Cyber abuse and posttraumatic stress disorder among racially diverse women who have resided in domestic violence shelters: A longitudinal approach.

Cyber abuse and posttraumatic stress disorder among racially diverse women who have resided in domestic violence shelters: A longitudinal approach - PMC Skip to main content An official website of the United States government Here's how you know Here's how you know Search Log in Primary site navigation Search Log in Search PMC Full-Text Archive Search in PMC Journal List User Guide As a library, NLM provides access to scientific literature. Inclusion in an NLM database does not imply endorsement of, or agreement with, the contents by NLM or the National Institutes of Health. Learn more: PMC Disclaimer | PMC Copyright Notice Psychol Trauma . Author manuscript; available in PMC: 2026 Jun 1. Published in final edited form as: Psychol Trauma. 2025 Jan 27;17(Suppl 1):S105–S114. doi: 10.1037/tra0001868 Cyber abuse and posttraumatic stress disorder among racially diverse women who have resided in domestic violence shelters: A longitudinal approach Alejandra Gonzalez 1 , James K Haws 2, 3 , Nuha Alshabani 4, 5 , Caron Zlotnick 6, 7, 8 , Dawn M Johnson 9 Author information Article notes Copyright and License information 1 School of Psychology, Xavier University 2 Department of Psychiatry, University of Colorado – Anschutz, Aurora, CO 3 Adult & Child Center for Outcomes Research & Delivery Science, University of Colorado – Anschutz, Aurora, CO 4 Department of Psychiatry, Boston Medical Center– Boston MA, USA 5 Chobanian & Avedisian School of Medicine, Boston University– Boston MA, USA 6 Women and Infants Hospital, Departments of Psychiatry & Human Behavior, Medicine, and Obstetrics and Gynecology 7 Department of Psychiatry and Human Behavior, Warren Alpert Medical School, Brown University 8 Department of Psychiatry and Mental Health, University of Cape Town 9 Department of Psychology, University of Akron ✉ Correspondence concerning this article should be addressed to Alejandra Gonzalez, School of Psychology, Xavier University, 3800 Victory Parkway, Cincinnati, OH 45207. gonzaleza22@xavier.edu Alejandra Gonzalez – conceptualization, writing (original draft), formal analysis; James K. Haws – formal analysis, conceptualization, writing (original draft); Nuha Alshabani – conceptualization, writing (original draft); Caron Zlotnick – methodology, writing (review and editing); Dawn M. Johnson – funding acquisition, methodology, investigation, supervision, writing (original draft). Roles Caron Zlotnick : Honorary Professor Issue date 2025 Jun. PMC Copyright notice PMCID: PMC12202182  NIHMSID: NIHMS2057457  PMID: 39869706 The publisher's version of this article is available at Psychol Trauma Abstract Objective: The current study presents a longitudinal examination of the impact of cyber abuse (CA) on posttraumatic stress disorder (PTSD) severity among a racially diverse sample of women with intimate partner violence (IPV). Method: Using data collected from a completed randomized control trial with women who sought safety in a domestic violence shelter (W-DVS; N = 172) we conducted a secondary data analysis. Participants completed measures of CA, PTSD severity, and other types of IPV at each time point. To examine predictors of the latent change factor, we estimated the influence of CA on each latent change factor while controlling for other (i.e., non-cyber abuse) types of IPV and treatment condition across time. Results: Initial results suggested that CA has a negative impact on PTSD severity over time for W-DVS, as CA predicted changes in PTSD severity across five time points when controlling for treatment condition and PTSD symptom severity at the previous time points. However, CA did not predict a change in symptom severity when controlling for other forms of (non-cyber abuse) IPV. Conclusions: Although more CA is associated with more severe PTSD symptoms from the time women are in shelter, to post-shelter, 3-, 9-, and 15-months post-shelter, this is no longer the case when overall IPV is considered. This study extends our understanding of how CA can exacerbate psychological health consequences, including PTSD severity, of W-DVS, suggesting that cyber abuse should be assessed in tandem with other IPV types rather than as a separate phenomenon. Keywords: cyber abuse, IPV, domestic violence shelter, PTSD, latent change score models With technological advances and their integration into daily life, concerns have increased about their use in perpetrating intimate partner violence (IPV) through monitoring, harassment, and threats ( Rogers et al., 2023 ). The behaviors used to perpetrate IPV through technology (i.e., cyber abuse) range from cyberbullying (i.e., unwanted and repetitive communication intended to harm), cyberfraud (i.e., gaining access to something they are not entitled to, such as a financial account), cybersexual abuse (i.e., violating the sexual agency of a partner or ex-partner), and cyberstalking (i.e., unwanted and repetitive behavior that causes the receiver to fear for their safety; Clevenger & Navarro, 2021 ). A systematic review of cyber abuse (CA) shows victimization rates range from 1.1% to 77.1% depending on the behaviors of CA assessed ( Taylor & Xia, 2018 ). The current study seeks to illuminate the impacts of CA on the mental health of women with IPV histories who have sought emergency shelter due to their experiences of abuse. As women who seek domestic violence shelter (W-DVS) services often experience revictimization during