Using individual networks to identify treatment targets for eating disorder treatment: a proof-of-concept study and initial data

Cheri A Levinson, Rowan A Hunt, Ani C Keshishian, Mackenzie L Brown, Irina Vanzhula, Caroline Christian, Leigh C Brosof, Brenna M Williams, Cheri A Levinson, Rowan A Hunt, Ani C Keshishian, Mackenzie L Brown, Irina Vanzhula, Caroline Christian, Leigh C Brosof, Brenna M Williams

Abstract

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: 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: 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: We discuss implications of these data, how to use idiographic network analysis to personalize treatment, and identify areas that need future research.

Trial registration: Clinicaltrials.gov, NCT04183894. Registered 3 December 2019-Retrospectively registered, https://ichgcp.net/clinical-trials-registry/NCT04183894 . NCT04183894 (ClinicalTrials.gov identifier).

Keywords: Eating disorders; Idiographic modeling; Network analysis; Personalized treatment; Precision treatment.

Conflict of interest statement

The authors declare that they have no competing interests.

© 2021. The Author(s).

Figures

Fig. 1.
Fig. 1.
15-item (top) and 8-item (bottom) contemporaneous (left) and temporal (right) individual networks for example participant. See Additional file 1: Table S1 for full items associated with each node abbreviation
Fig. 2.
Fig. 2.
15-item and 8-item contemporaneous and temporal individual networks for additional participants. See Figs. 1/S1 for networks of participants 7 and 30. See Additional file 1: Table S1 for full items associated with each node abbreviation
Fig. 2.
Fig. 2.
15-item and 8-item contemporaneous and temporal individual networks for additional participants. See Figs. 1/S1 for networks of participants 7 and 30. See Additional file 1: Table S1 for full items associated with each node abbreviation
Fig. 2.
Fig. 2.
15-item and 8-item contemporaneous and temporal individual networks for additional participants. See Figs. 1/S1 for networks of participants 7 and 30. See Additional file 1: Table S1 for full items associated with each node abbreviation
Fig. 2.
Fig. 2.
15-item and 8-item contemporaneous and temporal individual networks for additional participants. See Figs. 1/S1 for networks of participants 7 and 30. See Additional file 1: Table S1 for full items associated with each node abbreviation

