Effectiveness of mobile health intervention in medication adherence: A systematic review and meta-analysis

doi:10.21203/rs.3.rs-3440357/v1

Background: Low medication adherence is known to pose a great risk of poor treatment outcomes among patients with chronic diseases. Recently, mobile applications (apps) have been recognized as effective interventions, enabling patients to adhere to their prescriptions. This study aimed to establish the effectiveness of mobile app interventions for medication adherence and the affecting features and dropout rates by focusing on randomized controlled trials (RCTs).

Method: This study conducted a systematic review and meta-analysis of mobile app interventions targeting medication adherence in patients with chronic diseases. Electronic searches of eight databases were conducted on April 21, 2023. Comprehensive meta-analysis (CMA) software was used to estimate the standardized mean difference (SMD) of pooled outcomes, odds ratios (ORs), and confidence intervals (CIs). Subgroup analysis was applied to investigate and compare the effectiveness of the interventional strategies and their features. The risk of bias of the included RCTs was evaluated by applying the risk of bias (ROB) tool. Publication bias was also examined using the fail-safe N method.

Results: Twenty-nine studies with 2,684 participants were included. The meta-analysis findings showed a positive impact of mobile apps on improving medication adherence (SMD = 0.386, OR = 2.34). The subgroup analysis results revealed greater effectiveness of interventions using interactive strategies (SMD=1.367, OR=2.678), context-aware reminders (SMD = 0.664, OR = 2.452), data-sharing (SMD = 0.321, OR = 2.449), and pill dispenser (OR = 2.436).

Conclusion: The current study found that mobile interventions had significant effects on medication adherence improvement. Subgroup analysis showed that the roles of stakeholders in health providers’ interactions with patients and developers’ understanding of patients and disease characteristics are critical. Future studies should incorporate advanced technology reflecting acceptability and the needs of the target population.

medication adherence

mobile apps

chronic disease

systematic review

meta-analysis

Increases in chronic diseases like diabetes and hypertension, with diverse complications, in aging populations bring financial burdens to nations and are the leading cause of mortality worldwide.[1, 2] Issues related to non-adherence in patients with chronic diseases can cause the fast deterioration of disease conditions and increased risks of complications, which eventually lead to increased mortality.[3] The risk of non-adherence and its negative impact on health have resulted in diverse efforts to use health information technology, which has been considered beneficial and cost-effective, to improve medication behavior.[4]

The definition of medication adherence is “the process by which patients take their medications as prescribed” and involves the continuous efforts of patients as well as family and health providers.[5, 6] To prevent non-adherence or the poor implementation of a prescribed regimen (e.g., skipping, extra, or reduced doses), one of the most effective strategies is regularly monitoring drug intake.[7] Prior behavioral and educational interventions showed rather modest effects.[8] and uniform interventions were distinct from solutions for diverse non-adherence phenotype.[8]

Mobile health is considered a significant breakthrough in the field of disease management.[9] In response to increases in preparedness for uncertain and complex healthcare environments, technological advancement is expected to support healthcare professionals as well as patients. Mobile health, in particular, which is considered a subset of digital health, includes health information technology, telemedicine, and personalized medicine.[10] The use of this technology enables patients to engage in disease management anywhere and anytime, assuring frequent and consistent contact with patients, the timely provision of intervention with health information, and the facilitation and motivation of patients to conduct self-management.[11]

Mobile data provide health providers with real-time access and enables the monitoring of patient data.[12] This could also be a means to create diverse forms of health data that provide opportunities to improve healthcare systems. Information from this analysis offers possibilities for innovation, impactfully addressing various concerns and predicting future health. The correct use of patient data and information was also highlighted as safety and privacy concerns.

Along with Smartphones, diverse devices, such as wearable and Internet of Things (IoT) devices, are integrated into mobile health. This has been considered an unprecedented opportunity as cutting-edge technology has become a promising solution in the detection and management of diseases.[13, 14] The rapid development of this technology through the integration of smart devices into mobile health is recognized as suitable for supporting patients and healthcare providers.[15]

A number of systematic reviews have examined the effectiveness of mobile health in disease management and medication adherence. However, the reviews on the suitability of using mobile applications (apps) were restricted in scope to specific patient groups and types of health behavior or did not secure the robustness of results, including non-randomized clinical trials (RCTs). And application-based interventions, such as wearable and smart devices, have been insufficiently examined. A previous meta-analysis of 14 mobile app interventional studies with 1,785 patients with chronic diseases revealed a significant improvement in medication adherence. However, the quality of evidence was concerning as small sample-sized studies with a high risk of bias were included.[16] More recently, a 2021 meta-analysis of 12 RCTs also reported improvements, yet the data were from self-reported adherence scores and limited to cardiovascular disease.[17]

Given the increased interest in mobile health, significant studies have been conducted to identify the effectiveness of mobile apps for chronic disease management. A recent overview of previously conducted systematic reviews revealed the benefits and cost-effectiveness of the interventions applied.[18] Yet there is a lack of research analyzing specific features of apps or smart devices. A previous value assessment suggested elements requiring optimizations.[19] Thus, the present study aimed to determine the functions and features of mobile apps, focusing on varying degrees of technological applications for app development and effectiveness.