and after they leave shelter, we sought to understand the mental health impact of CA as a tool for continued abuse. CA may be particularly relevant to mental health for women during and after shelter as, unlike other non-cyber types of IPV, it may continue when the perpetrator might not know the woman’s location but can still access technology to abuse her. We focus on the impact of CA on posttraumatic stress disorder (PTSD) severity given that prior research has demonstrated PTSD symptoms are some of the most common mental health consequences of IPV victimization ( Spencer et al., 2019 ) and are associated with comorbid psychiatric disorders, loss of resources, and social maladjustment for W-DVS ( Akyazi et al., 2018 ; Johnson et al., 2008 ). Intimate Partner Violence and Cyber Abuse IPV refers to various forms of abuse (e.g., sexual, emotional, physical, terrorizing) perpetrated by an intimate partner (e.g., spouses, dating partners, and cohabitors; Cannon, et al., 2016 ). Currently, we know more about the impact of non-cyber forms of IPV (e.g., physical, psychological, sexual abuse) on mental health than we do about the impact of CA. In the last decade, researchers have found a strong relation between CA and other types of IPV, such that CA occurs in a broader context of IPV ( Duerksen & Woodin, 2021 ; Gilbar et al., 2023 ). In a meta-analysis, large effect sizes were demonstrated for correlations among non-cyber IPV and CA ( Gilbar et al., 2023 ). This association holds true for both psychological and sexual violence, and those who experience cybersexual abuse have rates of non-cyber sexual coercion seven times more than those who do not experience cybersexual abuse ( Taylor & Xia, 2018 ). Similarly, in a sample of undergraduate students, a majority reported both non-cyber and CA (62.5%). Only 8.2% reported exclusively experiencing CA ( Duerksen & Woodin, 2021 ). These results suggest that CA may be an extension of non-cyber IPV or, indeed, the same phenomenon enacted through the use of technology. Cyber Abuse and Mental Health Symptoms A scoping review on CA noted that mental health consequences are one of the most frequently reported negative outcomes, ranging from diagnosable conditions to increased suicidal ideation ( Rogers et al., 2023 ). However, the unique impact of CA on the mental health of W-DVS is still unknown. In studies of shelter residents, 60.0-78.0% of women reported unwanted calls and messages from their perpetrator ( Woodlock, 2017 ; Messing et al., 2020 ). The limited scholarship on CA for W-DVS has focused on describing the behaviors, defining CA, and determining its prevalence, with less attention on mental health outcomes. Both CA and its mental health consequences might negatively affect the long-term safety of W-DVS. Duersken and Woodin (2021) found that among women, CA was associated with a greater fear of their partner, after controlling for non-cyber victimization, and suggested that CA experiences may signal risk of future danger. They recommend that future research examine the impact of CA in populations of women that may be particularly affected by this type of abuse (e.g., W-DVS). Given the severity of abuse experienced by W-DVS, the effects of CA may be especially important to investigate within this subgroup of women with IPV histories. Moreover, when women seek shelter services their physical location might be unknown but their digital footprint could be accessed, which could pose a continued risk to their physical safety. Although the deleterious impact of IPV on PTSD symptoms is well established, the longitudinal association between CA and PTSD is still emerging ( Duerksen & Woodin, 2021 ; Rogers et al., 2023 ). Lu and colleagues (2018) found associations between CA and a PTSD screen in cross-sectional analyses with a sample of adolescents who experienced CA. However, this association was not found in their temporal analyses, suggesting that controlling for prior mental health concerns might have masked the impact of CA. In qualitative research on women’s experiences of sexual CA, negative mental health impacts, including PTSD, emerged as a theme ( Bates, 2017 ). Duerksen and Woodin (2021) were the first to quantitatively test the association between CA and PTSD symptoms in undergraduate women, finding that CA was significantly associated with increased alcohol use and greater fear of one’s partner but not with depression, stress, or PTSD symptoms. The authors contended that because CA occurs at a distance from the perpetrator it comes with a lower risk of bodily harm, creating a lower sense of threat, and thus has less impact on PTSD symptoms. This might hold true with specific sample characteristics (e.g., low levels of violence, in dating relationships, adolescents or emerging adults) but might not apply to W-DVS whose experience of violence is often more severe. Taken together, results suggest that CA has a negative psychological impact, with mixed results on PTSD symptoms, but longitudinal analyses with more robust measurement of PTSD symptoms are necessary to clarify these associations for W-DVS due to IPV. In sum, much of the research on CA and its relationship to mental health has been conducted with adolescents, young adults, and undergraduate students and has been limited to those with high socioeconomic status, high levels of education, and primarily white participants ( Gilbar et al., 2023 ; Rogers et al., 2023 ; Taylor & Xia, 2018 ). The vast majority of these have been cross-sectional studies with conflicting findings on the