Multi-Agent-Based Simulation of a Complex Ecosystem of Mental Health Care.

This paper discusses the creation of an Agent-Based Simulation that modeled the introduction of care coordination capabilities into a complex system of care for patients with Serious and Persistent Mental Illness. The model describes the engagement between patients and the medical, social and criminal justice services they interact with in a complex ecosystem of care. We outline the challenges involved in developing the model, including process mapping and the collection and synthesis of data to support parametric estimates, and describe the controls built into the model to support analysis of potential changes to the system. We also describe the approach taken to calibrate the model to an observable level of system performance. Preliminary results from application of the simulation are provided to demonstrate how it can provide insights into potential improvements deriving from introduction of care coordination technology.

Racial differences in antipsychotic use: Claims database analysis of Medicaid-insured patients with schizophrenia.

BACKGROUND: Database analyses have indicated that medical treatment for schizophrenia varies among racial groups. This study assessed antipsychotic use and healthcare utilization across races in Medicaid-insured patients with schizophrenia. METHODS: A Medicaid database of inpatient/outpatient medical claims and outpatient prescription claims for more than 28 million enrollees in 11 geographically diverse states was analyzed. The primary outcome, racial differences in antipsychotic use in 2012, was examined in 5 multivariable logistic regression models: (1) any antipsychotic, (2) first-generation (FG) long-acting injectables (LAIs), (3) FG oral antipsychotics, (4) second-generation (SG) LAIs, and (5) SG oral antipsychotics. RESULTS: Odds ratios and adjusted predicted probabilities were comparable for any antipsychotic use between black and white patients. Black patients were less likely to receive SG oral antipsychotics (P < .001) and more likely to receive SG or FG LAIs (P = .001 and P < .001, respectively) and FG oral antipsychotics (P = .003) vs white patients. Further, black patients had a higher mean number of emergency room visits (P < .001) and a lower mean number of hospitalizations (P < .05) vs white patients; the mean number of physician visits was comparable. CONCLUSIONS: Disparities in antipsychotic use and healthcare utilization across races in patients with schizophrenia warrant further investigation and elimination of these disparities should be a national goal.

Designing mHealth Apps to Incorporate Evidence-Based Techniques for Prolonging User Engagement.

Maintaining user engagement with mobile health (mHealth) apps can be a challenge. Previously, we developed a conceptual model to optimize patient engagement in mHealth apps by incorporating multiple evidence-based methods, including increasing health literacy, enhancing technical competence, and improving feelings about participation in clinical trials. This viewpoint aims to report on a series of exploratory mini-experiments demonstrating the feasibility of testing our previously published engagement conceptual model. We collected data from 6 participants using an app that showed a series of educational videos and obtained additional data via questionnaires to illustrate and pilot the approach. The videos addressed 3 elements shown to relate to engagement in health care app use: increasing health literacy, enhancing technical competence, and improving positive feelings about participation in clinical trials. We measured changes in participants' knowledge and feelings, collected feedback on the videos and content, made revisions based on this feedback, and conducted participant reassessments. The findings support the feasibility of an iterative approach to creating and refining engagement enhancements in mHealth apps. Systematically identifying the key evidence-based elements intended to be included in an app's design and then systematically testing the implantation of each element separately until a satisfactory level of positive impact is achieved is feasible and should be incorporated into standard app design. While mHealth apps have shown promise, participants are more likely to drop out than to be retained. This viewpoint highlights the potential for mHealth researchers to test and refine mHealth apps using approaches to better engage users.

Nitrite anion stimulates ischemic arteriogenesis involving NO metabolism.

Nitric oxide (NO) is a potential regulator of ischemic vascular remodeling, and as such therapies augmenting its bioavailability may be useful for the treatment of ischemic tissue diseases. Here we examine the effect of administering the NO prodrug sodium nitrite on arteriogenesis activity during established tissue ischemia. Chronic hindlimb ischemia was induced by permanent unilateral femoral artery and vein ligation. Five days postligation; animals were randomized to control PBS or sodium nitrite (165 µg/kg) therapy twice daily. In situ vascular remodeling was measured longitudinally using SPY angiography and Microfil vascular casting. Delayed sodium nitrite therapy rapidly increased ischemic limb arterial vessel diameter and branching in a NO-dependent manner. SPY imaging angiography over time showed that nitrite therapy enhanced ischemic gracillis collateral vessel formation from the profunda femoris to the saphenous artery. Immunofluorescent staining of smooth muscle cell actin also confirmed that sodium nitrite therapy increased arteriogenesis in a NO-dependent manner. The NO prodrug sodium nitrite significantly increases arteriogenesis and reperfusion of established severe chronic tissue ischemia.

