paper_lag.bib

@misc{2022NationalSurvey,
  title = {2022 {{National Survey}} on {{Drug Use}} and {{Health}}. {{Public}} Data Set.},
  journal = {Substance Abuse and Mental Health Services Administration},
  urldate = {2024-05-15},
  howpublished = {https://www.samhsa.gov/data/report/2022-nsduh-detailed-tables},
  file = {C:\Users\kpaquette2\Zotero\storage\BGRNANZQ\2022-nsduh-detailed-tables.html}
}

@article{acevedoDisparitiesTreatmentSubstance2018,
  title = {Disparities in the {{Treatment}} of {{Substance Use Disorders}}: {{Does Where You Live Matter}}?},
  shorttitle = {Disparities in the {{Treatment}} of {{Substance Use Disorders}}},
  author = {Acevedo, Andrea and Panas, Lee and Garnick, Deborah and {Acevedo-Garcia}, Dolores and Miles, Jennifer and Ritter, Grant and Campbell, Kevin},
  year = {2018},
  month = oct,
  journal = {The Journal of Behavioral Health Services \& Research},
  volume = {45},
  number = {4},
  pages = {533--549},
  issn = {1556-3308},
  doi = {10.1007/s11414-018-9586-y},
  urldate = {2024-05-23},
  abstract = {This study focused on (1) whether disparities in timely receipt of substance use services can be explained in part by the characteristics of the community in which the clients reside and (2) whether the effect of community characteristics on timely receipt of services was similar across racial/ethnic groups. The sample was composed of adults receiving publicly funded outpatient treatment in Washington State. Treatment data were linked to data from the US census. The outcome studied was ``Initiation and Engagement'' in treatment (IET), a measure noting timely receipt of services at the beginning of treatment. Community characteristics studied included community level economic disadvantage and concentration of American Indian, Latino, and Black residents in the community. Black and American Indian clients were less likely to initiate or engage in treatment compared to non-Latino white clients, and American Indian clients living in economically disadvantaged communities were at even greater risk of not initiating treatment. Community economic disadvantage and racial/ethnic makeup of the community were associated with treatment initiation, but not engagement, although they did not entirely explain the disparities found in IET.},
  langid = {english},
  keywords = {Community,Disparities,Quality,Treatment},
  file = {C:\Users\kpaquette2\Zotero\storage\X2CEISP3\Acevedo et al. - 2018 - Disparities in the Treatment of Substance Use Diso.pdf}
}

@article{baeMobilePhoneSensors2018,
  title = {Mobile Phone Sensors and Supervised Machine Learning to Identify Alcohol Use Events in Young Adults: {{Implications}} for Just-in-Time Adaptive Interventions},
  shorttitle = {Mobile Phone Sensors and Supervised Machine Learning to Identify Alcohol Use Events in Young Adults},
  author = {Bae, Sangwon and Chung, Tammy and Ferreira, Denzil and Dey, Anind K. and Suffoletto, Brian},
  year = {2018},
  month = aug,
  journal = {Addictive Behaviors},
  volume = {83},
  pages = {42--47},
  issn = {1873-6327},
  doi = {10.1016/j.addbeh.2017.11.039},
  abstract = {BACKGROUND: Real-time detection of drinking could improve timely delivery of interventions aimed at reducing alcohol consumption and alcohol-related injury, but existing detection methods are burdensome or impractical. OBJECTIVE: To evaluate whether phone sensor data and machine learning models are useful to detect alcohol use events, and to discuss implications of these results for just-in-time mobile interventions. METHODS: 38 non-treatment seeking young adult heavy drinkers downloaded AWARE app (which continuously collected mobile phone sensor data), and reported alcohol consumption (number of drinks, start/end time of prior day's drinking) for 28days. We tested various machine learning models using the 20 most informative sensor features to classify time periods as non-drinking, low-risk (1 to 3/4 drinks per occasion for women/men), and high-risk drinking ({$>$}4/5 drinks per occasion for women/men). RESULTS: Among 30 participants in the analyses, 207 non-drinking, 41 low-risk, and 45 high-risk drinking episodes were reported. A Random Forest model using 30-min windows with 1day of historical data performed best for detecting high-risk drinking, correctly classifying high-risk drinking windows 90.9\% of the time. The most informative sensor features were related to time (i.e., day of week, time of day), movement (e.g., change in activities), device usage (e.g., screen duration), and communication (e.g., call duration, typing speed). CONCLUSIONS: Preliminary evidence suggests that sensor data captured from mobile phones of young adults is useful in building accurate models to detect periods of high-risk drinking. Interventions using mobile phone sensor features could trigger delivery of a range of interventions to potentially improve effectiveness.},
  langid = {english},
  pmcid = {PMC5963979},
  pmid = {29217132},
  keywords = {Adult,Alcohol,Alcoholism,AWARE app,Biosensing Techniques,Cell Phone,Ecological Momentary Assessment,Female,Humans,Machine learning,Male,Monitoring Ambulatory,Prospective Studies,Smartphone sensors,Supervised Machine Learning,Surveys and Questionnaires,Young Adult},
  file = {C\:\\Users\\kpaquette2\\Zotero\\storage\\BHUTCVPP\\Bae et al. - 2018 - Mobile phone sensors and supervised machine learni.pdf;C\:\\Users\\kpaquette2\\Zotero\\storage\\IG4IG8CW\\Bae et al. - 2018 - Mobile phone sensors and supervised machine learni.pdf;C\:\\Users\\kpaquette2\\Zotero\\storage\\UNSTQIZZ\\Bae et al. - 2018 - Mobile phone sensors and supervised machine learni.pdf;C\:\\Users\\kpaquette2\\Zotero\\storage\\WBLU3B3Q\\Bae et al. - 2018 - Mobile phone sensors and supervised machine learni.pdf;C\:\\Users\\kpaquette2\\Zotero\\storage\\XCJXWHX4\\Bae et al. - 2018 - Mobile phone sensors and supervised machine learni.pdf;C\:\\Users\\kpaquette2\\Zotero\\storage\\65YWZYM9\\S030646031730446X.html;C\:\\Users\\kpaquette2\\Zotero\\storage\\6W7MUWBG\\S030646031730446X.html}
}

@article{bennettAdaptationPersonalizationCapacity2023,
  title = {Adaptation, Personalization and Capacity in Mental Health Treatments: A Balancing Act?},
  shorttitle = {Adaptation, Personalization and Capacity in Mental Health Treatments},
  author = {Bennett, Sophie D. and Shafran, Roz},
  year = {2023},
  month = jan,
  journal = {Current Opinion in Psychiatry},
  volume = {36},
  number = {1},
  pages = {28},
  issn = {0951-7367},
  doi = {10.1097/YCO.0000000000000834},
  urldate = {2024-05-15},
  abstract = {Purpose of review~           There are increasing calls for mental health treatments to be adapted for different groups to maximize their acceptability and benefit to patients. However, adaptations can be costly to develop and evaluate, difficult to implement in routine clinical practice and may reduce service capacity at a time when there is unprecedented unmet need. An alternative method is personalization on an individual level. This review provides an overview of the issues related to personalization and adaptation of mental health interventions.           Recent findings~           Several terms have been used to describe changes to existing therapies, these reflect different extents to which existing treatments have been changed. Evidence-based practice and modular therapies allow a level of flexibility within intervention delivery without formal changes and not all changes to therapy should be considered as a new/adapted treatment but instead regarded as `metacompetence'. Implementing existing interventions in new contexts is preferable to developing new interventions in many instances. New guidance outlines how researchers can adapt and transfer interventions to varied contexts.           Summary~           The review provides proposed definitions of different changes to therapy. Modified and personalized treatments may improve acceptability to patients whilst maximizing implementation of evidence-based practice within clinical services.},
  langid = {american},
  file = {C\:\\Users\\kpaquette2\\Zotero\\storage\\7IJ2JLBF\\Bennett and Shafran - 2023 - Adaptation, personalization and capacity in mental.pdf;C\:\\Users\\kpaquette2\\Zotero\\storage\\XGBGILEH\\Adaptation,_personalization_and_capacity_in_mental.6.html}
}

@article{bickmanAchievingPrecisionMental2016a,
  title = {Achieving {{Precision Mental Health}} through {{Effective Assessment}}, {{Monitoring}}, and {{Feedback Processes}}},
  author = {Bickman, Leonard and Lyon, Aaron R. and Wolpert, Miranda},
  year = {2016},
  month = may,
  journal = {Administration and Policy in Mental Health and Mental Health Services Research},
  volume = {43},
  number = {3},
  pages = {271--276},
  issn = {1573-3289},
  doi = {10.1007/s10488-016-0718-5},
  urldate = {2024-05-16},
  langid = {english},
  file = {C:\Users\kpaquette2\Zotero\storage\33HM5IBP\Bickman et al. - 2016 - Achieving Precision Mental Health through Effectiv.pdf}
}

@article{bidargaddiDesigningMHealthInterventions2020,
  title = {Designing M-{{Health}} Interventions for Precision Mental Health Support},
  author = {Bidargaddi, N. and Schrader, G. and Klasnja, P. and Licinio, J. and Murphy, S.},
  year = {2020},
  month = jul,
  journal = {Translational Psychiatry},
  volume = {10},
  number = {1},
  pages = {1--8},
  publisher = {Nature Publishing Group},
  issn = {2158-3188},
  doi = {10.1038/s41398-020-00895-2},
  urldate = {2024-05-16},
  abstract = {Mobile health (m-Health) resources are emerging as a significant tool to overcome mental health support access barriers due to their ability to rapidly reach and provide support to individuals in need of mental health support. m-Health provides an approach to adapt and initiate mental health support at precise moments, when they are most likely to be effective for the individual. However, poor adoption of mental health apps in the real world suggests that new approaches to optimising the quality of m-Health interventions are critically needed in order to realise the potential translational benefits for mental health support. The micro-randomised trial is an experimental approach for optimising and adapting m-Health resources. This trial design provides data to construct and optimise m-Health interventions. The data can be used to inform when and what type of m-Health interventions should be initiated, and thus serve to integrate interventions into daily routines with precision. Here, we illustrate this approach in a case study, review implementation issues that need to be considered while conducting an MRT, and provide a checklist for mental health m-Health intervention developers.},
  copyright = {2020 The Author(s)},
  langid = {english},
  keywords = {Depression,Psychiatric disorders},
  file = {C:\Users\kpaquette2\Zotero\storage\4B4J6XPG\Bidargaddi et al. - 2020 - Designing m-Health interventions for precision men.pdf}
}

@article{campbellBriefReportGender2018,
  title = {Brief {{Report}}: {{Gender Differences}} in {{Demographic}} and {{Clinical Characteristics}} of {{Patients}} with {{Opioid Use Disorder Entering}} a {{Comparative Effectiveness Medication Trial}}},
  shorttitle = {Brief {{Report}}},
  author = {Campbell, Aimee N. C. and {Barbosa-Leiker}, Celestina and {Hatch-Maillette}, Mary and Mennenga, Sarah E. and Pavlicova, Martina and Scodes, Jennifer and Saraiya, Tanya and Mitchell, Shannon Gwin and Rotrosen, John and Novo, Patricia and Nunes, Edward V. and Greenfield, Shelly F.},
  year = {2018},
  month = sep,
  journal = {The American journal on addictions},
  volume = {27},
  number = {6},
  pages = {465--470},
  issn = {1055-0496},
  doi = {10.1111/ajad.12784},
  urldate = {2024-05-27},
  abstract = {Background \& Objectives We investigated gender differences in individuals with opioid use disorder (OUD) receiving inpatient services and entering a randomized controlled trial comparing extended-release naltrexone to buprenorphine. Methods Participants (N= 570) provided demographic, substance use, and psychiatric information. Results Women were significantly younger, more likely to identify as bisexual, live with a sexual partner, be financially dependent, and less likely employed. Women reported significantly greater psychiatric comorbidity and risk behaviors, shorter duration but similar age of onset of opioid use. Discussion/Conclusions Findings underscore economic, psychiatric, and infection vulnerability among women with OUD. Scientific Significance Interventions targeting these disparities should be explored, as women may face complicated treatment initiation, retention, and recovery.},
  pmcid = {PMC6124662},
  pmid = {30106494},
  file = {C:\Users\kpaquette2\Zotero\storage\38SUAZJ4\Campbell et al. - 2018 - Brief Report Gender Differences in Demographic an.pdf}
}

@article{chihPredictiveModelingAddiction2014,
  title = {Predictive Modeling of Addiction Lapses in a Mobile Health Application},
  author = {Chih, Ming-Yuan and Patton, Timothy and McTavish, Fiona M. and Isham, Andrew J. and {Judkins-Fisher}, Chris L. and Atwood, Amy K. and Gustafson, David H.},
  year = {2014},
  month = jan,
  journal = {Journal of Substance Abuse Treatment},
  volume = {46},
  number = {1},
  pages = {29--35},
  issn = {1873-6483},
  doi = {10.1016/j.jsat.2013.08.004},
  abstract = {The chronically relapsing nature of alcoholism leads to substantial personal, family, and societal costs. Addiction-comprehensive health enhancement support system (A-CHESS) is a smartphone application that aims to reduce relapse. To offer targeted support to patients who are at risk of lapses within the coming week, a Bayesian network model to predict such events was constructed using responses on 2,934 weekly surveys (called the Weekly Check-in) from 152 alcohol-dependent individuals who recently completed residential treatment. The Weekly Check-in is a self-monitoring service, provided in A-CHESS, to track patients' recovery progress. The model showed good predictability, with the area under receiver operating characteristic curve of 0.829 in the 10-fold cross-validation and 0.912 in the external validation. The sensitivity/specificity table assists the tradeoff decisions necessary to apply the model in practice. This study moves us closer to the goal of providing lapse prediction so that patients might receive more targeted and timely support.},
  langid = {english},
  pmcid = {PMC3963148},
  pmid = {24035143},
  keywords = {Adult,Alcoholism,Bayes Theorem,Cell Phone,Cellular Phone,decision making,Decision Making,Female,Humans,Lapse prediction,machine learning,Machine learning,Male,mHealth,Middle Aged,Mobile Applications,Models Statistical,Predictive Value of Tests,Recurrence,Relapse,risk2_protocol_paper,ROC Curve,Secondary Prevention,Sensitivity and Specificity,Young Adult},
  file = {C\:\\Users\\kpaquette2\\Zotero\\storage\\525AS6D2\\Chih et al. - 2014 - Predictive Modeling of Addiction Lapses in a Mobil.pdf;C\:\\Users\\kpaquette2\\Zotero\\storage\\AJDAP67X\\Chih et al. - 2014 - Predictive modeling of addiction lapses in a mobil.pdf;C\:\\Users\\kpaquette2\\Zotero\\storage\\AKNIS7HY\\Chih et al. - 2014 - Predictive modeling of addiction lapses in a mobil.pdf;C\:\\Users\\kpaquette2\\Zotero\\storage\\DX62KEH7\\Chih et al. - 2014 - Predictive modeling of addiction lapses in a mobil.pdf;C\:\\Users\\kpaquette2\\Zotero\\storage\\T9CDTXJ7\\Chih et al. - 2014 - Predictive modeling of addiction lapses in a mobil.pdf;C\:\\Users\\kpaquette2\\Zotero\\storage\\VXECTSNS\\Chih2014.pdf;C\:\\Users\\kpaquette2\\Zotero\\storage\\D7AQLFGL\\S0740547213001827.html}
}

