|Year : 2021 | Volume
| Issue : 4 | Page : 447-455
Impact of integrated medication reminders, gamification, and financial rewards via smart phone application on treatment adherence in uncomplicated type II diabetes patients: a randomized, open-label trial
Tejas Kamat1, Amit Dang2, Dimple Dang2, Pawan Rane3
1 Diabetes Care Clinic, Goa, India
2 MarksMan Healthcare Communications, Hyderabad, Telangana, India
3 Healthway Hospital, Goa, India
|Date of Submission||24-Mar-2021|
|Date of Decision||10-May-2021|
|Date of Acceptance||21-May-2021|
|Date of Web Publication||12-Jan-2022|
Dr. Amit Dang
KYT-Adhere and MarksMan Healthcare Communications, H. No. 9-1-67, Plot No. 67, TNGO’s Colony, Behind Q City, Financial District, Hyderabad, Telangana 500032.
Source of Support: None, Conflict of Interest: None
Background: Poor medication adherence in type 2 diabetes mellitus (T2DM) leads to poor glycemic control. Materials and Methods: This randomized, open-labeled, controlled study recruited consenting adult patients with uncomplicated T2DM who were on daily oral antidiabetics with documented poor medication adherence (missing ≥20% of their prescribed doses in the past 15 days). Patients in the “incentive group” installed a digital therapeutics mobile app (KYT-Adhere) and received multiple daily medication reminders. Patients were asked to show the pill/(s) to the app before consuming the medication, after which the patients received “KYT-Points”; these would be converted into financial incentives after 3 months, provided that they maintained ≥80% medication adherence. These patients received incentives for 3 months and medication reminders for 6 months. “Control group” patients received standard care. Results: A total of 118/120 recruited patients completed the study; 59 each with similar baseline parameters were randomized to incentive and control groups. At baseline, medication adherence and HbA1c were comparable (adherence: 65.7±4.7% and 65.3±4.0%; HbA1c: 9.0±0.3% and 9.0±0.3% for incentive and control groups, respectively). Over the study duration, the incentive group showed a significant improvement in medication adherence (P < 0.001) and significant HbA1c reduction (P < 0.001). At study closure, the average medication adherence and HbA1c were significantly different between the two groups (adherence: 86.8±3.2% vs. 67.7±4.6%, P < 0.001; HbA1c: 7.3±0.2% vs. 8.2±0.3%, P < 0.001). Conclusion: Gamification through combining repeated medication reminders and rewards through a smartphone application brought about a behavioral change, which improved medication adherence and glycemic control among T2DM patients within 3 months and was sustained for 3 more months without rewards.
Keywords: Behavioral modifications, digital therapeutics, financial incentives, medication adherence, medication reminders, uncomplicated type 2 diabetes
|How to cite this article:|
Kamat T, Dang A, Dang D, Rane P. Impact of integrated medication reminders, gamification, and financial rewards via smart phone application on treatment adherence in uncomplicated type II diabetes patients: a randomized, open-label trial. J Diabetol 2021;12:447-55
|How to cite this URL:|
Kamat T, Dang A, Dang D, Rane P. Impact of integrated medication reminders, gamification, and financial rewards via smart phone application on treatment adherence in uncomplicated type II diabetes patients: a randomized, open-label trial. J Diabetol [serial online] 2021 [cited 2022 Aug 13];12:447-55. Available from: https://www.journalofdiabetology.org/text.asp?2021/12/4/447/335604
| Introduction|| |
India is home to the second largest number of adults with diabetes mellitus (DM) worldwide, with approximately 77 million patients with DM as on 2019; by 2045, there will be around 134.2 million adults with DM in India.
