Telemedicine interventions for medication adherence in mental illness: A systematic review

Telemedicine interventions for medication adherence in mental illness: A systematic review

Journal Pre-proof Telemedicine interventions for medication adherence in mental illness: A systematic review Saadia A. Basit, Nikhil Mathews, Mark E...

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Journal Pre-proof Telemedicine interventions for medication adherence in mental illness: A systematic review

Saadia A. Basit, Nikhil Mathews, Mark E. Kunik PII:

S0163-8343(19)30271-3

DOI:

https://doi.org/10.1016/j.genhosppsych.2019.11.004

Reference:

GHP 7478

To appear in:

General Hospital Psychiatry

Received date:

28 June 2019

Revised date:

18 October 2019

Accepted date:

10 November 2019

Please cite this article as: S.A. Basit, N. Mathews and M.E. Kunik, Telemedicine interventions for medication adherence in mental illness: A systematic review, General Hospital Psychiatry (2019), https://doi.org/10.1016/j.genhosppsych.2019.11.004

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© 2019 Published by Elsevier.

Journal Pre-proof Telemedicine Interventions for Medication Adherence in Mental Illness: A Systematic Review Saadia A. Basit, PharmD, BCPPa,b Nikhil Mathews, MDb Mark E. Kunik, MD, MPH a,b,c a. Michael E. DeBakey Veterans Affairs Medical Center 2002 Holcombe Blvd. Houston, Texas 77030 United States

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b. Menninger Department of Psychiatry and Behavioral Sciences Baylor College of Medicine 1977 Butler Blvd. Houston, Texas 77030 United States

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[email protected] [email protected] [email protected]

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c. VA South Central Mental Illness Research, Education and Clinical Center (a virtual center)

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Running Title: Telemedicine Interventions for Medication Adherence in Mental Illness

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Article Data: Number of Tables: 1 Figures: 1 Supplemental Appendices: 1 Word Count: 2788 Abstract Word Count: 191

Acknowledgments: The authors thank Sonora Hudson for assistance with medical editing. Funding Sources: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. However, it was partly the result of resources and use of facilities of the Houston VA HSR&D Center for Innovations in Quality, Effectiveness and Safety (CIN13-413). The opinions expressed reflect those of the authors’ and not necessarily those of the Department of Veterans Affairs, the U. S. government, or Baylor College of Medicine. 1

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Declaration of Interests: None

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Corresponding Author: Saadia A. Basit, PharmD, BCPP Michael E. DeBakey Veterans Affairs Medical Center 2002 Holcombe Blvd. Houston, Texas 77030 Phone: 713-791-1414 ext. 23390 Email: [email protected]

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Telemedicine Interventions for Medication Adherence in Mental Illness: A Systematic Review

Running Title: Telemedicine Interventions for Medication Adherence in Mental Illness

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Article Data: Number of Tables: 1 Figures: 1 Supplemental Appendices: 1 Word Count: 2901 Abstract Word Count: 190

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Declaration of Interests: None

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Abstract Objective: To conduct a systematic review to assess the evidence for telemedicine interventions for pharmacologic adherence in persons with depression, bipolar disorder, or schizophrenia. Method: We searched PubMed and PsycINFO in August 2018 without restrictions on years or language. We also searched tables of contents in 2 journals, meeting abstracts, reference lists from identified

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studies and review articles. The selection criteria required that articles be randomized controlled trials

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involving outpatient adults diagnosed with depression, bipolar disorder, or schizophrenia; that they

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involve telemedicine interventions; and that they include an outcome of medication adherence. Initially,

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1 author identified relevant titles. Two authors independently reviewed the abstracts and titles. Results: Of 512 articles identified through database searching, we identified 17 articles that we

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categorized by intensity of intervention and rated by quality of evidence. There were 3 low-, 3 medium-

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and 11 high-intensity interventions. The most common type of technology used was the phone. Efficacy for adherence was demonstrated by 9 studies.

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Conclusions: Telemedicine may improve medication adherence in patients with depression, bipolar

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disorder, or schizophrenia. Future studies are needed to better understand how technology can be tailored to different types of nonadherence.

