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The prevalence of type 2 diabetes mellitus (T2DM) is increasing worldwide. Physical activity (PA) is an important aspect of self-care and first line management for T2DM. SMS text messaging can be used to support self-management in people with T2DM, but the effectiveness of mobile text message–based interventions in increasing PA is still unclear.
This study aims to assess the effectiveness of mobile phone messaging on PA in people with T2DM by summarizing and pooling the findings of previous literature.
A systematic review was conducted to accomplish this objective. Search sources included 5 bibliographic databases (MEDLINE, Cochrane Library, CINAHL, Web of Science, and Embase), the search engine
We included 3.8% (6/151) of the retrieved studies. The results of individual studies were contradictory regarding the effectiveness of mobile text messaging on PA. However, a meta-analysis of the results of 5 studies showed no statistically significant effect (
We could not draw a definitive conclusion regarding the effectiveness of text messaging on PA, glycemic control, weight, or BMI among patients with T2MD, given the limited number of included studies and their high risk of bias. Therefore, there is a need for more high-quality primary studies.
PROSPERO International Prospective Register of Systematic Reviews CRD42020156465; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=156465
The burden of diabetes is increasing, and the number of people with type 2 diabetes mellitus (T2DM) worldwide has reached 387 million and is expected to increase to 592 million by 2035 [
Several studies have assessed the effect of mobile text messaging on the PA of patients with T2DM. It is crucial to summarize and aggregate the findings of such studies to produce more generalizable and definitive conclusions about the effectiveness of such interventions. A total of 4 previous systematic reviews did not provide evidence from studies with text messaging interventions that specifically targeted PA. Specifically, the first review focused on the impact of education on T2DM delivered via mobile text messaging [
A systematic review was conducted and reported in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement (
We used the following electronic databases in our search: MEDLINE, Cochrane Library, CINAHL, Web of Science, and Embase. These databases were searched on April 19, 2020, by the first author (MA). Auto alerts were set after searching the databases to conduct an automatic search weekly for 16 weeks (ending on August 9, 2020) and send us the retrieved studies. We also searched the search engine
The search terms were identified by consulting 2 experts in eHealth interventions for patients with diabetes and by checking systematic reviews of relevance to the review. These terms were chosen based on the target population (eg, type 2 diabetes, diabetes type 2, and type II diabetes), target intervention (eg, text messaging, text messages, and short messages), target outcome (eg, PA, physical exercise, HbA1c, and weight), and target study design (eg, trial, experiment, and randomized controlled trial [RCT]).
The population of interest was adult patients (≥18 years) with T2DM, regardless of their gender and ethnicity. We excluded patients with type 1 diabetes mellitus, gestational diabetes, and prediabetes. The target intervention in this review was mobile phone text messages (SMS text messaging and multimedia message service), but not mobile apps, web-delivered interventions, wearables, or emails. The aim of the text messages was to improve solely PA but not diet, lifestyle, diabetic literacy, or other aspects of self-care. The primary outcomes of interest were subjectively or objectively measured PA (eg, step counts), glycemic control (eg, HbA1c and fasting glucose), and anthropometric measures (eg, change in weight and BMI). Only RCTs were eligible for inclusion in this review. We considered studies published only in the English language. No restrictions were applied to the year of publication, country of publication, comparator, type of publication, or study setting.
We followed 2 steps of the study selection process. In the first step, 2 reviewers (MA and AA) independently sifted the titles and abstracts of all retrieved studies. In the second step, the 2 reviewers independently scrutinized the full texts of the studies included in the first step. In both steps, any disagreements among the reviewers were resolved through discussion and consensus. Cohen
To assess the risk of bias in the included studies, we used the Risk of Bias 2 tool, which is recommended by the Cochrane Collaboration [
We synthesized the extracted data using narrative and statistical approaches. Specifically, meta-analysis was carried out when at least two studies assessed the same outcome of interest and reported sufficient data for the analysis (eg, mean difference, SD, and number of participants in each intervention group). When the abovementioned conditions were not met, we narratively synthesized findings of the included studies. We grouped and synthesized the findings according to the measured outcomes (ie, PA, glycemic control, and weight change).
We conducted a meta-analysis using Review Manager 5.4, which is a software developed by Cochrane. We used the mean difference to assess the effect of each trial and the overall effect when the outcome data were continuous, and the outcome measure of each outcome was identical in the meta-analyzed studies. However, we used the standardized mean difference when, among studies, the outcome was measured using different tools. We selected a random effects model in the analysis because of the clinical heterogeneity among the meta-analyzed studies in terms of intervention characteristics (eg, its directionality, purpose, and frequency) and population characteristics (eg, sample size and mean age).
We assessed the clinical heterogeneity of the meta-analyzed studies by inspecting the characteristics of their interventions, outcomes, participants, and comparators. Further, we evaluated the statistical heterogeneity of the meta-analyzed studies. To do so, we calculated a chi-square
The overall quality of meta-analyzed evidence was examined using the Grading of Recommendations Assessment, Development, and Evaluation approach [
We retrieved 541 citations by searching the 6 bibliographic databases (
Flow chart of the study selection process.