Selection of studies[20]

Studies were included if they met the following criteria: P) Study participants were diagnosed with a disease and prescribed medication that required regular intake, I) the intervention and the use of mobile devices and apps that used features to promote medication adherence was clear, O) the study reported outcomes related to medication adherence and SD) were RCTs and studies published in English or Korean.

Studies were excluded if interventions 1) did not include mobile health technology, 2) solely use simple text messages or phone calls, 3) the outcomes did not include numerical medication adherence-related indicators, and 4) did not specify the randomization of participants (non-RCT) or were qualitative studies or systematic reviews.

Search strategy

Searches were conducted on April 13, 2023, in eight different electronic databases, including EMBASE, PubMed, Cochrane Library, CINAHL, KISS, KoreaMed, RISS, and NDSL core databases. Date limits were applied to articles published after 2012 when Smartphones penetrated the market.

Data extraction

Data were extracted independently by paired reviewers ( two main authors, SK and SY) and checked by coauthors (HR and SH).) A data extraction form developed by one main author (SK) was provided with an orientation to ensure consistency. Items included the study characteristics and outcome data on medication adherence (primary outcomes) and dropout rate (secondary outcomes).

Statistical analysis

Comprehensive Meta-Analysis (CMA version 3.0 + [21]) was used for meta-analyses in which a random effects model was applied for expected heterogeneity between studies with I² values greater than 50%. Revman (version 5.4.1, [22]) was used for risk of bias assessment. Numerical data on mean values (pre and post-test) and standard deviation were used to calculate standardized mean differences. The number of events in each study was used for pooled odds ratios of medication adherence. The logit event rate was computed to estimate the possible effect of study characteristics on dropouts using an analytic method from a previous study.[23] The proportion of dropouts (remaining from the initial recruited sample) in individual studies was input into the algorithm and was used to estimate the overall dropout rate.

Quality assessment

The Cochrane risk of bias tool (RoB version 5.4.1) was used to evaluate the included RCTs independently. Five randomization process domains, including deviation from intended interventions, missing outcome data, measurement of outcomes, and selection of the reported results, were assessed and graded as having “low risk,” “some concerns,” or “high risk.” Discussion with a third author was used to reach a consensus in cases of disagreements between reviewers. The overall risk of bias was graded as 1) “low” when a trial was determined to have a low risk of bias for all domains, 2) having “some concerns” when some concerns were raised for at least one domain, and 3) “high risk of bias” when at least one domain had a high risk of bias classification.

Publication bias

Publication bias was accessed by funnel plot analysis and Egger’s test to verify the validity and reliability of a meta-analysis,[24] followed by trim-and-fill and fail-safe N. The fail-safe N method was used for existing publication bias, where the number of fail-safe N[25] indicated the number of studies required to nullify the results of the meta-analysis. The robustness of the study results was determined when a fail-safe N exceeded 5K + 10 (K = number of studies).

The electronic search of eight databases identified 10,156 studies, and 13 were found from a manual search of the reference lists of relevant articles. After removing duplicates, the title and abstract of 9,412 articles were screened, and 49 remained eligible for full-text assessments. Of these, 29 met the inclusion criteria and were included in the systematic review and meta-analysis(Fig. 1).

Summary of study characteristics

A summary of the characteristics of the included studies is shown in Table 1. The studies were published within the last 12 years, ranging from 2012 to 2013, and were conducted in diverse countries, including the United States (n = 10), China (n = 4), Australia (n = 3), Austria (n = 1), Brazil (n = 1), Canada (n = 1), Denmark (n = 1), Ethiopia (n = 1), Ghana (n = 1), India (n = 1), Indonesia (n = 1), Palestine (n = 1), Thailand (n = 1), Spain (n = 1), and Switzerland (n = 1). The study participants had chronic diseases, including cardiovascular disease (n = 14), diabetes mellitus (n = 4), stroke (n = 2), cancer (n = 2), asthma and chronic obstructive pulmonary disease (n = 2), AIDS (n = 2), chronic kidney disease (n = 1), lung transplant recipients (n = 1), tuberculosis (n = 1), and psoriasis (n = 1).

