Poor adherence to complex medication regimens is a global problem that affects the treatment of chronic diseases, which involve polypharmacy and require long-term administration of medications (Cramer et al., 2008; Zullig et al., 2013). Hence, the success of the treatment of chronic diseases depends on medication adherence and medication persistence (Zeber et al., 2013). Poor medication adherence results in faster disease progression, adverse drug reactions, and increased risk of adverse outcomes leading to frequent emergency department (ED) visits and/or hospitalizations (DiMatteo et al., 2002; Jin et al., 2016). These outcomes will affect patient and caregiver quality of life and furthermore cause a significant financial burden on the provider–patient/health care system (Roebuck et al., 2011; World Health Organization [WHO], 2015). It was estimated that the annual avoidable cost to the U.S. health care system due to medication non-adherence ranges from $100 to $300 billion (Iuga & McGuire, 2014).
The global population of adults age ≥60 is expected to exceed 2 billion by 2050 (Garçon et al., 2016). With this growing trend, there is also an increased prevalence of chronic illnesses in the aging population (Chatterji et al., 2015; WHO, 2015). Cognitive and functional impairments due to aging, polypharmacy, and complex dosing regimens increase the risk of poor adherence to medication in this population (Chatterji et al., 2015; Yap et al., 2016a). In the United States, approximately 50% of chronically treated patients do not adhere to their prescription medications, which leads to exacerbated health care costs and poor patient outcomes (Ganguli et al., 2016). Hence, interventions to monitor medication adherence are necessary to improve treatment outcomes in older adults and to reduce burden on the health care system (Yap et al., 2016b).
According to the WHO (2015), various factors can affect medication adherence and medication persistence in patients, including socioeconomic, therapy-, condition-, patient-, and healthcare system–related factors. The most significant barrier to medication adherence in older patients is patient-related factors (Jin et al., 2016). Several studies have demonstrated that self-management of medication in older patients is significantly affected by age-related problems (e.g., physical inability, cognitive incapacity), health conditions, educational level, complexity of drug regimen (e.g., dosing schedule, polypharmacy), and patient–health care provider and patient–pharmacist relationships, thereby demanding solutions for better medication management (WHO, 2015). There are commercially available solutions for medication management from simple interventions, such as pillboxes, blister packs, and standalone smartphone applications, to complex interventions, such as electronic medication packaging (EMP) devices with medication containers and electronic components for monitoring, recording, or storing adherence information. Although there are solutions available for medication management, there is a gap in the literature in evaluating the effectiveness of such interventions on improving medication adherence in older patients.
EMP devices not only store prescription medications in boxes, cups, or disposable multi-dose drug dispensing (MDD) pouches, but also support patients and/or caregivers in monitoring medication adherence (Checchi et al., 2014). One of the most commonly used EMP devices in adherence-related studies was the medication events monitoring system (MEMS) (Lam & Fresco, 2015). EMP devices have the advantage of high accuracy and ability to identify adherence patterns, which make devices such as the MEMS more useful to measure adherence than biochemical and self-reported measures (Lam & Fresco, 2015). Depending on the technological features, these devices may be expensive and demand technical assistance (Lam & Fresco, 2015). Although such devices may cause inconvenience and anxiety of being under surveillance in some patients, it is still worthwhile to evaluate their effectiveness on improving medication adherence as it will lower hospitalization rates and ED visits, and in turn lower overall health care consumption (Blanchard et al., 2013; Lam & Fresco, 2015; Sokol et al., 2005).
Currently available EMP devices in the market have various technological features in addition to mere medication storage and adherence monitoring. The features provided by these solutions can be grouped into four broad classes of utility: (a) organize medications as per the schedule; (b) deliver medications on time; (c) remind patients on doses with audio and/or visual reminders; and (d) notify caregivers of a missed dose. Therefore, the purpose of the current scoping review was to find evidence from the literature to evaluate the effectiveness of EMP devices on improving medication adherence in older patients, focusing on devices with the ability to organize and deliver medications, remind patients on doses using audio and/or visual reminders, and notify caregivers of a missed dose. The objectives were to describe the literature, identify gaps, and make recommendations for future research.
