Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
Hostname: page-component-cd9895bd7-7cvxr Total loading time: 0 Render date: 2024-12-21T04:38:13.870Z Has data issue: false hasContentIssue false

Part II - Clinical and Research Methods in the Addictions

Published online by Cambridge University Press:  13 July 2020

Edited by
Steve Sussman
Steve Sussman
Affiliation:
University of Southern California
Get access

Summary

A summary is not available for this content so a preview has been provided. Please use the Get access link above for information on how to access this content.
Image of the first page of this content. For PDF version, please use the ‘Save PDF’ preceeding this image.'
Type
Chapter
Information
The Cambridge Handbook of Substance and Behavioral Addictions , pp. 51 - 118
Publisher: Cambridge University Press
Print publication year: 2020

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

References

Babbs, R. K., Sun, X., Felsted, J., et al. (2013). Decreased caudate response to milkshake is associated with higher body mass index and greater impulsivity. Physiology & Behavior, 121, 103–111.CrossRefGoogle ScholarPubMed
Benedict, C., Brooks, S. J., O’Daly, O. G., et al. (2012). Acute sleep deprivation enhances the brain’s response to hedonic food stimuli: an fMRI study. Journal of Clinical Endocrinology and Metabolism, 97(3), E443–447. doi: 10.1210/jc.2011-2759Google Scholar
Berkman, E. T. & Falk, E. B. (2013). Beyond brain mapping using neural measures to predict real-world outcomes. Current Directions in Psychological Science, 22(1), 45–50.CrossRefGoogle ScholarPubMed
Berridge, K. C. (2012). From prediction error to incentive salience: mesolimbic computation of reward motivation. European Journal of Neuroscience, 35(7), 1124–1143. doi: 10.1111/j.1460-9568.2012.07990.xGoogle Scholar
Blum, K., Cull, J. G., Braverman, E. R. & Comings, D. E. (1996). Reward deficiency syndrome. American Scientist, 84(2), 132–145.Google Scholar
Boswell, R. G., Sun, W., Suzuki, S. & Kober, H. (2018). Training in cognitive strategies reduces eating and improves food choice. Proceedings of the National Academy of Sciences, 115(48), E11238–E11247.CrossRefGoogle ScholarPubMed
Buhle, J. T., Silvers, J. A., Wager, T. D., et al. (2014). Cognitive reappraisal of emotion: a meta-analysis of human neuroimaging studies. Cerebral Cortex, 24(11), 2981–2990.Google Scholar
Burger, K. S. (2017). Frontostriatal and behavioral adaptations to daily sugar-sweetened beverage intake: a randomized controlled trial. The American Journal of Clinical Nutrition, 105(3), 555–563. doi: 10.3945/ajcn.116.140145CrossRefGoogle ScholarPubMed
Chen, G., Saad, Z. S., Britton, J. C., Pine, D. S. & Cox, R. W. (2013). Linear mixed-effects modeling approach to fMRI group analysis. Neuroimage, 73, 176–190.CrossRefGoogle ScholarPubMed
Crockford, D. N., Goodyear, B., Edwards, J., et al. (2005). Cue-induced brain activity in pathological gamblers. Biological Psychiatry, 58(10), 787–795.CrossRefGoogle ScholarPubMed
Dawe, S. & Loxton, N. J. (2004). The role of impulsivity in the development of substance use and eating disorders. Neuroscience and Biobehavioral Reviews, 28(3), 343–351. doi: 10.1016/j.neubiorev.2004.03.007CrossRefGoogle ScholarPubMed
Fowler, J. S., Volkow, N. D., Ding, Y. S., et al. (1998). PET and the study of drug action in the human brain. Pharmaceutical News, 5, 11–16.Google Scholar
Frank, G. K. W., Reynolds, J. R., Shott, M. E., et al. (2012). Anorexia nervosa and obesity are associated with opposite brain reward response. Neuropsychopharmacology, 37(9), 2031–2046.Google Scholar
Goldstein, R. Z. & Volkow, N. D. (2011). Dysfunction of the prefrontal cortex in addiction: neuroimaging findings and clinical implications. Nature Reviews Neuroscience, 12(11), 652.Google Scholar
Goldstein, R. Z., Tomasi, D., Alia-Klein, N., et al. (2009). Dopaminergic response to drug words in cocaine addiction. Journal of Neuroscience, 29(18), 6001–6006.Google Scholar
Goudriaan, A. E., et al. (2010). Brain activation patterns associated with cue reactivity and craving in abstinent problem gamblers, heavy smokers and healthy controls: an fMRI study. Addiction Biology, 15(4), 491–503.CrossRefGoogle ScholarPubMed
Haller, S. & Bartsch, A. J. (2009). Pitfalls in fMRI. European Radiology, 19(11), 2689–2706.CrossRefGoogle ScholarPubMed
Koehler, S., Hasselmann, E., Wüstenberg, T., et al. (2015). Higher volume of ventral striatum and right prefrontal cortex in pathological gambling. Brain Structure and Function, 220(1), 469–477. doi: 10.1007/s00429-013-0668-6CrossRefGoogle ScholarPubMed
Koob, G. F. & Volkow, N. D. (2010). Neurocircuitry of addiction. Neuropsychopharmacology, 35(1), 217–238. doi: http://dx.doi.org/10.1038/npp.2009.110CrossRefGoogle ScholarPubMed
Lieberman, M. D. & Cunningham, W. A. (2009). Type I and Type II error concerns in fMRI research: re-balancing the scale. Social Cognitive and Affective Neuroscience, 4(4), 423–428.CrossRefGoogle ScholarPubMed
Liu, J., Liang, J., Qin, W., et al. (2009). Dysfunctional connectivity patterns in chronic heroin users: an fMRI study. Neuroscience Letters, 460(1), 72–77. doi: 10.1016/j.neulet.2009.05.038Google Scholar
Martinez, D., Slifstein, M., Narendran, R., et al. (2009). Dopamine D1 receptors in cocaine dependence measured with PET and the choice to self-administer cocaine. Neuropsychopharmacology, 34(7), 1774.CrossRefGoogle ScholarPubMed
Morgan, V. L., Dawant, B. M., Li, Y. & Pickens, D. R. (2007). Comparison of fMRI statistical software packages and strategies for analysis of images containing random and stimulus-correlated motion. Computerized Medical Imaging and Graphics, 31(6), 436–446.CrossRefGoogle ScholarPubMed
Poldrack, R. A., Fletcher, P. C., Henson, R. N., et al. (2008). Guidelines for reporting an fMRI study. Neuroimage, 40(2), 409–414.Google Scholar
Potenza, M. N., Steinberg, M. A., Skudlarski, P., et al. (2003). Gambling urges in pathological gambling: a functional magnetic resonance imaging study. Archives of General Psychiatry, 60(8), 828–836. doi: 10.1001/archpsyc.60.8.828CrossRefGoogle ScholarPubMed
Volkow, N., Chang, L., Wang, G. J., et al. (2001). Low level of brain dopamine D2 receptors in methamphetamine abusers: association with metabolism in the orbitofrontal cortex. American Journal of Psychiatry, 158(12), 2015–2021.Google Scholar
Volkow, N. D., Fowler, J. S., Wang, G. J., et al. (2009). Imaging dopamine’s role in drug abuse and addiction. Neuropharmacology, 56, 3–8.Google Scholar
Vul, E. & Pashler, H. (2012). Voodoo and circularity errors. Neuroimage, 62(2), 945–948.CrossRefGoogle ScholarPubMed
Wang, G.-J., Volkow, N. D., Thanos, P. K. & Fowler, J. S. (2004). Similarity between obesity and drug addiction as assessed by neurofunctional imaging: a concept review. Journal of Addictive Diseases, 23(3), 39–53.CrossRefGoogle ScholarPubMed
Wilcox, C. E., Braskie, M. N., Kluth, J. T. & Jagust, W. J. (2010). Overeating behavior and striatal Dopamine with 6-[1 8 F]-Fluoro-L-m-Tyrosine PET. Journal of Obesity, 2010, 909348.Google Scholar
Woo, C. W., Krishnan, A. & Wager, T. D. (2014). Cluster-extent based thresholding in fMRI analyses: pitfalls and recommendations. Neuroimage, 91, 412–419.CrossRefGoogle ScholarPubMed
Yarkoni, T. (2009). Big correlations in little studies: Inflated fMRI correlations reflect low statistical power – Commentary on Vul et al. (2009). Perspectives on Psychological Science, 4(3), 294–298.CrossRefGoogle ScholarPubMed
Zhao, L. Y., Tian, J., Wang, W., et al. (2012). The role of dorsal anterior cingulate cortex in the regulation of craving by reappraisal in smokers. PLoS ONE, 7(8) e43598.Google Scholar