@article{choiTreatmentUsePerceived2014,
  title = {Treatment Use, Perceived Need, and Barriers to Seeking Treatment for Substance Abuse and Mental Health Problems among Older Adults Compared to Younger Adults},
  author = {Choi, Namkee G. and DiNitto, Diana M. and Marti, C. Nathan},
  year = {2014},
  month = dec,
  journal = {Drug and Alcohol Dependence},
  volume = {145},
  pages = {113--120},
  issn = {0376-8716},
  doi = {10.1016/j.drugalcdep.2014.10.004},
  urldate = {2024-05-26},
  abstract = {Background This study examined age group differences in and correlates of treatment use and perceived treatment need for substance use disorders (SUD) and mental health (MH) problems as well as self-reported barriers to treatment among people 65+ years old vs. 26--34, 35--49, and 50--64 years old. Methods Data are from the 2008 to 2012 National Survey on Drug Use and Health (NSDUH) (N=96,966). Age group differences were examined using descriptive bivariate analyses and binary logistic regression analyses. Results The 65+ age group was least likely to use treatment and perceive treatment need, but the 50--64 age group was more similar to the younger age groups than the 65+ age group. Controlling for age, other predisposing, and enabling factors, alcohol and illicit drug dependence and comorbid SUD and MH problems increased the odds of SUD treatment use. Of MH problems, anxiety disorder had the largest odds for MH treatment use. Bivariate analyses showed that lack of readiness to stop using and cost/limited insurance were the most frequent barriers to SUD and MH treatment, respectively, among older adults, and they were less likely than younger age groups to report stigma/confidentiality concerns for MH treatment. Conclusions Older adults will become a larger portion of the total U.S. population with SUD and/or MH problems. Healthcare providers should be alert to the need to help older adults with SUD and/or MH problems obtain treatment.},
  keywords = {Barriers to treatment,Mental disorders,Substance use disorders,Treatment},
  file = {C:\Users\kpaquette2\Zotero\storage\C53RUPRR\S0376871614018493.html}
}

@misc{chtc,
  title = {Center for High Throughput Computing},
  author = {{Center for High Throughput Computing}},
  year = {2006},
  publisher = {Center for High Throughput Computing},
  doi = {10.21231/GNT1-HW21}
}

@article{corredor-waldronTacklingSubstanceUse2022,
  title = {``{{Tackling}} the {{Substance Use Disorder Crisis}}: {{The Role}} of {{Access}} to {{Treatment Facilities}}''},
  shorttitle = {``{{Tackling}} the {{Substance Use Disorder Crisis}}},
  author = {{Corredor-Waldron}, Adriana and Currie, Janet},
  year = {2022},
  month = jan,
  journal = {Journal of Health Economics},
  volume = {81},
  pages = {102579},
  issn = {0167-6296},
  doi = {10.1016/j.jhealeco.2021.102579},
  urldate = {2024-05-26},
  abstract = {The continuing drug overdose crisis in the U.S. has highlighted the urgent need for greater access to treatment. This paper examines the impact of openings and closings of substance use disorder treatment facilities in New Jersey on emergency room visits for substance use disorder issues among nearby residents. We find that drug-related ER visits increase by 7.4\% after a facility closure and decrease by 6.5\% after an opening. The effects are smaller for the middle aged than for either younger or older people, and are also somewhat larger for Black residents, and for those on Medicaid. The results suggest that expanding access to treatment results in significant reductions in morbidity related to drugs.},
  keywords = {Emergency room,Overdose,Substance use disorder,Treatment facilities},
  file = {C:\Users\kpaquette2\Zotero\storage\6ZFBJ7UH\S0167629621001648.html}
}

@article{deifDepressionPrecisionMental2021,
  title = {Depression {{From}} a {{Precision Mental Health Perspective}}: {{Utilizing Personalized Conceptualizations}} to {{Guide Personalized Treatments}}},
  shorttitle = {Depression {{From}} a {{Precision Mental Health Perspective}}},
  author = {Deif, Reem and Salama, Mohamed},
  year = {2021},
  month = may,
  journal = {Frontiers in Psychiatry},
  volume = {12},
  publisher = {Frontiers},
  issn = {1664-0640},
  doi = {10.3389/fpsyt.2021.650318},
  urldate = {2024-05-16},
  abstract = {{$<$}p{$>$}Modern research has proven that the ``typical patient'' requiring standardized treatments does not exist, reflecting the need for more personalized approaches for managing individual clinical profiles rather than broad diagnoses. In this regard, precision psychiatry has emerged focusing on enhancing prevention, diagnosis, and treatment of psychiatric disorders through identifying clinical subgroups, suggesting personalized evidence-based interventions, assessing the effectiveness of different interventions, and identifying risk and protective factors for remission, relapse, and vulnerability. Literature shows that recent advances in the field of precision psychiatry are rapidly becoming more data-driven reflecting both the significance and the continuous need for translational research in mental health. Different etiologies underlying depression have been theorized and some factors have been identified including neural circuitry, biotypes, biopsychosocial markers, genetics, and metabolomics which have shown to explain individual differences in pathology and response to treatment. Although the precision approach may prove to enhance diagnosis and treatment decisions, major challenges are hindering its clinical translation. These include the clinical diversity of psychiatric disorders, the technical complexity and costs of multiomics data, and the need for specialized training in precision health for healthcare staff, besides ethical concerns such as protecting the privacy and security of patients' data and maintaining health equity. The aim of this review is to provide an overview of recent findings in the conceptualization and treatment of depression from a precision mental health perspective and to discuss potential challenges and future directions in the application of precision psychiatry for the treatment of depression.{$<$}/p{$>$}},
  langid = {english},
  keywords = {biomarkers,Depression,neural circuitry,Precision mental health,psychosocial marker},
  file = {C:\Users\kpaquette2\Zotero\storage\9CET4NX7\Deif and Salama - 2021 - Depression From a Precision Mental Health Perspect.pdf}
}

@article{derubeisHistoryCurrentStatus2019,
  title = {The History, Current Status, and Possible Future of Precision Mental Health},
  author = {DeRubeis, Robert J.},
  year = {2019},
  month = dec,
  journal = {Behaviour Research and Therapy},
  volume = {123},
  pages = {103506},
  issn = {0005-7967},
  doi = {10.1016/j.brat.2019.103506},
  urldate = {2024-05-16},
  abstract = {In evidence-based mental health practice, decisions must often be made for which there is little or no empirical basis. A common example of this is when there are multiple empirically supported interventions for a person with a given diagnosis, where the aim is to recommend the treatment most likely to be effective for that person. Data obtained from randomized clinical trials allow for the identification of patient characteristics that could be used to match patients to treatments. Historically, researchers have focused on individual moderators, single variables that interact statistically with treatment type, but these have rarely proved powerful enough to inform treatment decisions. Recently, researchers have begun to explore ways in which the use of multivariable algorithms might improve clinical decision-making. Common pitfalls have been identified, including the use of methods that provide overoptimistic estimates of the gains that can be expected from the applications of an algorithm in a clinical setting. It is too early to tell if these efforts will pay off and, if so, how much their use can increase the efficiency and effectiveness of mental health systems. It behooves the field to continue to learn and develop the most powerful methods that can produce generalizable knowledge that will advance the aims of precision mental health.},
  keywords = {Machine learning,Moderators of treatment response,Multivariable models,Personalized medicine,Precision medicine},
  file = {C:\Users\kpaquette2\Zotero\storage\3RIDDVRZ\S0005796719301925.html}
}

@article{fisherOpenTrialPersonalized2019,
  title = {Open Trial of a Personalized Modular Treatment for Mood and Anxiety},
  author = {Fisher, Aaron J. and Bosley, Hannah G. and Fernandez, Katya C. and Reeves, Jonathan W. and Soyster, Peter D. and Diamond, Allison E. and Barkin, Jonathan},
  year = {2019},
  month = may,
  journal = {Behaviour Research and Therapy},
  volume = {116},
  pages = {69--79},
  issn = {0005-7967},
  doi = {10.1016/j.brat.2019.01.010},
  urldate = {2022-10-27},
  abstract = {Psychosocial treatments for mood and anxiety disorders are generally effective, however, a number of treated individuals fail to demonstrate clinically-significant change. Consistent with the decades-old aim to identify `what works for whom,' personalized and precision treatments have become a recent area of interest in medicine and psychology. The present study followed the recommendations of Fisher (2015) to employ a personalized modular model of cognitive-behavioral therapy. Employing the algorithms provided by Fernandez, Fisher, and Chi (2017), the present study collected intensive repeated measures data prior to therapy in order to perform person-specific factor analysis and dynamic factor modeling. The results of these analyses were then used to generated personalized modular treatment plans on a person-by-person basis. Thirty-two participants completed therapy. The average number of sessions was 10.38. Hedges g's for the Hamilton Rating Scale for Depression (HRSD) and Hamilton Anxiety Rating Scale (HARS) were 2.33 and 1.62, respectively. The change per unit time was g = .24/session for the HRSD and g\,=\,0.17/session for the HARS. The current open trial provides promising data in support of personalization, modularization, and idiographic research paradigms.},
  langid = {english},
  keywords = {Adolescent,Adult,Algorithms,Anxiety,Anxiety Disorders,Cognitive Behavioral Therapy,Depression,Depressive Disorder Major,Female,Humans,Idiography,Male,Middle Aged,N-of-1,Personalized treatment,Precision medicine,Precision Medicine,Psychotherapy,Young Adult},
  file = {C\:\\Users\\kpaquette2\\Zotero\\storage\\3PBLDMRL\\Fisher et al. - 2019 - Open trial of a personalized modular treatment for.pdf;C\:\\Users\\kpaquette2\\Zotero\\storage\\G3949TNK\\Fisher et al. - 2019 - Open trial of a personalized modular treatment for.pdf;C\:\\Users\\kpaquette2\\Zotero\\storage\\S99CBAPH\\S0005796719300294.html}
}

@article{flavinAvailabilityBuprenorphineTreatment2020,
  title = {Availability of {{Buprenorphine Treatment}} in the 10 {{States With}} the {{Highest Drug Overdose Death Rates}} in the {{United States}}},
  author = {Flavin, Lila and Malowney, Monica and Patel, Nikhil A. and Alpert, Michael D. and Cheng, Elisa and Noy, Gaddy and Samuelson, Sarah and Sreshta, Nina and Boyd, J. Wesley},
  year = {2020},
  month = jan,
  journal = {Journal of Psychiatric Practice{\textregistered}},
  volume = {26},
  number = {1},
  pages = {17},
  issn = {1527-4160},
  doi = {10.1097/PRA.0000000000000437},
  urldate = {2024-05-15},
  abstract = {Objective:~           The objective of this study was to assess the accuracy of the Substance Abuse and Mental Health Services Administration (SAMHSA) database for patients who use it to seek buprenorphine treatment.           Design and Measurements:~           Buprenorphine providers within a 25-mile radius of the county with the highest drug-related death rates within the 10 states with the highest drug-related death rates were identified and called to determine whether the provider worked there, prescribed buprenorphine, accepted insurance, had appointments, or charged for visits.           Results:~           The number of providers listed in each county ranged from 1 to 166, with 5 counties having {$<$}10 providers. In 3 counties no appointments were obtained, and another 3 counties had {$\leq$}3 providers with availability. Of the 505 providers listed, 355 providers (70.3\%) were reached, 310 (61.4\%) of the 505 listings were correct numbers, and 195 (38.6\%) of the 505 providers in the listings provided buprenorphine. Of the 173 clinics that provided buprenorphine and were asked about insurance, 131 (75.7\%) accepted insurance. Of the 167 clinics that provided buprenorphine and were asked about Medicaid, 105 (62.9\%) accepted it. Wait times for appointments ranged from 1 to 120 days, with an average of 16.8 days for those that had a waitlist. Among the 39 providers who reported out-of-pocket costs, the average cost was \$231 (range: \$90 to \$600). One hundred forty of the 505 providers listed in the database had appointments available (27.7\%). Three hundred sixty-five of the 505 providers did not have appointments available (72.3\%) for various reasons, including the fact that 120 providers (32.9\% of the 365 providers) could not be reached, and 137 of the numbers (37.5\% of the 365 listed numbers) were wrong. Other reasons appointments could not be obtained included the fact that providers did not treat outpatients, were not accepting new patients, were out of office, or required a referral.           Conclusion:~           Although the SAMHSA buprenorphine practitioner locator is used by patients and providers to locate treatment options, only a small portion of clinicians in the database ultimately offered initial appointments, implying that the database is only marginally useful for patients.},
  langid = {american},
  file = {C:\Users\kpaquette2\Zotero\storage\6RXZECRS\Availability_of_Buprenorphine_Treatment_in_the_10.3.html}
}

