{"paper_id":"cdc79075-1702-42a2-984f-18849abe1bf2","body_text":"Psychology & Health\nISSN: 0887-0446 (Print) 1476-8321 (Online) Journal homepage: www.tandfonline.com/journals/gpsh20\nPhysical and psychosocial factors are crucial\nfor maintaining physical and mental health in\nendometriosis: a longitudinal analysis\nJohanna Netzl, Burkhard Gusy, Barbara Voigt, Jalid Sehouli & Sylvia\nMechsner\nTo cite this article: Johanna Netzl, Burkhard Gusy, Barbara Voigt, Jalid Sehouli & Sylvia\nMechsner (2025) Physical and psychosocial factors are crucial for maintaining physical and\nmental health in endometriosis: a longitudinal analysis, Psychology & Health, 40:7, 1156-1177,\nDOI: 10.1080/08870446.2024.2302486\nTo link to this article:  https://doi.org/10.1080/08870446.2024.2302486\n© 2024 The Author(s). Published by Informa\nUK Limited, trading as Taylor & Francis\nGroup\nPublished online: 22 Jan 2024.\nSubmit your article to this journal \nArticle views: 3316\nView related articles \nView Crossmark data\nCiting articles: 2 View citing articles \nFull Terms & Conditions of access and use can be found at\nhttps://www.tandfonline.com/action/journalInformation?journalCode=gpsh20\n\nPsychology & health\n2025, Vol. 40, No . 7, 1156–1177\nPhysical and psychosocial factors are crucial for \nmaintaining physical and mental health in \nendometriosis: a longitudinal analysis\nJohanna Netzl a,b, Burkhard Gusy a, Barbara Voigt c, Jalid Sehouli b and Sylvia \nMechsnerb\naDepartment of Psychology, Freie Universität Berlin, Berlin, g ermany; bendometriosis c entre charité, \nDepartment of gynaecology with c enter for o ncological surgery, charité – Universitätsmedizin Berlin, \nc orporate Member of Freie Universität Berlin and humboldt-Universität zu Berlin, Berlin, g ermany; \ncDepartment of Psychosomatic Medicine, c enter for Internal Medicine and Dermatology, charité – \nUniversitätsmedizin Berlin, c orporate Member of Freie Universität Berlin and humboldt-Universität zu \nBerlin, Berlin, g ermany\nABSTRACT\nObjective: To test the associations of physical and psychosocial fac-\ntors with physical and mental health in individuals living with endo-\nmetriosis (EM) by means of cross-sectional and longitudinal analyses.\nMethods and Measures: Data were gathered via an online survey \nbetween February and August 2021. At survey date t1, sociodemo -\ngraphic, EM-related and psychosocial factors as well as physical \nand mental health of people with EM were assessed. At survey \ndate t2 three months later, physical and mental health was reas -\nsessed. The sample consisted of n_t1 = 723 (30.60 ± 6.31 years) and \nn_t2 = 216 (30.56 ± 6.47 years) cis women with EM. Statistical analy -\nses included bivariate and partial correlation analyses and hierar -\nchical regression analyses.\nResults: The participants’ physical health was within the average \nrange and their mental health was below-average at t1 and t2. \nCross-sectional analyses revealed that worse health was associated \nwith longer diagnostic delay, more surgeries, greater pelvic pain \nand lower sense of coherence, self-efficacy, sexual satisfaction and \nsatisfaction with the gynecological treatment. In longitudinal anal -\nyses, pelvic pain and participants’ satisfaction with the gynecologi -\ncal treatment remained significantly associated with health.\nConclusion: Treatment should address both pelvic pain and psy -\nchosocial factors to improve long-term physical and mental health \nin EM.\n© 2024 t he a uthor(s). Published by Informa UK limited, trading as taylor & Francis group\nCONTACT sylvia Mechsner  sylvia.mechsner@charite.de   endometriosis c entre charité, Department of \ngynaecology with c enter for o ncological surgery, charité –Universitätsmedizin Berlin, c orporate Member of Freie \nUniversität Berlin and humboldt-Universität zu Berlin, 13353 Berlin, g ermany\nt his article was originally published with errors, which have now been corrected in the online version. Please see \nc orrection https://doi.org/10.1080/08870446.2024.2376801\nhttps://doi.org/10.1080/08870446.2024.2302486\nt his is an o pen a ccess article distributed under the terms of the c reative c ommons a ttribution license ( http://creativecommons.\norg/licenses/by/4.0/ ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is \nproperly cited. t he terms on which this article has been published allow the posting of the a ccepted Manuscript in a repository \nby the author(s) or with their consent.\nARTICLE HISTORY\nReceived 6 April 2023\nAccepted 2 January 2024\nKEYWORDS\nendometriosis; \nlongitudinal; mental \nhealth; physical health; \npsychosocial; PROMIS\n\nPSyChOl OGy & hEAl Th 1157\n1.  Introduction\nEndometriosis (EM) is a chronic inflammatory gynecological disease defined as the \ngrowth of tissue similar to the uterine lining outside of the uterus (Giudice, 2010). \nEM affects about 10% of people born with female-assigned reproductive organs and \nis associated with infertility and pelvic pain, in some cases chronic pelvic pain (Bulun, \n2018). Research shows that EM is associated with impaired mental health (l aganà \net  al., 2015; Pope et  al., 2015; van Barneveld et  al., 2021), a reduced quality of life \n(de Graaff et  al., 2016; Siedentopf et  al., 2008) and debilitated sexual functioning \n(Aerts et  al., 2018; Pérez-l ópez et  al., 2020). It takes 4–11 years from the first onset \nof symptoms (Taylor et  al., 2021) and the consultation of several doctors (De Graaff \net  al., 2013; Greene et  al., 2009) to receive the correct diagnosis. This long diagnostic \ndelay has repeatedly been described as particularly distressing by individuals affected \nby the disease (Ballard et  al., 2006; Denny, 2004; Jones et  al., 2004) as it can lead to \nan exacerbation of pain symptoms, hopelessness, difficulties in patient–\ndoctor-relationships and a general lack of trust in the medical system (Facchin et  al., \n2017b). Research highlights the relevance of the relationship between patient and \npractitioner in this context (Kundu et  al., 2015; Roomaney et  al., 2019) as communi -\ncation about pain and the recognition of EM symptoms by the attending doctor have \nbeen shown to be of particular importance on the path to diagnosis (Bullo & Weckesser, \n2021; Drinkell et  al., 2023; Fernley, 2021; Manderson et  al., 2008). According to l ukas \net  al. ( 2018) these factors are associated with the satisfaction with medical support \nin individuals affected by EM. Satisfaction with medical support, in turn, is an import -\nant factor in the individual’s adherence in general (Anhang Price et  al., 2014; l ewin \net  al., 2001) and thus can be assumed to be critical for their overall health.