associations between CA and PTSD symptoms. To our knowledge, no research to date has examined the impact of CA on PTSD severity among W-DVS, a subset of women with IPV histories who experience more severe victimization, loss of resources, and are generally more demographically diverse ( Johnson, et al., 2016 ). Thus, we seek to illuminate the unique impacts of CA on the mental health of W-DVS. Current Study The current study seeks to address gaps in research on CA in women with IPV histories by conducting a longitudinal examination of the impacts of CA on PTSD severity in a racially diverse sample of W-DVS. We conducted secondary data analyses from a randomized controlled trial (RCT) evaluating the treatment of IPV-related PTSD in women initially residing in DV shelters ( Johnson et al., 2020 ). This RCT found no differences between either treatment condition on any outcome, including PTSD severity and PTSD across follow-up. We tested whether CA predicted changes in PTSD severity from baseline to post-shelter (PS) and 3-, 9-, and 15-months post-shelter (3PS, 9PS, and 15PS, respectively) after controlling for lag-1 PTSD severity, overall IPV severity, and treatment condition. We hypothesized that CA would predict increases in PTSD severity across follow-up. Methods Below we provide an overview of the methodology relevant to the present study. The reader is referred to the original trial (see Johnson et al., 2020 ) for a more comprehensive description of study methods. Data collection occurred between February 2013 and February 2017. Participants and Procedures Participants included 172 W-DVS from six shelters in the Midwest. To be eligible, participants had to meet DSM-IV-TR ( American Psychiatric Association (APA), 2000 ) criteria for PTSD, endorse at least one instance of IPV in the month prior to shelter admittance, and the PTSD diagnosis must have been connected to IPV. Participants were excluded if they endorsed symptoms that suggested they required different or more intensive treatment (i.e., endorsed current suicidal ideation with intent and plan, exhibited symptoms of psychosis, met DSM-IV-TR criteria for substance dependence in the last 3 months, met DSM-IV-TR criteria for lifetime bipolar disorder) or reported attendance to concurrent individual counseling or therapy. Safety procedures described by Sullivan and Cain (2004) were followed throughout the study given the high risk of revictimization in our participants. The original study was a randomized double-blind clinical trial evaluating the efficacy of present centered therapy plus safety planning (PCT+) and HOPE treatment in women-identifying shelter residents. All research procedures were approved by the University Institutional Review Board and the CEO and/or directors of participating shelters. Both PCT+ and HOPE are present-centered treatments. HOPE is an empowerment-based, cognitive-behavioral intervention that addresses the impact of IPV-related PTSD symptoms on women’s ability to effectively access shelter and community resources to meet their goals and establish safety. PCT+ was adapted from PCT ( Schnurr et al., 2007 ) to include safety planning and focuses on women’s current concerns, empowering women with IPV histories to determine what issues they want to work on during each session. Several recruitment strategies were implemented, including the provision of study brochures and flyers, in-person meetings with shelter residents where research assistants were available to answer questions about the study, and shelter staff direct referrals. Participants first completed a brief initial screen for inclusion criteria and were provided more information about the study. Participants who were eligible and provided informed consent completed a comprehensive, 2-3 hours in length, baseline assessment. Both treatments were initiated during shelter with participants eligible to receive up to 16 sessions of therapy, some of which occurred in shelter (i.e. maximum of 10 sessions) and the remaining occurred during the first three months after leaving shelter where the frequency of sessions, as well as the specific break down of shelter and post-shelter sessions being dependent on participants’ length of shelter stay (see Johnson et al., 2020 for more details regarding treatment delivery). Follow-up assessments occurred within one-week after shelter exit (i.e., PS), 3PS, 9PS, and 15PS. Baseline assessments were conducted in shelter and post-shelter assessments were conducted at a participants’ home or another negotiated safe location (e.g., friend’s house, community room in library). Participants received US $40 for the baseline assessment, US $50 for each follow-up assessment, and US $10 to cover childcare and transportation costs when treatment was delivered outside of shelter. Participants who completed at least 75.0% of all assessments were entered into a raffle for one of two US $250 gift cards. Retention rates for each timepoint were 89.5% at PS ( n = 154), 82.6% at 3PS ( n = 142), 78.5% at 9PS ( n = 135), and 73.8% at 15PS ( n = 127). Measures Participants completed a comprehensive assessment battery that included both self-report measures and clinical interviews. The measures used in the current study are described below. IPV Severity. The Severity of Violence Against Women Scale (SVAW; Marshall, 1992 ) was used to assess overall severity of non-cyber IPV experienced by participants. The SVAW includes 46 items that assess the frequency of serious threats of violence as well as mild to serious instances of physical violence