Kornelius Winds1,2*
Theresa Marka1,2Bernhard Salcher3Nicole Rieser1Christine Skrivanek4Michelle Hochrainer5Julia Trost-Schrems1Lucas J. Rainer1,2,4,6
Wolfgang Hitzl7Christoph Augner4,8Belinda Plattner1,2- 1University Clinics for Child and Adolescent Psychiatry and Psychotherapeutic Medicine, Salzburger Landeskliniken, Paracelsus Medical University, Salzburg, Austria
- 2Centre for Cognitive Neuroscience Salzburg, Paris Lodron University Salzburg, Salzburg, Austria
- 3Department of Environment and Biodiversity, Paris Lodron University Salzburg, Salzburg, Austria
- 4University Clinics for Psychiatry, Psychotherapy and Psychosomatics, Salzburger Landeskliniken, Paracelsus Medical University, Salzburg, Austria
- 5University Clinics for Pediatrics, Paracelsus Medical University, Salzburg, Austria
- 6University Clinics for Neurology, Salzburger Landeskliniken, Paracelsus Medical University, Salzburg, Austria
- 7Research Office Biostatistics, Paracelsus Medical University, Salzburg, Austria
- 8Institute for Human Resources Research in Health Care, Salzburger Landeskliniken, Paracelsus Medical University, Salzburg, Austria
Background: Today, online communication is shaped by a billion-dollar social media (SM) and social networking site (SNS) industry. Visual content consumed by children and adolescents has been shown to influence behavioral patterns, state emotions, and self-esteem (SE). In this study, we introduced a novel intervention creating visual content through a professional photoshoot and investigated its impact on state emotions and SE in child and adolescent psychiatric (CAP) patients.
Methods: Standardized and validated self-rating questionnaires were used to assess SE, state emotions, coping mechanisms, psychopathological symptoms, and internet use behavior at baseline. SE and state emotions were monitored at different time points around a professional photoshoot within 45 CAP patients (30 female patients; mean age, 15.1 years) using a longitudinal design.
Results: Within-subject repeated-measures ANOVA and bootstrapped paired-sample t-tests showed a significant fluctuation in state emotions and SE throughout the intervention. Spearman correlations and univariate logistic regressions revealed that internalizing symptomatology and maladaptive coping significantly worsened the outcome of the intervention on state emotions and SE in girls. Internet-related variables heightened the positive effect of the intervention in boys and lowered SE in girls during the intervention.
Conclusion: The photo intervention had various gender-specific effects. Boys did benefit from the intervention in terms of longitudinal outcome on positive state emotions (PE) and SE, even positively influenced by SNS and SM. Thus, it might be concluded that online social comparison was processed more beneficial in boys. In contrast, when working with visual content in girls, psychopathology and coping must be considered. Internet consumption in general, especially SM and SNS, was related to low SE in girls. Nevertheless, when therapeutically accompanied, the “glow up moment” during the shoot (high on PE and SE; low on negative state emotions) could be used as an index moment for therapeutic reflection.
1 Introduction
With over five billion users worldwide, the internet plays a key role in the way we communicate today (1). Social media (SM) and social networking sites (SNS) have been found to promote pro-social functions, such as social support, friendship enhancement and maintenance, and to decrease loneliness (2).
In a parallel vein, SM and SNS are linked to a billion-dollar industry (3). The source of income for SM and SNS companies is product placement aiming to influence consumption behavior (4). Product presentation in SMS and SNS is characterized by behavior-relevant visual triggers, by communication through apparently like-minded peers (influencers), and by purchase recommendation with an ostensible non-commercial interest (5–9). The industry and its framework are accompanied by high-scale research on reachability of the main target group, which are young people in the phase of self-reflective identity seeking (10, 11). Products are presented by generally attractive, successful people of young age, leading to a continuous confrontation with perfect images of peers (12). Recently, an article in the Wall Street Journal leaked internal investigation results indicating that the use of Instagram increased body image dissatisfaction in adolescents, especially girls, leading to the development of eating disorders and depressions (13). Negative body image is associated with dissatisfied self-image and poses a high risk of developing low self-esteem (SE) (14), depression, and poor quality of life (14, 15). The irritation caused by idealized images on SM and SNS might be intentional, triggering the need to consume in order to adapt or to create an own desirable online personality (16).
SM and SNS aim at the basic human urge to communicate (17) and the human desire to be recognized by others (18). Needs such as visibility, acceptance, belonging, and at the same time recreational distraction are particularly present in adolescence (12, 19, 20), a phase characterized by various biological (21), psychological, and social changes (22) and affective regulation and identity formation processes (23). In particular, adolescents with low SE are more prone to instant messaging addiction (24) as well as problematic internet use (PIU) (25), while high SE may play a protective role in terms of PIU (26). PIU in adolescents can intensify maladjustment leading to altered self-concept and low self-satisfaction (27, 28), psychopathological symptoms, distinctive personality traits (29, 30), and problematic behavior among users (31, 32).
As psychopathological symptoms are often associated with poor SE and a negative self-perception (28, 33), creating a positive self-image is a major goal in psychotherapy with children and adolescents. Classic psychotherapeutic approaches are mainly language based and low SE is difficult to target, especially when it gets to personal appearance and body image. Being aware of the importance of visual content experiences online when it comes to social comparison and the impact on self-perception and identity development, an exclusively verbally conveyed approach seems insufficient when working with adolescent patients. Using photography as a therapeutic tool for psychiatric patients has been found to improve social skills, impulse control, and SE (34, 35). Different approaches such as the use of personal photographs as a representation for self-analysis (36, 37) or taking and posting a daily photo on SNS have been found to be related to positive therapeutic outcomes in terms of social interaction and emotion regulation (38–42).