\nThe association between (chronic) pelvic pain symptoms and health outcomes \n(physical health, mental health, quality of life) in EM has been widely demonstrated \n(Brooks et  al., 2020; Comptour et  al., 2020; Facchin et  al., 2017a; Márki et  al., 2017; \nNetzl et  al., 2022; van Barneveld et  al., 2021), whereas psychosocial factors relevant \nto the treatment of EM are still understudied. In previous studies, for example, dis -\npositions, personality traits, resilience, coping styles or stress associated with EM \nhave been examined. The results pointed toward a reduced optimism (Morán-Sánchez \net  al., 2020) and higher trait anxiety (Quiñones et  al., 2015) in people affected by \nEM and an association between painful EM and higher harm avoidance as well as \nlower exploratory excitability (Facchin et  al., 2016). Other research suggests that \nresilience in individuals living with EM may be an important factor in their life sat -\nisfaction (Romaniuk & Oniszczenko, 2023). In other studies, the importance of coping \nstyles for the emotional well-being in EM has been investigated (Eriksen et  al., 2008; \nQuiñones et  al., 2015; Rees et  al., 2020) and a bidirectional relationship between EM \nand chronic stress has been suggested (Petrelluzzi et  al., 2008; Reis et  al., 2020). \nGenerally, the experience of EM is considered a disruption in the individual’s life, \nposing the challenge to integrate this chronic condition into one’s self-concept \n(Facchin et  al., 2017b ) and to regain interpretative control over one’s own life \n(Wischmann, 2008) or a sense of coherence (SOC). The concept of SOC is defined \nas a global orientation that expresses the extent to which one has a pervasive, \nenduring though dynamic feeling of confidence in the comprehensibility, \n\n1158 J. NETZl ET Al.\nmanageability and meaningfulness of the internal and external demands in one’s \nlife (l angeland, 2014). A higher SOC is associated with lower levels of anxiety and \ndepression, greater physical well-being and lower perceived stress (Zirke et  al., 2007). \nThe concept of self-efficacy is closely related to SOC and refers to the belief in one’s \nabilities, skills and competencies across various situations. h igher self-efficacy is \nassociated with higher self-esteem and optimism and lower anxiety (Beierlein et  al., \n2012). In people living with EM, lower self-efficacy was associated with poorer mental \nhealth (Facchin et  al., 2017a), as were negative feelings toward the medical profes -\nsions and impaired sexual functioning (Roomaney et  al., 2019). Uncontroversially, EM \nis known to be a heavy sexual burden (la Rosa et  al., 2020; Sullivan-Myers et  al., \n2023) and several researchers pointed out the association between impaired sexual \nfunctioning and psychological distress in individuals living with the disease (de Graaff \net  al., 2016; Facchin et  al., 2017b; Roomaney et  al., 2019). These studies provided \nimportant insights into the associations between psychosocial factors and well-being \nin EM. Still, it remains necessary to investigate additional psychosocial factors in EM \nto improve the understanding of this complex disease and its physical, psychological \nand social consequences and, thus, overcome them. Moreover, there have been \nrepeated calls to include longitudinal data in the research (Facchin et  al., 2017a; \nMárki et  al., 2017).\nThe goal of this study was to increase the knowledge about physical and psycho -\nsocial factors associated with self-reported physical and mental health in individuals \nliving with EM. First, cross-sectional associations between putative physical and psy -\nchosocial predictor variables and physical and mental health were explored. \nSociodemographic (age, income, relationship status, EM-related strain on the relation -\nship), EM-related (diagnostic delay, time since receiving the EM diagnosis, number of \nsurgeries, number of pelvic pain types, pelvic pain intensity, number of pelvic pain \ndays per month, hormonal treatment, infertility) and psychosocial variables (SOC, \nself-efficacy, sexual satisfaction, satisfaction with the gynecological treatment) were \nincluded. Based on the referenced literature, the included psychosocial factors were \nassumed to be of high relevance for the well-being in EM. The goal was to test them \nin a comprehensive statistical model. The statistical procedure was repeated for phys -\nical and mental health assessed at t2 three months later, controlling for physical or \nmental health at survey date t1. By including data from a second survey date, we \naimed to increase the knowledge about longer-term associations between physical \nand psychosocial factors and self-reported health in people living with EM, and con -\nsequently derive treatment implications.\n2.  Material and methods\nThis online study was conducted at the Endometriosis Centre of Charité—\nUniversitätsmedizin Berlin. Data were collected between February and August 2021 \nusing Research Electronic Data Capture (REDCap) tools hosted at Charité—\nUniversitätsmedizin Berlin (harris et  al., 2009, 2019). REDCap is a secure, web-based \nsoftware platform designed to support data capture for research studies, providing \n(1) an intuitive interface for validated data capture, (2) audit trails for tracking data \nmanipulation and export procedures, (3) automated export procedures for seamless \n\nPSyChOl OGy & hEAl Th 1159\ndata downloads to common statistical packages and (4) procedures for data integra -\ntion and interoperability with external sources.\n2.1.  Ethical approval\nThe study was conducted in accordance with the Declaration of helsinki, and approved \nby the Ethics Committee of Charité—Universitätsmedizin Berlin (Number: EA4/248/19). \nInformed consent to collect and publish the data has been obtained from all partic -\nipants via an opt-in item. If participants did not consent, the survey ended \nautomatically.\n2.2.  Participants and procedure\nParticipation in the study was publicly advertised (e.g. on Facebook and Instagram). \nThe participants had to meet the following inclusion criteria: being at least 18 years \nold and pre-menopausal, speaking fluent German, not suffering from a malignant \ndisease or infection, no current pregnancy and no ovariectomy. If any of these criteria \nwere not met, the survey ended automatically. After obtaining informed consent, \nparticipants were asked to generate a pseudonym to match the data from survey \ndates t1 and t2. At the end of survey date t1, participants were asked to send an \ne-mail to the study team so that their e-mail-addresses could be saved independently \nof the study data. After three months, participants were sent the browser link to \nsurvey date t2.\nInitially, 734 participants met the inclusion criteria and completed the survey at \nt1. All of them had been born with a uterus and ovaries and 723 (98%) reported a \nfemale, 5 (1%) a male and 6 (1%) a diverse gender identity. To avoid bias in the \nanalyses, these 11 (2%) people were excluded. EM diagnoses by laparoscopy ( n = 624, \n86%), magnetic resonance imaging ( n = 25, 4%) or sonography ( n = 74, 10%) were \naccepted. The final sample comprised n_t1 = 723 cis women with EM at t1 (cis or \ncisgender describes individuals who identify with the gender they were assigned at \nbirth: cis women were assigned the female gender at birth and identify with the \nfemale gender), of whom n_t2 = 216 cis women with EM also completed the ques -\ntionnaire at the second survey date t2. All of these n_t2 = 216 participants had received \nthe EM diagnosis by laparoscopic surgery. To rule out attrition bias, participants who \ncompleted t2 ( n_t2 = 216) were tested against participants who only completed t1 \n(n = 507) in sociodemographic and EM-related data. In these analyses, no significant \ndifferences were found ( Table 1 ).\n2.3.  Measures and instruments\nParticipants responded to a comprehensive item battery collecting sociodemographic, \nclinical and EM-/pelvic-pain-related information. Participants were asked to rate the \nstrain EM puts on their current relationship (0 = not at all to 100 = extremely strained \nby EM). household net income was assessed with one item (<500€, 501–800€, 801–\n1000€, 1001–1500€, 1501–2000€, 2001–2500€, 2501–3000€, >3000€). Participants were \n\n1160 J. NETZl ET Al.