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Substance Dependence

Substance Dependence is a condition characterized by a strong, compulsive desire to consume a particular substance, such as drugs or alcohol, despite the harmful consequences.
This disorder involves physical and psychological dependence, leading to tolerance, withdrawal symptoms, and continued use despite negative impacts on health and well-being.
Effective treatment often requires a combination of behavioral therapies and medication management.
Understanding the complexities of Substance Dependence is crucial for developing better interventions and improving outcomes for those affected by this challenging condition.

Most cited protocols related to «Substance Dependence»

Alzheimer's Disease Risk Factors Study

Cited 47 times

A total of 22 subjects were recruited in this study including 8 young controls (4 men and 4 women, age range: 24–40 years, mean 31) and 14 older adults (6 men and 8 women, age range: 50–73 years, mean 61). In order to match the prevalence of AD in the older population, 1 AD and 2 MCI patients were included as positive controls whose diagnoses were based on consensus conference case reviews. The rest of the older individuals were selected from a carefully characterized cohort known as The Wisconsin Registry for AD Prevention (WRAP), many of whom have a parent with AD and are considered at high risk to develop AD (20 (link)). The study was approved by the Institutional Review Board at the University of Wisconsin. All participants provided informed consent prior to participation. The consent process included an initial screening for MRI and PET compatibility and discussion of major safety exclusion criteria. Study exclusion criteria included contraindications to MRI and PET; less than 10 years of education; pregnancy; major head trauma, psychiatric disease such as schizophrenia and substance dependence, or abnormal structural MRI and neuropsychological testing as part of study participation. Excluded medications include psychoactive medications, neuroleptics, short or long acting nitrates, and warfarin or other drugs that may affect CBF (such as caffeine within 3 hours and nicotine within 1 hour of the imaging exam).

Xu G., Rowley H.A., Wu G., Alsop D.C., Shankaranarayanan A., Dowling M., Christian B.T., Oakes T.R, & Johnson S.C. (2010). Reliability and Precision of Pseudo-continuous Arterial Spin Labeling Perfusion MRI on 3.0 T and Comparison with 15O-water PET in Elderly Subjects at Risk for Alzheimer’s Disease. NMR in biomedicine, 23(3), 286-293.

Publication 2010

Aged Antipsychotic Agents Caffeine Conferences Craniocerebral Trauma Diagnosis Ethics Committees, Research Mental Disorders Nicotine Nitrates Parent Patients Pharmaceutical Preparations Pregnancy Psychotropic Drugs Safety Schizophrenia Substance Dependence Warfarin Woman

Developing the Questionnaire for Impulsive-Compulsive Disorders in Parkinson's Disease

Cited 47 times

First, existing screening and diagnostic instruments for ICDs and other compulsive behaviors that have been used in PD and the general population were reviewed(1 ;6 (link);19 (link);21 (link)–23 (link)). Second, input was solicited from outside experts in the area of ICDs in PD (MNP, JM, and VV) and from an expert in questionnaire development (JAS). Third, a preliminary ICD section of the QUIP was structured to be consistent with diagnostic criteria or defining clinical characteristics as described in the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV-TR)(1 ). This consisted of an introductory question and four additional questions that addressed cognitive symptoms, affective symptoms, lack of ability to reduce or stop the behaviors, and activities that enable continuation of the behaviors. The compulsive medication use section was modeled on both Giovannoni’s proposed criteria for hedonistic homeostatic dysregulation and DSM-IV substance dependence criteria. While minor wording changes were made in subsequent drafts, the structure of these sections remained consistent throughout the instrument development process. The other compulsive behaviors section was designed with conciseness in mind (an introductory question for each of the three behaviors plus two common additional questions). Guiding principles in the design of the QUIP included making it self-administered, brief yet comprehensive, and consistent in wording across different ICDs and other compulsive behaviors.
Next, the preliminary QUIP was administered to a sample of healthy controls (10 research staff members who work with neurodegenerative disease and psychiatric populations), and modifications were made based on the feedback received. Finally, the QUIP was administered to five PD patients and their informed others, and additional modifications were made based on the feedback received from them.
The final version that was validated queried about behaviors that occurred at any time since the onset of PD (either inactive or active) that lasted at least four weeks. We chose the time frame of “anytime during PD” due to the observation that a substantial number of PD patients who have experienced an ICD during PD are currently asymptomatic due to clinical management, but may be at elevated risk of developing an ICD in the future. Another version of the QUIP that queries only about active behaviors is also available; it is identical to the validated version except for the time frame queried. The final version of the QUIP is divided into three sections: (1) five questions (including an introductory question that defines and gives examples of problem behaviors) for the four ICDs reported in PD; (2) three distinct introductory questions and two common additional questions for hobbyism, punding, and walkabout; and (3) five questions (including an introductory question) for compulsive medication use. The Flesch-Kincaid Readability Test assessed the QUIP to require a 12^th grade reading level.