@article{fronkStressAllostasisSubstance2020,
  title = {Stress {{Allostasis}} in {{Substance Use Disorders}}: {{Promise}}, {{Progress}}, and {{Emerging Priorities}} in {{Clinical Research}}},
  shorttitle = {Stress {{Allostasis}} in {{Substance Use Disorders}}},
  author = {Fronk, Gaylen E. and Sant'Ana, Sarah J. and Kaye, Jesse T. and Curtin, John J.},
  year = {2020},
  journal = {Annual Review of Clinical Psychology},
  volume = {16},
  number = {1},
  pages = {401--430},
  doi = {10.1146/annurev-clinpsy-102419-125016},
  urldate = {2020-05-05},
  abstract = {Clinicians and researchers alike have long believed that stressors play a pivotal etiologic role in risk, maintenance, and/or relapse of alcohol and other substance use disorders (SUDs). Numerous seminal and contemporary theories on SUD etiology posit that stressors may motivate drug use and that individuals who use drugs chronically may display altered responses to stressors. We use foundational basic stress biology research as a lens through which to evaluate critically the available evidence to support these key stress--SUD theses in humans. Additionally, we examine the field's success to date in targeting stressors and stressor reactivity in treatments for SUDs. We conclude with our recommendations for how best to advance our understanding of the relationship between stressors and drug use, and we discuss clinical implications for treatment development. Expected final online publication date for the Annual Review of Clinical Psychology, Volume 16 is May 7, 2020. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.},
  pmid = {32040338},
  file = {C:\Users\kpaquette2\Zotero\storage\7T78Y6GE\Fronk et al. - 2020 - Stress Allostasis in Substance Use Disorders Prom.pdf}
}

@misc{gabryPriorDistributionsRstanarm2023,
  title = {Prior {{Distributions}} for Rstanarm {{Models}}},
  author = {Gabry, Jonah and Goodrich, Ben},
  year = {2023},
  journal = {CRAN R-Project},
  urldate = {2023-08-03},
  howpublished = {https://cran.r-project.org/web/packages/rstanarm/vignettes/priors.html},
  file = {C:\Users\kpaquette2\Zotero\storage\MAKXL8AQ\priors.html}
}

@misc{goodrichRstanarmBayesianApplied2023,
  title = {Rstanarm: {{Bayesian Applied Regression Modeling}} via {{Stan}}},
  author = {Goodrich, Ben and Gabry, Jonah and Ali, Imad and Brilleman, Sam},
  year = {2023},
  urldate = {2023-08-03},
  abstract = {Estimates previously compiled regression models using the rstan     package, which provides the R interface to the Stan C++ library for Bayesian     estimation. Users specify models via the customary R syntax with a formula and     data.frame plus some additional arguments for priors.},
  file = {C:\Users\kpaquette2\Zotero\storage\FPD5AFV9\rstanarm.html}
}

@article{greenfieldSubstanceAbuseTreatment2007,
  title = {Substance Abuse Treatment Entry, Retention, and Outcome in Women: {{A}} Review of the Literature},
  shorttitle = {Substance Abuse Treatment Entry, Retention, and Outcome in Women},
  author = {Greenfield, Shelly F. and Brooks, Audrey J. and Gordon, Susan M. and Green, Carla A. and Kropp, Frankie and McHugh, R. Kathryn and Lincoln, Melissa and Hien, Denise and Miele, Gloria M.},
  year = {2007},
  month = jan,
  journal = {Drug and Alcohol Dependence},
  volume = {86},
  number = {1},
  pages = {1--21},
  issn = {0376-8716},
  doi = {10.1016/j.drugalcdep.2006.05.012},
  urldate = {2024-05-26},
  abstract = {This paper reviews the literature examining characteristics associated with treatment outcome in women with substance use disorders. A search of the English language literature from 1975 to 2005 using Medline and PsycInfo databases found 280 relevant articles. Ninety percent of the studies investigating gender differences in substance abuse treatment outcomes were published since 1990, and of those, over 40\% were published since the year 2000. Only 11.8\% of these studies were randomized clinical trials. A convergence of evidence suggests that women with substance use disorders are less likely, over the lifetime, to enter treatment compared to their male counterparts. Once in treatment, however, gender is not a significant predictor of treatment retention, completion, or outcome. Gender-specific predictors of outcome do exist, however, and individual characteristics and treatment approaches can differentially affect outcomes by gender. While women-only treatment is not necessarily more effective than mixed-gender treatment, some greater effectiveness has been demonstrated by treatments that address problems more common to substance-abusing women or that are designed for specific subgroups of this population. There is a need to develop and test effective treatments for specific subgroups such as older women with substance use disorders, as well as those with co-occurring substance use and psychiatric disorders such as eating disorders. Future research on effectiveness and cost-effectiveness of gender-specific versus standard treatments, as well as identification of the characteristics of women and men who can benefit from mixed-gender versus single-gender treatments, would advance the field.},
  keywords = {Gender differences,Predictors,Retention,Substance abuse,Treatment entry,Treatment outcome,Women},
  file = {C\:\\Users\\kpaquette2\\Zotero\\storage\\VIQZ6SMS\\Greenfield et al. - 2007 - Substance abuse treatment entry, retention, and ou.pdf;C\:\\Users\\kpaquette2\\Zotero\\storage\\5IZ5IIYI\\S0376871606001773.html}
}

@article{guerreroDisparitiesLatinoSubstance2013,
  title = {Disparities in {{Latino}} Substance Use, Service Use, and Treatment: {{Implications}} for Culturally and Evidence-Based Interventions under Health Care Reform},
  shorttitle = {Disparities in {{Latino}} Substance Use, Service Use, and Treatment},
  author = {Guerrero, Erick G. and Marsh, Jeanne C. and Khachikian, Tenie and Amaro, Hortensia and Vega, William A.},
  year = {2013},
  month = dec,
  journal = {Drug and Alcohol Dependence},
  volume = {133},
  number = {3},
  pages = {805--813},
  issn = {0376-8716},
  doi = {10.1016/j.drugalcdep.2013.07.027},
  urldate = {2024-05-23},
  abstract = {Background The goal of this systematic literature review was to enhance understanding of substance use, service use, and treatment among Latino subgroups to improve access to care and treatment outcomes in an era of health care reform. Methods The authors used 13 electronic databases and manually searched the literature from January 1, 1978, to May 30, 2013. One hundred (69\%) of 145 primary research articles met the inclusion criteria. Two blinded, independent reviewers scored each article. Consensus discussions and a content expert reconciled discrepancies. Results Current rates of alcohol and substance abuse among Latinos are comparable to or surpass other U.S. ethnic groups. Disparities in access and quality of care are evident between Latinos and other ethnic groups. As a heterogeneous group, Latinos vary by geographic region in terms of substance of choice and their cultural identity takes precedence over general ethnic identity as a likely determinant of substance abuse behaviors. There is growing research interest in systems influencing treatment access and adherence among racial/ethnic and gender minority groups. However, studies on Latinos' service use and immediate treatment outcomes have been both limited in number and inconsistent in findings. Conclusions This review identified human capital, quality of care, and access to culturally responsive care as key strategies to eliminate disparities in health and treatment quality. Implications are discussed, including the need for effectiveness studies on Latinos served by systems of care that, under health care reform, are seeking to maximize resources, improve outcomes, and reduce variation in quality of care.},
  keywords = {Disparities,Effective treatment,Health care reform,Latino substance use,Service use},
  file = {C:\Users\kpaquette2\Zotero\storage\HIDYGZDI\S0376871613002925.html}
}

@article{gustafsonSmartphoneApplicationSupport2014,
  title = {A Smartphone Application to Support Recovery from Alcoholism: A Randomized Clinical Trial},
  shorttitle = {A Smartphone Application to Support Recovery from Alcoholism},
  author = {Gustafson, David H. and McTavish, Fiona M. and Chih, Ming-Yuan and Atwood, Amy K. and Johnson, Roberta A. and Boyle, Michael G. and Levy, Michael S. and Driscoll, Hilary and Chisholm, Steven M. and Dillenburg, Lisa and Isham, Andrew and Shah, Dhavan},
  year = {2014},
  month = may,
  journal = {JAMA psychiatry},
  volume = {71},
  number = {5},
  pages = {566--572},
  issn = {2168-6238},
  doi = {10.1001/jamapsychiatry.2013.4642},
  abstract = {IMPORTANCE: Patients leaving residential treatment for alcohol use disorders are not typically offered evidence-based continuing care, although research suggests that continuing care is associated with better outcomes. A smartphone-based application could provide effective continuing care. OBJECTIVE: To determine whether patients leaving residential treatment for alcohol use disorders with a smartphone application to support recovery have fewer risky drinking days than control patients. DESIGN, SETTING, AND PARTICIPANTS: An unmasked randomized clinical trial involving 3 residential programs operated by 1 nonprofit treatment organization in the Midwestern United States and 2 residential programs operated by 1 nonprofit organization in the Northeastern United States. In total, 349 patients who met the criteria for DSM-IV alcohol dependence when they entered residential treatment were randomized to treatment as usual (n = 179) or treatment as usual plus a smartphone (n = 170) with the Addiction-Comprehensive Health Enhancement Support System (A-CHESS), an application designed to improve continuing care for alcohol use disorders. INTERVENTIONS: Treatment as usual varied across programs; none offered patients coordinated continuing care after discharge. A-CHESS provides monitoring, information, communication, and support services to patients, including ways for patients and counselors to stay in contact. The intervention and follow-up period lasted 8 and 4 months, respectively. MAIN OUTCOMES AND MEASURES: Risky drinking days--the number of days during which a patient's drinking in a 2-hour period exceeded 4 standard drinks for men and 3 standard drinks for women, with standard drink defined as one that contains roughly 14 g of pure alcohol (12 oz of regular beer, 5 oz of wine, or 1.5 oz of distilled spirits). Patients were asked to report their risky drinking days in the previous 30 days on surveys taken 4, 8, and 12 months after discharge from residential treatment. RESULTS: For the 8 months of the intervention and 4 months of follow-up, patients in the A-CHESS group reported significantly fewer risky drinking days than did patients in the control group, with a mean of 1.39 vs 2.75 days (mean difference, 1.37; 95\% CI, 0.46-2.27; P\,=\,.003). CONCLUSIONS AND RELEVANCE: The findings suggest that a multifeatured smartphone application may have significant benefit to patients in continuing care for alcohol use disorders. TRIAL REGISTRATION: clinicaltrials.gov Identifier: NCT01003119.},
  langid = {english},
  pmcid = {PMC4016167},
  pmid = {24671165},
  keywords = {Adult,Aftercare,Alcoholism,Case Management,Cell Phone,Cellular Phone,Cognitive Therapy,Female,Humans,Male,Middle Aged,Midwestern United States,Motivational Interviewing,Patient Compliance,Patient Education as Topic,Personal Autonomy,Psychotherapy Group,Recurrence,risk2_protocol_paper,Secondary Prevention,Software,Substance Abuse Treatment Centers,Temperance,Therapy Computer-Assisted},
  file = {C\:\\Users\\kpaquette2\\Zotero\\storage\\NVKQLPED\\Gustafson et al. - 2014 - A smartphone application to support recovery from .pdf;C\:\\Users\\kpaquette2\\Zotero\\storage\\R55DS3KQ\\Gustafson DH et al. - 2014 - A smartphone application to support recovery from .pdf;C\:\\Users\\kpaquette2\\Zotero\\storage\\TPYHPVH7\\Gustafson et al. - 2014 - A Smartphone Application to Support Recovery From .pdf}
}

@article{hsiehSampleSizeTables1989,
  title = {Sample Size Tables for Logistic Regression},
  author = {Hsieh, F.},
  year = {1989},
  journal = {Statistics in Medicine},
  volume = {8},
  pages = {795--802},
  abstract = {Sample size tables are presented for epidemiologic studies which extend the use of whittemores formula. The tables are easy to use both simple and multiple logistic regressions. Monte Carlo simulatins are performed which show three important results. Firstly, the sample size tables are suitable for studies with either high or low event proportions, secondly, although the tables can be inaccurate for risk factors having double exponential distributions. Finally, the power of a study varies both with the number of events and the number of individual risks.},
  keywords = {risk2_protocol_paper},
  file = {C:\Users\kpaquette2\Zotero\storage\PJVT39E4\HsiehF1989a.pdf}
}

@article{johnsonReceivingAddictionTreatment2020,
  title = {Receiving Addiction Treatment in the {{US}}: {{Do}} Patient Demographics, Drug of Choice, or Substance Use Disorder Severity Matter?},
  shorttitle = {Receiving Addiction Treatment in the {{US}}},
  author = {Johnson, Kimberly and Rigg, Khary K. and Hopkins Eyles, Cary},
  year = {2020},
  month = jan,
  journal = {International Journal of Drug Policy},
  volume = {75},
  pages = {102583},
  issn = {0955-3959},
  doi = {10.1016/j.drugpo.2019.10.009},
  urldate = {2024-05-26},
  abstract = {Background Understanding addiction treatment needs and utilization is important to health systems and government to develop policies that effectively address substance use disorders (SUDs) at a population level. This study aims to describe differences in treatment receipt by diagnostic category, drug of choice, and demographic characteristics in an effort to identify potential disparities in receipt of care. Methods Using data from the 2017 National Survey on Drug Use and Health, we calculated the proportion of various groups that received treatment and conducted binary logistic regression to determine the association between addiction treatment receipt and SUD severity, drug of choice, and patient demographics. Results Using DSM-5, 16.7 million Americans (age 12 and older) are estimated to have a mild SUD, 5.2 million a moderate SUD, and 4.6 million a severe SUD. The two greatest predictors of treatment receipt are addiction severity and probation status. People with severe opioid use disorder have the greatest probability of treatment receipt with a 55.6\% predicted probability of receiving treatment, while people with a mild alcohol use disorder are the least likely to receive treatment with a predicted probability of 1.8\% receiving treatment. People with mild alcohol use disorder make up the largest proportion of people identified as needing but not receiving treatment when assuming all people with a diagnosis need treatment. Conclusions Improving use of existing specialty addiction treatment capacity to address the needs of the 8.5 million Americans with moderate or severe SUD and better use of the general health care system to treat mild or stable SUD may be a better focus for health system planners and government than adding new capacity for specialty treatment.},
  file = {C:\Users\kpaquette2\Zotero\storage\N4SYP8SN\S095539591930283X.html}
}