As detailed in
Characteristics of studies and population.
Study | Year | Country | Study design | Sample size | Age (years), mean (SD) | Sex (male) | Health condition | Setting |
Agboola et al [ |
2016 | United States | RCTa | 126 | 51.4 (11.5) | 48.4% | T2DMb | Health centers |
Arovah et al [ |
2018 | Indonesia | RCT | 43 | 65.5 (5.8) | 37.2% | T2DM | Public hospital |
Lari et al [ |
2018 | Iran | RCT | 73 | 47.6 (9.1) | 53.4% | T2DM | Diabetes clinics |
Lari et al [ |
2018 | Iran | RCT | 76 | 48.2 (8.8) | 57.9% | T2DM | Diabetes clinics |
Polgreen et al [ |
2018 | United States | RCT | 138 | 44.6 (15.9) | 23.3% | T2DM | Community |
Ramirez and Wu [ |
2017 | United States | RCT | 28 | 52 (9.0) | 33% | T2DM | Ambulatory care clinic |
aRCT: randomized controlled trial.
bT2DM: type 2 diabetes mellitus.
The interventions in the included studies were text messages only (n=1), text messages and educational CD about PA (n=1), and text messages and pedometers (n=4;
Characteristics of interventions.
Study | Intervention | Directionality | Purpose | Frequency | Period | Theory used |
Agboola et al [ |
SMS and pedometers | 1- and 2-way | Education, motivation, reminder, and feedback | 2/day | 24 weeks | Transtheoretical model and grounded theory |
Arovah et al [ |
SMS and pedometers | 2-way | Motivation and reminder | 1-3/day | 12 weeks | Social Cognitive Theory |
Lari et al [ |
SMS | 2-way | Education | Phase 1: 2-3/day; phase 2: 2/week | Phase 1: 2 weeks; Phase 2: 10 weeks | Health promotion models |
Lari et al [ |
SMS + educational CD | 1-way | Education | 2/week | 12 weeks | Health promotion models |
Polgreen et al [ |
Intervention 1: SMS text messaging (reminder) + SMS text messaging (goal setting) + pedometer; intervention 2: SMS text messaging (reminder)+pedometer | 2-way | Reminders, feedback, and setting goals | Intervention 1: 2/day; intervention 2: 1/day | 24 weeks | N/Aa |
Ramirez and Wu [ |
Intervention 1: SMS text messaging + pedometer | 2-way | Education reminders and feedback | ≥4/week | 12 weeks | Social Cognitive Theory |
aN/A: not applicable.
The comparison group received pedometers in 4 of the studies or no intervention in 2 studies (
Characteristics of comparators and outcomes.
Study | Comparator | Period (week) | Follow-up (week) | Outcome | Outcome measure |
Agboola et al [ |
Pedometers | 24 | 24 | PAa, glycemic control, and weight | Step count, weight scale, and HbA1cb |
Arovah et al [ |
Pedometers | 12 | 12 and 24 | PA and glycemic control | Step count, PARc questionnaire, HbA1c, fasting glucose, and 2-hour glucose |
Lari et al [ |
No intervention | N/Ad | 4 and 12 | PA | METe questionnaire |
Lari et al [ |
No intervention | N/A | 4 and 12 | PA | MET questionnaire |
Polgreen et al [ |
Pedometers | 24 | 12 and 24 | PA and BMI | Step count, weight scale, and stadiometer |
Ramirez and Wu [ |
Pedometers | 12 | 6 and 12 | PA | Step count |
aPA: physical activity.
bHbA1c: glycated hemoglobin.
cPAR: physical activity rating.
dN/A: not applicable.
eMET: metabolic equivalent of task.
Although all studies used an appropriate random allocation sequence for the randomization process and had comparable groups, only 2 studies concealed the allocation sequence until participants were enrolled and assigned to interventions. Accordingly, only these 2 studies were rated as having a low risk of bias in the randomization process (
Review authors’ judgments about each risk of bias domain.
Outcome data were not available for all participants in the included studies, and there was no evidence that the findings were not biased by missing outcome data. However, the reasons for missing outcome data were not related to the true value of the outcome in all studies. Thus, all studies were judged as having a low risk of bias in the domain of missing outcome data.
In 4 studies, the outcomes of interest were assessed using appropriate measures (eg, pedometer and HbA1c), which were comparable between the intervention groups. Therefore, these studies were rated as having a low risk of bias when measuring the outcome. However, the remaining 2 studies were judged as having a high risk of bias in this domain because they used subjective outcome measures that depended on participants’ recall, and participants and outcome assessors were not blinded in the 2 studies (
Only 1 study was judged as having a low risk of bias in the selection of the reported studies (
All included studies assessed the effect of using text messages on PA among patients with T2DM. A total of 3 studies showed a statistically significant effect of text messages on PA [
The 3 remaining studies did not find a statistically significant effect of text messages on PA [
A total of 5 studies were included in the statistical analysis (ie, meta-analysis), as they reported sufficient and appropriate data for the analysis [
Forest plot of 6 studies assessing the effect of text messaging on physical activity.
