Using individual networks to identify treatment targets for eating disorder treatment: a proof-of-concept study and initial data.
Eating disorders
Idiographic modeling
Network analysis
Personalized treatment
Precision treatment
Journal
Journal of eating disorders
ISSN: 2050-2974
Titre abrégé: J Eat Disord
Pays: England
ID NLM: 101610672
Informations de publication
Date de publication:
04 Nov 2021
04 Nov 2021
Historique:
received:
26
08
2021
accepted:
23
10
2021
entrez:
5
11
2021
pubmed:
6
11
2021
medline:
6
11
2021
Statut:
epublish
Résumé
Eating disorders (EDs) are severe mental illnesses, with high morbidity, mortality, and societal burden. EDs are extremely heterogenous, and only 50% of patients currently respond to first-line treatments. Personalized and effective treatments for EDs are drastically needed. The current study (N = 34 participants with an ED diagnosis collected throughout the United States) aimed to investigate best methods informing how to select personalized treatment targets utilizing idiographic network analysis, which could then be used for evidence based personalized treatment development. We present initial data collected via experience sampling (i.e., ecological momentary assessment) over the course of 15 days, 5 times a day (75 total measurement points) that were used to select treatment targets for a personalized treatment for EDs. Overall, we found that treatment targets were highly variable, with less than 50% of individuals endorsing central symptoms related to weight and shape, consistent with current treatment response rates for treatments designed to target those symptoms. We also found that different aspects of selection methods (e.g., number of items, type of centrality measure) impacted treatment target selection. We discuss implications of these data, how to use idiographic network analysis to personalize treatment, and identify areas that need future research. Clinicaltrials.gov, NCT04183894. Registered 3 December 2019-Retrospectively registered, https://clinicaltrials.gov/ct2/show/NCT04183894 . NCT04183894 (ClinicalTrials.gov identifier). Eating disorders are severe psychiatric illnesses that carry high mortality, morbidity, and societal and personal burden. Treatments for eating disorders only work in 50% of patients, signifying a great need to improve treatments. One reason that treatments may not work, is because eating disorders vary substantially from one individual to the next, which existing treatments do not fully consider. The current study (N = 34 participants with an eating disorder diagnosis) uses a new modeling technique to identify which symptoms should be targeted in treatment in a personalized manner. As expected, we found that, using this modeling technique, symptoms that should be targeted in treatment vary considerably. We discuss how to use this modeling technique to identify individual treatment targets and ways in which the field can use this strategy to improve existing and create new treatments.
Sections du résumé
BACKGROUND
BACKGROUND
Eating disorders (EDs) are severe mental illnesses, with high morbidity, mortality, and societal burden. EDs are extremely heterogenous, and only 50% of patients currently respond to first-line treatments. Personalized and effective treatments for EDs are drastically needed.
METHODS
METHODS
The current study (N = 34 participants with an ED diagnosis collected throughout the United States) aimed to investigate best methods informing how to select personalized treatment targets utilizing idiographic network analysis, which could then be used for evidence based personalized treatment development. We present initial data collected via experience sampling (i.e., ecological momentary assessment) over the course of 15 days, 5 times a day (75 total measurement points) that were used to select treatment targets for a personalized treatment for EDs.
RESULTS
RESULTS
Overall, we found that treatment targets were highly variable, with less than 50% of individuals endorsing central symptoms related to weight and shape, consistent with current treatment response rates for treatments designed to target those symptoms. We also found that different aspects of selection methods (e.g., number of items, type of centrality measure) impacted treatment target selection.
CONCLUSIONS
CONCLUSIONS
We discuss implications of these data, how to use idiographic network analysis to personalize treatment, and identify areas that need future research.
TRIAL REGISTRATION
BACKGROUND
Clinicaltrials.gov, NCT04183894. Registered 3 December 2019-Retrospectively registered, https://clinicaltrials.gov/ct2/show/NCT04183894 . NCT04183894 (ClinicalTrials.gov identifier).