Inspired by the project “Glow-up” created by Theresa Marka, in which she offered a professional photoshoot (PS) to her university fellows, we designed a study implementing a PS for child and adolescent psychiatric (CAP) patients (https://textmarka.com/category/project-glow-up/accessed on 05.10.2023). We chose a multiple time point assessment design in order to monitor the whole process including the phase of expectation, the actual shooting process, the reaction to the photos, and a follow-up in terms of SE and state emotions. We then analyzed the impact of psychopathological symptoms, coping strategies, and internet behavior on the course of the interventional outcome in CAP patients.
2 Methods, materials, and statistics
2.1 Participants and procedure
The study took place at the University Clinics for Child and Adolescent Psychiatry and Psychotherapeutic Medicine, Salzburger Landeskliniken, Paracelsus Medical University between June 2021 and February 2022. The department offers inpatient, outpatient, and day-clinic treatment and is the major supply hospital in the state of Salzburg, Austria.
A total sample of 45 children and adolescents was enrolled in the study. Inclusion criteria were (1) a minimum age of 12 (2), a maximum age of 20 (3), being a patient at University Clinics for Child and Adolescent Psychiatry and Psychotherapeutic Medicine, and (4) a written declaration of consent signed by patients and caregivers. Exclusion criteria were (1) inability to fill out self-rating questionnaires due to intellectual disability or insufficient German language proficiency (2), acute suicidality, and (3) eating disorder or acute severe psychiatric disorders with thought disorder and loss of reality (such as psychotic disorders and complex posttraumatic stress disorders) present at the time of the study. Inclusion and exclusion criteria were clinically assessed by the clinician responsible for psychiatric treatment.
The same intervention procedure was chosen for each participant. After the invitation to participate in the study and consent, a baseline test was collected, further test points were immediately determined before the PS, during the PS, upon receipt of the pictures, and 3 months after receipt of the pictures. This made it possible to record emotions and SE in detail throughout the entire process (see Figure 1).
Figure 1 Test application in the longitudinal study design.
The PS took place in urban locations within walking distance from the clinics. Led by a professional photographer (Theresa Marka), the session spanned between 1.5 and 2 h, allowing participants ample time to feel at ease in front of the camera. The photography equipment utilized included a Canon 5D Mark II camera body with EF 24–70 mm f/2.8l and EF 70–200 mm f/2.8l lenses, set to program auto mode. Prior to the PS, the participants received a concise introduction to camera handling. The photographer conducted the PS individually with the participants, initiating the session with a stroll outside the clinic while engaging in light conversation to establish a sense of comfort and safety. The three designated photo locations were carefully selected to offer distinct settings. These included a serene nature spot, an urban locale adorned with vibrant graffiti capturing the essence of youth, and a backdrop featuring an architectural urban building. At the onset of the shoot, the photographer provided guidance on posing to help participants relax, subsequently minimizing interference to preserve authenticity. Employing professional techniques, diverse perspectives, including close-ups and long shots, wide-angle and portrait compositions, and full-body shots and shots from varying angles such as low and high, to capture a comprehensive view of the participants were incorporated during the PS. Post-PS, the pictures were imported to the photographer’s laptop and underwent minimal alteration, limited to color correction using the Adobe Lightroom application to achieve a more accurate representation of the scene, focusing on white balance adjustment, exposure and contrast, saturation and vibrance, hue adjustment, and tonal curve adjustments. This process was used to maintain a natural and authentic representation of the participants while ensuring that the colors were balanced. After the color correction, the pictures were exported as JPEGs onto a USB flash drive.
Approximately 1 week after the PS, each participant received between 120 and 150 photos on a personal USB flash drive. This procedure of receiving the photos was accompanied by one of the authors of the study and the photographer. The photos were shown to each participant and they were given opportunities to talk about the photos and their perceptions. After their talk, they completed the questionnaires of T4 (see Figure 1). To ensure privacy, the pictures were deleted from all data storage devices used during the procedure after handing out the USB flash drive. The participants were allowed to do whatever they want with the photos. There was no obligation to post the photos online.
2.2 Measures
2.2.1 Positive and negative affect schedule
The Positive and Negative Affect Schedule (PANAS) is widely used to assess emotions (43, 44). It consists of 20 adjectives, which correlate to certain sensations and feelings. Items are rated on a five-point Likert scale ranging from 1 (= not at all) to 5 (= extremely). Summary scales for positive state emotions (PE) and negative state emotions (NE) are summarized based on 10 items, respectively (44). Depending on instruction, it is possible to measure affect in different contexts such as (i) the present moment, (ii) any past moment (day, week, or year), or (iii) in general (on average). The scale provides information to measure emotional responses to events as well as emotional fluctuations throughout a specific time frame (44). It is reliable and valid and shows a high internal consistency with a stability at appropriate levels over a period of 2 months (44). The German version of the PANAS has been derived from the English version (43).
2.2.2 Self-esteem inventory for children and adolescents (German: Selbstwertinventar für Kinder und Jugendliche)
The Self-Esteem inventory for Children and Adolescents (SEKJ) contains three scales to assess important domains of SE, namely, height, stability over time, and academic contingency bound (45). All scales consist of 10 to 12 items/statements (32 items in total) scoring on a five-point rating scale to assess the extent of either agreement or disagreement. The internal consistency for all three scales scores between Cronbach α = 0.81 and 0.86 for 10- to 12-year-olds and between Cronbach α = 0.87 and 0.90 for 13- to 16-year-olds (46). The formulations of the specific scale items are based on systematic construct definitions and guarantee item validity of the inventory and were confirmed with two factor analyses (45). Expectation conform scale intercorrelations as well as associations with other factors such as depression, school phobia, rumination, and self-concept provide clear evidence for convergent/discriminant validity of the scales. The SEKJ was standardized in a sample of n = 3.121 pupils aged 10–16 years (45). The norm sample was representative for this age group in Germany in terms of geographical, gender, and school-type distribution (45).