Despite the availability of a wide range of effective and safe treatment options for treating type 2 DM (T2DM), acceptable glycemic control (defined as Hba1c <7%) is not achieved in at least 45% of the T2DM patients, predisposing them with high risk of developing several diabetes-related complications, which further lead to increased morbidity and healthcare costs. One of the most prominent reasons for poor glycemic control among patients with T2DM is the poor rates of adherence to antidiabetic medications, which has been reported in all corners of the world, including India.,,,,,, An accepted definition of medication adherence is “correctly taking at least 80% of prescribed medication doses.”, Identification of factors leading to poor medication adherence in diabetes has been the focus of research from a long time. Any intervention aiming to introduce behavioral modification that leads to improvement in medication adherence has the potential to significantly lower the emergence of complications due to T2DM, which would be associated with a similar lowering in the cost due to the illness.
Providing rewards to improve medication adherence has been tried in the past for a large variety of health conditions.,,,, Secondly, directly observed therapy (DOT) has been successfully proven to enhance treatment adherence in chronic conditions such as tuberculosis treatment in the past and has been endorsed worldwide. Advances in mobile phone technology have made it possible to perform DOT remotely by means of apps developed for this purpose. Finally, gamification (which is the application of principles of games to stimulate users to perform routine activities) is also found to improve medication adherence in conditions such as DM and cancer treatment.
With this background, a new smartphone digital therapeutics (DTx) application named KYT-Adhere (Dexium Technologies Pvt. Ltd, India) is now available, which combines these concepts of “medication reminders,” “rewards,” “gamification,” and “DOT” for enhancing medication adherence in patients with any condition. Backed by artificial intelligence (AI) and machine learning (ML) algorithms, KYT-Adhere is a disease-customizable, invite-only DTx intervention that performs DOT, automatically calculates the extent of adherence, and provides rewards for patients in the form of redeemable KYT points, in return to maintaining a pre-specified level of medication adherence, thereby using the concepts of gamification to improve medication adherence.
The present study is the first to explore the clinical application of this smartphone app in diabetes, with the main objective of evaluating the impact of providing medication reminders and rewards to patients on the medication adherence rate in Asian Indian patients with uncomplicated T2DM, as measured using an AI-based DOT.
| Materials and Methods|| |
Study design and participants
This randomized, parallel-group, open-label controlled study screened patients with uncomplicated T2DM attending the outpatient clinic of a diabetes speciality center in Goa, India for eligibility using predetermined criteria. Consenting adult Asian Indian patients (aged 18–65 years) of either gender diagnosed with uncomplicated T2DM at least 6 months prior, who were on oral antidiabetic therapy with no change in treatment schedule anticipated for the next 6 months, having poor adherence to diabetic medication (defined as missing ≥20% of, or consuming <80% of, their prescribed doses of oral antidiabetic medication in the past 15 days, as documented by patient recall), owning a smartphone with an active internet connection, and able to understand English language were recruited into the study. Patients having any DM-related complications, receiving any parenteral medication for DM, having type 1 DM, pregnant patients, and already having good medication adherence were excluded from the study. We also excluded patients with poorly controlled T2DM, T2DM patients in whom a change in oral antidiabetic therapy is anticipated in the next 6 months, and also those patients who had any comorbid illness that would, as per the investigators’ discretion, interfere with completion of the study.
At baseline visit, randomization was done and app was installed in patients randomized to the “incentive” group. Rewards in the form of money for maintaining medication adherence were accumulated by the patients over 3 months, which were redeemed at the interim visit at 3 months (±7 days). Adherence incentives were stopped at this point, and the extent of retention of the behavioral change in the absence of financial incentives was measured over the subsequent 3 months during the closure visit at 6 months ±14 days post-randomization [Figure 1]. The incentive group patients received medication reminders for the entire duration of the study.
The study protocol was approved by an independent Ethics Committee in Goa (Aavishkar Ethics Committee; CDSCO registration number ECR/138/Indt/GA/2013/RR-19). The trial was conducted in accordance with the ICH-GCP guidelines and all relevant provisions of the Declaration of Helsinki. All patients provided written informed consent before enrollment and participated in the study out of their own will.