Keywords: mental health, telemedicine, medication adherence, cell phone

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1. INTRODUCTION Nonadherence to psychotropic medication is a major obstacle to the treatment of mental illness. Approximately 60% of patients with schizophrenia discontinue their antipsychotics within 90 days of initiation, and roughly 75% do so within 18 months.1,2 For people with schizophrenia, nonadherence is associated with poor outcomes, including increased rates of hospitalization, violence, and arrest.1,3 Similarly, in depression, nonadherence rates over 6 months near 60%4; and nonadherence is linked to

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relapse, recurrence, and higher healthcare costs. 4,5

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The literature conceptualizes 2 types of nonadherence: intentional nonadherence is the affirmative

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decision to forgo prescribed medication; while unintentional nonadherence may be secondary to

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forgetfulness, carelessness, or poor health literacy.6 Mental illness itself appears to play a role in exacerbating nonadherence. For example, the diagnosis of a depressive disorder is a known risk factor for

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medication nonadherence.7 Additionally, negative symptoms in schizophrenia are correlated with medication nonadherence.8 Symptoms of mental illness often include apathy/abulia, cognitive

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impairment, diminished insight, and impaired reality testing, which contribute to unintentional

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nonadherence. Meanwhile, side-effects of psychotropics also lead to intentional nonadherence.9 Overall, patients with psychiatric illnesses may be susceptible to a particularly vicious cycle in which nonadherence worsens illness and illness, in turn, worsens nonadherence.

The complex nature of medication nonadherence in patients with mental illnesses has led to the development of multifaceted interventions, including patient education and collaborative care to promote adherence.10 In recent decades, telemedicine - “the use of electronic information and communications technologies to provide and support health care when distance separates the participants” 11 - has emerged as an increasingly prominent part of adherence interventions for various 5

Journal Pre-proof chronic disease states.12 The World Health Organization has emphasized the proliferation of mobile technology (mHealth) as a means for improving health and identifies improved treatment adherence as 1 promise of mHealth.13

A growing body of literature highlights various forms of mHealth as a novel approach to improve medication adherence in mental health disorders; however, reviews have not specifically focused on

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medication adherence as a primary outcome. For example, 1 review examines

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automated/semiautomated interventions for treatment adherence in schizophrenia spectrum disorders

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but included only 1 study with a primary outcome of medication adherence.14 Another review focuses only on text-message interventions in mental illness and surveys a range of outcomes, including

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medication adherence.15 Our systematic review is, to our knowledge, the first to broadly examine

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telemedicine interventions for patients with mental illness (depression, bipolar disorder, and schizophrenia) while honing in on studies with medication adherence as an outcome and with

2.1 Search Strategy

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2. METHODS

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interventions that are targeted to improve adherence.

To identify relevant studies, we conducted 2 separate search strategies in PubMed and PsycINFO in August 2018, using 4 different search strategies. In PubMED, the first strategy involved a combination of medical subject heading (MeSH) terms and text words (in title/abstract); and the second strategy used text words only (in title/abstract), done to account for articles that had not been indexed. In PsycINFO, the first search strategy was a subject-heading-only search, and the second strategy involved a combination of subject headings and keywords. All subject headings were automatically exploded to include all narrower terms. The search criteria are described in detail in Appendix A.

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Hand searches of tables of contents were conducted from the past 5 years in 2 high-impact psychopharmacology journals: Journal of Clinical Psychopharmacology and Journal of Psychopharmacology. Meeting abstracts were reviewed from the American Society of Health-System Pharmacy Midyear Clinical Meeting Abstracts Archive from 2013 to 2017, as well as the College of Psychiatric and Neurologic Pharmacists website. Reference lists from eligible articles and relevant review

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articles were also searched.

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2.2 Study Selection Criteria

We considered randomized controlled trials of adults diagnosed with schizophrenia, bipolar disorder, or

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depression (diagnosed using any recognized diagnostic criteria). The interventions of the studies had to

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be primarily telemedicine,11 including text messaging, telephone calls to patients, remote adherence monitoring devices, and video conferencing. An outcome of the studies had to include psychotropic

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medication adherence. There were no restrictions as to how medication adherence was assessed,

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including timing or frequency. The intervention had to be conducted on an outpatient basis. The

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comparators/controls included both treatment as usual (nontelemedicine) and/or active controls (telemedicine). We excluded articles in languages other than English.

