This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Formative Research, is properly cited. The complete bibliographic information, a link to the original publication on https://formative.jmir.org, as well as this copyright and license information must be included.
Poor adherence to oral medications is common in people with type 2 diabetes and can lead to an increased chance of health complications. Text messages may provide an effective delivery method for an intervention; however, thus far, the majority of these interventions do not specify either a theoretical basis or propose specific mechanisms of action. This makes it hard to determine how and whether an intervention is having an effect. The text messages included in the current intervention have been developed to deliver specific behavior change techniques. These techniques are the “active ingredients” of the intervention and were selected to target psychological constructs identified as predictors of medication adherence.
There are 2 aims of this study: (1) to assess whether a text message intervention with specified behavior change techniques can change the constructs that predict medication adherence behaviors in people with type 2 diabetes and (2) to assess whether changes to psychological constructs are associated with changes in self-reported medication adherence.
We conducted a randomized controlled, 6-month feasibility trial. Adults prescribed oral medication for type 2 diabetes (N=209) were recruited from general practice and randomized to either receive a text message–based intervention or care as usual. Data were analyzed with repeated measures analysis of covariance and Spearman rho correlation coefficients.
For 8 of the 14 constructs that were measured, a significant time-by-condition interaction was found: necessity beliefs, intention, maintenance self-efficacy, recovery self-efficacy, action control, prompts and cues, social support, and satisfaction with experienced consequences all increased in the intervention group compared to the control group. Changes in action self-efficacy, intention, automaticity, maintenance self-efficacy, and satisfaction with experienced consequences were positively associated with changes in self-reported medication adherence.
A relatively low-cost, scalable, text message–only intervention targeting medication adherence using behavior change techniques can influence psychological constructs that predict adherence. Not only do these constructs predict self-reported medication adherence, but changes in these constructs are correlated with changes in self-reported medication adherence. These findings support the promise of text message–based interventions for medication adherence in this population and suggest likely mechanisms of action.
ISRCTN Registry ISRCTN13404264; https://www.isrctn.com/ISRCTN13404264
Poor adherence to oral treatments is common in people with type 2 diabetes [
An updated Cochrane review of 182 interventions to improve medication adherence concluded that “current methods of improving medication adherence for chronic health problems are mostly complex and not very effective,” [
Having an explicit theoretical basis provides clarity in terms of the proposed mechanism of action of an intervention; that is, what the intervention is intended to do and how. This helps with both the development and evaluation of digital health interventions. In development, having a logic model that describes how an intervention is intended to work can help designers choose what elements to include. By defining the constructs the intervention is targeting, components such as behavior change techniques (BCTs) that are hypothesized to affect these constructs can be chosen.
BCTs have been described as the “active ingredients” of an intervention and include techniques such as problem solving. There is currently a taxonomy of 93 BCTs with descriptions that can be used by intervention designers [
In terms of evaluation, an explicit logic model helps with understanding why a change in behavior either has or has not happened [
Prior to this study, a rapid systematic review was conducted that identified psychological constructs related to medication adherence from systematic reviews and meta-analyses as well as BCTs that target these constructs [
A library of text messages was developed to deliver specified BCTs that target multiple constructs relevant to this model (see
Our previous research has confirmed that the messages in the library are good examples of the BCTs they were written to represent and are acceptable to the target population [
Proposed theoretical model based on the Health Action Process Approach [
Example messages with associated BCTs (replicated from Bartlett et al [
Target and category of message | BCTa/belief or concern | Example messages |
Medication adherence, BCT | 1.4b Action planning | “Plan when, where and how you are going to take your medication.” |
Medication adherence, BCT | 15.1b Verbal persuasion about capability | “If you are struggling with your diabetes tablets then don't worry, you will be able to master it in time. You will get on top of it.” |
Medication adherence, BCT | 7.1b Prompts/cues | “It can be difficult to remember to take your tablets. Why not set an alarm to remind you to take them?” |
Medication adherence, beliefs, and concerns | Health care system–related concerns | “Lots of questions? Check who the best person to see might be.” |
Diet management | Signposting | “Stuck for new ideas? You can search recipes for mains, desserts and snacks online at Diabetes.org.uk.” |
aBCT: behavior change technique.