A New Approach to Enhancing Engagement in eHealth Apps.

This viewpoint presents a 3-phase conceptual model of the process of user engagement with eHealth apps. We also describe how knowledge gleaned from psychosocial, behavioral, and cognitive science can be incorporated into this model to enhance user engagement with an eHealth app in each phase of the engagement process.

Self-injurious behavior in the nursing home setting.

BACKGROUND: Self-injurious behavior (SIB) in older adults is defined as harm inflicted on oneself without conscious suicidal intent. SIB as a separate entity distinct from suicidal intent is poorly understood. However, it is of great concern to the patients' families and caregivers and it poses serious clinical challenges for clinicians. METHODS: We searched the database of PubMed, Ovid Medline, and ScienceDirect for reports published between 1970 and 2009 using combination of the following keywords: "self-injurious behavior", "self-destructive behavior", "self-mutilating behavior", "older adults", "geriatric population", and "nursing homes". The term "self-harm behavior" which also appears in the literature is broader in scope than "self-injurious behavior". It encompasses high suicide intent and failed suicide attempts; therefore, we excluded this term in order to focus purely on "self-injurious behavior". Our search yielded 10 publications concerning SIB in older adults, four of which included studies investigating SIB in nursing homes. RESULTS: Clinical studies of SIB in older adult nursing home residents are sparse. This limited literature suggests that SIB is a prevalent phenomenon and is reported to be as high as 14% in one study of nursing home subjects aged 65 and older. It is reported to be strongly associated with dementia and a risk of accidental death. It has been suggested that SIB among demented patients occurs in the context of poor impulse control and physical isolation. CONCLUSION: SIB is likely a common phenomenon in older adult nursing home residents. There is little evidence-based treatment guidance for SIB in older population.

A hypothesized role for dendritic remodeling in the etiology of mood and anxiety disorders.

An elegant theory that links hippocampal neurogenesis to mood and anxiety disorders and to the mechanism of action of antidepressant drugs has gained widespread attention. However, depression and anxiety disorders involve multiple areas of the brain, such as the amygdala and prefrontal cortex, where neurogenesis does not appear to occur in the adult mammalian brain. A complementary theory is proposed here in which neurogenesis is seen as an epiphenomenon of a more widespread alteration in dendritic length and spine number. According to this theory, exposure to chronic stress and stressful life events increases excitotoxic glutamatergic neurotransmission in multiple brain areas. To protect neurons from consequent apoptosis, dendrites retract and spine number decreases, thus limiting the number of exposed glutamate receptors. Drugs that reduce glutamatergic neurotransmission under these circumstances, many of which have already been shown helpful in treating mood and anxiety disorders, may prevent this dendritic retraction and thus protect synaptic connections throughout the brain.

Understanding the elements of a holistic wound assessment.

Wounds have become a significant public health challenge and consume a large amount of healthcare resources. Wounds can have severe negative effects on patients' quality of life, causing psychological and social distress, and may lead to significant periods of lost employment, resulting in financial loss. For nurses to manage wounds effectively, they need to be competent in undertaking holistic wound assessments. This article supports nurses' understanding by providing information on various types of wounds and their differing aetiologies. It also outlines the elements of a wound assessment and discusses some of the challenges that may be encountered during this process.

Use of technology for care coordination initiatives for patients with mental health issues: a systematic literature review.

This systematic literature review investigates the use of technology for the coordination and management of mental health care with an emphasis on outcomes. Searches of MEDLINE/PubMed, Scopus, and EMBASE were conducted between January 1, 2003, and January 4, 2018, to identify articles that assessed patient outcomes associated with care coordination, evaluated technology to improve care, or discussed management of mental health care using technology. A total of 21 articles were included in a qualitative review based on the recommendations set forth by the PRISMA statement. Among the various health technologies, electronic health records were most commonly used for care coordination, with primary care being the most frequent setting. Care coordination was shown to provide easier patient access to health care providers and to improve communication between caregiver and patient, especially in cases where geographic location or distance is a challenge. Barriers to coordinated care included, but were not limited to, insufficient funding for health information technology, deficient reimbursement plans, limited access to technologies, cultural barriers, and underperforming electronic health record templates. In conclusion, many studies showed the benefit of coordinated and collaborative care through the use of technology; however, further research and development efforts are needed to continue technological innovation for advanced patient care.

