دانلود مقاله ISI انگلیسی شماره 38846
عنوان فارسی مقاله

پنج شاخص تنظیم احساسات در شرکت کنندگان با سابقه خودآسیبی غیرخودکشی : مطالعه خاطرات روزانه

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
38846 2014 11 صفحه PDF سفارش دهید محاسبه نشده
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عنوان انگلیسی
Five Indices of Emotion Regulation in Participants With a History of Nonsuicidal Self-Injury: A Daily Diary Study
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Behavior Therapy, Volume 45, Issue 1, January 2014, Pages 56–66

کلمات کلیدی
آسیب به خود - صدمه به خود - عاطفه منفی - مطالعه خاطرات
پیش نمایش مقاله
پیش نمایش مقاله پنج شاخص تنظیم احساسات در شرکت کنندگان با سابقه خودآسیبی غیرخودکشی : مطالعه خاطرات روزانه

چکیده انگلیسی

Abstract Theory has proposed that nonsuicidal self-injury (NSSI) is a response to intense frequent negative affect (NA) that is difficult to control. Therefore, individual differences that are related to emotion dysregulation should be higher in individuals who engage in NSSI compared to healthy controls. Though current research supports this prediction, this research could be strengthened by corroborating evidence from daily diary studies. The current study used a daily diary protocol to thoroughly examine the emotional correlates of NSSI. Individuals with and without a history of NSSI rated their affect daily for 14 days. This information was used to score multiple indices of emotionality (e.g., mean level, within-person variation, reactivity). The results showed that compared to controls, individuals who engaged in NSSI had higher mean levels, within-person variation, and lower emotional differentiation of NA, but groups did not differ on inertia of NA or reactivity of NA. Moreover, individuals with a history of NSSI reported lower levels of positive affect and lower inertia of positive affect. These results are discussed in terms of affect regulation models of NSSI and treatment implications.