bNumerical identifiers from the taxonomy [
Participants were recruited from 16 general practices in England between January 2019 and June 2019. Potentially eligible patients were contacted about the study by the practice and invited to send a text message to express interest. On receipt of the text message, further information about the study was given either online or by post, and eligibility was assessed by the researchers by phone. Eligible patients were those who were ≥35 years of age, able to use a mobile phone to send and receive text messages, and taking oral medication for type 2 diabetes (including lipid and blood pressure–lowering medications for diabetes). Patients taking oral medication either with or without concomitant insulin were eligible. Patients who had been admitted to hospital in the previous 3 months with hypo- or hyperglycemia, were pregnant, were within 3 months postpartum, were planning a pregnancy within the trial, or had a serious medical condition that, in the opinion of the investigator, made them unable to take part were ineligible. Informed consent was given either online or by post. See
Support Through Mobile Messaging and Digital Health Technology for Diabetes (SuMMiT-D) feasibility CONSORT (Consolidated Standards of Reporting Trials) flow diagram.
Data were collected for a 6-month, parallel-group, randomized controlled feasibility trial as part of the Support Through Mobile Messaging and Digital Health Technology for Diabetes (SuMMiT-D) program of work. Participants were randomized in a 1:1 ratio to the intervention or control arm. Randomization was completed through a validated secure web-based program (Sortition) using a nondeterministic minimization algorithm to ensure groups were balanced for age, study site, gender, duration of diabetes, and number of medications. The allocated intervention was then delivered directly through an online platform. Aside from those conducting qualitative interviews and the engineering team, all other research team members and health care staff were blinded. For further details of the design and development, see the protocol paper [
Ethical approval for the study was granted by National Health Service (NHS) West of Scotland Research Ethics Committee 05 (reference number 18/WS/0173). The trial is registered in the ISRCTN registry (ISRCTN13404264).
Participants in the intervention group were sent up to 4 text messages per week for 6 months. There were 2 categories of messages: (1) those targeting medication adherence based on BCTs identified as relevant for this population [
Participants in the control group received 1 message per month for 6 months thanking them for their participation in the study; this was in addition to their usual care.
Assessments were completed online or on paper. At baseline, participants completed a demographic questionnaire and provided their postcode, and at baseline and 6 months, participants completed the 5-item Medication Adherence Report Scale (MARS) [
The hypothesized mechanisms of action questionnaire was developed for this study and measured key constructs targeted by the messages (see
Properties of the psychological construct scales.
