Does Mhealth Increase Adherence to Medication? Results of a Systematic Review

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Peer-reviewed

Research Article

Result of electronic adherence monitoring on adherence and outcomes in chronic atmospheric condition: A systematic review and meta-assay

• Amy Hai Yan Chan,
• Holly Foot,
• Christina Joanne Pearce,
• Rob Horne,
• Juliet Michelle Foster,
• Jeff Harrison

x

• Published: March 21, 2022
• https://doi.org/10.1371/journal.pone.0265715

Figures

Abstract

Introduction

Electronic adherence monitoring (EAM) is increasingly used to improve adherence. Yet, there is express testify on the effect of EAM in across chronic conditions and on patient acceptability. Nosotros aimed to assess the consequence of EAM on adherence and clinical outcomes, beyond all ages and all chronic conditions, and examine acceptability in this systematic review and meta-assay.

Methods

A systematic search of Ovid MEDLINE, EMBASE, Social Piece of work Abstracts, PsycINFO, International Pharmaceutical Abstracts and CINAHL databases was performed from database inception to Dec 31, 2020. Randomised controlled trials (RCTs) that evaluated the effect of EAM on medication adherence as part of an adherence intervention in chronic conditions were included. Written report characteristics, differences in adherence and clinical outcomes between intervention and control were extracted from each study. Estimates were pooled using random-effects meta-analysis, and presented as mean differences, standardised mean differences (SMD) or run a risk ratios depending on the data. Differences by written report-level characteristics were estimated using subgroup meta-analysis to identify intervention characteristics associated with improved adherence. Effects on adherence and clinical outcomes which could not exist meta-analysed, and patient acceptability, were synthesised narratively. The Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guideline was followed, and Adventure of bias (RoB) assessed using the Cochrane Collaboration's RoB tool for RCTs. The review is registered with PROSPERO CRD42017084231.

Findings

Our search identified 365 studies, of which 47 studies involving 6194 patients were included. Data from 27 studies (n = 2584) were extracted for the adherence outcome. The intervention group (n = 1267) had significantly better adherence compared to control (due north = 1317), (SMD = 0.93, CI:0.69 to i.17, p<0.0001) with high heterogeneity across studies (Iⁱⁱ = 86%). There was a significant difference in event co-ordinate to intervention complication (p = 0.01); EAM simply improved adherence when used with a reminder and/or wellness provider back up. Clinical outcomes were measured in 38/47 (81%) of studies; of these data from 14 studies were included in a meta-assay of clinical outcomes for HIV, hypertension and asthma. In total, 13/47 (28%) studies assessed acceptability; patient perceptions were mixed.

Interpretation

Patients receiving an EAM intervention had significantly improve adherence than those who did not, but improved adherence did not consistently translate into clinical benefits. Acceptability data were mixed. Farther research measuring effects on clinical outcomes and patient acceptability are needed.

Citation: Chan AHY, Pes H, Pearce CJ, Horne R, Foster JM, Harrison J (2022) Effect of electronic adherence monitoring on adherence and outcomes in chronic conditions: A systematic review and meta-analysis. PLoS I 17(three): e0265715. https://doi.org/10.1371/journal.pone.0265715

Editor: Ivan D. Florez, Universidad de Antioquia, Republic of colombia

Received: March 18, 2021; Accepted: March seven, 2022; Published: March 21, 2022

Copyright: © 2022 Chan et al. This is an open admission commodity distributed under the terms of the Creative Commons Attribution License, which permits unrestricted apply, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are inside the paper and its Supporting Information files.

Funding: The author(south) received no specific funding for this work.

Competing interests: AC provides freelance consultancy to UCL-Business spin-out visitor Spoonful of Sugar Ltd., and Janssen-Cilag, and has received research grants from A+ charitable trust (Auckland District Health Lath), Maurice and Phyllis Paykel trust, Universitas 21, New Zealand Chemist's Education Research Fund, Auckland Academic Wellness Alliance, Asthma United kingdom of great britain and northern ireland, Academy of Auckland, Wellness Enquiry Council, and Oakley Mental Health Foundation, outside the submitted work, is the holder of the Robert Irwin Postdoctoral Fellowship; HF provides freelance consulting UCL-Business organization spin-out company Spoonful of Sugar Ltd.; RH has received enquiry grants from NIHR, and Asthma UK, and consultancy fees from Medical Innovation Academic Consortium (CASMI), AbbVie, Amgen, Biogen, Idec, Gilead Sciences, GlaxoSmithKline, Janssen, Pfizer, Roche, Shire Pharmaceuticals, MSD, Astellas, AstraZeneca, DRSU, Novartis, Universitatsklinikum Hamburg-Eppendorf, and Teva Pharmaceuticals, and is the Founder and Director of UCL-Business spin-out company Spoonful of Sugar Ltd., outside the submitted piece of work. All other authors declare no financial relationships with any organisations that might have an interest in the submitted piece of work in the previous three years and no other relationships or activities that could appear to have influenced the submitted piece of work. This does non modify our adherence to all PLOS One policies on sharing data and materials.

Introduction

Poor medication adherence costs the US health system betwixt $100 and $300 billion of avoidable health care costs annually, and is associated with increased morbidity and mortality [ane, 2]. Despite the big body of literature on adherence, medication adherence remains suboptimal [one]. Interventions to improve adherence accept had simply modest impacts on adherence, and take uncertain long-term sustainability due to the brusque trial durations and need for intensive resources [1, three]. Digital solutions can address some of these concerns by potentially improving intervention sustainability through automation and reduce resources for implementation [iv]. Exploring new ways of delivering healthcare is essential with the COVID-xix pandemic and increased pressures on health systems [5–7].

Electronic adherence monitoring (EAM) [iv] use electronic devices that record medication-taking, commonly the time and engagement of each dose. These medication monitors are increasingly used as part of strategies to better adherence. EAM is seen as the aureate standard of adherence measurement due to their objectivity and data recording accurateness [4, eight], and tin be used to improve adherence through straight patient reminders for medication-taking [9], and/or by facilitating adherence feedback to the patient [10, 11]. Previous reviews have looked at the upshot of certain features of EAM and associated electronic devices such as reminders [seven, 12–15], medication packaging [thirteen], or adherence feedback [x], on adherence. The reviews generally study a positive event on adherence [7, 12–fifteen] however no reviews have examined EAM specifically, or beyond all chronic conditions rather than specific conditions [14, 15]. Christensen et al. for instance conducted a systematic review of studies on EAM for oral antihypertensive medicines, and found that nearly reported average adherence rates above 80%, though adherence did vary from 0 to 101% [16]. The authors did not perform a meta-analysis. In a systematic review and meta-analysis past Yaegashi et al., adherence equally measured past EAM was reported to be 71% for antipsychotics in schizophrenia [17]. Lee et al. conducted a meta-analysis of RCTs of EAM in children with asthma and reported that the EAM group was ane.l times more probable to adhere to inhalers compared with the command group [eighteen]. However, these reviews have non included clinical outcome data [thirteen], or where there is outcome data, the review has not been systematic [9] or did not include a meta-analysis [10], or focused on specific populations or medication [17, 18].

Given the costs of poor adherence, and the increasing investment into EAM to improve adherence, there is a demand for a high quality systematic review and meta-assay. The findings will inform public wellness conclusion-making and futurity strategies to improve adherence and outcomes across chronic weather condition. This systematic review examines the effect of EAM across all chronic conditions on adherence and clinical outcomes.

Materials and methods

This systematic review was conducted based on Guidelines of the Cochrane Collaboration as described in the Cochrane Handbook of Systematic Reviews of Interventions, version 6.0 (updated July 2019) [19] and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). The review is registered with PROSPERO CRD42017084231.

Search strategy and option criteria

We did a systematic search of the literature using Ovid Medline, EMBASE, Social Work Abstracts, PsycINFO, International Pharmaceutical Abstracts, EBM Reviews–Cochrane Central Register of Controlled Trials and CINAHL from database inception to June ane, 2020. Indexing terms based on electronic monitoring, adherence, and intervention were used to develop the search strategy; full details are in S1 Appendix. No language or participant blazon limit was used. This search strategy was supplemented by a manual search of the reference lists of the identified studies to find other relevant studies. All titles and abstracts were screened separately by 2 authors independently (Air conditioning first, with a 2d screen by JH/CP/HF). Total texts were obtained for eligible studies or abstracts that did not have sufficient information for review. Studies that did non encounter the inclusion criteria or had reasons for exclusion were not reviewed further, and reasons for exclusion documented.