and sexual aggression perpetrated by an intimate partner. Participants rated each item on a 4-item Likert-type scale ranging from 0 ( never ) to 3 ( 4 or more times ), and items were summed to reflect overall severity of IPV. IPV severity was assessed over the month prior to shelter admittance at baseline, and over the past month at each follow-up assessment. Sample items include “threatened to hurt you” and “physically forced you to have sex.” Marshall (1992) reported excellent internal consistency of the SVAW, and the current study found the same ( α = .96). Cyber Abuse. As there were no well-validated measures of CA appropriate for an adult women population at the start of this study, a modified version of the Electronic Intrusion Scale (EIS; Doucette et al., 2021 ) was used to measure cyber abuse from their partner. The original EIS consisted of three items. Three items were added to assess CA through social network sites and instant messaging. The six items assessed whether their partner accessed their friend list, checked up on them on a social networking profile, checked their cellphone call list, checked their text messages, or became jealous after reading their social networking profile. Participants were asked to indicate whether they had experienced each of the six CA behaviors (i.e., 0 = no, 1 = yes) in the month prior to shelter at baseline and during the last month at each follow-up assessment. A count variable was created by summing the number of CA behaviors endorsed (i.e., 0-6) with higher scores reflecting more CA. Our analysis of the current data revealed that the original scale demonstrated acceptable reliability ( α = .84). The modified scale's internal consistency reliability coefficient was .80. These findings support the reliability of both the original scale and the modified scale in measuring CA. Posttraumatic Stress Disorder Severity. IPV-related PTSD symptom severity was assessed with the Clinician Administered PTSD Scale (CAPS; Blake et al., 1995 ). The CAPS is a structured interview that assesses the intensity and frequency of DSM-IV-TR (APA, 2000) symptoms of PTSD over the last month. Frequency and Intensity are rated on 5-point Likert-type scales (0 = never to 4 = daily or almost every day ; 0 = absent to 4 = extreme, respectively ). Frequency and intensity scores are summed to reflect overall severity of PTSD symptoms. Blake et al. (1995) report internal consistency reliability coefficients of the CAPS ranging from .73 to .85. Convergent and discriminant validity of the CAPS is well established across 10 years of research ( Weathers et al., 2001 ). The CAPS was administered by advanced doctoral students in Counseling Psychology trained and supervised by the last author. Internal consistency reliability of the CAPS total score was .91. Inter-rater reliability for PTSD diagnosis derived from the CAPS was calculated for 20 randomly selected interviews (κ = 1). Data Analysis Prior to analyses, we examined data for missingness and analytic assumptions. In all our models, missing data were handled using full information maximum likelihood estimation with robust standard errors. This method allows for the inclusion of all available data in the analysis, maximizing statistical power and reducing bias associated with missing data (Enders, 2010; 2023). Missing values were estimated based on the observed data and the covariance structure of the variables in the model, which is widely used in longitudinal data analysis under conditions of missingness at random or missingness completely at random. Using this methodology, we were able to minimize the potential impact of missing data on our results and conclusions. All models were fit using the lavaan ( Rosseel, 2012 ) package in R (Version 4.2.3; R Core Team), including the confirmatory factor analysis, multigroup and longitudinal measurement invariance test, and the latent change score models. Confirmatory Factor Analysis. We computed confirmatory factor analyses following descriptive statistics analysis to identify the best-fitting PTSD factor-analytic model. We used multiple fit indices to assess and compare model fit, including Comparative Fit Index (CFI; values ≥ .95 indicate good fit), Root Mean Square Error of Approximation (RMSEA; values ≤ .05 indicate good fit), and Standard Root Mean Residual (SRMR; values ≤ .08 indicate adequate fit). When comparing model fit for the nested models, a significant χ 2 value indicated a better fit for the complex model. To compare the model fit of non-nested models, we used the model with the smaller Bayesian Information Criterion (BIC) value ( Kass & Raferty, 1995 ). We tested the models proposed in the literature ( Marshall et al., 2013 ), including the unidimensional DSM-IV-TR three-factor model, the four-factor numbing model, and the four-factor dysphoria model using the fit indices described above. Although not the focus of this manuscript, there is a large body of research examining the best factor structure for PTSD, including psychometric issues ( Charak et al., 2014 ; King et al., 1998 ), theoretical concerns ( Borsboom, 2017 ; Haws et al., 2023 ; Hoffman & Hayes, 2019 ; Kotoy et al., 2017), and clinical implications ( Haws et al., 2023 ; Hoffman & Hayes, 2019 ), which has demonstrated mixed results (e.g., Lommen et al., 2014 ; Contractor et al., 2018 ). Interested readers are pointed toward these manuscripts on the topic (e.g., Armour et al., 2015 ; Lommen et al., 2014 ; Contractor et al., 2018 ). Multigroup Measurement Invariance. After identifying the