Construct | Example item | Interitem correlation at baseline | Paper adapted from | |||
|
|
Correlation coefficient (Rs) |
|
|
||
Action self-efficacy | “I am confident that I can take my diabetes tablets as prescribed” | 0.82 | 203 | <.001 | Schwarzer et al [ |
|
Necessity | “My health in the future will depend on my diabetes tablets” | 0.53 | 203 | <.001 | Horne et al [ |
|
Concerns | “I sometimes worry about the long-term effects of my diabetes tablets” | 0.19 | 203 | .007 | Horne et al [ |
|
Intention | “I will take my diabetes tablets as prescribed every day over the next 3 months” | 0.81 | 204 | <.001 | Presseau et al [ |
|
Automaticity | “Taking my diabetes tablets as prescribed is something I do without thinking” | 0.50 | 199 | <.001 | Gardner et al [ |
|
Maintenance self-efficacy | “I am confident that I am able to take my diabetes tablets as prescribed even when something disrupts my routine” | 0.54 | 200 | <.001 | Greer et al [ |
|
Recovery self-efficacy | “If I don’t take my diabetes tablets for any reason, I am confident that I am able to start taking them again even if I feel no different to when I was not taking them” | 0.63 | 201 | <.001 | Greer et al [ |
|
Action planning | “I have made a detailed plan about exactly where to take my diabetes tablets” | 0.72 | 200 | <.001 | Greer et al [ |
|
Coping planning | “I have made a detailed plan for how to deal with unpleasant side effects of taking my diabetes tablets as prescribed” | 0.46 | 200 | <.001 | Greer et al [ |
|
Action control | “During the last 4 weeks I consistently monitored when, where, and how I took my diabetes tablets” | 0.23 | 201 | .001 | Sniehotta et al [ |
|
Prompts and cues | “I use things around me to help me to take my diabetes tablets as prescribed (e.g. notes, phone reminders)” | 0.63 | 202 | <.001 | N/Aa | |
Social support | “I have felt supported in taking my diabetes tablets as prescribed” | 0.29 | 202 | <.001 | Presseau et al [ |
|
Satisfaction with experienced consequences | “I am content with what I have experienced as a result of taking my diabetes tablets” | 0.75 | 203 | <.001 | Baldwin et al [ |
|
Risk perception | “I feel very at risk of developing complications, or experiencing worsening of existing complications from my diabetes if I do not take my tablets” | 0.64 | 201 | <.001 | N/A |
aN/A: not applicable.
The index of multiple deprivation (IMD) is a measure of relative deprivation used by the English government. Areas (32,844 across England) are ranked according to a variety of domains, including income, employment, health, and crime, and then the ranked list is divided into deciles [
Repeated-measures analysis of covariance (ANCOVA) was conducted for each construct, with time as a within-subject factor at 2 levels (baseline and 6-month follow-up); group (intervention or control) as a between-subject factor; and age, gender, and IMD included as covariates. As a sensitivity analysis, univariate ANCOVA for each construct were conducted, with construct at follow-up as the dependent variable; gender and experimental group as fixed factors; and construct at baseline, age, and IMD as covariates.
Standardized residual change scores were calculated using linear regression for each construct (baseline to follow-up) and MARS (baseline to follow-up). Spearman rho correlation coefficients were then calculated to assess the relationship between change in standardized residuals for each construct and change in self-reported adherence.
Participants (N=209) had a mean age of 63.4 years (SD 10.16), were 41.1% (86/209) female, and were recruited from all 10 of the IMD deciles with a mean of 6.38 (SD 2.73). Thirty-one participants were excluded from this analysis, as they did not complete the follow-up assessments analyzed here, and one participant died prior to follow-up. There were no significant differences between groups in age, gender, or IMD at baseline or between those who completed and did not complete the questionnaire measures at follow-up (see
Demographics.
Variable | Overall, mean (SD) (N=209) | Intervention, mean (SD) (N=103) | Control, mean (SD), (N=106) | Between-group differences at baselinea | Differences between completers (n=177) and noncompleters (n=31)—hypothesized mechanisms of action questionnaire assessing constructsa | Differences between completers (n=168) and noncompleters (n=40) in MARSab |
Age, (years) | 63.44 (10.16) | 63.47 (10.64) | 63.42 (9.72) | .98 | .96 | .91 |
Female | 86 (41.1)c | 42 (40.8)c | 44 (41.5)c | .51 | .36 | .22 |
IMDd decilese | 6.38 (2.73) | 6.10 (2.71) | 6.65 (2.73) | .15 | .55 | .98 |
aValues in this column are
bMARS: 5-item Medication Adherence Report Scale.
cValues in this cell are number and percentage.
dIMD: index of multiple deprivation.
en=208: 1 postcode was incorrect and could not by mapped onto the IMD.
A significant interaction between time and experimental group was seen in necessity (
Repeated-measures analysis of covariance effect of the text message intervention on psychological constructs.