2.3 Data-Collection Process and Data Items We extracted the following characteristics from included studies: study design, sample size, sample demographic data (age, minority distribution, sex), diagnoses, control/comparator information, intervention description (deliverer/resources needed, duration of study, frequency of dose, number of sessions, session time and total time, content), primary outcomes, and relevant secondary outcomes. We also identified whether the study was conducted in the Veterans Affairs healthcare system and whether

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Journal Pre-proof the sample resided in a rural setting. Data on cost analysis were also collected. Study quality was assessed using the Jadad scale for reporting randomized controlled trials.16

Author (__) conducted the database search and title review. (__) and (__) independently screened abstracts for review. Disagreements were resolved by (__). (__) and (__) also independently reviewed full texts. Disputes were resolved via discussion among (__), (__) and (__). SB and (__) independently

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collected data from the eligible full texts, and (__) resolved disputes in the Jadad scores.

3. RESULTS

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See Figure 1 for a PRISMA flow diagram17 showing the results of our searches with our inclusion criteria.

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No articles were identified through the meeting abstract and table of contents searches. Screening of

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titles led to exclusion of 67 irrelevant titles. Of the 369 abstracts screened, 28 full-text articles were assessed for eligibility. Of the abstracts excluded, 3 met criteria in the initial screening; however, we could

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not obtain the English version for 2 of the articles18,19 and did not receive a response regarding request

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for a full-text or a meeting abstract for 1 article.20 One article was excluded for multiple reasons.21 The most common reason for exclusion was the intervention was not targeted toward adherence. Two

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studies by Beebe et al.22,23 were excluded because they included the same patient population as an additional full-text article (Beebe24). One article was excluded because it included the same population as another full-text article.25 We identified an additional article from a systematic review26 and 2 additional articles from references of eligible full-text articles.27,28 Of note, we included the study by Hammonds et al.29, though it included patients based on an antidepressant prescription, because 89.5% of the patients had a depression diagnosis.

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Journal Pre-proof We categorized the final 17 studies by intensity of intervention (refer to Table 1), organized them in descending order of quality within each category, and also noted the quality rank of each study within each category.24-40 Based on the Jadad criteria (range: 0-5)), 7 studies were high quality (score 45),24,27,28,30-32,34 and 10 studies were low quality (score 1-3).25,26,29,33, 36-40 The intensity was based on extent of staff assistance (low intensity = no staff assistance) and intervention frequency and duration. Only 2 studies26,40 selectively included subjects with adherence problems. The intervention durations ranged

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from 30 days to 12 months. The sample sizes ranged from 29 to 962 (mean: 236.53, median: 113.5). The

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studies used 9 types of technology alone and in combination: automated text message, automated

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smartphone reminder app, telemonitoring, phone, text message, electronic medical records (EMR), interactive video technology, online messaging and Internet. Two studies indicated the proportion of

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patients or practice sites located in rural areas.35,36 No studies measured cognitive impairment. Of the 17

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3.1 Low-Intensity Studies

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studies, 131 was restricted to a specific medical condition (HIV).

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The 3 low-intensity interventions used an automated smartphone reminder app 29 and automated text message.26,30 The 2015 study by Hammonds et al.29 was conducted in patients with various diagnoses,

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including depression; while the other 2 studies were conducted in stable patients with bipolar disorder30 and schizophrenia.26 These interventions did not require staff assistance and served as reminders to take medications Significant improvements in self-reported adherence were identified at 3 months for the 2 automated text message studies.26,30 While the study by Hammonds et al.29 did not identify a significant difference in adherence between groups at 30 days, a trend revealed that participants using the reminder app were 3 times more likely to be adherent. The positive studies26,30 had larger sample sizes and were longer in duration than the negative study by Hammonds et al.29 (n = 57).

