Temporal Associations between Disordered Eating and Non-suicidal Self-injury: Examining Symptom Overlap over One Year

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Temporal Associations between Disordered Eating and Non-suicidal Self-injury: Examining Symptom Overlap over One Year

Brianna J. Turner, Angelina Yiu, Brianne K. Layden, Laurence Claes,Shannon Zaitsoff, Alexander L. Chapman

PII: S0005-7894(14)00109-9DOI: doi: 10.1016/j.beth.2014.09.002Reference: BETH 510

To appear in: Behavior Therapy

Received date: 11 September 2013Accepted date: 5 September 2014

Please cite this article as: Turner, B.J., Yiu, A., Layden, B.K., Claes, L., Zaitsoff, S.& Chapman, A.L., Temporal Associations between Disordered Eating and Non-suicidalSelf-injury: Examining Symptom Overlap over One Year, Behavior Therapy (2014), doi:10.1016/j.beth.2014.09.002

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http://dx.doi.org/10.1016/j.beth.2014.09.002

http://dx.doi.org/10.1016/j.beth.2014.09.002

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Longitudinal Relationship of DE and NSSI 1

Temporal Associations between Disordered Eating and Non-suicidal Self-injury: Examining

Symptom Overlap over One Year

Brianna J. Turner, M.A.1, Angelina Yiu, B.A.1, Brianne K. Layden, M.A.1, Laurence Claes,

Ph.D.2, Shannon Zaitsoff, Ph.D.1, & Alexander L. Chapman, Ph.D, R.Psych1.

1 Simon Fraser University 2 Catholic University of Leuven

Authors’ Note: We would like to thank the Social Sciences and Humanities Research Council for

providing financial support for this study, and the Canadian Institute for Health Research for

providing doctoral funding to the first author.

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Abstract

Disordered eating (DE) and non-suicidal self-injury (NSSI) commonly co-occur. This

study compared several models of the longitudinal relationship between DE and NSSI, including

concurrent and prospective models, and examined the possible moderating roles of self-

objectification, impulsivity, and emotion dysregulation in these relationships. Individuals with

NSSI (N = 197) recruited from online forums completed measures of NSSI and DE every three

months for one year. We tested the associations between NSSI and DE using hierarchical linear

models. Results supported a concurrent relationship, wherein frequency of NSSI positively

covaried with concurrent DE severity. Body surveillance moderated the concurrent relationship

between NSSI and DE. Individuals who engaged in more body surveillance endorsed high levels

of DE pathology, whereas those lower in body surveillance engaged in more frequent NSSI only

at higher levels of DE. In addition, whereas DE did not prospectively predict NSSI, frequency of

NSSI predicted more severe DE three months later. The prospective relationship between DE

and later NSSI was moderated by emotion dysregulation, such that highly dysregulated

individuals had a stronger relationship between DE and later NSSI, whereas this relationship was

not significant among individuals low in emotion dysregulation. These findings add valuable

information regarding the co-occurrence of self-damaging behaviors.

Key words: Non-suicidal self-injury; eating disorder; longitudinal; prospective; self-

objectification; emotion dysregulation.

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Temporal Associations between Disordered Eating and Non-suicidal Self-injury: Examining

Symptom Overlap over One Year

Self-damaging behavior, defined as any deliberate behavior with a high potential for

physical self-harm, can take a variety of forms. For example, disordered eating (DE) includes a

range of maladaptive or atypical eating and weight control behaviors such as restricting one’s

food intake, binge eating, and compensatory behaviors (e.g., self-induced vomiting, laxative or

diuretic abuse, over-exercising; Stice, Marti, Shaw, & Jaconis, 2009) that often result in tissue

damage and serious health concerns when they are repeated over time. Other self-damaging

behaviors, such as non-suicidal self-injury (NSSI), result in tissue damage as a direct and

immediate consequence of the behavior. Although these behaviors are often painful (Claes,

Vandereycken, & Vertommen, 2006; Selby et al., 2010) and carry a risk of negative emotional

and social consequences (Leibenluft, Gardener, & Cowdry, 1987), they often serve to reduce

unwanted emotions and other internal experiences (Haynos & Fruzzetti, 2011; Heatherton &

Baumeister, 1991; Klonsky, 2007; Smyth et al., 2007). Current theories posit that the immediate

reduction in negative affect provides powerful reinforcement such that these behaviors are

repeated despite negative consequences that can accumulate over the long term (Chapman,

Gratz, & Brown, 2006; Fairburn, Cooper, & Shafran, 2003; Haynos & Fruzzetti, 2011;

Heatherton & Baumeister, 1991). Given their similar features, it is perhaps not surprising that

there is considerable co-occurrence of DE and NSSI. A substantial proportion of individuals with

DE report that they have engaged in NSSI (32-70%; see Svirko & Hawton, 2007, for a review),

and many individuals with NSSI report engaging in DE (as many as 60%; Darche, 1990; Ross,

Heath, & Toste, 2009).

Shared risk factors may also explain the co-occurrence of NSSI and DE. DE and NSSI

have similar correlates, including childhood abuse (Muehlenkamp, Kerr, Bradley, & Adams-

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Larson, 2010; Wonderlich et al., 2007), emotion dysregulation (Muehlenkamp, Peat, Claes, &

Smits, 2012), impulsivity (Peterson & Fischer, 2012), negative body regard (Muehlenkamp,

Swanson, & Brausch, 2005; Tylka & Sabik, 2010), and anxiety and mood symptoms (Hudson et

al., 2007; Touchette et al., 2011). Thus, individuals at risk for developing one self-damaging

behavior may be more likely to engage in the other.

Despite growing awareness and concern about the overlap between self-damaging

behaviors, little is known about the temporal relationship between DE and NSSI. Some evidence

suggests that the presence of both DE and NSSI predicts a more chronic or severe course of

behavior over time. For example, among women with bulimia nervosa, engagement in NSSI

predicted continued engagement in binge eating twelve years later (Fichter, Quadflieg, &

Hedlund, 2008). Conversely, although bulimia symptoms were positively related to lifetime

(retrospective) NSSI frequency among university students with a history of NSSI, they did not

prospectively predict NSSI frequency one year later (Glenn & Klonsky, 2011). A recent

investigation of undergraduate women found that whereas NSSI and purging behavior were not

significantly associated at baseline, NSSI predicted greater engagement in purging eight months

later, and vice versa (i.e. purging predicted more frequent NSSI at 8-month follow-up; Peterson

& Fischer, 2012). These findings suggest that NSSI and DE may be reciprocally related over

time. Whereas NSSI seems to be prospectively associated with DE behavior, findings regarding

the prospective association between DE and NSSI have been more mixed. Further, it is unclear

whether NSSI and DE are independently related after accounting for underlying factors

associated with each behavior, such as psychological distress.