Item | Control, mean (SD) | Intervention, mean (SD) | Main effect time, |
Interaction time×group, |
Significant covariates, Covariate: |
||
|
BLb | FUc | BL | FU |
|
|
|
Action self-efficacy | 8.87 (1.37) | 8.65 (2.05) | 8.55 (1.82) | 8.80 (1.49) | .88 | .10 | N/Ad |
Necessity | 7.67 (1.71) | 7.73 (1.83) | 7.44 (1.63) | 8.15 (1.53) | .72 | Age: |
|
Concerns | 5.73 (1.67) | 5.73 (1.84) | 5.85 (1.65) | 5.56 (1.64) | .18 | Age: |
|
Intention | 9.10 (1.06) | 8.77 (1.62) | 8.61 (1.51) | 9.14 (1.24) | .11 | N/A | |
Automaticity | 7.51 (1.71) | 7.51 (1.95) | 7.12 (1.85) | 7.59 (1.77) | .65 | .06 | N/A |
Maintenance self-efficacy | 8.48 (1.34) | 8.29 (1.44) | 7.91 (1.59) | 8.19 (1.44) | .58 | N/A | |
Recovery self-efficacye | 8.55 (1.27) | 8.56 (1.56) | 8.10 (1.56) | 8.67 (1.33) | .65 | N/A | |
Action planning | 6.94 (2.21) | 7.24 (2.10) | 6.88 (2.01) | 7.49 (1.96) | .72 | .32 | Age: |
Coping planning | 5.88 (1.83) | 6.32 (1.66) | 6.05 (1.63) | 6.70 (1.77) | .11 | .42 | N/A |
Action control | 7.10 (1.79) | 7.05 (1.81) | 6.99 (1.74) | 7.88 (1.59) | .12 | N/A | |
Prompts and cues | 5.38 (2.07) | 5.59 (2.09) | 4.91 (1.75) | 6.26 (1.99) | .22 | N/A | |
Social support | 4.71 (1.55) | 4.74 (1.71) | 4.95 (1.75) | 6.11 (1.65) | .24 | Age: |
|
Satisfaction with experienced consequences | 7.78 (1.91) | 7.60 (1.68) | 7.47 (1.81) | 8.16 (1.62) | .45 | Age: |
|
Risk perception | 8.08 (1.53) | 8.78 (1.76) | 8.07 (1.44) | 8.22 (1.61) | .70 | .53 | N/A |
aTest statistic and degrees of freedom are only reported for
bBL: baseline.
cFU: follow-up.
dN/A: not applicable (no significant covariates were found).
ePotentially there is less confidence in this result as recovery self-efficacy was significantly different between groups at baseline such that intervention (mean 8.07, SD 1.54) was higher than the control (mean 7.98, SD 1.52; t199=2.59;
Standardized residual change scores in 5 of the 14 psychological constructs were significantly positively correlated with those in self-reported medication adherence, such that increases in the construct represented improvements in medication adherence action self-efficacy (
In this analysis we have shown that first, provision of a text message–based intervention using behavior change techniques results in improvements to multiple psychological constructs compared to usual care. Second, we have identified that changes in psychological constructs are correlated with changes in self-reported medication adherence. These findings support the hypothesized mechanisms of action that are amenable to change through a low-cost, scalable intervention, and that when changed, may have an effect on medication adherence in people with type 2 diabetes. These findings, although tentative, provide a strong base on which to progress to a full efficacy trial.
The intervention messages use a wide variety of BCTs that are thought to target different points in the process of adherence. The incorporation of a wide variety of techniques, including some BCTs that have not been applied in this context previously, constitutes one of the strengths of this intervention, as this represents a new way to approach medication adherence. However, a corresponding weakness is that this could make looking at each individual link between BCTs and constructs more difficult, as several BCTs might have affected the same construct.