References

Abrams, D. B., Monti, P. M., Carey, K. B., Pinto, R. P. & Jacobus, S. I. (1988). Reactivity to smoking cues and relapse: Two studies of discriminant validity. Behaviour Research and Therapy, 26(3), 225–233. doi:10.1016/0005-7967(88)90003-4Google Scholar
Ait-Daoud, N., Seneviratne, C., Smith, J. B., et al. (2012). Preliminary evidence for cue-induced alcohol craving modulated by serotonin transporter gene polymorphism rs1042173. Frontiers in Psychiatry, 3. doi:10.3389/fpsyt.2012.00006CrossRefGoogle ScholarPubMed
American Psychiatric Association (2013). Diagnostic and Statistical Manual of Mental Disorders (5th edition). Washington, DC: American Psychiatric Association Publishing.Google Scholar
Amlung, M. T., Acker, J., Stojek, M. K., Murphy, J. G. & MacKillop, J. (2011). Is talk “cheap”? An initial investigation of the equivalence of alcohol purchase task performance for hypothetical and actual rewards. Alcoholism: Clinical and Experimental Research, 36(4), 716–724. doi:10.1111/j.1530-0277.2011.01656.xCrossRefGoogle ScholarPubMed
Arnsten, A. F. T. (2009). Stress signalling pathways that impair prefrontal cortex structure and function. Nature Reviews Neuroscience, 10(6), 410–422. https://doi.org/10.1038/nrn2648CrossRefGoogle ScholarPubMed
Avants, S., Margolin, A., Kosten, T. R. & Cooney, N. L. (1995). Differences between responders and nonresponders to cocaine cues in the laboratory. Addictive Behaviors, 20(2), 215–224. doi:10.1016/0306-4603(94)00066-2Google Scholar
Balodis, I. M., Wynne-Edwards, K. E. & Olmstead, M. C. (2010). The other side of the curve: Examining the relationship between pre-stressor physiological responses and stress reactivity. Psychoneuroendocrinology, 35(9), 1363–1373. https://doi.org/10.1016/j.psyneuen.2010.03.011CrossRefGoogle ScholarPubMed
Balodis, I. M., Kober, H., Worhunsky, P. D., et al. (2012). Diminished frontostriatal activity during processing of monetary rewards and losses in pathological gambling. Biological Psychiatry, 71(8), 749–757.Google Scholar
Becirevic, A., Reed, D. D., Amlung, M., et al. (2017). An initial study of behavioral addiction symptom severity and demand for indoor tanning. Experimental and Clinical Psychopharmacology, 25(5), 346–352. doi:10.1037/pha0000146CrossRefGoogle ScholarPubMed
Carter, B. L. & Tiffany, S. T. (1999). Meta-analysis of cue-reactivity in addiction research. Addiction, 94(3), 327–340. doi:10.1046/j.1360-0443.1999.9433273.xCrossRefGoogle ScholarPubMed
Chen, X., Gianferante, D., Hanlin, L., et al. (2017). HPA-axis and inflammatory reactivity to acute stress is related with basal HPA-axis activity. Psychoneuroendocrinology, 78, 168–176. https://doi.org/10.1016/j.psyneuen.2017.01.035Google Scholar
Clark, G. I., Rock, A. J., Mckeith, C. F. & Coventry, W. L. (2016). Cue-reactive rationality, visual imagery and volitional control predict cue-reactive urge to gamble in poker-machine gamblers. Journal of Gambling Studies, 33(3), 807–823. doi:10.1007/s10899-016-9650-6Google Scholar
DeLongis, A. Folkman, S. & Lazarus, R. S. (1988). The impact of daily stress on health and mood: Psychological and social resources as mediators. Journal of Personality and Social Psychology, 54(3), 486–495. https://doi.org/10.1037/0022-3514.54.3.486CrossRefGoogle ScholarPubMed
Dickerson, S. S. & Kemeny, M. E. (2004). Acute stressors and cortisol responses: A theoretical integration and synthesis of laboratory research. Psychological Bulletin, 130(3), 355–391. https://doi.org/10.1037/0033-2909.130.3.355CrossRefGoogle ScholarPubMed
Dixon, M. J., Harrigan, K. A., Santesso, D. L., et al. (2013a). The impact of sound in modern multiline video slot machine play. Journal of Gambling Studies, 30(4), 913–929. doi:10.1007/s10899-013-9391-8CrossRefGoogle Scholar
Dixon, M. J., Collins, K., Harrigan, K. A., Graydon, C. & Fugelsang, J. A. (2013b). Using sound to unmask losses disguised as wins in multiline slot machines. Journal of Gambling Studies, 31(1), 183–196. doi:10.1007/s10899-013-9411-8CrossRefGoogle Scholar
Dixon, M. J., Graydon, C., Harrigan, K. A., et al. (2014). The allure of multi-line games in modern slot machines. Addiction, 109(11), 1920–1928. doi:10.1111/add.12675Google Scholar
Dixon, M. J., Larche, C. J., Stange, M., Graydon, C. & Fugelsang, J. A. (2017). Near-misses and stop buttons in slot machine play: An investigation of how they affect players, and may foster erroneous cognitions. Journal of Gambling Studies, 34(1), 161–180. doi:10.1007/s10899-017-9699-xCrossRefGoogle Scholar
Dolinsky, Z. S. & Babor, T. F. (1997). Ethical, scientific and clinical issues in ethanol administration research involving alcoholics as human subjects. Addiction, 92, 1087–1097.CrossRefGoogle ScholarPubMed
Drummond, D. C., et al. (2000). Craving research: Future directions. Addiction, 95(8s2), 247–255. doi:10.1046/j.1360-0443.95.8s2.13.xCrossRefGoogle ScholarPubMed
Enoch, M.-A., et al. (2009). Ethical considerations for administering alcohol or alcohol cues to treatment-seeking alcoholics in a research setting: Can the benefits to society outweigh the risks to the individual? Alcoholism: Clinical and Experimental Research, 33(9), 1508–1512. doi:10.1111/j.1530-0277.2009.00988.xGoogle Scholar
Epstein, L. H., Handley, E. A., Dearing, K. K., et al. (2006). Purchases of food in youth. Influence of price and income. Psychological Science, 17(1), 82–89. doi:10.1111/j.1467-9280.2005.01668.xCrossRefGoogle ScholarPubMed
Epstein, L. H., Salvy, S. J., Carr, K. A., Dearing, K. K. & Bickel, W. K. (2010). Food reinforcement, delay discounting and obesity. Physiology & Behavior, 100(5), 438–445. doi:10.1016/j.physbeh.2010.04.029CrossRefGoogle ScholarPubMed
Epstein, L. H., Finkelstein, E., Raynor, H., et al. (2015). Experimental analysis of the effect of taxes and subsides on calories purchased in an on-line supermarket. Appetite, 95, 245–251. doi:10.1016/j.appet.2015.06.020CrossRefGoogle Scholar
Ferguson, C. J., Coulson, M. & Barnett, J. (2011). A meta-analysis of pathological gaming prevalence and comorbidity with mental health, academic and social problems. Journal of Psychiatric Research, 45(12), 1573–1578. doi:10.1016/j.jpsychires.2011.09.005Google Scholar
Ferrer-García, M., Gutiérrez-Maldonado, J., Pla-Sanjuanelo, J., et al. (2017). A randomised controlled comparison of second-level treatment approaches for treatment-resistant adults with bulimia nervosa and binge eating disorder: Assessing the benefits of virtual reality cue exposure therapy. European Eating Disorders Review, 25(6), 479–490. doi:10.1002/erv.2538CrossRefGoogle ScholarPubMed
Filbey, F. M., Claus, E., Audette, A. R., et al. (2007). Exposure to the taste of alcohol elicits activation of the mesocorticolimbic neurocircuitry. Neuropsychopharmacology, 33(6), 1391–1401. doi:10.1038/sj.npp.1301513Google Scholar
Filbey, F. M., Ray, L., Smolen, A., et al. (2008). Differential neural response to alcohol priming and alcohol taste cues is associated with DRD4 VNTR and OPRM1 genotypes. Alcoholism: Clinical and Experimental Research, 32(7), 1113–1123. doi:10.1111/j.1530-0277.2008.00692.xCrossRefGoogle ScholarPubMed
Filbey, F. M., Schacht, J. P., Myers, U. S., Chavez, R. S. & Hutchison, K. E. (2009). Individual and additive effects of the CNR1 and FAAH genes on brain response to marijuana cues. Neuropsychopharmacology, 35(4), 967–975. doi:10.1038/npp.2009.200Google Scholar
Fosnocht, A. Q. & Briand, L. A. (2016). Substance use modulates stress reactivity. Behavioral and Physiological Outcomes, 166, 32–42. https://doi.org/10.1126/science.1249098.SleepGoogle Scholar
Franken, I. H. a., et al. (1999). Cue reactivity and effects of cue exposure in abstinent posttreatment drug users. Journal of Substance Abuse Treatment, 16(1), 81–85. doi:10.1016/s0740-5472(98)00004-xGoogle Scholar
Goeders, N. E. (2003). The impact of stress on addiction. European Neuropsychopharmacology, 13(6), 435–441. https://doi.org/10.1016/j.euroneuro.2003.08.004CrossRefGoogle ScholarPubMed
Goldman, R. L., Borckardt, J. J., Frohman, H. A., et al. (2011). Prefrontal cortex transcranial direct current stimulation (tDCS) temporarily reduces food cravings and increases the self-reported ability to resist food in adults with frequent food craving. Appetite, 56(3), 741–746. doi:10.1016/j.appet.2011.02.013CrossRefGoogle ScholarPubMed
Goodman, W. K., Janson, J. & Wolf, J. M. (2017). Meta-analytical assessment of the effects of protocol variations on cortisol responses to the Trier Social Stress Test. Psychoneuroendocrinology, 80, 26–35. https://doi.org/10.1016/j.psyneuen.2017.02.030Google Scholar
Gruenewald, T. L., Kemeny, M. E., Aziz, N. & Fahey, J. L. (2004). Acute threat to the social self: Shame, social self-esteem, and cortisol activity. Psychosomatic Medicine, 66(6), 915–924. https://doi.org/10.1097/01.psy.0000143639.61693.efGoogle Scholar
Hartston, H. (2012). The case for compulsive shopping as an addiction. Journal of Psychoactive Drugs, 44(1), 64–67. doi:10.1080/02791072.2012.660110Google Scholar
Jarmolowicz, D. P. & Schneider, T. D. (2020). Behavioral economics and addictive disorders. In Sussman, S. (Ed.) The Cambridge Handbook of Substance and Behavioral Addictions, Cambridge, UK: Cambridge University Press, 12–22.Google Scholar
Kirschbaum, C., Pirke, K.-M. & Hellhammer, D. H. (1993). The “Trier Social Stress Test” – A tool for investigating psychobiological stress responses in a laboratory setting. Neuropsychobiology, 28, 76–81.CrossRefGoogle Scholar
Kirschbaum, C. & Hellhammer, D. H. (1994). Review: Salivary cortisol in psychoneuroendocrine research: Recent developments and applications. Psychoneuroendocrinology, 19(4), 313–333.CrossRefGoogle ScholarPubMed
Kirschbaum, C., Kudielka, B. M., Gaab, J., Schommer, N. C. & Hellhammer, D. H. (1999). Impact of gender, menstrual cycle phase, and oral contraceptives on the activity of the hypothalamus-pituitary-adrenal axis. Psychosomatic Medicine, 61(2), 154–162. https://doi.org/10.1097/00006842-199903000-00006CrossRefGoogle ScholarPubMed
Koob, G. F. (1999). Corticotopin-releasing factor, norephinephrine, and stress. Stress: The International Journal on the Biology of Stress, 1800, 47–59. https://doi.org/10.1016/j.bbagen.2009.07.018Google Scholar
Koob, G. & Kreek, M. J. (2007). Stress, dysregulation of drug reward pathways, and the transition to drug dependence. American Journal of Psychiatry, 164(8), 1149–1159. https://doi.org/10.1176/appi.ajp.2007.05030503CrossRefGoogle ScholarPubMed
Kudielka, B. M., Buske-Kirschbaum, A., Hellhammer, D. H. & Kirschbaum, C. (2004). HPA axis responses to laboratory psychosocial stress in healthy elderly adults, younger adults, and children: Impact of age and gender. Psychoneuroendocrinology, 29(1), 83–98. https://doi.org/10.1016/S0306-4530(02)00146-4Google Scholar
Kuhn, S. & Gallinat, J. (2011). Common biology of craving across legal and illegal drugs – A quantitative meta-analysis of cue-reactivity brain response. European Journal of Neuroscience, 33(7), 1318–1326. doi:10.1111/j.1460-9568.2010.07590.xGoogle Scholar
Kushner, M. G., Abrams, K., Donahue, C., et al. (2007). Urge to gamble in problem gamblers exposed to a casino environment. Journal of Gambling Studies, 23(2), 121–132. doi:10.1007/s10899-006-9050-4Google Scholar
Laudat, M. H., Cerdas, S., Fournier, C., et al. (1988). Salivary cortisol measurement: A practical approach to assess pituitary-adrenal function. Journal of Clinical Endocrinology and Metabolism, 66(2), 343–348. https://doi.org/10.1210/jcem-66-2-343Google Scholar
Loo, J. A., Yan, W., Ramachandran, P. & Wong, D. T. (2010). Comparative human salivary and plasma proteomes. Journal of Dental Research, 89(10), 1016–1023. https://doi.org/10.1177/0022034510380414Google Scholar
MacKillop, J. & Lisman, S. A. (2008). Effects of a context shift and multiple context extinction on reactivity to alcohol cues. Experimental and Clinical Psychopharmacology, 16(4), 322–331. doi:10.1037/a0012686CrossRefGoogle ScholarPubMed
MacKillop, J., et al. (2010).Behavioral economic analysis of cue-elicited craving for alcohol. Addiction, 105(9), 1599–1607. doi:10.1111/j.1360-0443.2010.03004.xCrossRefGoogle ScholarPubMed
MacKillop, J., et al. (2012). Behavioral economic analysis of withdrawal- and cue-elicited craving for tobacco: An initial investigation. Nicotine & Tobacco Research, 14(12), 1426–1434., doi:10.1093/ntr/nts006Google Scholar
Maxwell, A. L., Loxton, N. J. & Hennegan, J. M. (2017). Exposure to food cues moderates the indirect effect of reward sensitivity and external eating via implicit eating expectancies. Appetite, 111, 135–141. doi:10.1016/j.appet.2016.12.037CrossRefGoogle ScholarPubMed
Márquez, S. & De la Vega, R. (2015). [Exercise addiction: An emergent behavioral disorder]. Nutricion Hospitalaria, 31(6). doi:10.3305/nh.2015.31.6.8934Google ScholarPubMed
McRae, A. L., Saladin, M. E., Brady, K. T., et al. (2006). Stress reactivity:  Biological and subjective responses to the cold pressor and Trier Social stressors. Human Psychopharmacology, 21(August), 377–385. https://doi.org/10.1002/hup.778CrossRefGoogle Scholar
Metrik, J., et al. (2016). Cue-elicited increases in incentive salience for marijuana: Craving, demand, and attentional bias. Drug and Alcohol Dependence, 167, 82–88. doi:10.1016/j.drugalcdep.2016.07.027Google Scholar
Meyer, G., Hauffa, B. P., Schedlowski, M., et al. S. (2000). Casino gambling increases heart rate and salivary cortisol in regular gamblers. Biological Psychiatry, 48(9), 948–953. https://doi.org/10.1016/S0006-3223(00)00888-XGoogle Scholar
Meyer, G., Schwertfeger, J., Exton, M. S., et al. (2004). Neuroendocrine response to casino gambling in problem gamblers. Psychoneuroendocrinology, 29(10), 1272–1280. https://doi.org/10.1016/j.psyneuen.2004.03.005CrossRefGoogle ScholarPubMed
Miller, G. A., Levin, D. N., Kozak, M. J., et al. (1987). Individual differences in imagery and the psychophysiology of emotion. Cognition and Emotion, 1(4), 367–390. https://doi.org/10.1080/02699938708408058Google Scholar
Miller, K. A. & Mays, D. (2020). Tanning as an addiction: The state of the research and implications for intervention. In Sussman, S. (Ed.) The Cambridge Handbook of Substance and Behavioral Addictions. Cambridge, UK: Cambridge University Press, pp. 362–372.Google Scholar
Mónok, K., Berczik, K., Urbán, R., et al. (2012). Psychometric properties and concurrent validity of two exercise addiction measures: A population wide study. Psychology of Sport and Exercise, 13(6), 739–746. doi:10.1016/j.psychsport.2012.06.003Google Scholar
Mogensen, M. & Jemec, G. B. (2010). The potential carcinogenic risk of tanning beds: clinical guidelines and patient safety advice. Cancer Management and Research, 2, 277–282.CrossRefGoogle ScholarPubMed
National Institute on Drug Abuse [NIDA] (2017). Trends & Statistics. NIDA, 24 April 2017. www.drugabuse.gov/related-topics/trends-statisticsGoogle Scholar
NIAAA. (2005). National Advisory Council on Alcohol Abuse and Alcoholism – Recommended Council Guidelines on Ethyl Alcohol Administration in Human Experimentation. Retrieved from www.niaaa.nih.gov/Resources/ResearchResources/Pages/job22.aspxGoogle Scholar
Niaura, R., Abrams, D., Demuth, B., Pinto, R. & Monti, P. (1989). Responses to smoking-related stimuli and early relapse to smoking. Addictive Behaviors, 14(4), 419–428. doi:10.1016/0306-4603(89)90029-4CrossRefGoogle ScholarPubMed
Niaura, R., Abrams, D. B., Shadel, W. G., et al. (1999). Cue exposure treatment for smoking relapse prevention: A controlled clinical trial. Addiction, 94(5), 685–695. doi:10.1046/j.1360-0443.1999.9456856.xGoogle Scholar
Niaura, R. (2002). Does “unlearning” ever really occur: Comment on Conklin & Tiffany. Addiction, 97(3), 357. doi:10.1046/j.1360-0443.2002.0055a.xGoogle Scholar
Norberg, M. M., et al. (2016). Craving cannabis: A meta-analysis of self-report and psychophysiological cue-reactivity studies. Addiction, 111(11), 1923–1934. doi:10.1111/add.13472Google Scholar
Park, C., Park, S. M., Gwak, A. R., et al. (2015). The effect of repeated exposure to virtual gambling cues on the urge to gamble. Addictive Behaviors, 41, 61–64. doi:10.1016/j.addbeh.2014.09.027CrossRefGoogle ScholarPubMed
Pavlov, I. P. & Anrep, G. V. (2003). Conditioned Reflexes. Mineola, NY: Dover Publications.Google Scholar
Pedram, P., Wadden, D., Amini, P., et al. (2013). Food addiction: Its prevalence and significant association with obesity in the general population. PLoS ONE, 8(9). doi:10.1371/journal.pone.0074832Google Scholar
Phan, J. M., Schneider, E., Peres, J., et al. (2017). Social evaluative threat with verbal performance feedback alters neuroendocrine response to stress. Hormones and Behavior, 96(September), 104–115. https://doi.org/10.1016/j.yhbeh.2017.09.007Google Scholar
Powell, J. (2006). Conditioned responses to drug-related stimuli: Is context crucial. Addiction, 90(8), 1089–1095. doi:10.1046/j.1360-0443.1995.90810897.xGoogle Scholar
Pruessner, J. C., Wolf, O. T., Hellhammer, D. H., et al. (1997). Free cortisol levels after awakening: A reliable biological marker for the assessment of adrenocortical activity. Life Sciences, 61(26), 2539–2549.Google Scholar
Pulido, C., Brown, S. A., Cummins, K., Paulus, M. P. & Tapert, S. F. (2010). Alcohol cue reactivity task development. Addictive Behaviors, 35(2), 84–90. doi:10.1016/j.addbeh.2009.09.006Google Scholar
Radley, J. J., Arias, C. M. & Sawchenko, P. E. (2006). Regional differentiation of the medial prefrontal cortex in regulating adaptive responses to acute emotional stress. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience, 26(50), 12967–12976. https://doi.org/10.1523/JNEUROSCI.4297-06.2006Google Scholar
Reed, D. D. (2015). Ultra-violet indoor tanning addiction: A reinforcer pathology interpretation. Addictive Behaviors, 41, 247–251. doi:10.1016/j.addbeh.2014.10.026Google Scholar
Reed, D. D, Naudé, G. P., Gelino, B. W. & Amlung, M. (2020). Behavioral economic considerations of novel addictions and nonaddictive behavior: Research and analytic methods. In Sussman, S. (Ed.) The Cambridge Handbook of Substance and Behavioral Addictions, Cambridge, UK: Cambridge University Press, pp. 73–86.Google Scholar
Rose, S. & Dhandayudham, A. (2014). Towards an understanding of Internet-based problem shopping behaviour: The concept of online shopping addiction and its proposed predictors. Journal of Behavioral Addictions, 3(2), 83–89. doi:10.1556/jba.3.2014.003Google Scholar
Saladin, M. E., Gray, K. M., Carpenter, M. J., et al. (2012). Gender differences in craving and cue reactivity to smoking and negative affect/stress cues. The American Journal on Addictions, 21(3), 210–220. doi:10.1111/j.1521-0391.2012.00232.xGoogle Scholar
Sayette, M. A., Shiffman, S., Tiffany, S. T., et al. (2000). The measurement of drug craving. Addiction, 95(8s2), 189–210. doi:10.1046/j.1360-0443.95.8s2.8.xGoogle Scholar
Schwabe, L., Haddad, L. & Schachinger, H. (2008). HPA axis activation by a socially evaluated cold-pressor test. Psychoneuroendocrinology, 33(6), 890–895. https://doi.org/10.1016/j.psyneuen.2008.03.001Google Scholar
Sephton, S. E., Lush, E., Dedert, E. A., et al. (2013). Diurnal cortisol rhythm as a predictor of lung cancer survival. Brain, Behavior, and Immunity, 30(Supplement), S163–S170. https://doi.org/10.1016/j.bbi.2012.07.019Google Scholar
Shaffer, H. J., Hall, M. N. & Bilt, J. V. (1999). Estimating the prevalence of disordered gambling behavior in the United States and Canada: A research synthesis. American Journal of Public Health, 89(9), 1369–1376. doi:10.2105/ajph.89.9.1369Google Scholar
Singh, A., Petrides, J. S., Gold, P. W., Chrousos, G. P. & Deuster, P. A. (1999). Differential hypothalamic-pituitary-adrenal axis reactivity to psychological and physical stress. The Journal of Clinical Endocrinology & Metabolism, 84(6), 1944–1948.Google Scholar
Sinha, R. (2008). Chronic stress, drug use, and vulnerability to addiction. Annals of the New York Academy of Sciences, 1141, 105–130. https://doi.org/10.1196/annals.1441.030.ChronicGoogle Scholar
Sinha, R., Talih, M., Malison, R., et al. (2003). Hypothalamic-pituitary-adrenal axis and sympatho-adreno-medullary responses during stress-induced and drug cue-induced cocaine craving states. Psychopharmacology, 170(1), 62–72. https://doi.org/10.1007/s00213-003-1525-8Google Scholar
Sterling, R. C., Dean, J., Weinstein, S. P., Murphy, J. & Gottheil, E. (2004). Gender differences in cue exposure reactivity and 9-month outcome. Journal of Substance Abuse Treatment, 27(1), 39–44. doi:10.1016/j.jsat.2004.03.008Google Scholar
Sussman, S. Y. (2017). Substance and Behavioral Addictions: Concepts, Causes, and Cures. Cambridge, United Kingdom: Cambridge University Press.Google Scholar
Symes, B. A. & Nicki, R. M. (1997). a preliminary consideration of cue-exposure, response-prevention treatment for pathological gambling behaviour: Two case studies. Journal of Gambling Studies, 13(2), 145–157. https://doi.org/10.1023/A:1024951301959Google Scholar
Szegedi, A., Lörch, B., Scheurich, A., et al. (2000). Cue exposure in alcohol dependent patients: preliminary evidence for different types of cue reactivity. Journal of Neural Transmission, 107(6), 721–730. doi:10.1007/s007020070073CrossRefGoogle ScholarPubMed
Templeton, J. A., Dixon, M. J., Harrigan, K. A. & Fugelsang, J. A. (2014). Upping the reinforcement rate by playing the maximum lines in multi-line slot machine play. Journal of Gambling Studies, 31(3), 949–964. doi:10.1007/s10899-014-9446-5Google Scholar
Testa, M., et al. (2006). Understanding alcohol expectancy effects: Revisiting the placebo condition. Alcoholism: Clinical and Experimental Research, 30(2), 339–348. doi:10.1111/j.1530-0277.2006.00039.xGoogle Scholar
Vadlin, S., Åslund, C. & Nilsson, K. W. (2015). Development and content validity of a screening instrument for gaming addiction in adolescents: The Gaming Addiction Identification Test (GAIT). Scandinavian Journal of Psychology, 56(4), 458–466. doi:10.1111/sjop.12196Google Scholar
Van Hedger, K., Bershad, A. K. & de Wit, H. (2017). Pharmacological challenge studies with acute psychosocial stress. Psychoneuroendocrinology, 85(August), 123–133. https://doi.org/10.1016/j.psyneuen.2017.08.020CrossRefGoogle ScholarPubMed
Watt, M. J., Weber, M. A., Davies, S. R. & Forster, G. L. (2017). Impact of juvenile chronic stress on adult cortico-accumbal function: Implications for cognition and addiction. Progress in Neuro-Psychopharmacology and Biological Psychiatry, 79(June), 136–154. https://doi.org/10.1016/j.pnpbp.2017.06.015CrossRefGoogle ScholarPubMed
Weinstein, A. & Weinstein, Y. (2014). Exercise addiction – Diagnosis, bio-psychological mechanisms and treatment issues. Current Pharmaceutical Design, 20(25), 4062–4069. doi:10.2174/13816128113199990614Google Scholar
Wohl, M. J. A., Matheson, K., Young, M. M. & Anisman, H. (2008). Cortisol rise following awakening among problem gamblers: Dissociation from comorbid symptoms of depression and impulsivity. Journal of Gambling Studies, 24(1), 79–90. https://doi.org/10.1007/s10899-007-9080-6Google Scholar
World Health Organization. (2015, December). Health in 2015: From MDGs to SDGs. Retrieved March 02, 2018, from www.who.int/gho/publications/mdgs-sdgs/en/Google Scholar