Eating disorders are severe psychiatric illnesses that carry high mortality, morbidity, and societal and personal burden. Treatments for eating disorders only work in 50% of patients, signifying a great need to improve treatments. One reason that treatments may not work, is because eating disorders vary substantially from one individual to the next, which existing treatments do not fully consider. The current study (N = 34 participants with an eating disorder diagnosis) uses a new modeling technique to identify which symptoms should be targeted in treatment in a personalized manner. As expected, we found that, using this modeling technique, symptoms that should be targeted in treatment vary considerably. We discuss how to use this modeling technique to identify individual treatment targets and ways in which the field can use this strategy to improve existing and create new treatments.
Autres résumés
Type: plain-language-summary
(eng)
Eating disorders are severe psychiatric illnesses that carry high mortality, morbidity, and societal and personal burden. Treatments for eating disorders only work in 50% of patients, signifying a great need to improve treatments. One reason that treatments may not work, is because eating disorders vary substantially from one individual to the next, which existing treatments do not fully consider. The current study (N = 34 participants with an eating disorder diagnosis) uses a new modeling technique to identify which symptoms should be targeted in treatment in a personalized manner. As expected, we found that, using this modeling technique, symptoms that should be targeted in treatment vary considerably. We discuss how to use this modeling technique to identify individual treatment targets and ways in which the field can use this strategy to improve existing and create new treatments.
Identifiants
pubmed: 34736538
doi: 10.1186/s40337-021-00504-7
pii: 10.1186/s40337-021-00504-7
pmc: PMC8567590
doi:
Banques de données
ClinicalTrials.gov
['NCT04183894']
Types de publication
Journal Article
Langues
eng
Pagination
147Commentaires et corrections
Type : ErratumIn
Informations de copyright
© 2021. The Author(s).
Références
Ægisdóttir S, White MJ, Spengler PM, Maugherman AS, Anderson LA, Cook RS, Nichols CN, Lampropoulos GK, Walker BS, Cohen G, Rush JD. The meta-analysis of clinical judgment project: fifty-six years of accumulated research on clinical versus statistical prediction. Couns Psychol. 2006;34(3):341–82. https://doi.org/10.1177/0011000005285875 .
doi: 10.1177/0011000005285875
Agras WS. Cognitive behavior therapy for the eating disorders. Psychiatr Clin North Am. 2019;42(2):169–79. https://doi.org/10.1016/j.psc.2019.01.001 .
doi: 10.1016/j.psc.2019.01.001
pubmed: 31046920
Arcelus J. Mortality rates in patients with anorexia nervosa and other eating disorders: a meta-analysis of 36 studies. Arch Gen Psychiatry. 2011;68(7):724. https://doi.org/10.1001/archgenpsychiatry.2011.74 .
doi: 10.1001/archgenpsychiatry.2011.74
pubmed: 21727255
Askew AJ, Peterson CB, Crow SJ, Mitchell JE, Halmi KA, Agras WS, Haynos AF. Not all body image constructs are created equal: Predicting eating disorder outcomes from preoccupation, dissatisfaction, and overvaluation. Int J Eat Disord. 2020;53(6):954–63. https://doi.org/10.1002/eat.23277 .
doi: 10.1002/eat.23277
pubmed: 32304257
Bringmann LF, Ferrer E, Hamaker EL, Borsboom D, Tuerlinckx F. Modeling nonstationary emotion dynamics in dyads using a time-varying vector-autoregressive model. Multivar Behav Res. 2018;53(3):293–314. https://doi.org/10.1080/00273171.2018.1439722 .
doi: 10.1080/00273171.2018.1439722
Burger J, A Bayesian Approach to Incorporating Clinical Expertise in Personalized Symptom Networks 2020.
Carter JC, Mercer-Lynn KB, Norwood SJ, Bewell-Weiss CV, Crosby RD, Woodside DB, Olmsted MP. A prospective study of predictors of relapse in anorexia nervosa: Implications for relapse prevention. Psychiatry Res. 2012;200(2–3):518–23. https://doi.org/10.1016/j.psychres.2012.04.037 .
doi: 10.1016/j.psychres.2012.04.037
pubmed: 22657951
David SJ, Marshall AJ, Evanovich EK, Mumma GH. Intraindividual dynamic network analysis – implications for clinical assessment. J Psychopathol Behav Assess. 2018;40(2):235–48. https://doi.org/10.1007/s10862-017-9632-8 .
doi: 10.1007/s10862-017-9632-8
pubmed: 29937621
Dawes RM, Faust D, Meehl PE. Clinical Versus Actuarial Judgment. 1989;8.