2.2.3 Compulsive internet use scale and scale for the assessment of internet and computer game addiction
We used a combination of the Compulsive Internet Use Scale (CIUS) (47) and the Scale for the Assessment of Internet and Computer Game Addiction (AICA) (48) to assess internet consumption behavior. The CIUS is a widely used self-report questionnaire that contains 14 items to assess internet use and its compulsiveness (47). The items measure loss of control, preoccupation (mental and behavioral), conflict (intrapersonal and interpersonal), withdrawal symptoms, and coping or mood modification on a five-point Likert scale. The CIUS sum score ranges from 0 to 56 points (47). A cutoff score of 21 points identifies a subthreshold PIU, whereas 28 points confirm PIU (49). The items of the CIUS are based on the Diagnostic and Statistical Manual of Mental Disorders version 4 criteria for dependence, obsessive–compulsive disorder, and behavioral addictions (47). The CIUS shows good reliability (Cronbach α = 0.89–0.90), good internal consistency (Cronbach α = 0.87), and good validity (47). The AICA was developed to assess the use of online formats (gaming, streaming, SM, and SNS) and appears in different versions (48). The screening version is a self-questionnaire tool based on the Diagnostic and Statistical Manual of Mental Disorders version 5 criteria for internet gaming disorder (50), consisting of six items measuring specific internet use within the last 12 months (51). When compared to external ratings of psychotherapists, scores of the self-report version of the AICA obtained a good diagnostic accuracy (sensitivity = 80,5%; specificity = 82,4%) as well as sound psychometric properties (52).
2.2.4 Youth self-report
The Youth Self-Report (YSR) is a widely used self-report instrument, measuring behavioral and emotional problems among youth aged 11–18 years during the past 6 months (53). A total of 118 items can be rated on a three-point scale from 0 (= not true) to 2 (= very true). The scores lead to a total problem scale, two broadband scales (internalizing and externalizing problems) and eight empirically derived first-order syndrome scales (somatic complaints, social withdrawal, thought problems, social problems, anxiety/depression, aggressive behaviors, delinquent behaviors, and attention problems) (53). The YSR shows good validity, reliability, and internal consistency (54).
2.2.5 Questionnaire for the evaluation of emotional regulation in children and adolescents (German: Fragebogen zur Erhebung der Emotionsregulation bei Kindern und Jugendlichen)
The Questionnaire for the evaluation of emotional regulation in children and adolescents (FEEL-KJ) is a standardized self-report questionnaire to assess emotion regulation (55). It contains 15 different strategies of emotion regulation consisting of five maladaptive, seven adaptive, and three other strategies. They are rated on five-point Likert scales evaluating the emotions sadness, fear, and anger. The questionnaire shows an internal consistency between Cronbach α = 0.69 and 0.93 (55).
2.2.6 Narrative follow-up assessment
As part of the follow-up, the following questions were assessed based on written narrative. Question 1: How do you feel about the photos today? Question 2: Do you sometimes look at them? Question 3: Have you posted them on the Internet? For analyses, question 1 was coded into three categories: positive, ambivalent, and negative feelings toward the photos; question 2 was coded into two categories: yes/no revising the photos; and question 3 was coded into two categories: yes/no posting the photos.
2.3 Statistical and data analysis
Differences in demographics, internalizing and externalizing problems (YSR), maladaptive and adaptive strategies (FEEL-KJ), height of SE (SEKJ), PE and NE (PANAS), general internet, and specific internet behaviors (CIUS and AICA) between girls and boys were analyzed using chi-square tests for categorial variables and Mann–Whitney U-tests for continuous variables. Effect sizes of significant findings in chi-square tests were calculated using Cramers V with values >0.10 interpreted as small effects, >0.30 interpreted as medium effects, and 0.50 interpreted as large effects (56). Effect sizes of significant Mann–Whitney U-test findings were calculated using Cohen’s d with values >0.20 interpreted as small effects, >0.50 interpreted as medium effects, and 0.80 interpreted as large effects (56).
To test for the difference in mean scores of variables assessed in repeated observations, a within-subject repeated-measures ANOVA was performed using SPSS Version 28. Effect sizes were calculated using Cohen’s d (56). To test potential differences between the levels of PE, NE (PANAS), and SE (SEKJ) over time, we used bootstrapped paired-sample t-tests. For those sections in the interventional course that showed a significant rise or decline, simple linear regressions were administered to test if individual variables [internalizing and externalizing problems (YSR), maladaptive and adaptive strategies (FEEL-KJ), and general and specific internet use (CIUS and AICA)] significantly influence the section history. To assess possible influences of internet use-related variables, internalizing and externalizing symptomatology, and adaptive and maladaptive strategies on PE, NE, and SE at the different time points of the intervention, we performed two-tailed Spearman correlations.
All analyses were conducted in SPSS version 28.
2.4 Ethics
This study was approved on the 25 June 2021 by the ethics committee (ethic committee vote number: 1,091/2,021) of the state of Salzburg and was performed according to the Declaration of Helsinki 1995 (as revised in Edinburgh in 2000). All participants and their legal custodians provided written informed consent prior to participating in the study.
3 Results
3.1 Descriptive measures
Our study population consisted of 45 patients with a mean age of 15.51 years, of which n = 30 (66.7%) were female and n = 15 (33.3%) were male. A total of 24 participants received in-patient treatment, 13 received outpatient treatment, and 8 received day-clinic treatment.
Mean average use of the internet was 5.35 h/day on weekdays and 7.08 h/day on weekends. There was no difference in gender regarding the internet use on weekdays (U = 197.5, Z = −0.317, p = 0.751) or weekends (U = 174.5, Z = −0.899, p = 0.369). Psychopathology was obtained from our internal medical records. In the study, patients with diagnoses from the following categories were included: substance use disorders (n = 1), affective disorders (n = 3), neurotic, stress-related, and somatoform disorders (n = 23), personality disorders (n = 7), and disruptive behavior disorders (n = 11). There were no significant gender differences obtained, except for disruptive behavior disorders (male individuals, n = 7) [χ2 (1, 45) = 6.016, p = 0.026].