The apps for the individual interventions had features ranging from simple reminders to artificial intelligence (AI) platforms for confirming medication intake. The included apps featured links to devices, mostly pill boxes (n = 6) and Bluetooth-enabled ones (n = 9; e.g., Smart inhaler). Medication adherence was measured in two ways: by self-reporting (n = 21) and electronic pill counts (n = 6). Self-reporting adherence was measured by the Morisky Medication Adherence Scale in eight studies, the Voils Extent Scale in one study, and the Hill-Bone Compliance to High Blood Pressure Therapy Scale in one study. In three studies, drug compliance questionnaires were part of the disease management measurement.

Table 1

Summarized characteristics of the included studies
Author (year) /Country	Number of participants /Group mean age (years)	Disease	Control	Intervention	Devices	Intervention length	Medication adherence outcome	Drop out /attrition rate
Brath, H. et al. (2013)[27] Austria	Con: 53 Int: 53 69.4 ± 4.8	DM	Usual care	1. Mobile health-based remote medication adherence measurement system - Electronic medication blister (Tracking the dosage and timing of medication intake) - Drug type dosage time stamp (NFC) - Remote telemonitoring of medication adherence 2. Interaction: Weekly Phone call (coordinator) 3. Reminder: Context-aware medication reminder 4. Data usage: Medication recording only	1. Mobile device - Mobile phone 2. Dispenser -Electronic medication blister (OtCM, DSM TCG B.V., Mauritslaan, the Netherlands)	20 weeks	Pill count (automatic) : Electronic medication blister	0%
Dabbs, A. D. et al. (2016)[28] United States	Con: 102 Int: 99 Con: 61.7 ± 3.4 Int: 61.7 ± 3.2	Lung transplant recipients	Usual care	1. Pocket PATH - Recording daily health indicators - Risk management with automatic messages (sent for out-of-preestablished-parameters) 2. Data usage: Medication recording only	1. Mobile device - Smartphone	12 months	Self-reporting medication adherence score : Health Habits Survey	0%
Kim, J. Y. et al. (2016)[29] United States	Con: 43 Int: 52 Con: 57.7 ± 8.7 Int: 57.5 ± 8.6	CVD (hypertension)	Usual care	1. HealthyCircles (patients) - Educational materials: disease information, health behavior recommendations - Reminder for monitoring (according to setting ) 2. Interaction: A dashboard to link with their families, caregivers, and health care professionals. 3. Data usage: Medication recording only	1. Mobile device - Smartphone 2. Bluetooth device -Blood pressure monitor	6 months	Self-reporting medication adherence score : Morisky Medication Adherence Scale 8-item (MMAS-8)	0%
Shah, V. et al. (2016)[30] United States	Con: 11 Int: 13 Con: 60.6 ± 4.1 Int: 60.5 ± 8.9	CVD (percutaneous coronary intervention)	Usual care	1. My IDEA app - Recording patient symptoms (e.g., shortness of breath and fatigue) - Educational material: the importance of medication adherence - Video training on the management of diseases 2. Data usage: Medication recording only	1. Mobile device - Smartphone	3 months	Pill count (manual)	0%
Guo, Y. et al. (2017)[31] China	Con: 96 Int: 113 Con: 70.9 ± 17.4 Int: 67.4 ± 10.6	CVD (atrial fibrillation)	Usual care	1. mAF app - Personal health record -Clinical decision support: automatic stroke evaluation and recommendations made accordingly. - Patient education program - Patients’ self-monitoring HR and BP - Follow-up reminder 2. Data usage: Sharing data with health provider	1. Mobile device - Smartphone	3 months	Self-reporting medication adherence score : Adherence Estimator	42/113 = 36.3%
Himelhoch, S. et al. (2017)[32] United States	Con: 9 Int: 19 Con: 53.1 ± 4.4 Int: 44.6 ± 10.7	AIDS	Usual care	1. Heart2HAART app - Real-time information about medication adherence - Use ecological momentary assessments 2. Interaction: Tailored education, recommendations, and encouragement 3. Reminder: Simple medication reminder 4. Data usage: Medication recording only	1. Mobile device - Smartphone	3 months	Pill count (manual)	0%
Kleinman, N. J. et al. (2017)[33] India	Con: 46 Int: 44 Con: 48.0 ± 9.5 Int: 48.8 ± 9.0	DM	Usual care	1. Gather Health: an m-Health diabetes management platform - Reminder: Simple medication reminder - Health data visualization (medical history, laboratory results, clinical visits, and medication adherence) - Ongoing support: algorithmically generated “feed” alerts highlighting who needs attention 2. Interaction: Each site’s health coach regularly responded to patient questions and system-generated alerts. 3. Data usage: Sharing data with health provider	1. Mobile device - Smartphone	6 months	Self-reporting medication adherence record :Gather Health app	0%
Labovitz, D. L. et al. (2017)[34] United States	Con: 13 Int: 15 Con: 55.5 ± 16.6 Int: 58.3 ± 9.8	Stroke	Usual care	1.AI platform; app is Health Insurance Portability and Accountability Act (HIPAA) compliant - Reminder: Context-aware medication reminder - Dosing instructions - Late dose-triggered notifications - Automated text messages to clinicians for misuse 2. Data usage: Sharing data with health provider	1. Mobile device - Smartphone	12 weeks	Self-reporting medication adherence record :AI (artificial intelligence) app	0%