best-fitting PTSD model, we completed multigroup measurement invariance tests between the two treatment conditions, starting with the least restrictive model. We used the effects coding method of scaling that constrains the average loadings at 1.0 ( Little, 2013 ). While comparing the two treatment conditions on the best-fitting PTSD model, we began with the configural model. In the configural model, the two treatment conditions varied on all parameter estimates, including factor loadings, intercepts, residual error variance, factor means, and factor variances and covariances. To test the equivalence of factor loadings between groups, we estimated weak/metric invariance by constraining factor loadings for each item/indicator. To test strong factor invariance between conditions, we constrained the item/indicator intercepts and the factor loadings. We established noninvariance of statistical parameters with a significant χ 2 difference test value ( p < .005; Little 2013 ), and a CFI value difference > .01. Longitudinal Measurement Invariance. Following the identification of the best-fitting PTSD model and completing multigroup measurement invariance tests, we examined the longitudinal invariance symptom clusters, starting with the least restrictive model with subsequent models specified by progressively constraining parameters using the same methods as the multigroup measurement invariance tests. While testing the longitudinal measurement invariance, we began with the configural model where temporal groups varied on all parameter estimates. To test the equivalence of factor loadings across time, we estimated weak/metric invariance by constraining factor loadings for each item/indicator across time. We constrained item/indicator intercept and the constrained factor loadings across time to test strong factorial invariance. Finally, like the multigroup measurement invariance tests, we calculated the χ 2 value for each model and compared the values between the models. Latent Change Score Models. Latent change score models were used to examine differences in PTSD severity change across time. To do so, we created latent variables for each outcome at each wave (e.g., PTSD latent scores at baseline and post-shelter). The change between the latent variables across two-time points was used as the primary dependent variable (i.e., ΔPTSD), reflecting the average symptom severity change between time points. Cyber abuse was examined as a predictor of latent change with treatment conditions, IPV severity, and lag-1 PTSD symptom severity as control covariates. Results Preliminary Analyses Most participants identified as White ( n = 80, 46.5%) or Black ( n = 76, 44.2%), heterosexual ( n = 160, 93.0%), had completed some college ( n = 62, 36.0%) or graduated high school/received GED ( n = 49, 28.5%), and had a personal income less than $10,000 per year ( n = 151, 87.8%). See Table 1 for additional demographic characteristics. Overall, 152 participants (88.3%) reported at least one CA victimization experience at baseline, with 77 participants (44.8%) endorsing all six CA behaviors assessed. Regarding non-cyber IPV, 167 (97.1%) reported non-cyber abuse at baseline on the SVAW, with 166 participants (96.5%) reporting threats, 159 (92.4%) reporting physical abuse, and 108 (62.8%) reporting sexual abuse (see Table 2 for victimization rates across time). Table 1. Demographic Characteristics of Participants Variable n % or M (SD) Race Black or African American 76 44.2% White 80 46.5% More than one race 15 8.7% Ethnicity Hispanic/Latina 6 3.5% Non-Hispanic/Latina 165 95.9% Sexual orientation Bisexual 12 7.0% Heterosexual 160 93.0% Education level Completed junior high school 6 3.5% Completed some high school, but did not graduate 42 24.4% Graduated from high school or received GED 49 28.5% Completed some college 62 36.0% Graduated from college 13 7.6% Personal income Less than $10,000 151 87.8% $10,000—15,000 12 7.0% $15,000—20,000 3 1.7% $20,000—25,000 2 1.2% More than $25,000 4 2.3% Treatment Sessions 172 9.5 (6.0) PCT+ 89 10.1 (5.8) HOPE 83 8.9 (6.2) Open in a new tab Note. Total n values do not always sum to 172. Table 2. PTSD Symptom, Intimate Partner Violence (IPV) Victimization, and Cyber Abuse Scores at each time point Number of participants at each time point Baseline n = 172 PS n = 154 3PS n = 142 9PS n = 135 15PS n = 127 PTSD symptom score M ( SD ) 75.1 (21.1) 50.8 (27.5) 38.0 (24.0) 31.4 (27.3) 28.5 (26.5) Type of abuse reported Cyber abuse n (%) 152 (88.3) 52 (30.2) 33 (19.2) 28 (16.3) 25 (14.5) Overall IPV n (%) 167 (97.1) 69 (40.1) 31 (18.0) 33 (19.2) 26 (15.1) Threats n (%) 166 (96.5) 69 (40.1) 31 (18.0) 33 (19.2) 26 (15.1) Physical abuse n (%) 159 (92.4) 25 (14.5) 8 (4.7) 12 (7.0) 10 (5.8) Sexual abuse n (%) 108 (62.8) 16 (9.3) 4 (2.3) 6 (3.5) 7 (4.1) Contact with perpetrator Physical Contact n (%) 37 (21.5) 66 (38.4) 64 (37.2) 58 (33.7) 63(36.6) Contact by phone, email, or other means n (%) 56 (32.5) 55 (31.9) 57 (33.1) 61 (35.5) 56 (32.6) Open in a new tab Note . N = 172. Percentages were calculated using the original sample size. PS = post-shelter, 3PS = 3-months post-shelter, 9PS = 9-months post-shelter, 15PS = 15-months post-shelter. At baseline, participants were asked if they had contact with the IPV perpetrator since entering the shelter. PTSD Confirmatory Factor Analysis. All models evidence good model fit across fit indices. The best fitting model was the four-factor numbing model, which is comprised of intrusions, avoidance, numbness, and arousal factors, χ 2 (2) = .39, p = .82, CFI = 