The sample size for the study was calculated based on observations that “good” medication adherence is found in around 54% of Indian patients with T2DM., Assuming that with the intervention (integrative reminders and financial incentives), the medication adherence will improve from “poor” to “good” in at least 50% of the recruited patients (“incentive” group), and a similar improvement will be seen in maximum of 20% of the patients even without the intervention (“control” group), and with a power = 80% and α = 0.05, with 10% non-inferiority margin, and a loss to follow-up of 25%, the minimum sample size was calculated as 57 patients per group, and it was decided to recruit a total of 120 patients in the study. The sample size was calculated online using the website https://www.openepi.com/.
Randomization and concealment
Because of the nature of the intervention, blinding was not possible; however, to minimize allocation bias in the resulting open-label study design, generation of random allocation sequence was performed independently using a computer-generated random numbers table that was maintained by a partner organization located in Hyderabad, India, and communicated via email to the clinicians involved in patient recruitment in Goa. Randomization was stratified based on gender, and eligible patients were randomly assigned in 1:1 ratio to the two groups.
Description of interventions
After randomization, all enrolled patients (irrespective of the treatment group) attended a 30-min interactive sensitization program using standardized patient handouts, in which patients were provided information about diabetes and importance of treatment adherence. This session was conducted by a trained assistant physician.
Patients randomized into the “incentive group” were asked to install the KYT-Adhere app in-clinic, and a demonstration about using the app was provided. The details of the KYT-Adhere app used in the study are: Android, version 1.0, app size 73.30 MB, compatible with Android 5.0 and above, coding format: Kotlin; iOS, version 4.0, app size 101.32 MB, compatible with iOS 12.0 and above, coding format: Swift; KYT Admin: version 4.0, technology: PHP, MySQL, PHP version 7.4.12, compatible with Chrome, Safari, and Firefox browsers, code framework: Laravel 5.8. Patients entered their basic details and the details of a caregiver. All the patients randomized into the incentive group were able to understand the functionality of the app after the demonstration session. The app provided up to four medication reminders per dose to the patients over a 2-h window (one in-app reminder at 0 min of the dose, one text reminder at 60th minute, one reminder to patient caregiver at 90th minute, and one automated telephone call to patient at 115th minute); after the patient consumed the medication, further reminders for that dose were stopped.
Before consuming the tablet, patients were asked to open the app and hold the tablet in front of the smartphone camera. The AI algorithm of the app captured the tablet, performed “pill identification and pill counting,” and time-stamped the capture. Subsequent to the capturing of the pill, it was assumed that the patient has consumed the medication, and the app calculated the patient’s “adherence score” as a percentage. If a dose was missed, patients were asked to enter the reason for missing when they opened the app to record their next dose consumption. The number of doses consumed, skipped doses, and % adherence were displayed to the patient in the app; inspection of the “Performance Ring” would give the patient information about the current adherence % with an encouragement to maintain the adherence above 80%. After each verified time-stamped pill, patients received “KYT points” of random quantity (ranging from 1 to 6 points per dose) in the form of scratch cards. At the end of the study period of 3 months, if the patient has maintained at least 80% of adherence throughout the study, the patient would be able to convert these earned “KYT points” into financial incentive (adherence incentive), wherein a proportionate quantity of money would be directly transferred into their bank account (1 KYT point = 1 INR). Upon maintaining at least 80% adherence throughout the study, patients could receive a maximum of INR 300 per patient per dose at the interim visit. In case of multiple eligible tablets for the study, the maximum total amount of adherence incentive was capped at INR 600 per patient. The higher the adherence score, the higher would be the incentive. In addition to the adherence incentives, each patient in the incentive group also received “visit incentives” of INR 100 per visit. Patients in the “control group” did not install the app and only received “visit incentives.”
Outcomes and data collected
The primary outcome was the improvement in medication adherence from baseline, and the secondary outcome was the improvement in glycemic control.