@article{jonesComplianceEcologicalMomentary2019a,
  title = {Compliance with Ecological Momentary Assessment Protocols in Substance Users: A Meta-analysis},
  shorttitle = {Compliance with Ecological Momentary Assessment Protocols in Substance Users},
  author = {Jones, Andrew and Remmerswaal, Danielle and Verveer, Ilse and Robinson, Eric and Franken, Ingmar H. A. and Wen, Cheng K. Fred and Field, Matt},
  year = {2019},
  month = apr,
  journal = {Addiction (Abingdon, England)},
  volume = {114},
  number = {4},
  pages = {609--619},
  issn = {0965-2140},
  doi = {10/gfsjzg},
  urldate = {2020-12-19},
  abstract = {Background and Aims While there are considerable benefits to Ecological Momentary Assessment (EMA), poor compliance with assessment protocols has been identified as a limitation, particularly in substance users. Our aim was to identify the pooled compliance rate of EMA studies in substance users and examine variables that may influence compliance with EMA protocols, such as the length and frequency of assessments. Design A meta-analysis and meta-regression of all possible studies (randomized controlled trials and longitudinal) which incorporated EMA protocols, examining substance use. Setting Studies took place from 1998 to 2017, in numerous countries world-wide. Participants One hundred and twenty-six studies were identified, contributing a total of 19~431 participants (52.32\% male, mean age~=~28.86). Measurements Compliance data, the proportion of responses to the study protocol, were extracted from each study alongside prompt frequency, total length of assessment period, substance use population and device used to administer EMA prompts. Findings The pooled compliance rate across all studies was 75.06\% [95\% confidence interval (CI)~=~72.37\%, 77.65\%]. There was no evidence that compliance rates were significantly associated with prompt frequency [Q(3) = 7.35, P~=~0.061], length of assessment period [Q(2) = 2.40, P~=~0.301], substance type [Q(3) = 6.30, P~=~0.098] or device administration [Q(4) = 4.28, P~=~0.369]. However, dependent samples (69.80\%) had lower compliance rates than non-dependent samples [76.02\%; Q(1) = 4.13, P~=~0.042]. Conclusions The pooled compliance rate for Ecological Momentary Assessment studies in substance-using populations from 1998 to 2017 was lower than the recommended rate of 80\%, and was not associated with frequency or duration of assessments.},
  pmcid = {PMC6492133},
  pmid = {30461120},
  file = {C:\Users\kpaquette2\Zotero\storage\3JYWB7K7\Jones et al. - 2019 - Compliance with ecological momentary assessment pr.pdf}
}

@article{kilaruIncidenceTreatmentOpioid2020,
  title = {Incidence of {{Treatment}} for {{Opioid Use Disorder Following Nonfatal Overdose}} in {{Commercially Insured Patients}}},
  author = {Kilaru, Austin S. and Xiong, Aria and Lowenstein, Margaret and Meisel, Zachary F. and Perrone, Jeanmarie and Khatri, Utsha and Mitra, Nandita and Delgado, M. Kit},
  year = {2020},
  month = may,
  journal = {JAMA Network Open},
  volume = {3},
  number = {5},
  pages = {e205852},
  issn = {2574-3805},
  doi = {10.1001/jamanetworkopen.2020.5852},
  urldate = {2024-05-23},
  abstract = {Timely initiation and referral to treatment for patients with opioid use disorder seen in the emergency department is associated with reduced mortality. It is not known how often commercially insured adults obtain follow-up treatment after nonfatal opioid overdose.To investigate the incidence of follow-up treatment following emergency department discharge after nonfatal opioid overdose and patient characteristics associated with receipt of follow-up treatment.A retrospective cohort study was conducted using an administrative claims database for a large US commercial insurer, from October 1, 2011, to September 30, 2016. Data analysis was performed from May 1, 2019, to September 26, 2019. Adult patients discharged from the emergency department after an index opioid overdose (no overdose in the preceding 90 days) were included. Patients with cancer and without continuous insurance enrollment were excluded.The primary outcome was follow-up treatment in the 90 days following overdose, defined as a combined outcome of claims for treatment encounters or medications for opioid use disorder (buprenorphine and naltrexone). Analysis was stratified by whether patients received treatment for opioid use disorder in the 90 days before the overdose. Logistic regression models were used to identify patient characteristics associated with receipt of follow-up treatment. Marginal effects were used to report the average adjusted probability and absolute risk differences (ARDs) in follow-up for different patient characteristics.A total of 6451 patients were identified with nonfatal opioid overdose; the mean (SD) age was 45.0 (19.3) years, 3267 were women (50.6\%), and 4676 patients (72.5\%) reported their race as non-Hispanic white. A total of 1069 patients (16.6\%; 95\% CI, 15.7\%-17.5\%) obtained follow-up treatment within 90 days after the overdose. In adjusted analysis of patients who did not receive treatment before the overdose, black patients were half as likely to obtain follow-up compared with non-Hispanic white patients (ARD, -5.9\%; 95\% CI, -8.6\% to -3.6\%). Women (ARD, -1.7\%; 95\% CI, -3.3\% to -0.5\%) and Hispanic patients (ARD, -3.5\%; 95\% CI, -6.1\% to -0.9\%) were also less likely to obtain follow-up. For each additional year of age, patients were 0.2\% less likely to obtain follow-up (95\% CI, -0.3\% to -0.1\%).Efforts to improve the low rate of timely follow-up treatment following opioid overdose may seek to address sex, race/ethnicity, and age disparities.},
  file = {C\:\\Users\\kpaquette2\\Zotero\\storage\\7GES2DUJ\\Kilaru et al. - 2020 - Incidence of Treatment for Opioid Use Disorder Fol.pdf;C\:\\Users\\kpaquette2\\Zotero\\storage\\35GYAHMS\\2766239.html}
}

@article{kuerbisSubstanceAbuseOlder2014,
  title = {Substance {{Abuse Among Older Adults}}},
  author = {Kuerbis, Alexis and Sacco, Paul and Blazer, Dan G. and Moore, Alison A.},
  year = {2014},
  month = aug,
  journal = {Clinics in Geriatric Medicine},
  volume = {30},
  number = {3},
  pages = {629--654},
  publisher = {Elsevier},
  issn = {0749-0690, 1879-8853},
  doi = {10.1016/j.cger.2014.04.008},
  urldate = {2024-05-26},
  langid = {english},
  pmid = {25037298},
  keywords = {Alcohol,Assessment,Assessment tools,Brief interventions,Older adults,Prescription medication,Substance use,Treatment},
  file = {C:\Users\kpaquette2\Zotero\storage\B4TC67R8\Kuerbis et al. - 2014 - Substance Abuse Among Older Adults.pdf}
}

@book{kuhnAppliedPredictiveModeling2018,
  title = {Applied {{Predictive Modeling}}},
  author = {Kuhn, Max and Johnson, Kjell},
  year = {2018},
  month = mar,
  edition = {1st ed. 2013, Corr. 2nd printing 2018 edition},
  publisher = {Springer},
  address = {New York},
  doi = {10.1007/978-1-4614-6849-3},
  abstract = {Winner of the 2014 Technometrics Ziegel Prize for Outstanding BookApplied Predictive Modeling covers the overall predictive modeling process, beginning with the crucial steps of data preprocessing, data splitting and foundations of model tuning.~ The text then provides intuitive explanations of numerous common and modern regression and classification techniques, always with an emphasis on illustrating and solving real data problems.~ Addressing practical concerns extends beyond model fitting to topics such as handling class imbalance, selecting predictors, and pinpointing causes of poor model performance{\texthorizontalbar}all of which are problems that occur frequently in practice.~The text illustrates all parts of the modeling process through many hands-on, real-life examples.~ And every chapter contains extensive R code for each step of the process.~ The data sets and corresponding code are available in the book's companion AppliedPredictiveModeling R package, which is freely available on the CRAN archive.~This multi-purpose text can be used as an introduction to predictive models and the overall modeling process, a practitioner's reference handbook, or as a text for advanced undergraduate or graduate level predictive modeling courses.~ To that end, each chapter contains problem sets to help solidify the covered concepts and uses data available in the book's R package.~Readers and students interested in implementing the methods should have some basic knowledge of R.~ And a handful of the more advanced topics require some mathematical knowledge.},
  isbn = {978-1-4614-6848-6},
  langid = {english}
}

@misc{kuhnTidymodelsCollectionPackages2020,
  title = {Tidymodels: A Collection of Packages for Modeling and Machine Learning Using Tidyverse Principles},
  author = {Kuhn, Max and Wickham, Hadley},
  year = {2020},
  urldate = {2023-08-03},
  annotation = {https://www.tidymodels.org/},
  file = {C:\Users\kpaquette2\Zotero\storage\XC47Q6J5\www.tidymodels.org.html}
}

@misc{kuhnTidyposteriorBayesianAnalysis2022,
  title = {Tidyposterior: {{Bayesian Analysis}} to {{Compare Models}} Using {{Resampling Statistics}}},
  shorttitle = {Tidyposterior},
  author = {Kuhn, Max},
  year = {2022},
  urldate = {2023-08-03},
  abstract = {Bayesian analysis used here to answer the question: "when looking at resampling results, are the differences between models 'real'?" To answer this, a model can be created were the performance statistic is the resampling statistics (e.g. accuracy or RMSE). These values are explained by the model types. In doing this, we can get parameter estimates for each model's affect on performance and make statistical (and practical) comparisons between models. The methods included here are similar to Benavoli et al (2017) {$<$}https://jmlr.org/papers/v18/16-305.html{$>$}.},
  copyright = {MIT + file LICENSE}
}

@article{kullSigmoidsHowObtain2017,
  title = {Beyond Sigmoids: {{How}} to Obtain Well-Calibrated Probabilities from Binary Classifiers with Beta Calibration},
  shorttitle = {Beyond Sigmoids},
  author = {Kull, Meelis and Filho, Telmo M. Silva and Flach, Peter},
  year = {2017},
  month = jan,
  journal = {Electronic Journal of Statistics},
  volume = {11},
  number = {2},
  pages = {5052--5080},
  publisher = {{Institute of Mathematical Statistics and Bernoulli Society}},
  issn = {1935-7524, 1935-7524},
  doi = {10.1214/17-EJS1338SI},
  urldate = {2023-08-01},
  abstract = {For optimal decision making under variable class distributions and misclassification costs a classifier needs to produce well-calibrated estimates of the posterior probability. Isotonic calibration is a powerful non-parametric method that is however prone to overfitting on smaller datasets; hence a parametric method based on the logistic sigmoidal curve is commonly used. While logistic calibration is designed for normally distributed per-class scores, we demonstrate experimentally that many classifiers including Naive Bayes and Adaboost suffer from a particular distortion where these score distributions are heavily skewed. In such cases logistic calibration can easily yield probability estimates that are worse than the original scores. Moreover, the logistic curve family does not include the identity function, and hence logistic calibration can easily uncalibrate a perfectly calibrated classifier. In this paper we solve all these problems with a richer class of parametric calibration maps based on the beta distribution. We derive the method from first principles and show that fitting it is as easy as fitting a logistic curve. Extensive experiments show that beta calibration is superior to logistic calibration for a wide range of classifiers: Naive Bayes, Adaboost, random forest, logistic regression, support vector machine and multi-layer perceptron. If the original classifier is already calibrated, then beta calibration learns a function close to the identity. On this we build a statistical test to recognise if the model deviates from being well-calibrated.},
  keywords = {Beta distribution,Binary classification,classifier calibration,logistic function,posterior probabilities,sigmoid},
  file = {C:\Users\kpaquette2\Zotero\storage\7C2YDI7G\Kull et al. - 2017 - Beyond sigmoids How to obtain well-calibrated pro.pdf}
}

@article{listerSystematicReviewRuralspecific2020,
  title = {A Systematic Review of Rural-Specific Barriers to Medication Treatment for Opioid Use Disorder in the {{United States}}},
  author = {Lister, Jamey J. and Weaver, Addie and Ellis, Jennifer D. and Himle, Joseph A. and Ledgerwood, David M.},
  year = {2020},
  month = may,
  journal = {The American Journal of Drug and Alcohol Abuse},
  volume = {46},
  number = {3},
  pages = {273--288},
  publisher = {Taylor \& Francis},
  issn = {0095-2990},
  doi = {10.1080/00952990.2019.1694536},
  urldate = {2024-05-15},
  abstract = {Opioid-related deaths have risen dramatically in rural communities. Prior studies highlight few medication treatment providers for opioid use disorder in rural communities, though literature has yet to examine rural-specific treatment barriers. We conducted a systematic review to highlight the state of knowledge around rural medication treatment for opioid use disorder, identify consumer- and provider-focused treatment barriers, and discuss rural-specific implications. We systematically reviewed the literature using PsycINFO, Web of Science, and PubMed databases (January 2018). Articles meeting inclusion criteria involved rural samples or urban/rural comparisons targeting outpatient medication treatment for opioid use disorder, and were conducted in the U.S. to minimize healthcare differences. Our analysis categorized consumer- and/or provider-focused barriers, and coded barriers as related to treatment availability, accessibility, and/or acceptability. Eighteen articles met inclusion, 15 which addressed consumer-focused barriers, while seven articles reported provider-focused barriers. Availability barriers were most commonly reported across consumer (n~=~10) and provider (n~=~5) studies, and included the lack of clinics/providers, backup, and resources. Acceptability barriers, described in three consumer and five provider studies, identified negative provider attitudes about addiction treatment, and providers' perceptions of treatment as unsatisfactory for rural patients. Finally, accessibility barriers related to travel and cost were detailed in four consumer-focused studies whereas two provider-focused studies identified time constraints. Our findings consistently identified a lack of medication providers and rural-specific implementation challenges. This review highlights a lack of rural-focused studies involving consumer participants, treatment outcomes, or barriers impacting underserved populations. There is a need for innovative treatment delivery for opioid use disorder in rural communities and interventions targeting provider attitudes.},
  pmid = {31809217},
  keywords = {barriers,medication treatment,opioid,review,Rural,United States},
  file = {C:\Users\kpaquette2\Zotero\storage\K8GKATTB\Lister et al. - 2020 - A systematic review of rural-specific barriers to .pdf}
}