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Journal Pre-proof 3.2 Medium-Intensity Studies The medium-intensity interventions included 3 studies using automated text message31 and a combination of telemonitoring and telephone.32,33 The level of staff assistance was least intense for the study by Moore et al.,31 which studied an intervention involving face-to-face psychoeducation delivered before the intervention by a nonclinical research staff (project coordinator) to intervention and control groups. Automated text messages consisting of personalized reminders and reinforcements were sent to

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the intervention group. These studies used telemonitoring devices to monitor adherence, although

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Frangou et al.33 used different methods of measurement for the nonintervention groups (self-report and

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pill counting). Specifically, Frangou et al.33 studied the application of @HOME, a platform consisting of various modules that allows remote monitoring by clinicians. This study used the Medication Event

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Monitoring System (MEMS) that fits a standard pharmacy bottle and alerts healthcare providers when

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adherence is less than 50%. Moore et al.31 used similar technology to monitor adherence via the MEMS. Velligan et al.32 used the Med-eMonitor™ (MM) to monitor adherence remotely in all groups. The MM in

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the intervention group cued patients to take their medications (see Table 1 for additional functions of the

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MM). As in the other 2 studies in the medium-intensity category, MM alerts staff of nonadherence. Two

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of these interventions32,33 demonstrated better adherence than their comparators. Velligan et al.32 demonstrated that the PharmCAT (nontelemedicine intervention) and MM were associated with better adherence than the standard of care at 3, 6 months, and 9 months; but there was no difference in adherence between the 2 comparators. The positive studies32,33 had larger sample sizes than the negative study31 and were longer in duration.

3.3 High-Intensity Studies Finally, we identified 11 studies with high-intensity interventions. Unlike the lower-intensity studies, these interventions required ongoing staff involvement. Six interventions included nurses alone24,27,28 or in

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Journal Pre-proof combination with other healthcare professionals.34,36,37 Rickleset al.25 and Capoccia et al.38 used pharmacists as a major part of the intervention. The interventions included monitoring36 to more involved interactions such as problem-solving, psychoeducation, and adherence counseling.24,25,27,28,34,35,37-40All high-intensity interventions used telephones as the primary technology except the online messaging intervention by Simon 37 Beebe et al.28 used a combination of telephone and text messaging. Five studies 25,27,35-37 identified a significant improvement in adherence in the intervention

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group. The average sample size of the positive studies was larger than that of the negative

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studies.24,28,34,38-40

3.4 Cost

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Cost information was reported for 2 studies. Salzer et al.,40 using weekly telephone calls for 52 weeks,

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reported that the cost per participant, not including overhead costs, was $239.50. Velligan et al.,32 using telemonitoring and telephone, reported the average cost of treatment per patient per month to be $180

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4. DISCUSSION

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for PharmCAT and $130 for MM (most of the cost of MM consisted of monitor and web support).

Telemedicine interventions for adherence have the advantage over face-to-face interventions of addressing the nonadherence when and where it occurs (e.g., in between visits and outside of the pharmacy/clinic). This systematic review explored and categorized the literature of randomized controlled trials of telemedicine interventions for medication adherence in people with depression, bipolar disorder and schizophrenia. We sorted the interventions by intensity, intending that this information can be used as a guide for healthcare facilities seeking to identify tools to target adherence while efficiently using labor and time resources. The rigorous search strategy yielded 17 included studies, which used a variety of telemedicine tools. Phone technology was used as a primary intervention in all

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Journal Pre-proof studies (i.e., cellular, smart, or telephone) except one (Simon 37). The most studied condition was schizophrenia. The sample sizes of the 9 positive studies ranged from 29 to 928 (mean: 276.67; median: 142), while the sample sizes of the 8 negative studies ranged from 30 to 962 (mean: 191.38; median: 65.5). Some negative studies with smaller sample sizes may have been underpowered. The durations of the high-intensity interventions ranged from 3 to 12 months, longer than the lower-intensity interventions. Furthermore, a majority of the low- and medium-intensity studies were positive, while less

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than half of the high-intensity studies were positive. Of the low and high-intensity interventions,

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approximately one third were high-quality studies within each category; while in the medium-intensity

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interventions, two thirds were high quality. Of the 9 positive studies, one third were high quality, and half of the negative studies were high quality.