Search Strategy and Selection Criteria
MEDLINE and EMBASE databases using the OvidSP gateway were systematically searched through June 27, 2018. All searches were conducted in the English language. Keywords were used to generate refined queries with inclusion and exclusion criteria, focusing on medication adherence and features of the intervention. An updated search was conducted on February 1, 2019, using the same criteria but did not identify any new studies.
The following Boolean search string was used to conduct the search: (((strip or pouch or medication) adj2 (dispens* or pack*)).ti,ab. OR (pill organi#er or medication organi#er). mp. OR automated medication dis-pens*.ti,ab. OR automated medication distribut*.ti,ab. OR automated drug distribut*.ti,ab. OR automated drug dispens*.ti,ab. OR automated dose-dispens*.ti,ab. OR automated dose distribut*.ti,ab. OR automated dispensing system*.ti,ab. OR multidose drug dispens*.ti,ab. OR multi-dose drug dispens*.ti,ab. OR multidose drug distribut*.ti,ab. OR multi-dose drug distribut*.ti,ab. OR unit-dose dispens*.ti,ab. OR unit-dose distribut*.ti,ab. OR (automat* adj2 (dispens* or distribut*) adj2 (device* or system* or scheme*)). ti,ab. OR (automat* adj2 dose dis-pens*).ti,ab. OR (automat* adj2 dose distribut*).ti,ab. OR ((multidose or multi-dose) adj2 dispens*).ti,ab. OR ((multidose or multi-dose) adj2 distribut*).ti,ab. OR (unit-dose adj2 (dispens* or distribut*)).ti,ab. OR medication dispens*.ti,ab. OR (electronic adj2 medication).ti,ab. OR AMDD$.ti,ab.) AND ((medication adherence or patient compliance). sh. OR adheren*.ti,ab. OR nonadheren*.ti,ab. OR nonadheren*. ti,ab. OR non-complian*.ti,ab. OR noncomplian*.ti,ab.).
Studies were selected for evidence synthesis according to the following eligibility criteria:
Source attributes: Include peer-reviewed publications and exclude abstracts, conference reports, case reports, systematic reviews, ongoing trials, and unpublished articles.
Study design: Include randomized controlled trials (RCTs), non-RCTs, and proof-of-concept studies.
Patient demographics: Include studies involving adults (age >18 years) irrespective of their gender, ethnicity, or other factors.
Intervention: Include studies in which the method of medication delivery involved an EMP device.
Intervention features: EMP devices with all following features: (a) organize medications; (b) deliver medications; (c) remind patients on doses using audio and/or visual signal; and (d) notify caregivers of missed medications.
Outcome: Include studies that reported medication adherence as one of the primary or secondary outcomes.
Combined search results from MEDLINE and EMBASE databases were imported into Mendeley, and duplicate entries were removed. Articles were screened after reviewing the titles and abstracts. The authors found a limited number of articles that met all eligibility criteria. Although the goal of the scoping review was to evaluate the effectiveness of EMP devices specifically in older patients, only one study was found that only included an older patient population (age >55 years). The authors discussed the consequences and reached consensus to include studies that involved all adults (age >18 years). From the studies selected for full-text review, articles were selected based on the new eligibility criteria. Each full-text article was reviewed independently by two authors (R.K., J.P.D.M.). The following data were extracted independently for evidence synthesis: study details, characteristics of the study, patients and interventions, and study results.
The electronic search identified 1,058 articles of which 384 were duplicate records. Among the remaining 674 articles, 646 were deemed irrelevant after reviewing the titles and abstracts. Among the remaining 28 articles, 23 were excluded after full-text review as they did not meet inclusion criteria; thus, a total of five articles were included. One study was identified that involved patients in the age range of 2 to 69 years. This study was included for review because (a) the average age of participants was 44.65 years, (b) it met all other eligibility criteria, and (c) due to limitation in the availability of studies (Figure 1).
Study selection process.