Descriptive findings on sample characteristics and the results gained for internet behavior, psychopathological symptoms, adaptive/maladaptive strategies, PE, NE, and SE are shown in Table 1 for each gender separately. Noteworthy, 46.7% of the girls and 80% of the boys showed subthreshold or full PIU. There were no significant differences in internet application use between the genders, respectively; 44.4% of our sample reported adaptive strategies below norm, and 73.3% reported maladaptive strategies above norm; 65.9% of our sample presented with SE below norm at T1. Significant differences between adaptive strategies within norm between girls and boys were present (p = 0.029).
Table 1 Descriptives for age and outcome measures of instruments used: CIUS (generalized internet behavior), AICA (application-related internet behavior), YSR (internalizing and externalizing problems), FEEL-KJ (coping strategies), PANAS (positive and negative state emotions), and SEKJ (Self-esteem).
3.2 Course of state emotions and SE over points of measurement
To tests for the difference in mean scores of variables assessed in repeated observations, a within-subject repeated-measures ANOVA was performed.
In girls, Mauchly’s test indicated that the assumption of sphericity had been violated for PANAS PE χ2 (9) = 20.577, p = 0.015, and for PANAS NE χ2 (9) = 18.586, p = 0.029, and therefore, a correction of degrees of freedom was done by using Greenhouse–Geisser estimates of sphericity (ε = 0.691 and ε = 0.724, respectively). A repeated-measures ANOVA with Greenhouse–Geisser correction determined that mean PANAS PE differed statistically significantly between time points [F(2.76, 66.30) = 17.83, p < 0.001], ω2 = 0.228 as well as PANAS NE [F(2.90, 69.54) = 34.15, p < 0.001], ω2 = 0.391. Mauchly’s test indicated that the assumption of sphericity had been met for SEKJ SE, χ2 (2) = 0.855, p = 0.652. Multivariate tests showed a significant difference of SE means over all time points, V = 0.28, F(2, 22) = 4.342, p = 0.026, ω2 = 0.026.
In boys, Mauchly’s test indicated that the assumption of sphericity had been violated for PANAS NE, χ2(9) = 18.026, p = 0.037, and therefore, a correction of degrees of freedom was done by using Greenhouse–Geisser estimates of sphericity (ε = 0.601). A repeated-measures ANOVA with Greenhouse–Geisser correction determined that mean PANAS NE differed statistically significantly between time points [F(2.40, 26.43) = 10.31, p < 0.001], ω2 = 0.318. Mauchly’s test indicated that the assumption of sphericity had been met for PANAS PE, χ2(9) = 7.809, p = 0.558, and SEKJ SE, χ2(2) = 3.669, p = 0.160. Multivariate tests showed that PANAS PE and SEKJ SE means differed statistically significantly over all time points, V = 0.678, F(4, 10) = 5.263, p = 0.015, ω2 = 0.070 and V = 0.469, F(2, 13) = 5.742, p = 0.016, ω2 = 0.020.
After significance of the interventional course was detected in within-subject repeated-measures ANOVA, bootstrapped paired-sample t-tests were performed to test for significance of mean changes in PE and NE as well as SE in both genders over the different time points of the course of the intervention (see Figures 2–4).
Figure 2 Longitudinal mean findings for positive state emotions (PE) as measured by Positive and Negative Affect Schedule (PANAS) over time by gender. T1 = 1 month prior to the photoshoot, T2 = immediately before the photoshoot, T3 = during the photoshoot, T4 = after the photoshoot/during receipt of photos, T5 = 3 months after the photoshoot/at follow-up. Significance [* < 0.5 level, ** < 0.01 level (two-tailed)] was calculated with paired-sample bootstrapped t-tests for difference in PE between consecutive time points. Alphabetic character (A) refers to significant linear regression model for adaptive strategies and internalizing problems in girls.
Figure 3 Longitudinal mean findings for negative state emotions (NE) as measured by Positive and Negative Affect Schedule (PANAS) over time by gender. T1 = 1 month prior to the photoshoot, T2 = immediately before the photoshoot, T3 = during the photoshoot, T4 = after the photoshoot/during receipt of photos, T5 = 3 months after the photoshoot/at follow-up. Significance [* < 0.5 level, ** < 0.1 level (two-tailed)] was calculated with paired-sample bootstrapped t-tests for difference in NE between consecutive time points. Alphabetic characters refer to significant linear regression models for (A) adaptive strategies; (B) internalizing problems, externalizing problems, Assessment of Internet and Computer Game Addiction (AICA) video streaming, and AICA online gaming; and (C) maladaptive strategies and externalizing problems in girls. Alphabetic characters refer to significant linear regression models for (D) maladaptive strategies in boys.
Figure 4 Longitudinal mean findings for self-esteem (SE) as measured by self-worth inventory for children and adolescents (SEKJ) over time by gender. T1 = 1 month prior to the photoshoot, T4 = after the photoshoot/during receipt of photos, T5 = 3 months after the photoshoot/at follow-up. Significance [* < 0.5 level, ** < 0.1 level (two-tailed)] was calculated with paired-sample bootstrapped t-tests for difference in SE between consecutive time points.
Girls showed a significant rise in PE between T1 (m = 16.48, SD = 8.38) and T2 (m = 19.92, SD = 10.26); t(24) = −2.171, p = 0.047 and T2 (m = 19.92, SD = 10.26) and T3 (m = 29.32, SD = 7.77); t(24) = −7.935, p < 0.001; as well as a significant decline between T4 (m = 26.60, SD = 10.10) and T5 (m = 17.64, SD = 10.92); t(24) = 4.837, p < 0.001 (Figure 2).