Deloitte Access Economics. Social and economic cost of eating disorders in the United States of America. Academy for Eating Disorders 2020.
DuBois RH, Rodgers RF, Franko DL, Eddy KT, Thomas JJ. A network analysis investigation of the cognitive-behavioral theory of eating disorders. Behav Res Ther. 2017;97:213–21. https://doi.org/10.1016/j.brat.2017.08.004 .
doi: 10.1016/j.brat.2017.08.004
pubmed: 28826067
Epskamp S, Borsboom D, Fried EI. Estimating psychological networks and their accuracy: A tutorial paper. Behav Res Methods. 2018;50(1):195–212. https://doi.org/10.3758/s13428-017-0862-1 .
doi: 10.3758/s13428-017-0862-1
pubmed: 28342071
Epskamp S, Cramer AOJ, Waldorp LJ, Schmittmann VD, Borsboom D, qgraph: Network visualizations of relationships in psychometric data. J Stat Softw, 2012;48(1), 1–18. https://doi.org/10.18637/jss.v048.i04
Fairburn CG. Cognitive behavior therapy and eating disorders. Guilford Press; 2008.
Fairburn CG, Cooper Z, Shafran R. Cognitive behaviour therapy for eating disorders: A “transdiagnostic” theory and treatment. Behav Res Ther. 2003;41(5):509–28.
doi: 10.1016/S0005-7967(02)00088-8
Fernandez KC, Fisher AJ, Chi C. Development and initial implementation of the dynamic assessment treatment algorithm (DATA). PLoS ONE. 2017;12(6): e0178806. https://doi.org/10.1371/journal.pone.0178806 .
doi: 10.1371/journal.pone.0178806
pubmed: 28654667
pmcid: 5487014
First MB, Williams JBW, Karg RS, Spitzer RL, Structured clinical interview for DSM-5 – research version (SCID-5 for DSM-5, research version; SCID-5-RV). American Psychiatric Association, 2015
Fisher AJ, Reeves JW, Lawyer G, Medaglia JD, Rubel JA. Exploring the idiographic dynamics of mood and anxiety via network analysis. J Abnorm Psychol. 2017;126(8):1044–56. https://doi.org/10.1037/abn0000311 .
doi: 10.1037/abn0000311
pubmed: 29154565
Forbush KT, Siew CSQ, Vitevitch MS. Application of network analysis to identify interactive systems of eating disorder psychopathology. Psychol Med. 2016;46(12):2667–77. https://doi.org/10.1017/S003329171600012X .
doi: 10.1017/S003329171600012X
pubmed: 27387196
Frumkin MR, Piccirillo ML, Beck ED, Grossman JT, Rodebaugh TL. Feasibility and utility of idiographic models in the clinic: a pilot study. Psychother Res. 2020. https://doi.org/10.1080/10503307.2020.1805133 .
doi: 10.1080/10503307.2020.1805133
pubmed: 32838671
pmcid: 7902742
Galmiche M, Déchelotte P, Lambert G, Tavolacci MP. Prevalence of eating disorders over the 2000–2018 period: a systematic literature review. Am J Clin Nutr. 2019;109(5):1402–13. https://doi.org/10.1093/ajcn/nqy342 .
doi: 10.1093/ajcn/nqy342
pubmed: 31051507
Garb HN. Clinical judgment and decision making. Annu Rev Clin Psychol. 2005;1(1):67–89. https://doi.org/10.1146/annurev.clinpsy.1.102803.143810 .
doi: 10.1146/annurev.clinpsy.1.102803.143810
pubmed: 17716082
Gibbons RD, Weiss DJ, Frank E, Kupfer D. Computerized adaptive diagnosis and testing of mental health disorders. Annu Rev Clin Psychol. 2016;12(1):83–104. https://doi.org/10.1146/annurev-clinpsy-021815-093634 .
doi: 10.1146/annurev-clinpsy-021815-093634
pubmed: 26651865
Griffiths C, Williamson H, Zucchelli F, Paraskeva N, Moss T. A systematic review of the effectiveness of acceptance and commitment therapy (ACT) for body image dissatisfaction and weight self-stigma in adults. J Contemp Psychother. 2018;48(4):189–204. https://doi.org/10.1007/s10879-018-9384-0 .
doi: 10.1007/s10879-018-9384-0
pubmed: 30369631
pmcid: 6182714
Hamaker EL, In Why researchers should think “within-person”: a paradigmatic rationale: Mehl MR, Conner TS., Handbook of research methods for studying daily life. (p. 43.61). The Guilford Press 2012.