نتیجه گیری انگلیسی

Results Due to the nested nature of the data (days within-subjects), multilevel modeling (MLM) was used for all analyses (aside from emotional differentiation, see below). MLM is able to accurately parse variance apart at the between- and within-subject levels to allow for accurate parameter estimates of nested data (Singer & Willett, 2003). Moreover, given that MLM is robust to missing data (Singer, 1998), this allowed for the use of data from all participants who completed more than one day of diary entries. All models were run in SAS 9.3 (SAS Institute Inc., 2011) PROC MIXED (or PROC NLMIXED where appropriate), with the settings recommended by Singer (1998). To accommodate the nested nature of the data, an unstructured variance-covariance matrix was used. This type of variance-covariance matrix applies no restrictions to the variance components, which can provide a better fit to the data than methods with restrictions (e.g., autocorrelation, compound symmetry; Singer & Willett, 2003). Alternative variance-covariance structures were explored; however, none provided a significantly better fit than the unstructured. More importantly, the results for the fixed effects remained unchanged. The parameter estimates for all models are displayed in Table 1. Table 1. Unstandardized Regression Coefficients and Standard Errors for Multi-Level Modeling Results Negative Affect Positive Affect Mean Level Fixed Effects Intercept (γ00) 1.60 (.06)⁎⁎ 2.87 (.08)⁎⁎ NSSI Group (γ01) .29 (.09)⁎⁎ -.25 (.11)⁎ Variance Components (in natural log units) Between Intercept (α00) -1.57 (.18)⁎⁎ -1.08 (.19)⁎⁎ NSSI group (α01) .10 (.20) .09 (.28) Within Intercept (τ00) -2.57 (.16)⁎⁎ -1.37 (.11)⁎⁎ NSSI group (τ01) .80 (.22) .15 (.15) Inertia Fixed Effects Intercept (γ00) 1.58 (.06)⁎⁎ 2.85 (.08)⁎⁎ NSSI Group (γ01) .30 (.09)⁎⁎ -.25 (.11)⁎ Lag NA/PA (γ10) .06 (.05) .16 (.04)⁎⁎ NSSI Group by Lag NA/PA (γ11) -.04 (.06) -.15 (.06)⁎ Variance Components Between (σBw2) .23 (.03)⁎⁎ .37 (.01)⁎⁎ Within (σWn2) .19 (.008)⁎⁎ .32 (.05)⁎⁎ Emotional Reactivity Fixed Effects Intercept (γ00) 1.60 (.06)⁎⁎ 2.86 (.08)⁎⁎ NSSI Group (γ01) .30 (.09)⁎⁎ -.24 (.11)⁎ Events (γ10) .10 (.01)⁎⁎ .22 (.02)⁎⁎ NSSI Group by Events (γ11) .02 (.02) .01 (.03) Variance Components Between (σBw2) .23 (.03)⁎⁎ .35 (.05)⁎⁎ Within (σWn2) .16 (.007)⁎⁎ .28 (.01)⁎⁎ Note. ⁎⁎p < .01, ⁎p < .05. NSSI = nonsuicidal self-injury; NSSI Group: 0 = Non-NSSI, 1 = NSSI; NA = Negative Affect; PA = Positive Affect; Lag = previous day; Events = Unpleasant events for negative affect and pleasant events for positive affect. Table options Effect sizes are reported as pseudo-r2 values, which indicate the percentage of the explainable variance explained by the addition of a given parameter over a model without that parameter ( Bryk & Raudenbush, 1992). This effect size was chosen for multiple reasons. First, it allowed for consistency across analyses, some of which involved group differences of a mean, and some of which involved group differences in a slope. Second, it allows for the calculation of effect sizes for both between- and within-person variability separately, which was desirable as some analyses involved between-subject predictors (e.g., NSSI group) and others involved cross-level interactions (e.g., NSSI group by unpleasant events). To save space, pseudo-r2 values are only reported for the most relevant level (e.g., within-subjects for a cross-level interaction). Mean Level and Variability Group differences in mean level and variability of affect were examined using the mixed-effects location scale model proposed by Hedeker, Mermelstein, and Demirats (2008). This model allows for the simultaneous modeling of location (mean) and scale (variance) parameters. Mean level, between-person variation, and within-person variations were modeled as a function of NSSI group. Specifically, the Level 1 (within-person) model was equation(1) yij=b0j+rijyij=b0j+rij Turn MathJax on and the Level 2 (between-person) model was equation(2) View the MathML sourceb0j=γ00+γ01NSSIGroupj+u0j Turn MathJax on Hence, the combined MLM model was equation(3) View the MathML sourceyij=γ00+γ01NSSIGroupj+u0j+rij Turn MathJax on where yij is the NA/PA of the ith day for participant j, γ00 is the grand mean for the non-NSSI group, γ10 is the difference between the NSSI group and non-NSSI group (i.e., NSSI group was coded as 0 = non-NSSI, 1 = NSSI), u0j is the between-person residual, and rij is the within-person residual. Further, because we assume that u0j ~ N(0, σBw2) and rij ~ N(0, σWn2), both variance components can be modeled as a function of NSSI group using the log link function. equation(4) View the MathML sourceηBw=logσBw2 Turn MathJax on equation(5) View the MathML sourceηBw=α00+α01NSSIGroupj Turn MathJax on equation(6) View the MathML sourceηWn=logσWn2 Turn MathJax on equation(7) View the MathML sourceηWn=τ00+τ10NSSIGroupj+wij Turn MathJax on where α00 is the between-person variance for the non-NSSI group in natural log units, α01 is the difference in the between-person variation between the NSSI group and non-NSSI group in natural log units, τ00 is the within-person variance for the non-NSSI group in natural log units, τ10 is the difference in within-person variation between the NSSI group and non-NSSI group in natural log units, and wij is the within-person residual. Therefore, the significance of α01 and τ10 indicate the presence of group differences in between-person and within-person variability, respectively. As predicted, across the 14-day period, the NSSI group