@inproceedings{lundbergUnifiedApproachInterpreting2017,
  title = {A Unified Approach to Interpreting Model Predictions},
  booktitle = {Proceedings of the 31st {{International Conference}} on {{Neural Information Processing Systems}}},
  author = {Lundberg, Scott M. and Lee, Su-In},
  year = {2017},
  month = dec,
  series = {{{NIPS}}'17},
  pages = {4768--4777},
  publisher = {Curran Associates Inc.},
  address = {Red Hook, NY, USA},
  urldate = {2023-09-26},
  abstract = {Understanding why a model makes a certain prediction can be as crucial as the prediction's accuracy in many applications. However, the highest accuracy for large modern datasets is often achieved by complex models that even experts struggle to interpret, such as ensemble or deep learning models, creating a tension between accuracy and interpretability. In response, various methods have recently been proposed to help users interpret the predictions of complex models, but it is often unclear how these methods are related and when one method is preferable over another. To address this problem, we present a unified framework for interpreting predictions, SHAP (SHapley Additive exPlanations). SHAP assigns each feature an importance value for a particular prediction. Its novel components include: (1) the identification of a new class of additive feature importance measures, and (2) theoretical results showing there is a unique solution in this class with a set of desirable properties. The new class unifies six existing methods, notable because several recent methods in the class lack the proposed desirable properties. Based on insights from this unification, we present new methods that show improved computational performance and/or better consistency with human intuition than previous approaches.},
  isbn = {978-1-5108-6096-4},
  file = {C:\Users\kpaquette2\Zotero\storage\85DSYI44\Lundberg and Lee - 2017 - A unified approach to interpreting model predictio.pdf}
}

@book{markTrackingQualityAddiction2020,
  title = {Tracking the Quality of Addiction Treatment over Time and across States},
  author = {Mark, Tami and Dowd, William N. and Council, Carol L.},
  year = {2020},
  month = jul,
  publisher = {RTI Press},
  abstract = {The objective of this study was to track trends in the signs of higher-quality addiction treatment as defined by the National Institute on Drug Abuse, the National Institute on Alcohol Abuse and Addiction, and the Substance Abuse and Mental Health Services Administration. We analyzed the National Survey of Substance Abuse Treatment Services from 2007 through 2017 to determine the percent of facilities having the characteristics of higher quality. We analyzed the percent by state and over time.{$\bullet$} We found improvements between 2007 and 2017 on most measures, but performance on several measures remained low.{$\bullet$} Most programs reported providing evidence-based behavioral therapies.{$\bullet$} Half or fewer facilities offered medications for opioid use disorder; mental health assessments; testing for hepatitis C, HIV, and sexually transmitted diseases; self-help groups; employment assistance; and transportation assistance.{$\bullet$} There was significant state-level variation across the measures.},
  googlebooks = {bNBiEAAAQBAJ},
  langid = {english},
  keywords = {Self-Help / Substance Abuse & Addictions / General}
}

@book{marlattRelapsePreventionMaintenance1985,
  title = {Relapse {{Prevention}}: {{Maintenance Strategies}} in the {{Treatment}} of {{Addictive Behaviors}}},
  shorttitle = {Relapse {{Prevention}}},
  editor = {Marlatt, G. Alan and Gordon, Judith R.},
  year = {1985},
  month = feb,
  edition = {First edition},
  publisher = {The Guilford Press},
  address = {New York},
  isbn = {978-0-89862-009-2},
  langid = {english},
  annotation = {https://psycnet.apa.org/record/2005-08721-000}
}

@misc{mba2024FederalPoverty2024,
  title = {2024 {{Federal Poverty Rates Published}}: {{Why}} That Matters for Your Student Loans},
  shorttitle = {2024 {{Federal Poverty Rates Published}}},
  author = {MBA, DVM, Tony Bartels},
  year = {2024},
  month = jan,
  journal = {VIN Foundation},
  urldate = {2024-09-02},
  abstract = {Each year in January, the U.S. Department of Health and Human Services (HHS) updates the federal poverty guidelines used to determine financial eligibility for certain programs. Your federal student loan monthly payments are impacted by these updates when you use an income-driven repayment (IDR) plan. The VIN Foundation Student Loan Repayment Simulator is now using the recently updated 2024 poverty guidelines for IDR calculations.},
  howpublished = {https://vinfoundation.org/2024-federal-poverty-rates-published-why-that-matters-for-your-student-loans/},
  langid = {american},
  file = {C:\Users\kpaquette2\Zotero\storage\BFXYDL4J\2024-federal-poverty-rates-published-why-that-matters-for-your-student-loans.html}
}

@article{mchughSexGenderDifferences2018,
  title = {Sex and Gender Differences in Substance Use Disorders},
  author = {McHugh, R. Kathryn and Votaw, Victoria R. and Sugarman, Dawn E. and Greenfield, Shelly F.},
  year = {2018},
  month = dec,
  journal = {Clinical Psychology Review},
  volume = {66},
  pages = {12--23},
  issn = {1873-7811},
  doi = {10.1016/j.cpr.2017.10.012},
  abstract = {The gender gap in substance use disorders (SUDs), characterized by greater prevalence in men, is narrowing, highlighting the importance of understanding sex and gender differences in SUD etiology and maintenance. In this critical review, we provide an overview of sex/gender differences in the biology, epidemiology and treatment of SUDs. Biological sex differences are evident across an array of systems, including brain structure and function, endocrine function, and metabolic function. Gender (i.e., environmentally and socioculturally defined roles for men and women) also contributes to the initiation and course of substance use and SUDs. Adverse medical, psychiatric, and functional consequences associated with SUDs are often more severe in women. However, men and women do not substantively differ with respect to SUD treatment outcomes. Although several trends are beginning to emerge in the literature, findings on sex and gender differences in SUDs are complicated by the interacting contributions of biological and environmental factors. Future research is needed to further elucidate sex and gender differences, especially focusing on hormonal factors in SUD course and treatment outcomes; research translating findings between animal and human models; and gender differences in understudied populations, such as those with co-occurring psychiatric disorders and gender-specific populations, such as pregnant women.},
  langid = {english},
  pmcid = {PMC5945349},
  pmid = {29174306},
  keywords = {Female,Gender differences,Humans,Male,Risk factors,Sex Characteristics,Sex Factors,Substance use disorders,Substance-Related Disorders,Treatment outcomes,Women},
  file = {C:\Users\kpaquette2\Zotero\storage\8PEKTPWZ\McHugh et al. - 2018 - Sex and gender differences in substance use disord.pdf}
}

@article{mckennaTreatmentUseSources2017,
  title = {Treatment Use, Sources of Payment, and Financial Barriers to Treatment among Individuals with Opioid Use Disorder Following the National Implementation of the {{ACA}}},
  author = {McKenna, Ryan M.},
  year = {2017},
  month = oct,
  journal = {Drug and Alcohol Dependence},
  volume = {179},
  pages = {87--92},
  issn = {0376-8716},
  doi = {10.1016/j.drugalcdep.2017.06.028},
  urldate = {2024-05-26},
  abstract = {Introduction Despite increasing rates of opioid misuse and hospitalizations, rates of treatment for those with opioid use disorder (OUD) are very low. This study examined the impact of the Patient Protection and Affordable Care Act's (ACA) insurance expansion on improving rates of insurance, health care access, and treatment for those with OUD. Methods Data on individuals ages 18--64 with OUD come from the 2008--2014 National Survey on Drug Use and Health (N=4100). Multivariable logistic regression analyses were performed to estimate the trends of health care insurance, treatment and barriers to care across the stages of ACA implementation: pre-ACA (2008--2009), partial-ACA (2010--2013), and national implementation (2014). All models were adjusted for predisposing, enabling, and need factors. Results In both adjusted and unadjusted comparisons, national implementation of the ACA was associated with significant improvements in outcome measures for those with OUD. Multivariable analyses indicate that, after national implementation, those with OUD were significantly less likely to be uninsured and were less likely to report financial barriers as a reason for not receiving substance use treatment, relative to the pre-ACA period. Individuals were also more likely to receive substance use treatment and were more likely to report that insurance paid for treatment after national implementation of the ACA relative to the pre-ACA period. These results persisted when national implementation was compared relative to partial-implementation. Conclusions National implementation of the ACA has helped to reduce rates of uninsurance, barriers to care, and improve rates of substance use treatment for those with OUD.},
  keywords = {Access to care,Affordable care act,Heroin use,Opioid use disorder,Prescription drug misuse}
}

@inproceedings{mitchellModelCardsModel2019,
  title = {Model {{Cards}} for {{Model Reporting}}},
  booktitle = {Proceedings of the {{Conference}} on {{Fairness}}, {{Accountability}}, and {{Transparency}}},
  author = {Mitchell, Margaret and Wu, Simone and Zaldivar, Andrew and Barnes, Parker and Vasserman, Lucy and Hutchinson, Ben and Spitzer, Elena and Raji, Inioluwa Deborah and Gebru, Timnit},
  year = {2019},
  month = jan,
  eprint = {1810.03993},
  primaryclass = {cs},
  pages = {220--229},
  doi = {10.1145/3287560.3287596},
  urldate = {2024-05-23},
  abstract = {Trained machine learning models are increasingly used to perform high-impact tasks in areas such as law enforcement, medicine, education, and employment. In order to clarify the intended use cases of machine learning models and minimize their usage in contexts for which they are not well suited, we recommend that released models be accompanied by documentation detailing their performance characteristics. In this paper, we propose a framework that we call model cards, to encourage such transparent model reporting. Model cards are short documents accompanying trained machine learning models that provide benchmarked evaluation in a variety of conditions, such as across different cultural, demographic, or phenotypic groups (e.g., race, geographic location, sex, Fitzpatrick skin type [15]) and intersectional groups (e.g., age and race, or sex and Fitzpatrick skin type) that are relevant to the intended application domains. Model cards also disclose the context in which models are intended to be used, details of the performance evaluation procedures, and other relevant information. While we focus primarily on human-centered machine learning models in the application fields of computer vision and natural language processing, this framework can be used to document any trained machine learning model. To solidify the concept, we provide cards for two supervised models: One trained to detect smiling faces in images, and one trained to detect toxic comments in text. We propose model cards as a step towards the responsible democratization of machine learning and related artificial intelligence technology, increasing transparency into how well artificial intelligence technology works. We hope this work encourages those releasing trained machine learning models to accompany model releases with similar detailed evaluation numbers and other relevant documentation.},
  archiveprefix = {arXiv},
  langid = {english},
  keywords = {Computer Science - Artificial Intelligence,Computer Science - Machine Learning},
  file = {C:\Users\kpaquette2\Zotero\storage\EGQMJQCN\Mitchell et al. - 2019 - Model Cards for Model Reporting.pdf}
}

@article{mohrPersonalSensingUnderstanding2017,
  title = {Personal {{Sensing}}: {{Understanding Mental Health Using Ubiquitous Sensors}} and {{Machine Learning}}},
  shorttitle = {Personal {{Sensing}}},
  author = {Mohr, David C. and Zhang, Mi and Schueller, Stephen M.},
  year = {2017},
  journal = {Annual Review of Clinical Psychology},
  volume = {13},
  number = {1},
  pages = {23--47},
  doi = {10.1146/annurev-clinpsy-032816-044949},
  urldate = {2018-06-05},
  abstract = {Sensors in everyday devices, such as our phones, wearables, and computers, leave a stream of digital traces. Personal sensing refers to collecting and analyzing data from sensors embedded in the context of daily life with the aim of identifying human behaviors, thoughts, feelings, and traits. This article provides a critical review of personal sensing research related to mental health, focused principally on smartphones, but also including studies of wearables, social media, and computers. We provide a layered, hierarchical model for translating raw sensor data into markers of behaviors and states related to mental health. Also discussed are research methods as well as challenges, including privacy and problems of dimensionality. Although personal sensing is still in its infancy, it holds great promise as a method for conducting mental health research and as a clinical tool for monitoring at-risk populations and providing the foundation for the next generation of mobile health (or mHealth) interventions.},
  pmid = {28375728},
  keywords = {Humans,machine learning,Machine Learning,Mental Disorders,mental health,mHealth,Neurophysiological Monitoring,pervasive health,sensors,Telemedicine,wearables},
  file = {C\:\\Users\\kpaquette2\\Zotero\\storage\\3NB2JYFJ\\Mohr et al. - 2017 - Personal Sensing Understanding Mental Health Usin.pdf;C\:\\Users\\kpaquette2\\Zotero\\storage\\3NIFCIMX\\Mohr et al. - 2017 - Personal Sensing Understanding Mental Health Usin.pdf}
}

@article{moshontzProspectivePredictionLapses2021,
  title = {Prospective {{Prediction}} of {{Lapses}} in {{Opioid Use Disorder}}: {{Protocol}} for a {{Personal Sensing Study}}},
  shorttitle = {Prospective {{Prediction}} of {{Lapses}} in {{Opioid Use Disorder}}},
  author = {Moshontz, Hannah and Colmenares, Alejandra J. and Fronk, Gaylen E. and Sant'Ana, Sarah J. and Wyant, Kendra and Wanta, Susan E. and Maus, Adam and Jr, David H. Gustafson and Shah, Dhavan and Curtin, John J.},
  year = {2021},
  month = dec,
  journal = {JMIR Research Protocols},
  volume = {10},
  number = {12},
  pages = {e29563},
  publisher = {JMIR Publications Inc., Toronto, Canada},
  doi = {10.2196/29563},
  urldate = {2022-02-21},
  abstract = {Background: Successful long-term recovery from opioid use disorder (OUD) requires continuous lapse risk monitoring and appropriate use and adaptation of recovery-supportive behaviors as lapse risk changes. Available treatments often fail to support long-term recovery by failing to account for the dynamic nature of long-term recovery. Objective: The aim of this protocol paper is to describe research that aims to develop a highly contextualized lapse risk prediction model that forecasts the ongoing probability of lapse. Methods: The participants will include 480 US adults in their first year of recovery from OUD. Participants will report lapses and provide data relevant to lapse risk for a year with a digital therapeutic smartphone app through both self-report and passive personal sensing methods (eg, cellular communications and geolocation). The lapse risk prediction model will be developed using contemporary rigorous machine learning methods that optimize prediction in new data. Results: The National Institute of Drug Abuse funded this project (R01DA047315) on July 18, 2019 with a funding period from August 1, 2019 to June 30, 2024. The University of Wisconsin-Madison Health Sciences Institutional Review Board approved this project on July 9, 2019. Pilot enrollment began on April 16, 2021. Full enrollment began in September 2021. Conclusions: The model that will be developed in this project could support long-term recovery from OUD---for example, by enabling just-in-time interventions within digital therapeutics.},
  copyright = {Unless stated otherwise, all articles are open-access distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work ("first published in JMIR Research Protocols...") is properly cited with original URL and bibliographic citation information. The complete bibliographic information, a link to the original publication on http://www.researchprotocols.org/, as well as this copyright and license information must be included.},
  langid = {english},
  file = {C:\Users\kpaquette2\Zotero\storage\B5CI756H\e29563.html}
}