The final sample of five studies (two non-RCTs, one prospective stratified RCT, one prospective RCT, and one prospective feasibility study) included experiences with four different devices—MD.2 Automated Medication Dispensing System; Med-eMonitor™ Electronic Medication Monitor; MedaCube™ Automated Home Medication Dispenser; and an unknown EMD—and different medical conditions including renal transplant, schizophrenia, HIV, psychiatric disorders, Alzheimer's disease, and an unintentionally non-adherent cohort of patients with multiple comorbidities. The number of patients ranged from 12 to 134, and the interventions lasted from 3 to 15 months. Medication adherence was reported in all five studies in addition to other clinical outcomes (Table 1 and Table 2).
Design and Outcomes of Included Studies
The first study was from 2004, in which Buckwalter et al. studied how an electronically managed medication dispensing system can increase medication compliance and in turn improve quality of life for older patients. The authors reported the results of two preliminary studies conducted by (a) the Visiting Nurses Association to evaluate the effectiveness of the MD.2, and (b) a health care management company to compare MD.2 with the use of plastic medication boxes called Medi-sets. The first study was a proof-of-concept study aimed to assess the frequency and content of nursing care and home health aide visits after installing MD.2. The second study, which involved community-dwelling older adults or those with disabilities, was a non-RCT where patients were assigned to intervention (MD.2) and control (Medi-sets) groups based on their cognitive and physical functioning assessments. The intervention group indicated a reduction in hospitalization rates, number of ED visits, number of prescriptions being taken, and number of missed medications (Table 2) (Buckwalter et al., 2004).
The second included study was from 2012, in which Haberer et al. examined the feasibility, medication adherence, and acceptability of the Med-eMonitor, a real-time electronic adherence monitoring device. This study involved 52 patients with HIV who were on HIV antiretroviral therapy. The study used three different measures to monitor and report adherence as defined by the authors: (a) unadjusted Med-eMonitor adherence (i.e., “the number of compartment openings for all antiretroviral drugs divided by the number of prescribed doses”); (b) adjusted Med-eMonitor adherence (i.e., “the number of compartment openings with confirmation of dose ingestion from patients by pressing a button on the device divided by the number of prescribed doses”); and (c) un-announced pill count adherence (i.e., “the number of pills prescribed minus the number of pills counted, all divided by the number of pills prescribed for the interval between home visits”) (Haberer et al., 2012, p. 3). The median adherence was observed as 97.1% for un-announced pill count, 96.8% for unadjusted Med-eMonitor, and 89.8% for adjusted Med-eMonitor (Table 2) (Haberer et al., 2012).
The third included study was from 2013, in which Velligan et al. compared how an electronic intervention and its combination with in-person intervention affected adherence to oral antipsychotic drugs in patients with schizophrenia. Using a computer-generated algorithm and stratified by gender and age, 142 patients were randomized into three treatment arms: (1) Med-eMonitor—an electronic intervention in which a therapist programmed prescription information, assisted patients to correctly load the medications into the device, and regularly supervised patients via telephone to ensure compliance; (2) PharmCAT—an in-person intervention in which a cognitive adaptation training (CAT) therapist used a psychological treatment approach to improve medication adherence by compensating the cognitive deficits in patients; and (3) treatment as usual (TAU) (Draper et al., 2009; Velligan et al., 2013). Although all three groups were given a Med-eMonitor to measure compliance, the adherence prompts were activated only for the Med-eMonitor group (Velligan et al., 2013). Adherence was measured in two ways as defined by the authors: (a) Med-eMonitor–based assessment by comparing the number of prescribed doses with the number of compartment openings within a dosing window followed by a confirmation of dose ingestion from patients by pressing a button; and (b) unannounced pill counts in which pills were counted during monthly home visits. The results indicated a significantly higher adherence in Med-eMonitor (91%) and PharmCAT (90%) groups compared to the TAU group (72%). Adherence measured by monthly pill counts indicated higher adherence in the PharmCAT group (91%) compared to Med-eMonitor (86%) and TAU (80%) groups. The authors concluded that the electronic and in-person interventions significantly improved adherence compared to TAU; however, they did not have similar impacts in improving clinical outcomes (Velligan et al., 2013).