Graetz, I. et al. (2018)[35] United States	Con: 23 Int: 25 Con: 59.3 Int: 60.6	Cancer	Patient care monitor (PCM) app	1. PCM app with reminder - PCM app: Web-based app to report symptoms and aromatase inhibitor adherence - Reminder: Simple medication reminder 2. Interaction: If the patient had symptoms, the nurse would call immediately 3. Data usage: Sharing data with health provider	1. Mobile device - Smartphone - Home computer	6–8 weeks	Self-reporting medication adherence score :Morisky Medication Adherence Scale (MMAS-4)	2/23 = 8.7%
Morawski, K. et al. (2018)[36] United States	Con: 202 Int: 209 Con: 52.4 ± 10.1 Int: 51.7 ± 10.5	CVD (hypertension)	Usual care	1. Medication Adherence Improvement Support App For Engagement - Blood Pressure (MedISAFE-BP) - Reminder: Context-aware medication reminder - Weekly adherence reports - Provide peer support 2. Data usage: Sharing data with health provider	1. Mobile device - Smartphone	12 weeks	Self-reporting medication adherence score :Morisky Medication Adherence Scale 8-item (MMAS-8)	25/209 = 12%
Sarfo, F. et al. (2018)[37] Ghana	Con: 30 Int: 30 Con: 55.9 ± 13.7 Int: 54.3 ± 11.9	Stroke	SMS messages (not with medication adherence)	1. Phone-Based Intervention Under Nurse Guidance After Stroke (PINGS) app - Monitoring and reporting BP measurements - Monitoring medication intake (timestamp) 2. Interaction: Phone-based intervention under nurse guidance 3. Reminder: Context-aware medication reminder 4. Data usage: Sharing data with health provider	1. Mobile Device - Smartphone 2. Bluetooth device - UA-767Plus BT BP device	3 months	Pill count (manual)	0%
Svendsen, M. T. et al. (2018)[38] Denmark	Con: 66 Int: 68 48 ± 10.4	Psoriasis	Usual care	1. An app supporting adherence - Reminder: Simple medication reminder - Informing patients regarding their adherence 2. Data usage: Sharing data with health provider	1. Mobile device - Smartphone 2. Dispenser - Pill dispenser (information on adherence was obtained with a chip attached to the dispenser that was synchronized with the app)	26 weeks	Pill count (automatic) : Pill dispenser	7/68 = 10.3%
Gregoriano, C. et al. (2019)[39] Switzerland	Con: 79 Int: 79 Con: 69.0 ± 8.8 Int: 64.7 ± 12.4	Asthma COPD	Usual care	1. Audio-reminder app - Reminder: Simple medication reminder - Feedback on intake pattern of inhaled medication 2. Interaction: Support calls from the study pharmacist or study nurses 3. Data usage: Medication recording only	1. Mobile device - Smartphone 2. Dispenser - Smart-inhaler	6 months	Pill count (automatic) : Smart-inhaler	4/79 = 5.1%
Marquez Contreras, E. et al. (2019)[40] Spain	Con: 77 Int: 77 Con: 57.1 ± 10 Int: 57.7 ± 9	CVD (hypertension)	Usual care	1. AlerHTA - Promote health education in arterial hypertension - Appointments reminder 2. Reminder: Simple medication reminder 3. Data usage: Medication recording only	1. Mobile device - Smartphone 2. Dispenser - Medication Event Monitoring System (MEMS)	12 months	Pill count (automatic) :Medication Event Monitoring System (MEMS)	4/77 = 5.2%
Santo, K. et al. (2019)[41] Australia	Con: 54 Int: 53 Con: 56.8 ± 8.64 Int: 58.4 ± 9.04	CVD (coronary heart disease)	Usual care	1. Advanced medication reminder app - Reminder: Simple medication reminder - Scheduled reminders (up to three times at 10-min intervals) - Other medication-related features (e.g., refill reminders, adherence statistics, export and sharing information) 2. Data usage: Medication recording only	1. Mobile device - Smartphone	3 months	Self-reporting medication adherence score :Morisky Medication Adherence Scale 8-item (MMAS-8)	3/54 = 5.6%
Ding, H. et al. (2020)[42] Australia	Con: 93 Int: 91 Con: 70.8 ± 12.4 Int: 69.5 ± 12.3	CVD (chronic heart failure)	Usual care	1. Innovative telemonitoring enhanced care program for chronic heart failure (ITEC-CHF) - Body weight monitoring - Interaction: Structured telephone support 2. Data usage: Sharing data with health provider	1. Mobile device - Tablet PC (Galaxy Tab A, Samsung) 2. Bluetooth device - Electronic weight scale	6 months	Self-reporting medication adherence score :Heart Failure Compliance Questionnaire	24/91 = 26.4%
Greer, J. A. et al. (2020)[43] United States	Con: 90 Int: 91 Con: 53.8 ± 12.1 Int: 52.9 ± 13.7	Cancer	Standard oncology care	1. Mobile application - Medication scheduling - Reminder: Simple medication reminder - Weekly reports of adherence and symptoms (via push notifications) - Educational resources - Tracking physical activity (via Fitbit) 2. Data usage: Sharing data with health provider	1. Mobile device - Smartphone 2. Dispenser - Electronic pill caps (The MEMS Cap [AARDEX Group] and GlowCap [Vitality])	12–14weeks	Pill count (automatic) :Electronic pill caps	11/91 = 13.8%
Sartori, A. C. et al. (2020)[44] Brazil	Con: 248 Int: 248 Con: 56.5 ± 12.4 Int: 57.3 ± 10.5	CVD (hypertension) DM	Usual care	1. WhatsApp application - Reminder: Simple medication reminder - Motivational image - Disease information	1. Mobile device - Smartphone	16 weeks	Self-reporting medication adherence score :Morisky-Green Test(MGT)	45/248 = 18.1%
Yu, C. et al. (2020)[45] China	Con: 499 Int: 501 Con: 57.1 ± 9.20 Int: 57.41 ± 8.99	CVD (isolated CABG)	Usual care	1. Heart Health Application - Reminder: Simple medication reminder - Cardiac health education - Medication reports 2. Data usage: Medication recording only	1. Mobile device - Smartphone	6 months	Self-reporting medication adherence score :Morisky Medication Adherence Scale 8-item (MMAS-8)	8/501 = 1.6%