Inclusion criteria were: a) the intervention evaluated EAM every bit an intervention to improve medication adherence; b) participants were individuals with chronic conditions, defined every bit a long-term, persistent wellness condition lasting iii months or more than [twenty]; c) ane of the event measures was medication adherence, though this did not need to exist measured by electronic monitoring, and clinical outcomes did not need to be assessed; and d) the report was a RCT or a controlled clinical trial (to ensure the highest quality of evidence was included). Studies were even so eligible for inclusion even if only 1 group had adherence monitored electronically and the other did not. Cluster trials were eligible for inclusion. EAM was defined as any mechanism or device that measures and records adherence electronically, regardless of whether or not the EAM devices had a reminder function. All studies meeting the inclusion criteria were included regardless of how adherence was measured (via EAM or not), the adherence measurement (objective or self-report), definition of adherence (taking, timing adherence, or difference between 2 fourth dimension-points) or analysis method (e.g. mean ± SD, odds ratios). Adherence had to be measured for all participants at the individual level and non at a group level e.g. adherence of private patients non adherence of patients within 1 chemist's shop site. Studies that used a downstream measure out to approximate adherence e.g. adherence knowledge as a proxy for adherence, or used electronic monitoring for adherence measurement only, rather than to amend adherence, were excluded. Studies using a inside-subjects blueprint or historical controls were excluded due to the risk of bias arising from factors other than the intervention itself. Studies using contemporary controls were included.

Data analysis

Air-conditioning and HF extracted the following data for each study: general study information (author, twelvemonth of publication); report design; study population (historic period, sex, wellness condition); report elapsing; type of EAM used; description of the intervention and control conditions including details on intervention complexity (i.e. how many components were included in the intervention in addition to EAM eastward.chiliad. whether the intervention used EAM alone, EAM + EAM reminder, or EAM + health professional input, or all of the aforementioned components); method of adherence feedback; timing of the adherence feedback to the individual (immediate or delayed); presence of participant blinding to adherence monitoring function of EAM; how adherence was measured; outcome measures–effect on adherence, clinical outcomes and other findings; and any data on patient perceptions of EAM.

Studies were classified based the chronic condition of the participants in the report, and on how EAM were used in the adherence intervention–either directly-to-patient to improve adherence (e.g. via a reminder or visual feedback), or through an indirect provider-to-patient interaction (e.g. adherence feedback by the health provider), or both. The effect on clinical outcomes, where reported, was classified as "significantly improved", "trend towards comeback merely not significant", "no consequence", or "worsened". Patient perceptions of the EAM intervention were categorised equally perceptions of EAM or of the adherence intervention.

Grade was used to charge per unit the quality of evidence according to risk of bias, consistency, directness, precision and reporting bias [21]. The gamble of bias (RoB) in each included written report was assessed independently, using the Cochrane Collaboration's RoB tool for RCTs [22], past AC and HF/CP. A funnel plot was used to evaluate the effect of publication bias.

Data were pooled from studies which reported medication adherence of participants in the intervention and command groups. The primary result mensurate was the difference in medication adherence between intervention and control groups, expressed as the hateful divergence (MD) with 95% confidence intervals (CIs), and derived using random-furnishings models to account for both within-study and between-study variance (tau-squared [τ²]).SMD was used to account for different measures of adherence reporting. The SMD expresses the intervention effect in standard units rather than the original units of measurement and shows the difference in hateful effects between the intervention and control groups divided by the pooled standard departure of participants' outcomes [19]. A positive SMD (i.e. greater than 0) indicates meliorate adherence in the intervention group compared to control. To arrange for differences between studies in adherence measures, adherence definitions, and analysis methods, the generic inverse variance issue blazon was used. All estimates are presented as SMDs. Equally medication adherence differs significantly among different health conditions, the adherence outcome was analysed by chronic disease. There were two secondary outcomes: the difference in medication adherence between intervention and control groups in studies that a) measured taking adherence (i.e. studies that measured the percent of prescribed doses taken, regardless of timing [23]) and b) studies that used an objective measure of adherence.

Nosotros contacted authors for studies that did non have data or could not be converted into the required format for meta-analysis (i.eastward. SMD and standard deviation). The primary issue mensurate was called for studies that had multiple medications or dosing regimens or adherence measures (eastward.thousand. timing and taking adherence); or reported multiple intervention or control groups. If outcomes were reported at multiple time points, nosotros extracted these and included the latest reported time signal. We excluded post-intervention follow-upwards data. If multiple measures of adherence were used, nosotros included the most objective mensurate in the review. Reporting in the report of one or more of the outcomes listed here was non an inclusion criterion for this review. Intention-to-treat (ITT) or 'total analysis set' analyses were used where these were reported. For studies that did not report data in a form that allowed meta-analysis, this data were reported narratively (east.thou. as medians and interquartile ranges for each group).

Studies that included all as principal or secondary outcomes, decisions were fabricated in the post-obit order: the well-nigh recent (or cease) time-bespeak in the intervention menses; the group with the largest number of participants; the dosing regimen with the least daily doses; taking adherence; and the intervention group that most closely represented EAM alone and the control grouping that most closely represented usual care.

The Ԛ test [24] and the I^two alphabetize were used to identify and quantify written report heterogeneity respectively. Cochrane RevMan Software version 5.4 [25] was used for all statistical analyses, and p-values <0.05 denoted statistical significance.

Study-level characteristics: Subgroup analysis.

We conducted pre-specified subgroup analyses to investigate the event of the post-obit report-level characteristics on adherence: 1) age; 2) healthcare setting; 3) intervention complexity (i.eastward. EAM alone, EAM + EAM reminder, EAM + health professional input or all aforementioned components); 4) method of adherence feedback; v) timing of adherence feedback to the participant (immediate or delayed); 6) written report duration; and 7) participant blinding to the EAM adherence monitoring office.

Effect on clinical outcomes. Based on the heterogeneity of the different illness measures, we conducted meta-analyses only when this was meaningful, that is, when treatments, participants, and the underlying clinical question were similar plenty for pooling to make sense, for example, where studies used similar upshot measures. We therefore performed a meta-analysis by grouping together like measure types co-ordinate to the chronic disease. For studies that did non report data in a course that immune

meta-analysis, this data were reported narratively (e.m. as medians and interquartile ranges for each group).

Course was used to charge per unit the quality of evidence according to risk of bias, consistency, directness, precision and reporting bias [21]. The risk of bias (RoB) in each included study was assessed independently, using the Cochrane Collaboration's RoB tool for RCTs [22], by AC and HF/CP. Funnel plots were used to evaluate the result of publication bias.

Information were pooled from studies which reported the clinical outcome of interest in the intervention and control groups. Continuous data (information that tin can accept any numerical value) was analysed as mean differences (MDs) using a random-effects model and 95% conviction intervals (CIs) if the measures used in the studies were reported on the same scale. If data were reported using different measures or scales, SMDs were used to account for the unlike methods of measurement (eastward.g. dissimilar asthma control questionnaires. If both alter from baseline and endpoint scores were available for continuous data, change from baseline scores were used. For information reported as rates or proportions, this was analysed as risk ratios using a random-effects model and by inverse variance. If a study reported outcomes at multiple fourth dimension points, we used the mensurate taken at the final follow-upwardly. Intention-to-treat (ITT) or 'full analysis set up' analyses were used where these were reported.

Patient acceptability of the EAM intervention. Don patient acceptability were synthesised narratively.