\nasked whether they suffered from dysmenorrhoea, non-menstrual cyclical pelvic pain \n(e.g. during ovulation), non-cyclical pelvic pain, chronic pelvic pain, pain related to \nsexual intercourse, dyschezia and dysuria, and to rate the typical intensity of each of \nthe reported types of pain (0  = no pain to 100 = worst pain imaginable). An average \npelvic pain intensity item was calculated.\nTable 1.  s ociodemographic, eM-related and psychosocial data at t1 and physical and mental \nhealth scores at t1 and t2.\nVariable\nt1 sample ( n_t1 = 723) t2 sample ( n_t2 = 216)\nN\nM ± sD/Frequency \n(%) N\nM ± sD/Frequency \n(%) pa\nage 723 30.60 ± 6.31 216 30.56 ± 6.47 .915\nMin, Max 18, 51 18, 47\nRelationship 723 553 (77%) 216 160 (74%) .829\neM-related strain on the \nrelationship\n541 66.98 ± 20.10 157 64.95 ± 21.63 .132\nMin, Max [0–100] 0, 100 0, 100\no ther gynecologic \ndiseaseb\n723 159 (22%) 216 42 (19%) .280\no ther pain disorder b 723 271 (38%) 216 78 (36%) .619\nchildren b 720 125 (17%) 30 (14%) .107\nInfertility 718 174 (24%) 216 45 (21%) .163\nhormonal treatment 723 380 (53%) 216 125 (58%) .062\nt ime since eM diagnosis \n(years)\n721 3.45 ± 4.18 216 3.49 ± 4.52 .862\nMin, Max 0, 30 0, 30\nDiagnostic delay (years) 705 9.11 ± 6.45 209 8.91 ± 6.51 .596\nMin, Max 0, 32 0, 31\nNumber of surgeries 721 1.43 ± 1.24 215 1.39 ± 1.20 .209\nMin, Max 0, 6 0, 6\nDysmenorrhoea b 723 364 (50%) 216 109 (51%) .967\nNon-menstrual cyclical \npelvic pain b\n723 390 (54%) 216 108 (50%) .165\nNon-cyclical pelvic pain b 723 503 (70%) 216 154 (71%) .511\nchronic pelvic pain b 723 157 (22%) 216 41 (19%) .245\nPain at sexual \nintercourseb\n723 526 (73%) 216 153 (71%) .449\nDyscheziab 723 408 (56%) 216 119 (55%) .636\nDysuriab 723 237 (33%) 216 59 (27%) .041\nNumber of pelvic pain \ntypes\n723 3.58 ± 1.63 216 3.44 ± 1.68 .145\nMin, Max 0, 7 0, 7\nPelvic pain days per \nmonth\n723 12.26 ± 9.44 216 11.69 ± 9.33 .290\nMin, Max 0, 31 0, 31\nPelvic pain intensity 723 29.52 ± 16.90 216 27.82 ± 16.76 .077\nMin, Max [0–100] 0, 93 0, 89\nsoc l -9 723 4.17 ± 1.10 216 4.22 ± 1.10 .366\naKsU 714 3.65 ± 0.71 216 3.60 ± 0.72 .188\nKFsP-F – sexual \nsatisfaction c\n696 13.59 ± 5.35 216 14.00 ± 4.96 .154\nFIPs 690 3.32 ± 1.00 216 3.25 ± 0.95 .253\nPhysical health t1 d 723 45.54 ± 8.34 216 45.90 ± 8.24 .336\nMental health t1 d 723 37.98 ± 7.04 216 38.59 ± 7.27 .068\nPhysical health t2 d – 216 46.28 ± 8.44\nMental health t2 d – 216 40.45 ± 7.68\naIndependent samples t-tests were used for continuous variables and χ 2-tests were used for categorical variables \nto compare participants who completed t2 ( n_t2 = 216) with those who only completed t1 ( n = 507, descriptive \nresults not shown).\nbVariables are reported descriptively only.\ncl evene’s test showed that the variances for KFsP-F sexual satisfaction were not equal, p = .009.\ndt -scores: M = 50 ± 10.\n\nPSyChOl OGy & hEAl Th 1161\nPhysical and mental health were measured using the Patient Reported Outcomes \nMeasurement Information System 29 v2.1 (PROMIS-29 Profile v2.1, hays et  al., 2018). \nUsing item response theory methods for item selection, the PROMIS initiative devel -\noped a variety of item banks to assess different health domains. The complete ques -\ntionnaire can be found online and is openly accessible (‘PROMIS-29 Profile v2.1’ , 2023). \nThe PROMIS Profile 29 assesses the seven health domains physical function (e.g. ‘Are \nyou able to do chores such as vacuuming or yard work?’), anxiety (e.g. ‘I felt fearful’), \ndepression (e.g. ‘I felt worthless’), fatigue (e.g. ‘I feel fatigued’), sleep disturbance (e.g. \n‘I had a problem with my sleep’), ability to participate in social roles and activities \n(e.g. ‘I have trouble doing all of my regular leisure activities with others’) as well as \npain interference (e.g. ‘how much did pain interfere with your day to day activities?’) \nwith four questions each. Additionally, it comprises one item on the average general \npain intensity in the past seven days (0 = no pain to 10 = worst pain imaginable). \nPhysical functioning and the ability to participate in social roles and activities are \nassessed without a specific timeframe, whereas all other health domains come with \nthe instruction to refer to the past seven days. Reliability scores for the subscales in \nthe study sample were Cronbachs α = .831 to .946 (t1) and Cronbachs α = .828 to \n.945 (t2). Raw scores and T-scores for each domain and raw scores for the general \npain intensity item of sample n_t1 are presented in Appendix A. Based on the scoring \ninstructions provided by hays et  al. ( 2018) and Spritzer and hays ( 2018), physical and \nmental health summary T-scores were calculated with M = 50 ± 10. These T-scores are \nderived from the general U.S. population and are commonly used as default values \nas there are no country-specific scoring algorithms to date (Fischer et  al., 2018). h igher \nscores in these summary scores represent better physical or mental health. Additionally, \nthey include a direct comparison with the U.S. general population, with M = 50 ± 10 \nrepresenting the average range. In the calculation of the PROMIS physical and mental \nhealth summary scores all seven health domains, that is physical, psychological and \nsocial factors, are used, each with a different weighting.\nSense of Coherence SOC was assessed using the Sense of Coherence Scale l -9 SOC \nl -9 (Schumacher, Wilz, Gunzelmann, & Brähler, 2000; possible scores: 1–7, Cronbachs \nα = .859 (t1), Cronbachs α = .876 (t2)), self-efficacy was assessed according to the \nSelf-Efficacy Scale—Short Form AKSU (Beierlein et  al., 2012; possible scores: 1–5, \nCronbachs α = .847 (t1), Cronbachs α = .852 (t2)). Sexual satisfaction was measured \nalong one scale of a German adaptation of the Brief Index of Sexual Functioning \nKFSP-F (hartmann et al., 2002; possible scores: 3–21, Cronbachs α = .792 (t1), Cronbachs \nα = .858 (t2)) and satisfaction with the gynecological treatment according to the \nFreiburg Index of Patient Satisfaction FIPS (Schoenthaler et  al., 2012) with the specific \ninstruction to think about ‘the gynecological examinations and treatments carried out’ \n(possible scores: 1–6, Cronbachs α = .778 (t1), Cronbachs α = .774 (t2)). h igher scores \non all of these scales represent a larger portion of the measured concept, except for \nthe FIPS scale, where lower scores represent greater treatment satisfaction.\n2.4.  Statistical analyses\nDescriptive statistics were calculated by Mean ± Standard deviation. Parametric Pearson \ncorrelation analyses between the outcome variables physical and mental health \n\n1162 J. NETZl ET Al.