Examination of potential moderators of the concurrent and prospective association of

NSSI and DE may help to clarify previous findings and identify factors potentiating the link

between these two self-damaging behaviors. Extant literature has emphasized the possible role of

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individual differences in the link between NSSI and DE, including self-objectification

(Muehlenkamp et al., 2005; Tylka & Sabik, 2010), impulsivity (Peterson & Fischer, 2012), and

emotion dysregulation (Muehlenkamp et al., 2012). Each of these factors could be expected to

increase vulnerability for both NSSI and DE. For instance, self-objectification, or the tendency to

view ones’ body as an object rather than part of oneself, may reduce inhibitions toward actions

that may cause physical damage (i.e. people may be harder on objects they do not perceive a

sense of ownership over and connection to), thus increasing the likelihood that individuals would

engage in more than one self-damaging behavior. Difficulty with impulsivity and poor inhibitory

control may make it difficult for people to resist urges to engage in both NSSI and DE. Indeed,

previous research shows that compared to individuals who engage in NSSI alone, those who

engage in both NSSI and DE tend to score higher on each of these factors (Claes et al., 2013;

Muehlenkamp et al., 2012; Petersen & Fischer, 2012; Svirko & Hawton, 2007). Thus, in a

sample of self-injurers, self-objectification, impulsivity, and emotion dysregulation could be

expected to moderate the relationship between NSSI and DE by strengthening the association

between these two behaviors. Whether these factors strengthen the concurrent (e.g., co-variation

between NSSI and DE within a three-month period) or the prospective (e.g., co-variation in

which NSSI predicts more severe DE three months later) relationship between NSSI and DE has

not been addressed in the literature.

Aims and Hypotheses

This study compared several exploratory models of the interplay between NSSI and DE

symptoms over one year in a sample of self-injurers. Given that NSSI and DE are conceptualized

as serving similar emotion regulatory functions (Haynos & Fruzzetti, 2011; Klonsky, 2007), we

expected that these behaviors would positively covary within concurrent time periods. That is,

during times of greater emotional distress we expected that both behaviors would increase,

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whereas during times of less emotional distress the behaviors would both decrease (see Booth et

al., 2010). Thus, hypothesis 1a was that DE at TimeT would be positively, concurrently

associated with NSSI at TimeT and would account for unique and significant within-person

variance in this outcome. Similarly, we expected that NSSI at TimeT would be positively,

concurrently associated with DE at TimeT and would account for significant within-person

variance in DE. Given the prominent role that psychological distress is thought to play in each

behavior (Muehlenkamp et al., 2012), hypothesis 1b was that covarying for distress would

diminish the magnitude of the concurrent association between NSSI and DE.

We also examined whether there was a reciprocal, prospective (time-lagged) association

between NSSI and DE, such that DE symptoms at one time point (TimeT) would be positively

associated with frequency of NSSI at subsequent time points (TimeT+1) and vice versa.

Consistent with previous research (Fichter et al., 2008; Peterson & Fischer, 2012), we expected

that NSSI frequency at TimeT would be positively associated with subsequent DE severity at

TimeT+1 (hypothesis 2a). Although findings regarding the prospective relationship between DE

and later NSSI have been mixed (Glenn & Klonsky, 2011; Peterson & Fischer, 2012), we also

expected a positive relationship between NSSI frequency at TimeT and subsequent DE severity

over a three-month interval (at TimeT+1; hypothesis 2b).

The second aim of this study was to clarify individual differences that may moderate the

relationship between NSSI and DE. We hypothesized that self-objectification (hypothesis 3a),

impulsivity (hypothesis 3b), and emotion dysregulation (hypothesis 3c) would moderate the

concurrent relationship between NSSI and DE, such that individuals who score high on these

traits (i.e. one standard deviation above the mean) would exhibit a stronger, more positive

concurrent relationship between the two behaviors compared to those who scored low on these

traits (i.e. one standard deviation below the mean). Similarly, we expected that each of these

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traits would moderate the prospective relationship between NSSI and DE (hypotheses 4a, 4b, and

4c, respectively), such that those with high scores would exhibit a stronger prospective

relationship between the two behaviors compared to those with low scores.

Methods

Participants

The sample was composed of 211 individuals who reported at least one instance of NSSI

in their lifetime. Participants were mostly female (n = 197, 93.4%) and Caucasian (n = 193

91.5%). The mean age was 22.94 (SD = 7.15, range = 16 - 57). Most participants resided in the

United States (n = 109, 51.7%), Canada (n = 37, 17.5%), the United Kingdom (n = 26, 12.3%),

and Australia (n = 15, 7.1%), and the remaining participants resided in Europe, Russia, Mexico,

Israel, New Zealand, Japan, and South Africa. Many participants reported an annual household

income of less than $50,000 per year (n = 104, 49.3%) and had completed either high school (n =

73, 34.6%) or some college/university (n = 95, 45.0%).

Procedures

Participants were recruited from online self-injury forums on social networking websites

including Facebook.com, LiveJournal.com, and DailyStrength.org. Advertisements were posted

on the community forums describing the study. Interested participants who contacted the study

coordinator via email received a copy of the informed consent form and a link and password for

the online questionnaire portal. Of the 211 participants who completed the baseline

questionnaires, 197 indicated that they would be interested in completing follow-up

questionnaires every three months for one year. Participants were emailed one week prior to their

follow-up date (e.g., one week before their 3-month follow-up was due), and were then sent

reminder emails roughly once per week. Participants in the first cohort received a $5 gift

certificate for Amazon.com or PayPal.com for each time point completed and a $20 bonus for

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completion of all five time-points. The payment scheme was modified part way through the

study to reflect the length of questionnaires, and participants in the second cohort received $10

for each time point and a $25 bonus for completion of all five time-points.