Results

Our search identified 565 records, of which 365 were screened after duplicates were removed. 66 full-text manufactures were assessed for eligibility and 47 studies involving 6194 patients met the inclusion criteria for inclusion in this systematic review (Fig 1). Tabular array 1 describes the principal characteristics of the studies. Study population size ranged from 6 [26] to 784 [14] participants (mean = 128, median = 80). Most (due north = 41, 87%) were in adults with just 6 studies in children. The near common conditions were in asthma (north = 10, 21%) [11, 27–35], or human being immunodeficiency virus (HIV) (n = 9, 19%) [36–44], or hypertension (north = 6, thirteen%) [14, 45–49].

The most common EAM device type was an electronic cap fitted onto an oral medication bottle (the 'Medication Event Monitoring Organisation (MEMS)' (n = fourteen, 30%) [26, 37, 39, 40, 42, 43, 47, 48, 50–55] or similar (n = ten, 21%) [38, 41, 46, 49, 56–61]. The four (ix%) other studies of oral medicines used electronic medication blister cards [xiv, 62–64]. Some used EAM devices that fitted to a specific medication formulation such as inhalers (northward = 12, 26%) [11, 27–35, 65, 66] or eyedrops (northward = 2, iv%) [67, 68]. Five (11%) [36, 44, 45, 69, 70] used an integrated medication management system (MMS) which included recording of dosing times and symptoms, reminders most lifestyle and /or medication-taking, and information about disease control.

Most studies, except two early studies [46, 63], used electronic monitoring to measure adherence, though this was ofttimes used with other measures such as serum medication levels [39, 40, 59, 63], self-study [fourteen, 38, 39, 41, 43, 49, 51–55, 64–66, 71], adherence questionnaire [36, 45, 51, 61], pill count [38, 48, 51, 59, 69, 70], canister weight [65, 66],or prescription refill data [52, 55].

Of the 47 included articles, 27 (57%) studies provided sufficient data for the primary upshot meta-assay, through the published manuscript or author contact. Fourteen (30%) authors were uncontactable, iii (6%) studies did not study on adherence differences in both control and intervention groups and two (4%) authors could non provide further information. From these 27 included studies, 25 were eligible for the secondary outcome analysis of studies measuring taking adherence, and 24 of studies using objective adherence measures.

Outcome of EAM on medication adherence

The principal event analysis of pooled information from 27 studies (n = 2584) showed that the intervention group (n = 1267) had significantly better adherence than control (north = 1317), (MD = 0.93, CI: 0.69 to i.17, p = <0.0001). Statistically pregnant heterogeneity was present (Q = 187.65, p = <0.0001) and of substantial caste (Iⁱⁱ = 86%). The woods plot for all studies is shown in Fig two.

Fig 2. Wood plot of effect of the electronic adherence monitoring intervention compared to command on medication adherence for studies with bachelor information (n = 27) by chronic condition.

SE, standard error; CI, confidence intervals for upshot size.

https://doi.org/x.1371/journal.pone.0265715.g002

The secondary upshot assay of the 25 studies (n = 1127 in the intervention, due north = 1175 in the control) that measured taking adherence showed a positive event size (MD = 0.95, CI: 0.69 to 1.22, p = <0.0001). Simlarly, analysis of the 24 studies using objective measures of adherence (n = 1131 intervention, n = 1164 control group) as well showed a statistically pregnant positive consequence size (Physician = 1.02, CI: 0.76 to 1.28, p = <0.0001).

Study-level characteristics: Subgroup analysis.

Divide subgroup analyses are shown in Tabular array two. All subgroups had positive effect sizes, with no meaning differences amid subgroups except for the "complexity of intervention" variable (p = 0.01). EAM-only interventions did not improve adherence (SMD = 0.24, CI:-0.35 to 0.84) as much compared to interventions where the EAM was used with a reminder and / or health professional input (SMD ranged from 0.73 (EAM + health professional input) to 1.51 (EAM + EAM reminder) (CI range: -0.54–2.22).

Event on clinical outcomes

Table 3 shows the furnishings of EAM on clinical outcomes across the 47 included studies summarised narratively. Nine studies (19%) did not assess clinical effect [26, 38, 54–57, 65, 66, 68]–most these (vii/9) were of a shorter study duration (half dozen months or less). There were 38 (81%) studies that reported clinical outcomes; ten (26%) reported statistically significant improvements [27, 28, 30, 32, 37, 39, 46, 47, 52, 62].

Due to the wide heterogeneity of the types of measures used to assess clinical outcomes, meta-analyses could only be conducted by disease grouping, where there were three or more studies reporting on clinical effect in a similar way. Across the 38 studies that reported on clinical outcomes, meta-analyses could be conducted using the following upshot measures–viral load for HIV, blood pressure for hypertension; and asthma control measures for asthma. For studies in HIV, v [36, 37, 72–74] of the 9 HIV studies reported on proportion of patients with undetectable viral load as an outcome;. In hypertension, 4 [49, 75–77] of the seven studies reported on hateful change in blood pressure (systolic and diastolic); and in asthma, 5 of the nine studies reported on change in asthma control [28, 32, 34, 78, 79], in a fashion that could exist included in the meta-analysis. Table 4 shows the testify profile for these 3 clinical outcomes, and for adherence.

There were 3 studies that reported outcomes in transplant patients, even so only 2 reported numbers of patients with rejection [51, 58] while the other reported on v-yr issue-free survival rates [64]. The remaining 11 studies reporting on outcomes were in a range of health conditions: heart failure (2/x); diabetes (2/x); glaucoma; bipolar disorder; percutaneous coronary intervention; psychosis; COPD; coronary artery illness; and schizophrenia, all reporting on outcomes using different measures (east.one thousand. symptom scores or hospitalisation rates) and therefore could non exist synthesised via meta-assay.

Fig 3 shows the consequence of EAM on clinical outcomes in HIV, hypertension and asthma. For all outcomes, the analysis crossed the boundary of now issue, but showed a non-significant effect favouring the EAM group. For HIV, those receiving the intervention had a i.08 (95% CI 0.91–1.29, p = 0.39) chance of having an undetectable viral load. In hypertension, the EAM group achieved a lower systolic and diastolic blood pressure by 2mmHg, though the 95% CI was wide. For asthma, a SMD of 0.09 (95% CI, -0.07–0.24, p = 0.43) was seen, indicating a minor simply positive improvement in asthma command favouring the intervention, though this was not meaning. Table 3 describes narratively the outcomes for other health conditions, which report a range of upshot from significant improvements in outcome to no issue. There were xv studies that reported no effect on outcomes; these had improvements in adherence from 2% [43, 48] to 34% [31], and had a lack of blinding or real-time feedback, with nearly (11/fifteen studies) not feeding back adherence in real-time to the participant.

Fig 3.

Wood plot of effect of the electronic adherence monitoring intervention compared to control on clinical outcomes past a) viral load for Human being Immunodeficiency Virus; b) blood pressure for hypertension; c) asthma command for asthma.

https://doi.org/10.1371/periodical.pone.0265715.g003

Not all studies reporting clinical do good had improvements in adherence. Four studies [45, 58, 61, 64] had no meaning effect on adherence but reported clinical benefit. Three [36, 41, 69] had improvements in some but non all clinical parameters (due east.m. reduced viral load, only no effect on CD4 count [36]), and 2 [44, 63] had a negative result on clinical outcomes but a positive [44] or no effect [63] on adherence. In a study of patients with bipolar affective disorder, the EAM intervention group reported higher rates of feet, low and somatism than the controls [63]. Wu et al. reported worsened quality of life in patients with HIV who received the EAM intervention [44] than those who did non, merely no differences in disease control.

Assessment of risk of bias

Almost studies had at least one domain rated equally having a high risk of bias (north = 40, 85%, S1 and S2 Files). In that location was a high performance bias in most studies (n = 35, 74%). This is expected every bit blinding of patients and health professionals is difficult due to the nature of the EAM intervention. Most studies (n = 38, 81%) had low detection bias every bit adherence and clinical outcome measures were objectively measured. Whist the risk of selective reporting bias was low within each study, overall, there was moderate selective reporting bias as twenty of the 47 studies did non report data in a way that could exist meta-analysed.

Assessment of publication bias across studies

The funnel plot (S3 File) indicated acceptable plot symmetry for both adherence and clinical outcomes data, suggesting limited publication bias, though the large amount of study heterogeneity needs to be considered.