Weintraub D., Stewart S., Shea J.A., Lyons K.E., Pahwa R., Driver-Dunckley E.D., Adler C.H., Potenza M.N., Miyasaki J., Siderowf A.D., Duda J.E., Hurtig H.I., Colcher A., Horn S.S., Stern M.B, & Voon V. (2009). Validation of the Questionnaire for Impulsive-Compulsive Disorders in Parkinson’s Disease (QUIP). Movement disorders : official journal of the Movement Disorder Society, 24(10), 1461-1467.

Publication 2009

Affective Symptoms Compulsive Behavior Diagnosis Homeostasis Implantable Defibrillator Neurobehavioral Manifestations Neurodegenerative Disorders Patients Pharmaceutical Preparations Population Group Problem Behavior Reading Frames Substance Dependence

Comprehensive Assessment Battery for Substance Use Trials

Cited 32 times

The assessment battery for both trials included: (a) basic demographic characteristics; (b) substance use, measured with urine- and breathalyzer-confirmed self-report using the Substance Use Calendar (SUC), an assessment adapted from the Time Line Follow Back interview (Fals-Stewart, O’Farrell, Freitas, McFarlin, & Rutigliano, 2000 (link); Sobell & Sobell, 1992 ); (c) a brief version of the Addiction Severity Index (ASI; McLellan, Alterman, Cacciola, Metzger, & O’Brien, 1992 (link)); (d) level of HIV risk behaviors, measured with the HIV Risk Behavior Scale (HRBS; Darke, Hall, Heather, Ward, & Wodak, 1991 (link)); and (e) readiness to change, measured with the University of Rhode Island Change Assessment (URICA; DiClemente & Hughes, 1990 (link)). The MET study also included the Substance Dependence Severity Scale (SDSS; Miele, et al., 2000a (link); Miele, et al., 2000b (link)), an interview assessing the severity and frequency of DSM-IV dependence symptoms. As outcome measures, both studies utilized the SUC to assess frequency of substance use, as well as client records to assess treatment retention.
Baseline assessment in both trials included a revised version of the Short Index of Problems (SIP), a self-report inventory of adverse consequences associated with drug and alcohol use described earlier. The SIP instructs participants to indicate how often each of the listed consequences has occurred during the past three months (“never,” “once or a few times,” “once or twice a week,” “daily or almost daily”; scored 0-3). Item responses are summed to produce a total score and five subscale scores. The revised version (SIP-R) used in the MET and MI studies included minor modifications from prior versions. For instance, one of the ‘impulse control’ subscale items, “I have had an accident while drinking or intoxicated”, was replaced with “Drinking or using one drug has caused me to use other drugs more”. Also, two items from the InDUC that assess problems with work and legal trouble, not included on the original SIP, were added to this revised version to provide broader coverage of the ‘social’ domain, resulting in a 17-item SIP-R (seeAppendix).

Kiluk B.D., Dreifuss J.A., Weiss R.D., Morgenstern J, & Carroll K.M. (2012). The Short Inventory of Problems – Revised (SIP-R): Psychometric properties within a large, diverse sample of substance use disorder treatment seekers. Psychology of addictive behaviors : journal of the Society of Psychologists in Addictive Behaviors, 27(1), 307-314.