Measures

Non-suicidal Self-injury. The Questionnaire for Non-suicidal Self-injury (QNSSI;

Kleindienst et al., 2008) is a 34-item self-report measure that assesses the frequency, methods,

and functions of NSSI. Originally in German, the measure was translated into English for

research conducted in our laboratory (see Turner, Layden, & Chapman, 2012). The frequency of

NSSI is measured using a single item. After defining NSSI as any behavior that involves

deliberately injuring oneself, the QNSSI asks: “How often have you hurt yourself on average in

the last 3 months?” Responses are scored as follows: 0 = “I haven’t hurt myself in the last 3

months,” 1 = “Once a month or less often,” 2 = “2-3 times per month,” 3 = “1-2 times per week,”

4 = “3-6 times per week,” and 5 = “Daily or more than once a day.” While the psychometric

properties of the functional scales have been evaluated in previous research (Turner et al., 2012),

to our knowledge no published research has examined the properties of the item assessing NSSI

frequency. In the current study, the test-retest reliability of this item was low to moderate over

three-month (Spearman rs = .38 - .59) and six-month (Spearman rs = .32 - .54) intervals. These

rankings, however, may be expected to have relatively low stability, particularly in non-clinical

samples where NSSI frequency may vary considerably over three-month intervals. The

frequency item was correlated with items assessing the absolute frequency (i.e. a count of

number of NSSI episodes) of NSSI over the lifetime (r = .29, p = .002), and with an ordinal scale

of lifetime NSSI frequency (r = .23, p = .001), although these correlations were small. These

small correlations make theoretical sense, however, given the potential changes in such

behaviors over time. Previous research has reported the test-retest reliability of dichotomous

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(Gratz, 2001) and interval-level NSSI scales (Fliege et al., 2006; Glenn & Klonsky, 2011; Gratz,

2001), with reliability estimates generally ranging from .49 to .91. To our knowledge, previous

studies have not reported reliability of ordinal-type scales assessing NSSI frequency specifically.

Although we would expect the stability of NSSI frequency in the past three months to be lower

than the stability of dichotomous and count data regarding past behavior, the possibility of

psychometric limitations with regard to this item cannot be ruled out.

Eating Disorder Symptoms. The Eating Disorder Diagnostic Scale (EDDS; Stice,

Telch, & Rizvi, 2000) is a 22-item measure assessing symptoms of eating disorders as indicated

by the DSM-IV-TR (APA, 2000). Items from the EDDS assess DE-related attitudes and

behaviors over a range of time periods consistent with DSM-IV criteria (e.g., fear of weight gain

during the past three months; average weekly frequency of fasting, excessive exercise, self-

induced vomiting, and laxative/diuretic use during the past three months; average weekly

frequency of binge eating during the past six months). We used a composite, total score to index

overall DE severity, computed by summing the items in their original metric (Stice, Fisher, &

Martinez, 2004). Scores on this measure ranged from 0 to 93. Previous research demonstrates

that the composite score has good internal consistency (α = .89), test-retest reliability over one

week (r = .89), and convergence with other validated self-report and interview-based measures

of DE symptoms (Stice et al., 2000). In this study, the internal consistency of the composite

score was good at each time point (αs = .81 - .87). The EDDS can also be used to screen for

probable eating disorder diagnoses. Diagnoses derived from the EDDS demonstrated good

agreement with interview-based diagnoses (average κ = .83; Stice et al., 2000), and acceptable

sensitivity and specificity (> .77; Stice et al., 2004).

Self-objectification. The Objectified Body Consciousness Scale (OBCS; McKinley &

Hyde, 1996) is a 24-item self-report measure that includes three subscales: body surveillance

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(engagement in appearance-related body monitoring, and placing a high value on how the body

looks as opposed to how the body feels), body shame (experiencing shame when the participant

perceives that his or her body does not conform to social standards), and appearance control

beliefs (beliefs that the participant can control his or her weight with sufficient effort, as opposed

to believing that weight is determined by heritable or uncontrollable factors). We used the three

subscale scores to measure aspects of self-objectification; scores on each subscale were

computed as the mean of at least six of the eight items (McKinley & Hyde, 1996). Scores on

each scale range from 1 to 7. The OBCS subscales have acceptable internal consistency (αs = .68

- .89 in undergraduate students, αs = .70 - .76 in middle-aged women), good test-retest reliability

over two weeks (r = .73 - .79), and converge with related constructs, including body

consciousness, body esteem, and eating disorder symptoms (McKinley & Hyde, 1996). In this

study, the OBCS subscales demonstrated good internal consistency (αs = .81 - .83).

Impulsivity. The Barratt Impulsiveness Scale – 11 (BIS-11; Patton, Stanford, & Barratt,

1995) is a 30-item self-report measure that assesses features of impulsivity. For the purposes of

this study, the total score was used to index overall impulsivity. Total scores range from 30 to

120, and are calculated by summing the ratings on each item; higher scores reflect greater

impulsivity. The total score of the BIS-11 demonstrates good internal consistency (α = .83), test-

retest reliability over one month (r = .83), convergence with clinical problems related to

impulsivity (Stanford et al., 2009), and had good internal consistency in this study (α = .86).

Emotion Dysregulation. The Difficulties in Emotion Regulation Scale (DERS; Gratz &

Roemer, 2004) is a 36-item self-report measure. For this study, we used the total score

(calculated by summing all 36 items) to index overall emotion dysregulation, with higher scores

indicating greater dysregulation. Total scores range from 36 to 180. The DERS total score has

demonstrated excellent internal consistency (α = .93), test-retest reliability over four to eight

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weeks (ρ = .88), and convergence with other measures of emotion regulation and experiential

avoidance (Gratz & Roemer, 2004). In this study, the total score demonstrated excellent internal

consistency (α = .91).

Psychological Distress. We used the General Severity Index (GSI) of the Brief Symptom

Inventory (Derogatis, 1993) to assess psychological distress at each time point. The GSI is a

weighted sum of nine symptom dimensions (somatization, obsessive-compulsive, interpersonal

sensitivity, depression, anxiety, hostility, phobic anxiety, paranoid ideation, and psychoticism),

and is used to index overall symptom severity or psychological distress. Scores on the GSI range

from 0 to 212. The GSI has demonstrated excellent internal consistency (α > .90; see Pereda,

Forns, & Pero, 2007 for a review), test-retest reliability over two weeks (r = .90; Derogatis,

1993), and convergence with measures of symptom severity (Derogatis, 1993) and distressing

life events (e.g., unemployment, divorce; Gilbrar & Ben-Zur, 2002). The GSI had excellent

internal consistency in this study (α = .96).