References

    1. 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):341–382. doi: 10.1177/0011000005285875.
    1. Agras WS. Cognitive behavior therapy for the eating disorders. Psychiatr Clin North Am. 2019;42(2):169–179. doi: 10.1016/j.psc.2019.01.001.
    1. 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. doi: 10.1001/archgenpsychiatry.2011.74.
    1. 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):954–963. doi: 10.1002/eat.23277.
    1. 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. doi: 10.1080/00273171.2018.1439722.
    1. Burger J, A Bayesian Approach to Incorporating Clinical Expertise in Personalized Symptom Networks 2020.
    1. 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):518–523. doi: 10.1016/j.psychres.2012.04.037.
    1. David SJ, Marshall AJ, Evanovich EK, Mumma GH. Intraindividual dynamic network analysis – implications for clinical assessment. J Psychopathol Behav Assess. 2018;40(2):235–248. doi: 10.1007/s10862-017-9632-8.
    1. Dawes RM, Faust D, Meehl PE. Clinical Versus Actuarial Judgment. 1989;8.
    1. Deloitte Access Economics. Social and economic cost of eating disorders in the United States of America. Academy for Eating Disorders 2020.
    1. 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;97:213–221. doi: 10.1016/j.brat.2017.08.004.
    1. Epskamp S, Borsboom D, Fried EI. Estimating psychological networks and their accuracy: A tutorial paper. Behav Res Methods. 2018;50(1):195–212. doi: 10.3758/s13428-017-0862-1.
    1. 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. 10.18637/jss.v048.i04
    1. Fairburn CG. Cognitive behavior therapy and eating disorders. Guilford Press; 2008.
    1. Fairburn CG, Cooper Z, Shafran R. Cognitive behaviour therapy for eating disorders: A “transdiagnostic” theory and treatment. Behav Res Ther. 2003;41(5):509–528. doi: 10.1016/S0005-7967(02)00088-8.
    1. Fernandez KC, Fisher AJ, Chi C. Development and initial implementation of the dynamic assessment treatment algorithm (DATA) PLoS ONE. 2017;12(6):e0178806. doi: 10.1371/journal.pone.0178806.
    1. 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
    1. 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):1044–1056. doi: 10.1037/abn0000311.
    1. Forbush KT, Siew CSQ, Vitevitch MS. Application of network analysis to identify interactive systems of eating disorder psychopathology. Psychol Med. 2016;46(12):2667–2677. doi: 10.1017/S003329171600012X.
    1. 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 doi: 10.1080/10503307.2020.1805133.
    1. 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):1402–1413. doi: 10.1093/ajcn/nqy342.
    1. Garb HN. Clinical judgment and decision making. Annu Rev Clin Psychol. 2005;1(1):67–89. doi: 10.1146/annurev.clinpsy.1.102803.143810.
    1. 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. doi: 10.1146/annurev-clinpsy-021815-093634.
    1. 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. doi: 10.1007/s10879-018-9384-0.
    1. 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.
    1. 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):901–930. doi: 10.1037/a0038822.
    1. Howe E, Bosley HG, Fisher AJ. Idiographic network analysis of discrete mood states prior to treatment. Couns Psychother Res. 2020;20(3):470–478. doi: 10.1002/capr.12295.
    1. Insel TR. The NIMH research domain criteria (RDoC) project: precision medicine for psychiatry. Am J Psychiatry. 2014;171(4):395–397. doi: 10.1176/appi.ajp.2014.14020138.
    1. Kaiser T, Laireiter A-R, Process-symptom-bridges in psychotherapy: An idiographic network approach. J Person Orient Res, 2018;49–62. 10.17505/jpor.2018.06
    1. Keel PK, Brown TA. Update on course and outcome in eating disorders. Int J Eat Disord. 2010;43(3):195–204. doi: 10.1002/eat.20810.
    1. Keel PK, Mitchell JE. Outcome in bulimia nervosa. Am J Psychiatry. 1997;154(3):313–321. doi: 10.1176/ajp.154.3.313.
    1. 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):2263–2268. doi: 10.1176/appi.ajp.162.12.2263.
    1. 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):2086–2094. doi: 10.1002/eat.23398.
    1. 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;276:585–591. doi: 10.1016/j.jad.2020.07.075.
    1. 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. doi: 10.1016/j.brat.2020.103731.
    1. 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):1233–1243. doi: 10.1002/eat.22952.
    1. 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):340–354. doi: 10.1037/abn0000254.
    1. Levitt DH. Drive for thinness and fear of fat: separate yet related constructs? Eat Disord. 2003;11(3):221–234. doi: 10.1080/10640260390218729.
    1. Linehan MM, Cognitive-behavioral treatment of borderline personality disorder (1st ed.). Guilford Publications 1993.
    1. Mansueto AC, Wiers R, Weert JCM van, Schouten BC, Epskamp S, Investigating the feasibility of idiographic network models. 2020. 10.31234/
    1. 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):1106–1121. doi: 10.1037/emo0000419.
    1. Piccirillo ML, Beck ED, Rodebaugh TL. A clinician’s primer for idiographic research: considerations and recommendations. Behav Ther. 2019;50(5):938–951. doi: 10.1016/j.beth.2019.02.002.
    1. Piccirillo ML, Rodebaugh TL. Foundations of idiographic methods in psychology and applications for psychotherapy. Clin Psychol Rev. 2019;71:90–100. doi: 10.1016/j.cpr.2019.01.002.
    1. Reeves JW, Fisher AJ. An examination of idiographic networks of posttraumatic stress disorder symptoms. J Trauma Stress. 2020;33(1):84–95. doi: 10.1002/jts.22491.
    1. 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;50:353–366. doi: 10.1017/S0033291719003404.
    1. 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):232–241. doi: 10.1016/S0924-9338(97)83297-X.
    1. 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.
    1. 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):339–360. doi: 10.1002/(sici)1098-108x(199712)22:4<339::aid-eat1>;2-n.
    1. 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):1923–1928. doi: 10.1017/S0033291718002702.
    1. Wilson GT, Grilo CM, Vitousek KM. Psychological treatment of eating disorders. Am Psychol. 2007;62(3):199–216. doi: 10.1037/0003-066X.62.3.199.
    1. Wright AG, Woods WC. Personalized models of psychopathology. Annual Rev Clin Psychol. 2020;16:49–74. doi: 10.1146/annurev-clinpsy-102419-125032.

Source: PubMed

Спонсоры и соавторы

Заболевания

Медикаментозные вмешательства

Подписаться