Visual-feedback therapy for antipsychotic medication adherence

Schepp, Karen G.

doi:10.1097/01.yic.0000177016.59484.ce

Cited by 13 publications

(5 citation statements)

References 17 publications

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“…This category included any intervention component which enabled the participant to view their own adherence data, either in real time through a mobile phone or website interface or via periodically generated reports. Frequently, this was one part of multicomponent interventions, but some studies evaluated the effect of feedback alone as an adherence-enhancing intervention [84,85,88,102,103]. Other systems included feedback on more than just adherence data (eg, symptomatic progression as assessed by self-report) [44,46,120].…”

Section: Resultsmentioning

confidence: 99%

Technological Interventions for Medication Adherence in Adult Mental Health and Substance Use Disorders: A Systematic Review

Steinkamp¹,

Goldblatt²,

Borodovsky³

et al. 2019

JMIR Ment Health

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Background Medication adherence is critical to the effectiveness of psychopharmacologic therapy. Psychiatric disorders present special adherence considerations, notably an altered capacity for decision making and the increased street value of controlled substances. A wide range of interventions designed to improve adherence in mental health and substance use disorders have been studied; recently, many have incorporated information technology (eg, mobile phone apps, electronic pill dispensers, and telehealth). Many intervention components have been studied across different disorders. Furthermore, many interventions incorporate multiple components, making it difficult to evaluate the effect of individual components in isolation. Objective The aim of this study was to conduct a systematic scoping review to develop a literature-driven, transdiagnostic taxonomic framework of technology-based medication adherence intervention and measurement components used in mental health and substance use disorders. Methods This review was conducted based on a published protocol (PROSPERO: CRD42018067902) in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses systematic review guidelines. We searched 7 electronic databases: MEDLINE, EMBASE, PsycINFO, the Cochrane Central Register of Controlled Trials, Web of Science, Engineering Village, and ClinicalTrials.gov from January 2000 to September 2018. Overall, 2 reviewers independently conducted title and abstract screens, full-text screens, and data extraction. We included all studies that evaluate populations or individuals with a mental health or substance use disorder and contain at least 1 technology-delivered component (eg, website, mobile phone app, biosensor, or algorithm) designed to improve medication adherence or the measurement thereof. Given the wide variety of studied interventions, populations, and outcomes, we did not conduct a risk of bias assessment or quantitative meta-analysis. We developed a taxonomic framework for intervention classification and applied it to multicomponent interventions across mental health disorders. Results The initial search identified 21,749 results; after screening, 127 included studies remained (Cohen kappa: 0.8, 95% CI 0.72-0.87). Major intervention component categories include reminders, support messages, social support engagement, care team contact capabilities, data feedback, psychoeducation, adherence-based psychotherapy, remote care delivery, secure medication storage, and contingency management. Adherence measurement components include self-reports, remote direct visualization, fully automated computer vision algorithms, biosensors, smart pill bottles, ingestible sensors, pill counts, and utilization measures. Intervention modalities include short messaging service, mobile phone apps, websites, and interactive voice response. We provide graphical representations of intervention component categories and an element-wise breakdown of multicomponent interventions. Conclusions Many technology-based medication adherence and monitoring interventions have been studied across psychiatric disease contexts. Interventions that are useful in one psychiatric disorder may be useful in other disorders, and further research is necessary to elucidate the specific effects of individual intervention components. Our framework is directly developed from the substance use disorder and mental health treatment literature and allows for transdiagnostic comparisons and an organized conceptual mapping of interventions.

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Section: Resultsmentioning

confidence: 99%

Technological Interventions for Medication Adherence in Adult Mental Health and Substance Use Disorders: A Systematic Review

Steinkamp¹,

Goldblatt²,

Borodovsky³

et al. 2019

JMIR Ment Health

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“…Most studies, except two early studies [ 46 , 63 ], used electronic monitoring to measure adherence, though this was frequently used with other measures such as serum medication levels [ 39 , 40 , 59 , 63 ], self-report [ 14 , 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 ].…”

Section: Resultsmentioning

confidence: 99%

Effect of electronic adherence monitoring on adherence and outcomes in chronic conditions: A systematic review and meta-analysis

et al. 2022

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Introduction Electronic adherence monitoring (EAM) is increasingly used to improve adherence. However, there is limited evidence on the effect of EAM in across chronic conditions and on patient acceptability. We aimed to assess the effect of EAM on adherence and clinical outcomes, across all ages and all chronic conditions, and examine acceptability in this systematic review and meta-analysis. Methods A systematic search of Ovid MEDLINE, EMBASE, Social Work Abstracts, PsycINFO, International Pharmaceutical Abstracts and CINAHL databases was performed from database inception to December 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. Study 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 risk ratios depending on the data. Differences by study-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 be meta-analysed, and patient acceptability, were synthesised narratively. The Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guideline was followed, and Risk 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 (n = 1317), (SMD = 0.93, CI:0.69 to 1.17, p<0.0001) with high heterogeneity across studies (I2 = 86%). There was a significant difference in effect according to intervention complexity (p = 0.01); EAM only improved adherence when used with a reminder and/or health provider support. Clinical outcomes were measured in 38/47 (81%) of studies; of these data from 14 studies were included in a meta-analysis 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 better adherence than those who did not, but improved adherence did not consistently translate into clinical benefits. Acceptability data were mixed. Further research measuring effects on clinical outcomes and patient acceptability are needed.

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“…Adherence improved in the interventional group but declined for controls (CA of þ9.5% and -20.5%, respectively; p¼0.026). 17 Cramer et al 18 also investigated effects of visual feedback from MEMS on adherence to antipsychotic medications. Overall adherence was 76% in the intervention group and 57% in controls (p¼0.08).…”

Section: Mental Healthmentioning

confidence: 99%

The Effects of Feedback on Adherence to Treatment: A Systematic Review and Meta-analysis of RCTs

Seewoodharry

Maconachie

Gillies

et al. 2017

American Journal of Preventive Medicine

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Visual-feedback therapy for antipsychotic medication adherence

Cited by 13 publications

References 17 publications

Technological Interventions for Medication Adherence in Adult Mental Health and Substance Use Disorders: A Systematic Review

Technological Interventions for Medication Adherence in Adult Mental Health and Substance Use Disorders: A Systematic Review

Effect of electronic adherence monitoring on adherence and outcomes in chronic conditions: A systematic review and meta-analysis

The Effects of Feedback on Adherence to Treatment: A Systematic Review and Meta-analysis of RCTs

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