Abu-El-Noor, et al. (2021)[46] Palestine	Con: 94 Int: 97 Con: 57.5 ± 11.9 Int: 55.4 ± 10.9	CVD (hypertension)	Daily routine	1. Phone app - Reminder: Simple medication reminder - Follow-up appointments and educational information about hypertension management 2. Data usage: Medication recording only	1. Mobile device - Smartphone	3 months	Self-reporting medication adherence score :Hill-Bone CHBPTS	15.74%
Criner, G. J. et al. (2021)[47] United States	Con: 70 Int: 67 Con: 66.6 ± 8.21 Int: 66.7 ± 8.72	COPD	BreathMate -device without audiovisual reminders	1.Budesonide/Formoterol pMDI Medication Reminders via Smartphone app - Reminder: Simple medication reminder - Weekly Clinical COPD Questionnaire (CCQ) 2. Data usage: Sharing data with health provider	1. Mobile device - Smartphone 2. Dispenser - BreathMate device	6 months	Pill count (automatic) :BreathMate device	18/67 = 26.9%
Ong, S. W. et al. (2021)[48] Canada	Con: 93 Int: 89 Con: 58 ± 16 Int: 54 ± 14	CKD	MyMedRec -Manually enter and maintain health records	1. eKidneyCare - Medication review (monthly) - Report to clinicians (e.g., changes, additions, medication problems) 2. Interaction: Opening communication channels between patients and their clinical team 3. Data usage: Sharing data with health provider	1. Mobile device - Smartphone 2. Bluetooth device - BP monitor	1 year	Pill count (manual)	7/89 = 7.9%
Wungrath, J. et al. (2021)[49] Thailand	Con: 30 Int: 30 Con: 59.17 ± 13.52 Int: 61.57 ± 11.23	DM	Standard routine health education activities	1. Line application: video clip with content related to the 5R principles (right medicine, right dose, right route, right patient, and right time). 2. Interaction: Telephone-based counseling	1. Mobile device - Mobile phone	8 weeks	Self-reporting medication adherence score :Diabetes Medication Adherence Behavior(DMAB)	0%
Indraratna, P. et al. (2022)[50] Australia	Con: 83 Int: 81 Con: 61.7 ± 12.6 Int: 61.3 ± 12.3	CVD (acute coronary syndrome Or heart failure)	Usual care	1. TeleClinical Care(TCC) - Push educational notifications to promote healthy behavior choices - Weekly record of blood pressure and weight readings - Bluetooth-enabled peripheral devices 2. Data usage: Medication recording only	1. Mobile device - Smartphone 2. Bluetooth device - sphygmomanometer - weighing scale - activity monitor (Xiaomi MiBand 2)	6 months	Self-reporting medication adherence score :Morisky-Green-Levine 4-item (MGL)	3/81 = 3.7%
Li, Y. et al. (2022)[51] China	Con: 152 Int: 148 Con: 62.27 ± 9.87 Int: 61.38 ± 8.88	CVD (coronary heart disease)	Conventional hospital-based follow-up care	1. DTx (digital therapeutics) app - Discharge module: Patient data regarding medication and lifestyle intervention plan integrated into the system with a follow-up protocol. - Home management module: Patients’ bio-signal data transferred via Bluetooth with medication reminder (context-aware) - Follow-up module: Displays the latest discharge summaries 2. Data usage: Medication recording only	1. Mobile device - Smartphone 2. Bluetooth device - BP and HR monitor	1 year	Pill count (manual)	5/148 = 3.4%
Ni, Z. et al. (2022)[52] China	Con: 114 Int: 116 Con: 62 ± 11 Int: 61 ± 11	CVD (coronary heart disease)	WeChat with educational materials only	1. Message Express with WeChat - Connection to health professionals - Educational materials - Reminder: Simple medication reminder 2. Data usage: Medication recording only	1. Mobile device - Smartphone	90 days	Self-reporting medication adherence score :Voils Extent Scale	13/116 = 11.2%
Putra, R. P. et al. (2022)[53] Indonesia	Con: 25 Int: 25	CVD (coronary heart disease)	Usual care	“Friends of Heart” application -Educational material -Questionnaire to test knowledge	1. Mobile device - Smartphone	30 days	Self-reporting medication adherence score : Medication Adherence Questionnaire(MAQ)	0%
Manyazewal, T. et al. (2023)[54] Ethiopia	Con: 57 Int: 52	TB	Usual care	1. MERM(evriMED500®) - Adherence recording - Monitoring adherence for healthcare providers - Reminder: Simple medication reminder 2. Interaction: Discuss with the healthcare provider why the patient didn’t take their medication every 15 days. 3. Data usage: Medication recording only	1. Mobile device - Smartphone (Wisepill evriMED application, set up on computers) 2. Dispenser -evriMED500MERM (Pill box)	2 months	Self-reporting medication adherence record :Wisepill evriMED app	5/57 = 8.8%
Schnall, R. et al. (2023)[55] United States	Con: 101 Int: 99 Con: 49.34 ± 10.2 Int: 49.19 ± 10.9	AIDS	A mobile app without medication adherence-related features	1. WiseApp - Medication trackers - Health surveys - Chatrooms - To-Do list outlining tasks for the day - Reminder: Simple medication reminder 2. Data usage: Sharing data with health provider	1. Mobile device - Smartphone 2. Dispenser - CleverCap 3. Bluetooth device - FitBit (fitness tracker that connects to the WiseApp)	6 months	Pill count (automatic) : Clever Cap	23/99 = 23%
Con = control group, Int = intervention group, DM = diabetes mellitus, CVD = cardiovascular disease, COPD = chronic obstructive pulmonary disease, CKD = chronic kidney disease,