Planned Analyses

We used hierarchical linear modelling (HLM) to test the hypotheses using HLM 7.0

software (Bryk, Raudenbush, & Congdon, 2010). HLM has several advantages over traditional

repeated measures approaches for longitudinal data, including explicitly modelling within- and

between-person variability, and allowing participants with missing data to contribute to

parameter estimates using maximum likelihood estimation (Black, Harel, & Matthews, 2011;

Little & Rubin, 1987). We used random effects models to allow between-person variability in

slopes and intercepts. All variables were converted to z-scores prior to entry into the HLM

models. Level 1 predictors were group-mean centered and Level 2 predictors were grand-mean

centered to facilitate the interpretability of the coefficients and reduce collinearity. Time was

uncentered and modeled as 0 at the intercept, and then the number of days from baseline to

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completion of each follow-up questionnaire. Following the recommendations of Singer and

Willett (2003), hypotheses were tested using a series of increasingly complex models, beginning

with unconditional means (Model 1) and growth (Model 2) models, shown below, and ending

with the moderation models.

Model 1

Level 1 Yti = π0i + eti

Level 2 π0i = γ 00 + r0i

Model 2

Level 1 Yti = π0i + π1i(TimeT) + eti

Level 2 π0i = γ00 + r0i

π1i = γ 10 + r1i

Hypothesis 1a was examined by modelling the predictor (e.g., DE symptoms at TimeT) as

a Level 1 time-varying predictor of the outcome (e.g., NSSI at TimeT), with TimeT as a Level 1

covariate, as shown in the following example equation. Participant age was included as a Level 2

covariate in concurrent and prospective models as age was associated with study drop-out, and

therefore was associated with missing data in both concurrent and prospective models (see

Missing Data and Attrition, below).

Level 1 NSSIT = π0i + π1i(TimeT) + π2i(DET) + eti

Level 2 π0i = γ 00 + γ 01(Age) + r0i

π1i = γ 10 + γ 11 + r1i

π2i = γ 20 + γ 21 + r2i

Hypothesis 1b was examined by adding psychological distressT as a Level 1 covariate to

the above models. The proportion of additional variance explained by adding distress as a

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covariate was examined using guidelines published by Scientific Software International, Inc.,

wherein the reduction in Level 1 residual variance is modeled as a proportion of the Level 1

residual variance in the model without the covariate.

We next examined the prospective (time-lagged) multilevel models predicting outcomes

over the follow-up period (TimeT + 1) from predictors assessed at prior time points (TimeT).

Prospective analyses examined associations across four lags: from T1 to T2, T2 to T3, T3 to T4,

and from T4 to T5. For hypotheses 2a and 2b, the time-lagged outcome (either DE or NSSI at

TimeT+1) was modelled as a function of lagged time (TimeT +1), the outcome variable at TimeT (to

account for the autocorrelation between the outcome at TimeT and TimeT+1), and the predictor at

TimeT, with participant age as a Level 2 covariate. The effect of Time T + 1 was fixed in all

prospective models. The following is an example of the equation predicting DET +1 from NSSIT

with Age as a Level 2 covariate:

Level 1 DET +1 = π0i + π1i(TimeT +1) + π2i(DET) + π3i(NSSIT) + eti

Level 2 π0i = γ 00 + γ 01(Age) + r0i

π1i = γ 10 + γ 11 + r1i

π2i = γ 20 + γ 21 + r2i

π3i = γ 30 + γ 31 + r3i

To investigate hypotheses 3 and 4, each of the moderators of interest were added as Level

2 moderators of the relevant Level 1 intercepts and slopes in separate models. In these models,

we were primarily interested in whether the cross-level interaction between the Level 2

moderator and the Level 1 slope was significant. Significant interactions at Level 2 were

clarified by examining the simple slopes and regions of significance (using utilities provided by

Sibley, 2008, and Preacher, Curran, & Bauer, 2006).

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Results

Sample Characteristics

Participants reported clinically significant levels of NSSI, including engaging in NSSI 2-

3 times per month on average and using multiple methods of NSSI (M = 5.06, SD = 2.51), such

as cutting (95.7%), hitting (56.4%), and scratching until bleeding occurred (56.4%). The majority

of participants (68.2%) had received medical attention for their NSSI. With regard to DE, 14.2%

of participants met the EDDS cut-offs for a DSM-IV eating disorder, and an additional 11.2%

met sub-threshold criteria. Highlighting the clinical severity of this sample, nearly half of

participants (49.2%) reported at least one previous suicide attempt, 53.6% had received

psychotherapy within the past year, and 16.1% had been admitted to a psychiatric unit within the

past year. Please see Table 1 for further details of the NSSI and DE behaviors endorsed by the

participants.

Missing Data and Attrition

Of the 197 individuals who consented to participate in the longitudinal phase of the

study, eight participants provided email addresses that were invalid by the time the first follow-

up was due. Of the eligible participants, 119 (60%) completed at least one follow-up assessment

(see Table 2 for sample sizes at each time point). Participants completed an average of 2.41 (SD

= 1.53) assessments in this study; 15.8% (n = 34) completed all five assessments, 11.6% (n = 25)

completed four assessments, and 12.1% (n = 26) completed three assessments. Further inspection

of missing data suggested that complete data were available for 62.27% of the values of interest.

We used Little’s Missing Completely at Random (MCAR) test (1988) to examine

patterns of missing data in the demographic variables (i.e. age and sex) and primary variables of

interest (i.e. NSSI, DE, OBCS, BIS, DERS, and GSI). Results supported the assumption that the

data were missing at random (χ2[709] = 764.10, p = .07). Further, comparing participants who

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provided baseline data only to those who provided at least some follow-up data revealed no

significant differences with respect to sex (χ2[1] = .93, p =.34), DE severity, frequency of NSSI,

self-objectification, impulsivity, emotion dysregulation, or psychological distress (ts = -0.58 -

0.94, ps > .10). Participants who did not provide follow-up data were younger (t[208] = -4.06, p

< .001) than those who did.1 Simulation studies have demonstrated that when data are missing at

random or completely at random, statistical techniques based on maximum likelihood estimation,

such as HLM, produce unbiased parameter estimates (Black, Harel, & Matthews, 2011). This is

particularly true when variables that are associated with missingness are included as covariates;

thus, we included participant age as a covariate in our concurrent and prospective models.