When looking at NE in girls, they highly significantly declined between T1 (m = 22.28, SD = 9.26) and T2 (m = 12.64, SD = 7.55); t(24) = 5.741, p < 0.001; T2 (m = 12.64, SD = 7.55) and T3 (m = 4.28, SD = 4.34); t(24) = 7.539, p < 0.001 and rose between T3 (m = 4.28, SD = 4.34) and T4 (m = 7.80, SD = 7.08); t(24) = −2.729, p = 0.013; T4 (m = 7.80, SD = 7.08) and T5 (m = 18.00, SD = 10.61); t(24) = −5.037, p < 0.001 (Figure 3). Nevertheless, an overall significant decline in NE was detected between T1 (m = 22.28, SD = 9.26) and T5 (m = 18.00, SD = 10.61); t(24) = 2.392, p = 0.028 (Figure 3). A highly significant rise in SE in girls was found between T1 (m = 25.13, SD = 11.39) and T4 (m = 30.00, SD = 11.14); t(23) = −2.939, p = 0.009; as well as a significant decline between T4 (m = 30.00, SD = 11.14) and T5 (m = 25.88, SD = 9.99); t(23) = 2.103, p = 0.042. (Figure 4).
Boys showed a highly significant rise in PE between T1 (m = 14.50, SD = 6.41) and T5 (m = 22.64, SD = 8.04); t(13) = −4.964, p = 0.005 (Figure 2). Regarding NE, a highly significant decline was found between T1 (m = 19.00, SD = 5.80) and T2 (m = 9.92, SD = 7.33); t(12) = 4.410, p = 0.004 and between T2 (m = 9.92, SD = 7.33) and T3 (m = 5.15, SD = 6.16); t(12) = 5.029, p = 0.001 as well as a rise between T4 (m = 9.15, SD = 8.45) and T5 (m = 17.31, SD = 8.23); t(12) = −3.105, p = 0.005 (Figure 3).
Concerning SE in boys, a highly significant rise was shown between T1 (m = 26.07, SD = 9.09) and T4 (m = 30.07, SD = 9.36); t(14) = −2.997, p = 0.010 (Figure 4).
3.3 Correlations
To assess possible influences of internet use-related variables, internalizing and externalizing symptomatology, and adaptive and maladaptive strategies on perceived emotions and SE during the different time points of the intervention, we performed two-tailed Spearman correlations. Results are shown in Supplementary Table 1 and Table 2.
For YSR internalizing problems, girls showed significant negative correlations with PE at T1, T2, and T3 and significant positive correlations with NE at T1 and T2. In terms of FEEL-KJ adaptive strategies, significant positive correlations could be found with PE at T4 and T5 and significant negative correlations could be found with NE at T1 and T5. For FEEL-KJ maladaptive strategies, significant negative correlations could be found with PE at T1, T2, T3, and T5 and significant positive correlations could be found with NE at T1 (see Supplementary Table 1). Concerning possible influences on SE (SEKJ), girls showed significant negative correlations with CIUS total score, AICA SM use, AICA SNS use, YSR internalizing problems, and FEEL-KJ maladaptive strategies at receipt of the photos (T4). At baseline (T1), negative correlations were found with CIUS total score. At baseline (T1) and at follow-up (T5), significant negative correlations with AICA SNS, YSR internalizing problems, and FEEL-KJ maladaptive strategies were detected (see Table 2).
Table 2 Two-tailed Spearman correlations between self-esteem (SE) measured by SEKJ at the different time points with CIUS (general internet behavior), AICA (application-related behavior), YRS (internalizing and externalizing problems), and FEEL-KJ (coping strategies).
Boys showed significant positive correlations between PE at receipt of the photos (T4) with AICA video streaming, AICA SNS, and AICA SM. Furthermore, significant positive correlations between FEEL-KJ maladaptive strategies with NE at T1 and T5 could be found (see Supplementary Table 1). When assessing possible influences on SE (SEKJ) in boys, significant negative correlations with YSR internalizing problems at baseline (T1) and receipt of the photos (T4) were found. Furthermore, significant positive correlations were detected between AICA streaming and SEKJ T5 (see Table 2).
3.4 Simple linear regression
For those sections in the interventional course (Figures 2–4; PANAS, SEKJ) that showed a significant rise or decline, simple linear regressions were administered to test if individual variables significantly influence the section history. We used the following independent variables: internalizing problems (YSR), externalizing problems (YSR), adaptive strategies (FEEL-KJ), maladaptive strategies (FEEL-KJ), internet use (CIUS), and specific internet use (AICA).
In girls, several significant regression equations were found: Adaptive strategies significantly negatively predicted [R2 of 0.26, F(1,28) = 9.69, p = 0.004, t(28) = −3.11, β = −0.10] and internalizing problems significantly positively predicted rise in PE from T2 to T3 [R2 = 0.20, F(1,28) = 7.036, p = 0.013, t(28) = 2.653, β = 0.226] (see Figure 2A).
Adaptive strategies positively predicted decline in NE from T1 to T2 [R2 = 0.18, F(1,28) = 5.993, p = 0.021, t(28) = 2.448, β = 0.117] (see Figure 3A). Internalizing problems [R2 = 0.16, F(1,28) = 5.379, p = 0.028, t(28) = −2.319, β = −0.205], externalizing problems [R2 = 0.16, F(1,28) = 5.499, p = 0.026, t(28) = −2.345, β = −0.226], AICA-video streaming [R2 = 0.14, F(1,28) = 4.372, p = 0.046, t(28) = −2.091, β = −0.385], and AICA-computer gaming [R2 = 0.20, F(1,28) = 6.813, p = 0.014, t(28) = −2.610, β = −0.396] negatively predicted decline in NE from T2 to T3 (see Figure 3B). The rise in NE from T4 to T5 was positively predicted by FEEL-KJ maladaptive strategies [R2 of 0.23, F(1,23) = 6.90, p = 0.015, t(23) = 2.63, β = 0.28] and externalizing problems [R2 = 0.20, F(1, 23) = 5.638, p = 0.026, t(23) = 2.374, β = 0.429] (see Figure 3C).