Publication 2012

Accidents Addictive Behavior Amyotrophic lateral sclerosis 1 Pharmaceutical Preparations Retention (Psychology) Substance Dependence Substance Use TimeLine Urine

Telephone-Based Clinical Reappraisal of NCS-A

Cited 32 times

The clinical reappraisal sample included a probability sample of 347 adolescent NCS-A respondents in addition to one parent of each adolescent drawn within strata defined by DSM-V/CIDI diagnoses. The sample was confined to households with telephones because the K-SADS clinical reappraisal interviews were administered by phone. Telephone administration is now widely accepted in clinical reappraisal studies based on evidence of comparable validity to in-person administration in both adults29 (link), 30 and adolescents.31 (link), 32 (link) A great advantage of telephone administration is that a centralized and closely supervised clinical interview staff can carry out the interviews throughout the country without the geographic restrictions required for face-to-face clinical assessment. A disadvantage is that the small part of the population without telephones cannot be included in clinical calibration studies when interviews are done by telephone. In addition, telephone interviews cannot as readily use non-verbal communications to facilitate probing and scoring.
Respondents who met DSM-IV/CIDI criteria for one or more of the more uncommon disorders assessed in the NCS-A (e.g., agoraphobia, bipolar disorder, panic disorder, substance dependence with abuse) were sampled at a higher rate than respondents in a second sampling stratum who met criteria only for more common disorders. The lowest sampling fraction was for a third stratum of respondents who did not meet criteria for any lifetime DSM-IV/CIDI disorder. Respondents were selected into the clinical reappraisal sample with probabilities proportional to sample weight so as to reduce the effects of weighting. Each adolescent and parent respondent was given a $50 incentive for participation in the clinical reappraisal survey (over and above the $50 incentive for participation in the main survey). K-SADS interviews were conducted over the phone an average of 77 days after the CIDI interviews. Clinicians first conducted the interview with adolescents and then a parent. The focus was on lifetime prevalence, which is one reason the time interval between interviews was made much longer than the 1–3 days typical for validation studies that focus on the assessment of current prevalence. A long time interval was used in order to avoid respondent reluctance to participate in an intensive re-interview within a shorter time period from the original interview. A danger in doing this, though, is that the time interval is long enough that there might have been true change in lifetime diagnostic status, introducing a conservative bias into estimates of concordance between CIDI and K-SADS assessments.

Kessler R.C., Avenevoli S., Green J.G., Gruber M.J., Guyer M., He Y., Jin R., Kaufman J., Sampson N.A., Zaslavsky A.M, & Merikangas K.R. (2009). The National Comorbidity Survey Adolescent Supplement (NCS-A): III. Concordance of DSM-IV/CIDI diagnoses with clinical reassessments. Journal of the American Academy of Child and Adolescent Psychiatry, 48(4), 386-399.

Publication 2009

Adolescent Agoraphobia Bipolar Disorder Diagnosis Drug Abuse Face Households Panic Disorder Parent Sadness Substance Abuse Substance Dependence

Development and Validation of Perceived Substance Abuse Stigma Scale

Cited 31 times

An initial pool of 12 items was created by modifying the discrimination–devaluation measure perceived by Link et al. (1997) (link) to refer to “someone who has been treated for substance use” instead of mental illness. The content validity of the preliminary scale was assessed by ratings of seven experts. Expert raters were considered to be professionals who had previously published an article in a peer-reviewed journal on the stigma of substance abuse. An electronic bibliography search identified a total of 23 such experts for whom e-mail addresses could be obtained.
The identified experts were contacted via e-mail, asked to participate, and provided an Internet link to our web-based rating task. Those who responded (n = 7) were provided with a description of stigma and perceived stigma and the initial 12-item version of the PSAS adapted from Link et al. (1997) (link). Raters then rated each item on two dimensions. First, they rated each item for fit, whether each item reflected a component of stigma as directed toward those with substance problems using a 4-point Likert-type rating scale ranging from 1 (not at all) to 4 (very much). Second, they rated each item in terms of overall quality on a 4-point Likert-type scale ranging from 1 (poor) to 4 (excellent).
Across the 12 items, the mean rating for fit was 3.25 and for quality, 3.32. Three items had average fit ratings below a 3 (moderate fit) and were removed from the scale. One of these three items also had a mean quality rating below a 3. The remaining nine items all had average ratings above 3 with average fit ratings of 3.46 and average quality ratings of 3.44, indicating that these items fit the content of the scale and were clear and well written. Raters were also asked to suggest domains of stigma relevant to substance addiction that were not assessed by our scale. None of the raters suggested any additional domains for inclusion in the scale.
These initial efforts resulted in a nine-item scale that was used as part of a questionnaire packet. The items were rated on a 7-point Likert scale ranging from strongly disagree (1) to strongly agree (7). Six items were reverse-scored.