References

Ainslie, G. (1975). Specious reward: A behavioral theory of impulsiveness and impulse control. Psychological Bulletin, 82(4), 463–496. doi: 10.1037/h0076860Google Scholar
Amlung, M. T., Acker, J., Stojek, M. K., Murphy, J. G. & MacKillop, J. (2012). Is talk “cheap”? An initial investigation of the equivalence of alcohol purchase task performance for hypothetical and actual rewards. Alcoholism: Clinical and Experimental Research, 36(4), 716–724. https://doi.org/10.1111/j.1530-0277.2011.01656.xCrossRefGoogle ScholarPubMed
Aston, E. R., Farris, S. G., MacKillop, J. & Metrik, J. (2017). Latent factor structure of a behavioral economic marijuana demand curve. Psychopharmacology, 234(16), 2421–2429. https://doi.org/10.1007/s00213-017-4633-6Google Scholar
Aston, E. R., MacKillop, J., Cassidy, R. & Metrik, J. (2015). Initial validation of a marijuana purchase task. Drug and Alcohol Dependence, 146, e212. https://doi.org/10.1016/j.drugalcdep.2014.09.044Google Scholar
Aston, E. R., Metrik, J. & MacKillop, J. (2015). Further validation of a marijuana purchase task. Drug and Alcohol Dependence, 152, 32–38. https://doi.org/10.1016/j.drugalcdep.2015.04.025Google Scholar
Atchley, P. & Warden, A. C. (2012). The need of young adults to text now: Using delay discounting to assess informational choice. Journal of Applied Research in Memory and Cognition, 1(4), 229–234. doi: 10.1016/j.jarmac.2012.09.001Google Scholar
Becirevic, A., Reed, D. D. & Amlung, M. (2017). An initial investigation of the effects of tanning-related cues on demand and craving for indoor tanning. Psychological Record, 67(2), 149–160. https://doi.org/10.1007/s40732-017-0246-zGoogle Scholar
Becirevic, A., Reed, D. D., Amlung, M., et al. (2017). An initial study of behavioral addiction symptom severity and demand for indoor tanning. Experimental and Clinical Psychopharmacology, 25(5), 346–352. https://doi.org/10.1037/pha0000146Google Scholar
Bickel, W. K. & Madden, G. J. (1999). A comparison of measures of relative reinforcing efficacy and behavioral economics: Cigarettes and money in smokers. Behavioural Pharmacology, 10(6–7), 627–637. https://doi.org/10.1097/00008877-199908001-00023Google Scholar
Bickel, W. K., Degrandpre, R. J. & Higgins, S. T. (1993). Behavioral economics: A novel experimental approach to the study of drug dependence. Drug and Alcohol Dependence, 33(2), 173–192. https://doi.org/10.1016/0376-8716(93)90059-YGoogle Scholar
Bickel, W. K., DeGrandpre, R. J., Higgins, S. T. & Hughes, J. R. (1990). Behavioral economics of drug self-administration. I. Functional equivalence of response requirement and drug dose. Life Sciences, 47(17), 1501–1510.Google Scholar
Bickel, W. K., DeGrandpre, R. J., Hughes, J. R. & Higgins, S. T. (1991). Behavioral economics of drug self‐administration. II. A unit‐price analysis of cigarette smoking. Journal of the Experimental Analysis of Behavior, 55(2), 145–154.CrossRefGoogle Scholar
Bickel, W. K., Jarmolowicz, D. P., Mueller, E. T. & Gatchalian, K. M. (2011). The behavioral economics and neuroeconomics of reinforcer pathologies: Implications for etiology and treatment of addiction. Current Psychiatry Reports, 13(5), 406–415. https://doi.org/10.1007/s11920-011-0215-1CrossRefGoogle ScholarPubMed
Bickel, W. K., Johnson, M. W., Koffarnus, M. N., MacKillop, J. & Murphy, J. G. (2014). The behavioral economics of substance use disorders: reinforcement pathologies and their repair. Annual Review of Clinical Psychology, 10, 641–677. https://doi.org/10.1146/annurev-clinpsy-032813-153724Google Scholar
Bickel, W. K., Odum, A. L. & Madden, G. J. (1999). Impulsivity and cigarette smoking: Delay discounting in current, never, and ex-smokers. Psychopharmacology, 146(4), 447–454. doi: 10.1007/PL00005490Google Scholar
Borges, A. M., Kuang, J., Milhorn, H. & Yi, R. (2016). An alternative approach to calculating area-under-the-curve (AUC) in delay discounting research. Journal of the Experimental Analysis of Behavior, 106(2), 145–155. doi: 10.1002/jeab.219Google Scholar
Bradshaw, C. M. & Killeen, P. R. (2012). A theory of behaviour on progressive ratio schedules, with applications in behavioural pharmacology. Psychopharmacology (Berl), 222(4), 549–564. https://doi.org/10.1007/s00213-012-2771-4Google Scholar
Branch, M. N. (2006). How research in behavioral pharmacology informs behavioral science. Journal of the Experimental Analysis of Behavior, 85(3), 407–423. https://doi.org/10.1901/jeab.2006.130-04Google Scholar
Broadbent, J. & Dakki, M. A. (2015). How much is too much to pay for internet access? A behavioral economic analysis of internet use. Cyberpsychology, Behavior, and Social Networking, 18(8), 457–461. https://doi.org/10.1089/cyber.2014.0367Google Scholar
Bruner, N. R. & Johnson, M. W. (2014). Demand curves for hypothetical cocaine in cocaine-dependent individuals. Psychopharmacology, 231(5), 889–897. https://doi.org/10.1007/s00213-013-3312-5Google Scholar
Catania, A. C. & Reynolds, G. S. (1968). A quantitative analysis of the responding maintained by interval schedules of reinforcement. Journal of the Experimental Analysis of Behavior, 11(3S2), 327–383.Google Scholar
Cheever, N. A., Rosen, L. D., Carrier, L. M. & Chavez, A. (2014). Out of sight is not out of mind: The impact of restricting wireless mobile device use on anxiety levels among low, moderate and high users. Computers in Human Behavior, 37, 290–297. doi: 10.1016/j.chb.2014.05.002CrossRefGoogle Scholar
Chung, S. & Herrnstein, R. J. (1967). Choice and delay of reinforcement. Journal of the Experimental Analysis of Behavior, 10(1), 67–74. doi: 10.1901/jeab.1967.10-67Google Scholar
Collins, R. L., Parks, G. A. & Marlatt, G. A. (1985). Social determinants of alcohol consumption: The effects of social interaction and model status on the self-administration of alcohol. Journal of Consulting and Clinical Psychology, 53(2), 189–200. https://doi.org/10.1037/0022-006X.53.2.189Google Scholar
Collins, R. L., Vincent, P. C., Yu, J., Liu, L. & Epstein, L. H. (2014). A behavioral economic approach to assessing demand for marijuana. Experimental and Clinical Psychopharmacology, 22(3), 211–221. https://doi.org/10.1037/a0035318Google Scholar
Comer, S. D., Bickel, W. K., Yi, R., et al. (2010). Human behavioral pharmacology, past, present, and future: Symposium presented at the 50th annual meeting of the Behavioral Pharmacology Society. Behavioural Pharmacology, 21(4), 251–277. https://doi.org/10.1097/Fbp.0b013e32833bb9f8CrossRefGoogle ScholarPubMed
Dariotis, J. K. & Johnson, M. W. (2015). Sexual discounting among high-risk youth ages 18–24: Implications for sexual and substance use risk behaviors. Experimental and Clinical Psychopharmacology, 23(1), 49–58. doi: 10.1037/a0038399Google Scholar
DeGrandpre, R. J., Bickel, W. K., Hughes, J. R. & Higgins, S. T. (1992). Behavioral economics of drug self-administration. Psychopharmacology, 108(1–2), 1–10.CrossRefGoogle ScholarPubMed
DeGrandpre, R. J., Bickel, W. K., Hughes, J. R., Layng, M. P. & Badger, G. (1993). Unit price as a useful metric in analyzing effects of reinforcer magnitude. Journal of the Experimental Analysis of Behavior, 60(3), 641–666.Google Scholar
Du, W., Green, L. & Myerson, J. (2002). Cross-cultural comparisons of discounting delayed and probabilistic rewards. The Psychological Record, 52(4), 479–492.Google Scholar
Epstein, L. H., Dearing, K. K. & Roba, L. G. (2010). A questionnaire approach to measuring the relative reinforcing efficacy of snack foods. Eating Behaviors, 11(2), 67–73. https://doi.org/10.1016/j.eatbeh.2009.09.006Google Scholar
Epstein, L. H., Salvy, S. J., Carr, K. A., Dearing, K. K. & Bickel, W. K. (2010). Food reinforcement, delay discounting and obesity. Physiology & Behavior, 100(5), 438–445. doi: 10.1016/j.physbeh.2010.04.029Google Scholar
Evenden, J. L. & Ryan, C. N. (1996). The pharmacology of impulsive behaviour in rats: The effects of drugs on response choice with varying delays of reinforcement. Psychopharmacology, 128(2), 161–170. doi: 10.1007/s002130050121Google Scholar
Fagerström, K. O. (1978). Measuring degree of physical dependence to tobacco smoking with reference to individualization of treatment. Addictive Behaviors, 3(3–4), 235–241. https://doi.org/10.1016/0306-4603(78)90024-2Google Scholar
Felton, M. & Lyon, D. O. (1966). The post‐reinforcement pause. Journal of the Experimental Analysis of Behavior, 9(2), 131–134.Google Scholar
Few, L. R., Acker, J., Murphy, C. & MacKillop, J. (2012). Temporal stability of a cigarette purchase task. Nicotine & Tobacco Research, 14(6), 761–765. https://doi.org/10.1093/ntr/ntr222Google Scholar
Field, M., Santarcangelo, M., Sumnall, H., Goudie, A. & Cole, J. (2006). Delay discounting and the behavioural economics of cigarette purchases in smokers: The effects of nicotine deprivation. Psychopharmacology, 186(2), 255–263. https://doi.org/10.1007/s00213-006-0385-4Google Scholar
Gray, J. C., Amlung, M. T., Palmer, A. A. & MacKillop, J. (2016). Syntax for calculation of discounting indices from the monetary choice questionnaire and probability discounting questionnaire. Journal of the Experimental Analysis of Behavior, 106(2), 156–163. doi: 10.1002/jeab.221Google Scholar
Green, L., Myerson, J., Shah, A. K., Estle, S. J. & Holt, D. D. (2007). Do adjusting-amount and adjusting-delay procedures produce equivalent estimates of subjective value in pigeons? Journal of the Experimental Analysis of Behavior, 87(3), 337–347. doi: 10.1901/jeab.2007.37-06Google Scholar
Greenwald, M. K. & Hursh, S. R. (2006). Behavioral economic analysis of opioid consumption in heroin-dependent individuals: Effects of unit price and pre-session drug supply. Drug and Alcohol Dependence, 85(1), 35–48. https://doi.org/http://dx..org/10.1016/j.drugalcdep.2006.03.007Google Scholar
Griffiths, R. R., Bigelow, G. E. & Henningfield, J. E. (1980). Similarities in animal and human drug-taking behavior. In Mello, N. K. (Ed.), Advances in Substance Abuse (Volume 1). Greenwich, CT: JAI Press, pp. 1–90.Google Scholar
Griffiths, R. R., Brady, J. V. & Bradford, L. D. (1979). Predicting the abuse liability of drugs with animal drug self-administration procedures: Psychomotor stimulants and hallucinogens. In Thompson, T. & Dews, P. B. (Eds.), Advances in Behavioral Pharmacology (Volume 2). New York: Academic Press, pp. 163–208.Google Scholar
Heatherton, T. F., Kozlowski, L. T., Frecker, R. C. & Fagerström, K. O. (1991). The Fagerström test for nicotine dependence: A revision of the Fagerström Tolerance Questionnaire. British Journal of Addiction, 86(9), 1119–1127. https://doi.org/10.1111/j.1360-0443.1991.tb01879.xGoogle Scholar
Henley, A. J., DiGennaro Reed, F. D., Kaplan, B. A. & Reed, D. D. (2016). Quantifying efficacy of workplace reinforcers: An application of behavioral economic demand to evaluate hypothetical work performance. Translational Issues in Psychological Science, 2(2), 174–183. https://doi.org/10.1037/tps0000068Google Scholar
Herrmann, E. S., Hand, D. J., Johnson, M. W., Badger, G. J. & Heil, S. H. (2014). Examining delay discounting of condom-protected sex among opioid-dependent women and non-drug-using control women. Drug and Alcohol Dependence, 144, 53–60. doi: 10.1016/j.drugalcdep.2014.07.026Google Scholar
Herrnstein, R. J. (1971). Quantitative hedonism. Journal of Psychiatric Research, 8(3–4), 399–412. https://doi.org/Doi 10.1016/0022-3956(71)90033-1CrossRefGoogle ScholarPubMed
Hodos, W. (1961). Progressive ratio as a measure of reward strength. Science, 134(3483), 943–944. Retrieved from www.ncbi.nlm.nih.gov/pubmed/13714876Google Scholar
Hurlbut, S. C. & Sher, K. J. (1992). Assessing alcohol problems in college students. Journal of the American College Health Association, 41(2), 49–58. https://doi.org/10.1080/07448481.1992.10392818Google Scholar
Hursh, S. R. (1980). Economic concepts for the analysis of behavior. Journal of the Experimental Analysis of Behavior, 34(2), 219–238. https://doi.org/http://dx..org/10.1901/jeab.1980.34-219Google Scholar
Hursh, S. R. (1984). Behavioral economics. Journal of the Experimental Analysis of Behavior, 42(3), 435–452. https://doi.org/http://dx..org/10.1901/jeab.1984.42-435Google Scholar
Hursh, S. R. (1993). Behavioral economics of drug self-administration – An introduction. Drug and Alcohol Dependence, 33(2), 165–172. https://doi.org/10.1016/0376-8716(93)90058-XGoogle Scholar
Hursh, S. R. (2014). Behavioral economics and analysis of consumption and choice. In McSweeney, F. K. & Murphy, E. S. (Eds.), The Wiley Blackwell Handbook of Classical and Operant Conditioning. West Sussex, UK: John Wiley & Sons, pp. 275–305.Google Scholar
Hursh, S. R. & Roma, P. G. (2013). Behavioral economics and empirical public policy. Journal of the Experimental Analysis of Behavior, 99(1), 98–124. https://doi.org/10.1007/s00213-008-1120-0Google Scholar
Hursh, S. R. & Silberberg, A. (2008). Economic demand and essential value. Psychological Review, 115(1), 186–198. https://doi.org/10.1037/0033-295x115.1.186Google Scholar
Hursh, S. R., Madden, G. J., Spiga, R., DeLeon, I. G. & Francisco, M. T. (2013). The Translational Utility of Behavioral Economics: The Experimental Analysis of Consumption and Choice. Washington, DC: American Psychological Association. https://doi.org/http://dx..org/10.1037/13938-008Google Scholar
Jacobs, E. A. & Bickel, W. K. (1999). Modeling drug consumption in the clinic using simulation procedures: Demand for heroin and cigarettes in opioid-dependent outpatients. Experimental and Clinical Psychopharmacology, 7(4), 412–426. https://doi.org/10.1037//1064-1297.7.4.412Google Scholar
Jarmolowicz, D. P. & Lattal, K. A. (2010). On distinguishing progressively increasing response requirements for reinforcement. The Behavior Analyst, 33(1), 119–125.Google Scholar
Jarmolowicz, D. P. & Schneider, T. D. (2020). Behavioral economics and addictive disorders. In Sussman, S. (Ed.) The Cambridge Handbook of Substance and Behavioral Addictions, Cambridge, UK: Cambridge University Press, pp. 12–22.Google Scholar
Jarmolowicz, D. P., Bickel, W. K. & Gatchalian, K. M. (2013). Alcohol-dependent individuals discount sex at higher rates than controls. Drug and Alcohol Dependence, 131(3), 320–323. doi: 10.1016/j.drugalcdep.2012.12.014Google Scholar
Jarmolowicz, D. P., Landes, R. D., Christensen, D. R., et al. (2014). Discounting of money and sex: Effects of commodity and temporal position in stimulant-dependent men and women. Addictive Behaviors, 39(11), 1652–1657. doi: 10.1016/j.addbeh.2014.04.026Google Scholar
Jarmolowicz, D. P., Lemley, S. M., Mateos, A. & Sofis, M. J. (2016). A multiple-stimulus-without-replacement assessment for sexual partners: Purchase task validation. Journal of Applied Behavior Analysis, 49(3), 723–729. https://doi.org/10.1002/jaba.313Google Scholar
Jarmolowicz, D. P., Reed, D. D. & Bickel, W. K. (2015). Neuroeconomics: Implications for understanding and treating addictive behavior. In Feldstein Ewing, S. W. & Filbey, F. M. (Eds.), Neuroimaging and Psychosocial Addiction Treatment: An Integrative Guide for Researchers and Clinicians. New York, NY: Palgrave Macmillan, pp. 141–157.Google Scholar
Jarmolowicz, D. P., Reed, D. D., Bruce, A. S., et al. (2016). Using EP50 to forecast treatment adherence in individuals with multiple sclerosis. Behavioural Processes, 132, 94–99. doi: 10.1016/j.beproc.2016.09.003Google Scholar
Johnson, M. W. & Bickel, W. K. (2008). An algorithm for identifying nonsystematic delay-discounting data. Experimental and Clinical Psychopharmacology, 16(3), 264–274. doi:10.1037/1064-1297.16.3.264CrossRefGoogle ScholarPubMed
Johnson, M. W. & Bruner, N. R. (2012). The Sexual Discounting Task: HIV risk behavior and the discounting of delayed sexual rewards in cocaine dependence. Drug and Alcohol Dependence, 123(1–3), 15–21. https://doi.org/10.1016/j.drugalcdep.2011.09.032Google Scholar
Johnson, M. W., Herrmann, E. S., Sweeney, M. M., LeComte, R. S. & Johnson, P. S. (2017). Cocaine administration dose-dependently increases sexual desire and decreases condom use likelihood: The role of delay and probability discounting in connecting cocaine with HIV. Psychopharmacology, 234(4), 599–612. doi: 10.1007/s00213-016-4493-5Google Scholar