\nassessed at t1 and the putative predictor variables collected at t1 were calculated to \npreselect predictor variables for the subsequent regression analyses. The following \nputative predictor variables were examined: sociodemographic (age, income, relation -\nship status, EM-related strain on the relationship), EM-related (diagnostic delay, time \nsince receiving the EM diagnosis, number of surgeries, number of pelvic pain types, \npelvic pain intensity, number of pelvic pain days per month, hormonal treatment, \ninfertility) and psychosocial variables (SOC, AKSU, KFSP-F—sexual satisfaction, FIPS). \nPutative predictor variables that showed significant bivariate associations with the \noutcome variable were then entered stepwise (enter method) into multiple regression \nanalyses with step I sociodemographic, step II EM-related and step III psychosocial \nvariables. All three steps were calculated for physical and mental health at t1. Pairwise \nexclusion of missing values was used to keep the sample size at a maximum and to \navoid limiting the data set: Since the item on EM-related strain on the relationship \nhad only been answered by participants who were in a relationship at the time, a \nlistwise exclusion of missing values would have resulted in the exclusion of all par -\nticipants who were single and thus in a less representative sample. To evaluate the \npredictive power of each set of predictor variables, R2 and the change in variance \nexplanation are reported for each step. To test whether the results were different \nwhen only those participants who had received the EM diagnosis by laparoscopy \n(n = 624) were considered cross-sectional correlation and regression analyses were \nrepeated for this subsample (Appendix C).\nAnalyses were repeated for the outcome variables physical and mental health \nassessed three months later at t2, with physical or mental health at t1 included as a \ncontrol variable to partialize out its influence (Reinders, 2006). Partial correlation \nanalyses between the health outcome at t2 and the sociodemographic, physical and \npsychosocial putative predictor variables collected at t1 were performed, controlling \nfor the same health outcome at t1. Next, multiple regression analyses with the steps \nI physical health at t1 or mental health at t1 and II putative predictor variables that \nwere significantly associated in the partial correlation analyses were performed. Again, \npairwise exclusion of missing values was used and R2 is reported.\nStatistical analyses were performed using IBM SPSS Statistics for Windows, Version \n26.0 (IBM Corp, 2019) and p-values of p ≤ 0.05 were considered statistically significant. \nStatistical assumptions of multiple linear regression analysis (Field, 2009) were tested \nfor all regression analyses (Appendices B and C).\n3.  Results\nThe descriptive results of sociodemographic, EM-related and psychosocial data col -\nlected at t1 are presented in Table 1 . At t1, participants were on average 30 years old \nand three quarters of them were in a relationship. Median monthly net household \nincome was 2001–2500€ with percentile 25 = 1001–1500€ and percentile 75 = >3000€. \nMost participants did not have children and about one quarter reported being infer -\ntile. The average time since EM diagnosis was 3.5 years and half of the sample received \nhormonal treatment. The average delay in diagnosis from the first onset of EM symp -\ntoms was 9 years. Most participants suffered from pelvic pain symptoms and one-fifth \nof them reported chronic pelvic pain.\n\nPSyChOl OGy & hEAl Th 1163\nThe PROMIS physical and mental health summary T-scores at both t1 and t2 are \nshown in Table 1 . Compared to the general U.S. population ( M = 50 ± 10), participants \nreported about half a standard deviation below average physical health and one \nstandard deviation below average mental health at t1 (physical health: 45.54 ± 8.34, \nmental health: 37.98 ± 7.04) and t2 (physical health: 46.28 ± 8.44, mental health: \n40.45 ± 7.68).\n3.1.  Cross-sectional analyses\nTable 2  presents the results of Pearson correlation analyses between the putative \npredictor variables and physical and mental health scores at t1. Both higher physical \nhealth and higher mental health were associated with higher income, lower EM-related \nstrain on the relationship, shorter diagnostic delay, fewer surgeries, fewer pelvic pain \ntypes, lower pelvic pain intensity, fewer pelvic pain days per month, a higher SOC, \nhigher self-efficacy, higher sexual satisfaction and higher satisfaction with the gyne -\ncological treatment.\nPutative predictor variables that showed significant correlations with the health out -\ncome were then entered into hierarchical multiple regression analyses in three steps: I \nsociodemographic, II EM-related and III psychosocial predictor variables. The results are \npresented in Table 3. All three steps were significant for both physical and mental health \nat t1 ( p < .001). The variance explained by sociodemographic variables was 7.3% for \nphysical and 14.5% for mental health at t1. The inclusion of EM-related variables (step II) \nincreased the percentage of variance explained by 24.6% for physical and 12.7% for \nmental health at t1. Step III, which additionally included psychosocial predictor variables, \nled to an increase in variance explanation of 8.4% for physical and of 25.8% for mental \nTable 2.  c ross-sectional correlation analyses between physical and mental health scores at t1 \nand putative predictor variables.\nPutative predictor \nvariable\nPhysical health t1 a Mental health t1 a\nN r p N r p\ns ociodemographic variables\n Income 719 .160 <.001 719 .167 <.001\n eM-related strain \non the relationship\n541 −0.218 <.001 541 −0.344 <.001\neM-related variables\n Diagnostic delay 705 −0.115 .002 705 −0.077 .041\n Number of \nsurgeries\n721 −0.187 <.001 721 −0.079 .035\n Number of pelvic \npain types\n723 −0.435 <.001 723 −0.347 <.001\n Pelvic pain intensity 723 −0.454 <.001 723 −0.408 <.001\n Pelvic pain days \nper month\n723 −0.472 <.001 723 −0.360 <.001\nPsychosocial variables\n soc l -9 723 .359 <.001 723 .626 <.001\n aKsU 714 .297 <.001 714 .356 <.001\n KFsP-F – sexual \nsatisfaction\n696 .099 .009 696 .144 <.001\n FIPs 690 −0.343 <.001 690 −0.363 <.001\nNote. Pearson product-moment correlation coefficient.\naNo significant associations were found with age, relationship status, time since eM diagnosis, hormonal treatment \nand infertility ( p > .05).\n\n1164 J. NETZl ET Al.\nhealth at t1. The percentage of variance explained by the overall models was 40.3% for \nphysical health at t1 and 53.0% for mental health at t1 (see R2 in the footnotes of Table 3).\nIn cross-sectional regression analyses, physical health was higher if the number of \nsurgeries, the pelvic pain intensity and the number of pelvic pain days per month were \nlower and if SOC, self-efficacy and satisfaction with the gynecological treatment were \nhigher ( Table 3). Mental health was higher if the EM-related strain on the relationship, \nthe pelvic pain intensity and the number of pelvic pain days per month were lower \nand if SOC and satisfaction with the gynecological treatment were higher ( Table 3 ).\nTable 3.  Results of the cross-sectional multiple regression analyses for the outcome variables \nphysical health at t1 and mental health at t1.\no utcome variable Predictor variable a\nc oefficientsb\nB (s td.-error) β pb 95%-cI b\nPhysical health t1 c c onstant 46.06 (2.65) <.001 40.85, 51.27\nIncome 0.18 (0.14) .05 .209 −0.10, 0.45\neM-related strain on \nthe relationship\n−0.01 (0.02) −0.01 .774 −0.04, 0.03\nDiagnostic delay d −0.09 (0.05) −0.07 .051 −0.18, 0.00\nNumber of surgeries −0.67 (0.24) −0.10 .004 −1.14, −0.21\nNumber of pelvic pain \ntypes\n−0.16 (0.33) −0.03 .640 −0.80, 0.50\nPelvic pain intensity −0.10 (0.03) −0.19 .002 −0.15, −0.04\nNumber of pelvic pain \ndays per month\n−0.22 (0.04) −0.25 <.001 −0.30, −0.15\nsoc-l9 1.22 (0.33) .16 <.001 0.58, 1.86\naKsU 1.38 (0.48) .12 .004 0.44, 2.31\nKFsP-F - sexual \nsatisfaction\n0.06 (0.06) .04 .314 −0.06, 0.18\nFIPs −1.23 (0.31) −0.15 <.001 −1.83, −0.62\nMental health t1 e c onstant 33.15 (1.99) <.001 29.25, 37.06\nIncome 0.02 (0.10) .01 .843 −0.18, 0.23\neM-related strain on \nthe relationship\n−0.04 (0.01) −0.10 .005 −0.06, −0.01\nDiagnostic delay −0.02 (0.03) −0.02 .482 −0.09, 0.04\nNumber of surgeries −0.03 (0.18) −0.01 .885 −0.37, 0.32\nNumber of pelvic pain \ntypes\n0.18 (0.25) .04 .476 −0.31, 0.66\nPelvic pain intensity −0.09 (0.02) −0.21 <.001 −0.13, −0.04\nNumber of pelvic pain \ndays per month\n−0.11 (0.03) −0.15 <.001 −0.16, −0.05\nsoc-l9 3.16 (0.24) .49 <.001 2.68, 3.64\naKsU 0.20 (0.36) .02 .580 −0.50, 0.90\nKFsP-F - sexual \nsatisfaction\n0.01 (0.05) .01 .854 −0.08, 0.10\nFIPs −1.02 (0.23) −0.15 <.001 −1.48, −0.57\nNote. linear hierarchical regression analyses (enter method).