Houben M, Van Den Noortgate W, Kuppens P. The relation between short-term emotion dynamics and psychological well-being: A meta-analysis. Psychol Bull. 2015;141(4):901–30. https://doi.org/10.1037/a0038822 .
doi: 10.1037/a0038822
pubmed: 25822133
Howe E, Bosley HG, Fisher AJ. Idiographic network analysis of discrete mood states prior to treatment. Couns Psychother Res. 2020;20(3):470–8. https://doi.org/10.1002/capr.12295 .
doi: 10.1002/capr.12295
Insel TR. The NIMH research domain criteria (RDoC) project: precision medicine for psychiatry. Am J Psychiatry. 2014;171(4):395–7. https://doi.org/10.1176/appi.ajp.2014.14020138 .
doi: 10.1176/appi.ajp.2014.14020138
pubmed: 24687194
Kaiser T, Laireiter A-R, Process-symptom-bridges in psychotherapy: An idiographic network approach. J Person Orient Res, 2018;49–62. https://doi.org/10.17505/jpor.2018.06
Keel PK, Brown TA. Update on course and outcome in eating disorders. Int J Eat Disord. 2010;43(3):195–204. https://doi.org/10.1002/eat.20810 .
doi: 10.1002/eat.20810
pubmed: 20186717
Keel PK, Mitchell JE. Outcome in bulimia nervosa. Am J Psychiatry. 1997;154(3):313–21. https://doi.org/10.1176/ajp.154.3.313 .
doi: 10.1176/ajp.154.3.313
pubmed: 9054777
Keel PK, Dorer DJ, Franko DL, Jackson SC, Herzog DB. Postremission predictors of relapse in women with eating disorders. Am J Psychiatry. 2005;162(12):2263–8. https://doi.org/10.1176/appi.ajp.162.12.2263 .
doi: 10.1176/appi.ajp.162.12.2263
pubmed: 16330589
Levinson CA, Cash E, Welch K, Epskamp S, Hunt RA, Williams BM, Keshishian AC, Spoor SP. Personalized networks of eating disorder symptoms predicting eating disorder outcomes and remission. Int J Eat Disord. 2020;53(12):2086–94. https://doi.org/10.1002/eat.23398 .
doi: 10.1002/eat.23398
pubmed: 33179347
pmcid: 7864225
Levinson CA, Christian C, Ram SS, Vanzhula I, Brosof LC, Michelson LP, Williams BM. Eating disorder symptoms and core eating disorder fears decrease during online imaginal exposure therapy for eating disorders. J Affect Disord. 2020;276:585–91. https://doi.org/10.1016/j.jad.2020.07.075 .
doi: 10.1016/j.jad.2020.07.075
pubmed: 32794449
Levinson CA, Vanzhula IA, Smith TW, Stice E. Group and longitudinal intra-individual networks of eating disorder symptoms in adolescents and young adults at-risk for an eating disorder. Behav Res Ther. 2020;135: 103731. https://doi.org/10.1016/j.brat.2020.103731 .
doi: 10.1016/j.brat.2020.103731
pubmed: 33010651
pmcid: 7688499
Levinson CA, Vanzhula I, Brosof LC. Longitudinal and personalized networks of eating disorder cognitions and behaviors: targets for precision intervention a proof of concept study. Int J Eat Disord. 2018;51(11):1233–43. https://doi.org/10.1002/eat.22952 .
doi: 10.1002/eat.22952
pubmed: 30291641
Levinson CA, Zerwas S, Calebs B, Forbush K, Kordy H, Watson H, Hofmeier S, Levine M, Crosby RD, Peat C, Runfola CD, Zimmer B, Moesner M, Marcus MD, Bulik CM. The core symptoms of bulimia nervosa, anxiety, and depression: a network analysis. J Abnorm Psychol. 2017;126(3):340–54. https://doi.org/10.1037/abn0000254 .
doi: 10.1037/abn0000254
pubmed: 28277735
pmcid: 5378619
Levitt DH. Drive for thinness and fear of fat: separate yet related constructs? Eat Disord. 2003;11(3):221–34. https://doi.org/10.1080/10640260390218729 .
doi: 10.1080/10640260390218729
pubmed: 16801253
Linehan MM, Cognitive-behavioral treatment of borderline personality disorder (1st ed.). Guilford Publications 1993.