These findings have shown that this intervention can have an effect on multiple constructs that may influence people at different points in the process of improving medication adherence from forming an intention, acting on that intention, to monitoring and adjusting these actions until adherence becomes habitual (see
Medication adherence has been self-reported here using the MARS. In the future, we plan to take additional measures of adherence (eg, from medical records) so that the relationships between these constructs, self-reported adherence, and adherence measured through more direct means can be explored. Future work could also explore the use of objective measures of medication adherence, such as urine samples [
The eventual aim is that the brief text message intervention can be delivered at scale, through general practice. In terms of future scalability, basing the intervention solely on text messages is highly cost-effective. Recent research has indicated combining text messages with interactive voice recognition can be an effective intervention for medication adherence in this population [
The measures of psychological constructs used in this study were by necessity brief to minimize participant burden. There is increasing recognition that high questionnaire burden in trials has undesirable consequences, such as reducing recruitment, increasing dropout in low socioeconomic status or minority ethnic groups, and producing unintended reactions to this measurement [
Future research could use these findings for the following purposes: to investigate those constructs that did not change in this instance, and whether there are more effective BCTs to target these constructs than those used here; to explore those constructs where changes did not correlate with changes in medication adherence; and to improve the measurement of constructs where correlations between the 2 items were weak. This work would help to gather additional information that could be used to optimize interventions for this population.
The findings reported indicate that certain constructs are both amenable to change by text message and, when changed, are associated with changes in self-reported medication adherence (eg, intention, maintenance self-efficacy, and satisfaction with experienced consequences). These constructs could indicate the importance of continued feedback and adjustment within medication adherence interventions; following initial changes to intention, it may be necessary to support people to maintain and highlight the positive effects of changes made to support satisfaction with continued adherence. BCTs that target these constructs may be useful for focusing on future research into medication adherence.
This feasibility trial was not powered to look at direct effects of the intervention on the outcome. The findings do provide a clear indication of the potential value of an intervention such as this, but in the planned trial of this intervention participant numbers will be sufficiently high to ascertain efficacy of the intervention and allow for mediation analysis to further explore the potential mechanisms of action suggested here. By identifying likely mechanisms of action of the intervention beforehand, efficacy results will be more easily interpreted. In addition, with a larger sample, it may be possible to conduct subanalysis to explore whether changes in constructs are associated with particular participant characteristics, and this could provide evidence to inform future tailoring strategies. Incorporating tailoring increases the complexity of an intervention and potentially reduces the scalability. However, if future tailored interventions were compared with this nontailored intervention, an evidence base could be built on how to tailor in the most effective way, which would only introduce additional complexity where there is likely to be maximum benefit.
A text message intervention based on behavior change techniques can affect psychological constructs that are correlated with medication adherence. The use of a logic model enabled clear proposed mechanisms of action to be defined and tested. Future research can explore these potential mechanisms further to improve the understanding of adherence behavior and intervention design.
CONSORT-eHEALTH checklist (V 1.6.1).
analysis of covariance
behavior change technique
Consolidated Standards of Reporting Trials
index of multiple deprivation
Medication Adherence Report Scale
Medical Research Council
National Institute for Health Research
National Health Service
Support Through Mobile Messaging and Digital Health Technology for Diabetes
The authors would like to acknowledge the support of the University of Oxford Primary Care Clinical Trials Research Unit; the National Institute for Health Research (NHIR); Greater Manchester, West Midlands, South West Peninsula and Thames Valley, and South Midlands Clinical Research Networks; participating general practitioner surgeries; and the SuMMiT-D team.
This publication presents independent research funded by the NIHR under its Programme Grants for Applied Research (#RP-PG-1214-20003). AF is supported by the NIHR Oxford Biomedical Research Centre and is an NIHR senior investigator. The views expressed are those of the authors and do not necessarily reflect those of the NIHR or Department of Health and Social Care. The SuMMiT-D research team acknowledges the support of the NIHR Clinical Research Network.
None declared.