1.00, RMSEA = .00 [.00, .09], SRMR = .01. PTSD Multigroup Measurement Invariance. We conducted multigroup measurement invariance tests from configural to strong on the four-factor numbing PTSD model, comparing the two treatment conditions. The multigroup strong factorial invariance model demonstrated excellent model fit for each treatment condition: PCT: χ 2 (2) = 6.84, p = .30, CFI = .97, RMSEA = .01 [.00, .30], SRMR = .04; HOPE: χ 2 (2) = 1.59, p = .45, CFI = .98, RMSEA = .01 [.00, .20], SRMR = .02. Given that no significant differences were found between the two treatment conditions, including measurement invariance tests, we collapsed across treatment conditions for subsequent analyses. However, treatment condition was still controlled for in the subsequent analyses. PTSD Longitudinal Measurement Invariance. The longitudinal configural factorial invariance model evidenced good model fit, χ 2 (15) = 16.14, p = .34, CFI = .99, RMSEA = .03 [.00, .07], SRMR = .03. The longitudinal weak factorial invariance model evidenced good model fit, χ 2 (17) = 16.21, p = .51, CFI = 1.00, RMSEA = .00 [.00, .07], SRMR = .01. The longitudinal strong factorial invariance model evidenced good model fit, χ 2 (20) = 28.93, p = .09, CFI = .98, RMSEA = .05 [.00, .09], SRMR = .05. Based on the suggestion that a change in CFI greater than .01 reflects non-invariance over time ( Little, 2013 ), the best-fitting model was the weak factorial model, which was used for the PTSD latent change score analysis. PTSD Latent Change Score Models In the PTSD latent change score model, presented in Figure 1 , we estimated changes in PTSD from baseline to 15PS. We used cyber abuse to predict the PTSD latent change factor and the rate of change in symptom severity across the five waves while controlling for treatment 1 and lag-1 PTSD symptom severity. From baseline to PS, participants experienced a reduction in PTSD severity (ΔPTSD1 = −.24, SE = .11, p = .03). At PS, the presence of CA predicted PTSD symptom severity change while controlling for treatment and lag-1 PTSD severity ( β = .58, SE = .24, p = .02), meaning the presence of CA predicted increases in PTSD severity. When controlling for IPV severity, the presence of CA did not predict PTSD symptom severity change ( β = .15, SE = .28, p = .60). From PS to 3PS, participants experienced a reduction in PTSD severity (ΔPTSD2 = −.32, SE = .08, p < .001). At PS, the presence of CA did not predict PTSD symptom severity change while controlling for treatment, IPV severity, and lag-1 PTSD severity at 3PS ( β = −.39, SE = .30, p = .19). From 3PS to 9PS, participants experienced a reduction in PTSD symptom severity (ΔPTSD3 = −.40, SE = .10, p < .001). The presence of CA predicted PTSD symptom severity change while controlling for treatment and lag-1 PTSD severity ( β = .86, SE = .41, p < .05). However, when controlling for other forms of IPV, the presence of CA did not predict PTSD symptom severity change ( β = .51, SE = .47, p = .28). From 9PS to 15PS participants experienced a reduction in PTSD severity (ΔPTSD4 = −.35, SE = .11, p = .002). The presence of CA did not predict PTSD symptom severity change while controlling for treatment, IPV severity, and PTSD severity ( β = .39, SE = .76, p = .61). Figure 1. Multiple Indicator Univariate Latent Change Score Model. Open in a new tab Note. Multiple indicator univariate latent change score model. PTSD is measured at five-time points (baseline, post-shelter, 3 months post-shelter, 9 months post-shelter, & 15 months post-shelter), each measured using four indicators/factors (reexperiencing, avoidance, numbing & hyperarousal). We assumed measurement invariance and correlated residual errors over time. Not all estimates are included in the model; see text for a detailed description and results of model parameters. Note: ΔPTSD 1 = latent change score between baseline and post-shelter; ΔPTSD 2 = latent change score between post-shelter and 3-months post-shelter; ΔPTSD 3 = latent change score between -months and 9 months post-shelter; ΔPTSD 4 = latent change score between 9 months and 12 months post-shelter; CA = Cyber Abuse; Tx = Treatment; IPV = Intimate Partner Violence; R = Reexperiencing; A = Avoidance; N = Numbing; H = Hyperarousal. Discussion The current study investigated whether CA was associated with PTSD symptom change in women with IPV histories who resided in a DV shelter at the time of enrollment and were assessed across five time-points (i.e., baseline, PS, 3PS, 9PS, and 15PS). More severe CA was initially associated with PTSD symptom change across time, when accounting for treatment condition and PTSD symptom severity at the previous time points. When considering the overall severity of IPV experienced by women from baseline to 15 months after they leave the shelter, CA was no longer independently associated with PTSD symptom change. In other words, although the presence of CA predicts PTSD symptom change, when considered in the presence of other types of IPV it is no longer associated. These results suggest that CA may not have a distinct impact on PTSD symptom severity among W-DVS. Importantly, the finding that CA was not individually associated with PTSD symptom change across time when accounting for overall IPV severity is consistent with some research testing the cross-sectional association between CA, other forms of IPV, and PTSD symptoms ( Duerksen and Woodin, 2021 ). Coupled with the large percentage of women in this study who experienced CA in addition to other forms of IPV, these results are consistent with prior