Understanding Design Approaches and Evaluation Methods in mHealth applications Targeting Substance Use: Protocol for a Systematic Review (Preprint)

2022 ◽  
Author(s):  
Sahiti Kunchay ◽  
Ashley Linden-Carmichael ◽  
Stephanie Lanza ◽  
Saeed Abdullah

BACKGROUND Substance use and use disorders in the US have had significant and devastating impacts on individuals and communities, and the escalating substance use crisis calls for urgent and innovative solutions to effectively detect and provide interventions for individuals in times of need. Recent mHealth-based approaches offer promising new opportunities to address these issues through ubiquitous devices. However, the design rationale, theoretical framing, and the mechanisms through which users' perspectives and experiences guide the design and deployment of such systems have not been analyzed in any prior systematic reviews. OBJECTIVE In this paper, we systematically review these approaches and applications for their feasibility, efficacy, and usability. Further, we evaluate whether human-centered research principles and techniques guide the design and development of these systems, and examine how the current state-of-art systems apply to real-world contexts. In an effort to gauge the applicability of these systems, we also investigate whether these approaches consider the effects of stigma and the privacy concerns related to collecting data on substance use. Lastly, we examine persistent challenges in the design and large-scale adoption of substance use intervention applications and draw inspiration from other domains of mHealth to suggest actionable reforms into the design and deployment of these applications. METHODS Four databases (PubMed, IEEE, JMIR and ACM DL) were searched over a five-year period (2016 - 2021) for articles evaluating connected mHealth approaches for substance use (alcohol use, marijuana use, opioid use, tobacco use, and substance co-use). Articles that will be included describe an mHealth detection or intervention targeting substance use and provided outcomes data and a discussion of design techniques and user perspectives. Independent evaluation will be conducted by one author, followed by secondary reviewer(s) who will check and validate themes and data. RESULTS This is a protocol for a systematic review, therefore results are not yet available. We are currently in the process of selecting the studies for inclusion in the final analysis. CONCLUSIONS To the best of our knowledge, this is the first systematic review to assess real-world applicability, scalability, and use of human-centered design and evaluation techniques in mHealth approaches targeting substance use. This study is expected to identify gaps in current substance use detection and intervention mHealth technologies and inform and motivate future development of such systems.

2021 ◽  
Vol 12 ◽  
Author(s):  
Alexandria S. Coles ◽  
Dunja Knezevic ◽  
Tony P. George ◽  
Christoph U. Correll ◽  
John M. Kane ◽  
...  

Objectives: Co-occurring substance use disorders (SUDs) among individuals with schizophrenia are a prevalent and complex psychiatric comorbidity, which is associated with increased symptom severity, worsened illness trajectory and high rates of treatment non-adherence. Recent evidence suggests that the use of long-acting injectable (LAI) antipsychotics may provide an effective treatment option for individuals with this dual-diagnosis.Methods: A systematic review of the literature was conducted using the databases PubMed, PsychInfo and Google Scholar for English-language studies, investigating the use of LAIs in co-occurring schizophrenia and substance use disorders (SCZ-SUDs).Results: Eight reports [one case study (n = 1), one case series (n = 8), three open-label retrospective studies (n = 75), and three randomized controlled trials (n = 273)] investigated the use of LAI antipsychotics in 357 participants with SCZ-SUDs [alcohol use disorder: 5 studies, n = 282; cocaine use disorder: 5 studies, n = 85; amphetamine use disorder: 1 study, n = 1; cannabis use disorder: 3 studies, n = 160; opioid use disorder: 3 studies, n = 19; methylenedioxymethamphetamine (MDMA) use disorder: 2 studies, n = 9; ketamine use disorder: 1 study, n = 4] and were included in this systematic review. Findings indicate significant improvements in substance use related outcomes across 7 of 8 studies, while in 6 of 8 studies, significant improvements in psychopathology-related outcomes were reported.Conclusions: LAI antipsychotics may be an efficacious intervention option for the treatment of SCZ-SUDs. However, varying methodological rigor, generally small sample sizes and heterogeneity of samples, settings, substances of abuse, tested LAIs and comparators, as well as psychosocial cotreatments and level of reported detail across studies requires that these findings be considered preliminary and interpreted with caution. Further research is required to better understand the effects of LAIs among individuals with SCZ-SUDs.


2017 ◽  
Vol 24 (6) ◽  
pp. 1204-1210 ◽  
Author(s):  
Chelsea Canan ◽  
Jennifer M Polinski ◽  
G Caleb Alexander ◽  
Mary K Kowal ◽  
Troyen A Brennan ◽  
...  