Expert Consensus Survey on Digital Health Tools for Patients With Serious Mental Illness: Optimizing for User Characteristics and User Support.

BACKGROUND: Digital technology is increasingly being used to enhance health care in various areas of medicine. In the area of serious mental illness, it is important to understand the special characteristics of target users that may influence motivation and competence to use digital health tools, as well as the resources and training necessary for these patients to facilitate the use of this technology. OBJECTIVE: The aim of this study was to conduct a quantitative expert consensus survey to identify key characteristics of target users (patients and health care professionals), barriers and facilitators for appropriate use, and resources needed to optimize the use of digital health tools in patients with serious mental illness. METHODS: A panel of 40 experts in digital behavioral health who met the participation criteria completed a 19-question survey, rating predefined responses on a 9-point Likert scale. Consensus was determined using a chi-square test of score distributions across three ranges (1-3, 4-6, 7-9). Categorical ratings of first, second, or third line were designated based on the lowest category into which the CI of the mean ratings fell, with a boundary >6.5 for first line. Here, we report experts' responses to nine questions (265 options) that focused on (1) user characteristics that would promote or hinder the use of digital health tools, (2) potential benefits or motivators and barriers or unintended consequences of digital health tool use, and (3) support and training for patients and health care professionals. RESULTS: Among patient characteristics most likely to promote use of digital health tools, experts endorsed interest in using state-of-the-art technology, availability of necessary resources, good occupational functioning, and perception of the tool as beneficial. Certain disease-associated signs and symptoms (eg, more severe symptoms, substance abuse problems, and a chaotic living situation) were considered likely to make it difficult for patients to use digital health tools. Enthusiasm among health care professionals for digital health tools and availability of staff and equipment to support their use were identified as variables to enable health care professionals to successfully incorporate digital health tools into their practices. The experts identified a number of potential benefits of and barriers to use of digital health tools by patients and health care professionals. Experts agreed that both health care professionals and patients would need to be trained in the use of these new technologies. CONCLUSIONS: These results provide guidance to the mental health field on how to optimize the development and deployment of digital health tools for patients with serious mental illness.

Remission in schizophrenia: applying recent consensus criteria to refine the concept.

Although the concept of remission has been widely accepted and utilized in depression and anxiety disorders, there has been much less emphasis on defining remission in schizophrenia. Recently, an expert consensus definition of remission in schizophrenia was proposed along specific operational criteria for the attainment of remission focusing on the three core dimensions of psychopathology identified within schizophrenia: psychoticism, disorganization and negative symptoms. To date, the criteria have been applied retrospectively to several clinical studies, and these have demonstrated that the proposed definition of remission correlates significantly with established measures of symptom severity, functioning and quality of life, and appears achievable for a significant proportion of patients receiving at least 3 months of pharmacotherapy. In this article we extend the notion of remission to include an examination of the possible association of several modifiable and unmodifiable factors and co-morbidities on remission status. We also propose an investigation into the likelihood of different patient populations in achieving remission as well as assessing the impact of remission on health care costs and family burden. Since cognitive dysfunction and negative symptoms may be strongly correlated with a lower likelihood of achieving remission, we recommend retrospective and/or prospective studies to determine the relationship between neurocognitive status and the predominance of negative symptoms at treatment start and the probability of achieving remission. Taken together, these studies should help identify key predictors of remission, further define the remitted state, reduce therapeutic pessimism, raise treatment expectations and chart a strategy for further research in this important area.

Patient-based and clinician-based support for the remission criteria in schizophrenia.

This analysis characterizes patients with schizophrenia or schizoaffective disorder treated with risperidone who met remission criteria. In a 50-week, open-label trial, stable patients received long-acting injectable risperidone every 2 weeks. Remission criteria included severity (absent-mild ratings on core symptoms of the Positive and Negative Syndrome Scale) and duration (> or =6 months) components. The patients not remitted (severity component only) at baseline (n=394) are the subjects of this report. Measures applied included the PANSS, Clinical Global Impressions-Severity, patient-rated mental health status (Short Form-36), and Drug Attitude Inventory. Among patients who met remission criteria during the study (n=82), mean scores for all 30 PANSS items reflected absent-mild ratings at endpoint. The highest items represented an 'interpersonal' cluster, although mean ratings were still minimal to mild. Remitted patients experienced substantial improvements in Short Form-36 and Drug Attitude Inventory scores at endpoint. Although improvement occurred, it was less robust in patients who remained nonremitted (n=312). Logistic regression analysis found that remission (severity component only) was associated with a 97.1% probability of a 'not ill' rating on the Clinical Global Impressions-Severity. These remission criteria identified patients who differed from the nonremitted population on symptoms of psychopathology, medication attitude, health status, and overall clinical status, supporting the clinical validity of the remission criteria.