A total of 2 studies examined the effect of text messages on glycemic control, as assessed by HbA1c [
Forest plot of 2 studies assessing the effect of the text messaging on HbA1c.
A total of 2 studies assessed anthropometric measures as outcomes (weight or BMI) [
Secondary outcome measures reported in the examined studies included the following variables and parameters: reports of usability, satisfaction and adherence to the RCT as discussed in the study by Agboola et al [
This systematic review assessed the effectiveness of mobile text messaging as a method of promoting PA alone in people with T2DM. The meta-analysis of the results of 5 studies (6 comparisons) showed no statistically significant effect of mobile text messaging on PA in comparison with no intervention. The insignificant effect may be attributed to the fact that 3 studies showed a statistically significant effect of mobile text messaging on PA, whereas 2 studies did not find any significant effect of text messages on PA. There are several potential reasons for the significant increase in PA in 3 studies. First, the intervention in 1 study [
Our review found no statistically significant effect of mobile text messaging on glycemic control as assessed by HbA1c, fasting plasma glucose, and 2-hour plasma glucose. Our findings are consistent with those of previous studies that showed no significant difference in HbA1c levels in people with T2DM following text messaging interventions [
The narrative synthesis in this review showed no statistically significant effect of mobile text messaging on either weight or BMI. We could not synthesize these measures in our meta-analysis because of the high heterogeneity in the included studies. Our findings are consistent with those of previous reviews, and a meta-analysis showed no statistically significant association between BMI and weight following mobile messaging interventions in people with T2DM [
Our study is the first review and meta-analysis that focused on the effectiveness of text messages targeting only PA among T2DM patients. This enabled us to ensure that the effect of text messaging on PA outcomes is attributed to PA-related message content and to no other content such as diet, lifestyle, and general diabetes education. Our study is considered a robust and high-quality review given that we followed well-recommended guidelines (ie, PRISMA) in developing, executing, and reporting it.
To run as sensitive a search as possible, we searched the most popular databases in the health and information technology fields using a very comprehensive list of search terms. The risk of publication bias is minimal in this review because we searched gray literature databases (ie, Web of Science and Google Scholar) and conducted backward and forward reference list checking. We did not restrict our search to specific countries of publication, year of publication, comparators, or settings; thus, this resulted in a more comprehensive review.
The risk of selection bias was minimal in the current review as 2 authors (MA and AA) independently selected the studies, extracted data, and assessed the risk of bias and quality of evidence, and they had a very good interrater agreement in all processes. When possible, we meta-analyzed the results of the included studies, and this improved the power of studies and the estimates of the likely size of the effect of text messaging on different outcomes.
The intervention of interest in this review was restricted to PA-related text messaging, so we did not examine the impact of other digital interventions, such as mobile apps, wearables, or other eHealth tools. We also focused on patients with T2DM, rather than patients with other types of diabetes. Accordingly, our results may not be generalizable to other eHealth interventions or patients with type 1 diabetes mellitus or gestational diabetes mellitus. In this review, we included only RCTs published in the English language; thus, it is possible that we missed results from some non-English RCTs. We applied these restrictions owing to the high internal validity of RCTs over other study designs [
The current review found relatively few studies assessing the effectiveness of text messages in promoting PA in T2DM; thus, RCTs with larger sample sizes are needed. Future studies should seek to include objective outcome measures (eg, PA, glycemic control, and anthropometric measures), be consistent in terms of selected outcome measures, and measure outcomes after longer follow-up periods to be able to compare study findings and make firm conclusions about intervention effectiveness. More research is needed to determine the type of text message content, frequency of messaging, and duration of intervention that is most likely to result in positive outcomes. Additional research needs to include an estimation of the cost-effectiveness of text messages and an examination of their long-term impact.
We could not draw a definitive conclusion regarding the effectiveness of text messaging on PA, glycemic control, weight, or BMI among patients with T2MD, given the low number of included studies and their high risk of bias. Thus, the findings of this study suggest that texting messaging should not substitute but rather supplement clinical support. In addition, there is a pressing need for further RCTs with large sample sizes, low risk of bias, and more consistency regarding intervention duration, outcome measures, follow-up period, and comparator.
PRISMA (Preferred Reporting Item for Systematic Reviews and Meta-Analyses) checklist.
Search query used for searching MEDLINE.
Data extraction form.
Reviewers’ judgements about each “risk of bias” domain for each included randomized controlled trial.
Grading of Recommendations Assessment, Development, and Evaluation profile.
glycated hemoglobin
physical activity
Preferred Reporting Item for Systematic Reviews and Meta-Analyses
randomized controlled trial
type 2 diabetes mellitus
This review was supported by a doctoral scholarship from the Ministry of Higher Education, Saudi Arabia.
None declared.