The fourth included study was from 2016, in which Henriksson et al. conducted a prospective RCT in patients after renal transplantation to study the influence of an electronic medication dispenser (EMD) on immunosuppressive medication compliance and clinical outcomes versus patients who received a standard of care (without EMD). The trial lasted for 12 months during which six patients in the intervention group withdrew from the study prematurely for various reasons: uncomfortable using the device (n = 4), experienced a stroke (n = 1), and death (n = 1). In this study, the authors analyzed various parameters that affected medication compliance in the intervention group and observed that: (a) patients missed their doses on 2.2% of occasions; (b) 48% of these missed doses occurred in patients ages 16 to 35; (c) missed doses were significantly higher in women than men; (d) the frequency of missed doses was more common in the evening than morning, toward the second half of the week than the first half of the week, and during the second half of the year than during the first half of the year; and (e) over time, there was a 20% increase in the rate of missed doses (Henriksson et al., 2016).
The fifth and final included study was from 2018, in which Hoffmann et al. conducted a 6-month cohort study in an unintentionally non-adherent cohort of older patients (average age = 75.1 years) with multiple comorbidities. The objective of this study was to assess the feasibility and effectiveness of an automated home medication dispenser (AHMD), Meda-Cube, in improving adherence in the intended user population. The study involved 21 patient–caregiver dyads and patients were prescribed an average of 11 medications over the study period. Mean medication adherence rate increased from 49% (at study enrollment) to 96.8% (after using the AHMD for 6 months), which indicated a net improvement of 47.8%. Among 21 dyads, 10 patients lived with their caregivers and showed an increase in mean adherence from 53.9% to 97.4%. In the remaining 11 patients who did not live with their caregivers, mean adherence rate increased from 44.5% to 96.2% (Hoffmann et al., 2018).
Risk of Bias
The intended target population for the current study was older patients. However, there was only one study that assessed the impact of EMP devices with the described features specifically in older patients. Hence, the age restriction was changed to include four additional studies that met all other inclusion criteria. This change may have an impact while drawing conclusions on the effectiveness of adherence interventions in older patients.
One of the studies included in the current review measured medication adherence only in the intervention group and not in the control group. The quality of this RCT needs to be evaluated while considering it as evidence to support the effectiveness of the intervention as it may affect the statistical comparison of study results.
Impact of EMP Devices on Medication Adherence in Older Patients
Results from the current scoping review indicate that EMP device interventions may result in increased medication adherence in older patients. However, it was observed that there is less evidence available on this topic, and there was only one study that evaluated the effect of EMP devices with the selected features specifically in older patients (age >55 years). Lack of evidence in the target population (i.e., older adults) could be attributed to the exclusion of older patients from clinical studies, which has been a concern reported in earlier systematic reviews (Costa et al., 2015). The PREDICT Study (Increasing Participation of the Elderly in Clinical Trials) involving participation from nine European countries reported that the underrepresentation of older adults in studies is mainly due to health care professional–and patient-related barriers to participate in clinical trials (Crome et al., 2011). In addition, a recent systematic review that studied the association of EMP devices with medication adherence also reported lack of evidence in the literature validating the effectiveness of EMP devices in patients (Checchi et al., 2014). In response to the above call for more evidence in medication adherence, a cluster-randomized trial was conducted in China in adult patients with pulmonary tuberculosis to assess the effectiveness of an electronic medication monitor box with audio reminders (Liu et al., 2015). Although findings from this study indicated a substantial improvement in medication adherence, the target population had a mean age of 43 years, again not focusing on older adults. Hence, randomized studies with larger samples of older patients and long-term evidence of EMP device use are required to evaluate the effectiveness because the impact of adherence inventions may reduce over time (Haynes et al., 2008).