Descriptive and Preliminary Data Analysis

We examined the correlations between NSSI frequency and DE severity at each time

point, and their associations with each of the moderators of interest (see Table 2). NSSI and DE

had small, positive relationships with one another at each time point (rs = .17 - .31). Further,

whereas body shame had medium correlations with DE (rs = .40 - .58), the other moderators had

small or inconsistent correlations with DE and NSSI (rs = -.16 - .33).

To clarify the within- and between-person variability in the outcomes of interest, we

fitted an unconditional means model (Model 1) to each of the outcomes and calculated the intra-

class correlation coefficients (ICC). The average initial NSSI score was 1.84, indicating that the

average participant in this study engaged in NSSI 2-3 times per month at the beginning of the

study. The ICC revealed that 51.41% of the variability in NSSI was found within participants.

The unconditional growth model (Model 2) revealed that on average, NSSI frequency decreased

slightly over the course of the study (γ = -.001, p = .003).

With respect to DE severity, the unconditional means model (Model 1) revealed that the

average initial DE score was 30.93 (range = 0 - 93), indicating that the average participant in this

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study endorsed moderate symptoms of DE at the beginning of the study. Most variability in DE

was between participants (72.13%). The unconditional growth model (Model 2) revealed that on

average, DE severity decreased over the course of the study (γ = -.001, p < .001).

Concurrent Models

Consistent with hypothesis 1a, DE symptoms significantly and positively co-varied with

NSSI at any given time (γ = .29, p = .004, see Table 3). In other words, an increase in NSSI

frequency within a given three-month period was associated with an increase in DE symptom

severity. Consistent with hypothesis 1b, the magnitude of the effect diminished slightly after

controlling for psychological distress (γ = .22, p = .02), although the relationship between DE

and NSSI remained significant. DE explained 15.94% of the within-person variability in NSSI

(beyond what was explained by the effects of time and age), and adding psychological distress

accounted for an additional 3.63% of the within-person variability in NSSI.

Also consistent with hypotheses 1a and 1b, when we examined DE as the outcome, we

found that NSSI was positively, concurrently associated with DE (γ = .15, p = .003, see Table 3),

even after controlling for psychological distress (γ = .10, p = .04). NSSI frequency explained

28.11% of the within-person variability in DE (beyond what was explained by time and age), and

psychological distress accounted for a further 14.39% of the within-person variability in DE.

Prospective Models

Consistent with hypothesis 2a, DE symptoms at TimeT did not predict subsequent NSSI at

TimeT+1 (γ = .12, p = .37, see Table 3). Contrary to hypothesis 2b, however, NSSI prospectively

predicted DE (γ = .12, p = .009, see Table 3), indicating that more frequent NSSI at one time

point was associated with greater DE severity three months later.

Moderators of the Concurrent NSSI-DE Relationship

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Partially supporting hypothesis 3a, body surveillance significantly moderated the

concurrent relationship between DE symptoms and NSSI frequency (γ = -.28, p = .004, see Table

4), whereas body shame (γ = .06, p = .43) and appearance control beliefs (γ = -.05, p = .58) did

not. Simple slope analyses revealed a stronger, more positive association between DE and NSSI

at lower values of body surveillance compared to higher values (see Figure 1). Among

participants who scored one standard deviation below the mean on body surveillance, there was a

positive association between NSSI and DE (γ = .55, SE = .16, t = 3.57, p < .001), whereas among

those who scored one standard deviation above the mean, the relationship between NSSI and DE

was not significant (γ = -.01, SE = .12, t = -.10, p = .92). Similarly, only body surveillance

significantly interacted with NSSI frequency when predicting concurrent DE symptom severity

(surveillance γ = -.14, p < .001; shame γ = -.04, p = .57; control γ = .04, p = .52; see Table 4).

There was a stronger relationship between NSSI and DE among participants who scored one

standard deviation below the mean on body surveillance (γ = .24, SE = .04, t = 5.49, p < .001),

whereas the relationship between NSSI and DE was not significant among those who scored one

standard deviation above the mean (γ = -.05, SE = .07, t = -.63, p = .53). As shown in Figure 1,

participants who scored high on body surveillance exhibited more severe DE symptoms than

those who scored low on body surveillance, regardless of NSSI frequency. Although body shame

did not moderate the association between NSSI and DE, the addition of body shame as a Level 2

covariate explained an additional 44.09% of the between-person variance in DE (see also Table 2

for correlations). Further, models that included body shame improved model fit (deviance =

795.94) compared to models that included other moderators (deviances = 842.34 - 909.58).

Contrary to hypothesis 3b, impulsivity did not moderate the concurrent relationship

between DE and NSSI (γ = -.06, p = .33), or between NSSI and DE (γ = -.05, p = .35, see Table

4). Inconsistent with hypothesis 3c, there was no significant moderating effect of emotion

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dysregulation for DE predicting concurrent NSSI (γ = .002, p = .98) or NSSI predicting

concurrent DE (γ = -.04, p = .49, see Table 4).2 Further, models that included impulsivity or

emotion dysregulation explained minimal additional between-person variance in the outcomes

(1.7% - 6.5%), did not explain additional within-person variance in the outcomes, and resulted in

worse fit compared to the models without these moderators.

Moderators of the Prospective NSSI-DE Relationship

Although none of the self-objectification variables (hypothesis 4a; γs = -.09 - .18, ps >

.07) or impulsivity (hypothesis 4b; γ = -.02, p = .83) moderated the prospective relationship

between DET and later NSSIT+1, consistent with hypothesis 4c, emotion dysregulation moderated

this prospective relationship (γ = .27, p < .001; see Table 4). None of the variables of interest

moderated the prospective relationship between NSSIT and later DET+1 (γV = -.08 - .08, ps >

.10; see Table 4). Consistent with our expectations, simple slopes analyses revealed a stronger,

positive relationship between DE and later NSSI at high levels of emotion dysregulation (see

Figure 1). Among participants who scored one standard deviation below the mean on emotion

dysregulation, there was a negative and non-significant association between DE and later NSSIs

(γ = -.16, SE = .14, t = -1.14, p = .26), whereas among those who scored one standard deviation

above the mean the relationship between DE and later NSSI was positive and significant (γ = .38,

SE = .13, t = 2.98, p = .004).