Luoma J.B., O’Hair A.K., Kohlenberg B.S., Hayes S.C, & Fletcher L. (2010). The Development and Psychometric Properties of a New Measure of Perceived Stigma Toward Substance Users. Substance use & misuse, 45(1-2), 47-57.

Publication 2010

AN 12 Discrimination, Psychology Mental Disorders NPEPPS protein, human Substance Abuse Substance Dependence Substance Use

Most recents protocols related to «Substance Dependence»

Longitudinal Neuroimaging of PTSD Patients

Participant recruitment for the study commenced from August 2009 and ended June 2014. Participants were 51 treatment-seeking patients, 36 of whom had viable imaging data at baseline and 27 of these with follow-up MRI data. For this manuscript we include data from the 27 PTSD patients (13 females; age = 40.9 ± 11.8 years) who completed MRIs at both visits (see CONSORT diagram in Supplementary Fig. S1).
The PTSD sample had developed PTSD after experiencing assault, childhood abuse, motor vehicle accidents, or during police duties. PTSD was diagnosed according to DSM-IV criteria by masters or doctoral level clinical psychologists using the Clinician Administered PTSD Scale (CAPS; [19 (link)]. Symptoms were detected according to the ‘2/1’ method, in which symptoms were experienced for at least twice a month and caused moderate levels of distress. Levels of depression and anxiety were assessed using the Depression, Anxiety, and Stress Scale (DASS). Patients that reported psychosis, bipolar disorder, substance dependence, neurological disorders, or moderate to severe brain injury were excluded from the study. Patients taking medication (10 on selective serotonin reuptake inhibitors) were included on the condition that the dosage was stable for the previous two months and continued to be stable for the duration of the study. In addition, 21 controls (9 females; mean age 36.9 ± 14.0 years) who were age and gender-matched to the PTSD group were included in the study. Control participants had not experienced a Criterion A stressor and did not have an Axis I disorder, as assessed by the Mini International Neuropsychiatric Interview (MINI version 5.5) [20 (link)].
All participants underwent clinical and imaging assessments at baseline and again 12 weeks later. This study was approved by the Western Sydney Area Health Service Human Research Ethics Committee and informed consent was obtained from participants.

Korgaonkar M.S., Felmingham K.L., Malhi G.S., Williamson T.H., Williams L.M, & Bryant R.A. (2023). Changes in neural responses during affective and non-affective tasks and improvement of posttraumatic stress disorder symptoms following trauma-focused psychotherapy. Translational Psychiatry, 13, 85.

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Publication 2023

Anxiety Bipolar Disorder Brain Injuries Drug Abuse Epistropheus Ethics Committees, Research Females Gender Homo sapiens Magnetic Resonance Imaging Nervous System Disorder Patients Pharmaceutical Preparations Physicians Post-Traumatic Stress Disorder Psychotic Disorders Selective Serotonin Reuptake Inhibitors Substance Dependence Traffic Accidents