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By developing a better understanding of nonadherence, healthcare systems can tailor adherence

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interventions, based on the reasons for nonadherence. The Perceptions and Practicalities Approach to nonadherence indicates that nonadherence is attributed to a combination of perceptual factors (patients’

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beliefs about treatment) and practical factors (capability and resources). Patients may have the resources

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(e.g., access to a pharmacy and financial capability) to adhere to the medication but may not be

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motivated to take the medication because of their beliefs about medications/illness.41,42Patients with unintentional nonadherence may benefit from low- to medium-intensity interventions, such as automated text messaging and app reminders.26,29-31 However, these interventions would not address the issue of practical factors that affect unintentional nonadherence (e.g., access to pharmacies). Those with intentional nonadherence may benefit from more complex interventions, such as Telephone Intervention Problem Solving,24,27,28 Telephone Medication Management,40 and Pharmacy Guided Education and Monitoring,25 which consisted of routine telephone conversations addressing patient-specific problems. The intervention studied by Montes et al.36 was even more intense than these because it consisted of telephone-based monitoring by nurses in addition to psychiatrist intervention if adherence issues were

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Journal Pre-proof identified. Some interventions could address both types of nonadherence, such as those studied by Frangou et al.33 and Velligan et al.32 In these studies, adherence was remotely monitored via telemonitoring devices; and issues with adherence were addressed on an individual basis. Telemonitoring may be useful to gain a better understanding of patients’ adherence initially and could be discontinued if patients demonstrate adherence over a certain period. Future studies might also focus on cost analyses

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of telemonitoring devices.

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One dominant theme among the interventions examined is the use of telephone interventions to promote adherence. Phones are relatively inexpensive, mobile and ubiquitous resources that can be used

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to enhance patient monitoring and outcomes. Beebe and colleagues24,27,28 used a telephone to carry out

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a manualized problem-solving approach to target medication adherence, while other studies used a

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telephone to contact the patient on an as-needed basis to address adherence issues.32,33 Cellphones were used for texting26,28,30,31 and can also be used for voice-based mobile interventions. Smartphones were

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used for reminders via a medication reminder app.29 Phone-based interventions can be carried out at

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various frequencies, depending on patients’ needs. The high-intensity intervention by Simon et al37

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demonstrated that online messaging by nurses can lead to better adherence outcomes than treatment as usual. This was the only high-intensity intervention that did not use the telephone as a primary technology. Studies comparing patient satisfaction for online messaging versus telephone interventions could facilitate in developing targeted interventions for specific populations.37

Serious mental illness, itself, is likely a barrier to uptake and utilization of telehealth technologies. In a study of 1,568 patients with serious mental illness, 72% reported owning a mobile device; and 42% of the device users endorsed interest in mobile services for appointment or medication reminders.43 Of the 28% of device nonusers, 24.5% endorsed a lack of interest; and 20.3% reported that they cannot use a phone.

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Journal Pre-proof Affordability (41%) was the most common barrier to ownership. The study also found that use of mobile technology was more common among people with mood disorders (86%) than schizophrenia or schizoaffective disorder (63%). Most studies considered in our review included patients with serious mental illness (schizophrenia and bipolar disorder). Thus, telehealth interventions in this population are remarkable for often being effective, despite imperfect patient engagement and utilization.

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Findings from this review should be interpreted within several limitations. We searched only 2 major

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databases and may have unintentionally excluded articles by not searching other databases. The Jadad

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scores revealed that more than half of the studies were of low quality. It is still worthwhile to take these studies into consideration because so few studies on this topic appear in the literature. Although our

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initial search did not exclude non-English language articles, we did not include 2 non-English language

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articles and may have missed potential articles for inclusion.

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It may be useful for future studies to look at stepped-care interventions. For example, an intervention

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might start with low- to medium-intensity approaches and escalate to high intensity (i.e., telephone-

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based problem solving) in patients who require it. Only 2 studies26,40 selectively included patients with adherence problems. Future studies could focus on patients with known adherence problems and stratify them based on the reason for nonadherence (e.g., intentional and unintentional). It might also be useful to explore patients’ preferences for the type of telemedicine tool used. For example, some might prefer a daily interactive and automated text message reminder, such as studied by Moore et al.,31 while others might experience alert fatigue from this. Patients on multiple medications dosed multiple times per day might start to ignore reminders. Some patients might have demanding schedules and be unable to participate in weekly telephone calls (e.g., Telephone Intervention Problem

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Journal Pre-proof Solving, Telephone Medication Management). Future studies should focus on exploring the feasibility,

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patient perspective, and cost effectiveness of telemedicine adherence interventions.