Expert Consensus Survey on Medication Adherence in Psychiatric Patients and Use of a Digital Medicine System.

BACKGROUND: There is an unmet need to objectively assess adherence problems that are a common cause of unexplained or unexpected suboptimal outcome. A digital medicine system (DMS) has been developed to address this need in patients with serious mental illness. OBJECTIVE: To conduct a quantitative expert consensus survey to (1) assess relative importance of causes of suboptimal outcomes, (2) examine modalities used to assess adherence, (3) provide guidance on when and how to use the DMS in clinical practice once available, and (4) suggest interventions for specific reasons for nonadherence. METHODS: A panel of 58 experts in psychiatry completed a 23-question survey (October 13 through December 23, 2013) and rated their responses on a 9-point Likert scale. A χ² test of score distributions was used to determine consensus (P < .05). RESULTS: The panel rated adherence as the most important factor in suboptimal outcomes and yet the least likely to be assessed accurately. All predefined uses of the DMS received high mean first-line ratings (≥ 7.4). The experts recognized the utility of the DMS in managing adherence problems, identified clinical situations appropriate for DMS, and assessed potential benefits and challenges of this technology. Consensus was reached on first-line interventions for 10 of 11 reasons for nonadherence. CONCLUSIONS: The results provide a guide to clinicians on the evaluation of suboptimal outcomes, when and how to use the DMS, and the most appropriate interventions to address detected adherence problems.

Why do psychiatric patients stop antipsychotic medication? A systematic review of reasons for nonadherence to medication in patients with serious mental illness.

BACKGROUND: Antipsychotic medication reduces the severity of serious mental illness (SMI) and improves patient outcomes only when medicines were taken as prescribed. Nonadherence to the treatment of SMI increases the risk of relapse and hospitalization and reduces the quality of life. It is necessary to understand the factors influencing nonadherence to medication in order to identify appropriate interventions. This systematic review assessed the published evidence on modifiable reasons for nonadherence to antipsychotic medication in patients with SMI. METHODS: Articles published between January 1, 2005, and September 10, 2015, were searched on MEDLINE through PubMed. Abstracts were independently screened by 2 randomly assigned authors for inclusion, and disagreement was resolved by another author. Selected full-text articles were divided among all authors for review. RESULTS: A qualitative analysis of data from 36 articles identified 11 categories of reasons for nonadherence. Poor insight was identified as a reason for nonadherence in 55.6% (20/36) of studies, followed by substance abuse (36.1%, 13/36), a negative attitude toward medication (30.5%, 11/36), medication side effects (27.8%, 10/36), and cognitive impairments (13.4%, 7/36). A key reason directly associated with intentional nonadherence was a negative attitude toward medication, a mediator of effects of insight and therapeutic alliance. Substance abuse was the only reason consistently associated with unintentional nonadherence, regardless of type and stage of SMI. DISCUSSION: Although adherence research is inherently biased because of numerous methodological limitations and specific reasons under investigation, reasons for nonadherence consistently identified as significant across studies likely reflect valid existing associations with important clinical implications. CONCLUSION: This systematic review suggests that a negative attitude toward medication and substance abuse are consistent reasons for nonadherence to antipsychotic medication among people with SMI. Adherence enhancement approaches that specifically target these reasons may improve adherence in a high-risk group. However, it is also important to identify drivers of poor adherence specific to each patient in selecting and implementing intervention strategies.

Predicting Future High-Cost Schizophrenia Patients Using High-Dimensional Administrative Data.