Multidimensional Predictors of Substance Dependence

All statistical analyses were conducted using R version 4.2.1; there were no missing data in the analyses. To compare participants regardless of their preferred nicotine consumption method (cigarettes or e-cigarettes), a composite nicotine dependence variable was created by taking the highest value of the FTCD and e-FTCD scores; this gave each participant a single nicotine dependence score. Age of onset of regular use for each of the substances was calculated by subtracting reported years of regular use from current age; a composite nicotine age of onset was created by taking the lowest age between cigarette and e-cigarette age of onset variables. Three different hierarchical multiple regressions were conducted, in which dependent variables were levels of dependence on alcohol, cannabis, and nicotine, respectively. In the regression analyses, predictor variables were added in four stages: the first models contained demographic variables (age, sex, and education), the second models contained personality variables (hopelessness, anxiety sensitivity, impulsivity, and sensation seeking), the third models contained ages of onset of regular use across substances, and the fourth models contained dependence on the other two substances. Regarding nicotine dependence, the fourth model included an additional variable: dual use of cigarettes and e-cigarettes. The variables were added in this order as it was the most chronologically plausible order, as demographic variables are from birth, personality is mostly stable from childhood, and age of first use comes before substance dependence. Finally, post-hoc analyses were conducted on significant variables in the final models for each of the regressions. Participants were categorized by dependence on each substance (dependent vs. non-dependent) using the cut-offs described in Section 2.2. For continuous variables, the means of each group were compared via Welch's t-tests; for categorical variables (e.g., sex and dual use of cigarettes/e-cigarettes), the ratios were compared via Pearson's χ² tests.

Shephard A., Dölek Ş, & Barrett S.P. (2023). Investigating predictors of problematic alcohol, cannabis, and nicotine use among legal users of all three substances. Frontiers in Psychiatry, 14, 1110415.

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Publication 2023

Anxiety Birth Cannabis Ethanol Hypersensitivity Nicotine Nicotine Dependence Substance Dependence

Substance Use and Personality Profiles

Canadian adults (19 years old or older; N = 516) were recruited, as part of a larger study, through Qualtrics Panels, a survey company with a large pool of potential participants. Researchers provided Qualtrics Panels with eligibility criteria, who then recruited participants to complete an online survey measuring demographic information, substance dependence, substance use history, and personality. Eligibility criteria included fluency in English and recreational use (past 30 days) of alcohol, cannabis, and nicotine (via cigarettes or e-cigarettes); potential participants who did not meet these criteria were screened out of the study. Participants were compensated for their part in the larger study. Mean participant age was 39.8 (SD = 12.4), there was roughly an equal number of males (n = 257) and females (n = 258), and most participants were Caucasian (69.8%). Regarding education, 26.2% had completed community or technical college and 41.7% had obtained a university degree.

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Publication 2023

Adult Cannabis Caucasoid Races Eligibility Determination Ethanol Females Males Nicotine Substance Dependence Substance Use

Identifying Patterns in Opioid Prescribing for Chronic Non-Cancer Pain

Ethical approval for this study was granted by LJMU Research Ethics Committee. A data sharing agreement for the extraction of patient level data was drawn up between LJMU and Liverpool CCG, in accordance with the Data Protection Act 2018. The inclusion criteria for patients were: age over 18 years; CNCP diagnosis; in receipt of any opioid prescription between August 2016 and August 2018. Patients with a history of substance dependence (current read code for dependence or free text entry indicating dependence in patient’s record), and those prescribed opioids to manage cancer pain (current read code for cancer diagnosis; prescribed opioid predominantly used in management of cancer pain not CNCP) were excluded. While we were interested in opioid prescribing for CNCP, the extracted data demonstrated that there was a great deal of heterogeneity in the coding of linked problems (providing reason why an opioid prescription was issued) with over 60,000 distinct reported problems. Categories of CNCP were created by grouping together similar conditions and conferring with a consultant anaesthetist (BF) to develop typologies. The most common linked problem for which opioids were prescribed was for musculoskeletal pain (n = 16,137) specifically back pain (n = 10,974) and arthritis (n = 7,154). For a full list of the 78 categories and frequency of linked prescriptions see S1 File. Upon further investigation of the linked problems, it was evident that there were anomalies in coding of linked problems, with some codes reflecting that a patient may have initially requested an appointment for an alternative reason. See S2 File for full list of linked problems.

Begley E.K., Poole H.M., Sumnall H.R., Frank B.F, & Montgomery C. (2023). Opioid prescribing and social deprivation: A retrospective analysis of prescribing for CNCP in Liverpool CCG. PLOS ONE, 18(3), e0280958.