Quality of show

Tabular array 4 shows the Grade ratings for adherence and clinical outcomes for the three weather condition where this could be meta-analysed.

Run a risk of bias for adherence (S1 and S2 Files) was rated equally serious for the adherence issue due to the high number of studies with unclear allocation concealment and the take chances of functioning bias, equally a results of the inability to bullheaded the intervention to the participants and upshot assessors in most studies, which could affect result reporting. For clinical outcomes yet, the nature of the outcomes (viral load and blood pressure) are objective measures that are unlikely affected by noesis of grouping allocation; as such show was not downgraded for HIV and hypertension but was so for asthma. Inconsistency was downgraded and rated equally serious for adherence and blood pressure level, given the high heterogeneity of the studies.

Indirectness, imprecision and publication bias were non downgraded for adherence, simply evidence was downgraded for chance of bias and inconsistency. Quality was upgraded given the strong association and large consequence size. This gave an overall rating of moderate certainty in the upshot estimate for adherence. For clinical outcomes, overall certainty in the evidence was deemed low, mainly as a issue of inconsistency and imprecision as a result of the small sample sizes per condition.

Patient acceptability perceptions of the EAM intervention

Fourteen studies evaluated patient acceptability of the EAM intervention (see S4 File). Nine assessed usability of the device [11, 30, 37, 38, 44, 49, 67, 69, 70] though one study did not report the results [67]; 4 focused on the acceptability of the adherence feedback and interaction with wellness providers [47, 53, 54, 56]; and one report focused on both the device and the health provider interaction [62].

Patient perceptions of EAM.

Of the nine studies that reported patient perceptions of the device, perceptions were negative in nearly half [37, 44, 69, 70]. In the Velligan et al. study, participants preferred getting medication support from staff rather than from EAM [70], and had negative feedback, primarily about the device'due south reminder beeps. Similarly, Wu et al. reported that the EAM device was as well large and too loud, leading to unwelcome questions and possible revelation of the patient'south HIV condition [44]. In dissimilarity, the EAM device used by Frick et al., which included a button to silence the alarm, received positive feedback, with 99% stating they would utilize the vial again and 97% finding the warning helpful [38]. Other studies reported mixed results, with some participants enjoying EAM equally they felt it helped them stay on schedule with their doses, whilst others "hated the device" and felt their lives were regulated by the EAM device, and found EAM to exist a nuisance [69], unnecessary and unattractive [37, lxxx].

Patient perceptions of the adherence intervention.

Patient perceptions of the adherence intervention were more positive than for the device [37]. Patients and health providers found the adherence review and discussions the most beneficial parts of the intervention and "looked forward" to receiving their adherence information [47, 53, 56, 62]. The adherence feedback did non brand patients feel uncomfortable [53] and was not perceived to be intrusive [62], or a burden [54].

Other findings.

Although most studies did non aim to evaluate patient perceptions, four studies attributed recruitment issues, patient drop-outs and non-participation to issues with device acceptability by patients (S4 File) [14, 37, 45, 51]. Training was also identified as a factor to consider for intervention acceptability [41].

Discussion

This is the offset systematic review and meta-analysis of EAM and the effect on medication adherence and clinical outcomes, beyond all chronic weather condition. To our noesis, this systematic review and meta-analysis is the largest in this field, comprising 47 RCTs in the systematic review and 27 studies in the meta-assay of adherence. Whilst there have been systematic reviews beyond conditions, these have been for multiple types of interventions rather than specifically examining the effect of EAM. Patients receiving an EAM intervention had significantly improve adherence compared to those who did not, with a big magnitude of effect (SMD = 0.93). SMD measures effect when studies study efficacy as a continuous measurement, with nada meaning the intervention and command groups take equivalent effects, with SMD increasing equally the difference between the intervention and control grouping increases. An SMD over 0.eight is considered a big outcome [81]. Putting this into perspective, when SMD = 0, the probability that the intervention outperforms control is 0.5 (no better than take chances); and when SMD = 1, the probability increases to 0.76. In this review, SMD = 0.93, meaning for individuals who receive EAM, at that place is a approximately a 0.7 probability that their adherence will improve than if they didn't receive the EAM. As our review establish all the same, this may not consistently translate to clinical benefits, as this appears to vary depending on the population and chronic condition. This highlights the potential of EAM to improve medication adherence in patients with chronic conditions. Similar upshot sizes were seen in studies measuring simply taking adherence, and in studies using objective adherence measures. Nosotros constitute that the effect of EAM appeared particularly big for asthma and HIV, similar to findings from other reviews, though the number of studies per condition are small, which limits our confidence in the findings. Lee et al. reported that in children with asthma, those receiving an EAM intervention were one.5 times more likely to attach than those in the control group [18]. Similarly Christensen et al. noted high adherence rates reported beyond included studies for HIV populations receiving EAM [sixteen], though the authors did not carry a meta-assay. Our findings are like to previous reviews of the result of reminders or adherence feedback on adherence, but the magnitude of effect in prior reviews was smaller and not-significant [82] or not quantified if findings in studies that only reported results narratively [seven, ix, ten, 12, 13]. Previous reviews may have only been limited to one wellness condition [17, 18], or evaluated simply one aspect of EAM (eastward.g. reminders, adherence feedback, or the packaging), which may explain our larger magnitude of consequence. Nosotros found improvements in clinical outcomes in HIV, hypertension and in asthma, but none of these reached statistical significance due to the small number of studies that were able to be included.

There are several implications for physicians, researchers, and payers. First, our review found that intervention complexity was important for intervention effectiveness. Studies using an EAM device by itself, without reminders or wellness provider input, did not improve adherence. This aligns with previous literature showing that circuitous interventions–i.e. those involving more than than i intervention element–are more constructive [83]. A systematic review of electronic packaging interventions on adherence, including both RCTs and non-RCTs, constitute that complex interventions with EAM were the most constructive for improving adherence [84]. Second, the delivery format of the EAM intervention did non appear to greatly influence the magnitude of outcome. Intervention effectiveness was not influenced by how adherence feedback was provided or the timing of the feedback, nor past the age of the participants, with EAM being effective in both children and adults.

Third, although there were significant improvements in adherence, few RCTs reported corresponding benefits in clinical outcomes. Those that did show clinical improvements reported a greater magnitude of increase in adherence. Information technology is possible that a minimum threshold of percent adherence change is needed earlier any clinical change can be achieved, however the threshold is unknown for most conditions and depends on the medication pharmacology [85]. Whilst at that place are many studies demonstrating the association between adherence and clinical outcomes [86, 87], information technology is not known whether the relationship is a linear, exponential or logarithmic one, and the relationship is likely to exist affected past the disease, medication and patient [85]. We found that, on average, merely one-half of those interventions that improve adherence translate to respective improvements in clinical outcomes.

In that location are several limitations to consider. The impact on adherence was pooled from 27 studies; over 40% of the studies (20/47) did not report adherence data in style that could exist meta-analysed. The impact on clinical outcomes is too less clear. Several studies did not measure clinical outcome information or where information were measured, the outcomes were non relevant to the status. For example, Elixhauser et al. used a general psychological symptom questionnaire [88] to assess the result of an intervention to meliorate lithium adherence, rather than a validated mania calibration, which would have better reflect lithium adherence and response [63]. Even in conditions where illness command can exist easily measured and validated illness control questionnaires exist, such equally in asthma, centre failure, or diabetes, there was large heterogeneity in the measures used and outcomes studied that precluded inclusion in a meta-assay [11, 14, 27–31, 45–48, 52, 53, 61, 69]. This lack of standardisation in outcome measures across the aforementioned illness state makes inter-study analyses and comparisons difficult. In this review, we performed a meta-analysis to synthesise clinical outcomes simply only where meta-analysis was meaningful. Every bit outcome measures were highly varied, nosotros opted but group together measures for the same status where these made clinical sense. The limitation of this arroyo is the small number of studies that were able to be included, which reduced our confidence in the findings. Additionally, for the studies that could not be included in the meta-analysis, this could only be described narratively and whether studies reported statistically significant benefits. This has limitations as it does not provide information about overall effect size. Questions besides remain about the sustainability of the intervention effects–simply 21 of the 38 studies that assessed clinical outcomes were of 6 months duration or longer. Whether these benefits are maintained in the long-term for chronic weather condition is non known equally initial intervention effects may wane over time. Whilst most studies assessed adherence well, by triangulating data from more than one objective adherence measure to evaluate adherence, the measurement of clinical outcomes is less consistent. Hereafter research should use validated markers of illness control that can be used in unlike research studies and clinical settings. As adherence is only a mediator of therapeutic outcomes, adherence studies should ideally always include a measure of clinical outcomes as an endpoint, as achieving adherence is meaningless if patients are not getting any clinical benefits. This is seen in the studies that reported clinical benefits, only no effect on adherence. Of note, a limitation for our review is how quality of testify was assessed. We used the Cochrane Risk of Bias 1.0 tool for testify certainty grading; however, we note that random sequence generation and allocation darkening were ofttimes rated equally 'unclear risk of bias' due to absenteeism of sufficient detail in reported studies. If the Cochrane Risk of Bias two.0 tool were used, this may potentially downgrade the prove certainty to 'very serious' risk of bias.