BACKGROUND: The burden of serious and persistent mental illness such as schizophrenia is substantial and requires health-care organizations to have adequate risk adjustment models to effectively allocate their resources to managing patients who are at the greatest risk. Currently available models underestimate health-care costs for those with mental or behavioral health conditions. OBJECTIVES: The study aimed to develop and evaluate predictive models for identification of future high-cost schizophrenia patients using advanced supervised machine learning methods. METHODS: This was a retrospective study using a payer administrative database. The study cohort consisted of 97,862 patients diagnosed with schizophrenia (ICD9 code 295.*) from January 2009 to June 2014. Training (n = 34,510) and study evaluation (n = 30,077) cohorts were derived based on 12-month observation and prediction windows (PWs). The target was average total cost/patient/month in the PW. Three models (baseline, intermediate, final) were developed to assess the value of different variable categories for cost prediction (demographics, coverage, cost, health-care utilization, antipsychotic medication usage, and clinical conditions). Scalable orthogonal regression, significant attribute selection in high dimensions method, and random forests regression were used to develop the models. The trained models were assessed in the evaluation cohort using the regression R2, patient classification accuracy (PCA), and cost accuracy (CA). The model performance was compared to the Centers for Medicare & Medicaid Services Hierarchical Condition Categories (CMS-HCC) model. RESULTS: At top 10% cost cutoff, the final model achieved 0.23 R2, 43% PCA, and 63% CA; in contrast, the CMS-HCC model achieved 0.09 R2, 27% PCA with 45% CA. The final model and the CMS-HCC model identified 33 and 22%, respectively, of total cost at the top 10% cost cutoff. CONCLUSION: Using advanced feature selection leveraging detailed health care, medication utilization features, and supervised machine learning methods improved the ability to predict and identify future high-cost patients with schizophrenia when compared with the CMS-HCC model.

Integrated multisystem analysis in a mental health and criminal justice ecosystem.

BACKGROUND: Patients with a serious mental illness often receive care that is fragmented due to reduced availability of or access to resources, and inadequate, discontinuous, and uncoordinated care across health, social services, and criminal justice organizations. This article describes the creation of a multisystem analysis that derives insights from an integrated dataset including patient access to case management services, medical services, and interactions with the criminal justice system. METHODS: Data were combined from electronic systems within a US mental health ecosystem that included mental health and substance abuse services, as well as data from the criminal justice system. Cox models were applied to test the associations between delivery of services and re-incarceration. Additionally, machine learning was used to train and validate a predictive model to examine effects of non-modifiable risk factors (age, past arrests, mental health diagnosis) and modifiable risk factors (outpatient, medical and case management services, and use of a jail diversion program) on re-arrest outcome. RESULTS: An association was found between past arrests and admission to crisis stabilization services in this population (N = 10,307). Delivery of case management or medical services provided after release from jail was associated with a reduced risk for re-arrest. Predictive models linked non-modifiable and modifiable risk factors and outcomes and predicted the probability of re-arrests with fair accuracy (area under the receiver operating characteristic curve of 0.67). CONCLUSIONS: By modeling the complex interactions between risk factors, service delivery, and outcomes, systems of care might be better enabled to meet patient needs and improve outcomes.

Resveratrol suppresses nuclear factor-kappaB in herpes simplex virus infected cells.

Resveratrol inhibits herpes simplex virus (HSV) replication by an unknown mechanism. Previously it was suggested that this inhibition may be mediated through a cellular factor essential for HSV replication [Docherty, J.J., Fu, M.M., Stiffler, B.S., Limperos, R.J., Pokabla, C.M., DeLucia, A.L., 1999. Resveratrol inhibition of herpes simplex virus replication. Antivir. Res. 43, 145-155]. After examining numerous cellular factors, we report that resveratrol suppresses NF-kappaB (NF-kappaB) activation in HSV infected cells. Reports have indicated that HSV activates NF-kappaB during productive infection and this may be an essential aspect of its replication scheme [Patel, A., Hanson, J., McLean, T.I., Olgiate, J., Hilton, M., Miller, W.E., Bachenheimer, S.L., 1998. Herpes simplex type 1 induction of persistent NF-kappa B nuclear translocation increases the efficiency of virus replication. Virology 247, 212-222; Gregory, D., Hargett, D., Holmes, D., Money, E., Bachenheimer, S.L., 2004. Efficient replication by herpes simplex virus type 1 involves activation of the IkappaB kinase-IkappaB-RelA/p65 pathway. J. Virol. 78, 13582-13590]. Electromobility shift assays determined that resveratrol, in a dose dependent and reversible manner, suppressed activation of NF-kappaB in Vero cells infected with HSV-1, HSV-2 and acyclovir resistant HSV-1. Furthermore, resveratrol did not protect IkappaBalpha, a cytoplasmic NF-kappaB inhibitor, from degradation in HSV-1 infected cells. Immunohistochemical studies demonstrated that RelA/p65, a component of the dimeric NF-kappaB complex, translocated to the nucleus of HSV-1 infected cells in the presence of resveratrol. Finally, direct effects on viral transcription and DNA synthesis were evaluated. Real-time RT-PCR analysis showed that resveratrol treatment of infected cells resulted in reductions of mRNA for ICP0, ICP4, ICP8 and HSV-1 DNA polymerase by 2.1-, 3.3-, 3.8- and 3.1-fold, respectively. Plus, mRNA for glycoprotein C, an HSV late gene, was completely absent in the presence of resveratrol. Lastly, quantitative PCR showed that resveratrol significantly blocked HSV DNA synthesis. Cumulatively, these data indicate that resveratrol (i) suppresses HSV induced activation of NF-kappaB within the nucleus and (ii) impairs expression of essential immediate-early, early and late HSV genes and synthesis of viral DNA.