For boys, only one regression equation was detected: Maladaptive strategies significantly negatively predicted the decline in NE from T1 to T2 [R2 = 0.36, F(1,11) = 6.12, p = 0.031, t(11) = −2.47, p = 0.031. β = −0.34] (see Figure 3D).
3.5 Narrative follow-up on assessment
Seventy-three percent of boys and 50% of the girls reported positive feelings towards the photos. Fifty-one percent of the participants reported revising the photos, and 24% of the sample posted the photos online. For further details, see Table 1.
4 Discussion
Through the use of SM and SNS, social comparison with idealized young people, especially in terms of appearance, is omnipresent among adolescents (57, 58). Mediated by the use of photo editing, the perception of the average external appearance diverges from the actual reality of the norm (e.g., thin, toned, well-exercised, healthy eating, and weight loss-encouraging individuals) (59, 60). This perceptual shift can lead to negative influence on mental health, such as body (61) and face dissatisfaction (62). In the study presented, we developed a new approach to CAP patients by offering a professional PS in the treatment setting. We evaluated the outcome on SE and state emotions at different points of the intervention (in anticipation of the PS, during the PS, at receiving the photos, and long-term outcome).
Internet use in our population was problematic (PIU) in 50% of girls and 80% of boys. In girls, the results are consistent with clinical populations; in boys, they exceed the previously reported rates (63, 64). A possible explanation might be an increase in prevalence of PIU during the COVID pandemic (65, 66). Compared to the test norm, our subjects showed increased maladaptive defense strategies and reduced adaptive ones, as well as reduced SE, especially the girls.
During the photo intervention, substantial fluctuations in emotionality and SE could be detected. Girls reported a significant rise in PE and SE and a decline in NE from baseline to the PS, possibly caused by anticipation. Being invited to a PS was possibly associated with good expectations, receiving centered attention and the photo-shooting atmosphere might have enhanced wellbeing. In girls, the positive emotional effect and increase in SE, however, could not be sustained after receiving the pictures. In boys, overall, in terms of positive emotionality and SE, the intervention had a beneficial outcome. Reacting less enthusiastically in the expectation phase from baseline to PS, an overall outcome of heightened positive emotions at follow-up could be observed. SE significantly rose and remained at higher levels. Only NE over the course of the intervention were similar to those in the girls.
In general, adolescents tend to experience high-intensity emotions and higher emotional instability compared to children and adults (67, 68). The gender differences in affect converge with studies reporting elevated emotional reactivity in female when compared with male adolescents (69–74), especially in terms of negative affect (71–74). A possible explanation might be higher probability of negative life events and increased cognitive vulnerability in girls (72). In addition, adolescent boys have been shown to have more efficient regulatory mechanisms for negative affect as well as greater hedonic balance levels (75).
4.1 The role of defense strategies in relation to the intervention
In girls, coping mechanisms were relevant for the emotional perception and experienced SE at different time points of the intervention. Maladaptive strategies diminished PE before and during the shooting and mediated a rise in NE after the intervention and were associated with low levels of SE throughout the intervention. Adaptive strategies lowered positive expectations before the intervention and, at the same time, relevantly diminished NE and allowed PE to remain higher after the intervention. High levels of adaptive strategies allowed SE to remain elevated after the intervention. No significant influence on the course of the intervention due to coping strategies was found in boys.
Hence, our results reflect a remarkable gender difference in emotion regulation and SE as a function of coping with an interventional offer. In adolescence, a decreased use of adaptive strategies and an increased use of maladaptive strategies have been described (76, 77). There is consensus that in stressful situations, different coping strategies are more likely to be used within the genders, respectively (78–80). Especially in girls, a stronger decrease in humor enhancement and cognitive problem solving ability has been reported (77). The higher vulnerability of girls to mental illness in adolescence can therefore also be interpreted as a function of limited defense strategies (81, 82).
4.2 The role of psychopathological symptoms in relation to the intervention
Girls with internalizing symptoms experienced a lower baseline but a significantly higher rise in positivity and decline in negativity during the intervention. This emotional uplift triggered by the intervention could be specifically therapeutically used in internalizing patients, for instance, as index moment in cognitive behavioral therapy. Furthermore, internalizing symptoms were associated with lower SE throughout the intervention in both genders. Internalizing problems are defined as being directed inwardly and express internal distress (83). They include symptoms related to depression and anxiety and lower SE (84–86). Female adolescents are more likely to experience depression compared to male adolescents and prevalence rates are constantly rising (72, 87). Externalizing symptoms were found to be associated with a rise in negative emotions after the intervention in girls. Externalizing problems include symptoms related to aggression, reduced reward dependence, hostile affect, and delinquency (86, 88). Prevalence rates of externalizing problems such as deviant behaviors and conduct behaviors are increasingly relevant in girls, while prevalence rates for this diagnostic category decreased in male adolescents over time (89, 90).
4.3 The role of internet-related variables in relation to the intervention
High levels of SNS use and internet use in girls were associated with low levels of SE during the intervention. SM use could be linked to low SE when being confronted with the pictures. Interestingly, this negative outcome in relation to SNS or SM could not be observed in boys. In contrast, the use of streaming, SM and SNS lead to elevated positive emotion at receipt of the photos.