Junco, R. & Cotten, S. R. (2012). No A 4 U: The relationship between multitasking and academic performance. Computers & Education, 59(2), 505–514. doi: 10.1016/j.compedu.2011.12.023Google Scholar
Kaplan, B. A., Amlung, M., Reed, D. D., et al. (2016). Automating scoring of delay discounting for the 21- and 27-item monetary choice questionnaires. The Behavior Analyst, 39(2), 293–304. doi: 10.1007/s40614-016-0070-9Google Scholar
Kaplan, B. A., Foster, R. N. S., Reed, D. D., et al. (2018). Understanding alcohol motivation using the alcohol purchase task: A methodological systematic review. Drug and Alcohol Dependence, 191, 117–140. https://doi.org/10.1016/J.DRUGALCDEP.2018.06.029CrossRefGoogle ScholarPubMed
Kaplan, B. A., Gelino, B. W. & Reed, D. D. (2018). A behavioral economic approach to green consumerism: Demand for reusable shopping bags. Behavior and Social Issues, 27, 20–30. https://doi.org/10.5210/bsi.v.27i0.8003Google Scholar
Kirby, K. N. & Maraković, N. N. (1996). Delay-discounting probabilistic rewards: Rates decrease as amounts increase. Psychonomic Bulletin & Review, 3(1), 100–104. doi: 10.3758/BF03210748Google Scholar
Kirby, K. N., Petry, N. M. & Bickel, W. K. (1999). Heroin addicts have higher discount rates for delayed rewards than non-drug-using controls. Journal of Experimental Psychology: General, 128(1), 78–87. doi: 10.1037/0096-3445.128.1.78Google Scholar
Koffarnus, M. N. & Bickel, W. K. (2014). A 5-trial adjusting delay discounting task: Accurate discount rates in less than one minute. Experimental and Clinical Psychopharmacology, 22(3), 222–228. doi: 10.1037/a0035973CrossRefGoogle ScholarPubMed
Kwon, M., Lee, J., Won, W., et al. (2013). Development and validation of a smartphone addiction scale (SAS). PLoS ONE, 8(2), 7.Google Scholar
Lawyer, S. R. (2008). Probability and delay discounting of erotic stimuli. Behavioural Processes, 79(1), 36–42. doi: 10.1016/j.beproc.2008.04.009Google Scholar
Lea, S. E. (1976). Titration of schedule parameters by pigeons. Journal of the Experimental Analysis of Behavior, 25(1), 43–54. doi: 10.1901/jeab.1976.25-43Google Scholar
Lemley, S. M., Kaplan, B. A., Reed, D. D., Darden, A. C. & Jarmolowicz, D. P. (2016). Reinforcer pathologies: Predicting alcohol related problems in college drinking men and women. Drug and Alcohol Dependence, 167, 57–66. https://doi.org/10.1016/j.drugalcdep.2016.07.025Google Scholar
Leslie, J. C. (2003). A history of reinforcement: The role of reinforcement schedules in behavior pharmacology. The Behavior Analyst Today, 4(1), 98–108. http://dx.doi.org/10.1037/h0100017Google Scholar
MacKillop, J. & Tidey, J. W. (2011). Cigarette demand and delayed reward discounting in nicotine-dependent individuals with schizophrenia and controls: An initial study. Psychopharmacology, 216(1), 91–99. https://doi.org/10.1007/s00213-011-2185-8Google Scholar
MacKillop, J., Murphy, J. G., Ray, L. A., et al. (2008). Further validation of a cigarette purchase task for assessing the relative reinforcing efficacy of nicotine in college smokers. Experimental and Clinical Psychopharmacology, 16(1), 57–65. https://doi.org/10.1037/1064-1297.16.1.57Google Scholar
Madden, G. J. & Bickel, W. K. (1999). Abstinence and price effects on demand for cigarettes: A behavioral-economic analysis. Addiction, 94(4), 577–588. https://doi.org/10.1046/j.1360-0443.1999.94457712.xGoogle Scholar
Madden, G. J. & Bickel, W. K. (2010). Impulsivity: The Behavioral and Neurological Science of Discounting (1st edition). Washington, DC: American Psychological Association.Google Scholar
Madden, G. J., Petry, N. M. & Johnson, P. S. (2009). Pathological gamblers discount probabilistic rewards less steeply than matched controls. Experimental and Clinical Psychopharmacology, 17(5), 283–290. https://doi.org/10.1037/A0016806Google Scholar
Mazur, J. E. (1987). An adjusting procedure for studying delayed reinforcement. In Commons, M. L., Mazur, J. E., Nevin, J. A. & Rachlin, H. (Eds.), Quantitative Analysis of Behavior: Volume 5. The Effect of Delay and Intervening Events on Reinforcement Value. Hillsdale, NJ: Erlbaum, pp. 55–73. Retrieved from https://search.proquest.com/docview/617226485?accountid=14556Google Scholar
Metrik, J., Aston, E. R., Kahler, C. W., et al. (2016). Cue-elicited increases in incentive salience for marijuana: Craving, demand, and attentional bias. Drug and Alcohol Dependence, 167, 82–88. https://doi.org/10.1016/j.drugalcdep.2016.07.027Google Scholar
Mischel, W. & Ebbesen, E. B. (1970). Attention in delay of gratification. Journal of Personality and Social Psychology, 16(2), 329–337. doi: 10.1037/h0029815Google Scholar
Mischel, W., Ebbesen, E. B. & Raskoff Zeiss, A. (1972). Cognitive and attentional mechanisms in delay of gratification. Journal of Personality and Social Psychology, 21(2), 204–218. doi: 10.1037/h0032198Google Scholar
Mischel, W., Shoda, Y. & Peake, P. K. (1988). The nature of adolescent competencies predicted by preschool delay of gratification. Journal of Personality and Social Psychology, 54(4), 687–696. doi: 10.1037/0022-3514.54.4.687Google Scholar
Mischel, W., Shoda, Y. & Rodriguez, M. L. (1989). Delay of gratification in children. Science, 244(4907), 933–938. doi: 10.1126/science.2658056Google Scholar
Mulhauser, K., Short, E. M. & Weinstock, J. (2018). Development and psychometric evaluation of the pornography purchase task. Addictive Behaviors, 84(December 2017), 207–214. https://doi.org/10.1016/j.addbeh.2018.04.016Google Scholar
Murphy, J. G. & MacKillop, J. (2006). Relative reinforcing efficacy of alcohol among college student drinkers. Experimental and Clinical Psychopharmacology, 14(2), 219–227. https://doi.org/10.1037/1064-1297.14.2.219Google Scholar
Murphy, J. G., MacKillop, J., Skidmore, J. R. & Pederson, A. A. (2009). Reliability and validity of a demand curve measure of alcohol reinforcement. Experimental and Clinical Psychopharmacology, 17(6), 396–404. https://doi.org/10.1037/a0017684Google Scholar
Murphy, J. G., MacKillop, J., Tidey, J. W., Brazil, L. A. & Colby, S. M. (2011). Validity of a demand curve measure of nicotine reinforcement with adolescent smokers. Drug and Alcohol Dependence, 113(2–3), 207–214. https://doi.org/10.1016/J.DRUGALCDEP.2010.08.004Google Scholar
Myerson, J. & Green, L. (1995). Discounting of delayed rewards: Models of individual choice. Journal of the Experimental Analysis of Behavior, 64(3), 263–276. doi: 10.1901/jeab.1995.64-263Google Scholar
Myerson, J., Baumann, A. A. & Green, L. (2014). Discounting of delayed rewards: (A)theoretical interpretation of the Kirby questionnaire. Behavioural Processes, 107, 99–105. doi: 10.1016/j.beproc.2014.07.021Google Scholar
Myerson, J., Green, L. & Warusawitharana, M. (2001). Area under the curve as a measure of discounting. Journal of the Experimental Analysis of Behavior, 76(2), 235–243. doi: 10.1901/jeab.2001.76-235Google Scholar
Navarick, D. J. & Fantino, E. (1972). Interresponse time as a factor in choice. Psychonomic Science, 27(1), 4–6. doi: 10.3758/BF03328868Google Scholar
O'Donnell, S. & Epstein, L. H. (2019). Smartphones are more reinforcing than food for students. Addictive Behaviors, 90, 124–133.Google Scholar
Odum, A. L., Baumann, A. A. L. & Rimington, D. D. (2006). Discounting of delayed Hypothetical money and food: Effects of amount. Behavioural Processes, 73(3), 278–284. doi: 10.1016/j.beproc.2006.06.008Google Scholar
Petry, N. M. (2000). Effects of increasing income on polydrug use: A comparison of heroin, cocaine and alcohol abusers. Addiction, 95(5), 705–717. https://doi.org/10.1046/j.1360-0443.2000.9557056.xGoogle Scholar
Petry, N. M. (2001a). A behavioral economic analysis of polydrug abuse in alcoholics: Asymmetrical substitution of alcohol and cocaine. Drug and Alcohol Dependence, 62(1), 31–39. https://doi.org/10.1016/S0376-8716(00)00157-5Google Scholar
Petry, N. M. (2001b). The effects of housing costs on polydrug abuse patterns: A comparison of heroin, cocaine, and alcohol abusers. Experimental and Clinical Psychopharmacology, 9(1), 47–58. https://doi.org/10.1037/1064-1297.9.1.47Google Scholar
Petry, N. M. & Bickel, W. K. (1998). Polydrug abuse in heroin addicts: A behavioral economic analysis. Addiction, 93(3), 321–335. https://doi.org/10.1046/j.1360-0443.1998.9333212.xGoogle Scholar
Petry, N. M. & Bickel, W. K. (1999). A behavioral economic analysis of polydrug abuse in heroin addicts. In Chaloupka, F. J., Grossman, M., Bickel, W. K. & Saffer, H. (Eds.), The Economic Analysis of Substance Use and Abuse: An Integration of Econometrics and Behavioral Economic Research. Chicago, IL: University of Chicago Press, pp. 213–250. https://doi.org/10.1046/j.1360-0443.1998.9333212.xGoogle Scholar
Pickover, A. M., Messina, B. G., Correia, C. J., Garza, K. B. & Murphy, J. G. (2016). A behavioral economic analysis of the nonmedical use of prescription drugs among young adults. Experimental and Clinical Psychopharmacology, 24(1), 38–47. https://doi.org/10.1037/pha0000052Google Scholar
Pope, H. G., Kean, J., Nash, A., et al. (2010). A diagnostic interview module for anabolic-androgenic steroid dependence: Preliminary evidence of reliability and validity. Experimental and Clinical Psychopharmacology, 18(3), 203–213. https://doi.org/10.1037/a0019370Google Scholar
Rachlin, H. (2006). Notes on discounting. Journal of the Experimental Analysis of Behavior, 85(3), 425–435. doi: 10.1901/jeab.2006.85-05Google Scholar
Rachlin, H. & Green, L. (1972). Commitment, choice and self-control. Journal of the Experimental Analysis of Behavior, 17(1), 15–22. doi: 10.1901/jeab.1972.17-15Google Scholar
Rachlin, H., Raineri, A. & Cross, D. (1991). Subjective probability and delay. Journal of the Experimental Analysis of Behavior, 55(2), 233–244. doi: 10.1901/jeab.1991.55-233Google Scholar
Reed, D. D. (2015). Ultra-violet indoor tanning addiction: A reinforcer pathology interpretation. Addictive Behaviors, 41, 247–251. https://doi.org/10.1016/j.addbeh.2014.10.026Google Scholar
Reed, D. D., Becirevic, A., Atchley, P., Kaplan, B. A. & Liese, B. S. (2016a). Validation of a novel delay discounting of text messaging questionnaire. The Psychological Record, 66(2), 253–261. doi: 10.1007/s40732-016-0167-2Google Scholar
Reed, D. D., Kaplan, B. A. & Becirevic, A. (2015). Basic research on the behavioral economics of reinforcer value. In Autism Service Delivery: Bridging the Gap Between Science and Practice. Springer Science + Business Media, pp. 279–306. https://doi.org/10.1007/978-1-4939-2656-5_10Google Scholar
Reed, D. D., Kaplan, B. A., Becirevic, A., Roma, P. G. & Hursh, S. R. (2016b). Toward quantifying the abuse liability of ultraviolet tanning: A behavioral economic approach to tanning addiction. Journal of the Experimental Analysis of Behavior, 106(1), 93–106. https://doi.org/10.1002/jeab.216Google Scholar
Reed, D. D., Kaplan, B. A., Roma, P. G. & Hursh, S. R. (2014). Inter-method reliability of progression sizes in a hypothetical purchase task: Implications for empirical public policy. Psychological Record, 64(4), 671–679. https://doi.org/10.1007/s40732-014-0076-1Google Scholar
Reed, D. D., Naudé, G. P., Salzer, A. R., et al. (2020). Behavioral economic measurement of cigarette demand: A descriptive review of published approaches to the Cigarette Purchase Task. Experimental and Clinical Psychopharmacology. Advance online publication. doi: 10.1037/pha0000347Google Scholar
Richards, J. B., Mitchell, S. H., de Wit, H. & Seiden, L. S. (1997). Determination of discount functions in rats with an adjusting-amount procedure. Journal of the Experimental Analysis of Behavior, 67(3), 353-366. doi: 10.1901/jeab.1997.67-353Google Scholar
Richards, J. B., Zhang, L., Mitchell, S. H. & de Wit, H. (1999). Delay or probability discounting in a model of impulsive behavior: Effect of alcohol. Journal of the Experimental Analysis of Behavior, 71(2), 121–143. doi: 10.1901/jeab.1999.71-121Google Scholar
Roberts, J. A., Petnji Yaya, L. H. & Manolis, C. (2014). The invisible addiction: Cell-phone activities and addiction among male and female college students. Journal of Behavioral Addictions, 3(4), 254–265. doi: 10.1556/JBA.3.2014.015Google Scholar
Ross, E. & Kenakin, T. (2001). Pharmacodynamics – Mechanisms of drug action and the relationship between drug concentration and effect. In Hardman, J. G. & Gilman, A. J. (Eds.), The Pharmacological Basis of Therapeutics (10th edition). London: McGraw.Google Scholar
Samuelson, P. A. (1937). A note on measurement of utility. Review of Economic Studies, 4, 155–161.Google Scholar
Saville, B. K., Gisbert, A., Kopp, J. & Telesco, C. (2010). Internet addiction and delay discounting in college students. Psychological Record, 60(2), 273–286.Google Scholar
Shoda, Y., Mischel, W. & Peake, P. K. (1990). Predicting adolescent cognitive and self-regulatory competencies from preschool delay of gratification: Identifying diagnostic conditions. Developmental Psychology, 26(6), 978–986. doi: 10.1037/0012-1649.26.6.978Google Scholar
Skinner, B. F. (1953). Science and Human Behavior. Macmillan: Oxford.Google Scholar
Skinner, B. F. (1961). What is the experimental analysis of behavior? Journal of the Experimental Analysis of Behavior, 9(3), 213–218. https://doi.org/http://dx.doi.org/10.1037/11324-008Google Scholar
Sobell, L. C. & Sobell, M. B. (1992). Timeline follow-back. In Litten, R. Z. & Allen, J. P. (Eds.), Measuring Alcohol Consumption. Totowa, NJ: Humana Press, pp. 41–72. https://doi.org/10.1007/978-1-4612-0357-5_3Google Scholar
Sobell, M. B. & Sobell, L. C. (1973). Individualized behavior therapy for alcoholics – Republished. Behavior Therapy, 4(1), 49–72. https://doi.org/https://doi.org/10.1016/S0005-7894(73)80074-7Google Scholar
Stafford, D., LeSage, M. G. & Glowa, J. R. (1998). Progressive-ratio schedules of drug delivery in the analysis of drug self-administration: A review. Psychopharmacology, 139(3), 169–184. https://doi.org/10.1007/s002130050702Google Scholar
Strickland, J. C., Lile, J. A. & Stoops, W. W. (2017). Unique prediction of cannabis use severity and behaviors by delay discounting and behavioral economic demand. Behavioural Processes, 140, 33–40. doi: 10.1016/j.beproc.2017.03.017Google Scholar
Strickland, J. C., Lile, J. A., Rush, C. R. & Stoops, W. W. (2016). Comparing exponential and exponentiated models of drug demand in cocaine users. Experimental and Clinical Psychopharmacology, 24(6), 447–455. https://doi.org/10.1037/pha0000096Google Scholar
Strickland, J. C., Reynolds, A. R. & Stoops, W. W. (2016). Regulation of cocaine craving by cognitive strategies in an online sample of cocaine users. Psychology of Addictive Behaviors, 30(5), 607–612. https://doi.org/10.1037/adb0000180Google Scholar
Vincent, P. C., Collins, R. L., Liu, L., et al. (2017). The effects of perceived quality on behavioral economic demand for marijuana: A web-based experiment. Drug and Alcohol Dependence, 170, 174–180. https://doi.org/10.1016/j.drugalcdep.2016.11.013Google Scholar
Weinstock, J., Mulhauser, K., Oremus, E. G. & D’Agostino, A. R. (2016). Demand for gambling: Development and assessment of a gambling purchase task. International Gambling Studies, 16(2), 316–327. https://doi.org/10.1080/14459795.2016.1182570Google Scholar
Winger, G., Hursh, S. R., Casey, K. L. & Woods, J. H. (2002). Relative reinforcing strength of three methyl-d-aspartate antagonists with different onsets of action. Journal of Pharmacology and Experimental Therapeutics, 301(2), 690–697.Google Scholar
Yoon, J. H. & Higgins, S. T. (2008). Turning k on its head: Comments on use of an ED50 in delay discounting research. Drug and Alcohol Dependence, 95(1–2), 169–172. doi: 10.1016/j.drugalcdep.2007.12.011Google Scholar
Yurasek, A. M., Murphy, J. G., Clawson, A. H., Dennhardt, A. A. & MacKillop, J. (2013). Smokers report greater demand for alcohol on a behavioral economic purchase task. Journal of Studies on Alcohol and Drugs, 74(4), 626–34. https://doi.org/http://dx.doi.org/10.15288/jsad.2013.74.626Google Scholar