\nat he number of participants without missing values in the respective predictor variable and included in the pairwise \nanalysis can be found in table 2 , column N.\nbB = unstandardised beta coefficient. s td. error = standard error of the unstandardised beta coefficient. B = standardised \ncoefficient. p = p-value, probability. 95%- cI = 95% confidence interval for the unstandardised beta coefficient B.\ncstep I: sociodemographic variables, R2 = .073, p <.001, F(2, 516) = 20.23, p <.001; step II: sociodemographic and \neM-related variables, R2 = .319, p <.001, F(7, 511) = 34.16, p <.001; step III: sociodemographic, eM-related and \npsychosocial variables, R2 = .403, p <.001, F(11, 507) = 31.05, p <.001. t he degrees of freedom are based on the \nminimum pairwise n.\ndDiagnostic delay remained a significant predictor variable for physical health at t1 when only those participants \nwho had received the eM diagnosis by laparoscopy ( n = 624) were considered ( p < .05, a ppendix c ).\nestep I: sociodemographic variables, R2 = .145, p <.001, F(2, 516) = 43.89, p <.001; step II: sociodemographic and \neM-related variables, R2 = .272, p <.001, F(7, 511) = 27.22, p <.001; step III: sociodemographic, eM-related and \npsychosocial variables, R2 = .530, p <.001, F(11, 507) = 52.06, p <.001. t he degrees of freedom are based on the \nminimum pairwise n.\n\nPSyChOl OGy & hEAl Th 1165\nWhen only participants who had received the EM diagnosis by laparoscopy ( n = 624) \nwere included, results were the same in all correlation analyses and both regression \nanalyses with the exception of diagnostic delay remaining a significant predictor for \nphysical health at t1 (see footnotes of Table 3  and Appendix C). In cross-sectional \nregression analyses, physical health of those participants who had received the EM \ndiagnosis by laparoscopy was higher if the diagnostic delay was shorter, the number \nof surgeries, the pelvic pain intensity and the number of pelvic pain days per month \nwere lower and if SOC, self-efficacy and satisfaction with the gynecological treatment \nwere higher.\n3.2.  Longitudinal analyses\nTable 4  presents the results of partial correlation analyses between the putative pre -\ndictor variables assessed at t1 and physical and mental health at t2. In these analyses, \nthe health outcome assessed at t1 was a control variable. When controlling for physical \nhealth at t1, higher physical health at t2 was significantly associated with lower \nEM-related strain on the relationship, fewer pelvic pain types, lower pelvic pain inten -\nsity, fewer pelvic pain days per month and higher satisfaction with the gynecological \ntreatment at t1. h igher mental health at t2 was significantly associated with higher \nsatisfaction with the gynecological treatment at t1, when controlling for mental health \nat t1. Figure 1  shows significant results from both cross-sectional and longitudinal \ncorrelation analyses.\nThose putative predictor variables that showed significant associations with the \nhealth outcome in the partial correlation analyses were then entered into hierarchical \nmultiple regression analyses as a second step. In the first step, the health outcome \nat t1 was entered into the analyses. The results are presented in Table 5 . Both steps \nwere significant for both physical and mental health at t2 ( p < .001). The variance \nexplained by entering the health outcome at t1 was 59.7% for physical and 60.3% \nfor mental health. The inclusion of physical and psychosocial predictor variables led \nTable 4. Partial correlation analyses between physical and mental health scores at t2 and putative \npredictor variables at t1, controlling for physical and mental health at t1.\nc ontrol variable Putative predictor variable Physical health t2 a\nPhysical health t1 N r p\neM-related strain on the \nrelationship\n157 −0.159 .047\nNumber of pelvic pain types 216 −0.201 .003\nPelvic pain intensity 216 −0.197 .004\nPelvic pain days per month 216 −0.154 .023\nFIPs 216 −0.249 <.001\nMental health t2 b\nMental health t1 N r p\nFIPs 216 −0.214 .002\naNo significant associations were found with the variables age, income, relationship status, diagnostic delay, time \nsince eM diagnosis, number of surgeries, hormonal treatment, infertility, soc-l9, aKsU and KFsP-F - sexual sat -\nisfaction ( p > .05).\nbNo significant associations were found with the variables age, income, relationship status, eM-related strain on the \nrelationship, diagnostic delay, time since eM diagnosis, number of surgeries, number of pelvic pain types, pelvic \npain intensity, number of pelvic pain days per month, hormonal treatment, infertility, soc-l9, aKsU and KFsP-F \n- sexual satisfaction ( p > .05).\n\n1166 J. NETZl ET Al.\nto an increase in variance explanation of 4.5% for physical and 1.8% for mental health \nat t2. The percentage of variance explained by the overall models was 64.2% for \nphysical and 62.1% for mental health at t2 (see R2 in the footnotes of Table 5 ). In \naddition to the health outcome at t1, satisfaction with the gynecological treatment \nat t1 remained a significant predictor in both analyses: Physical and mental health \nat t2 were higher if satisfaction with the gynecological treatment at t1 had been \nhigher ( Table 5 ).\nFigure 1. significant associations between physical and mental health scores in cross-sectional cor -\nrelation analyses. Bold variables denote significant associations in longitudinal partial correlation \nanalyses with physical health at t2 (control variable: physical health at t1). Italics denote significant \nassociations in longitudinal partial correlation analyses with mental health at t2 (control variable: \nmental health at t1).\nTable 5. Results of the longitudinal multiple regression analyses for the outcome variables physical \nhealth at t2 and mental health at t2.\no utcome variable Predictor variable a\nc oefficientsb\nB (s td.-error) β pb 95%-cI b\nPhysical health t2 c c onstant 23.60 (4.00) <.001 15.70, 31.50\nPhysical health t1 0.67 (0.06) .66 <.001 0.56, 0.79\neM-related strain on \nthe relationship\n−0.03 (0.02) −0.09 .096 −0.07, 0.01\nNumber of pelvic pain \ntypes\n−0.30 (0.49) −0.06 .541 −1.27, 0.67\nPelvic pain intensity −0.02 (0.05) −0.03 .721 −0.11, 0.08\nNumber of pelvic pain \ndays\n−0.04 (0.06) −0.04 .555 −0.16, 0.08\nFIPs −1.36 (0.43) −0.15 .002 −2.20, −0.51\nMental health t2 d c onstant 14.23 (0.78) <.001 9.42, 19.03\nMental health t1 0.78 (0.05) .74 <.001 0.68, 0.87\nFIPs −1.14 (0.36) −0.14 .002 −1.84, −0.44\nNote. linear hierarchical regression analyses (enter method).\nat he number of participants without missing values in the respective predictor variable and included in the pairwise \nanalysis can be found in table 4 , column N.\nbB = unstandardised beta coefficient. s td. error = standard error of the unstandardised beta coefficient. B = standardised \ncoefficient. p = p-value, probability. 