Our results emphasise the demand to consider the patient in healthcare interventions. Less than a third of the studies reported on patient acceptability, all the same findings evidence that patient perceptions of the devices were ofttimes negative. In contrast, patient perceptions about the adherence interventions were positive, especially about the provision of adherence feedback and the opportunity to interact with a health provider. Issues relating to the loudness of the reminder and device size are common themes that future interventions involving EAM should consider. Devices where patients are able to silence reminders and / or personalise the reminder setting may be more adequate [eighty]. Training resources need to be considered, specially as technologies alter and more EAM devices become available. Our findings highlight the importance of including feasibility and patient acceptability measures in future inquiry.

By combining information from RCTs, our systematic review and meta-analysis found that EAM can accept a meaning effect on medication adherence in chronic weather condition. How these adherence improvements translate into clinical benefit is less clear. A quarter of the studies reporting adherence improvements had corresponding clinical benefits. The lack of standardised outcome measures that reliably and accurately reflect disease control prevents us from definitively answering how EAM affects clinical outcomes. Future inquiry should mensurate clinical outcomes using standardised and validated tools; be of adequate study elapsing to assess the sustainability of improvements in adherence and disease control in the medium- to long-term; and chiefly, evaluate the patient acceptability of the EAM intervention.

Supporting information

S1 File. Summary risk of bias graph (n = 27) for adherence outcome using Cochrane Collaboration'south tool for assessing take a chance of bias for randomised controlled trials.

Studies are categorised as 'Depression hazard' of bias (green), 'High risk' of bias (cherry) or 'Unclear risk' of bias (yellow).

https://doi.org/10.1371/journal.pone.0265715.s003

(DOCX)

Acknowledgments

Air-conditioning was supported past a Lottery Wellness Doctoral scholarship when this work was started, and is currently supported by the Robert Irwin Postdoctoral Fellowship.

Previous presentation at meeting: Presented as an oral presentation at the 31^st Briefing of the European Health Psychology Society and British Psychological Gild Division of Health Psychology Conference, 23^rd– 27^th Baronial 2017.