Abstract Objective Improved methods to identify nonmedical opioid use can help direct health care resources to individuals who need them. Automated algorithms that use large databases of electronic health care claims or records for surveillance are a potential means to achieve this goal. In this systematic review, we reviewed the utility, attempts at validation, and application of such algorithms to detect nonmedical opioid use. Materials and Methods We searched PubMed and Embase for articles describing automatable algorithms that used electronic health care claims or records to identify patients or prescribers with likely nonmedical opioid use. We assessed algorithm development, validation, and performance characteristics and the settings where they were applied. Study variability precluded a meta-analysis. Results Of 15 included algorithms, 10 targeted patients, 2 targeted providers, 2 targeted both, and 1 identified medications with high abuse potential. Most patient-focused algorithms (67%) used prescription drug claims and/or medical claims, with diagnosis codes of substance abuse and/or dependence as the reference standard. Eleven algorithms were developed via regression modeling. Four used natural language processing, data mining, audit analysis, or factor analysis. Discussion Automated algorithms can facilitate population-level surveillance. However, there is no true gold standard for determining nonmedical opioid use. Users must recognize the implications of identifying false positives and, conversely, false negatives. Few algorithms have been applied in real-world settings. Conclusion Automated algorithms may facilitate identification of patients and/or providers most likely to need more intensive screening and/or intervention for nonmedical opioid use. Additional implementation research in real-world settings would clarify their utility.


2015 ◽  
Vol 9 ◽  
pp. SART.S30120 ◽  
Author(s):  
Brittany B. Dennis ◽  
Monica Bawor ◽  
Leen Naji ◽  
Carol K. Chan ◽  
Jaymie Varenbut ◽  
...  

Background While a number of pharmacological interventions exist for the treatment of opioid use disorder, evidence evaluating the effect of pain on substance use behavior, attrition rate, and physical or mental health among these therapies has not been well established. We aim to evaluate these effects using evidence gathered from a systematic review of studies evaluating chronic non-cancer pain (CNCP) in patients with opioid use disorder. Methods We searched the Medline, EMBASE, PubMed, PsycINFO, Web of Science, Cochrane Database of Systematic Reviews, ProQuest Dissertations and theses Database, Cochrane Central Register of Controlled Trials, World Health Organization International Clinical Trials Registry Platform Search Portal, and National Institutes for Health Clinical Trials Registry databases to identify articles evaluating the impact of pain on addiction treatment outcomes for patients maintained on opioid agonist therapy. Results Upon screening 3,540 articles, 14 studies with a combined sample of 3,128 patients fulfilled the review inclusion criteria. Results from the meta-analysis suggest that pain has no effect on illicit opioid consumption [pooled odds ratio (pOR): 0.70, 95%CI 0.41–1.17; I 2 = 0.0] but a protective effect for reducing illicit non-opioid substance use (pOR: 0.57, 95%CI 0.41–0.79; I 2 = 0.0). Studies evaluating illicit opioid consumption using other measures demonstrate pain to increase the risk for opioid abuse. Pain is significantly associated with the presence of psychiatric disorders (pOR: 2.18; 95%CI 1.6, 2.9; I 2 = 0.0%). Conclusion CNCP may increase risk for continued opioid abuse and poor psychiatric functioning. Qualitative synthesis of the findings suggests that major methodological differences in the design and measurement of pain and treatment response outcomes are likely impacting the effect estimates.


2021 ◽  
Author(s):  
Apoorva Anand ◽  
Jacob Bigio ◽  
Emily MacLean ◽  
Talya Underwood ◽  
Nitika Pant Pai ◽  
...  

Introduction: Testing is critical to controlling the COVID-19 pandemic. Antigen-detecting rapid diagnostic tests (Ag-RDTs) that can be used at the point of care have the potential to increase access to COVID 19 testing, particularly in settings with limited laboratory capacity. This systematic review synthesized literature on specific use cases and performance of Ag RDTs for detecting SARS-CoV-2, for the first comprehensive assessment of Ag RDT use in real-world settings. Methods: We searched three databases (PubMed, EMBASE and medRxiv) up to 12 April 2021 for publications on Ag-RDT use for large-scale screening, irrespective of symptoms, and surveillance of COVID-19, excluding studies of only presumptive COVID-19 patients. We tabulated data on the study setting, populations, type of test, diagnostic performance and operational findings. We assessed risk of bias using QUADAS-2 and an adapted tool for prevalence studies. Results: From 4313 citations, 39 studies conducted in asymptomatic and symptomatic adults were included. Study sample sizes varied from 40 to >5 million. Of 39 studies, 37 (94.9%) investigated lateral flow Ag-RDTs and two (5.1%) investigated multiplex sandwich chemiluminescent enzyme immunoassay Ag-RDTs. Six categories of testing (screening/surveillance) initiatives were identified: mass screening (n=13), targeted screening (n=11), healthcare entry testing (n=6), at-home testing (n=4), surveillance (n=4) and prevalence survey (n=1). Across studies, Ag-RDT sensitivity varied from 40% to 100%. Ag-RDTs were noted as convenient, easy-to-use and low cost, with a rapid turnaround time and high user acceptability. Risk of bias was generally low or unclear across the studies. Conclusion: This systematic review demonstrates the use of Ag-RDTs across a wide range of real-world settings for screening and surveillance of COVID-19 in both symptomatic and asymptomatic individuals. Ag-RDTs were overall found to be easy-to-use, low cost and rapid tools, when consideration is given to their implementation and interpretation. The review was funded by FIND, the global alliance for diagnostics.


Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8387
Author(s):  
Adrian Cosma ◽  
Ion Emilian Radoi

The use of gait for person identification has important advantages such as being non-invasive, unobtrusive, not requiring cooperation and being less likely to be obscured compared to other biometrics. Existing methods for gait recognition require cooperative gait scenarios, in which a single person is walking multiple times in a straight line in front of a camera. We address the challenges of real-world scenarios in which camera feeds capture multiple people, who in most cases pass in front of the camera only once. We address privacy concerns by using only motion information of walking individuals, with no identifiable appearance-based information. As such, we propose a self-supervised learning framework, WildGait, which consists of pre-training a Spatio-Temporal Graph Convolutional Network on a large number of automatically annotated skeleton sequences obtained from raw, real-world surveillance streams to learn useful gait signatures. We collected and compiled the largest pretraining dataset to date of anonymized walking skeletons called Uncooperative Wild Gait, containing over 38k tracklets of anonymized walking 2D skeletons. We make the dataset available to the research community. Our results surpass the current state-of-the-art pose-based gait recognition solutions. Our proposed method is reliable in training gait recognition methods in unconstrained environments, especially in settings with scarce amounts of annotated data.


2020 ◽  
Vol 14 ◽  
pp. 117822181990128
Author(s):  
Christine Timko ◽  
Amia Nash ◽  
Mandy D Owens ◽  
Emmeline Taylor ◽  
Andrea K Finlay

Evidence indicates that substance use and mental health treatment is often associated with reduced criminal activity. The present systematic review examined this association among military veterans, and aimed to provide a comprehensive summary of needed research to further contribute to reduced criminal activity among veterans. This systematic review was derived from a scoping review that mapped existing research on justice-involved veterans’ health. For the current systematic review, a subset of 20 publications was selected that addressed the question of whether criminal activity declines among veterans treated for substance use and mental health disorders. Generally, veterans improved on criminal outcomes from pre- to post-treatment for opioid use, other substance use, or mental health conditions, and more sustained treatment was associated with better outcomes. This occurred despite high rates of criminal involvement among veterans prior to entering treatment. Needed are substance use and mental health treatment studies that include women justice-involved veterans, follow criminally-active veterans for longer periods of time, and use validated and reliable measures of criminal activity with fully transparent statistical procedures. Future randomized trials should evaluate new treatments against evidence-based treatments (versus no-treatment control conditions). Subsequent studies should examine how to link veterans to effective treatments, facilitate sustained treatment engagement, and ensure the availability of effective treatments, and examine mechanisms (mediators and moderators) that explain the association of treatment with reduced criminal activity among veterans. Best practices are needed for reducing criminal activity among the minority of justice-involved veterans who do not have diagnosed substance use and/or mental health disorders.


2018 ◽  
Vol 25 (3) ◽  
pp. 131-141 ◽  
Author(s):  
Nicola A Holmes ◽  
Joseph EM van Agteren ◽  
Diana S Dorstyn

Introduction Mental health interventions disseminated via, or accessed using, digital technologies are an innovative new treatment modality for managing co-morbid depression and substance use disorder. The present systematic review assessed the current state of this literature. Methods A search of the Cochrane Library, Embase, Pubmed, PsycInfo and Scopus databases identified six eligible studies ( Nparticipants = 862), utilising quasi-experimental or randomised controlled designs. Reporting quality was evaluated and Hedges’ g effect sizes (with 95% confidence intervals and p-values) were calculated to determine treatment effectiveness. Process outcomes (e.g. treatment satisfaction, attrition rates) were also examined. Results Quality ratings demonstrated high internal validity, although external validity was low. Effect size data revealed medium to large and short-term improvements in severity of depression and substance use symptoms in addition to global improvement in social, occupational and psychological functioning. Longer-term treatment effectiveness could not be established, due to the limited available data. Preliminary findings suggest that there was high client satisfaction, therapeutic alliance and client engagement. Discussion Mobile phone devices and the Internet can help to increase access to care for those with mental health co-morbidity. Large-scale and longitudinal research is, however, needed before digital mental healthcare becomes standard practice. This includes establishing critical therapeutic factors including optimum levels of assistance from clinicians.


2021 ◽  
Author(s):  
Christoph Käding ◽  
Jakob Runge

<p>The Earth’s climate is a highly complex and dynamical system. To better understand and robustly predict it, knowledge about its underlying dynamics and causal dependency structure is required. Since controlled experiments are infeasible in the climate system, observational data-driven approaches are needed. Observational causal inference is a very active research topic and a plethora of methods have been proposed. Each of these approaches comes with inherent strengths, weaknesses, and assumptions about the data generating process as well as further constraints.<br>In this work, we focus on the fundamental case of bivariate causal discovery, i.e., given two data samples X and Y the task is to detect whether X causes Y or Y causes X. We present a large-scale benchmark that represents combinations of various characteristics of data-generating processes and sample sizes. By comparing most of the current state-of-the-art methods, we aim to shed light onto the real-world performance of evaluated methods. Since we employ synthetic data, we are able to precisely control the data characteristics and can unveil the behavior of methods when their underlying assumptions are met or violated. Further, we give a comparison on a set of real-world data with known causal relations to complete our evaluation.</p>


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