TB = tuberculosis

Risk of bias

Of 29 studies accessed using the Cochrane risk of bias tool (RoB 5.4.1), 5 studies (17.2%)[28, 33, 37, 38, 41] were rated as having “low risk of bias”, one study (3.4%)[35] had “some concerns”, and 14 studies (48.3%)[30, 31, 36, 39, 42, 45–47, 50–55] were rated as having “high risk of bias”. Frequently, the risks of bias was due to the lack of participants blinding (9 studies, 31%)[30, 36, 46, 47, 50–52, 54, 55] and assessors (7 studies, 24.1%).[39, 45, 47, 50–52, 55] A substantial number of studies did not describe the randomization methods (10 out of 29)[27, 29–32, 34, 43, 45, 49, 53] or allocation concealment (15 out of 29)[27, 29–32, 34, 39, 40, 43–46, 48, 49, 53](Fig. 2).

Publication Bias

Asymmetry in the funnel plot and Eggers test provided evidence for publication bias (t = 3.65, df = 27, p = 0.001). The fail-safe analysis indicated the robustness of the current meta-analysis results where 808 studies were required to reject the meta-analysis results (Fig. 3).

Effectiveness of mobile interventions on medication adherence

Twenty studies with sufficient primary data to calculate odds ratios were pooled, and significant effects were obtained (OR = 2.34 [95% CI: 1.99, 2.74]). Nine studies reported medication adherence scores, yielding a significant combined SMD of 0.39 [95% CI: 0.01, 0.77] (Fig. 4).