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Publication 2023

Anesthetist Arthritis Back Pain Cancer Pain Consultant Diagnosis Ethics Committees, Research Genetic Heterogeneity Malignant Neoplasms Management, Pain Opioids Patients Prescriptions Substance Dependence

Inhibitory Control Training for Binge-Spectrum Eating Disorders

We recruited adults with clinically significant binge-spectrum EDs (N = 63), including BN-spectrum and BED, from the community for participation in a parent trial of CBT-E augmented by inhibitory control training (clinicaltrials.gov identifier: NCT04076553). Participants were included in the parent trial if they were between 18 and 55 years old, experienced an average of at least one objective binge eating episode per week over the previous 12 weeks, had stable psychiatric medication for the past 3 months (if applicable), had a reliable Internet connection, and were located in the USA and willing and able to participate in remote intervention and assessments. Participants were excluded if they were not fluent in English, were below a BMI of 18.5, were planning to begin (in the next 6 months) or currently participating in another weight loss treatment or psychotherapy for binge eating and/or weight loss. Participants were not eligible if they had a diagnosis of autism spectrum disorder or intellectual disability, were currently experiencing other severe psychopathology that would limit their ability to engage in the treatment program (e.g., severe depression, substance dependence, active psychotic disorder), or demonstrated high levels of inhibitory control (and thus would not benefit from the inhibitory control training portion of the treatment). Participants were also excluded from the parent trial if at least half of their binge episodes were composed nearly entirely of fruit/vegetables (i.e., 80% or more of the total food consumed during binges were raw fruits and vegetables) because of the intention to test inhibitory control training toward more traditional binge foods (e.g., pizza, ice cream). Participants were not eligible if they had experienced a recent head trauma, neurological condition, or brain condition that would interfere with completion of daily computer trainings.
The current study represents a secondary analysis of data from the parent trial. For the current study’s analyses, participants were included if they completed at least one session of treatment. Of these 63 individuals, 11 (17.5% of sample) dropped out of treatment prior to completing all 12 sessions. Attrition rates in our study are comparable to rates in other trials of CBT for EDs [3 (link)]. Participants completed 10.48 treatment sessions on average (SD = 3.51). In the current sample, 29 participants were randomized to the “sham” training condition, and 34 to the inhibitory control training condition. Table 1 depicts participant demographic information, diagnoses, and BMI.Table 1

Participant demographics and baseline characteristics by diagnosis (N = 63)

	Bulimia nervosa (incl. subthreshold; n = 27)		Binge eating disorder (n = 36)
	Mean or n	SD or %	Mean or n	SD or %
Age	37.2	12.7	42.9	9.6
Gender
Male	1	3.7%	6	16.7
Female	26	96.3%	30	83.3
Race
White	21	77.8%	31	86.1%
African American	2	7.4%	4	11.1%
Asian	2	7.4%	0	0%
Multiracial	1	3.7%	1	2.8%
Unknown/prefer not to say	1	3.7%	0	0%
Ethnicity
Hispanic/Latinx	4	14.8%	3	8.3%
Non-Hispanic	23	85.2%	33	91.7%
Disordered eating behaviors
Binge episodes past 3 months	91.4	58.9	91.3	48.6
Compensatory behaviors 3 months	71.7	77.3	0.5	1.4
BMI	31.3	8.3	36.2	12.6
Fear of weight gain
GFFS 10-item scores	30.9	5.7	23.8	7.4

BMI body mass index, GFFS Goldfarb Fear of Fat Scale

Butler R.M., Lampe E., Trainor C, & Manasse S.M. (2023). Fear of weight gain during cognitive behavioral therapy for binge-spectrum eating disorders. Eating and Weight Disorders, 28(1), 29.

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Publication 2023

Adult Autism Spectrum Disorders Brain Diseases Bulimia Nervosa Craniocerebral Trauma Diagnosis Disorder, Binge-Eating Ethnicity Fear Feeding Behaviors Food Fruit Hispanics Ice Cream Index, Body Mass Intellectual Disability Negroid Races Nervous System Disorder Parent Pharmaceutical Preparations Psychological Inhibition Psychotherapy Psychotic Disorders Substance Dependence Tooth Attrition Vegetables

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What is Substance Dependence?