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(iTAB): Improving antiretroviral dose timing among HIV-infected persons with co-occurring bipolar disorder. AIDS Behav. 2015;19(3):459-471. 32. Velligan D, Mintz J, Maples N, et al. A randomized trial comparing in person and electronic interventions for improving adherence to oral medications in schizophrenia. Schizophr Bull. 2013;39(5):999-1007. 33. Frangou S, Sachpazidis I, Stassinakis A, Sakas G. Telemonitoring of medication adherence in patients with schizophrenia. Telemed J E Health. 2005;11(6):675-683. 34. Simon Ge, Ludman Ej, Operskalski BH. Randomized trial of a telephone care management program for outpatiens starting antidepressant treatment. Psychiatr Serv. 2006;57(10):1441-45.

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improve depression care and outcomes in primary care. Am J Health-Syst Pharm. 2004;61:364-72. 39. Perahia DGS, Quail D, Gandhi P, et al. A randomized controlled trial of duloxetine alone vs.

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duloxetine pluls a telephone intervention in the treatment of depression. J Affect Disord.

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2008;108:33-41.

40. Salzer MS, Tunner T, Charney NJ. A low-cost, telephone intervention to enhance schizophrenia

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treatment: A demonstration study. Schizophr Res. 2004;66(1):75-76.

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41. Chapman SCE, Home R. Medication nonadherence and psychiatry. Curr Opin Psychiatry.

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2013;25(5):446-52.

42. Horne R, Clatworthy . Adherence to advice and treatment. In French D, Vedhara K, Kaptein AA, Weinman J (Eds). Health Psychology, 2nd ed. Chichester: British Psychological Society and Blackwell Publishing Ltd., 2010; pp. 175-88.43. 43. Been-Zeev D, Davis KE, Kaiser S, et al. Mobile technologies among people with serious mental illness: Opportunities for future services. Adm Policy Ment Health. 2013;40(4):340-343.

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Journal Pre-proof Table 1. Included Studies Intensity

Quality

Study

Sample

Technology

Intervention Content

Staff-Assisted

LOW

5/high

Menon et 30 al.

132; bipolar I disorder

Automated Text Message

Greeting, reminder to take medication at specific dose and times, and positive message.

No

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LOW

3/low

Montes et 26 al.

254; schizophrenia; adherence problems

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Control Content Received pharmacological and psychosocial treatment as indicated.

Adherence Measurement SR (MMAS-8)

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Automated text message

Intervention Frequency 2x/week

Reminders to take medications

No

1

Daily

TAU

SR (MAQ)

Adherence Outcomes 3-month MMAS-8 increased in IG from 4.8 to 6.7 vs. TAU from 5.0 to 5.4. 3month MMAS8 in IG greater than TAU (p<0.001). At 3-month postintervention FU, IG maintained higher MMAS8 (6.3 vs. 5.3, p<0.001). 3-month MAQ improvement greater in IG vs. TAU (25% vs. 17.5%, p=0.02). 3month postintervention FU MAQ improvement maintained in IG (-1.1 vs. -

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LOW

2/low

Hammonds 29 et al.

MEDIUM

4/high

Moore et 31 al.

MEDIUM

4/high

Velligan et 32 al.

57; various diagnoses including depression 50; HIV+ with co-occurring bipolar disorder

142; schizophrenia or schizoaffective disorder

Automated Smartphone reminder app

Reminders to take medication.

No

Not stated

Automated text message

Personalized reminders and reinforcement texts with automated features

Preintervention psychoeducation delivered faceto-face to both groups by nonclinical staff

Each dosing time

Telemonitoring via MM device, telephone

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MM device – smart pill container cues medication-taking, alerts patients when taking the wrong medication, records side-effect complaints, and alerts staff of nonadherence. Telephone interventions addressed adherence and practical concerns.

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MM therapist checked secure website every 3 days to monitor adherence and contacted patients if needed.

Therapist contacted patient as needed

Instructed to continue taking medications as prescribed Active control with a standard of care adherence psychoeducation and daily text mood inquiries

PC adherence rate

TAU – case management and psychiatry appointments

MM data; PC adherence rate

Active control: PharmCAT: application of environmental supports maintained on weekly home visits by a case worker

MEMS data; SR (VAS)

0.8, p=0.04). 30-day PC NS between groups (p=0.057). 30-day MEMS, VAS, MEMS dose timing windows NS between groups (p=0.43, p=0.54, p=0.42). Average MM adherence over 9 months was better in MM (91%) vs. PharmCAT (90%) and TAU (72%). PharmCAT and MM were better than TAU at all time points through treatment and FU (p<0.0001). Average monthly PC adherence

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MEDIUM

2/low

Frangou et 33 al.