Table 2

Results of subgroup analysis
Outcome	Categories	Subgroups	No. of studies	SMD (95%CI)	Heterogeneity
Medication adherence score (mean ± SD)	Interaction	No	6	0.031(-0.273 to 0.336)	Q value = 40.242, df = 5, I² = 87.575
	Interaction	Yes	3	1.367(-0.279 to 3.013)	Q value = 73.534, df = 2, I² = 97.280
	Reminder	No	3	1.367(-0.279 to 3.013)	Q value = 73.534, df = 2, I² = 97.280
		Simple	4	-0.152(-0.524 to 0.221)	Q value = 25.878, df = 3, I² = 88.407
		Context-aware	2	0.664(-0.431 to 1.758)	Q value = 6.642, df = 1, I² = 84.944
	Data usage∗	Only record	5	-0.114(-0.426 to 0.198)	Q value = 26.310, df = 4, I² = 84.797
	Data usage∗	Sharing data with health provider	3	0.321(-0.085 to 0.727)	Q value = 7.297, df = 2, I² = 72.593
Medication adherence rate (%)	Interaction	No	12	2.203(1.823 to 2.663)	Q value = 16.923, df = 11, I² = 35.001
	Interaction	Yes	8	2.678(2.005 to 3.578)	Q value = 1.911, df = 7, I² = 0.000
	Reminder	No	6	2.443(1.861 to 3.207)	Q value = 6.132, df = 5, I² = 18.460
		Simple	10	2.228(1.778 to 2.792)	Q value = 12.963, df = 9, I² = 30.571
		Context-aware	4	2.452(1.662 to 3.616)	Q value = 0.631, df = 3, I² = 0.000
	Data usage∗	Only record	9	2.415(1.906 to 3.061)	Q value = 5.479, df = 8, I² = 0.000
	Data usage∗	Sharing data with health provider	9	2.449(1.909 to 3.141)	Q value = 7.569, df = 8, I² = 0.000
	Dispenser	No	12	2.279(1.866 to 2.783)	Q value = 10.923, df = 11, I² = 0.000
	Dispenser	Yes	8	2.436(1.877 to 3.160)	Q value = 8.976, df = 7, I² = 22.017

∗ Analysis was conducted for studies stating the use of data

The number of studies allowed for additional subgroup analyses based on intervention characteristics and app features, including the use of interactive strategies, types of reminders, use of data, and dispensers linked to the apps (Table 2). Higher adherence scores (SMD = 1.367 [95% CI: -0.279 to 3.013 ]) and rates (OR = 2.678, [95% CI: 2.005 to 3.578]) were observed for studies using health providers to interact with patients during the intervention. Similarly, adherence scores (SMD = 0.321, [95% CI: -0.085 to 0.727]) and rates (OR = 2.449, [95% CI: 1.909 to 3.141]) were higher when data were shared with health providers than when they were not. In regard to reminder type, the patients’ medication-taking performance was better for context-aware reminders (SMD = 0.664, [95% CI: -0.431 to 1.758], OR = 2.452, [95% CI: 1.662 to 3.616]) than simple reminders (SMD = -0.152, [95% CI: -0.524 to 0.221], OR = 2.228, [95% CI: 1.778 to 2.792]). In the reporting of adherence rates, 8 studies added medication dispensers and showed greater adherence rates (OR = 2.436, [95% CI: 1.877 to 3.160]) than those without (OR = 2.279, [95% CI: 1.866 to 2.783]).

Table 3

Dropout rates by intervention characteristics and reminder type
	Sample size (n)	No. of studies	Dropout count (n)	Dropout rate (%)
Interaction No	2120	18	222	10.5
Interaction Yes	564	11	42	7.4
1 month	25	1	0	0
2 months	107	3	7	6.5
3 months	756	10	109	14.4
4 months	301	2	45	15.0
6 months	1082	9	87	8.0
12 months	413	4	16	3.9
No reminder	593	9	76	12.8
Simple reminder	1617	14	158	9.8
Context-aware reminder	474	6	30	6.3

Additional analyses were performed to examine how intervention characteristics affected dropout rates (Table 3). Relatively lower dropout rates were revealed in app interventions using context-aware reminders and interactive strategies with health providers. Dropout rates increased continuously with intervention period increases up to 4 months and then decreased.

This study included RCTs using mobile apps to improve medication adherence in patients with chronic diseases. The findings indicated statistically significantly higher medication adherence in the intervention group than in the control group, and dropout rates differed according to the characteristics of the interventions. The subgroup analysis results indicated the importance of comprehensively designed interactive applications, and achievable levels of personalized features are essential. About half of the included studies (14 out of 29) targeted patients with cardiovascular disease. Perhaps medication adherence is a high priority for patients with these diseases.[56] Thus, sustainable interventions to reduce nonadherence are required to decrease the relative hazard and the findings of this study revealed the potential of using mobile apps as a solution.