Substance Dependence is a complex condition characterized by a strong, compulsive desire to consume a particular substance, such as drugs or alcohol, despite harmful consequences. This disorder involves both physical and psychological dependence, leading to tolerance, withdrawal symptoms, and continued use even when it negatively impacts a person's health and well-being. Effective treatment often requires a combination of behavioral therapies and medication management to address the multifaceted nature of this challenging condition.

What are the common symptoms of Substance Dependence?

The main symptoms of Substance Dependence include a craving or strong urge to use the substance, increased tolerance (needing more of the substance to achieve the same effect), withdrawal symptoms when the substance is stopped, and continued use despite negative impacts on the person's life, such as health problems, relationship issues, or problems at work or school. People with Substance Dependence may also experience mood changes, sleep disturbances, and difficulty controlling their substance use.

How can PubCompare.ai assist with Substance Dependence research?

PubCompare.ai can help researchers optimize their Substance Dependence studies in several ways. First, the platform allows you to screen protocol literature more efficiently by leveraging AI to pinpoint critical insights. This helps researchers identify the most effective protocols related to Substance Dependence for their specific research goals. The platform's AI-driven analysis can also highlight key differences in protocol effectiveness, enabling you to choose the best option for reproducibility and accuracy. By using PubCompare.ai, researchers can enhance the quality and efficiency of their Substance Dependence studies, leading to better outcomes and more impactful findings.

Are there different types or variations of Substance Dependence?

Yes, there are several variations and types of Substance Dependence, including dependence on alcohol, opioids, stimulants (such as cocaine or amphetamines), cannabis, and sedatives or hypnotics (such as benzodiazepines). Each type of Substance Dependence can have its own unique symptoms, withdrawal patterns, and treatment approaches. Additionally, some people may develop dependence on multiple substances, known as polysubstance dependence, which can be particularly challenging to treat. Understanding the specific type of Substance Dependence is crucial for developing effective interventions and management strategies.

How can Substance Dependence be treated?

The treatment of Substance Dependence typically involves a combination of behavioral therapies and medication management. Behavioural therapies, such as cognitive-behavioral therapy (CBT) and contingency management, can help individuals develop coping strategies, address underlying psychological factors, and build a support network. Medications, such as opioid agonists (e.g., methadone, buprenorphine) for opioid dependence or acamprosate and naltrexone for alcohol dependence, can help manage withdrawal symptoms, reduce cravings, and prevent relapse. A comprehensive, individualized approach that addresses the physical, psychological, and social aspects of Substance Dependence is often the most effective way to achieve long-term recovery.

More about "Substance Dependence"

Substance Dependence, also known as Drug Addiction or Substance Use Disorder (SUD), is a complex and challenging condition characterized by a strong, compulsive desire to consume a particular substance, such as drugs or alcohol, despite the harmful consequences.
This disorder involves both physical and psychological dependence, leading to tolerance, withdrawal symptoms, and continued use despite negative impacts on health and well-being.
Effective treatment often requires a combination of behavioral therapies, such as cognitive-behavioral therapy (CBT) and contingency management, as well as medication management, including the use of pharmacotherapies like methadone, buprenorphine, and naltrexone.
Understanding the intricacies of Substance Dependence is crucial for developing better interventions and improving outcomes for those affected by this condition.
Researchers and clinicians may utilize various statistical software packages, such as SAS version 9.4, Stata 15, SPSS Statistics 22, Avance III 600 MHz, STATA MP11, Trio scanner, Stata 11, Stata 14, 400 MHz spectrometer, and Stata 13, to analyze data and inform their studies on Substance Dependence.
These tools can help researchers identify patterns, trends, and associations that can inform the development of more effective treatment strategies and prevention programs.
Ackonwledging the complexities of Substance Dependence and the need for comprehensive, evidence-based approaches is key to addressing this challenging condition and improving the lives of those affected.
By combining the insights gained from research, clinical experience, and advanced analytical tools, we can continue to make progress in understanding and managing Substance Dependence.