Baseline adherence was measured using patient recall for all randomized patients. During the baseline visit, all patients were instructed to retain their empty tablet strips after consuming the tablets and to bring the empty tablet strips while coming to the two subsequent visits (interim visit at 3 months and closure visit at 6 months). The patients were given a reminder 7 days prior to their scheduled interim and closure visits through a text message to bring their empty tablet strips to avoid discontinuation from the study. Medication adherence during the interim and closure visits was measured using pill counts for all patients. For patients in the incentive group, an in-app feature for measuring medication adherence was used in addition to pill-count method in the interim visit. In all the cases, adherence was expressed as percentage of tablets that the patient actually consumed as opposed to the number of tablets that the patient was expected to consume. Glycemic control was measured using HbA1c (glycated hemoglobin), which was measured once per visit for each patient.
Demographic details collected during baseline visit included age, duration of diabetes, presence of comorbidities, details of antidiabetic and other medications, family history of diabetes; height and weight were measured and baseline body mass index (BMI) was calculated. Baseline medication adherence was calculated through the recall method for all patients, and baseline HbA1c was measured.
Data handling, analysis, and availability
The app is completely HIPAA-compliant, and all patient data captured through the app were handled securely and used only for the purpose of this study. As data verification happened in the backend based on AI/ML protocols, human interference with patient data was non-existent. All patient data underwent standard encryption and secure storage through multiple security features.
All patient data were entered in Microsoft Excel. Comparisons of means between groups and within groups were done using Student’s t-test and repeated-measures analysis of variance (ANOVA), and comparison of proportions was done using the χ2 test. SPSS version 20.0 was used for statistical analysis, and P-values ≤ 0.05 were considered statistically significant. The data that support the findings of this study are available from the corresponding author upon reasonable request. The CONSORT-NPT extension guidelines for non-pharmacological treatments were used for preparing the protocol and reporting the study results.
Amendment in methodology due to COVID-19
Due to the restrictions in place during the COVID-19 pandemic, the interim and closure visits were done through telemedicine consultation, unlike the face-to-face baseline visit. Pill counting in these two visits was achieved by patients showing empty pill strips over telemedicine and destroying them in the presence of the consultant, and HbA1c was measured by arranging for home-based blood sample collection.
| Results|| |
Patient recruitment started on January 1, 2020, and after screening 105 patients for eligibility, recruitment of the target sample size of 120 patients was accomplished by March 20, 2020. Two patients dropped out of the study within 2 days of randomization and hence were not included in the analysis (one patient randomized to the “incentive group” was not comfortable with the app usage, and one patient randomized to the control group withdrew consent because she wanted to be randomized into the incentive group) [Figure 2]. Interim visit and closure visits were completed by the 118 patients after an average of 90.96±3.22 days (range 84–97 days) and 185.75±4.48 days (range 174–196 days), respectively, from the date of randomization.
The baseline features of both the groups were well-balanced [Table 1]. Metformin was a part of antidiabetic therapy for all 118 patients, with 19 patients receiving metformin monotherapy. The second diabetic drug for the remaining 99 patients was vildagliptin (n = 22), teneligliptin (n = 23), sitagliptin (n = 10), remogliflozin (n = 22), empagliflozin (n = 12), and glimepiride (n = 10). Comorbid hypertension well controlled by antihypertensive medication was found in 21 patients; 2 patients each took non-steroidal anti-inflammatory drugs and inhaled salbutamol for osteoarthritis of knee and asthma, in addition to antihypertensive drugs. Apart from these 21 patients, concomitant medications were taken by 36 patients, and the medications included calcium supplements, multivitamin preparations, and Ayurvedic formulations.
|Table 1: Baseline features of patients randomized to incentive and control groups|
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There were totally 79 incidents of app malfunctioning during the “incentive phase” of the first 3 months, all of which were resolved within 6 h through IT support.
All 59 patients in the incentive group received two doses of antidiabetic drugs in a day, either the same drug (given in twice daily dosing) or two different formulations in the morning and evening dose. Only those doses for which adherence in the past 15 days was <80% were considered: out of the 59 patients randomized in the incentive group, incentive was considered for one daily dose in 11 patients and two daily doses in 48 patients. A total of 20,665 medication reminders were sent out through the app to these 59 patients, over a period of 3 months. As per patient feedback during the interim visit, most patients perceived the first in-app reminder to be the most effective among all the reminders.