108; schizophrenia

Telemonitoring via @HOME device, telephone, electronic medication dispenser (MEMS)

MEMS transmitted data to clinical team via @HOME device; alerts issued by @HOME to clinical team if <50% adherence over 1 week.

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If alerts issued, contact was made as needed outside regular appointment by clinicians.

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Patients transmitted data once daily in evenings from MEMS; as-needed contact from team

TAU

Weekly

TAU: case management, psychosocial rehabilitation,

PC group: by pharmacists at each medication visit

TAU: SR adherence rate PC group: adherence rate IG: MEMS adherence rate

Jo HIGH

5/high

Beebe et 27 al.

29; schizophrenia

Telephone

TIPS - manualized telephone nursing intervention addressed knowledge

Nurse

3

Adherence rate using PC and IM antipsychotic

over 9 months was better in PharmCAT (91%) vs. MM (86%, p=0.04) or TAU (80%, p=0.0001). NS difference MM vs. TAU (p=0.072). 8-week adherence NS from baseline in TAU (p=0.49). 8-week adherence NS between PC and SR (p=0.7). 8-week adherence in IG (92.3%) better than SR (77.3%, p=0.001) or PC (78.5%, p=0.007). IG had greater adherence than TAU

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HIGH

HIGH

5/high

5/high

Beebe et 28 al.

Simon et 34 al.

of medication, attending appointments, coping with symptoms, abstaining from substances and social support. TIPS and daily text message (adapted TIPS protocol)

30; schizophrenia spectrum disorders

Telephone and text message

207; depression

Telephone, electronic decision support system

Nurse

prevention, residential treatment, and employment services

record

across all 3 months (80% vs. 60.1%, p=.0298).

Daily texts and weekly telephone calls

Text messageonly group

Adherence rate using PC and IM antipsychotic record

In 3-month study, NS group x time interaction (p=0.31).

Proportion of patients receiving adequate treatment (continuing antidepressant for 90 days or more at minimally adequate dose; using pharmacy records) Adherence rate using PC and IM antipsychotic record; serum antipsychotic

Proportion of patients receiving adequate treatment NS between groups at 6 months (p=0.17) .

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TIPS-only group

Assessment of symptoms, use of antidepressant, side effects; psychiatrist receives structured report; care manager facilitates doctorpatient communication for FU

Nurse care manager, psychiatrist

Initial, 4 weeks, 12 weeks

TAU

TIPS

Nurse

Weekly

Medication FU appointments with a psychiatrist ~ every 4-6 weeks and case

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4/high

Beebe

24

119; schizophrenia spectrum disorder

Telephone

4

Adherence rate, serum antipsychotic levels, and MARS NS between

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HIGH

HIGH

3/low

3/low

Gensichen 35 et al.

Montes et 36 al.

626; depression

928; schizophrenia

Telephone

Telephone

Assistants monitored symptoms and adherence using depression monitoring list; encouraged selfmanagement (e.g., adherence, activation for pleasurable activities)

Health care assistants and family physicians

Nurses contacted patients to assess adherence and reported information to psychiatrist. If adherence issues, patient scheduled for additional psychiatrist visit within 7 days.

Nurses and psychiatrists

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levels; SR (MARS)

groups at 9 months.

SR (Modified Morisky Scale)

Scheduled to see psychiatrist 4 months after inclusion; no telephone calls

Indirect evaluation by psychiatrist and nurse according to the RAT

12-month adherence greater in IG vs. TAU (2.70 vs. 2.53; p=0.042; mean difference, 0.17 [CI, 0.01 to 0.34]). At 12 weeks, a larger proportion of patients in IG vs. TAU (96.7% vs. 91.2%, p=0.0007) were adherent, based on RAT. IG more likely to be adherent than TAU (8.5% increase vs. 1.1% increase, OR=3.3, p=0.0001). Percentage of patients who

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2x/weekfor 1 month, then monthly for 11 months

management appointments ~ every 6-8 weeks TAU

5

Every 4 weeks and as needed face-to-face psychiatrist visit

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HIGH

3/low

Rickles et 25 al.