App-based interventions featuring interactions with healthcare providers showed substantial improvement in medication adherence. Particularly, the subgroup analysis revealed the importance of data-sharing with healthcare providers and a previous study reported that health data fostered positive patient-clinician relationships.[57] This is consistent with previous review studies reporting better outcomes when healthcare providers were involved.[17, 58] Apps could ensure patient-clinician interaction and, when they were data-based, obtain better disease management for patients. In disease surveillance, early detection, and timely intervention, the further incorporation of advanced features (e.g., AI, electronic health records) would optimize patient support for disease management[59] Thus, the key to encouraging the wider use of health apps is a strategy to overcome existing barriers to health services that require long terms and traveling great distances.

The current study analyzed medication adherence using data recorded electronically. As the lack of accuracy and sensitivity of self-reported measures has been reported, electronic event monitoring or pill counting has consistently been suggested for measuring medication management.[60, 61] As a healthcare monitoring system, tracking and analyzing health data can not only identify the reason for non-adherence but also provide data-driven recommendations for individuals. The current subgroup analysis revealed the significance of dispensers. Changes were made to electronic adherence monitoring devices to achieve highly accurate data for medication adherence. By detecting user adherence, health providers were able to draw patterns of medication behavior for how misuse occurs,[62, 63] incorporating individualized healthcare planning.[63]

Although they were unable to estimate effectiveness, recently published studies integrated diverse devices (e.g., electronic weight scales).[29, 37, 42, 48, 50, 51, 55, 64–66] Moreover, the rapid growth of wearable devices (e.g., wearable biometric sensors) has brought dramatic changes to monitoring patients’ behavior and health conditions.[67] Previous reviews pointed out the importance of these devices, and studies suggested that well-established data-sharing with health providers could promote optimal disease management planning.[66, 68] Although it was unable to confirm with statistical analysis, ease of use and functionality would key to success that focusing on implementation of multiple system, these are often missed out. As a previous study identified,[69] the effectiveness of future applications is likely to be influenced by how each device is integrated.

Health monitoring devices play a role in compliance with medication regimens,[70] and the advantages could also be related to the motivation for self-care. A previous review revealed that medication compliance was better in patients who monitored their health indicators daily[71, 72] Advanced sensor system technology can provide novel methods for improving the effectiveness of healthcare interventions.[73] Furthermore, the clear visualization of monitoring data long term and from multiple sensors allows better accessibility to health data in a comprehensive manner.[74] Future reviews focusing on rigorous analyses of technological strategies that could reduce non-adherence and improve self-care are needed.

The current analysis showed that the effectiveness of reminders was questionable. A previous study identified that the impact on adherence varied depending on the types of reminder and alarm frequency. Simple reminders did not result in better medication adherence outcomes, consistent with suggestions from a previous study.[75] In this regard, context-aware reminders have been suggested. Context-aware reminders were initiated when a dose was due, had not been taken, or was still safe to take in the included studies that used diverse technologies from sensors in pill blisters to AI for medication intake recognition.[76] Future studies guiding reminder optimization in the context of individual patients would ensure not only the quality but also the person-centeredness of mobile health interventions.

Limitations

The current review was not without limitations. First, grey literature and unpublished papers were not searched, so some papers that might have affected the findings were not included. The language in the electronic database searches was limited to English and Korean. Thus, caution is needed in interpreting the pooled estimates of the intervention effects. Methodological discrepancies in measuring medication adherence between studies made it impossible to pool estimates in all 29 included studies, so they were divided into two reports, making it challenging to use the pooled estimates to ascertain the effectiveness of mobile interventions on medication adherence. In addition, the quality assessment revealed that most studies lacked concealing and blinding, which could have affected the overall quality of this review.

This review was conducted to provide robust evidence for the effectiveness of mobile app interventions on medication adherence in a population with chronic diseases. The findings demonstrated that mobile apps could assist with medication intake required over a long period and time management. The inclusion of health providers in data-driven interactions showed promising outcomes in improving medication adherence. While the greater effectiveness of particular features or devices was not conclusive, the findings indicated the importance of carefully considering end-user acceptance and preferences. Recommendations for future studies include incorporating more diverse and advanced technologies into mobile app interventions and conducting robustly designed studies.

Ethical Approval

Not applicable.

Funding

This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT)(No. 2022R1A5A8033794).

Availability of data and materials

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

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No competing interests reported.

Effectiveness of mobile health intervention in medication adherence: A systematic review and meta-analysis

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Abstract

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Introduction

Methods

Statistical analysis

Results

Discussion

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