95%- cI = 95% confidence interval for the unstandardised beta coefficient B.\ncstep I: physical health at t1, R2 = .597, p <.001, F(1, 152) = 225.436, p <.001; step II: physical health at t1 and \nsociodemographic, eM-related and psychosocial variables which showed a significant association in partial cor -\nrelation analyses, R2 = .642, p = .004, F(6, 147) = 43.896, p <.001. t he degrees of freedom are based on the \nminimum pairwise n.\ndstep I: mental health at t1, R2 = .603, p <.001, F(1, 214) = 325.178, p <.001; step II: mental health at t1 and \nsociodemographic, eM-related and psychosocial variables which showed a significant association in partial cor -\nrelation analyses, R2 = .621, p = .002, F(2, 213) = 174.695, p <.001. t he degrees of freedom are based on the \nminimum pairwise n.\n\nPSyChOl OGy & hEAl Th 1167\n4.  Discussion\nPrevious research has shown that EM symptoms such as menstrual irregularities, \ndysmenorrhoea, chronic pelvic pain, dyspareunia and infertility often negatively affect \nthe individual’s psychological and social functioning (laganà et  al., 2017; Vitale et  al., \n2016, 2017). Consequently, EM is considered a disabling condition that may signifi -\ncantly compromise social relationships, sexuality and mental health. In this study, \nphysical and mental health in people living with EM and its associations with physical \nand psychosocial characteristics were examined. In line with previous findings of \nimpaired health in people with EM (Brandes, 2007), participants in this study reported \ntheir mental health to be one standard deviation below and, thus, significantly poorer \nthan the general population’s average. Conversely, the results for physical health were \nwithin the average range. This is in contrast to the high level of pain symptoms \nreported by participants ( Table 1) and could be explained by the design of the PROMIS \nProfile 29. Firstly, the PROMIS pain intensity item does not focus on pelvic, menstrual \nor premenstrual pain or the worst pain experienced in a specific time frame but the \naverage general pain intensity in the past seven days. Secondly, some of the items \nmeasuring physical functioning may not detect temporary forms of impairment, for \nexample: ‘Are you able to go for a walk of at least 15 min?’ . Most people living with \nEM would answer in the affirmative and, assuming no chronic pain symptoms are \npresent, which was the case for the majority of the study sample ( Table 1 ), would \nbe most impaired in their physical functioning during their menstruation but not \npermanently. This has potentially led to an underestimation of the participants’ pelvic \npain intensity and the related physical impairment and highlights the difficulty to \nadequately measure the burdens associated with EM with established, non-EM-specific \nquestionnaires. Nevertheless, the results of both the correlation and regression anal -\nyses in this study confirm the notion that pelvic pain is a crucial factor for the \nwell-being in EM (Brooks et  al., 2020; Comptour et  al., 2020; Facchin et  al., 2017a; \nMárki et  al., 2017; Netzl et  al., 2022; van Barneveld et  al., 2021) and demonstrate the \nsignificance of psychosocial factors at the same time. These findings have important \nimplications for EM treatment as they highlight the necessity of a biopsychosocial \napproach to improve both physical and mental health (Netzl et  al., 2023).\nThe cross-sectional correlation analyses have shown that both physical and mental \nhealth in people living with EM suffer if the diagnostic delay is long and pelvic pain \nis present and intense. In addition to that, they indicate that surgeries should be \nkept at the minimum number necessary to preserve the individual’s health. Notably, \nthe role of the diagnostic delay was of particular importance for physical health in \nthe cases where the diagnosis had been made by means of surgery. A potential \nexplanation for this finding might be that in most cases, treatment is only started \nafter the diagnosis of EM is confirmed and as certain types of EM can only be reliably \ndiagnosed by means of surgery this may consequently lead to a delay in treatment. \nhormonal symptom-oriented treatments, however, could already be implemented at \nthe time of a suspected diagnosis based on a thorough anamnesis of pain symptoms \n(Mechsner, 2016; Sillem, 2015). The importance of an early start of EM treatment is \nunderlined by the fact that earlier stages of the disease show higher metabolic activity \nand are more reactive to hormonal treatment than advanced stages (Schweppe, 2011). \n\n1168 J. NETZl ET Al.\nFurthermore, the cross-sectional analyses have revealed positive associations between \nthe health outcomes and the household income of participants. The level of income \nis one of the main factors in the calculation of socioeconomic status (l ampert et  al., \n2013) and a lower socioeconomic status is generally associated with poorer health \nand poorer access to health care (Binder & Rieder, 2014; Donkin et  al., 2017; lampert \net  al., 2019). Thus, special attention must be paid to the treatment of financially and \nsocially disadvantaged individuals with EM. As expected, a higher SOC, higher \nself-efficacy, higher sexual satisfaction and higher satisfaction with the gynecological \ntreatment were associated with better physical and mental health. In line with the \npresented result on the importance of participants’ treatment satisfaction, previous \nresearch has shown that the patient-doctor-relationship is an important factor in the \nwell-being of people with EM (Roomaney et  al., 2019). Adequate education about the \ndisease and its treatment options is crucial to improving patient-doctor relationships \nand the individual’s trust in the medical system (l ukas et  al., 2018). Additionally, \nproviding individuals living with a chronic illness with disease-specific knowledge can \nhelp protect them from misinformation on the internet (Dinh et  al., 2020) and improve \nself-efficacy and self-care (Wu et  al., 2016). These results suggest that by attending \nto psychological and interpersonal factors such as the individual’s SOC, self-efficacy \nand sexual functioning, their overall health could be improved.\nIncluded in joint models, sociodemographic, EM-related and psychosocial pre -\ndictor variables all significantly contributed to the variance explanation in both \nphysical and mental health. As expected, the increase in variance explanation for \nphysical health was greater when EM-related—primarily physical factors with a \nfocus on pain—predictor variables were included, while variance explanation for \nmental health increased particularly strongly when psychosocial predictor variables \nwere included. By providing these detailed analyses, this study contributes to a \nbetter understanding of the complex associations between physical and psycho -\nsocial factors and subjective health in people with EM. These results illustrate the \nnecessity and importance of setting individual priorities within a multiprofessional \ntreatment approach depending on each individual’s current treatment needs and \npotential for improvement.