research supporting that CA might be frequently co-occurring with other forms of IPV rather than experienced separately ( Gilbar et al., 2023 ; Taylor & Xia, 2018 ). It is also possible that there might be a plateau effect because of the presence of severe IPV victimization. CA can be distinct from other forms of IPV as it can occur when individuals are not in the physical presence of the perpetrator and be used to maintain control, even when women have sought emergency shelter. However, CA can also overlap with other forms of IPV such that CA may be used to threaten, surveil, or harass (i.e., psychological abuse), coerce or violate women’s sexual agency (i.e., sexual abuse) and facilitate further physical abuse (e.g., tracking and assaulting an individual). Thus, CA should be routinely assessed along with other forms of IPV, as important safety and clinical concerns are likely missed when CA is not assessed. More research is needed to clarify the relationship between CA, non-cyber IPV, and psychological distress, such as the unique impact of CA on distress and interventions that can support healing and safety, particularly among individuals with IPV histories where research on CA is sparse. Lastly, measures that assess for IPV should ask about CA victimization. Our results that CA victimization was associated with PTSD change over time, before controlling for overall IPV severity, were consistent with cross-sectional results in adolescents ( Lu et al., 2018 ). It is possible that because W-DVS are often escaping severe physical or sexual violence, CA could be used to facilitate a continuation of such abuse with technology. For example, perpetrators might utilize technology to threaten women who have sought shelter or to attempt to find them. However, since our measure of CA did not ask who the partner was that perpetrated the abuse, we cannot ascertain whether this was a continuation of abuse or new instances of IPV by another person. Moreover, Lu and colleagues (2018) used a 12-item measure of CA, which may have better captured different technology-facilitated IPV behaviors compared to our 6-item measure. The sample of women in this study was also different in that they all met PTSD diagnostic criteria and had recent IPV exposure (i.e., within the month prior to shelter admittance). Our results suggest that CA is common among W-DVS, with most participants reporting CA at baseline and almost half the sample endorsing all of the CA items. Although this is a higher rate of CA than reported in previous studies (i.e., 1.0-78.0%; Messing et al., 2020 ; Taylor & Xia, 2018 ; Woodlock, 2017 ), this is expected as all participants experienced IPV victimization prior to entering shelter and W-DVS generally experience more severe IPV. Further, previous research likely underreported the prevalence of CA as this form of IPV may be harder to recognize ( Messing et al., 2020 ). An encouraging finding was that less women reported CA and non-CA abuse (e.g., threats, physical abuse, sexual abuse) from the time they were in shelter to 15 months after leaving shelter. Yet, this does not account for the women who left the study and it is possible that those women continued to experience IPV. Given that some women continued to experience CA 15 months post-shelter, conducting qualitative research with these women could help explain how CA and non-cyber abuse might overlap and be distinct. Overall, our results are consistent with suggestions that CA might occur within a broader pattern of abuse ( Duerksen & Woodin, 2021 ; Gilbar et al., 2023 ). In the context of domestic violence, CA erodes a sense of safety, creates an omnipresence of the perpetrator, and provides additional methods of isolating the victim ( Woodlock, 2017 ). W-DVS have attempted to seek safety and separate from a violent relationship and may experience fear associated with the perpetrator maintaining control, surveillance, and harassment over them even when in a new location ( Melander, 2010 ). Women may feel a sense of hopelessness that they may never be free of the perpetrator ( Woodlock, 2017 ; Duersken & Woodin, 2021 ). Women residing in shelter may feel isolated from social supports, due to shelter policies and procedures ( Stylianou et al., 2021 ). CA can further facilitate isolation as attempts to end the abuse (e.g., changing phone number, closing social media accounts) further separate victims from social support ( Woodlock, 2017 ). Although CA is often viewed as “minor” compared to other in-person IPV experiences (e.g., physical assault) these methods of abuse are coercive, intimidating, ever-present and may exacerbate fear and isolation for those in seeking safety in shelter. This specific experience may not be well captured in measures of overall IPV. Strengths and Limitations A significant strength of the current study is its focus on W-DVS, who are understudied in the research on CA yet seem to experience high rates of this type of IPV. Another strength is the use of longitudinal data to examine the impact of CA as most of the previous literature has utilized cross-sectional designs. The use of a clinical interview to assess PTSD severity is a significant strength, as most prior research on CA and PTSD has only utilized self-report data of PTSD symptoms. However, this study also has important limitations. First, the current study was a clinical trial with a select sample of W-DVS and the experimental treatment targeted PTSD, which might have impacted the results. Findings may be less generalizable to women experiencing IPV