References

Adamson, S. J. & Sellman, J. D. (2003). A prototype screening instrument for cannabis use disorder: The Cannabis Use Disorders Identification Test (CUDIT) in an alcohol-dependent clinical sample. Drug and Alcohol Review, 22(3), 309–315.Google Scholar
Albrecht, U., Kirschner, N. E. & Grüsser, S. M. (2007). Diagnostic instruments for behavioural addiction: an overview. GMS Psycho-Social Medicine, 4.Google Scholar
Alexander, D. (2003). A marijuana screening inventory (experimental version): description and preliminary psychometric properties. The American Journal of Drug and Alcohol Abuse, 29(3), 619–646.Google Scholar
Alexander, D. E. & Leung, P. (2004). The Marijuana Screening Inventory (MSI‐X): Reliability, factor structure, and scoring criteria with a clinical sample. The American Journal of Drug and Alcohol Abuse, 30(2), 321–351.Google Scholar
Alexander, D. & Leung, P. (2006). The Marijuana Screening Inventory (MSI-X): Concurrent, convergent and discriminant validity with multiple measures. The American Journal of Drug and Alcohol Abuse, 32(3), 351–378.Google Scholar
American Psychiatric Association. (2000). Diagnostic Criteria from DSM-IV-TR. Washington, DC: American Psychiatric Publishing.Google Scholar
American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (DSM-5®). Washington, DC: American Psychiatric Association Publishing.Google Scholar
Andreassen, C. S., Griffiths, M. D., Hetland, J. & Pallesen, S. (2012). Development of a work addiction scale. Scandinavian Journal of Psychology, 53(3), 265–272.Google Scholar
Andreassen, C. S., Griffiths, M. D., Pallesen, S., et al. (2015). The Bergen Shopping Addiction Scale: Reliability and validity of a brief screening test. Frontiers in Psychology, 6. doi: https://doi.org/10.3389/fpsyg.2015.01374.Google Scholar
Andreassen, C. S., Torsheim, T., Brunborg, G. S. & Pallesen, S. (2012). Development of a Facebook addiction scale. Psychological Reports, 110(2), 501–517.Google Scholar
Armstrong, L., Phillips, J. G. & Saling, L. L. (2000). Potential determinants of heavier internet usage. International Journal of Human-Computer Studies, 53(4), 537–550.Google Scholar
Babor, T. F., de la Fuente, J. R., Saunders, J. & Grant, M. (2001). The Alcohol Use Disorders Identification Test: Guidelines for use in Primary Health Care. WHO Publication number 89.4, World Health Organization. Accessed November 7, 2018 from http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.505.4146&rep=rep1&type=pdfGoogle Scholar
Bamber, D., Cockerill, I. M. & Carroll, D. (2000). The pathological status of exercise dependence. British Journal of Sports Medicine, 34(2), 125–132.Google Scholar
Bancroft, J. & Vukadinovic, Z. (2004). Sexual addiction, sexual compulsivity, sexual impulsivity, or what? Toward a theoretical model. Journal of Sex Research, 41(3), 225–234.Google Scholar
Barke, A., Nyenhuis, N. & Kröner-Herwig, B. (2012). The German version of the internet addiction test: a validation study. Cyberpsychology, Behavior, and Social Networking, 15(10), 534–542.Google Scholar
Barth, R. J. & Kinder, B. N. (1987). The mislabeling of sexual impulsivity. Journal of Sex & Marital Therapy, 13(1), 15–23.Google Scholar
Bashford, J., Flett, R. & Copeland, J. (2010). The Cannabis Use Problems Identification Test (CUPIT): Development, reliability, concurrent and predictive validity among adolescents and adults. Addiction, 105(4), 615–625.Google Scholar
Benotsch, E. G., Kalichman, S. C. & Kelly, J. A. (1999). Sexual compulsivity and substance use in HIV-seropositive men who have sex with men: Prevalence and predictors of high-risk behaviors. Addictive Behaviors, 24(6), 857–868.Google Scholar
Berczik, K., Szabó, A., Griffiths, M. D., et al. (2012). Exercise addiction: Symptoms, diagnosis, epidemiology, and etiology. Substance Use & Misuse, 47(4), 403–417.Google Scholar
Blanchard, K. A., Morgenstern, J., Morgan, T. J., Lobouvie, E. W. & Bux, D. A. (2003). Assessing consequences of substance use: Psychometric properties of the inventory of drug use consequences. Psychology of Addictive Behaviors, 17(4), 328.Google Scholar
Blow, F. C., Gillespie, B. W., Barry, K. L., Mudd, S. A. & Hill, E. M. (1998). Brief screening for alcohol problems in elderly populations using the Short Michigan Alcoholism Screening Test – Geriatric Version (SMAST-G). Alcoholism: Clinical and Experimental Research, 22(3). Paper presented at: Research Society on Alcoholism; June 20–25, 1998; Hilton Head Island, SC.Google Scholar
Boubeta, A. R., Salgado, P. G., Folgar, M. I., Gallego, M. A. & Mallou, J. V. (2015). PIUS-a: Problematic Internet Use Scale in adolescents. Development and psychometric validation. Adicciones, 27(1), 27–63.Google Scholar
Boudreau, B. & Poulin, C. (2007). The South Oaks Gambling Screen-revised Adolescent (SOGS-RA) revisited: A cut-point analysis. Journal of Gambling Studies, 23(3), 299–308.Google Scholar
Brand, M., Laier, C., Pawlikowski, M., et al. (2011). Watching pornographic pictures on the Internet: Role of sexual arousal ratings and psychological–psychiatric symptoms for using Internet sex sites excessively. Cyberpsychology, Behavior, and Social Networking, 14(6), 371–377.Google Scholar
Brenner, V. (1997). Psychology of computer use: XLVII. Parameters of Internet use, abuse and addiction: the first 90 days of the Internet Usage Survey. Psychological Reports, 80(3), 879–882.Google Scholar
Brewer, J. A. & Potenza, M. N. (2008). The neurobiology and genetics of impulse control disorders: relationships to drug addictions. Biochemical Pharmacology, 75(1), 63–75.Google Scholar
Browne, M., Goodwin, B. C. & Rockloff, M. J. (2018). Validation of the Short Gambling Harm Screen (SGHS): A tool for assessment of harms from gambling. Journal of Gambling Studies, 34(2), 499–512.Google Scholar
Buelens, M. & Poelmans, S. A. (2004). Enriching the Spence and Robbins' typology of workaholism: Demographic, motivational and organizational correlates. Journal of Organizational Change Management, 17(5), 440–458.Google Scholar
Burke, H. R. & Marcus, R. (1977). MacAndrew MMPI alcoholism scale: Alcoholism and drug addictiveness. The Journal of Psychology, 96(1), 141–148.Google Scholar
Butcher, J. N. & Owen, P. L. (1978). Objective personality inventories: Recent research and some contemporary issues. In Clinical Diagnosis of Mental Disorders. Boston, MA: Springer, pp. 475–545.Google Scholar
Caplan, S. E. (2002). Problematic Internet use and psychosocial well-being: Development of a theory-based cognitive–behavioral measurement instrument. Computers in Human Behavior, 18(5), 553–575.Google Scholar
Caplan, S. E. (2010). Theory and measurement of generalized problematic Internet use: A two-step approach. Computers in Human Behavior, 26(5), 1089–1097.Google Scholar
Carnes, P. (1983). The Sexual Addiction. Minneapolis, MN: CompCare Publications.Google Scholar
Carnes, P. J., Green, B. A., Merlo, L. J., et al. (2012). PATHOS: A brief screening application for assessing sexual addiction. Journal of Addiction Medicine, 6(1), 29.Google Scholar
Carnes, P., Green, B. & Carnes, S. (2010). The same yet different: Refocusing the Sexual Addiction Screening Test (SAST) to reflect orientation and gender. Sexual Addiction & Compulsivity, 17(1), 7–30.Google Scholar
Chen, S. H., Weng, L. J., Su, Y. J., Wu, H. M. & Yang, P. F. (2003). Development of a Chinese Internet addiction scale and its psychometric study. Chinese Journal of Psychology, 45, 251–266.Google Scholar
Clark, L. A., Livesley, W. J., Schroeder, M. L. & Irish, S. L. (1996). Convergence of two systems for assessing specific traits of personality disorder. Psychological Assessment, 8(3), 294.Google Scholar
Clark, L. A., McEwen, J. L., Collard, L. M. & Hickok, L. G. (1993). Symptoms and traits of personality disorder: Two new methods for their assessment. Psychological Assessment, 5(1), 81–91.Google Scholar
Clark, M. A., Michel, J. S., Zhdanova, L., Pui, S. Y. & Baltes, B. B. (2016). All work and no play? A meta-analytic examination of the correlates and outcomes of workaholism. Journal of Management, 42(7), 1836–1873.Google Scholar
Clark, M. & Calleja, K. (2008). Shopping addiction: A preliminary investigation among Maltese university students. Addiction Research & Theory, 16(6), 633–649.Google Scholar
Clopton, J. R. (1978). Alcoholism and the MMPI. A review. Journal of Studies on Alcohol, 39(9), 1540–1558.Google Scholar
Clopton, J. R., Weiner, R. H. & Davis, H. G. (1980). Use of the MMPI in identification of alcoholic psychiatric patients. Journal of Consulting and Clinical Psychology, 48(3), 416.Google Scholar
Coen, S. P. & Ogles, B. M. (1993). Psychological characteristics of the obligatory runner: A critical examination of the anorexia analogue hypothesis. Journal of Sport and Exercise Psychology, 15(3), 338–354.Google Scholar
Coleman, E. (1991). Compulsive sexual behavior: New concepts and treatments. Journal of Psychology & Human Sexuality, 4(2), 37–52.Google Scholar
Coleman, E., Miner, M., Ohlerking, F. & Raymond, N. (2001). Compulsive Sexual Behavior Inventory: A preliminary study of reliability and validity. Journal of Sex & Marital Therapy, 27(4), 325–332.Google Scholar
Currie, S. R., Casey, D. M. & Hodgins, D. C. (2010). Improving the Psychometric Properties of the Problem Gambling Severity Index. Ottawa: Canadian Consortium for Gambling Research.Google Scholar
Currie, S. R., Hodgins, D. C., Casey, D. M., et al. (2012). Examining the predictive validity of low‐risk gambling limits with longitudinal data. Addiction, 107(2), 400–406.Google Scholar
Davis, C., Brewer, H. & Ratusny, D. (1993). Behavioral frequency and psychological commitment: Necessary concepts in the study of excessive exercising. Journal of Behavioral Medicine, 16(6), 611–628.Google Scholar
de Meneses-Gaya, C., Zuardi, A. W., Loureiro, S. R. & Crippa, J. A. S. (2009). Alcohol Use Disorders Identification Test (AUDIT): An updated systematic review of psychometric properties. Psychology & Neuroscience, 2(1), 83.Google Scholar
Delmonico, D. & Miller, J. (2003). The Internet Sex Screening Test: A comparison of sexual compulsives versus non-sexual compulsives. Sexual and Relationship Therapy, 18(3), 261–276.Google Scholar
Demetrovics, Z., Király, O., Koronczai, B., et al. (2016). Psychometric properties of the Problematic Internet Use Questionnaire Short-Form (PIUQ-SF-6) in a nationally representative sample of adolescents. PLoS ONE, 11(8), e0159409.Google Scholar
Denis, C. M., Cacciola, J. S. & Alterman, A. I. (2013). Addiction Severity Index (ASI) summary scores: Comparison of the Recent Status Scores of the ASI-6 and the Composite Scores of the ASI-5. Journal of Substance Abuse Treatment, 45(5), 444–450.Google Scholar
Dhalla, S. & Kopec, J. A. (2007). The CAGE questionnaire for alcohol misuse: A review of reliability and validity studies. Clinical & Investigative Medicine, 30(1), 33–41.Google Scholar
Dhalla, S., Zumbo, B. D. & Poole, G. (2011). A review of the psychometric properties of the CRAFFT instrument: 1999–2010. Current Drug Abuse Reviews, 4(1), 57–64.Google Scholar
Dowling, N. A. & Quirk, K. L. (2009). Screening for internet dependence: do the proposed diagnostic criteria differentiate normal from dependent internet use? CyberPsychology & Behavior, 12(1), 21–27.Google Scholar
Earleywine, M., LaBrie, J. W. & Pedersen, E. R. (2008). A brief Rutgers Alcohol Problem Index with less potential for bias. Addictive Behaviors, 33(9), 1249–1253.Google Scholar
Eleuteri, S., Tripodi, F., Petruccelli, I., Rossi, R. & Simonelli, C. (2014). Questionnaires and scales for the evaluation of the online sexual activities: A review of 20 years of research. Cyberpsychology: Journal of Psychosocial Research on Cyberspace, 8(1), article 2. http://dx.doi.org/10.5817/CP2014-1-2Google Scholar
Ewing, J. A. (1984). Detecting alcoholism: the CAGE questionnaire. JAMA, 252(14), 1905–1907.Google Scholar
Fagerström, K. (2011). Determinants of tobacco use and renaming the FTND to the Fagerström Test for Cigarette Dependence. Nicotine & Tobacco Research, 14(1), 75–78.Google Scholar
Feeney, J. A. & Noller, P. (1990). Attachment style as a predictor of adult romantic relationships. Journal of Personality and Social Psychology, 58(2), 281–291.Google Scholar
Feldstein, S. W. & Miller, W. R. (2007). Does subtle screening for substance abuse work? A review of the Substance Abuse Subtle Screening Inventory (SASSI). Addiction, 102(1), 41–50.Google Scholar
Ferris, J. A. & Wynne, H. J. (2001). The Canadian Problem Gambling Index. Ottawa, ON: Canadian Centre on Substance Abuse, pp. 1–59.Google Scholar
Fioravanti, G., Primi, C. & Casale, S. (2013). Psychometric evaluation of the generalized problematic internet use scale 2 in an Italian sample. Cyberpsychology, Behavior, and Social Networking, 16(10), 761–766.Google Scholar
Flowers, C. P. & Robinson, B. (2002). A structural and discriminant analysis of the Work Addiction Risk Test. Educational and Psychological Measurement, 62(3), 517–526.Google Scholar
Forcehimes, A. A., Tonigan, J. S., Miller, W. R., Kenna, G. A. & Baer, J. S. (2007). Psychometrics of the drinker inventory of consequences (DrInC). Addictive Behaviors, 32(8), 1699–1704.Google Scholar
Friedman, A. S. & Utada, A. (1989). A method for diagnosing and planning the treatment of adolescent drug abusers (the Adolescent Drug Abuse Diagnosis [ADAD] instrument). Journal of Drug Education, 19(4), 285–312.Google Scholar
Galimov, A. & Black, D. W. (2020). Prevention and treatment of compulsive buying disorder. In Sussman, S. (Ed.) The Cambridge Handbook of Substance and Behavioral Addictions. Cambridge, UK: Cambridge University Press, pp. 271–279.Google Scholar
Gambino, B. & Lesieur, H. (2006). The south oaks gambling screen (SOGS): A rebuttal to critics. Journal of Gambling Issues, 17, doi: http://dx.doi.org/10.4309/jgi.2006.17.10.Google Scholar
Gearhardt, A. N., Boswell, R. G. & White, M. A. (2014). The association of “food addiction” with disordered eating and body mass index. Eating Behaviors, 15(3), 427–433.Google Scholar
Gearhardt, A. N., Corbin, W. R. & Brownell, K. D. (2009). Preliminary validation of the Yale food addiction scale. Appetite, 52(2), 430–436.Google Scholar
Gearhardt, A. N., Corbin, W. R. & Brownell, K. D. (2016). Development of the Yale Food Addiction Scale Version 2.0. Psychology of Addictive Behaviors, 30(1), 113–121.Google Scholar
Gerstein, D., Hoffmann, J., Larison, C., et al. (1999). Gambling Impact and Behavior Study. National Opinion Research Center at the University of Chicago.Google Scholar
Ginzler, J. A., Garrett, S. B., Baer, J. S. & Peterson, P. L. (2007). Measurement of negative consequences of substance use in street youth: An expanded use of the Rutgers Alcohol Problem Index. Addictive Behaviors, 32(7), 1519–1525.Google Scholar
Gold, S. N. & Heffner, C. L. (1998). Sexual addiction: Many conceptions, minimal data. Clinical Psychology Review, 18(3), 367–381.Google Scholar
Gormally, J., Black, S., Daston, S. & Rardin, D. (1982). The assessment of binge eating severity among obese persons. Addictive Behaviors, 7(1), 47–55.Google Scholar