The number and proportion of missed doses were significantly lower in the incentive group [Table 2]. Patients randomized to the incentive group missed 12.17% (2417/19,859 doses) of their doses over a period of 6 months, compared with 31.89% (5995/18,798 doses) missed by the control group patients (P < 0.001). Assuming similar patient behavior, in the absence of intervention in the incentive group, the patients would have missed 31.89% of the total 19,859 doses (i.e., 6,333 doses); but the actual number of doses missed by the incentive group patients was 2417. Thus, the intervention was able to prevent (6333−2417=) 3916 doses from being missed, over a total study period of 185.75 days, at an average of 21.08 doses per day, among the 59 patients randomized to the incentive group.
|Table 2: Details of consumed and missed doses by patients randomized to incentive and control groups over the incentive phase, follow-up phase, and overall study duration|
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The three most frequent reasons given for missing the doses among incentive group patients were patient being busy, unable to access the app while consuming the dose, and forgot to buy medicines on time; for the control group patients, the three most frequent reasons were forgetfulness, being busy, and being out of reach to the medicine.
Average medication adherence rates increased from baseline values in both the groups, but the improvement in adherence in the incentive group was significantly higher than the control group across the three visits (repeated-measures ANOVA, F (1.203, 139.511) = 820.782, P < 0.001). During both interim and closure visits, the average adherence % as measured by the independent t-test was significantly higher (P < 0.001) in the incentive group when compared with the control group [Figure 3]A. The number of patients with adherence >80% among the incentive group was 59/59 (100%) and 57/59 (96.6%) in the interim and closure visits, respectively; in contrast, none of the 59 patients in the control group achieved adherence >80% in both the visits. In the interim visit, the average medication adherence among the patients randomized to the incentive group as measured by pill counts was slightly higher than that measured by the app, but the difference was statistically non-significant (pill count: 89.4±3.8%; app: 89.0±4.0%; P = 0.78).
|Figure 3: Changes in average levels of adherence (A) and HbA1c (B) in the two groups over the study period|
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All 59 patients in the incentive group had maintained adherence of 80% and above during the interim visit and hence were eligible to redeem their KYT points to actual financial incentives. The total adherence incentive given to the 59 patients during the interim visit amounted to INR 32,742 over a period of 90.96 days. This translates to an average of INR 6.10 per day per patient. The total amount of visit incentive given to all the patients attending the three visits amounted to INR 35,600.
Average values of HbA1c reduced from baseline values in both the groups, but the HbA1c reduction in the incentive group was significantly larger than that of the control group across the three visits (repeated-measures ANOVA, F (1.744, 202.359) = 2889.741, P < 0.001). During both interim and closure visits, the average HbA1c % (after adjusting for medication) as measured by the independent t-test was significantly lower (P < 0.001) in the incentive group when compared with the control group [Figure 3B].
The average weight in both the groups decreased from baseline over 6 months, but the reduction was not statistically significant when compared with baseline (P = 0.612 for incentive group, P = 0.825 for control group, paired t-test). Even though the incentive group recorded a slightly higher average weight loss over 6 months when compared with the control group (incentive group: 0.08±1.27 kg; control group: 0.03±1.17 kg), this difference was not statistically significant over the duration of the study (ANOVA: F (1.553, 180.102) = 0.222, P = 0.744). The changes in the BMI values mirrored the changes in the weight, with the BMI reduction in the incentive group over 6 months being slightly but not significantly lower than that seen with the control group [mean BMI reduction in the incentive group: 0.021±0.48 vs. control group: 0.004±0.42, F (1.576, 182.863) = 0.094, P = 0.866].
| Discussion|| |
Poor medication adherence leading to complications is a well-known problem in many chronic diseases, including T2DM. Making the patients remember to take their medication involves behavioral modification, and various avenues have been tried to bring this about, with variable degrees of success. Notably, it has been observed in the past that providing medication reminders and reinforcement can improve medication adherence.,, Despite this observation, poor medication adherence continues to be a problem in T2DM management. This suggests that reinforcement alone might not be sufficient to bring in a strong behavioral change that is sustained for a long time. In the present study, we have demonstrated that gamification in the form of scratch cards after consuming each dose as prescribed, and providing financial rewards for maintaining good adherence level, has the potential to convert a “boring” task into a “fun activity” with beneficial health outcomes, as evidenced by improvement in glycemic control. By combining reinforcement with a reward in the form of financial incentives, we were able to achieve a behavioral change within a period of 3 months, which was observed to sustain for 3 subsequent months even in the absence of financial incentives.