Mansueto AC, Wiers R, Weert JCM van, Schouten BC, Epskamp S, Investigating the feasibility of idiographic network models. 2020. https://doi.org/10.31234/osf.io/hgcz6
Moeller J, Ivcevic Z, Brackett MA, White AE. Mixed emotions: Network analyses of intra-individual co-occurrences within and across situations. Emotion. 2018;18(8):1106–21. https://doi.org/10.1037/emo0000419 .
doi: 10.1037/emo0000419
pubmed: 29389203
Piccirillo ML, Beck ED, Rodebaugh TL. A clinician’s primer for idiographic research: considerations and recommendations. Behav Ther. 2019;50(5):938–51. https://doi.org/10.1016/j.beth.2019.02.002 .
doi: 10.1016/j.beth.2019.02.002
pubmed: 31422849
Piccirillo ML, Rodebaugh TL. Foundations of idiographic methods in psychology and applications for psychotherapy. Clin Psychol Rev. 2019;71:90–100. https://doi.org/10.1016/j.cpr.2019.01.002 .
doi: 10.1016/j.cpr.2019.01.002
pubmed: 30665765
Reeves JW, Fisher AJ. An examination of idiographic networks of posttraumatic stress disorder symptoms. J Trauma Stress. 2020;33(1):84–95. https://doi.org/10.1002/jts.22491 .
doi: 10.1002/jts.22491
pubmed: 32103567
Robinaugh DJ, Hoekstra RHA, Toner ER, Borsboom D. The network approach to psychopathology: a review of the literature 2008–2018 and an agenda for future research. Psychol Med. 2020;50:353–66. https://doi.org/10.1017/S0033291719003404 .
doi: 10.1017/S0033291719003404
pubmed: 31875792
Sheehan DV, Lecrubier Y, Sheehan KH, Janavs J, Weiller E, Keskiner A, Schinka J, Knapp E, Sheehan MF, Dunbar GC. The validity of the Mini International Neuropsychiatric Interview (MINI) according to the SCID-P and its reliability. Eur Psychiatry. 1997;12(5):232–41. https://doi.org/10.1016/S0924-9338(97)83297-X .
doi: 10.1016/S0924-9338(97)83297-X
Sheehan DV, Lecrubier Y, Sheehan KH, Amorim P, Janavs J, Weiller E, Hergueta T, Baker R, Dunbar GC. The mini-international neuropsychiatric interview (MINI): the development and validation of a structured diagnostic psychiatric interview for DSM-IV and ICD-10. J Clin Psychiatry. 1998;59(Suppl 20):22–33.
pubmed: 9881538
Strober M, Freeman R, Morrell W. The long-term course of severe anorexia nervosa in adolescents: survival analysis of recovery, relapse, and outcome predictors over 10–15 years in a prospective study. Int J Eat Disord. 1997;22(4):339–60. https://doi.org/10.1002/(sici)1098-108x(199712)22:4%3c339::aid-eat1%3e3.0.co;2-n .
doi: 10.1002/(sici)1098-108x(199712)22:4<339::aid-eat1>3.0.co;2-n
pubmed: 9356884
Wang SB, Jones PJ, Dreier M, Elliott H, Grilo CM. Core psychopathology of treatment-seeking patients with binge-eating disorder: a network analysis investigation. Psychol Med. 2019;49(11):1923–8. https://doi.org/10.1017/S0033291718002702 .
doi: 10.1017/S0033291718002702
pubmed: 30232948
Wilson GT, Grilo CM, Vitousek KM. Psychological treatment of eating disorders. Am Psychol. 2007;62(3):199–216. https://doi.org/10.1037/0003-066X.62.3.199 .
doi: 10.1037/0003-066X.62.3.199
pubmed: 17469898
Wright AG, Woods WC. Personalized models of psychopathology. Annual Rev Clin Psychol. 2020;16:49–74.
doi: 10.1146/annurev-clinpsy-102419-125032