In general, problematic mobile phone and SM/SNS use is more prevalent among female adolescents (91, 92), whereas among male adolescents, there are higher prevalence rates in gambling and online gaming disorders (92). Social comparison, social feedback, and self-reflection are the three key mechanisms contributing to a possible relation between SM/SNS and SE (93). Building up SE is an important developmental task during adolescence and a predictor of later psychological wellbeing (94, 95), healthy peer attachment (96), and success in life (94). SE in female adolescents was found to be more social-oriented and reward-oriented compared to their male counterparts (80, 97–99). In accordance, female adolescents reported more often to benefit from the use of SM/SNS in order to satisfy their social needs (100, 101). On the other hand, social expectations can promote problematic SM/SNS use (102, 103). When it comes to boys, researchers have found that they show a more robust SE (75). Male adolescents show less concern with feelings of others due to a higher sensitivity threshold to others’ emotions (104). Male adolescents tend to be more involved in competitive internet activities as represented by online gaming (105, 106).
Adolescent girls are more exposed to stressors such as concerns about their body image (107). Tendency to self-objectification, to monitor attractive individuals (108), and to carry out social comparison on SM was found to be related to lower SE (109). The marketing-driven SM industry is geared towards upward social comparison (comparing oneself to others who are better off) (110, 111) and judgmental body image (112), leading to dissatisfaction, lower levels of SE, and emotional distress (113, 114). In particular, the algorithm-driven passive form of SM use is reported to be related to increased social comparison (115, 116), lower wellbeing (57), body dissatisfaction (115), and depression (117).
4.4 Limitations and strengths
The focus of our study was to assess the impact of a new intervention, namely, a PS, on adolescents undergoing psychiatric treatment. We were interested in examining the different time points of the intervention to determine how patients react at different stages of the intervention and whether certain factors such as internet use, coping mechanisms, or psychopathology had an impact on the course. However, our specific approach was associated with limitations: Firstly, our sample included significantly more girls, which, at the same time, reflects the reality of gender distribution of adolescents in psychiatric treatment (118). However, the unequal gender ratio and the smaller sample size of boys must be taken into account when interpreting the results. It is possible that detection of statistically relevant effects was more unlikely in the male sample. In general, the sample size was rather small, but it should be borne in mind that the interventional study design was rather elaborate. Another limitation is that our study took place in CAP patients with an expected ceiling effect in psychopathology as well as negative coping and SE. Therefore, the results cannot be generalized to non-clinical adolescents and future studies including a non-clinical comparison group would be of relevance. In particular, the negative effects of internet and SNS/SM use on the outcome of the intervention in girls could be related to the specific vulnerability of our sample.
One of the strengths of the study is the new approach to working with young people with psychiatric disorder. The study was met with great interest and willingness on the part of the young people to participate. This is also reflected in the fact that all of the young people who took part stayed on for the entire intervention and the majority of the participants (n = 40) remained accessible for the follow-up study. Three months after receiving the photos, 73% of the boys rated their photos positively and 63.3% of the girls rated them positively or at least ambivalently. Professional camera equipment and instruction led to higher-quality portrait photography compared to (self-guided) mobile phone camera use. Using a professional camera instead of a mobile phone camera demarcated the intervention from the topic of SNS and SM. The results converge with the literature and substantially add to our knowledge on new treatment approaches in CAP patients.
4.5 Conclusion
The novel intervention using PS was well received by patients. Girls experienced a positive development in SE and an increase in positive emotion, particularly in anticipation of and during the PS. Perhaps the offer represents a contrast to other interventions because it provides centered attention and focus while maintaining intimacy. Increase in PE and SE experienced could be used and processed therapeutically. Furthermore, our results add to our understanding on the relevance of coping mechanisms in the processing of offers in CAP patients. Our results suggest that it is relevant to reflect on SM behavior with girls in terms of the impact on their SE. For boys, the intervention appears to be largely beneficial. The use of SM and SNS does not seem to have a negative influence on their perception of their individual photos. Although practitioners are generally less likely to consider that interventions relating to external attributes might be relevant for boys, this does appear to be the case.
Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
Ethics statement
The studies involving humans were approved by Ethics committee of the state of Salzburg (ethic committee vote-number: 1091/2021). The studies were conducted in accordance with the local legislation and institutional requirements. Written informed consent for participation in this study was provided by the participants’ legal guardians/next of kin.
Author contributions
KW: Data curation, Formal Analysis, Investigation, Methodology, Project administration, Software, Visualization, Writing – original draft, Writing – review & editing. TM: Conceptualization, Project administration, Writing – review & editing. BS: Visualization, Writing – review & editing, Formal Analysis, Supervision. NR: Data curation, Project administration, Writing – review & editing. CS: Formal Analysis, Writing – review & editing, Methodology. MH: Conceptualization, Project administration, Writing – review & editing. JT-S: Conceptualization, Writing – review & editing, Project administration. LR: Formal Analysis, Methodology, Writing – review & editing, Data curation. WH: Writing – review & editing, Formal Analysis, Methodology. CA: Supervision, Writing – review & editing, Validation. BP: Conceptualization, Data curation, Formal Analysis, Methodology, Project administration, Supervision, Visualization, Writing – original draft, Writing – review & editing.
Funding
The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpsyt.2024.1310252/full#supplementary-material
Abbreviations
AICA, Scale for the Assessment of Internet and Computer Game Addiction; CAP, Child and adolescent psychiatric; CIUS, Compulsive Internet Use Scale; FEEL-KJ, Questionnaire for the evaluation of emotional regulation in children and adolescents; NE, Negative state emotions; PANAS, Positive and negative affect schedule; PE, Positive state emotions; PIU, Problematic internet use; PS, Photoshoot; SE, Self-esteem; SEKJ, Self-Esteem inventory for Children and Adolescents; SM, Social media; SNS, Social networking sites; YSR, Youth Self-Report.
References
1. Statista Research Department. Worldwide digital population April 2022. Available online at: https://www.statista.com/statistics/617136/digital-population-worldwide/#statisticContainer.
2. Wood MA, Bukowski WM, Lis E. The digital self: how social media serves as a setting that shapes youth’s emotional experiences. Adolesc Res Review (2016) 1:163–73.