References

1. ane. Nieuwlaat R, Wilczynski N, Navarro T, Hobson Northward, Jeffery R, Keepanasseril A, et al. Interventions for enhancing medication adherence. Cochrane Database Syst Rev. 2014;2014(11):CD000011. pmid:25412402
   • View Article
   • PubMed/NCBI
   • Google Scholar
2. 2. Iuga AO, McGuire MJ. Adherence and wellness intendance costs. Hazard Direction and Healthcare Policy. 2014;7:35–44. pmid:24591853
   • View Commodity
   • PubMed/NCBI
   • Google Scholar
3. 3. Linn AJ, Vervloet Grand, van Dijk L, Smit EG, Van Weert JCM. Furnishings of eHealth Interventions on Medication Adherence: A Systematic Review of the Literature. Periodical of Medical Cyberspace Inquiry. 2011;13(4):e103. pmid:22138112
   • View Article
   • PubMed/NCBI
   • Google Scholar
4. 4. Chan AHY, Reddel HK, Apter A, Eakin Grand, Riekert K, Foster JM. Adherence Monitoring and E-Health: How Clinicians and Researchers Tin can Apply Applied science to Promote Inhaler Adherence for Asthma. Journal of Allergy and Clinical Immunology: In Do. 2013;1(5):446. pmid:24565615
   • View Article
   • PubMed/NCBI
   • Google Scholar
5. 5. de Jongh T, Gurol‐Urganci I, Vodopivec‐Jamsek V, Car J, Atun R. Mobile phone messaging for facilitating self‐management of long‐term illnesses. Cochrane Database of Systematic Reviews. 2012;(12). pmid:23235644
   • View Article
   • PubMed/NCBI
   • Google Scholar
6. 6. Mahtani KR, Heneghan CJ, Glasziou PP, Perera R. Reminder packaging for improving adherence to self‐administered long‐term medications. Cochrane Database of Systematic Reviews. 2011;(9). pmid:21901694
   • View Article
   • PubMed/NCBI
   • Google Scholar
7. 7. Vervloet M, Linn AJ, van Weert JC, de Bakker DH, Bouvy ML, van Dijk Fifty. The effectiveness of interventions using electronic reminders to improve adherence to chronic medication: a systematic review of the literature. J Am Med Inform Assoc. 2012;19(5):696–704. pmid:22534082
   • View Article
   • PubMed/NCBI
   • Google Scholar
8. 8. Foster JM, Smith Fifty, Usherwood T, Sawyer SM, Rand CS, Reddel HK. The reliability and patient acceptability of the SmartTrack device: a new electronic monitor and reminder device for metered dose inhalers. Periodical of Asthma. 2012;49(half-dozen):657–62. pmid:22741746
   • View Article
   • PubMed/NCBI
   • Google Scholar
9. 9. Kini Five, Ho PM. Interventions to Ameliorate Medication Adherence: A Review. JAMA: Journal of the American Medical Association. 2018;320(23):2461–73. pmid:30561486
   • View Commodity
   • PubMed/NCBI
   • Google Scholar
10. ten. van Heuckelum Chiliad, van den Ende CHM, Houterman AEJ, Heemskerk CPM, van Dulmen S, van den Bemt BJF. The effect of electronic monitoring feedback on medication adherence and clinical outcomes: A systematic review. PLoS I [Electronic Resource]. 2017;12(ten):e0185453-e. pmid:28991903
    • View Article
    • PubMed/NCBI
    • Google Scholar
11. 11. Foster JM, Usherwood T, Smith 50, Sawyer SM, Xuan W, Rand CS, et al. Inhaler reminders improve adherence with controller handling in primary care patients with asthma. Periodical of Allergy and Clinical Immunology. 2014;134(half-dozen):1260. pmid:25062783
    • View Article
    • PubMed/NCBI
    • Google Scholar
12. 12. Tran Due north, Coffman JM, Sumino K, Cabana Dr.. Patient reminder systems and asthma medication adherence: a systematic review. J Asthma. 2014;51(5):536–43. pmid:24506699
    • View Article
    • PubMed/NCBI
    • Google Scholar
13. 13. Checchi KD, Huybrechts KF, Avorn J, Kesselheim As. Electronic medication packaging devices and medication adherence: A systematic review. JAMA—Periodical of the American Medical Association. 2014;312(12):1237. pmid:25247520
    • View Article
    • PubMed/NCBI
    • Google Scholar
14. 14. Christensen A, Christrup LL, Fabricius PE, Chrostowska M, Wronka 1000, Narkiewicz M, et al. The bear upon of an electronic monitoring and reminder device on patient compliance with antihypertensive therapy: a randomized controlled trial. Journal of hypertension. 2010;28(1):194. pmid:19770778
    • View Article
    • PubMed/NCBI
    • Google Scholar
15. xv. Wise J, Operario D. Use of electronic reminder devices to improve adherence to antiretroviral therapy: a systematic review. AIDS Patient Intendance & STDs. 2008;22(half dozen):495. pmid:18462071
    • View Commodity
    • PubMed/NCBI
    • Google Scholar
16. 16. Christensen A, Osterberg LG, Hansen EH. Electronic monitoring of patient adherence to oral antihypertensive medical treatment: a systematic review. Periodical of Hypertension. 2009;27(8):1540–51. pmid:19474761
    • View Article
    • PubMed/NCBI
    • Google Scholar
17. 17. Yaegashi H, Kirino S, Remington K, Misawa F, Takeuchi H. Adherence to oral antipsychotics measured past electronic adherence monitoring in schizophrenia: a systematic review and meta-analysis. CNS drugs. 2020;34(6):579–98. pmid:32219681
    • View Article
    • PubMed/NCBI
    • Google Scholar
18. 18. Lee JR, Leo S, Liao S, Ng WR, Tay TYN, Wang Y, et al. Electronic adherence monitoring devices for children with asthma: A systematic review and meta-analysis of randomised controlled trials. International Journal of Nursing Studies. 2021;122:104037. pmid:34391027
    • View Article
    • PubMed/NCBI
    • Google Scholar
19. 19. Higgins J, Thomas J, Chandler J, Cumpston G, Li T, Page M, et al. Cochrane Handbook for Systematic Reviews of Interventions version half dozen.one (updated September 2020): Cochrane; 2020. Available from: www.training.cochrane.org/handbook.
    • View Article
    • Google Scholar
20. xx. Bernell Southward, Howard SW. Use Your Words Carefully: What Is a Chronic Affliction? Frontiers in Public Health. 2016;4:159–. pmid:27532034
    • View Commodity
    • PubMed/NCBI
    • Google Scholar
21. 21. Guyatt GH, Oxman AD, Vist GE, Kunz R, Falck-Ytter Y, Alonso-Coello P, et al. GRADE: an emerging consensus on rating quality of evidence and strength of recommendations. BMJ. 2008;336(7650):924–half dozen. pmid:18436948
    • View Article
    • PubMed/NCBI
    • Google Scholar
22. 22. Higgins JPT, Altman DG, Gøtzsche PC, Jüni P, Moher D, Oxman Advertising, et al. The Cochrane Collaboration's tool for assessing chance of bias in randomised trials. BMJ. 2011;343:d5928. pmid:22008217
    • View Article
    • PubMed/NCBI
    • Google Scholar
23. 23. Demonceau J, Ruppar T, Kristanto P, Hughes DA, Fargher E, Kardas P, et al. Identification and assessment of adherence-enhancing interventions in studies assessing medication adherence through electronically compiled drug dosing histories: a systematic literature review and meta-assay. Drugs. 2013;73(half dozen):545–62. pmid:23588595
    • View Commodity
    • PubMed/NCBI
    • Google Scholar
24. 24. Hedges LV, Olkin I. Statistical methods for meta-analysis. San Diego, CA: Academic Press. San Diego, CA: Bookish Press; 1985.
25. 25. Review Manager (RevMan). Review Manager (RevMan). five.4 ed: The Cochrane Collaboration; 2020.
26. 26. Matteson-Kome ML, Winn J, Bechtold ML, Bragg JD, Russell CL. Improving Maintenance Medication Adherence in Adult Inflammatory Bowel Disease Patients: A Pilot Study. Health Psychology Research. 2014;2(1):1389. pmid:26973930
    • View Commodity
    • PubMed/NCBI
    • Google Scholar
27. 27. Burgess SW, Sly PD, Devadason SG. Providing feedback on adherence increases utilise of preventive medication by asthmatic children. Periodical of Asthma. 2010;47(ii):198. pmid:20170329
    • View Commodity
    • PubMed/NCBI
    • Google Scholar
28. 28. Chan AH, Stewart AW, Harrison J, Camargo CA Jr., Blackness PN, Mitchell EA. The effect of an electronic monitoring device with audiovisual reminder function on adherence to inhaled corticosteroids and school attendance in children with asthma: a randomised controlled trial. The Lancet Respiratory Medicine. 2015.
    • View Commodity
    • Google Scholar
29. 29. Charles T, Quinn D, Weatherall M, Aldington South, Beasley R, Holt S. An audiovisual reminder part improves adherence with inhaled corticosteroid therapy in asthma. Journal of Allergy and Clinical Immunology. 2007;119(4):811. pmid:17320942
    • View Article
    • PubMed/NCBI
    • Google Scholar
30. 30. Duncan CL, Hogan MB, Tien KJ, Graves MM, Chorney JM, Zettler Md, et al. Efficacy of a parent-youth teamwork intervention to promote adherence in pediatric asthma. Journal of pediatric psychology. 2013;38(6):617. pmid:23248342
    • View Commodity
    • PubMed/NCBI
    • Google Scholar