who do not have access to DV shelter for various reasons (e.g., lack of childcare, geographic location) and may experience additional economic, safety, and housing challenges impacting their experience of CA. Moreover, self-report measures were used to identify experiences of CA and other forms of IPV victimization. The measure of CA used in this study did not capture all aspects of technology-based abuse (e.g., stalkerware, financial abuse, threats) which might be relevant for W-DVS. Future research should expand existing IPV measures to assess for CA. Importantly, our assessment of CA was linked to their current partner which might not be the same partner whose abuse brought them to shelter. In addition, PTSD was assessed based on the DSM-IV-TR symptom criteria and the results might differ based on DSM-V criteria. We also do not know if the unequal time intervals between time points had an effect on the results. Lastly, while this study included more racial/ethnic minority women and economically marginalized women when compared to most other studies on CA, future studies should investigate whether results are similar among samples of LGBTQ+ and disabled individuals, as well as other genders (e.g., men, non-binary individuals) who might experience different mental health impacts from this type of abuse. Implications Results from this study extend our understanding of how CA can continue to negatively impact the psychological health of women who have left an abusive relationship and sought shelter services but may continue to experience IPV, such as CA. While the focus of the study illuminates how technology can be used to further victimize W-DVS and thereby contribute to higher symptoms of PTSD, we also believe that technology can be used to help women experiencing IPV. Al-Alosi (2020) describes how technology can be used to combat IPV by 1) helping women access resources and service providers, 2) facilitating contact with women’s social networks and online support groups, 3) increasing access to safety devices and safety planning, 4) gathering evidence of the abuse, and 5) empowering women who have experienced IPV. Disseminating information on these strategies to shelter workers and through low-cost and easily accessible online education materials could help women experiencing CA have increased knowledge on how to increase their safety and psychological health, as well as education on how to reduce CA exposure from the perpetrator. It would also be important for individuals working with W-DVS (e.g., shelter workers, other service providers) to be aware of the potential impact of CA and increase screening for PTSD in women who report CA experiences. Future research should examine the implementation, feasibility, and efficacy of these strategies or interventions that mitigate CA experiences and their psychological consequences. In conclusion, the results from this study highlight the negative consequences of CA, as part of women’s overall IPV experiences, on PTSD symptom change, even as women are seeking safety from IPV and receiving targeted psychological treatment. To our knowledge, this was the first study to longitudinally examine this impact with W-DVS and future research should test interventions that specifically aim to reduce the impact of CA on women’s psychological health. Moreover, the findings that CA is not individually associated with PTSD symptom change but rather appears to increase symptom severity in conjunction with other forms of IPV, highlights the need for future research to understand the mechanisms by which different types of IPV impact mental health. Moreover, exploring how fear and isolation due to CA are associated with risk for future danger is important ( Duersken & Woodin, 2021 ). Such research can support policies that address technology-facilitated gender-based violence and protect victims of CA. Clinical Impact Statement. This study highlights the negative longitudinal consequences of cyber abuse on PTSD severity for women who have sought shelter. Findings support that more severe cyber abuse was associated with greater PTSD severity across time, however, cyber abuse is no longer associated with PTSD severity when accounting for other types of IPV. Women who are fleeing to shelter for safety may be impacted by the omnipresent and coercive nature of cyber abuse. Clinicians should assess for cyber abuse, and the consequences of this form of IPV, and empower women to use technology for navigating to safety, social support, and resources. Acknowledgements We would like to thank Kathleen Alto, RaeAnn Anderson, Brittany Baker, April Barnes, Angela Burton, Jenifer Caldwell, Taylor L. Ceroni, Alexis DaFonseca, Larisa-Jayne Edwards, Vanessa Facemire, Katherine Fedele, Barbara Foster, Paige Giammusso, Lesa Hoffman, Samantha C. Holmes, Mitzi Hutchins, Nicole L. Johnson, Myriam Kadeba, Caitlin Martin-Wagar, Heike Mitchell, Laura Pappa, Sara Perez, Elsa Rojas-Ashe, Sarah Sanders, Lynn Shaffer, Yulia Shteynberg, Kristin Silver, Heather Sperry, Kristen Thornton, and the staff and residents of the Battered Women’s Shelter of Summit and Medina Counties, Someplace Safe, One Eighty, and Genesis House for their assistance in data collection and/or provision of therapy. We also thank our co-investigators, Doug Delahanty, Ted Miller, and Patrick A. Palmieri, who assisted on other aspects of this project not described within this manuscript. The authors have no relevant financial or non-financial interests to disclose. 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