Grant, J. E., Atmaca, M., Fineberg, N. A., et al. (2014). Impulse control disorders and “behavioural addictions” in the ICD‐11. World Psychiatry, 13(2), 125–127.Google Scholar
Grant, J. E., Odlaug, B. L. & Chamberlain, S. R. (2017). Gambling disorder, DSM-5 criteria and symptom severity. Comprehensive Psychiatry, 75, 1–5.Google Scholar
Grant, J. E., Potenza, M. N., Weinstein, A. & Gorelick, D. A. (2010). Introduction to behavioral addictions. The American Journal of Drug and Alcohol Abuse, 36(5), 233–241.Google Scholar
Grant, J. E., Steinberg, M. A., Kim, S. W., Rounsaville, B. J. & Potenza, M. N. (2004). Preliminary validity and reliability testing of a structured clinical interview for pathological gambling. Psychiatry Research, 128(1), 79–88.Google Scholar
Griffiths, M.D. (1996). Behavioural addiction: An issue for everybody? Journal of Workplace Learning, 8(3), 19–25.Google Scholar
Grigsby, T. J. (2019). Development and psychometric properties of the tobacco and nicotine consequences scale (TANCS) to screen for cigarette and e-cigarette misuse in community settings. Addictive Behaviors, 98, 106058.Google Scholar
Grigsby, T. J., Sussman, S., Chou, C. P. & Ames, S. L. (2017). Assessment of substance misuse. In Research Methods in the Study of Substance Abuse. Cham: Springer, pp. 197–233.Google Scholar
Grubbs, J. B., Sessoms, J., Wheeler, D. M. & Volk, F. (2010). The Cyber-Pornography Use Inventory: The development of a new assessment instrument. Sexual Addiction & Compulsivity, 17(2), 106–126.Google Scholar
Grupski, A. E., Hood, M. M., Hall, B. J., et al. (2013). Examining the Binge Eating Scale in screening for binge eating disorder in bariatric surgery candidates. Obesity Surgery, 23(1), 1–6.Google Scholar
Grüsser, S. M., Thalemann, R. & Griffiths, M. D. (2006). Excessive computer game playing: Evidence for addiction and aggression?. Cyberpsychology & Behavior, 10(2), 290–292.Google Scholar
Hannifin, J. (1990). The cannabis abuse syndrome screening test: a brief report. Unpublished report. Wellington: Drugs Advisory Committee.Google Scholar
Hasin, D. S., O’Brien, C. P., Auriacombe, M., et al. (2013). DSM-5 criteria for substance use disorders: Recommendations and rationale. American Journal of Psychiatry, 170(8), 834–851.Google Scholar
Hatfield, E. & Sprecher, S. (1986). Measuring passionate love in intimate relationships. Journal of Adolescence, 9(4), 383–410.Google Scholar
Hathaway, S. R. & McKinley, J. C. (1951). Minnesota Multiphasic Personality Inventory; Manual, revised.Google Scholar
Hausenblas, H. A. & Symons Downs, D. (2002). Exercise Dependence Scale-21 Manual. Retrieved from www.personal.psu.edu/dsd11/EDS/EDS21Manual.pdf.Google Scholar
Hjorthøj, C. R., Hjorthøj, A. R. & Nordentoft, M. (2012). Validity of timeline follow-back for self-reported use of cannabis and other illicit substances – systematic review and meta-analysis. Addictive Behaviors, 37(3), 225–233.Google Scholar
Hodgins, D. C. (2004). Using the NORC DSM Screen for Gambling Problems as an outcome measure for pathological gambling: Psychometric evaluation. Addictive Behaviors, 29(8), 1685–1690.Google Scholar
Hook, J. N., Hook, J. P., Davis, D. E., Worthington, E. L. Jr & Penberthy, J. K. (2010). Measuring sexual addiction and compulsivity: A critical review of instruments. Journal of Sex & Marital Therapy, 36(3), 227–260.Google Scholar
Horn, J. L., Wanberg, K. W. & Foster, F. M. (1990). Guide to the Alcohol Use Inventory (AUI). Minneapolis, MN: National Computer Systems.Google Scholar
Hueppelsheuser, M., Crawford, P. & George, D. (1997). The link between incest abuse and sexual addiction. Sexual Addiction & Compulsivity: The Journal of Treatment and Prevention, 4(4), 335–355.Google Scholar
Hunter, M. S., Nitschke, C. & Hogan, L. (1981). A scale to measure love addiction. Psychological Reports, 48(2), 582–582.Google Scholar
Hurlbut, S. C. & Sher, K. J. (1992). Assessing alcohol problems in college students. Journal of American College Health, 41(2), 49–58.Google Scholar
Jelenchick, L. A., Eickhoff, J., Christakis, D. A., et al. (2014). The Problematic and Risky Internet Use Screening Scale (PRIUSS) for adolescents and young adults: Scale development and refinement. Computers in Human Behavior, 35, 171–178.Google Scholar
Johansson, A. & Götestam, K. G. (2004). Internet addiction: Characteristics of a questionnaire and prevalence in Norwegian youth (12–18 years). Scandinavian Journal of Psychology, 45(3), 223–229.Google Scholar
Johnson, E. E., Hamer, R. M. & Nora, R. M. (1998). The Lie/Bet Questionnaire for screening pathological gamblers: A follow-up study. Psychological Reports, 83(Supplement 3), 1219–1224.Google Scholar
Johnson, E. E., Hamer, R., Nora, R. M., et al. (1997). The Lie/Bet Questionnaire for screening pathological gamblers. Psychological Reports, 80(1), 83–88.Google Scholar
Kalichman, S. C. & Cain, D. (2004). The relationship between indicators of sexual compulsivity and high risk sexual practices among men and women receiving services from a sexually transmitted infection clinic. Journal of Sex Research, 41(3), 235–241.Google Scholar
Kalichman, S. C. & Rompa, D. (1995). Sexual sensation seeking and sexual compulsivity scales: Validity and predicting HIV risk behavior. Journal of Personality Assessment, 65(3), 586–601.Google Scholar
Kalichman, S. C., Johnson, J. R., Adair, V., et al. (1994). Sexual sensation seeking: Scale development and predicting AIDS-risk behavior among homosexually active men. Journal of Personality Assessment, 62(3), 385–397.Google Scholar
Kardefelt‐Winther, D., Heeren, A., Schimmenti, A., et al. (2017). How can we conceptualize behavioural addiction without pathologizing common behaviours? Addiction, 112(10), 1709–1715.Google Scholar
Karila, L., Wéry, A., Weinstein, A., et al. (2014). Sexual addiction or hypersexual disorder: different terms for the same problem? A review of the literature. Current Pharmaceutical Design, 20(25), 4012–4020.Google Scholar
Kelley, K. J. & Gruber, E. M. (2010). Psychometric properties of the problematic internet use questionnaire. Computers in Human Behavior, 26(6), 1838–1845.Google Scholar
Kiluk, B. D., Dreifuss, J. A., Weiss, R. D., Morgenstern, J. & Carroll, K. M. (2013). 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, 27(1), 307–314.Google Scholar
Kim, C. T., Kim, D. I., Park, J. K. & Lee, S. J. (2002). A Study on Internet Addiction Counseling and the Development of Prevention Programs. Seoul: National IT Industrial Promotion Agency.Google Scholar
Kim, J. U. (2008). The effect of a R/T group counseling program on the Internet addiction level and self-esteem of Internet addiction university students. International Journal of Reality Therapy, 27(2), 4–12.Google Scholar
Kim, S. W., Grant, J. E., Potenza, M. N., Blanco, C. & Hollander, E. (2009). The Gambling Symptom Assessment Scale (G-SAS): A reliability and validity study. Psychiatry Research, 166(1), 76–84.Google Scholar
Király, O., Griffiths, M. D. & Demetrovics, Z. (2015). Internet gaming disorder and the DSM-5: Conceptualization, debates, and controversies. Current Addiction Reports, 2(3), 254–262.Google Scholar
Klein, M. (2003). Sex addiction: A dangerous clinical concept. Siecus Report, 31(5), 8–12.Google Scholar
Knight, J. R., Shrier, L. A., Bravender, T. D., et al. (1999). A new brief screen for adolescent substance abuse. Archives of Pediatrics & Adolescent Medicine, 153(6), 591–596.Google Scholar
Ko, C. H., Yen, C. F., Yen, C. N., et al. (2005). Screening for Internet addiction: an empirical study on cut‐off points for the Chen Internet Addiction Scale. The Kaohsiung Journal of Medical Sciences, 21(12), 545–551.Google Scholar
Krasnova, I. N. & Cadet, J. L. (2009). Methamphetamine toxicity and messengers of death. Brain Research Reviews, 60(2), 379–407.Google Scholar
Laconi, S., Rodgers, R. F. & Chabrol, H. (2014). The measurement of Internet addiction: A critical review of existing scales and their psychometric properties. Computers in Human Behavior, 41, 190–202.Google Scholar
Ladouceur, R., Bouchard, C., Rhéaume, N., et al. (2000). Is the SOGS an accurate measure of pathological gambling among children, adolescents and adults? Journal of Gambling Studies, 16(1), 1–24.Google Scholar
Laier, C., Pawlikowski, M., Pekal, J., Schulte, F. P. & Brand, M. (2013). Cybersex addiction: Experienced sexual arousal when watching pornography and not real-life sexual contacts makes the difference. Journal of Behavioral Addictions, 2(2), 100–107.Google Scholar
Lee, Y. S., Han, D. H., Kim, S. M. & Renshaw, P. F. (2013). Substance abuse precedes internet addiction. Addictive Behaviors, 38(4), 2022–2025.Google Scholar
Legleye, S., Karila, L., Beck, F. & Reynaud, M. (2007). Validation of the CAST, a general population Cannabis Abuse Screening Test. Journal of Substance Use, 12(4), 233–242.Google Scholar
Lesieur, H. R. & Blume, S. B. (1987). The South Oaks Gambling Screen (SOGS): A new instrument for the identification of pathological gamblers. American Journal of Psychiatry, 144(9), 1184–1188.Google Scholar
Líbano, M. D., Llorens, S., Salanova, M. & Schaufeli, W. (2010). Validity of a brief workaholism scale. Psicothema, 22(1), 143–150.Google Scholar
Littrell, J. (1991). Understanding and Treating Alcoholism: Biological, Psychological, and Social Aspects of Alcohol Consumption and Abuse (Volume 2). Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
MacAndrew, C. (1965). The differentiation of male alcoholic outpatients from nonalcoholic psychiatric outpatients by means of the MMPI. Quarterly Journal of Studies on Alcohol, 26(2), 238–246.Google Scholar
MacAndrew, C. (1981). What the MAC scale tells us about men alcoholics. An interpretive review. Journal of Studies on Alcohol, 42(7), 604–625.Google Scholar
Magruder-Habib, K., Harris, K. E. & Fraker, G. G. (1982). Validation of the Veterans Alcoholism Screening Test. Journal of Studies on Alcohol, 43(9), 910–926.Google Scholar
Mäkelä, K. (2004). Studies of the reliability and validity of the Addiction Severity Index. Addiction, 99(4), 398–410.Google Scholar
McBride, K. R., Reece, M. & Sanders, S. A. (2008). Using the Sexual Compulsivity Scale to predict outcomes of sexual behavior in young adults. Sexual Addiction & Compulsivity, 15(2), 97–115.Google Scholar
McCall, H., Adams, N., Mason, D. & Willis, J. (2015). What is chemsex and why does it matter? BMJ, 2015, e351.Google Scholar
McCourt, W. F., Williams, A. F. & Schneider, L. (1971). Incidence of alcoholism in a state mental hospital population. Quarterly Journal of Studies on Alcohol, 32(4), 1085–1088.Google Scholar
McCready, J. & Adlaf, E. (2006). Performance and Enhancement of the Canadian Problem Gambling Index (CPGI): Report and Recommendations. Prepared for Inter-provincial Funding Partners for Research Into Problem Gambling.Google Scholar
McDermott, P. A., Alterman, A. I., Brown, L., et al. (1996). Construct refinement and confirmation for the Addiction Severity Index. Psychological Assessment, 8(2), 182–189.Google Scholar
McLellan, A. T., Luborsky, L., Cacciola, J., et al. (1985). New data from the Addiction Severity Index: Reliability and validity in three centers. Journal of Nervous and Mental Disease, 173(3), 412–423.Google Scholar
McLellan, A. T., Luborsky, L., Woody, G. E. & O’Brien, C. P. (1980). An improved diagnostic evaluation instrument for substance abuse patients. Journal of Nervous and Mental Disease, 168, 26–33.Google Scholar
McMillan, L. H., Brady, E. C., O'Driscoll, M. P. & Marsh, N. V. (2002). A multifaceted validation study of Spence and Robbins'(1992) Workaholism Battery. Journal of Occupational and Organizational Psychology, 75(3), 357–368.Google Scholar
Meerkerk, G. J., Van Den Eijnden, R. J., Vermulst, A. A. & Garretsen, H. F. (2009). The compulsive internet use scale (CIUS): Some psychometric properties. Cyberpsychology & Behavior, 12(1), 1–6.Google Scholar
Meneses‐Gaya, C., Zuardi, A. W., Loureiro, S. R., et al. (2010). Is the full version of the AUDIT really necessary? Study of the validity and internal construct of its abbreviated versions. Alcoholism: Clinical and Experimental Research, 34(8), 1417–1424.Google Scholar
Meule, A. & Gearhardt, A. (2014). Food addiction in the light of DSM-5. Nutrients, 6(9), 3653–3671.Google Scholar
Meyers, K., McLellan, A. T., Jaeger, J. L. & Pettinati, H. M. (1995). The development of the Comprehensive Addiction Severity Index for Adolescents (CASI-A): An interview for assessing multiple problems of adolescents. Journal of Substance Abuse Treatment, 12(3), 181–193.Google Scholar
Miele, G. M., Carpenter, K. M., Cockerham, M. S., et al. (2000). Concurrent and predictive validity of the Substance Dependence Severity Scale (SDSS). Drug and Alcohol Dependence, 59(1), 77–88.Google Scholar
Miller, G. A. (1985). The Substance Abuse Subtle Screening Inventory (SASSI) Manual (2nd edition). Springville, IN: The SASSI Institute.Google Scholar
Miller, G. A. (1999). The Substance Abuse Subtle Screening Inventory (SASSI) Manual (3rd edition). Springville, IN: The SASSI Institute.Google Scholar
Miller, K. A. & Mays, D. (2020). Tanning as an addiction: The state of the research and implications for intervention. In Sussman, S. (Ed.) The Cambridge Handbook of Substance and Behavioral Addictions, Cambridge, UK: Cambridge University Press, pp. 362–372.Google Scholar
Miller, W. R. & Del Boca, F. K. (1994). Measurement of drinking behavior using the Form 90 family of instruments. Journal of Studies on Alcohol, Supplement, 12, 112–118.Google Scholar
Miller, W. R. & Marlatt, G. A. (1984). Manual for the Comprehensive Drinker Profile. Psychological Assessment Resources.Google Scholar
Miller, W. R., Leckman, A. L., Delaney, H. D. & Tinkcom, M. (1992). Long-term follow-up of behavioral self-control training. Journal of Studies on Alcohol, 53(3), 249–261.Google Scholar
Miller, W. R., Tonigan, J. S. & Longabaugh, R. (1995). The drinker inventory of consequences (DrInC). Project MATCH Monograph Series, 4.Google Scholar
Miner, M. H., Coleman, E., Center, B. A., Ross, M. & Rosser, B. S. (2007). The compulsive sexual behavior inventory: Psychometric properties. Archives of Sexual Behavior, 36(4), 579–587.Google Scholar
Minnich, A., Erford, B. T., Bardhoshi, G. & Atalay, Z. (2018). Systematic review of the Michigan Alcoholism Screening Test. Journal of Counseling & Development, 96(3), 335–344.Google Scholar
Minnich, A., Erford, B. T., Bardhoshi, G., et al. (2019). Systematic evaluation of psychometric characteristics of the Michigan Alcoholism Screening Test 13‐Item Short (SMAST) and 10‐Item Brief (BMAST) versions. Journal of Counseling & Development, 97(1), 15–24.Google Scholar
Morahan-Martin, J. & Schumacher, P. (2000). Incidence and correlates of pathological Internet use among college students. Computers in Human Behavior, 16(1), 13–29.Google Scholar
Morgenstern, J., Parsons, J., Muench, F., et al. (2004, May). Understanding and Treating Compulsive Sexual Behavior. New York, NY: Paper presented at the American Psychiatric Association Annual Conference.Google Scholar