31. 31. Onyirimba F, Apter A, Reisine South, Litt M, McCusker C, Connors M, et al. Straight clinician-to-patient feedback discussion of inhaled steroid use: Its effect on adherence. Annals of Allergy, Asthma and Immunology. 2003;90(iv):411. pmid:12722963
    • View Article
    • PubMed/NCBI
    • Google Scholar
32. 32. Morton RW, Elphick HE, Rigby As, Daw WJ, Male monarch DA, Smith LJ, et al. STAAR: a randomised controlled trial of electronic adherence monitoring with reminder alarms and feedback to meliorate clinical outcomes for children with asthma. Thorax. 2017;72(4):347–54. pmid:27815524
    • View Article
    • PubMed/NCBI
    • Google Scholar
33. 33. Sulaiman I, Greene Chiliad, MacHale E, Seheult J, Mokoka M, D'Arcy S, et al. A randomised clinical trial of feedback on inhaler adherence and technique in patients with severe uncontrolled asthma. European Respiratory Journal. 2018;51(one):1701126. pmid:29301919
    • View Article
    • PubMed/NCBI
    • Google Scholar
34. 34. Vasbinder EC, Goossens LMA, Rutten-Van Molken MPMH, De Wintertime BCM, Van Dijk L, Vulto AG, et al. Due east-Monitoring of Asthma Therapy to Amend Compliance in children (east-MATIC): A randomised controlled trial. European Respiratory Journal. 2016;48(3):758–67. pmid:27230437
    • View Article
    • PubMed/NCBI
    • Google Scholar
35. 35. Gregoriano C, Dieterle T, Breitenstein AL, Dürr Due south, Baum A, Giezendanner South, et al. Does a tailored intervention to promote adherence in patients with chronic lung disease bear upon exacerbations? A randomized controlled trial. Respir Res. 2019;20(i):273. pmid:31796013
    • View Article
    • PubMed/NCBI
    • Google Scholar
36. 36. Andrade ASA, McGruder HF, Wu AW, Celano SA, Skolasky RL, Selnes OA, et al. A Programmable Prompting Device Improves Adherence to Highly Active Antiretroviral Therapy in HIV-Infected Subjects with Memory Impairment. Clinical Infectious Diseases. 2005;41(half dozen):875. pmid:16107989
    • View Article
    • PubMed/NCBI
    • Google Scholar
37. 37. de Bruin M, Hospers HJ, van Breukelen GJP, Kok Yard, Koevoets WM, Prins JM. Electronic monitoring-based counseling to enhance adherence amidst HIV-infected patients: a randomized controlled trial. Health Psychology. 2010;29(4):421. pmid:20658830
    • View Commodity
    • PubMed/NCBI
    • Google Scholar
38. 38. Frick PA, Lavreys 50, Mandaliya K, Kreiss JK. Impact of an warning device on medication compliance in women in Mombasa, Kenya. International Journal of STD & AIDS. 2001;12(v):329.
    • View Article
    • Google Scholar
39. 39. Rosen MI, Dieckhaus K, McMahon TJ, Valdes B, Petry NM, Cramer J, et al. Improved adherence with contingency direction. AIDS Patient Care & STDs. 2007;21(1):30. pmid:17263651
    • View Article
    • PubMed/NCBI
    • Google Scholar
40. 40. Rigsby MO, Rosen MI, Beauvais JE, Cramer JA, Rainey PM, O'Malley SS, et al. Cue-dose Training with Budgetary Reinforcement: Airplane pilot Study of an Antiretroviral Adherence Intervention. Journal of General Internal Medicine. 2000;15(12):841. pmid:11119180
    • View Commodity
    • PubMed/NCBI
    • Google Scholar
41. 41. Sabin LL, DeSilva MB, Hamer DH, Xu K, Zhang J, Li T, et al. Using electronic drug monitor feedback to meliorate adherence to antiretroviral therapy among HIV-positive patients in China. AIDS and Behavior. 2010;xiv(3):580. pmid:19771504
    • View Article
    • PubMed/NCBI
    • Google Scholar
42. 42. Smith SR, Rublein JC, Marcus C, Brock TP, Chesney MA. A medication cocky-management program to improve adherence to HIV therapy regimens. Patient education and counseling. 2003;50(ii):187. pmid:12781934
    • View Commodity
    • PubMed/NCBI
    • Google Scholar
43. 43. Wilson IB, Barton Laws M, Safren SA, Lee Y, Lu M, Coady Due west, et al. Provider-focused intervention increases adherence-related dialogue but does non ameliorate antiretroviral therapy adherence in persons with HIV. Journal of acquired allowed deficiency syndromes. 2010;53(3):338. pmid:20048680
    • View Article
    • PubMed/NCBI
    • Google Scholar
44. 44. Wu AW, Snyder CF, Huang I, Skolasky R Jr., McGruder HF, Celano SA, et al. A Randomized Trial of the Impact of a Programmable Medication Reminder Device on Quality of Life in Patients with HIV. AIDS Patient Care and STDs. 2006;20(11):773. pmid:17134351
    • View Article
    • PubMed/NCBI
    • Google Scholar
45. 45. Erickson SR, Ascione FJ, Kirking DM. Utility of electronic device for medication management in patients with hypertension: a airplane pilot written report. Journal of the American Pharmacists Association: JAPhA. 2005;45(1):88. pmid:15730122
    • View Article
    • PubMed/NCBI
    • Google Scholar
46. 46. McKenney JM, Munroe WP, Wright JT Jr. Impact of an electronic medication compliance assistance on long-term blood force per unit area control. Periodical of clinical pharmacology. 1992;32(3):277. pmid:1564133
    • View Commodity
    • PubMed/NCBI
    • Google Scholar
47. 47. Ruppar TM. Randomized pilot study of a behavioral feedback intervention to better medication adherence in older adults with hypertension. Periodical of Cardiovascular Nursing. 2010;25(6):470. pmid:20856132
    • View Commodity
    • PubMed/NCBI
    • Google Scholar
48. 48. van Onzenoort HA, Verberk WJ, Kroon AA, Kessels AG, Neef C, van der Kuy PH, et al. Electronic monitoring of adherence, handling of hypertension, and blood pressure control. American Journal of Hypertension. 2012;25(ane):54. pmid:21993365
    • View Article
    • PubMed/NCBI
    • Google Scholar
49. 49. Mehta SJ, Volpp KG, Troxel AB, Twenty-four hour period SC, Lim R, Marcus N, et al. Electronic Pill Bottles or Bidirectional Text Messaging to Improve Hypertension Medication Adherence (Way 2 Text): a Randomized Clinical Trial. Journal of General Internal Medicine. 2019;34(11):2397–404. pmid:31396815
    • View Article
    • PubMed/NCBI
    • Google Scholar
50. 50. Forni Ogna V, Menetrey I, Muller O, Tousset E, Guihard Fifty, Fontana P, et al. Result of long-term adherence to clopidogrel on the VASP-PRI after elective coronary stent implantation: a randomized controlled study. British Journal of Clinical Pharmacology. 2016;82(6):1486–97. pmid:27447737
    • View Article
    • PubMed/NCBI
    • Google Scholar
51. 51. Joost R, Dorje F, Schwitulla J, Eckardt KU, Hugo C. Intensified pharmaceutical care is improving immunosuppressive medication adherence in kidney transplant recipients during the first post-transplant year: a quasi-experimental study. Nephrology Dialysis Transplantation. 2014;29(8):1597. pmid:24914089
    • View Article
    • PubMed/NCBI
    • Google Scholar
52. 52. Murray Medico, Young J, Hoke S, Tu W, Weiner Thousand, Morrow D, et al. Pharmacist intervention to ameliorate medication adherence in heart failure: a randomized trial. Ann Intern Med. 2007;146(ten):714–25. pmid:17502632
    • View Article
    • PubMed/NCBI
    • Google Scholar
53. 53. Rosen MI, Rigsby MO, Salahi JT, Ryan CE, Cramer JA. Electronic monitoring and counseling to improve medication adherence. Behaviour Research & Therapy. 2004;42(4):409. pmid:14998735
    • View Article
    • PubMed/NCBI
    • Google Scholar
54. 54. Russell C, Conn V, Ashbaugh C, Madsen R, Wakefield K, Webb A, et al. Taking immunosuppressive medications effectively (TIMELink): a pilot randomized controlled trial in adult kidney transplant recipients. Clinical transplantation. 2011;25(six):864. pmid:21077956
    • View Article
    • PubMed/NCBI
    • Google Scholar
55. 55. Yeh EA, Grover S, Powell V, Noguera A, Edwards Thou, Slater R, et al. Impact of an electronic monitoring device and behavioral feedback on adherence to multiple sclerosis therapies in youth: Results of a randomized trial. Neurology Conference: 69th American University of Neurology Annual Meeting, AAN. 2017;88(16 Supplement 1). pmid:28393317
    • View Article
    • PubMed/NCBI
    • Google Scholar
56. 56. De Geest S, Schafer-Keller P, Denhaerynck Grand, Thannberger Due north, Kofer Due south, Bock A, et al. Supporting medication adherence in renal transplantation (SMART): a airplane pilot RCT to meliorate adherence to immunosuppressive regimens. Clinical transplantation. 2006;20(three):359. pmid:16824155
    • View Commodity
    • PubMed/NCBI
    • Google Scholar
57. 57. Hardstaff R, Light-green Thou, Talbot D. Measurement of compliance posttransplantation—The results of a 12-month report using electronic monitoring. Transplantation proceedings. 2003;35(2):796. pmid:12644142
    • View Commodity
    • PubMed/NCBI
    • Google Scholar
58. 58. Henriksson J, Tyden G, Hoijer J, Wadstrom J. A Prospective Randomized Trial on the Event of Using an Electronic Monitoring Drug Dispensing Device to Improve Adherence and Compliance. Transplantation. 2016;100(1):203–9. pmid:26588006