Impact of Technological Features of Interventions on Medication Adherence
A study on medication adherence in older patients that actively compared an EMP device (MDD system) with control (without MDD system) measures showed that patients who received drugs via the MDD reported higher medication adherence compared to those who received only manually dispensed drugs; however, this study did not assess the impact of technological features of the automated MDD system on adherence (Kwint et al., 2013). There are also studies that reviewed how interventions such as simple drug reminder packaging and electronic reminders improved medication adherence (Boeni et al., 2014). However, there is a research gap to support the additive effect of different technological features of interventions on medication adherence. More studies are required to evaluate the effect of having one intervention strategy versus multiple intervention strategies in a single EMP device and assess how having more features would impact the level of medication adherence. It would be advantageous to identify which feature, or its combination with others, would make the most significant impact on medication adherence in older patients, for example, whether an organizer by itself or its combination with an automated reminder and MDD are more impactful. There are studies that assessed the impact of different technology-based adherence interventions on improving adherence in older patients in general (Checchi et al., 2014; Costa et al., 2015; Granger & Bosworth, 2011; Mistry et al., 2015; Stip et al., 2013); however, no studies were found that assessed how two or more technological features in different combinations impacted medication adherence. This gap in the literature begs the question of whether adding new features to a technology-based adherence intervention would have an additive effect on medication adherence, specifically in older patients.
The current review was restricted to studies that involved EMP devices with all four features as mentioned in the objectives; hence, studies that did not have all these features were not included, resulting in a lesser number of articles. In addition, restricting the study to publications in the English language contributed to less articles. The limitation in the number of controlled studies evaluating the effect of such EMP devices on medication adherence may have introduced bias and subsequent effects in the quality of this review.
As noted, EMP devices may improve medication adherence in older patients but with the disadvantage of having higher costs compared to manual dispensers (Seal & Felipe, 2017). Most studies selected in the current scoping review had important methodological shortcomings, and the impact of EMP devices on adverse drug events, potential adverse drug events, morbidity, and mortality were not assessed by any of the studies reviewed. Furthermore, resource use and cost data related to EMP devices have yet to be assessed. Hence, if policymakers decide to adopt this technology, it will not inevitably ensure better patient outcomes or greater efficiency without performing a health technology assessment first (Seal & Felipe, 2017). Policymakers and health care professionals should also note that the results summarized in the current review were achieved in a research study environment, and similar nursing care coordination might not be feasible outside of these constraints (Hoffmann et al., 2018; Velligan et al., 2013). Moreover, to reproduce the positive outcomes observed in the selected studies, health care professionals should carefully examine the study design before applying this evidence to practice. Future research should reflect this gap to facilitate the choice of EMP devices for an aging population and ultimately promote greater patient outcomes.
EMP devices may also help alleviate medication management–related burden on clinical nursing staff, personal support workers, and caregivers of patients with multiple chronic conditions. With the aid of EMP devices and real-time adherence monitoring, their time could be spent on other, equally important aspects of patient care, providing better and timely care for their patients, and serving more patients (Haberer et al., 2012). In addition, EMP devices could also prevent use of unnecessary resources such as additional costs and nursing services, which could in turn improve working conditions as well as quality of life and job satisfaction of nurses (Cimete et al., 2003). Use of EMP devices at home can help older adults self-manage their medications better, thereby improving their independence and extending their transfer to assisted-living centers solely for medication management support (Hoffmann et al., 2018).
The study results indicate that EMP devices improved medication adherence in older patients. However, due to insufficient evidence in the literature that supports their effectiveness in the aging population, it is recommended to conduct further clinical validation in older patients with larger samples to draw strong conclusions on the effectiveness of EMP devices.