Discussion

This study is one of the first empirical investigations of the temporal relationships

between DE and NSSI in a targeted sample of individuals with NSSI, and thus addresses an

important gap in the literature. Although previous clinical observations suggest that NSSI and

DE may be negatively related (e.g., Washburn, Gebhardt, Styer, Juzwin, & Gottlieb, 2012), with

one behavior increasing as the other decreases, our results demonstrate that NSSI and DE shared

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a positive concurrent relationship within three-month intervals in an untreated sample. That is, at

any given time point an increase in NSSI frequency was associated with more severe DE

behavior, and vice versa. Consistent with research underscoring the importance of psychological

distress in accounting for fluctuations in self-damaging behaviors (Booth et al., 2010), the

strength of the relationship between NSSI and DE diminished when distress was taken into

account; however, it remained significant, suggesting that there is a relationship between NSSI

and DE that is independent of psychological distress.

With respect to moderators of the concurrent relationship between NSSI and DE, our

results highlight the importance of self-objectification, and related constructs such as body

surveillance and shame, for understanding the relationship between NSSI and DE. Specifically,

results suggested that self-injurers who engage in high levels of body surveillance exhibited

more severe DE behaviors, regardless of frequency of NSSI. Among those low in body

surveillance, however, more severe DE was observed among those who were engaging in

frequent NSSI, whereas those engaging in less frequent NSSI endorsed less severe DE.

Similarly, when NSSI was examined as the outcome, results suggested that frequent NSSI

occurred mainly among those who were low in body surveillance but who were engaging in

severe DE. Together, these findings suggest that individuals who are low in body surveillance

but who are nonetheless engaging in severe DE may represent a particularly high-risk group who

are likely to rely on multiple self-damaging behaviors, possibly prompting more frequent NSSI

in this population. In reconciling this finding with previous literature that generally points to high

self-objectification as being a risk factor for NSSI (Muehlenkamp, 2012), it is important to note

that this study focused on the maintenance of NSSI among individuals with an established

history of this behavior, whereas much of the previous work has focused on initiation or presence

of NSSI (Muehlenkamp, 2012). It is possible that whereas greater body surveillance might

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contribute to the initial acts of NSSI, higher body surveillance may in some cases protect against

more frequent NSSI once the behavior has been established. Body surveillance may bring

attention to the negative physical consequences of NSSI, such as scarring and infection.

Alternatively, individuals who attend less to how their body looks and more to how it feels (i.e.

those who are low in surveillance) may be more aware of internal cues for distress, resulting in

greater urges for self-damaging behaviors. Future research should investigate specific

mechanisms (e.g., concern about scarring, interoceptive awareness) that may explain this pattern,

as well as individual differences that can account for variability in the developmental trajectories

following initiation of NSSI.

The results of this study also underscored the important role of body shame in accounting

for between-person variability in DE severity among those who engage in NSSI. Our results

suggest that body shame may function as an individual difference factor that distinguishes

individuals with NSSI who do versus do not engage in DE, with body shame accounting for 44%

of the between-person variability in DE severity. This finding is in line with a robust body of

literature suggesting that body shame and other negative attitudes toward the body are key risk

factors for DE (Muehlenkamp et al., 2005; Tylka & Sabik, 2010). It also underscores the need to

attend to body-related attitudes to further our understanding of why some individuals may

engage in multiple self-damaging behaviors, whereas others “specialize” in a single behavior.

In addition to examining concurrent associations between NSSI and DE, this study

investigated prospective relationships between NSSI and DE over three-month lagged intervals.

Consistent with other studies (Fichter et al., 2008; Peterson & Fischer, 2012), our results

demonstrated a prospective relationship between NSSI and later DE, such that greater frequency

of NSSI predicted more severe DE three months later. One possible explanation for this finding

is that the negative emotions that are often triggered by NSSI over the long term (Leibenluft et

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al., 1987) may result in an increased need for alternative emotion regulation behaviors, such as

DE. Alternatively, it may be that NSSI becomes decreasingly effective at reducing negative

emotions over time, prompting engagement in other maladaptive behaviors. Indeed, given that

NSSI is prospectively related to suicidal behavior (Asarnow et al., 2011; Whitlock et al., 2013),

some researchers have posited that NSSI in particular may function as a “gateway” to more

versatile self-damaging behavior (Whitlock et al., 2013). Consistent with this notion, Joiner’s

(2005) interpersonal theory of suicide suggests that experience harming oneself may

progressively diminish inhibitions against doing so while providing experience with the

potentially reinforcing qualities of such behaviors, thereby increasing an individual’s willingness

to try other methods. This emerging body of literature suggesting that NSSI may increase risk for

and severity of other self-damaging behaviors underscores the clinical importance of identifying

and reducing this behavior.

Perhaps helping to understand why previous findings regarding the prospective

relationship from DE to later NSSI have been inconsistent (Glenn & Klonsky, 2011; Peterson &

Fischer, 2012), our results suggested that DE predicted later NSSI only among those who

reported high levels of emotion dysregulation. This finding is consistent with previous work

suggesting that emotion dysregulation may function as an underlying vulnerability for using

multiple self-damaging behaviors (Muehlenkamp et al., 2012). The combination of severe DE

(which itself may be associated with physical, nutritional, and cognitive consequences that

increase dysregulation) and emotion dysregulation may strengthen the prospective relationship

between DE and later NSSI. On its own, however, DE may be less likely to prospectively predict

engagement in other self-damaging behaviors such as NSSI in those who are not vulnerable to

emotion dysregulation. Indeed, among those who are low in emotion dysregulation, engagement

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in severe DE was associated with less frequent NSSI, possibly suggesting that a single self-

damaging behavior may suffice to regulate emotions in this population.

Contrary to our expectations, this study did not find a moderating role of impulsivity in

the concurrent or prospective relationships between NSSI and DE. In addition, self-reported

impulsivity demonstrated inconsistent zero-order associations with NSSI and DE across time

points. This unexpected finding may be understood in light of research suggesting that the

strength of the relationship between NSSI and impulsivity differs depending on how impulsivity

is assessed (e.g., behavioral tasks versus self-report; Glenn & Klonsky, 2010; Janis & Nock,

2009). Previous research also suggests that some aspects of impulsivity, such as negative

urgency (i.e. hasty decision making in the presence of intense negative emotions), are important

in accounting for NSSI, whereas other aspects of impulsivity (e.g., lack of perseverance, lack of

premeditation, sensation seeking) are less consistently related to NSSI (Glenn & Klonsky, 2010;

Peterson & Fischer, 2012). The relationship between impulsivity and DE is similarly nuanced.