63; depression

Telephone

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PGEM: 3 calls consisting of rapport building, medication/adherence counseling, assessment of beliefs about antidepressants, progress evaluation and problem-solving.

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Pharmacist

Monthly

Did not receive telephone calls from pharmacist.

Pharmacy data (missed doses)

changed from non-adherent to adherent over 12 weeks was significant only in IG (10.4%, p=0.0013) vs. TAU (5.2%, p=0.43). A higher percentage of patients in IG vs. TAU (25.7% vs. 16.8%, p=0.0013) improved adherence at 12 weeks. 3-month missed dose rate NS between groups. 6month missed dose rate lower in IG vs. TAU (30% vs. 49%, p≤0.05). 3- and 6month difference NS with ITT

Journal Pre-proof analysis. HIGH

HIGH

3/low

2/low

Simon et 37 al.

Capoccia 38 et al.

208; depression

74; depression

Online messaging system; EMR

Telephone

Advice on self-care and antidepressant. Each contact included: outreach message, patient online assessment, structured response from care manager, FU with patient and physician as needed.

Nurse care manager, physician

Pharmacist addressed symptoms and medication-related concerns; initial calls – dose adjustment and management of adverse effects; refill authorizations; access to patient assistance programs facilitated. Other interventions – dose timing changes, change or discontinuation of antidepressant; provision of additional medication for insomnia or sexual dysfunction as needed. Initial call - review of

Pharmacist, primary care physician, study psychiatrist

Healthcare

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HIGH

2/low

Perahia et

962;

Telephone

Percentage of patients using antidepressant for over 90 days (refill data)

At 6 months, higher percentage of adherent patients in IG vs. TAU (81% vs. 61%, p=0.001)

Weekly x4 weeks, then every other week through week 12, then every other month during months 412.

TAU – collaborative care model; patients encouraged to use available resources including primary care physicians, pharmacists, nurses, and mental health providers.

Use of antidepressant for at least 25 of the past 30 days (SR)

3-month, 6month, 9month, and 12-month antidepressant adherence NS between IG and TAU (p=0.91).

Weeks 1, 4,

No telephone

PC: Number of

PC: NS

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7

TAU

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Welcome, Weeks 2, 6, and 10

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al.

HIGH

1/low

Salzer et 40 al.

depression

32; schizophreniaspectrum disorder; adherence problems

symptoms, discussion about depression and patients' emotional and rational responses to treatment. Next 2 calls - reviewed progress and experiences on treatment, FU on previous calls’ issues.

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and 9 after initiation of duloxetine.

intervention.

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pill taken as a percentage of the number of pills prescribed at each visit and SR: Morisky Medication Adherence Questionnaire

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Telephone

professional with experience in clinical management of major depressive disorder

TMM program: adherence counseling, validating treatment experience, and problem-solving

Yes; type of staff not stated.

8

Weekly

TAU

SR

difference in percentage of adherent patients between groups (leastsquares means from ANOVA) from baseline to week 2; week 2 to 6; week 6 to 12. SR: NS difference in percentage of adherent patients between groups at any visit. From baseline to last observation in 52-week study, NS difference in change in SR between groups (p=0.87)

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EMR – electronic medical record FU – follow up IG – intervention group IM – intramuscular MAQ - Morisky Green Adherence Questionnaire range: 0-4; lower scores indicating better adherence MARS – Medication Adherence Rating Scale MEMS – Medication Event Monitoring System MM – Med-eMonitor™ MMAS-8 - Morisky Medication Adherence Scale-8 range: 0-8; higher scores indicating better adherence NS – not significant PC – pill count PHQ-9 = 9-Item Patient Health Questionnaire PGEM – Pharmacist Guided Education and Monitoring RAT - Register of Adherence to Treatment SR – self-report TAU – treatment as usual TEAM - Telemedicine Enhanced Antidepressant Management TIPS - Telephone Intervention Problem Solving TMM – Telephone Medication Management VAS – Visual Analog Scale

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Figure Legends Figure 1. PRISMA diagram.

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Figure 1