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|Authors (Year)||Study Design||Intervention/EMP Device||Study Period (mo)||Sample Size/Group||Age (Years)||Medical Condition|
|Buckwalter et al. (2004)||Proof-of-concept||Automated medication dispensing system/MD.2||15||N = 12, all intervention||33 to 86||Primary and secondary psychiatric diagnosis|
|Non-RCT||6||N = 134; n = 89 MD.2, n = 45 Medi-set (control)||Not mentioned||Mental health issues and early to mid-stage Alzheimer's disease|
|Haberer et al. (2012)||Non-RCT (Proof-of-concept)||Electronic medication monitor/Med-eMonitor™||3||N = 52, all intervention||18 to 64 (mean = 49.8)||HIV|
|Velligan et al. (2013)||Prospective stratified RCT||Electronic medication monitor/Med-eMonitor||9||N = 142; n = 48, Med-eMonitor, n = 47 PharmCAT, n = 47 TAU (control)||18 to 60 (mean = 42.5)||Schizophrenia|
|Henriksson et al. (2016)||Prospective RCT||EMD/not mentioned||12||N = 80; n = 40 EMD, n = 40 usual care||2 to 69 (mean = 44.7)||Renal transplant|
|Hoffmann et al. (2018)||Prospective feasibility study||Automated home medication dispenser/MedaCube™||6||N = 21 patient–caregiver dyads||Mean = 75.1||Unintentionally non-adherent cohort with multiple comorbidities|
|Authors (Year)||Device||Device Features||Study Outcome||Findings Relevant to Medication Adherence|
|Buckwalter et al. (2004)||Automated medication dispensing system (MD.2)|
Note: Caregiver loaded the device with medications
Audio and visual reminders
Announces other preprogrammed instructions
Notifies caregivers/medical professionals in case of non-dispense
Monitors medication adherence
Generates report for adherence review
Frequency of home health aide visits
Dispensing rate statistics
Number of requests for technical support assistance from the device support center
Did not decrease due to other medical problems
Hospitalizations per patient
ED visits per patient
Prescriptions per patient
Missed doses per patient
|Intervention group indicated an overall reduction for all outcomes:
0.09 for MD.2 and 0.42 for Medi-set
0.18 for MD.2 and 0.42 for Medi-set
7.62 for MD.2 and 8.65 for Medi-set
2.9 for MD.2 and 7.31 for Medi-set
|Haberer et al. (2012)||Electronic medication monitor (Med-eMonitor™)|
Note: Licensed pharmacist loaded the device with medications
Audio and visual reminders
LCD display for medication instructions
Alerts when a wrong compartment is opened
Programmable ePRO features
Automatically connects with support personnel in case of emergencies
Records the date and time of opening the compartment
Button to confirm the ingestion of medication
Transmits adherence information in real-time
Records and stores adherence data
|(2) Median adherence was 97.1% (IQR 99.7% to 105.5%) for unannounced pill count, 96.8% (IQR 70.2% to 123.4%) for unadjusted Med-eMonitor, and 89.8% (IQR 77.1% to 102.5%) for adjusted Med-eMonitor|
|Velligan et al. (2013)||Electronic medication monitor (Med-eMonitor)|
Note: Patients (with external support) loaded the device with medications
Records the date and time of opening the compartment
Button to confirm the ingestion of medication
Stores adherence data
Transmits adherence information in real-time
Prompts users to take medication by sounding a chime
LCD points to the container to be opened for each specific dose
LCD display: pill description, reason for taking the medication, etc.
Alerts user when a wrong compartment is opened
Programmable ePRO features
Automatically connects with support personnel in case of emergencies
|Patient compliance||91% for Med-eMonitor, 90% for PharmCAT, and 72% for the treatment as usual group|
|Henriksson et al. (2016)||Electronic medication dispenser (EMD) (unknown)|
Note: Patients loaded the device with medications
Records and stores adherence data
Audio and visual reminders
Audio and visual signals for missed medications
Patient compliance with immunosuppressive medications
Influence of the EMD on clinical outcomes
Intervention group: 97.8%; control group: not assessed
|Hoffmann et al. (2018)||Automated home medication dispenser (AHMD) (MedaCube™)|
Note: Caregivers loaded the device withmedications
Bulk load medication (90 days' supply)
A drug database that uses pill pictures to assist medication programming and loading
Audio and visual reminders
Notifies caregivers in case of late or missed doses
Online portal for monitoring medication adherence
Lock mechanism to prevent inappropriate access to medications
24-hour battery support
|(2) Mean adherence increased from 49% (baseline) to 96.8% (after using AHMD for 6 months)
Median (IQR) MoCA score was 16 with IQR 8 at baseline and 16.5 with IQR 9.75 at 6-month follow up|