Resveratrol inhibition of varicella-zoster virus replication in vitro.

Resveratrol was found to inhibit varicella-zoster virus (VZV) replication in a dose-dependent and reversible manner. This decrease in virus production in the presence of resveratrol was not caused by direct inactivation of VZV or inhibition of virus attachment to MRC-5 cells. The drug effectively limited VZV replication if added during the first 30 h of infection. Western blot analysis and real-time RT-PCR studies demonstrated that protein and mRNA levels of IE62, an essential immediate early viral protein, were reduced when compared to controls. These results demonstrate that VZV replication is adversely affected by resveratrol which is negatively impacting IE62 synthesis.

Evaluation of a preclinical blood test for scrapie in sheep using immunocapillary electrophoresis.

An analytical method is described for detection of endogenous disease-associated prion protein in the buffy coat fraction from the blood of sheep infected with scrapie. The method has been improved and evaluated for its performance in the preclinical diagnosis of ovine transmissible spongiform encephalopathies. The test system uses a protocol for sample preparation that includes extraction and concentration and a test method that uses a liquid-phase competitive immunoassay for prion protein. Antibodies directed to a peptide sequence at the C-terminus of the prion protein (PrP) and a fluorescein-labeled peptide conjugate are used in the assay. Free zone capillary electrophoresis with laser-induced fluorescence for detection is used to separate the antibody-bound fluorescently labeled peptide and free labeled peptide. In this assay, the PrP competes with the fluorescently labeled peptide for limited antibody binding sites, which results in a reduction of the peak representing the immunocomplex of the antibody bound to the fluorescently labeled peptide. When blood samples from scrapie-infected sheep aged 7-12 months and of the scrapie-susceptible PrP genotypes VRQ/VRQ and VRQ/ARQ were analyzed, the abnormal PrP was found in blood samples. These results correlated with the post-mortem diagnosis of scrapie. The sheep were preclinical and appeared normal at the time of testing but later died with clinical disease approximately 12 months after testing. In older animals, and those with clinical signs, a smaller percentage of animals tested positive. This study has demonstrated that this technology can be used as a sensitive, rapid preclinical test to detect the disease-associated PrP in the blood of scrapie-infected sheep. Improvements in the extraction protocol and capillary electrophoresis conditions will enhance the robustness of this test.

Developing a Digital Medicine System in Psychiatry: Ingestion Detection Rate and Latency Period.

BACKGROUND: A digital medicine system (DMS) has been developed to measure and report adherence to an atypical antipsychotic, aripiprazole, in psychiatric patients. The DMS consists of 3 components: ingestible sensor embedded in a medication tablet, wearable sensor, and secure mobile and cloud-based applications. An umbrella study protocol was designed to rapidly assess the technical performance and safety of the DMS in multiple substudies to guide the technology development. METHODS: Two sequential substudies enrolled 30 and 29 healthy volunteers between March-April 2014 and February-March 2015, respectively, to assess detection accuracy of the ingestible sensor by the DMS and the latency period between ingestion and detection of the ingestion by the wearable sensor or the cloud-based server. RESULTS: The first substudy identified areas for improvement using early versions of the wearable sensor and the mobile application. The second substudy tested updated versions of the components and showed an overall ingestion detection rate of 96.6%. Mean latency times for the signal transmission were 1.1-1.3 minutes (from ingestion to the wearable sensor detection) and 6.2-10.3 minutes (from the wearable sensor detection to the server detection). Half of transmissions were completed in < 2 minutes, and ~90% of ingestions were registered by the smartphone within 30 minutes of ingestion. No serious adverse events, discontinuations, or clinically significant laboratory/vital signs findings were reported. CONCLUSIONS: The DMS implementing modified versions of the smartphone application and the wearable sensor has the technical capability to detect and report tablet ingestion with high accuracy and acceptable latency time. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT02091882.