Mobile technology for medication adherence in people with mood disorders: A systematic review

Rootes-Murdy, Kelly; Glazer, Kara; Wert, Michael J. Van; Mondimore, Francis M.; Zandi, Peter P.

doi:10.1016/j.jad.2017.11.022

Cited by 28 publications

(12 citation statements)

References 21 publications

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“…Recent related research in summarizing the vast and diverse field of psychiatric medication adherence technologies includes systematic reviews focusing on particular disorders such as mood disorders [27], schizophrenia and other psychotic disorders [28,29], and substance use disorders [30]. Other reviews evaluate specific technologic intervention types, including automated reminders [31], adherence monitoring [32], and virtual reality technologies [33].…”

Section: Introductionmentioning

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: Introductionmentioning

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

View full text Add to dashboard Cite

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“…Many researchers argued that the absence of such new informational systems in the hospitals may lead to inefficiency and the lack of patients' trust [3,4]. Introducing the effective information systems in the health section is considered an undeniable necessity in order to increase efficiency, efficacy, and quality of the services along with the higher customer satisfaction [5,6]. Data in Hospital Information System (HIS) are stored in a database in such a way that they could be available to the authorized users in the appropriate place and time, with a structure proportional to their special needs.…”

Section: Introductionmentioning

confidence: 99%

Modeling the Acceptance of Hospital Information Systems among Nurses: An Extended Technology Acceptance Model

et al. 2018

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Introduction: The perceived usefulness and perceived ease of use have been considered as the main factors affecting the acceptance of the new technologies since last few decades. However, it appears that these only two factors cannot describe the users’ behavior in the environments like the Hospital Information System. From the technology acceptance standpoint at the individual acceptance level, the present paper tends to develop a Technology Acceptance Model with introducing some external factors.Material and Methods: This study was conducted in 2017. The research population was included 185 nurses who works in Health Information Management (HIM) departments of Tehran University of Medical Sciences. A questionnaire was developed in order to gather the required data. The validity was obtained by panel of experts and the reliability was examined and then confirmed in a 50 person sample using the Cronbach’s Alpha (a=0.93). The Likert’s five item scale was applied. The data were analyzed using descriptive statistics, exploratory factor analysis, and path analysis.Results: The behavioral intention was affected significantly and positively by the factors of perceived usefulness, perceived ease of use, self-efficacy, end user support, social norm, trust, job relevance, and training, with trust having the highest level of effects. Also perceived ease of use had a significant effect on perceived usefulness along with an indirect effect on behavioral intention through perceived usefulness. The factors of anxiety, voluntariness, and facilitating conditions showed no significant effects on behavioral intention.Conclusion: The factors of trust, perceived usefulness, social norm, end user support, and self-efficacy have an impact on the behavioral intention of the users utilizing the Hospital Information System in the concerning hospitals. These factors could explain 72% of the changes of behavioral intention. Concentrating on them would lead to the improvement of the acceptance and Hospital Information System efficiency.

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“…Health care systems are increasingly using digital health technologies, such as smartphones, tablets, notebook computers, watches, other wearables, and mobile device apps for collecting health data and delivering health care-related services. The use of such technologies, also referred to as mobile technologies, is associated with improved outcomes for many patients [1][2][3][4][5][6]. For example, smartphone apps have facilitated diet tracking and led to significantly greater weight loss [7], and a game-based module has improved drug adherence, resulting in lower rates of side effects from chemotherapy among patients with breast cancer [8].…”

Section: Digital Health Technologies In Health Carementioning

confidence: 99%

Investigator Experiences Using Mobile Technologies in Clinical Research: Qualitative Descriptive Study

McKenna¹,

Geoghegan²,

Swezey³

et al. 2021

JMIR Mhealth Uhealth

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Background The successful adoption of mobile technology for use in clinical trials relies on positive reception from key stakeholders, including clinical investigators; however, little information is known about the perspectives of investigators using mobile technologies in clinical trials. Objective The aim of this study was to seek investigators’ insights on the advantages and challenges of mobile clinical trials (MCTs); site-level budgetary, training, and other support needs necessary to adequately prepare for and implement MCTs; and the advantages and disadvantages for trial participants using mobile technologies in clinical trials. Methods Using a qualitative descriptive study design, we conducted in-depth interviews with investigators involved in the conduct of MCTs. Data were analyzed using applied thematic analysis. Results We interviewed 12 investigators who represented a wide variety of clinical specialties and reported using a wide range of mobile technologies. Investigators most commonly cited 3 advantages of MCTs over traditional clinical trials: more streamlined study operations, remote data capture, and improvement in the quality of studies and data collected. Investigators also reported that MCTs can be designed around the convenience of trial participants, and individuals may be more willing to participate in MCTs because they can take part from their homes. In addition, investigators recognized that MCTs can also involve additional burden for participants and described that operational challenges, technology adoption barriers, uncertainties about data quality, and time burden made MCTs more challenging than traditional clinical trials. Investigators stressed that additional training and dedicated staff effort may be needed to select a particular technology for use in a trial, helping trial participants learn and use the technology, and for staff troubleshooting the technology. Investigators also expressed that sharing data collected in real time with investigators and trial participants is an important aspect of MCTs that warrants consideration and potentially additional training and education. Conclusions Investigator perspectives can inform the use of mobile technologies in future clinical trials by proactively identifying and addressing potential challenges.

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Mobile technology for medication adherence in people with mood disorders: A systematic review

Cited by 28 publications

References 21 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

Modeling the Acceptance of Hospital Information Systems among Nurses: An Extended Technology Acceptance Model

Investigator Experiences Using Mobile Technologies in Clinical Research: Qualitative Descriptive Study

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