Newcomb, M. D. & Felix-Ortiz, M. (1992). Multiple protective and risk factors for drug use and abuse: Cross-sectional and prospective findings. Journal of Personality and Social Psychology, 63(2), 280.Google Scholar
Ng, T. W., Sorensen, K. L. & Feldman, D. C. (2007). Dimensions, antecedents, and consequences of workaholism: A conceptual integration and extension. Journal of Organizational Behavior: The International Journal of Industrial, Occupational and Organizational Psychology and Behavior, 28(1), 111–136.Google Scholar
Northrup, J., Lapierre, C., Kirk, J. & Rae, C. (2015). The Internet Process Addiction Test: Screening for addictions to processes facilitated by the Internet. Behavioral Sciences, 5(3), 341–352.Google Scholar
Oates, W. E. (1971). Confessions of a Workaholic: The Facts about Work Addiction. New York: World Publishing Company.Google Scholar
Orford, J., Wardle, H., Griffiths, M., Sproston, K. & Erens, B. (2010). PGSI and DSM-IV in the 2007 British Gambling Prevalence Survey: Reliability, item response, factor structure and inter-scale agreement. International Gambling Studies, 10(1), 31–44.Google Scholar
Pallanti, S., DeCaria, C. M., Grant, J. E., Urpe, M. & Hollander, E. (2005). Reliability and validity of the pathological gambling adaptation of the Yale-Brown Obsessive-Compulsive Scale (PG-YBOCS). Journal of Gambling Studies, 21(4), 431–443.Google Scholar
Parsons, T., Bimbi, D., Perry, N. & Halkitis, J. (2001). Sexual compulsivity among gay/bisexual male escorts who advertise on the Internet. Sexual Addiction & Compulsivity: The Journal of Treatment and Prevention, 8(2), 101–112.Google Scholar
Pasman, L. & Thompson, J. K. (1988). Body image and eating disturbance in obligatory runners, obligatory weightlifters, and sedentary individuals. International Journal of Eating Disorders, 7(6), 759–769.Google Scholar
Pawlikowski, M., Altstötter-Gleich, C. & Brand, M. (2013). Validation and psychometric properties of a short version of Young’s Internet Addiction Test. Computers in Human Behavior, 29(3), 1212–1223.Google Scholar
Petit, A., Lejoyeux, M., Reynaud, M. & Karila, L. (2014). Excessive indoor tanning as a behavioral addiction: A literature review. Current Pharmaceutical Design, 20(25), 4070–4075.Google Scholar
Petry, N. M., Blanco, C., Stinchfield, R. & Volberg, R. (2013). An empirical evaluation of proposed changes for gambling diagnosis in the DSM‐5. Addiction, 108(3), 575–581.Google Scholar
Pokorny, A. D., Miller, B. A. & Kaplan, H. B. (1972). The brief MAST: A shortened version of the Michigan Alcoholism Screening Test. American Journal of Psychiatry, 129(3), 342–345.Google Scholar
Pontes, H. M. & Griffiths, M. D. (2015). Measuring DSM-5 Internet gaming disorder: Development and validation of a short psychometric scale. Computers in Human Behavior, 45, 137–143.Google Scholar
Poulin, C. (2002). An assessment of the validity and reliability of the SOGS-RA. Journal of Gambling Studies, 18(1), 67–93.Google Scholar
Read, J. P., Kahler, C. W., Strong, D. R. & Colder, C. R. (2006). Development and preliminary validation of the young adult alcohol consequences questionnaire. Journal of Studies on Alcohol, 67(1), 169–177.Google Scholar
Reid, R. C., Garos, S. & Carpenter, B. N. (2011). Reliability, validity, and psychometric development of the Hypersexual Behavior Inventory in an outpatient sample of men. Sexual Addiction & Compulsivity, 18(1), 30–51.Google Scholar
Reid, R. C., Garos, S. & Fong, T. (2012). Psychometric development of the hypersexual behavior consequences scale. Journal of Behavioral Addictions, 1(3), 115–122.Google Scholar
Reilly, C. & Smith, N. (2013). The evolving definition of pathological gambling in the DSM-5. National Center for Responsible Gaming, 1, 1–6.Google Scholar
Riggs, S. G. & Alario, A. J. (1989). Adolescent substance abuse. The Project ADEPT curriculum for primary care physician training, 27.Google Scholar
Robbins, T. W. & Clark, L. (2015). Behavioral addictions. Current Opinion in Neurobiology, 30, 66–72.Google Scholar
Robinson, B. E. (1989). Work Addiction: Hidden Legacies of Adult Children. Deerfield Beach, FL: Health Communications.Google Scholar
Robinson, B. E. (1998). The workaholic family: A clinical perspective. American Journal of Family Therapy, 26(1), 65–75.Google Scholar
Robinson, B. E. (2001). Workaholism and family functioning: A profile of familial relationships, psychological outcomes, and research considerations. Contemporary Family Therapy, 23(1), 123–135.Google Scholar
Robinson, B. E. & Post, P. (1994). Validity of the Work Addiction Risk Test. Perceptual and Motor Skills, 78(1), 337–338.Google Scholar
Robinson, S. M., Sobell, L. C., Sobell, M. B. & Leo, G. I. (2014). Reliability of the Timeline Followback for cocaine, cannabis, and cigarette use. Psychology of Addictive Behaviors, 28(1), 154.Google Scholar
Rohsenow, D. J., Abrams, D. B., Monti, P. M., et al. (2003). The Smoking Effects Questionnaire for adult populations: Development and psychometric properties. Addictive Behaviors, 28(7), 1257–1270.Google Scholar
Rounsaville, B. J., Kosten, T. R., Weissman, M. M. & Kleber, H. D. (1986). Prognostic significance of psychopathology in treated opiate addicts: A 2.5-year follow-up study. Archives of General Psychiatry, 43(8), 739–745.CrossRefGoogle Scholar
Ruddock, H. K., Christiansen, P., Halford, J. C. & Hardman, C. A. (2017). The development and validation of the Addiction-like Eating Behaviour Scale. International Journal of Obesity, 41(11), 1710–1717.Google Scholar
Rychtarik, R. G., Koutsky, J. R. & Miller, W. R. (1998). Profiles of the Alcohol Use Inventory: A large sample cluster analysis conducted with split-sample replication rules. Psychological Assessment, 10(2), 107–119.Google Scholar
Rychtarik, R. G., Koutsky, J. R. & Miller, W. R. (1999). Profiles of the Alcohol Use Inventory: Correction to Rychtarik, Koutsky, and Miller (1998). Psychological Assessment, 11(3), 396–402.Google Scholar
Sanchez-Craig, M., Annis, H. M., Bronet, A. R. & MacDonald, K. R. (1984). Random assignment to abstinence and controlled drinking: evaluation of a cognitive-behavioral program for problem drinkers. Journal of Consulting and Clinical Psychology, 52(3), 390–403.Google Scholar
Saunders, J. B., Aasland, O. G., Babor, T. F., De la Fuente, J. R. & Grant, M. (1993). Development of the alcohol use disorders identification test (AUDIT): WHO collaborative project on early detection of persons with harmful alcohol consumption‐II. Addiction, 88(6), 791–804.Google Scholar
Schneider, J. P., Sealy, J., Montgomery, J. & Irons, R. R. (2005). Ritualization and reinforcement: Keys to understanding mixed addiction involving sex and drugs. Sexual Addiction & Compulsivity, 12(2–3), 121–148.Google Scholar
Schwartz, R. H. & Wirtz, P. W. (1990). Potential substance abuse: Detection among adolescent patients using the Drug and Alcohol Problem (DAP) Quick Screen, a 30-item questionnaire. Clinical Pediatrics, 29(1), 38–43.Google Scholar
Scott, K. S., Moore, K. S. & Miceli, M. P. (1997). An exploration of the meaning and consequences of workaholism. Human Relations, 50(3), 287–314.Google Scholar
Selzer, M. L. (1971). The Michigan Alcoholism Screening Test: The quest for a new diagnostic instrument. American Journal of Psychiatry, 127(12), 1653–1658.Google Scholar
Selzer, M. L., Vinokur, A. & van Rooijen, L. (1975). A self-administered short Michigan alcoholism screening test (SMAST). Journal of Studies on Alcohol, 36(1), 117–126.Google Scholar
Shaughnessy, K., Byers, E. S. & Walsh, L. (2011). Online sexual activity experience of heterosexual students: Gender similarities and differences. Archives of Sexual Behavior, 40(2), 419–427.Google Scholar
Shek, D. T., Tang, V. M. & Lo, C. Y. (2008). Internet addiction in Chinese adolescents in Hong Kong: assessment, profiles, and psychosocial correlates. The Scientific World Journal, 8, 776–787.Google Scholar
Simons, J. S., Dvorak, R. D., Merrill, J. E. & Read, J. P. (2012). Dimensions and severity of marijuana consequences: Development and validation of the Marijuana Consequences Questionnaire (MACQ). Addictive Behaviors, 37(5), 613–621.Google Scholar
Skinner, H. A. (1982). The drug abuse screening test. Addictive Behaviors, 7(4), 363–371.Google Scholar
Smith, D. K., Hale, B. D. & Collins, D. (1998). Measurement of exercise dependence in bodybuilders. The Journal of Sports Medicine and Physical Fitness, 38(1), 66–74.Google Scholar
Smith, D. & Hale, B. (2005). Exercise-dependence in bodybuilders: Antecedents and reliability of measurement. Journal of Sports Medicine and Physical Fitness, 45(3), 401–408.Google Scholar
Smith, S. R. & Hilsenroth, M. J. (2001). Discriminative validity of the MacAndrew Alcoholism Scale with Cluster B personality disorders. Journal of Clinical Psychology, 57(6), 801–813.Google Scholar
Snir, R. & Harpaz, I. (2012). Beyond workaholism: Towards a general model of heavy work investment. Human Resource Management Review, 22(3), 232–243.Google Scholar
Sobell, L. C. & Sobell, M. B. (1992). Timeline follow-back. In Measuring Alcohol Consumption. Totowa, NJ: Humana Press, pp. 41–72.Google Scholar
Sohn, S. H. & Choi, Y. J. (2014). Phases of shopping addiction evidenced by experiences of compulsive buyers. International Journal of Mental Health and Addiction, 12(3), 243–254.Google Scholar
Spence, J. T. & Robbins, A. S. (1992). Workaholism: Definition, measurement, and preliminary results. Journal of Personality Assessment, 58(1), 160–178.Google Scholar
Stein, L. A., Lebeau, R., Clair, M., et al. (2010). Validation of a measure to assess alcohol-and marijuana-related risks and consequences among incarcerated adolescents. Drug and Alcohol Dependence, 109(1–3), 104–113.Google Scholar
Stephens, R. S., Roffman, R. A. & Curtin, L. (2000). Comparison of extended versus brief treatments for marijuana use. Journal of Consulting and Clinical Psychology, 68(5), 898–908.Google Scholar
Stinchfield, R. (2002). Reliability, validity, and classification accuracy of the South Oaks Gambling Screen (SOGS). Addictive Behaviors, 27(1), 1–19.Google Scholar
Sussman, S. (2010). Love addiction: Definition, etiology, treatment. Sexual Addiction & Compulsivity, 17(1), 31–45.Google Scholar
Sussman, S. (2017). Substance and Behavioral Addictions: Concepts, Causes, and Cures. Cambridge: Cambridge University Press.Google Scholar
Sussman, S. & Ames, S. L. (2008). Drug Abuse: Concepts, Prevention, and Cessation. Cambridge: Cambridge University Press.Google Scholar
Sussman, S. & Sussman, A. N. (2011). Considering the definition of addiction. International Journal of Environmental and Public Health, 8(10), 4025–4038.Google Scholar
Svanum, S., Levitt, E. E. & McAdoo, W. G. (1982). Differentiating male and female alcoholics from psychiatric outpatients: The MacAndrew and Rosenberg alcoholism scales. Journal of Personality Assessment, 46(1), 81–84.Google Scholar
Szabo, A. & Griffiths, M. D. (2004). The exercise addiction inventory: A new brief screening tool. Addiction Research and Theory, 12(5), 489–499.Google Scholar
Taris, T. W., Schaufeli, W. B. & Verhoeven, L. C. (2005). Internal and external validation of the Dutch Work Addiction Risk Test: Implications for jobs and non-work conflict. Journal of Applied Psychology: An International Review, 54, 37–60.Google Scholar
Thompson, J. K. & Pasman, L. (1991). The obligatory exercise questionnaire. The Behavior Therapist, 14, 137.Google Scholar
Toce-Gerstein, M., Gerstein, D. R. & Volberg, R. A. (2009). The NODS–CLiP: A rapid screen for adult pathological and problem gambling. Journal of Gambling Studies, 25(4), 541.Google Scholar
Tonigan, J. S. & Miller, W. R. (2002). The Inventory of Drug Use Consequences (InDUC): Test-retest stability and sensitivity to detect change. Psychology of Addictive Behaviors, 16(2), 165.Google Scholar
Vaccaro, A. G. & Potenza, M. N. (2020). Neurobiological foundations of behavioral addictions. In Sussman, S. (Ed.) The Cambridge Handbook of Substance and Behavioral Addictions. Cambridge, UK: Cambridge University Press, pp. 136–151.Google Scholar
Volberg, R. A., Munck, I. M. & Petry, N. M. (2011). A quick and simple screening method for pathological and problem gamblers in addiction programs and practices. The American Journal on Addictions, 20(3), 220–227.Google Scholar
Weiss, D. (2004). The prevalence of depression in male sex addicts residing in the United States. Sexual Addiction & Compulsivity, 11(1–2), 57–69.Google Scholar
Weiss, L. M. & Petry, N. M. (2008). Psychometric properties of the inventory of gambling situations with a focus on gender and age differences. The Journal of Nervous and Mental Disease, 196(4), 321–328.Google Scholar
West, R. & Brown, J. (2013). Theory of Addiction. Chichester: John Wiley & Sons.Google Scholar
White, H. R. & Labouvie, E. W. (1989). Towards the assessment of adolescent problem drinking. Journal of Studies on Alcohol, 50(1), 30–37.Google Scholar
Widyanto, L. & McMurran, M. (2004). The psychometric properties of the internet addiction test. Cyberpsychology & Behavior, 7(4), 443–450.Google Scholar
Widyanto, L., Griffiths, M. D. & Brunsden, V. (2011). A psychometric comparison of the Internet Addiction Test, the Internet-Related Problem Scale, and self-diagnosis. Cyberpsychology, Behavior, and Social Networking, 14(3), 141–149.Google Scholar
Widyanto, L., Griffiths, M., Brunsden, V. & McMurran, M. (2008). The psychometric properties of the Internet related problem scale: a pilot study. International Journal of Mental Health and Addiction, 6(2), 205–213.Google Scholar
Winters, K. C. & Henly, G. A. (1993). Adolescent diagnostic interview (ADI): Manual. Western Psychological Services.Google Scholar
Winters, K. C., Specker, S. & Stinchfield, R. (2002). Measuring pathological gambling with the diagnostic interview for gambling severity (DIGS). In: Marotta, J. J., Cornelius, J. A. & Eadington, W. R. (Eds.),The Downside: Problem and Pathological Gambling. Reno, NV: University of Nevada, pp. 143–148.Google Scholar
Winters, K. C., Stinchfield, R. D. & Fulkerson, J. (1993). Toward the development of an adolescent gambling problem severity scale. Journal of Gambling Studies, 9(1), 63–84.Google Scholar
Wong, J. L. & Besett, T. M. (1999). Sex differences on the MMPI–2 Substance Abuse Scales in psychiatric inpatients. Psychological Reports, 84(2), 582–584.Google Scholar
Yates, A., Edman, J. D., Crago, M. & Crowell, D. (2001). Using an exercise-based instrument to detect signs of an eating disorder. Psychiatry Research, 105(3), 231–241.Google Scholar
Young, K. S. (1996). Internet addiction diagnostic questionnaire (IADQ).Google Scholar
Young, K. S. (1998). Internet addiction: The emergence of a new clinical disorder. Cyberpsychology & Behavior, 1(3), 237–244.Google Scholar
Yudko, E., Lozhkina, O. & Fouts, A. (2007). A comprehensive review of the psychometric properties of the Drug Abuse Screening Test. Journal of Substance Abuse Treatment, 32(2), 189–198.Google Scholar