\nWhen examining the associations between predictor variables at t1 and physical \nand mental health at t2, physical and mental health at t1 were included as covariates \nto partialize out the variables’ influence (Reinders, 2006). In the partial correlation \nanalyses, EM-related variables continued to show significant associations with physical \nhealth at t2. In addition, participants’ satisfaction with the gynecological treatment \nwas significantly associated with both physical and mental health at t2 both in partial \ncorrelation and regression analyses. Thus, the individual’s evaluation of the gyneco -\nlogical treatment can be assumed to be a key factor in their long-term well-being \n(Kundu et  al., 2015).\nBy including physical and psychosocial factors as predictor variables and health as \ndependent variable, the multivariable models in this study assumed health to be a \nconsequence of these factors. Thus, the multivariable models were consistent with \nprevious albeit cross-sectional studies (Facchin et  al., 2015; Márki et  al., 2017). however, \nthe associations between the analysed constructs in this study are more complex and \nprobably not unidirectional: It is very likely that both physical and mental health also \n\nPSyChOl OGy & hEAl Th 1169\nhave an impact on physical, psychological, social and sexual factors and that these \nfactors themselves are interdependent and influence each other as well. Thus, the \npossibility of health influencing physical and psychosocial factors needs to be \nacknowledged.\nThere are some limitations to this study. First, the drop-out rate between the two \nsurvey points was quite high with less than a third of the initial participants com -\npleting the questionnaire at t2. Sample n_t2, however, was tested for a potential \nattrition bias against those participants who only completed the questionnaire at \nt1 ( Table 1 ) and as the two groups did not differ in any of the variables included \nin the analyses, the results of these tests were acceptable to continue with the \nplanned analyses. To test for attrition bias, t-tests were used, which are reasonably \nrobust with differing sample sizes, especially with large samples and equal variances \nin the test variables (Zimmerman, 1987), both of which were the case. The main \nreasons for dropping out between the two survey dates were, on the one hand, \nthat the survey was comparatively long as it comprised several questionnaires, and, \non the other hand, that we were not allowed to ask for participants’ contact infor -\nmation directly for data protection reasons. At the end of survey date t1, participants \nwere asked to send an e-mail to the study team to receive the browser link to \nsurvey date t2. Additionally, there was no intervention between the two survey \npoints and no major differences were observed between the health outcomes at t1 \nand t2. Moreover, this study only included two survey points, whereas a reliable \nstatement about the stability of a construct would benefit from at least three survey \npoints to account for measurement errors and ceiling effects. It should be noted, \nthat the data were collected during the COVID-19 pandemic, whose potential impact \non physical and mental health was not considered. In addition, not all statistical \nassumptions of the multiple linear regression analysis were met (Appendix B). Finally, \nparticipants who did not identify as female and responded to the survey were \nexcluded to avoid bias in the data. Their participation, however, deserves attention \nas transgender and non-binary individuals face health disparities and have an \nincreased risk for physical and mental health problems in general (Pulice-Farrow \net  al., 2021). Transmasculine or non-binary persons suffering from a gynecological \nand thus gendered disease such as EM may experience an increase in their gender \ndysphoria as a consequence, which can lead to a deterioration in their state of \nhealth (Pulice-Farrow et  al., 2021).\nDespite the high drop-out rate, one advantage of this study is the large sample \nsize. A thorough exploration of the associations between the putative predictor \nvariables and physical and mental health was presented. In this context, the large \nsample is particularly advantageous, as it can be assumed that the reported effects \nare quite stable. The selection of the putative predictor variables was both based \non previous research (Brooks et  al., 2020 ; Comptour et  al., 2020 ; Facchin et  al., \n2017a , 2017b ; Márki et  al., 2017; Netzl et  al., 2022; Roomaney et  al., 2019; van \nBarneveld et  al., 2021 ) and was consistent with a biopsychosocial approach to \nhealth (l arsen, 2022 ) by incorporating physical and psychosocial variables. The \npsychosocial variables considered in this study were SOC, self-efficacy, sexual sat -\nisfaction and satisfaction with the gynecological treatment, all of which emerged \nas important for the participants’ health from the analyses. The longitudinal study \n\n1170 J. NETZl ET Al.\ndesign is the main strength of this study. Incorporating the health outcome from \nt1 in the longitudinal analyses enhances their validity and attests to the stability \nof the identified associations. With this study, PROMIS Profile 29 data from a big \nsample of cis women living with EM are provided, which can be used as reference \nand comparison scores worldwide.\n4.1.  Conclusion\nThe findings of this study confirm that pelvic pain is a key factor in the well-being \nof people living with EM and that shortening the diagnostic delay remains a signif -\nicant goal in EM care. Additionally, the presented results demonstrate the relevance \nof psychosocial factors for both physical and mental health in EM and highlight the \nnecessity of mental health care in EM management. They suggest that by attending \nto psychological and interpersonal factors such as the individual’s SOC, self-efficacy, \nsexual satisfaction and treatment satisfaction in addition to treating their pelvic pain \nsymptoms, their overall and long-term health could be maintained and improved. \nMoreover, special attention should be paid to the gynecological care of financially \nand socially disadvantaged individuals. Future EM research and care should strive for \nan interdisciplinary, biopsychosocial perspective to adequately meet the challenges \nposed by this complex disease and its consequences.\nAcknowledgements\nJ.N. holds a doctoral scholarship by the Studienstiftung des deutschen Volkes (German National \nAcademic Scholarship Foundation). We thank all the people who participated in this study. In \naddition, we would like to thank the authors of the questionnaires that were used in this study: \nJ. Schumacher, G. Wilz, T. Gunzelmann, E. Brähler (SOC-l9), C. Beierlein, A. Kovaleva, C.J. Kemper, \nB. Rammstedt (AKSU), U. hartmann, K. heiser, C. Rüffer-hesse, G. Kloth (KFSP-F), M. Schoenthaler, \nE. Farin, W.K. Karzc, P . Ardelt, U. Wetterauer, A. Miernik (FIPS) and R.D. hays, K.l. Spritzer, B.D. \nSchalet, D. Cella and the PROMIS initiative (PROMIS Profile 29, physical and mental health sum -\nmary scores). We thank Andreas hetey from Charité – Universitätsmedizin Berlin, Clinical Trial \nOffice, for the support in the use of the REDCap software platform. We thank hannah Arnu for \nher support during the publication process.\nFunding\nNo funding was received for conducting this study.\nDisclosure statement\nThe authors report there are no competing interests to declare.\nData availability statement\nWe do not have the approval of the ethics committee or our participants’ consent to make the \ndata acquired and analysed in this study publicly available. 