@article{nahum-shaniBuildingHealthBehavior2015,
  title = {Building Health Behavior Models to Guide the Development of Just-in-Time Adaptive Interventions: {{A}} Pragmatic Framework},
  shorttitle = {Building Health Behavior Models to Guide the Development of Just-in-Time Adaptive Interventions},
  author = {{Nahum-Shani}, Inbal and Hekler, Eric B. and {Spruijt-Metz}, Donna},
  year = {2015},
  month = dec,
  journal = {Health psychology : official journal of the Division of Health Psychology, American Psychological Association},
  volume = {34},
  number = {0},
  pages = {1209--1219},
  issn = {0278-6133},
  doi = {10.1037/hea0000306},
  urldate = {2024-05-15},
  abstract = {Advances in wireless devices and mobile technology offer many opportunities for delivering just-in-time adaptive interventions (JITAIs)--suites of interventions that adapt over time to an individual's changing status and circumstances with the goal to address the individual's need for support, whenever this need arises. A major challenge confronting behavioral scientists aiming to develop a JITAI concerns the selection and integration of existing empirical, theoretical and practical evidence into a scientific model that can inform the construction of a JITAI and help identify scientific gaps. The purpose of this paper is to establish a pragmatic framework that can be used to organize existing evidence into a useful model for JITAI construction. This framework involves clarifying the conceptual purpose of a JITAI, namely the provision of just-in-time support via adaptation, as well as describing the components of a JITAI and articulating a list of concrete questions to guide the establishment of a useful model for JITAI construction. The proposed framework includes an organizing scheme for translating the relatively static scientific models underlying many health behavior interventions into a more dynamic model that better incorporates the element of time. This framework will help to guide the next generation of empirical work to support the creation of effective JITAIs.},
  pmcid = {PMC4732268},
  pmid = {26651462},
  file = {C:\Users\kpaquette2\Zotero\storage\NS56NTWG\Nahum-Shani et al. - 2015 - Building health behavior models to guide the devel.pdf}
}

@article{nahum-shaniJustTimeAdaptiveInterventions2018,
  title = {Just-in-{{Time Adaptive Interventions}} ({{JITAIs}}) in {{Mobile Health}}: {{Key Components}} and {{Design Principles}} for {{Ongoing Health Behavior Support}}},
  shorttitle = {Just-in-{{Time Adaptive Interventions}} ({{JITAIs}}) in {{Mobile Health}}},
  author = {{Nahum-Shani}, Inbal and Smith, Shawna N. and Spring, Bonnie J. and Collins, Linda M. and Witkiewitz, Katie and Tewari, Ambuj and Murphy, Susan A.},
  year = {2018},
  month = may,
  journal = {Annals of Behavioral Medicine: A Publication of the Society of Behavioral Medicine},
  volume = {52},
  number = {6},
  pages = {446--462},
  issn = {1532-4796},
  doi = {10.1007/s12160-016-9830-8},
  abstract = {Background: The just-in-time adaptive intervention (JITAI) is an intervention design aiming to provide the right type/amount of support, at the right time, by adapting to an individual's changing internal and contextual state. The availability of increasingly powerful mobile and sensing technologies underpins the use of JITAIs to support health behavior, as in such a setting an individual's state can change rapidly, unexpectedly, and in his/her natural environment. Purpose: Despite the increasing use and appeal of JITAIs, a major gap exists between the growing technological capabilities for delivering JITAIs and research on the development and evaluation of these interventions. Many JITAIs have been developed with minimal use of empirical evidence, theory, or accepted treatment guidelines. Here, we take an essential first step towards bridging this gap. Methods: Building on health behavior theories and the extant literature on JITAIs, we clarify the scientific motivation for JITAIs, define their fundamental components, and highlight design principles related to these components. Examples of JITAIs from various domains of health behavior research are used for illustration. Conclusions: As we enter a new era of technological capacity for delivering JITAIs, it is critical that researchers develop sophisticated and nuanced health behavior theories capable of guiding the construction of such interventions. Particular attention has to be given to better understanding the implications of providing timely and ecologically sound support for intervention adherence and retention.},
  langid = {english},
  pmcid = {PMC5364076},
  pmid = {27663578},
  keywords = {Behavioral Medicine,Health Behavior,Humans,Patient Compliance,Research Design,Telemedicine},
  file = {C\:\\Users\\kpaquette2\\Zotero\\storage\\QU9G2XID\\Nahum-Shani et al. - 2018 - Just-in-Time Adaptive Interventions (JITAIs) in Mo.pdf;C\:\\Users\\kpaquette2\\Zotero\\storage\\USPE5NPM\\Nahum-Shani et al. - 2018 - Just-in-Time Adaptive Interventions (JITAIs) in Mo.pdf;C\:\\Users\\kpaquette2\\Zotero\\storage\\KTEE7MZC\\4733473.html}
}

@article{olfsonHealthcareCoverageService2022,
  title = {Healthcare Coverage and Service Access for Low-Income Adults with Substance Use Disorders},
  author = {Olfson, Mark and Mauro, Christine and Wall, Melanie M. and Choi, C. Jean and Barry, Colleen L. and Mojtabai, Ramin},
  year = {2022},
  month = jun,
  journal = {Journal of Substance Abuse Treatment},
  volume = {137},
  pages = {108710},
  issn = {0740-5472},
  doi = {10.1016/j.jsat.2021.108710},
  urldate = {2024-05-26},
  abstract = {Introduction Although health coverage facilitates service access to adults in the general population, uncertainty exists over the extent to which this relationship extends to low-income adults with substance use disorders. Methods The health status and service use patterns of low-income adults with substance use disorders who had continuous, discontinuous, and no past year health coverage were compared using data from the 2015--2019 National Survey on Drug Use and Health (NSDUH). The NSDUH is a nationally representative survey of the civilian non-institutionalized population. Results In the weighted sample (unweighted n~=~9243), approximately 65.66\% of low-income adults with substance use disorders had continuous coverage, 17.03\% had discontinuous coverage, and 17.31\% had no insurance coverage during the past year. Although few group differences were observed in self-reported health status, the uninsured group compared to the discontinously and continuously covered groups, respectively, was less likely to report a past year substance use treatment visit (11.03\% vs. 14.83\% vs. 15.61\%), an outpatient care visit (53.39\% vs. 71.27\% vs. 79.04\%), an emergency department visit (33.33\% vs. 45.76\% vs. 45.57\%), or an inpatient admission (9.24\% vs. 15.11\% vs. 15.58\%). Conclusions Although the cross sectional design limits causal inferences, the correlations between lacking health insurance and low rates of substance use treatment and healthcare use raise the possibility that increasing healthcare coverage might increase access to substance use treatment and other needed healthcare services for low-income adults with substance use disorders.},
  keywords = {Health insurance,Service access,Substance use},
  file = {C\:\\Users\\kpaquette2\\Zotero\\storage\\LK7NYSJG\\Olfson et al. - 2022 - Healthcare coverage and service access for low-inc.pdf;C\:\\Users\\kpaquette2\\Zotero\\storage\\TF2HACUN\\S0740547221004360.html}
}

@article{parlier-ahmadExploratoryStudySex2021,
  title = {An Exploratory Study of Sex and Gender Differences in Demographic, Psychosocial, Clinical, and Substance Use Treatment Characteristics of Patients in Outpatient Opioid Use Disorder Treatment with Buprenorphine},
  author = {{Parlier-Ahmad}, Anna Beth and Martin, Caitlin E. and Radic, Maja and Svikis, Dace S.},
  year = {2021},
  journal = {Translational Issues in Psychological Science},
  volume = {7},
  number = {2},
  pages = {141--153},
  publisher = {Educational Publishing Foundation},
  address = {US},
  issn = {2332-2179},
  doi = {10.1037/tps0000250},
  abstract = {As treatment expansion in the opioid epidemic continues, it is important to examine how the makeup of individuals with opioid use disorder (OUD) is evolving. Treatment programs are increasingly utilizing buprenorphine, an effective OUD medication. This exploratory study examines sex and gender differences in psychosocial, clinical, and substance use treatment characteristics of a clinical population in outpatient medication treatment for OUD with buprenorphine. This is a secondary data analysis from a cross-sectional survey study with retrospective medical record review conducted with patients recruited from an office-based opioid treatment clinic between July--September 2019. Participants on buprenorphine for at least 28 days at time of survey completion were included (n = 133). Differences between men and women were explored with Pearson {$\chi^2$} and Fisher's exact tests for categorical variables and T-tests for continuous variables. The sample was 55.6\% women and nearly three fourths Black (70.7\%). Mean days in current treatment episode was 431.6 (SD = 244.82). Women were younger and more likely to be unemployed, identify as a sexual minority, and live alone with children than men. More women than men had a psychiatric comorbidity. Women reported more prescription opioid misuse while men had more heroin only opioid use. More men reported comorbid alcohol use and a history of drug overdose. One third of participants reported recent discrimination in a health care setting due to substance use. As buprenorphine-based outpatient treatment programs continue to expand, present study findings support evaluation of the unique needs of men and women in order to better tailor OUD-related services and improve treatment outcomes. (PsycInfo Database Record (c) 2021 APA, all rights reserved)},
  keywords = {Addiction,Buprenorphine,Drug Therapy,Human Sex Differences,Opioid Use Disorder,Outpatient Treatment,Substance Use Treatment,Test Construction},
  file = {C\:\\Users\\kpaquette2\\Zotero\\storage\\PVD4D6VB\\Parlier-Ahmad et al. - 2021 - An exploratory study of sex and gender differences.pdf;C\:\\Users\\kpaquette2\\Zotero\\storage\\Q2LQV7SN\\2021-14135-001.html}
}

@article{pinedoCurrentReexaminationRacial2019,
  title = {A Current Re-Examination of Racial/Ethnic Disparities in the Use of Substance Abuse Treatment: {{Do}} Disparities Persist?},
  shorttitle = {A Current Re-Examination of Racial/Ethnic Disparities in the Use of Substance Abuse Treatment},
  author = {Pinedo, Miguel},
  year = {2019},
  month = sep,
  journal = {Drug and Alcohol Dependence},
  volume = {202},
  pages = {162--167},
  issn = {0376-8716},
  doi = {10.1016/j.drugalcdep.2019.05.017},
  urldate = {2024-05-23},
  abstract = {Objective Racial/ethnic disparities in the use of substance abuse treatment services have been documented. The objective of this study was to re-examine if racial/ethnic disparities in the use of treatment still exist using current data collected post-implementation of the Affordable Care Act. Methods Data were pooled from the National Survey on Drug Use and Health survey years 2015, 2016, and 2017. Analyses were limited to adult White, Black, and Latino participants who met DSM-IV criteria for a past-year substance use disorder (n\,=\,12,070). Hierarchical multivariate logistic regression models examined the role of race/ethnicity on past-year use of (1) any substance abuse treatment services and (2) specialty treatment. Important covariates included socio-demographics, problem severity, and perceived treatment need. A sub-analysis was also conducted that was limited to participants who reported having health insurance to explore the role of insurance status on treatment utilization by race/ethnicity. Results Findings showed that Latinos and Blacks significantly underutilized specialty treatment relative to Whites. These relationships were statistically significant after controlling for socio-demographic characteristics, problem severity, and perceived treatment need. However, when analyses were limited to only those with health insurance, Black-White disparities became non-significant, while Latino-White disparities persisted. Conclusions Findings highlight that Black-White and Latino-White disparities in the use of substance abuse treatment still persist. However, Black-White disparities may be limited to only those who are uninsured. Public health implications are discussed.},
  keywords = {Blacks,Latinos,Racial/ethnic disparities,Specialty treatment,Substance use disorders,Treatment utilization},
  file = {C:\Users\kpaquette2\Zotero\storage\IRZR6QX2\Pinedo - 2019 - A current re-examination of racialethnic disparit.pdf}
}

@article{projectmatchresearchgroupMatchingAlcoholismTreatments1997,
  title = {Matching Alcoholism Treatments to Client Heterogeneity: {{Project MATCH Posttreatment}} Drinking Outcomes.},
  author = {Project Match Research Group, U. S.},
  year = {1997},
  journal = {Journal of Studies on Alcohol},
  volume = {58},
  number = {1},
  pages = {7--29},
  abstract = {Studied the benefits of matching alcohol-dependent clients to the Cognitive Behavioral Coping Skills Therapy, Motivational Enhancement Therapy, or 12-Step Facilitation Therapy with reference to various client attributes (CLAs). Two parallel but independent clinical trials were conducted, 1 with 952 alcohol-dependent Ss in outpatient therapy (mean age 38.9 yrs) and 1 with 774 Ss taking aftercare therapy (mean age 41.9 yrs) following 3-mo inpatient or day hospital treatment. Results show sustained improvement in drinking frequency and severity from pretreatment to 1-yr posttreatment with little difference across treatments for both groups. Findings suggest psychiatric severity should be considered when assigning clients to outpatient therapies. The lack of other robust matching effects suggests that providers need not take these CLAs into account when triaging clients to 1 or the other of these 3 individually delivered treatment approaches, despite their different treatment philosophies.}
}

@article{roosDevelopmentInitialTesting2024,
  title = {Development and Initial Testing of Mindful Journey: A Digital Mindfulness-Based Intervention for Promoting Recovery from {{Substance}} Use Disorder},
  shorttitle = {Development and Initial Testing of Mindful Journey},
  author = {Roos, Corey R. and Kiluk, Brian and Carroll, Kathleen M. and Bricker, Jonathan B. and Mun, Chung Jung and Sala, Margarita and Kirouac, Megan and Stein, Elena and John, Maya and Palmer, Robert and DeBenedictis, Andrew and Frisbie, Jena and Haeny, Angela M. and Barry, Declan and Fucito, Lisa M. and Bowen, Sarah and Witkiewitz, Katie and Kober, Hedy},
  year = {2024},
  journal = {Annals of Medicine},
  volume = {56},
  number = {1},
  publisher = {Taylor \& Francis},
  issn = {0785-3890},
  doi = {10.1080/07853890.2024.2315228},
  urldate = {2024-05-15},
  abstract = {There is a great unmet need for accessible adjunctive interventions to promote long-term recovery from substance use disorder (SUD). This study aimed to iteratively develop and test the initial feasibility and acceptability of Mindful Journey, a novel ...},
  langid = {english},
  pmid = {38382111},
  file = {C\:\\Users\\kpaquette2\\Zotero\\storage\\T4JDG72V\\Roos et al. - 2024 - Development and initial testing of mindful journey.pdf;C\:\\Users\\kpaquette2\\Zotero\\storage\\ML3C2R9P\\PMC10883107.html}
}