Whereas urgency and sensation seeking are prominent among individuals with bulimia nervosa

(BN), individuals with restrictive anorexia nervosa have more difficulty with premeditation and

perseverance (Claes, Vandereycken, & Vertommen, 2005). Impulsivity does not differentiate

individuals with BN who engage in multiple self-damaging behaviors, including NSSI, from

those who do not (Newton, Freeman, & Munro, 1993). Understanding the interplay between

impulsivity, NSSI, and DE may require multimodal and more comprehensive assessments of

impulsivity.

We believe these findings may have implications for clinical work with populations who

engage in multiple self-damaging behaviors. These findings underscore the importance of

assessing both NSSI and DE when working with patients who engage in self-damaging behavior,

given the positive temporal relationships between these behaviors. Fewer than 50% of treatment

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providers assess for NSSI in ED patients (Peebles, Wilson, & Lock, 2011) and, to our

knowledge, few studies have examined rates of assessment for ED in patients with NSSI.

Routine and repeated use of structured assessments of NSSI and DE can help therapists identify

and target important maladaptive behaviors, and to track how they covary over time. Future work

examining the interplay between these behaviors among individuals receiving psychotherapy is

necessary before conclusions can be drawn about implications for treatment.

Despite the strengths and novelty of this study, several limitations warrant consideration.

First, although three-month intervals provide insight into the broad strokes of the relationship

between NSSI and DE, future research should examine these associations over other intervals of

time (e.g., hours and days, or years). For example, by examining these constructs weekly, we

might find that individuals are more likely to engage in NSSI one week and DE the next week,

providing evidence for a “symptom-swapping” profile that was not observable across three-

month increments. It also is possible that within a shorter timeframe, the relationship among

behaviors may differ. Second, the frequency of NSSI was assessed using a single item. Single-

item assessments often suffer from limited reliability, and the psychometric properties of this

item in the current sample were marginal. It is unclear from this study whether the marginal

performance of this item is related to the qualities of the behavior it assesses (e.g., that NSSI

frequency is not particularly stable over three-month intervals, and may not be correlated with

lifetime frequency counts) or with psychometric limitations of the item. Future investigations

should use multi-item measures assessing NSSI frequency to provide more specific and reliable

information about NSSI. Third, the use of a self-selected sample of individuals who use online

self-injury forums limits the generalizability of our findings. Such individuals may be younger,

more technologically savvy, and more willing to disclose their NSSI and DE behavior compared

to individuals who do not use such forums. Future research examining the relationship between

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NSSI and DE in clinical and community samples will clarify the generalizability of our findings.

Fourth, although the analytic methods we used can flexibly handle instances of missing data, the

pattern of findings may have differed if we had had higher participant retention across

assessment points, and thus replication in larger samples is warranted. Fifth, the frequencies of

behaviors in the current study were aggregated across three-month intervals by asking

participants how often on average per week they engaged in NSSI and DE, which may restrict

the ranges of these variables. Despite these limitations, we believe this study provides an

important first step toward understanding the temporal associations between two clinically

relevant behaviors, NSSI and DE, as they unfold in an untreated community sample. Although

the present study does not speak directly to clinical assessment or treatment, future research in

this vein could illuminate important avenues and opportunities for intervention.

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Footnotes

1 We compared participants who did and did not complete each time point, and obtained similar

results: only age consistently differed between participants who did or did not complete any given time

point. The exception was that participants who did not complete the second time point were more

impulsive (t[207] = 2.35, p =.02) and psychiatrically severe (t[209] = 2.07, p =.04) than those who did.

These differences were not found for any other time point. Similar results were also obtained using a

logistic regression to predict drop-out status: only age uniquely predicted drop-out status (Model : χ2[9] =

16.56, Nagelkerke R2 = .29, p =.05, Age: OR = 1.14, 95% CI of OR = 1.02 - 1.29, p = .02). Thus, age was

retained as a covariate in concurrent and prospective models.

2 We also examined each of the three secondary BIS subscales, and the six DERS subscales to

examine whether there were significant moderating effects for any specific aspects of impulsivity or

emotion dysregulation, but did not find any significant interaction effects (γs =-.001 - .07, ps = .10 - .99).

More details on these results are available from the first author.

3 Given the small number of males in the sample, we repeated all of our analyses with the females

only. The pattern of results remained consistent, and thus analyses for the full sample are presented.

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Table 1: NSSI, Disordered Eating and Eating Disorder Diagnoses in the Present Sample

NSSI Characteristics at Baseline

Mean (SD)

NSSI Number of Methods 5.06 (2.51)

NSSI Age of Onset 13.14 (4.86)

Rates of Disordered Eating at Baseline

Percentage (n)

Binge Eating 31.8% (67)

Weekly Compensatory Behaviors 36.5% (77)

Vomiting 13.7% (29)

Laxative/Diuretic Misuse 8.1% (17)

Fasting 31.8% (67) Excessive Exercise 18.5% (39)

Full and Subthreshold Eating Disorders at Baseline

Percentage (n)

Anorexia Nervosa (AN) 0% (0)

Bulimia Nervosa (BN) 10.9% (23)

Binge Eating Disorder (BED) 3.3% (7)

Subthreshold AN 3.8% (6)

Subthreshold BN 6.7% (12)

Subthreshold BED 0.9% (2)

NSSI and DE Over the Course of One Year

Min Max Mean SD Skew Kurtosis

T1 NSSI 0 5 1.99 1.49 0.29 -0.92

T2 NSSI 0 5 1.72 1.28 0.44 -0.23

T3 NSSI 0 5 1.69 1.32 0.71 -0.28

T4 NSSI 0 5 1.70 1.35 0.73 -0.09

T5 NSSI 0 5 1.53 1.43 0.78 -0.22

T1 EDDS 0 80 32.22 17.74 0.10 -0.32

T2 EDDS 0 93 31.31 17.99 0.42 0.50

T3 EDDS 0 74 28.01 15.34 0.03 0.09

T4 EDDS 0 77 27.67 16.37 0.31 0.11

T5 EDDS 0 58 28.18 14.66 -0.01 -0.73

Note. NSSI: QNSSI frequency; EDDS: Eating disorder diagnostic scale composite score.

Baseline data on the EDDS were only available for 119 participants, so frequencies are reported

for this subsample.