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Journal of Clinical \nNursing, 25(17–18), 2609–2618. https://doi.org/10.1111/jocn.13305\nZimmerman, D. W. ( 1987). Comparative power of Student T test and Mann-Whitney U test for \nunequal sample sizes and variances. The Journal of Experimental Education , 55(3), 171–174. \nhttps://doi.org/10.1080/00220973.1987.10806451\nZirke, N., Schmid, G., Mazurek, B., Klapp, B. F., & Rauchfuss, M. ( 2007). Antonovsky’s Sense of \nCoherence in psychosomatic patients - a contribution to construct validation. Psycho-Social \nMedicine, 4, Doc03.\n\n1176 J. NETZl ET Al.\nAppendix A.  Descriptive statistics of the 7 PROMIS profile 29 scales\nTable A1.  Descriptive statistics of the 7 PRoMIs health domains at t1 of the complete sample at \nt1 ( n = 723).\nPRoMIs Profile 29 scale Min Max M ± sD Mdn (P25, P75) t -score M ± sD\nPhysical functioning 7 20 17.26 ± 2.76 18.00 (16.00, 20.00) 46.91 ± 7.28\na nxiety 4 20 10.91 ± 3.80 11.00 (8.00, 14.00) 60.95 ± 8.22\nDepression 4 20 11.34 ± 3.85 12.00 (8.00, 14.00) 60.92 ± 7.54\nFatigue 4 20 15.14 ± 3.76 16.00 (13.00, 18.00) 63.47 ± 8.31\nsleep disturbance 4 20 12.52 ± 3.93 12.00 (9.00, 15.00) 55.17 ± 8.31\na bility to participate in social \nroles and activities\n4 20 11.59 ± 3.49 12.00 (9.00, 14.00) 43.76 ± 6.81\nPain interference 4 20 11.73 ± 4.56 12.00 (8.00, 15.00) 60.14 ± 8.31\ngeneral pain intensity a 0 10 5.00 ± 2.60 5.50 (3.00, 7.00) –\naNo t -score available for the general pain intensity item.\nAppendix B.  Statistical assumptions of multiple linear regression \nanalysis\nThe assumptions of no multicollinearity and uncorrelated residuals were met by all four models \n(VIF < 10, tolerance value > .10, Durbin-Watson test = 1–3). The assumption of normally distrib -\nuted residuals was met as well with an exception of the longitudinal model for physical health: \nIn this model, the residuals showed slight deviations from a normal distribution. Partial plots \nbetween the residuals of the outcome variables and each predictor variable and plots of the \nstandardised residuals against the standardised predicted values indicated not perfectly linear \nrelationships and heteroscedasticity. The plots of the standardised residuals against the stan -\ndardised predicted values of the regression models for mental health at t1 and t2, however, \nwere satisfactory so that homoscedasticity could be assumed.\nAppendix C.  Results for the subsample of participants who had \nreceived the EM diagnosis by laparoscopy ( n = 624)\nTable A2.  c ross-sectional correlation analyses between physical and mental health scores at t1 \nand putative predictor variables including only those participants who had received the eM \ndiagnosis by laparoscopy ( n = 624).\nPutative predictor \nvariable\nPhysical health t1 a Mental health t1 a\nN r p N r p\ns ociodemographic variables\n Income 620 .166 <.001 620 .171 <.001\n eM-related strain \non the relationship\n467 −0.215 <.001 467 −0.343 <.001\neM-related variables\n Diagnostic delay 609 −0.133 <.001 609 −0.087 .033\n Number of \nsurgeries\n622 −0.174 <.001 622 −0.084 .037\n Number of pelvic \npain types\n624 −0.409 <.001 624 −0.319 <.001\n Pelvic pain intensity 624 −0.431 <.001 624 −0.379 <.001\n Pelvic pain days \nper month\n624 −0.455 <.001 624 −0.350 <.001\nPsychosocial variables\n soc l -9 624 .338 <.001 624 .633 <.001\n aKsU 617 .292 <.001 617 .388 <.001\n KFsP-F – sexual \nsatisfaction\n604 .081 .045 604 .151 <.001\n FIPs 600 −0.335 <.001 600 −0.342 <.001\nNote. Pearson product-moment correlation coefficient.\naNo significant associations were found with age, relationship status, time since eM diagnosis, hormonal treatment \nand infertility ( p > .05).\n\nPSyChOl OGy & hEAl Th 1177\nStatistical assumptions of multiple linear regression analysis\nThe following descriptions refer to the regression models presented in Table A3. The assumptions \nof no multicollinearity and uncorrelated residuals were met by both models (VIF < 10, tolerance \nvalue > .10, Durbin-Watson test = 1–3). The assumption of normally distributed residuals was met \nby both models as well. Partial plots between the residuals of the outcome variables and each \npredictor variable and plots of the standardised residuals against the standardised predicted val -\nues indicated not perfectly linear relationships and heteroscedasticity. The plots of the stan -\ndardised residuals against the standardised predicted values of the regression model for mental \nhealth at t1, however, were satisfactory so that homoscedasticity could be assumed.\nTable A3.  Results of the cross-sectional multiple regression analyses for the outcome variables \nphysical health at t1 and mental health at t1 including only those participants who had received \nthe eM diagnosis by laparoscopy ( n = 624).\nc oefficientsb\no utcome variable Predictor variable a B (s td.-error) β pb 95%-cI b\nPhysical health t1 c c onstant 46.57 (2.86) <.001 40.95, 52.20\nIncome 0.24 (0.15) .06 .116 −0.06, 0.54\neM-related strain on \nthe relationship\n−0.01 (0.02) −0.03 .450 −0.05, 0.02\nDiagnostic delay −0.11 (0.05) −0.08 .033 −0.21, −0.01\nNumber of surgeries −0.68 (0.26) −0.10 .010 −1.19, −0.17\nNumber of pelvic pain \ntypes\n−0.15 (0.36) −0.03 .687 −0.85, 0.56\nPelvic pain intensity −0.09 (0.03) −0.18 .008 −0.15, −0.02\nNumber of pelvic pain \ndays per month\n−0.22 (0.04) −0.25 <.001 −0.30, −0.13\nsoc-l9 1.04 (0.37) .14 .005 0.32, 1.76\naKsU 1.46 (0.54) .12 .007 0.39, 2.53\nKFsP-F - sexual \nsatisfaction\n0.06 (0.07) .04 .398 −0.07, 0.18\nFIPs −1.22 (0.34) −0.15 <.001 −1.88, −0.56\nMental health t1 d c onstant 31.29 (2.09) <.001 27.19, 35.40\nIncome 0.06 (0.11) .02 .588 −0.16, 0.28\neM-related strain on \nthe relationship\n−0.04 (0.01) −0.10 .007 −0.06, −0.01\nDiagnostic delay −0.03 (0.04) −0.03 .437 −0.10, 0.04\nNumber of surgeries −0.12 (0.19) −0.02 .525 −0.50, 0.25\nNumber of pelvic pain \ntypes\n0.21 (0.26) .05 .421 −0.30, 0.73\nPelvic pain intensity −0.09 (0.02) −0.21 <.001 −0.13, −0.04\nNumber of pelvic pain \ndays per month\n−0.10 (0.03) −0.14 .001 −0.16, −0.04\nsoc-l9 3.16 (0.27) .49 <.001 2.63, 3.68\naKsU 0.45 (0.40) .05 .260 −0.33, 1.23\nKFsP-F - sexual \nsatisfaction\n0.03 (0.05) .02 .512 −0.06, 0.13\nFIPs −0.91 (0.25) −0.13 <.001 −1.39, −0.42\nNote. linear hierarchical regression analyses (enter method).\nat he number of participants without missing values in the respective predictor variable and included in the pairwise \nanalysis can be found in table a2 , column N.\nbB = unstandardised beta coefficient. s td. error = standard error of the unstandardised beta coefficient. B = standardised \ncoefficient. p = p-value, probability. 95%- cI = 95% confidence interval for the unstandardised beta coefficient B.\ncstep I: sociodemographic variables, R2 = .073, p <.001, F(2, 448) = 17.61, p <.001; step II: sociodemographic and \neM-related variables, R2 = .302, p <.001, F(7, 443) = 27.38, p <.001; step III: sociodemographic, eM-related and \npsychosocial variables, R2 = .380, p <.001, F(11, 439) = 24.45, p <.001. t he degrees of freedom are based on the \nminimum pairwise n.\ndstep I: sociodemographic variables, R2 = .145, p <.001, F(2, 448) = 38.06, p <.001; step II: sociodemographic and \neM-related variables, R2 = .260, p <.001, F(7, 443) = 22.26, p <.001; step III: sociodemographic, eM-related and \npsychosocial variables, R2 = .532, p <.001, F(11, 439) = 45.29, p <.001. t he degrees of freedom are based on the \nminimum pairwise n.","source_license":"CC-BY-4.0","license_restricted":false}