@misc{rstudioteamRStudioIntegratedDevelopment2020,
  title = {{{RStudio}}: {{Integrated Development}} for {{R}}},
  author = {{RStudio Team}},
  year = {2020},
  address = {Boston, MA},
  howpublished = {RStudio, Inc}
}

@article{schmidtEthnicDisparitiesClinical2007,
  title = {Ethnic Disparities in Clinical Severity and Services for Alcohol Problems: Results from the {{National Alcohol Survey}}},
  shorttitle = {Ethnic Disparities in Clinical Severity and Services for Alcohol Problems},
  author = {Schmidt, Laura A. and Ye, Yu and Greenfield, Thomas K. and Bond, Jason},
  year = {2007},
  month = jan,
  journal = {Alcoholism, Clinical and Experimental Research},
  volume = {31},
  number = {1},
  pages = {48--56},
  issn = {0145-6008},
  doi = {10.1111/j.1530-0277.2006.00263.x},
  abstract = {BACKGROUND: This study reports lifetime estimates of the extent of unmet need for alcohol services across the 3 largest ethnic groups in America, and examines factors that may contribute to ethnic differences in service use. Prior studies report mixed findings as to the existence of ethnic disparities in alcohol services, with some suggesting that minorities are over-represented in treatment settings. METHODS: Drawing on the most recent National Alcohol Surveys, we compare rates and factors associated with the lifetime service use for alcohol problems among Whites, Blacks, and Hispanics who meet lifetime criteria for alcohol abuse or dependence. RESULTS: While bivariate analyses revealed few ethnic differences in service use, there were significant differences by ethnicity in multivariate models that included alcohol problem severity and its interactions with ethnicity. At higher levels of problem severity, both Hispanics and Blacks were less likely to have utilized services than comparable Whites. Hispanics, on the whole, reported higher-severity alcohol problems than Whites. Yet, they were less likely to have received specialty treatment and multiple types of alcohol services, and were more likely to cite economic and logistical barriers as reasons for not obtaining care. CONCLUSIONS: Future efforts to study ethnic disparities in alcohol services should utilize analytic approaches that address potential confounding between ethnicity and other factors in service use, such as alcohol problem severity. Our findings suggest that Hispanics and Blacks with higher-severity alcohol problems may utilize services at lower rates than comparable Whites, and that, particularly for Hispanics, this may in part be attributable to financial and logistical barriers to care.},
  langid = {english},
  pmid = {17207101},
  keywords = {Adolescent,Adult,Alcoholics Anonymous,Alcoholism,Black People,Ethnicity,Female,Health Services Accessibility,Hispanic or Latino,Humans,Logistic Models,Male,Middle Aged,Psychiatric Status Rating Scales,Social Work,Socioeconomic Factors,United States,White People}
}

@article{soysterPooledPersonspecificMachine2022,
  title = {Pooled and Person-Specific Machine Learning Models for Predicting Future Alcohol Consumption, Craving, and Wanting to Drink: {{A}} Demonstration of Parallel Utility},
  shorttitle = {Pooled and Person-Specific Machine Learning Models for Predicting Future Alcohol Consumption, Craving, and Wanting to Drink},
  author = {Soyster, Peter D. and Ashlock, Leighann and Fisher, Aaron J.},
  year = {2022},
  month = may,
  journal = {Psychology of Addictive Behaviors: Journal of the Society of Psychologists in Addictive Behaviors},
  volume = {36},
  number = {3},
  pages = {296--306},
  issn = {1939-1501},
  doi = {10.1037/adb0000666},
  abstract = {BACKGROUND AND AIMS: The specific factors driving alcohol consumption, craving, and wanting to drink, are likely different for different people. The present study sought to apply statistical classification methods to idiographic time series data in order to identify person-specific predictors of future drinking-relevant behavior, affect, and cognitions in a college student sample. DESIGN: Participants were sent 8 mobile phone surveys per day for 15 days. Each survey assessed the number of drinks consumed since the previous survey, as well as positive affect, negative affect, alcohol craving, drinking expectancies, perceived alcohol consumption norms, impulsivity, and social and situational context. Each individual's data were split into training and testing sets, so that trained models could be validated using person-specific out-of-sample data. Elastic net regularization was used to select a subset of a set of 40 variables to be used to predict either alcohol consumption, craving, or wanting to drink, forward in time. SETTING: A west-coast university. PARTICIPANTS: Thirty-three university students who had consumed alcohol in their lifetime. MEASUREMENTS: Mobile phone surveys. FINDINGS: Averaging across participants, accurate out-of-sample predictions of future drinking were made 76\% of the time. For craving, the mean out-of-sample R{$^2$} value was .27. For wanting to drink, the mean out-of-sample R{$^2$} value was .27. CONCLUSION: Using a person-specific constellation of psychosocial and temporal variables, it may be possible to accurately predict drinking behavior, affect, and cognitions before they occur. (PsycInfo Database Record (c) 2022 APA, all rights reserved).},
  langid = {english},
  pmid = {35041441},
  keywords = {Alcohol Drinking,Craving,Ethanol,Humans,Machine Learning,Students,Universities}
}

@article{spruijt-metzDynamicModelsBehavior2014,
  title = {Dynamic {{Models}} of {{Behavior}} for {{Just-in-Time Adaptive Interventions}}},
  author = {{Spruijt-Metz}, Donna and Nilsen, Wendy},
  year = {2014},
  month = jul,
  journal = {IEEE Pervasive Computing},
  volume = {13},
  number = {3},
  pages = {13--17},
  issn = {1536-1268, 1558-2590},
  doi = {10.1109/MPRV.2014.46},
  urldate = {2024-05-15},
  copyright = {https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html},
  langid = {english},
  file = {C:\Users\kpaquette2\Zotero\storage\FY6JRX5T\Spruijt-Metz and Nilsen - 2014 - Dynamic Models of Behavior for Just-in-Time Adapti.pdf}
}

@book{substanceabuseandmentalhealthservicesadministrationusFacingAddictionAmerica2016a,
  title = {Facing {{Addiction}} in {{America}}: {{The Surgeon General}}'s {{Report}} on {{Alcohol}}, {{Drugs}}, and {{Health}}},
  shorttitle = {Facing {{Addiction}} in {{America}}},
  author = {{Substance Abuse and Mental Health Services Administration (US)} and {Office of the Surgeon General (US)}},
  year = {2016},
  series = {Reports of the {{Surgeon General}}},
  publisher = {{US Department of Health and Human Services}},
  address = {Washington (DC)},
  urldate = {2024-05-15},
  abstract = {This Surgeon General's Report has been created because of the important health and social problems associated with alcohol and drug misuse in America. As described in this Report, a comprehensive approach is needed to address substance use problems in the United States that includes several key components: Enhanced public education to improve awareness about substance use problems and demand for more effective policies and practices to address them; Widespread implementation of evidence-based prevention policies and programs to prevent substance misuse and related harms; Improved access to evidence-based treatment services, integrated with mainstream health care, for those at risk for or affected by substance use disorders; Recovery support services (RSS) to assist individuals in maintaining remission and preventing relapse; and Research-informed public policies and financing strategies to ensure that substance misuse and use disorder services are accessible, compassionate, efficient, and sustainable. Recognizing these needs, the Report explains the neurobiological basis for substance use disorders and provides the biological, psychological, and social frameworks for improving diagnosis, prevention, and treatment of alcohol and drug misuse. It also describes evidence-based prevention strategies, such as public policies that can reduce substance misuse problems (e.g., driving under the influence [DUI]); effective treatment strategies, including medications and behavioral therapies for treating substance use disorders; and RSS for people who have completed treatment. Additionally, the Report describes recent changes in health care financing, including changes in health insurance regulations, which support the integration of clinical prevention and treatment services for substance use disorders into mainstream health care practice, and defines a research agenda for addressing alcohol and drug misuse as medical conditions. Thus, this first Surgeon General's Report on Alcohol, Drugs, and Health is not issued simply because of the prevalence of substance misuse or even the related devastating harms and costs, but also to help inform policymakers, health care professionals, and the general public about effective, practical, and sustainable strategies to address these problems. These strategies have the potential to substantially reduce substance misuse and related problems; promote early intervention for substance misuse and substance use disorders; and improve the availability of high-quality treatment and RSS for persons with substance use disorders.},
  langid = {english},
  lccn = {NBK424857},
  pmid = {28252892}
}

@article{waltersUsingMachineLearning2021,
  title = {Using Machine Learning to Identify Predictors of Imminent Drinking and Create Tailored Messages for At-Risk Drinkers Experiencing Homelessness},
  author = {Walters, Scott T. and Businelle, Michael S. and Suchting, Robert and Li, Xiaoyin and H{\'e}bert, Emily T. and Mun, Eun-Young},
  year = {2021},
  month = aug,
  journal = {Journal of Substance Abuse Treatment},
  volume = {127},
  pages = {108417},
  issn = {0740-5472},
  doi = {10.1016/j.jsat.2021.108417},
  urldate = {2022-12-12},
  abstract = {Adults experiencing homelessness are more likely to have an alcohol use disorder compared to adults in the general population. Although shelter-based treatments are common, completion rates tend to be poor, suggesting a need for more effective approaches that are tailored to this understudied and underserved population. One barrier to developing more effective treatments is the limited knowledge of the triggers of alcohol use among homeless adults. This paper describes the use of ecological momentary assessment (EMA) to identify predictors of ``imminent drinking'' (i.e., drinking within the next 4~h), among a sample of adults experiencing homelessness and receiving health services at a homeless shelter. A total of 78 mostly male (84.6\%) adults experiencing homelessness (mean age~=~46.6) who reported hazardous drinking completed up to five EMAs per day over 4~weeks (a total of 4557 completed EMAs). The study used machine learning techniques to create a drinking risk algorithm that predicted 82\% of imminent drinking episodes within 4~h of the first drink of the day, and correctly identified 76\% of nondrinking episodes. The algorithm included the following 7 predictors of imminent drinking: urge to drink, having alcohol easily available, feeling confident that alcohol would improve mood, feeling depressed, lower commitment to being alcohol free, not interacting with someone drinking alcohol, and being indoors. The research team used the results to develop intervention content (e.g., brief tailored messages) that will be delivered when imminent drinking is detected in an upcoming intervention phase. Specifically, we created three theoretically grounded message tracks focused on urge/craving, social/availability, and negative affect/mood, which are further tailored to a participant's current drinking goal (i.e., stay sober, drink less, no goal) to support positive change. To our knowledge, this is the first study to develop tailored intervention messages based on likelihood of imminent drinking, current drinking triggers, and drinking goals among adults experiencing homelessness.},
  langid = {english},
  keywords = {Ecological momentary assessment,Homeless,Intervention development,Machine learning,Substance use},
  file = {C\:\\Users\\kpaquette2\\Zotero\\storage\\2ESJVSJY\\Walters et al. - 2021 - Using machine learning to identify predictors of i.pdf;C\:\\Users\\kpaquette2\\Zotero\\storage\\X4U5YT3V\\Walters et al. - 2021 - Using machine learning to identify predictors of i.pdf;C\:\\Users\\kpaquette2\\Zotero\\storage\\HV86JQ7Z\\S0740547221001434.html}
}

@article{wangJustintheMomentAdaptiveInterventions2020,
  title = {Just-in-the-{{Moment Adaptive Interventions}} ({{JITAI}}): {{A Meta-Analytical Review}}},
  shorttitle = {Just-in-the-{{Moment Adaptive Interventions}} ({{JITAI}})},
  author = {Wang, Liyuan and Miller, Lynn Carol},
  year = {2020},
  month = oct,
  journal = {Health Communication},
  volume = {35},
  number = {12},
  pages = {1531--1544},
  publisher = {Routledge},
  issn = {1041-0236},
  doi = {10.1080/10410236.2019.1652388},
  urldate = {2024-05-15},
  abstract = {A just-in-time, adaptive intervention (JITAI) is an emerging type of intervention that provides tailored support at the exact time of need. It does so using enabling new technologies (e.g., mobile phones, sensors) that capture the changing states of individuals. Extracting effect sizes of primary outcomes produced by 33 empirical studies that used JITAIs, we found moderate to large effect sizes of JITAI treatments compared to (1) waitlist-control conditions (k~=~9), Hedges's g =~1.65 and (2) non-JITAI treatments (k~=~21), g =~0.89. Also, participants of JITAI interventions showed significant changes (k~=~13) in the positive direction (g~=~0.79). A series of sensitivity tests suggested that those effects persist. Those effects also persist despite differences in the behaviors of interests (e.g., blood glucose control, recovering alcoholics), duration of the treatments, and participants' age. Two aspects of tailoring, namely: (1) tailoring to what (i.e., both people's previous behavioral patterns and their current need states; with these effects additive) and (2) approach to tailoring (i.e., both using a human agent and an algorithm to decide tailored feedback; with these effects additive), are significantly associated with greater JITAI efficacy.},
  pmid = {31488002},
  file = {C:\Users\kpaquette2\Zotero\storage\ZQS3FLSG\Wang and Miller - 2020 - Just-in-the-Moment Adaptive Interventions (JITAI).pdf}
}