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Table 2: Correlations Among Moderators and Predictors of Interest within each time point

Time 1

N = 211

Time 2

n = 95

Time 3

n = 79

Time 4

n = 69

Time 5

n = 54 Intercorrelations

NSSI DE NSSI DE NSSI DE NSSI DE NSSI DE Surveil Shame Control BIS DERS

Body Surveillance .13 .34 .02 .28 -.06 .41 .19 .24 .08 .28 - .47 .25 .03 .14

Body Shame .18 .57 -.06 .42 -.04 .58 .06 .49 -.003 .40 - .05 .02 .37

Body Control .05 .18 .03 .24 .12 .24 -.10 .19 .09 .19 - -.10 -.08

BIS-11 Total .02 .19 .03 .10 -.13 -.02 .10 .09 .04 .24 - .33

DERS Total .19 .21 .01 .33 -.11 -.09 -.16 .26 .02 .19 -

NSSI Frequency - .17 - .17 - .31 - .23 - .24 - - - - -

Note. BIS-11 Total: total score from the Barratt Impulsiveness Scale; DERS total: total score from the Difficulty in Emotion Regulation

Scale.

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Table 3: HLM Models Investigating the Concurrent and Prospective Relationships

Predicting Concurrent NSSI T Predicting Concurrent DE T

Fixed Effects Fixed effects

Coefficient

Estimate (SE) t (df) p

Coefficient


Intercept .03 (.08) .35 (162) .73 Intercept .15 (.08) 1.79 (162) .08

Age * Int -.02 (.007) -2.31 (162) .02 Age * Int .01 (.009) 1.31 (162) .19

Time T -.0003 (.0003) -1.15 (163) .25 Time T -.0007 (.0002) -3.09 (163) .002

DE T .29 (.10) 2.89 (163) .004 NSSI T .15 (.05) 3.03 (163) .003

Predicting Prospective NSSI T+1 Predicting Prospective DE T+1

Fixed Effects Fixed effects

Coefficient


Coefficient


Intercept .01 (.14) .11 (85) .92 Intercept .16 (.13) 1.23 (87) .22

Age * Int -.01 (.007) -1.80 (85) .08 Age * Int .02 (.01) 1.68 (87) .10

Time T+1 -.0003 (.0005) -.59 (209) .56 Time T+1 -.002 (.0004) -4.16 (214) <.001

NSSI T -.25 (.07) -3.43 (86) <.001 DE T -.32 (.07) -4.48 (88) <.001

DE T .12 (.13) .90 (86) .37 NSSI T .12 (.05) 2.67 (88) .009

Note. All fixed effects are presented with robust standard errors. Age * Int = covariate for participant age.

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Table 4: Moderating effects of Self-Objectification, Impulsivity and Emotion Dysregulation

Predicting Concurrent NSSI Predicting Concurrent DE

Coefficient


Coefficient


Surveil (γ02) .04 (.07) .52 (155) .60 Surveil (γ02) .34 (.07) 5.17 (155) <.001

Surveil * DE (γ31) -.28 (.10) -2.90 (156) .004 Surveil * NSSI (γ31) -.14 (.04) -3.46 (156) <.001

Shame (γ02) .06 (.08) .78 (147) .43 Shame (γ02) .54 (.06) 8.69 (147) <.001

Shame * DE (γ31) -.05 (.09) -.63 (148) .53 Shame * NSSI (γ31) -.04 (.07) -.58 (148) .57

Control (γ02) .03 (.07) .48 (148) .63 Control (γ02) .21 (.07) 2.84 (148) .005

Control * DE (γ31) -.05 (.10) -.56 (149) .58 Control * NSSI (γ31) .04 (.05) .64 (149) .52

Impulsivity (γ02) .08 (.06) 1.33 (160) .19 Impulsivity (γ02) .13 (.07) 1.80 (160) .07

Impulse * DE (γ31) -.06 (.06) -.98 (161) .33 Impulse * NSSI (γ31) -.05 (.05) -.93 (161) .35

Emo Dysreg. (γ02) .12 (.07) 1.79 (160) .08 Emo Dysreg. (γ02) .24 (.07) 3.38 (160) <.001

EmoDys * DE (γ31) .002 (.08) .03 (161) .98 EmoDys * NSSI (γ31) -.04 (.06) -.69 (161) .49

Predicting Prospective NSSI Predicting Prospective DE

Coefficient


Coefficient Estimate (SE)

t (df) p

Surveil (γ02) -.05 (.10) -.54 (80) .59 Surveil (γ02) .28 (.08) 3.42 (82) <.001

Surveil * DE (γ31) .01 (.10) .11 (81) .91 Surveil * NSSI (γ31) .08 (.06) 1.42 (83) .16

Shame (γ02) -.08 (.11) -.73 (79) .47 Shame (γ02) .47 (.08) 5.94 (81) <.001

Shame * DE (γ31) .18 (.10) 1.86 (80) .07 Shame * NSSI (γ31) .04 (.06) .72 (82) .47

Control (γ02) -.009 (.09) -.10 (77) .92 Control (γ02) .13 (.10) 1.37 (77) .17

Control * DE (γ31) -.09 (.13) -.67 (78) .50 Control * NSSI (γ31) .04 (.05) .95 (78) .35

Impulsivity (γ02) -.03 (.08) -.43 (84) .67 Impulsivity (γ02) .12 (.10) 1.19 (86) .24

Impulse * DE (γ31) -.02 (.12) -.21 (85) .83 Impulse * NSSI (γ31) -.007 (.06) -.11 (87) .91

Emo Dysreg. (γ02) -.10 (.10) -1.08 (84) .29 Emo Dysreg. (γ02) .22 (.10) 2.20 (86) .03

EmoDys * DE (γ31) .27 (.08) 3.43 (85) <.001 EmoDys * NSSI (γ31) -.08 (.05) -1.55 (87) .13

Note. All fixed effects are presented with robust standard errors. Surveil = OBCS body surveillance subscale; Shame = OBCS body shame subscale; Control =

OBCS appearance control beliefs subscale; Impulsivity = BIS-11 total score; EmoDysreg = DERS total score.

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Figure 1: Surveillance Moderates the Concurrent Relationships between DE and NSSI.

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Figure 2: Emotion Dysregulation Moderates the Prospective Relationship between DE and Later NSSI.

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Highlights

• NSSI and DE were assessed every three months for one year.

• NSSI and DE are positively, contemporaneously related within three-month intervals.

• Frequency of NSSI predicted more severe DE three months later.

• The contemporaneous association of NSSI and DE was moderated by body surveillance.

• The prospective relationship of NSSI and DE was moderated by emotion dysregulation.

Temporal Associations between Disordered Eating and Non-suicidal Self-injury: Examining Symptom Overlap over One Year

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