Combining reminders and rewards in the form of financial incentives for improving medication adherence has been studied in the past by Petry et al. in managing patients with hypertension. Though the sample size in the Petry et al. study was small, it was observed that the combining medication reminders and financial incentives improved medication adherence within 3 months, which was sustained over a 3-month follow-up. However, an associated improvement in blood pressure control was not observed. Although Petry et al.’s study recruited all patients irrespective of baseline adherence, we were more selective in our approach: we recruited only patients who had missed ≥20% of their 15-day doses, thereby ensuring that the impact of the intervention to improve adherence was measured in the patient subset which had a documented problem in maintaining adherence. Probably, this is also the reason why we were able to record an improvement in the glycemic control.
Providing financial incentives might appear to be an expensive way of bringing about an improvement in adherence. However, it has been established that improving medication adherence in patients with DM reduces hospitalizations and visits to the emergency, thereby lowering healthcare costs. In this background, by spending a little over INR 6 per day per patient, we were able to improve the medication adherence and glycemic control among patients with poor medication adherence. Whether our approach is cost-effective or not needs to be evaluated by doing a well-designed cost-effectiveness study.
The adherence rate calculated through pill counting among the incentive group during the interim visit was slightly higher than that captured by the app through DOT. Accurately measuring medication adherence is considered to be a clinical and research challenge, and an AI-enabled DOT using a DTx app, as explored in our study, has the potential to provide a solution to this challenge.
We observed that providing incentives and reminders was able to prevent missing of nearly 4000 doses among 59 patients, within a span of 6 months. From the pharma industry viewpoint, in the absence of the intervention, missing of these many doses would have resulted in a significantly reduced revenue. Indeed, in 2011, it was estimated that poor medication adherence was responsible for a pharmaceutical revenue loss of USD 564 billion globally. This observation should encourage the pharma industry to actively seek out for interventions which may improve medication adherence. In this background, and in the light of DTx applications such as the one explored in the present study showing encouraging results in improving medication adherence, it is not a surprise that pharma is investing in DTx solutions to bring about an improvement in medication adherence. In fact, bringing about an improvement in medication adherence is expected to be advantageous for multiple stakeholders: patients (improved health outcomes), physicians (avoiding preventable complications leads to reduced work burden), pharma (improved sales), payers (improvement in overall health leading to lower reimbursement pay-outs), and government (reduced healthcare costs).
Our study had two incidental findings. The first one concerns the powerful role of digital health technologies in the practice of medicine and medical research. With increasing emphasis on online consultations and virtual clinical trials, the adoption of digital health including DTx has been hastened by the COVID-19 pandemic. Despite a reduced face-to-face interaction between the patients and the physician brought about by the pandemic, the daily reminders delivered virtually, coupled with rewards, was able to improve the adherence level among patients in our study, thereby demonstrating that DTx has a clear potential in the future practice of medicine. The second incidental finding in our study was that the weight reduction in the incentive group was non-significantly larger than those who did not receive rewards. One possible reason that might have contributed to this observation is that the behavioral change brought about pertaining to medication adherence has also extended to a corresponding behavioral change with respect to adhering to diet and exercise schedule, thereby leading to reduction in body weight. While our study was not powered sufficiently to detect a meaningful change in body weight, and also considering the small sample size, this finding should serve as a stepping stone to explore whether an association exists between improvement in medication adherence in diabetes and body weight.