@article{whoassistworkinggroupAlcoholSmokingSubstance2002,
  title = {The {{Alcohol}}, {{Smoking}} and {{Substance Involvement Screening Test}} ({{ASSIST}}): Development, Reliability and Feasibility},
  shorttitle = {The {{Alcohol}}, {{Smoking}} and {{Substance Involvement Screening Test}} ({{ASSIST}})},
  author = {{WHO ASSIST Working Group}},
  year = {2002},
  month = sep,
  journal = {Addiction (Abingdon, England)},
  volume = {97},
  number = {9},
  pages = {1183--1194},
  issn = {0965-2140},
  abstract = {AIMS: The Alcohol, Smoking and Substance Involvement Screening Test (ASSIST) was developed for the World Health Organization (WHO) by an international group of substance abuse researchers to detect psychoactive substance use and related problems in primary care patients. This report describes the new instrument as well as a study of its reliability and feasibility. SETTING: The study was conducted at participating sites in Australia, Brazil, Ireland, India, Israel, the Palestinian Territories, Puerto Rico, the United Kingdom and Zimbabwe. Sixty per cent of the sample was recruited from alcohol and drug abuse treatment facilities; the remainder was drawn from general medical settings and psychiatric facilities. METHODS: The study was concerned primarily with test item reliability, using a simple test-retest procedure to determine whether subjects would respond consistently to the same items when presented in an interview format on two different occasions. Qualitative and quantitative data were also collected to evaluate the feasibility of the screening items and rating format. PARTICIPANTS: A total of 236 volunteer participants completed test and retest interviews at nine collaborating sites. Slightly over half of the sample (53.6\%) was male. The mean age of the sample was 34 years and they had completed, on average, 10 years of education. RESULTS: The average test-retest reliability coefficients (kappas) ranged from a high of 0.90 (consistency of reporting 'ever' use of substance) to a low of 0.58 (regretted what was done under influence of substance). The average kappas for substance classes ranged from 0.61 for sedatives to 0.78 for opioids. In general, the reliabilities were in the range of good to excellent, with the following items demonstrating the highest kappas across all drug classes: use in the last 3 months, preoccupied with drug use, concern expressed by others, troubled by problems related to drug use, intravenous drug use. Qualitative data collected at the end of the retest interview suggested that the questions were not difficult to answer and were consistent with patients' expectations for a health interview. The data were used to guide the selection of a smaller set of items that can serve as the basis for more extensive validation research. CONCLUSION: The ASSIST items are reliable and feasible to use as part of an international screening test. Further evaluation of the screening test should be conducted.},
  langid = {english},
  pmid = {12199834},
  keywords = {Adult,Alcohol Drinking,Feasibility Studies,Female,Humans,Male,Research Design,Sensitivity and Specificity,Smoking,Substance Abuse Detection}
}

@article{witkiewitzModelingComplexityPosttreatment2007,
  title = {Modeling the Complexity of Post-Treatment Drinking: {{It}}'s a Rocky Road to Relapse},
  shorttitle = {Modeling the Complexity of Post-Treatment Drinking},
  author = {Witkiewitz, Katie and Marlatt, G. Alan},
  year = {2007},
  month = jul,
  journal = {Clinical Psychology Review},
  series = {New {{Approaches}} to the {{Study}} of {{Change}} in {{Cognitive Behavioral Therapies}}},
  volume = {27},
  number = {6},
  pages = {724--738},
  issn = {0272-7358},
  doi = {10.1016/j.cpr.2007.01.002},
  urldate = {2014-09-15},
  abstract = {The most widely cited road block to successful treatment outcomes for psychological and substance use disorders has been described as the return to the previous behavior, or ``relapse.'' The operational definition of ``relapse'' varies from study to study and between clinicians, but in general the term is used to indicate the return to previous levels of symptomatic behavior. One explanation for the variation in the operationalization of relapse is the wide variety of behaviors for which the term is applied, including (but not limited to): depression, substance abuse, schizophrenia, mania, sexual offending, risky sexual behavior, dieting, and the anxiety disorders. A second explanation for the multitude of definitions for relapse is the inherent complexity in the process of behavior change. In this paper we present the most recent treatment outcome research evaluating relapse rates, with a special focus on the substance use disorders. Following this review of the literature we present an argument for the operationalization of relapse as a dynamic process, which can be empirically characterized using dynamical systems theory. We support this argument by presenting results from the analysis of alcohol treatment outcomes using catastrophe modeling techniques. These results demonstrate the utility of catastrophe theory in modeling the alcohol relapse process. The implications of these analyses for the treatment of alcohol use disorders, as well as a discussion of future research incorporating nonlinear dynamical systems theory is provided.},
  keywords = {risk2_protocol_paper},
  file = {C\:\\Users\\kpaquette2\\Zotero\\storage\\HYRPLJWT\\Witkiewitz and Marlatt - 2007 - Modeling the complexity of post-treatment drinking.pdf;C\:\\Users\\kpaquette2\\Zotero\\storage\\RUEYI3AF\\Witkiewitz and Marlatt - 2007 - Modeling the complexity of post-treatment drinking.pdf;C\:\\Users\\kpaquette2\\Zotero\\storage\\EXXKR8RX\\S0272735807000062.html;C\:\\Users\\kpaquette2\\Zotero\\storage\\KYWAKVPE\\S0272735807000062.html}
}

@article{witkiewitzRelapsePreventionAlcohol2004,
  title = {Relapse Prevention for Alcohol and Drug Problems: {{That}} Was Zen, This Is Tao},
  shorttitle = {Relapse {{Prevention}} for {{Alcohol}} and {{Drug Problems}}},
  author = {Witkiewitz, Katie and Marlatt, G. Alan},
  year = {2004},
  month = may,
  journal = {American Psychologist},
  volume = {59},
  number = {4},
  pages = {224--235},
  issn = {1935-990X},
  doi = {10.1037/0003-066X.59.4.224},
  urldate = {2013-06-13},
  abstract = {Relapse prevention, based on the cognitive-behavioral model of relapse, has become an adjunct to the treatment of numerous psychological problems, including (but not limited to) substance abuse, depression, sexual offending, and schizophrenia. This article provides an overview of the efficacy and effectiveness of relapse prevention in the treatment of addictive disorders, an update on recent empirical support for the elements of the cognitive-behavioral model of relapse, and a review of the criticisms of relapse prevention. In response to the criticisms, a reconceptualized cognitive-behavioral model of relapse that focuses on the dynamic interactions between multiple risk factors and situational determinants is proposed. Empirical support for this reconceptualization of relapse, the future of relapse prevention, and the limitations of the new model are discussed. (PsycINFO Database Record (c) 2012 APA, all rights reserved) (journal abstract)},
  keywords = {addiction,addictive disorders,alcohol problems,Cognitive behavior therapy,cognitive-behavioral model,Drug abuse,drug problems,Drug Rehabilitation,Models,relapse prevention,risk2_protocol_paper,treatment efficacy,Treatment Outcomes},
  file = {C\:\\Users\\kpaquette2\\Zotero\\storage\\84R9SEMB\\Witkiewitz and Marlatt - 2004 - Relapse Prevention for Alcohol and Drug Problems .pdf;C\:\\Users\\kpaquette2\\Zotero\\storage\\JQ8H7SAA\\Witkiewitz and Marlatt - 2004 - Relapse Prevention for Alcohol and Drug Problems .pdf}
}

@article{wrightAppliedAmbulatoryAssessment2019,
  title = {Applied {{Ambulatory Assessment}}: {{Integrating Idiographic}} and {{Nomothetic Principles}} of {{Measurement}}},
  shorttitle = {Applied {{Ambulatory Assessment}}},
  author = {Wright, Aidan G.C. and Zimmermann, Johannes},
  year = {2019},
  month = dec,
  journal = {Psychological assessment},
  volume = {31},
  number = {12},
  pages = {1467--1480},
  issn = {1040-3590},
  doi = {10.1037/pas0000685},
  urldate = {2024-01-12},
  abstract = {Ambulatory assessment (also known as ecological momentary assessment) has enjoyed enthusiastic implementation in psychological research. The ability to assess thoughts, feelings, behavior, physiology, and context intensively and repeatedly in the moment in an individual's natural ecology affords access to data that can answer exciting questions about sequences of events and dynamic processes in daily life. Ambulatory assessment also holds unique promise for developing personalized models of individuals (i.e., precision or person-specific assessment) that might be transformative for applied settings such as clinical practice. However, successfully translating ambulatory assessment from bench to bedside is challenging because of the inherent tension between idiographic and nomothetic principles of measurement. We argue that the value of applied ambulatory assessment will be most fully realized by balancing the ability to develop personalized models with ensuring comparability among individuals.},
  pmcid = {PMC6754809},
  pmid = {30896209},
  file = {C:\Users\kpaquette2\Zotero\storage\4MXQD8WV\Wright and Zimmermann - 2019 - Applied Ambulatory Assessment Integrating Idiogra.pdf}
}

@article{wyantAcceptabilityPersonalSensing2023,
  title = {Acceptability of {{Personal Sensing Among People With Alcohol Use Disorder}}: {{Observational Study}}},
  shorttitle = {Acceptability of {{Personal Sensing Among People With Alcohol Use Disorder}}},
  author = {Wyant, Kendra and Moshontz, Hannah and Ward, Stephanie B. and Fronk, Gaylen E. and Curtin, John J.},
  year = {2023},
  month = aug,
  journal = {JMIR mHealth and uHealth},
  volume = {11},
  number = {1},
  pages = {e41833},
  publisher = {JMIR Publications Inc., Toronto, Canada},
  doi = {10.2196/41833},
  urldate = {2023-09-15},
  abstract = {Background: Personal sensing may improve digital therapeutics for mental health care by facilitating early screening, symptom monitoring, risk prediction, and personalized adaptive interventions. However, further development and the use of personal sensing requires a better understanding of its acceptability to people targeted for these applications. Objective: We aimed to assess the acceptability of active and passive personal sensing methods in a sample of people with moderate to severe alcohol use disorder using both behavioral and self-report measures. This sample was recruited as part of a larger grant-funded project to develop a machine learning algorithm to predict lapses. Methods: Participants (N=154; n=77, 50\% female; mean age 41, SD 11.9 years; n=134, 87\% White and n=150, 97\% non-Hispanic) in early recovery (1-8 weeks of abstinence) were recruited to participate in a 3-month longitudinal study. Participants were modestly compensated for engaging with active (eg, ecological momentary assessment [EMA], audio check-in, and sleep quality) and passive (eg, geolocation, cellular communication logs, and SMS text message content) sensing methods that were selected to tap into constructs from the Relapse Prevention model by Marlatt. We assessed 3 behavioral indicators of acceptability: participants' choices about their participation in the study at various stages in the procedure, their choice to opt in to provide data for each sensing method, and their adherence to a subset of the active methods (EMA and audio check-in). We also assessed 3 self-report measures of acceptability (interference, dislike, and willingness to use for 1 year) for each method. Results: Of the 192 eligible individuals screened, 191 consented to personal sensing. Most of these individuals (169/191, 88.5\%) also returned 1 week later to formally enroll, and 154 participated through the first month follow-up visit. All participants in our analysis sample opted in to provide data for EMA, sleep quality, geolocation, and cellular communication logs. Out of 154 participants, 1 (0.6\%) did not provide SMS text message content and 3 (1.9\%) did not provide any audio check-ins. The average adherence rate for the 4 times daily EMA was .80. The adherence rate for the daily audio check-in was .54. Aggregate participant ratings indicated that all personal sensing methods were significantly more acceptable (all P\&lt;.001) compared with neutral across subjective measures of interference, dislike, and willingness to use for 1 year. Participants did not significantly differ in their dislike of active methods compared with passive methods (P=.23). However, participants reported a higher willingness to use passive (vs active) methods for 1 year (P=.04). Conclusions: These results suggest that active and passive sensing methods are acceptable for people with alcohol use disorder over a longer period than has previously been assessed. Important individual differences were observed across people and methods, indicating opportunities for future improvement.},
  copyright = {Unless stated otherwise, all articles are open-access distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work ("first published in JMIR mHealth and uHealth...") is properly cited with original URL and bibliographic citation information. The complete bibliographic information, a link to the original publication on http://mhealth.jmir.org/, as well as this copyright and license information must be included.},
  langid = {english},
  keywords = {Clinical Psychology,Social and Behavioral Sciences,Substance Abuse and Addiction},
  file = {C\:\\Users\\kpaquette2\\Zotero\\storage\\55VMT2R2\\Wyant et al. - 2022 - Acceptability of Personal Sensing Among People wit.pdf;C\:\\Users\\kpaquette2\\Zotero\\storage\\R6LLQ57K\\Wyant et al. - 2023 - Acceptability of Personal Sensing Among People Wit.pdf;C\:\\Users\\kpaquette2\\Zotero\\storage\\25GFCPP5\\e41833.html}
}

@article{wyantMachineLearningModels2023,
  title = {Machine Learning Models for Temporally Precise Lapse Prediction in Alcohol Use Disorder},
  author = {Wyant, Kendra and Sant'Ana, Sarah June Kittleson and Fronk, Gaylen and Curtin, John J.},
  year = {2024},
  journal = {Psychopathology and Clinical Science},
  doi = {10.31234/osf.io/cgsf7},
  urldate = {2023-09-27},
  abstract = {We developed three separate models that provide hour-by-hour probabilities of a future lapse back to alcohol use with increasing temporal precision (i.e., lapses in the next week, next day, and next hour). Model features were based on raw scores and longitudinal change in theoretically implicated risk factors collected through ecological momentary assessment (EMA). Participants (N=151; 51\% male; mean age = 41; 87\% White, 97\% Non-Hispanic) in early recovery (1--8 weeks of abstinence) from alcohol use disorder provided 4x daily EMA for up to three months. We used grouped, nested cross-validation, with 1 repeat of 10-fold cross-validation for the inner loop and 3 repeats of 10-fold cross-validation for the outer loop to train models, select best models, and evaluate those best models on auROC. Models yielded median areas under the receiver operating curves (auROCs) of .90, .91, and .94 in the 30 held-out test sets for week, day, and hour level models, respectively. Some feature categories consistently emerged as being globally important to lapse prediction across our week, day, and hour level models (i.e., past use, future efficacy). However, most of the more punctuate, time varying constructs (e.g., craving, past stressful events, arousal) appear to have greater impact within the next hour prediction model. This research represents an important step toward the development of a smart (machine learning guided) sensing system that can both identify periods of peak lapse risk and recommend specific supports to address factors contributing to this risk.   General scientific summary: This study suggests that densely sampled self-report data can be used to predict lapses back to alcohol use with varying degrees of temporal precision. Additionally, the contextual features contributing to risk of lapse may offer important insight for treatment matching through a digital therapeutic.},
  langid = {american},
  keywords = {alcohol use disorder,Clinical Psychology,digital therapeutics,ecological momentary assessment,Social and Behavioral Sciences,Substance Abuse and Addiction},
  file = {C:\Users\kpaquette2\Zotero\storage\NKF33A5Z\Wyant et al. - 2023 - Machine learning models for temporally precise lap.pdf}
}