    • View Article
    • PubMed/NCBI
    • Google Scholar
59. 59. Kozuki Y, Schepp KG. Visual-feedback therapy for antipsychotic medication adherence. International clinical psychopharmacology. 2006;21(1):57. pmid:16317318
    • View Article
    • PubMed/NCBI
    • Google Scholar
60. lx. Reddy A, Huseman TL, Canamucio A, Marcus SC, Asch DA, Volpp K, et al. Patient and Partner Feedback Reports to Meliorate Statin Medication Adherence: A Randomized Control Trial. Periodical of Full general Internal Medicine. 2016:1–6. pmid:27612487
    • View Commodity
    • PubMed/NCBI
    • Google Scholar
61. 61. Sutton S, Kinmonth A, Hardeman W, Hughes D, Boase S, Prevost AT, et al. Does electronic monitoring influence adherence to medication? Randomized controlled trial of measurement reactivity. Annals of Behavioral Medicine. 2014;48(three):293. pmid:24573909
    • View Commodity
    • PubMed/NCBI
    • Google Scholar
62. 62. Brath H, Morak J, Kastenbauer T, Modre-Osprian R, Strohner-Kastenbauer H, Schwarz M, et al. Mobile health (mHealth) based medication adherence measurement—a pilot trial using electronic blisters in diabetes patients. Br J Clin Pharmacol. 2013;76 Suppl one:47–55. pmid:24007452
    • View Article
    • PubMed/NCBI
    • Google Scholar
63. 63. Elixhauser A, Eisen SA, Romeis JC, Homan SM. The effects of monitoring and feedback on compliance. Medical care. 1990;28(x):882. pmid:2232919
    • View Article
    • PubMed/NCBI
    • Google Scholar
64. 64. Dobbels F, De Bleser L, Berben L, Kristanto P, Dupont 50, Nevens F, et al. Efficacy of a medication adherence enhancing intervention in transplantation: The MAESTRO-Tx trial. Periodical of Heart & Lung Transplantation. 2017;06:06. https://dx.doi.org/x.1016/j.healun.2017.01.007.
    • View Article
    • Google Scholar
65. 65. Nides MA, Tashkin DP, Simmons MS, Wise RA, Li VC, Rand CS. Improving inhaler adherence in a clinical trial through the apply of the nebulizer chronolog. Chest. 1993;104(2):501–vii. pmid:8339641
    • View Commodity
    • PubMed/NCBI
    • Google Scholar
66. 66. Tashkin DP, Rand C, Nides M, Simmons M, Wise R, Coulson AH, et al. A nebulizer chronolog to monitor compliance with inhaler use. American Periodical of Medicine. 1991;91(4A):33S. pmid:1951401
    • View Commodity
    • PubMed/NCBI
    • Google Scholar
67. 67. Okeke CO, Quigley HA, Jampel HD, Ying Gs, Plyler RJ, Jiang Y, et al. Interventions Improve Poor Adherence with Once Daily Glaucoma Medications in Electronically Monitored Patients. Ophthalmology. 2009;116(12):2286. pmid:19815286
    • View Article
    • PubMed/NCBI
    • Google Scholar
68. 68. Hermann MM, Papaconstantinou D, Muether PS, Georgopoulos G, Diestelhorst G. Adherence with brimonidine in patients with glaucoma aware and not aware of electronic monitoring. Acta Opthalmologica. 2011;89(four):e300. pmid:21106046
    • View Article
    • PubMed/NCBI
    • Google Scholar
69. 69. Artinian NT, Harden JK, Kronenberg MW, Vander Wal JS, Daher E, Stephens Q, et al. Airplane pilot study of a Web-based compliance monitoring device for patients with congestive heart failure. Heart and Lung: Journal of Acute and Critical Care. 2003;32(four):226. pmid:12891162
    • View Article
    • PubMed/NCBI
    • Google Scholar
70. lxx. Velligan D, Mintz J, Maples N, Li X, Gajewski Southward, Carr H, et al. A randomized trial comparing in person and electronic interventions for improving adherence to oral medications in schizophrenia. Schizophrenia message. 2013;39(5):999. pmid:23086987
    • View Article
    • PubMed/NCBI
    • Google Scholar
71. 71. Okeke CO, Quigley HA, Jampel Hard disk, Ying Gs, Plyler RJ, Jiang Y, et al. Adherence with Topical Glaucoma Medication Monitored Electronically. The Travatan Dosing Assist Study. Ophthalmology. 2009;116(two):191. pmid:19084273
    • View Article
    • PubMed/NCBI
    • Google Scholar
72. 72. Rosen MI, Dieckhaus K, McMahon TJ, Valdes B, Petry NM, Cramer J, et al. Improved adherence with contingency direction. AIDS Patient Intendance and STDS. 2007;21(1):30–40. pmid:17263651
    • View Article
    • PubMed/NCBI
    • Google Scholar
73. 73. Sabin LL, DeSilva MB, Hamer DH, Xu K, Zhang J, Li T, et al. Using electronic drug monitor feedback to improve adherence to antiretroviral therapy among HIV-positive patients in Cathay. AIDS and Behavior. 2010;xiv(3):580–ix. pmid:19771504
    • View Article
    • PubMed/NCBI
    • Google Scholar
74. 74. Smith SR, Rublein JC, Marcus C, Brock TP, Chesney MA. A medication self-management plan to improve adherence to HIV therapy regimens. Patient Educ Couns. 2003;50(2):187–99. pmid:12781934
    • View Article
    • PubMed/NCBI
    • Google Scholar
75. 75. Erickson SR, Ascione FJ, Kirking DM. Utility of electronic device for medication management in patients with hypertension: a pilot study. Journal of the American Pharmaceutical Association. 2005;45(1):88–91. pmid:15730122
    • View Article
    • PubMed/NCBI
    • Google Scholar
76. 76. McKenney JM, Munroe WP, Wright JT Jr, Affect of an electronic medication compliance aid on long-term blood pressure control. Journal of Clinical Pharmacology. 1992;32(3):277–83. pmid:1564133
    • View Article
    • PubMed/NCBI
    • Google Scholar
77. 77. van Onzenoort HA, Verberk WJ, Kroon AA, Kessels AG, Neef C, van der Kuy PH, et al. Electronic monitoring of adherence, treatment of hypertension, and blood pressure command. American Journal of Hypertension. 2012;25(one):54–9. pmid:21993365
    • View Commodity
    • PubMed/NCBI
    • Google Scholar
78. 78. Duncan CL, Hogan MB, Tien KJ, Graves MM, Chorney JM, Zettler MD, et al. Efficacy of a parent-youth teamwork intervention to promote adherence in pediatric asthma. Journal of Pediatric Psychology. 2013;38(half-dozen):617–28. pmid:23248342
    • View Commodity
    • PubMed/NCBI
    • Google Scholar
79. 79. Foster JM, Usherwood T, Smith L, Sawyer SM, Xuan W, Rand CS, et al. Inhaler reminders improve adherence with controller treatment in master care patients with asthma. J Allergy Clin Immunol. 2014;134:1260–68. pmid:25062783
    • View Article
    • PubMed/NCBI
    • Google Scholar
80. fourscore. Foster JM, Reddel HK, Usherwood T, Sawyer SM, Smith Fifty. Patient-perceived acceptability and behaviour change benefits of inhaler reminders and adherence feedback: a qualitative study. Respiratory medicine. 2017;129:39–45. pmid:28732834
    • View Commodity
    • PubMed/NCBI
    • Google Scholar
81. 81. Faraone SV. Interpreting estimates of treatment furnishings: implications for managed intendance. P & T: a peer-reviewed journal for formulary management. 2008;33(12):700–11. pmid:19750051
    • View Article
    • PubMed/NCBI
    • Google Scholar
82. 82. Van Heuckelum One thousand, Van Den Ende E, Houterman A, Heemskerk C, Van Dulmen Due south, Van Den Bemt B. Electronic monitoring feedback to improve medication adherence and clinical outcomes. International Periodical of Clinical Pharmacy. 2016;38 (6):558. http://dx.doi.org/x.1007/s11096-015-0240-y.
    • View Article
    • Google Scholar
83. 83. McDonald HP, Garg AX, Haynes RB. Interventions to enhance patient adherence to medication prescriptions: scientific review. JAMA: Journal of the American Medical Association. 2002;288(22):2868–79. pmid:12472329
    • View Article
    • PubMed/NCBI
    • Google Scholar
84. 84. Checchi KD, Huybrechts KF, Avorn J, Kesselheim AS. Electronic medication packaging devices and medication adherence: a systematic review. JAMA: Periodical of the American Medical Association. 2014;312(12):1237–47. pmid:25247520
    • View Article
    • PubMed/NCBI
    • Google Scholar
85. 85. Assawasuwannakit P, Braund R, Duffull SB. Quantification of the Forgiveness of Drugs to Imperfect Adherence. CPT: pharmacometrics & systems pharmacology. 2015;four(three):e00004–e. pmid:26225235
    • View Article
    • PubMed/NCBI
    • Google Scholar
86. 86. DiMatteo MR, Giordani PJ, Lepper HS, Croghan TW. Patient adherence and medical handling outcomes: a meta-analysis. Med Care. 2002;forty(9):794–811. pmid:12218770
    • View Article
    • PubMed/NCBI
    • Google Scholar
87. 87. Horwitz RI, Horwitz SM. Adherence to treatment and health outcomes. Athenaeum of Internal Medicine. 1993;153(16):1863–viii. pmid:8250647
    • View Article
    • PubMed/NCBI
    • Google Scholar
88. 88. Kellner R. A symptom questionnaire. J Clin Psychiatry. 1987;48(7):268–74. pmid:3597327
    • View Article
    • PubMed/NCBI
    • Google Scholar

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