References

Adelman, C. (1993). Kurt Lewin and the origins of action research. Educational Action Research, 1(1), 7–24.Google Scholar
Agar, M. (1973). Ripping and Running: A Formal Ethnography of Urban Heroin Users. New York: Academic Press.Google Scholar
Agar, M. (1986). Speaking of Ethnography (Volume 2). Beverly Hills, CA: Sage.Google Scholar
Angrosino, M. (2007). Doing Ethnographic and Observational Research. Thousand Oaks, CA: Sage.Google Scholar
Bamber, D., Cockerill, I. M., Rodgers, S. & Carroll, D. (2000). “It's exercise or nothing”: A qualitative analysis of exercise dependence. British Journal of Sports Medicine, 34(6), 423–430.Google Scholar
Becker, H. S. (1953). Becoming a marihuana user. American Journal of Sociology, 59 (3), 235–242.Google Scholar
Bernard, H. R. & Bernard, H. R. (2013). Social Research Methods: Qualitative and Quantitative Approaches. Thousand Oaks, CA: Sage.Google Scholar
Bourgois, P. (1995). In Search of Respect: Selling Crack in El Barrio. Cambridge: Cambridge University Press.Google Scholar
Bourgois, P. (2000). Disciplining addictions: The bio-politics of methadone and heroin in the United States. Culture, Medicine and Psychiatry, 24(2), 165–195.Google Scholar
Bourgois, P., Martinez, A., Kral, A., et al. (2006). Reinterpreting ethnic patterns among white and African American men who inject heroin: A social science of medicine approach. PLoS Medicine, 3(10), e452.Google Scholar
Binde, P. (2016). Gambling-related embezzlement in the workplace: A qualitative study. International Gambling Studies, 16(3), 391–407.Google Scholar
Blumenthal, J. A., O'Toole, L. C. & Chang, J. L. (1984). Is running an analogue of anorexia nervosa?: An empirical study of obligatory running and anorexia nervosa. JAMA, 252(4), 520–523.Google Scholar
Bruce, A., Beuthin, R., Sheilds, L., Molzahn, A. & Schick-Makaroff, K. (2016). Narrative research evolving: Evolving through narrative research. International Journal of Qualitative Methods, 15(1), 1609406916659292.Google Scholar
Bryman, A. (2017). Quantitative and qualitative research: Further reflections on their integration. In: Brannen, J. (Ed.), Mixing Methods: Qualitative and Quantitative Research. London: Routledge, pp. 57–78.Google Scholar
Burton, D. (2004). Community-based participatory research on smoking cessation among Chinese Americans in Flushing, Queens, New York City. Journal of Inter-professional Care, 18, 443–445.Google Scholar
Cornelius, T., Earnshaw, V. A., Menino, D., Bogart, L. M. & Levy, S. (2017). Treatment motivation among caregivers and adolescents with substance use disorders. Journal of Substance Abuse Treatment, 75, 10–16.Google Scholar
Creswell, J. W. (2013). Qualitative Inquiry and Research Design: Choosing among Five Approaches (3rd edition). Sage Publications.Google Scholar
Cripps, B. (1995). Exercise addiction and chronic fatigue syndrome: Case study of a mountain biker. Exercise Addiction: Motivation for Participation in Sport and Exercise. Leicester: The British Psychological Society, pp. 22–33.Google Scholar
Curtis, C. & Davis, C. (2014). A qualitative study of binge eating and obesity from an addiction perspective. Eating Disorders, 22(1), 19–32.Google Scholar
Czarniawska, B. (2004). Narratives in Social Science Research. Sage.Google Scholar
De Quincey, T. (2013). Confessions of an English Opium-Eater and Other Writings. Oxford University Press.Google Scholar
Denzin, N. K. & Lincoln, Y. S. (Eds.). (2011). The Sage Handbook of Qualitative Research. Sage.Google Scholar
Dingle, G. A., Cruwys, T. & Frings, D. (2015). Social identities as pathways into and out of addiction. Frontiers in Psychology, 6, 1795.Google Scholar
Dittmar, H. (2001). Impulse buying in ordinary and “compulsive” consumers. In Weber, E. U., Baron, J. & Loomes, G. (Eds.), Cambridge Series on Judgement and Decision Making. Conflict and Tradeoffs in Decision Making.  New York, NY: Cambridge University Press, pp. 110–135.Google Scholar
Dhuffar, M. K. & Griffiths, M. D. (2015). Understanding conceptualisations of female sex addiction and recovery using interpretative phenomenological analysis. Psychology Research, 5(10), 585–603.Google Scholar
Engels, F. (2005). The condition of the working class in England. In: Michael Purdy & David Banks (Eds.) The Sociology and Politics of Health. London: Routledge, pp. 22–27.Google Scholar
Flick, U. (2013). The SAGE Handbook of Qualitative Data Analysis. Sage.Google Scholar
Garcia, V. & Gonzalez, L. (2011). Participatory research challenges in drug abuse studies among transnational Mexican migrants. The Open Anthropology Journal, 4, 3–11. http://doi.org/10.2174/1874912701104010003Google Scholar
Gardner, S. (2004). Participatory action research helps now. The Education Digest, 70 (3), 51.Google Scholar
Glaser, B. G. & Strauss, A. L. (1967). The Discovery of Grounded Theory: Strategies for Qualitative Research. New York: Aldine.Google Scholar
Geraghty, J. (2012). Smoking behaviour and interaction: The observation process in research. British Journal of Nursing, 21(5), 286–291. doi:10.12968/bjon.2012.21.5.286Google Scholar
Gergen, K. J. & Gergen, M. M. (1983). The social construction of helping relationships. New Directions in Helping, 1, 143–163.Google Scholar
Gewurtz, R., Moll, S., Poole, J. M. & Gruhl, K. R. (2016). Qualitative research in mental health and mental illness. In: O. Karin, A. Richard & Z. Izabela (Eds.) Handbook of Qualitative Health Research for Evidence-Based Practice. New York, NY: Springer, pp. 203–223.Google Scholar
Graham, M. D., Young, R. A., Valach, L. & Alan Wood, R. (2008). Addiction as a complex social process: An action theoretical perspective. Addiction Research & Theory, 16(2), 121–133.Google Scholar
Green, A. R., Larkin, M. & Sullivan, V. (2009). Oh stuff it! The experience and explanation of diet failure: An exploration using interpretative phenomenological analysis. Journal of Health Psychology, 14 (7), 997–1008.Google Scholar
Haralson, D. (2013). Grief reactions to drug loss: A grounded theory approach.  Doctoral dissertation, Colorado State University Libraries.Google Scholar
Howard, M., McMillen, C., Nower, L., et al. (2002). Denial in addiction: Toward an integrated stage and process model – Qualitative findings. Journal of Psychoactive Drugs, 34(4), 371–382.Google Scholar
Jamshed, S. (2014). Qualitative research method – Interviewing and observation. Journal of Basic and Clinical Pharmacy, 5(4), 87–88. http://doi.org/10.4103/0976-0105.141942Google Scholar
Jones, S. (1985). Depth Interviewing: Applied Qualitative Research. Aldershot, UK: Gower, pp. 45–55.Google Scholar
Kelly, P. J. (2005). Practical suggestions for community interventions using participatory action research. Public Health Nursing, 22: 65–73. doi: 10.1111/j.0737-1209.2005.22110Google Scholar
Klausner, M. & Hasselbring, B. (1990). Aching for Love: The Sexual Drama of the Adult Child: Healing Strategies for Women. San Francisco: Harper.Google Scholar
Koch, T., Selim, P. & Kralik, D. (2002). Enhancing lives through the development of a community-based participatory action research program. Journal of Clinical Nursing, 11, 109–117.Google Scholar
Kougiali, Z. G., Fasulo, A., Needs, A. & Van Laar, D. (2017). Planting the seeds of change: Directionality in the narrative construction of recovery from addiction. Psychology & Health, 32(6), 639–664.Google Scholar
Lankenau, S. E., Teti, M., Silva, K., et al. (2012). Initiation into prescription opioid misuse amongst young injection drug users. International Journal of Drug Policy, 23(1), 37–44.Google Scholar
Latuskie, K. A., Andrews, N. C., Motz, M., et al. (2019). Reasons for substance use continuation and discontinuation during pregnancy: A qualitative study. Women and Birth, 32(1), e57–e64.Google Scholar
Li, W., O’Brien, J. E., Snyder, S. M. & Howard, M. O. (2015). Characteristics of internet addiction/pathological internet use in U.S. university students: A qualitative-method investigation. PLoS ONE, 10(2), e0117372. http://doi.org/10.1371/journal.pone.0117372Google Scholar
Lima-Rodríguez, J. S., Guerra-Martín, M. D., Domínguez-Sánchez, I. & Lima-Serrano, M. (2015). Alcoholic patients’ response to their disease: Perspective of patients and family. Revista Latino-Americana de Enfermagem, 23(6), 1165–1172. http://doi.org/10.1590/0104-1169.0516.2662Google Scholar
Lindesmith, A. R. (1947). Opiate Addiction. Principia Press.Google Scholar
Maher, L. (1997). Sexed Work: Gender, Race, and Resistance in a Brooklyn Drug Economy. Oxford: Clarendon.Google Scholar
Martin, A. & Stenner, P. (2004). Talking about drug use: What are we (and our participants) doing in qualitative research? International Journal of Drug Policy, 15, 395–405.Google Scholar
Mars, S. G., Bourgois, P., Karandinos, G., Montero, F. & Ciccarone, D. (2014). “Every ‘never’I ever said came true”: Transitions from opioid pills to heroin injecting. International Journal of Drug Policy, 25(2), 257–266.Google Scholar
Mason, J. (1996). Qualitative Researching. Sage.Google Scholar
Mathur, M. (2009). Socialisation of street children in India: A socio-economic profile. Psychology and Developing Societies, 21(2), 299–325.Google Scholar
Mendenhall, T., Harper, P., Stephenson, H. & Santo Haas, G. (2011). The SANTA Project (Students against Nicotine and Tobacco Addiction): Using community-based participatory research to reduce smoking in a high-risk young adult population. Action Research, 9(2), 199-213.Google Scholar
Miles, M. & Huberman, A. (1994). Qualitative Data Analysis: An Expanded Source Book. Sage.Google Scholar
Moskalewicz, M. (2016). Lived time disturbances of drug addiction therapy newcomers: A qualitative, field phenomenology case study at Monar-Markot Center in Poland. International Journal of Mental Health and Addiction, 14(6), 1023–1038. doi:10.1007/s11469-016-9680-4Google Scholar
Neale, J., Allen, D. & Coombes, L. (2005). Qualitative research methods within the addictions. Addiction, 100, 1584–1593. doi:10.1111/j.1360-0443.2005.01230Google Scholar
Neale, J., Nettleton, S. & Pickering, L. (2014). Gender sameness and difference in recovery from heroin dependence: A qualitative exploration. International Journal of Drug Policy, 25 (1), 3–12.Google Scholar
Neale, J., Shepard, L. & Tompkins, C. N. E. (2007). Factors that help injecting drug users to access and benefit from services: A qualitative study. Substance Abuse Treatment, Prevention, and Policy, 2, 31.Google Scholar
Needle, R. H., Coyle, S., Cesari, H., et al. (1998). HIV risk behaviors associated with the injection process: Multiperson use of drug injection equipment and paraphernalia in injection drug user networks. Substance Use & Misuse, 33(12), 2403–2423.Google Scholar
Nichter, M., Quintero, G., Nichter, M., Mock, J. & Shakib, S. (2004). Qualitative research: contributions to the study of drug use, drug abuse, and drug use (r)-related interventions. Substance Use & Misuse, 39(10–12), 1907–1969.Google Scholar
Nurani, L. (2008). Critical review of ethnographic approach. Jurnal Sosioteknologi, 7(14), 441–447.Google Scholar
Ogden, J., Veale, D. & Summers, Z. (1997). The development and validation of the Exercise Dependence Questionnaire. Addiction Research, 5(4), 343–355.Google Scholar
Orosz, J. (1997). Qualitative Data Analysis: An Expanded Sourcebook (2nd edition). Public Administration Review, 57(6), 543.Google Scholar
Parkin, S. (2015). Colliding intervention in the spatial management of street-based injecting and drug-related litter within settings of public convenience (UK). Space and Polity, 1–20.Google Scholar
Preble, E. & Casey, J. J. (1969). Taking care of business – The heroin user's life on the street. International Journal of the Addictions, 4(1), 1–24.Google Scholar
Reinarman, C. (2005). Addiction as accomplishment: The discursive construction of disease. Addiction Research & Theory, 13(4), 307–320.Google Scholar
Rhodes, T. & Coomber, R. (2010). Qualitative methods and theory in addictions research. In: Miller, G., Strang, J. & Miller, P. (Eds.), Addiction Research Methods. Chichester: Wiley-Blackwell, pp. 59–78.Google Scholar
Rhodes, T. & Moore, D. (2001). On the qualitative in drugs research: Part one. Addiction Research & Theory, 9(4), 279–297.Google Scholar
Ronel, N. & Libman, G. (2003). Eating disorders and recovery: Lessons from Overeaters Anonymous. Clinical Social Work Journal, 31, 155–171.Google Scholar
Rossol, J. (2001). The medicalization of deviance as an interactive achievement: The construction of compulsive gambling. Symbolic Interaction, 24(3), 315–341. doi:10.1525/si.2001.24.3.315Google Scholar
Russell-Mayhew, S., von Ranson, K. M. & Masson, P. C. (2010). How does Overeaters Anonymous help its members? A qualitative analysis. European Eating Disorders Review, 18, 33–42.Google Scholar
Sachs, M. L. & Pargman, D. (1979). Running addiction: A depth interview examination. Journal of Sport Behavior, 2(3), 143–155.Google Scholar
Saldanha, K., D’Souza, B. & Madangopal, D. (2018). “It’s only a game of chance”: A portrait of gambling among street children in Mumbai. Journal of Adolescent Research, 33(6), 699–724.Google Scholar
Schensul, J. J., Chandran, D., Singh, S. K., et al. (2010). The use of qualitative comparative analysis for critical event research in alcohol and HIV in Mumbai, India. AIDS and Behavior, 14(Supplement 1), S113–S125. doi:10.1007/s10461-010-9736-6Google Scholar
Schneider, J. A., O'Leary, A. & Jenkins, S. R. (1995). Gender, sexual orientation, and disordered eating. Psychology and Health, 10(2), 113–128.Google Scholar
Seale, C. (1999). Grounding theory. The Quality of Qualitative Research, 87–105.Google Scholar
Seheult, C. (1995). Hooked on the “buzz”: history of a bodybuilding addict. Exercise Addiction: Motivation for Participation in Sport and Exercise. Leicester: The British Psychological Society, pp. 40–44.Google Scholar
Shannon, K., Bright, V., Allinott, S., et al. (2007). Community-based HIV prevention research among substance-using women in survival sex work: The Maka Project Partnership. Harm Reduction Journal, 4(1), 20.Google Scholar
Shefrin, H. M. & Thaler, R. H. (1988). The behavioral life‐cycle hypothesis. Economic Inquiry, 26(4), 609–643.Google Scholar
Simmons, J. (2006). The interplay between interpersonal dynamics, treatment barriers, and larger social forces: An exploratory study of drug-using couples in Hartford, CT. Substance Abuse Treatment and Prevention Policy, 1, 12. doi:10.1186/1747-597x-1-12Google Scholar
Simmons, J. & McMahon, J. M. (2012). Barriers to drug treatment for IDU couples: The need for couple-based approaches. Journal of Addictive Diseases, 31(3), 242–257. doi:10.1080/10550887.2012.702985Google Scholar
Simmons, J. & Singer, M. (2006). I love you … and heroin: Care and collusion among drug-using couples. Substance Abuse Treatment and Prevention Policy, 1, 7. doi:10.1186/1747-597x-1-7Google Scholar
Simmons, J., Rajn, S. & McMahon, J. M. (2012). Retrospective accounts of injection initiation in intimate partnerships. International Journal of Drug Policy, 23, 303–311.Google Scholar
Smith, B. (2016). Narrative analysis. Analysing Qualitative Data in Psychology, 2, 202–221.Google Scholar
Sterk, C. (2008). Fast Lives: Women Who Use Crack Cocaine. Temple University Press.Google Scholar
Strauss, A. & Corbin, J. (1990). Basics of Qualitative Research. Sage Publications.Google Scholar
Tsark, J. (2001). A participatory research approach to address data needs in tobacco use among Native Hawaiians. Asian American/Pacific Islander Journal of Health, 9, 40–48.Google Scholar
Wan, C. S. & Chiou, W. B. (2006). Why are adolescents addicted to online gaming? An interview study in Taiwan. CyberPsychology & Behavior, 9(6), 762–776.Google Scholar
Woo, J., Bhalerao, A., Bawor, M., et al. (2017). “Don’t Judge a Book Its Cover”: A qualitative study of methadone patients' experiences of stigma. Substance Abuse: Research and Ttreatment, 11, 1178221816685087. doi:10.1177/1178221816685087Google Scholar

Save book to Kindle

To save this book to your Kindle, first ensure [email protected] is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×