wrong.

    Experimental — accuracy not yet measured

  • In that Malaysian RCT, when the control group was used as reference, the odds ratio for the telephone group was 0.53, and that for text messaging was 0.62.

    “When the control group was used as reference, the odds ratio for the telephone group was 0.53, and that for text messaging was 0.62.”

    From The Empirical Foundations of Telemedicine Interventions in Primary Care (2016) · cites this paper as [95]

  • In that Malaysian RCT, differences between telephone and text messages were not statistically different, and both types reduced nonattendance more than the control group.

    “Differences between telephone and text messages were not statistically different, and both types reduced nonattendance more than the control group.”

    From The Empirical Foundations of Telemedicine Interventions in Primary Care (2016) · cites this paper as [95]

  • Two studies included in the review show that text message appointment reminders are effective at increasing attendance rates of patients with non-communicable diseases.

    “two studies included in our review show that they are also effective at increasing attendance rates of patients with NCDs [26, 30].”

    From A systematic review of randomized controlled trials of mHealth interventions against non-communicable diseases in developing countries (2016) · cites this paper as [30]

  • The most basic intervention in the category of remote monitoring & care support was an appointment-reminder system, where text messages were sent 24-48 h before the patients' scheduled appointments.

    “The most basic intervention in the category of remote monitoring & care support was an appointment-reminder system, where text messages were sent 24–48 h before the patients’ scheduled appointments [30].”

    From A systematic review of randomized controlled trials of mHealth interventions against non-communicable diseases in developing countries (2016) · cites this paper as [30]

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Related

Authors
Liew, S.-M., Tong, S. F., Mun Lee, V. K., Ng, C. J., Leong, K. C., & Teng, C. L.
Affiliations
  • National University of Singapore (SG)
  • University of Malaya (MY)
  • Deakin University (AU)
  • IMU University (MY)
ORCID
Venue
British Journal of General Practice
Publisher
Royal College of General Practitioners
Published
2009-12-01
Volume
59
Issue
569
Pages
916-920
ISSN
  • 0960-1643 (ISSN-L)
  • 1478-5242
DOI
10.3399/bjgp09x472250
PMC
PMC2784529
OpenAlex
W2021342127
Cited by
134
References
20
Citations by year
2026: 3 · 2025: 6 · 2024: 5 · 2023: 5 · 2022: 1 · 2021: 5 · 2020: 5 · 2019: 10 · 2018: 8 · 2017: 10 · 2016: 12 · 2015: 14 · 2014: 14 · 2013: 11 · 2012: 15
FWCI
11.29
Type
journal article
Language
en
Open access
Locations
  • British Journal of General Practice (publishedVersion)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
  • The University of Malaya Research Repository (University of Malaya) (submittedVersion)
  • Own your potential (DEAKIN) (submittedVersion)
  • Figshare (submittedVersion, cc-by)
Funders
Universiti Malaya
Topics
  • Health Sciences › Health Professions › Emergency Medical Services › Healthcare Operations and Scheduling Optimization
  • Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
  • Health Sciences › Medicine › Family Practice › Medication Adherence and Compliance
Keywords
appointment non-attendance, SMS reminders, telephone reminders, chronic disease follow-up, primary care, appointment scheduling, reminder systems, patient attendance
MeSH terms
Chronic Disease, Female, Follow-Up Studies, Humans, Male, Middle Aged, Patient Compliance, Primary Health Care, Reminder Systems, Cell Phone

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