Our study has some limitations. The low sample size is perhaps the greatest limitation of this study; the availability of the complete data from all the patients completing the study should serve as a stronger support to the points raised in this study. The study protocol was not registered in any clinical trials database. Finally, we did not explore the association of adherence improvements with confounding factors such as education, occupation, income, and socio-economic status.
| Conclusions|| |
We have demonstrated a significant improvement in medication adherence and glycemic control among Asian Indian patients with uncomplicated T2DM by gamification in the form of integrating medication reminders and rewards that was delivered through a smartphone application. This behavioral change was not only observed within 3 months, but also was sustained over the subsequent 3 months. While for this study we provided incentives amounting to INR 6.10 per patient per dose, the most optimal amount of financial incentive for this patient group remains to be explored by means of cost-effectiveness studies.
Manuscript writing and statistical analysis support were provided by Dr B. N. Vallish, MarksMan Healthcare Communications, India.
Financial support and sponsorship
The study was sponsored by Dexium Technologies Pvt. Ltd, Goa.
Conflicts of interest
Dr Tejas Kamat has no conflicts of interest. Dr Amit Dang, Ms Dimple Dang, and Dr Pawan Rane are co-founders of KYT Adhere.
| References|| |
IDF Diabetes Atlas. 9th ed. 2019. Available from: https://www.diabetesatlas.org/upload/resources/material/20200302_133351_IDFATLAS9e-final-web.pdf. Accessed March 24, 2021.
Polonsky WH, Henry RR. Poor medication adherence in type 2 diabetes: Recognizing the scope of the problem and its key contributors. Patient Prefer Adherence 2016;10:1299-307.
Alqarni AM, Alrahbeni T, Qarni AA, Qarni HMA. Adherence to diabetes medication among diabetic patients in the Bisha Governorate of Saudi Arabia—A cross-sectional survey. Patient Prefer Adherence 2019;13:63-71.
Aminde LN, Tindong M, Ngwasiri CA, Aminde JA, Njim T, Fondong AA, et al
. Adherence to antidiabetic medication and factors associated with non-adherence among patients with type-2 diabetes mellitus in two regional hospitals in Cameroon. BMC Endocr Disord 2019;19:35.
Dehdari L, Dehdari T. The determinants of anti-diabetic medication adherence based on the experiences of patients with type 2 diabetes. Arch Public Health 2019;77:21.
Acharya AS, Gupta E, Prakash A, Singhal N. Self-reported adherence to medication among patients with type II diabetes mellitus attending a tertiary care hospital of Delhi. J Assoc Physicians India 2019;67:26-9.
Waari G, Mutai J, Gikunju J. Medication adherence and factors associated with poor adherence among type 2 diabetes mellitus patients on follow-up at Kenyatta National Hospital, Kenya. Pan Afr Med J 2018;29:82.
Lin LK, Sun Y, Heng BH, Chew DEK, Chong PN. Medication adherence and glycemic control among newly diagnosed diabetes patients. BMJ Open Diabetes Res Care 2017;5:e000429.
García-Pérez LE, Alvarez M, Dilla T, Gil-Guillén V, Orozco-Beltrán D. Adherence to therapies in patients with type 2 diabetes. Diabetes Ther 2013;4:175-94.
Kleinsinger F. The unmet challenge of medication nonadherence. Perm J 2018;22:18-033.
Krass I, Schieback P, Dhippayom T. Adherence to diabetes medication: A systematic review. Diabet Med 2015;32:725-37.
Noordraven EL, Wierdsma AI, Blanken P, Bloemendaal AF, Staring AB, Mulder CL. Financial incentives for improving adherence to maintenance treatment in patients with psychotic disorders (money for medication): A multicentre, open-label, randomised controlled trial. Lancet Psychiatry 2017;4:199-207.
Martins N, Morris P, Kelly PM. Food incentives to improve completion of tuberculosis treatment: Randomised controlled trial in Dili, Timor-Leste. Br Med J 2009;339:b4248.