reported significantly more NA (M = 1.89) than the non-NSSI group (M = 1.60), t(116) = 3.27, p = .001, pseudo-r2 = .09. Conversely, the NSSI group reported significantly less PA (M = 2.61) compared to the non-NSSI group (M = 2.88), t(116) = -2.34, p = .021, pseudo-r2 = .05. In terms of variability of NA, the NSSI group did not have significantly more between-person variability (M = .22) than the non-NSSI group (M = .20), t(116) = .5, p = .619, pseudo-r2 = .05. However, the NSSI group did have significantly more within-person variation (M = .17) than the non-NSSI group (M = .07), t(116) = .5, p = .619, pseudo-r2 = .33. For PA, there were no significant differences between the two groups for between-person variability (NSSI M = .37, non-NSSI M = .33), t(116) = .33, p = .741, pseudo-r2 = .04, or within-person variability (NSSI: M = .29, non-NSSI: M = .25), t(116) = .96, p = .339, pseudo-r2 = .07. Inertia To examine group differences in emotional inertia, the combined model was equation(10) View the MathML sourceyij=γ00+γ01NSSIGroupj+γ10LagAffectij+γ11NSSIGroup*LagAffectij+u0j+rij Turn MathJax on where γ00 and γ01 can be interpreted as before, γ10 is the unstandardized slope relating affect on the previous day to affect on the current day for the non-NSSI group, and γ11 is the difference in this slope between the two groups. In this model, lag affect was centered within-person (Enders & Tofighi, 2008), which removes between-person variation. Hence, the slope can be interpreted as changes from an individual’s mean. Surprisingly, there was not a significant relationship between NA on the previous day and NA on the current day for the non-NSSI group, γ = .06, t(1003) = 1.25, p = .211, pseudo-r2 = .00. Moreover, this relationship was not significantly different for the NSSI group, γ = -.04, t(1003) = 0.70, p = .482, pseudo-r2 = .00. There was a significant relation between PA on the previous day and the current day for the non-NSSI group, γ = .29, t(1003) = 6.86, p < .001, pseudo-r2 = .01. Interestingly, the relation was significantly smaller for the NSSI group, γ = -.15, t(1003) = -2.64, p = .008, pseudo-r2 = .01, indicating reduced inertia of PA. Reactivity The combined model for reactivity was equation(11) View the MathML sourceyij=γ00+γ01NSSIGroupj+γ10Eventsij+γ11NSSIGroup*Eventsij+u0j+rij Turn MathJax on where the parameters can be interpreted similarly to those from the inertia model. Events were centered within-subject. There was a significant positive relation between unpleasant events and NA, γ = .10, t(1121) = 6.46, p < .001, pseudo-r2 = .03 for the non-NSSI group. However, individuals with a history of NSSI were not significantly more reactive to unpleasant events, γ = .02, t(1121) = 1.60, p = .290, pseudo-r2 = .00. The results were similar for pleasant events: there was a significant positive relation between pleasant events and PA, γ = .22, t(1121) = 10.05, p < .001, pseudo-r2 = .08, but this relation did not differ between groups, γ = .01, t(1121) = .40, p = .690, pseudo-r2 = .03. Differentiation The analyses for emotional differentiation took a slightly different approach from those above. First, for each participant, the correlation between each PANAS item with all other PANAS items for a subscale were calculated (e.g., distress and afraid, distressed and upset, etc.; Feldman Barrett et al., 2001). These correlations were then transformed using Fisher’s r to Z and then summed within the appropriate affect scale (e.g., NA). The scores were then transformed back to correlations and subtracted from 1 (to make them differentiation) before being compared with an independent samples t-test. For NA, the NSSI group had significantly less differentiation (M = .75) than the non-NSSI group (M = .82), t(112) = -2.08, p = .039, r2 = .03. The groups did not, however, differ on differentiation of PA, t(112) = -.22, p = .826, r2 = .00. For completeness, correlations among the measures (averaged across days) are reported in Table 2. Given that there were moderate to large correlations among some of the measures, several follow-up tests were conducted. Not all combinations were examined because in some cases, both affect measures were in the same model (e.g., mean level and variance, mean level and inertia) and in other cases the correlation was not significant (e.g., inertia). Therefore, the focus of the follow-up tests was on the variables that had the largest correlations that were not modeled simultaneously. In separate analyses, the results for emotional differentiation of NA were conducted controlling for mean level, within-person variability, and reactivity. Though the effect of group was only statistically significant when mean level was also in the model, the effect size (partial η2) was similar across models (range = .02–.05) and similar to the size of the effect without covariates. Hence, in spite of the large correlations among the measures, the results appear to be at least partially unique, though the correlations among the measures reduce the size of the effects. Table 2. Correlations Between Emotionality Indices Averaged Across Days 1 2 3 4 5 1. Mean Level __ .73⁎⁎ .27⁎⁎ .41⁎⁎ -.96⁎⁎ 2. Variance .65⁎⁎ __ .18⁎ .45⁎⁎ -.76⁎⁎ 3. Inertia .02 .03 __ .08 -.19⁎ 4. Reactivity .26⁎⁎ .38⁎⁎ -.01 __ -.44⁎⁎ 5. Differentiation -.83⁎⁎ -.72⁎⁎ -.01 -.32⁎⁎ __ Note. ⁎p < .05, ⁎⁎p < .001. Values below the diagonal reflect negative affect and values above the diagonal reflect positive affect. Variance is in natural log units to reduce skew.

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