Nunes EV, Rothenberg JL, Sullivan MA, Carpenter KM, Kleber HD. Behavioral therapy to augment oral naltrexone for opioid dependence: A ceiling on effectiveness? Am J Drug Alcohol Abuse 2006;32:503-17.
Sorensen JL, Haug NA, Delucchi KL, Gruber V, Kletter E, Batki SL, et al
. Voucher reinforcement improves medication adherence in HIV-positive methadone patients: A randomized trial. Drug Alcohol Depend 2007;88:54-63.
Stitzer ML, Polk T, Bowles S, Kosten T. Drug users’ adherence to a 6-month vaccination protocol: Effects of motivational incentives. Drug Alcohol Depend 2010;107:76-9.
Bayer R, Wilkinson D. Directly observed therapy for tuberculosis: History of an idea. Lancet 1995;345:1545-8.
Makris E, Hu L, Jones GB, Wright JM. Moving the dial on heart failure patient adherence rates. Patient Prefer Adherence 2020;14:2407-18.
KYT Adhere. Available from: https://kyt.ai/. Accessed March 24, 2021.
Kumar N, Unnikrishnan B, Thapar R, Mithra P, Kulkarni V, Holla R, et al
. Distress and its effect on adherence to antidiabetic medications among type 2 diabetes patients in Coastal South India. J Nat Sci Biol Med 2017;8:216-20.
Venkatesan M, Dongre AR, Ganapathy K. A community-based study on diabetes medication nonadherence and its risk factors in rural Tamil Nadu. Indian J Community Med 2018;43:72-6.
] [Full text]
Boutron I, Altman DG, Moher D, Schulz KF, Ravaud P; CONSORT NPT Group. CONSORT statement for randomized trials of nonpharmacologic treatments: A 2017 update and a CONSORT extension for nonpharmacologic trial abstracts. Ann Intern Med 2017;167:40-7.
Hussein WI, Hasan K, Jaradat AA. Effectiveness of mobile phone short message service on diabetes mellitus management; the SMS-DM study. Diabetes Res Clin Pract 2011;94:e24-6.
Shetty AS, Chamukuttan S, Nanditha A, Raj RK, Ramachandran A. Reinforcement of adherence to prescription recommendations in Asian Indian diabetes patients using short message service (SMS)—A pilot study. J Assoc Physicians India 2011;59:711-4.
Yoon KH, Kim HS. A short message service by cellular phone in type 2 diabetic patients for 12 months. Diabetes Res Clin Pract 2008;79:256-61.
Petry NM, Alessi SM, Byrne S, White WB. Reinforcing adherence to antihypertensive medications. J Clin Hypertens (Greenwich) 2015;17:33-8.
Encinosa W, Bernard D, Dor A. Does prescription drug adherence reduce hospitalizations and costs? Working paper 15691. National Bureau of Economic Research. Available from: http://www.nber.org/papers/w15691. Accessed March 24 , 2021.
Pednekar PP, Ágh T, Malmenäs M, Raval AD, Bennett BM, Borah BJ, et al
. Methods for measuring multiple medication adherence: A systematic review-report of the ISPOR medication adherence and persistence special interest group. Value Health 2019;22:139-56.
Forrissier T, Firlik K. Estimated Annual Pharmaceutical Revenue Loss due to Medication Non-Adherence. Capgemini consulting. Available from: https://www.capgemini.com/wp-content/uploads/2017/07/Estimated_Annual_Pharmaceutical_Revenue_Loss_Due_to_Medication_Non-Adherence.pdf. Accessed March 24, 2021.
Comstock J. Digital therapeutics, medication adherence, clinical trials were key pharma interests in Q2. Mobihealthnews. Available from: https://www.mobihealthnews.com/content/digital-therapeutics-medication-adherence-clinical-trials-were-key-pharma-interests-q2. Published July 18, 2016. Accessed March 24, 2021.
Bokolo AJ. Exploring the adoption of telemedicine and virtual software for care of outpatients during and after COVID-19 pandemic. Ir J Med Sci 2021;190:1-10.
[Figure 1], [Figure 2], [Figure 3]
[Table 1], [Table 2]