4. Appel G, Grewal L, Hadi R, Stephen AT. The future of social media in marketing. J Acad Mark Sci (2020) 48(1):79–95. doi: 10.1007/s11747-019-00695-1
5. Li Y, Xie Y. Is a picture worth a thousand words? An empirical study of image content and social media engagement. J Mark Res (2020) 57(1):1–19. doi: 10.1177/0022243719881113
6. Harris JM, Ciorciari J, Gountas J. Consumer neuroscience for marketing researchers. J Consumer Behaviour (2018) 17:239–52.
7. Pozharliev R, Rossi D, De Angelis M. A picture says more than a thousand words: Using consumer neuroscience to study instagram users’ responses to influencer advertising. Psychol Mark (2022) 39(7):1336–49. doi: 10.1002/mar.21659
8. Ye G, Hudders L, De Jans S, De Veirman M. The value of influencer marketing for business: A bibliometric analysis and managerial implications. J Advert (2021) 50(2):160–78. doi: 10.1080/00913367.2020.1857888
9. Labrecque LI, Swani K, Stephen AT. The impact of pronoun choices on consumer engagement actions: Exploring top global brands’ social media communications. Psychol Mark (2020) 37(6):796–814. doi: 10.1002/mar.21341
10. Lin CA, Kim T. Predicting user response to sponsored advertising on social media via the technology acceptance model. Comput Hum Behav (2016) 64:710–8.
11. Li F, Larimo J, Leonidou LC. Social media in marketing research: Theoretical bases, methodological aspects, and thematic focus. Psychology and Marketing (2023) 40. doi: 10.1002/mar.21746
12. Miranda S, Trigo I, Rodrigues R, Duarte M. Addiction to social networking sites: Motivations, flow, and sense of belonging at the root of addiction. Technol Forecast Soc Change (2023) 188:122280.
13. Wells G, Horwitz J, Seetharaman D. Facebook knows instagram is toxic for teen girls, company documents show. Wall Str J (2021) (600).
14. Paxton SJ, Neumark-Sztainer D, Hannan PJ, Eisenberg ME. Body dissatisfaction prospectively predicts depressive mood and low self-esteem in adolescent girls and boys. J Clin Child Adolesc Psychol (2006) 35(4):539–49. doi: 10.1207/s15374424jccp3504_5
15. Wynne C, Comiskey C, McGilloway S. The role of body mass index, weight change desires and depressive symptoms in the health-related quality of life of children living in urban disadvantage: Testing mediation models. Psychol Heal (2016) 31(2):147–65. doi: 10.1080/08870446.2015.1082560
16. Bergagna E, Tartaglia S. Self-esteem, social comparison, and facebook use. Eur J Psychol (2018) 14(4):831–45. doi: 10.5964/ejop.v14i4.1592
17. Harlow HF, Zimmermann RR. Affectional responses in the infant monkey: Orphaned baby monkeys develop a strong and persistent attachment to inanimate surrogate mothers. Sci (80- ) (1959) 130(3373):421–32. doi: 10.1126/science.130.3373.421
18. Kwon HE, So H, Han SP, Oh W. Excessive dependence on mobile social apps: A rational addiction perspective. Inf Syst Res (2016) 27(4):919–39. doi: 10.1287/isre.2016.0658
19. Sánchez-Hernández MD, Herrera MC, Expósito F. Does the number of likes affect adolescents’ Emotions? The moderating role of social comparison and feedback-seeking on instagram. J Psychol Interdiscip Appl (2021) 156(3):200–23.
20. Luo M, Hancock JT. Self-disclosure and social media: motivations, mechanisms and psychological well-being. Curr Opin Psychol (2020) 31:110–5.
21. Sisk CL, Zehr JL. Pubertal hormones organize the adolescent brain and behavior. Front Neuroendocrinol (2005) 26. doi: 10.1016/j.yfrne.2005.10.003
22. Somerville LH, Jones RM, Casey BJ. A time of change: Behavioral and neural correlates of adolescent sensitivity to appetitive and aversive environmental cues. Brain Cognition (2010) 72(1):124–33. doi: 10.1016/j.bandc.2009.07.003
23. Crone EA, Dahl RE. Understanding adolescence as a period of social-affective engagement and goal flexibility. Nat Rev Neurosci (2012) 13(9):636–50. doi: 10.1038/nrn3313
24. Ehrenberg A, Juckes S, White KM, Walsh SP. Personality and self-esteem as predictors of young people’s technology use. Cyberpsychol Behav (2008) 11(6):739–41. doi: 10.1089/cpb.2008.0030
25. Mei S, Yau YHC, Chai J, Guo J, Potenza MN. Problematic Internet use, well-being, self-esteem and self-control: Data from a high-school survey in China. Addict Behav (2016) 61:74–9. doi: 10.1016/j.addbeh.2016.05.009
26. Martinez-pecino R, Garcia-gavila M. Likes and problematic instagram use : the moderating role of self-esteem. Cyberpsychol Behav Soc Netw (2019) 22(6):412–6. doi: 10.1089/cyber.2018.0701
27. Campbell JD, Trapnell PD, Heine SJ, Katz IM, Lavallee LF, Lehman DR. Self-concept clarity: measurement, personality correlates, and cultural boundaries. J Pers Soc Psychol (1996) 70(1):141–56. doi: 10.1037/0022-3514.70.1.141
28. Zeigler-Hill V. The connections between self-esteem and psychopathology. J Contemp Psychother (2011) 41(3):157–64. doi: 10.1007/s10879-010-9167-8
29. Müller KW, Dreier M, Beutel ME, Duven E, Giralt S, Wölfling K. A hidden type of internet addiction? Intense and addictive use of social networking sites in adolescents. Comput Hum Behav (2016) 55:172–7.
30. Sampasa-Kanyinga H, Lewis RF. Frequent use of social networking sites is associated with poor psychological functioning among children and adolescents. Cyberpsychol Behav Soc Netw (2015) 18(7):380–5. doi: 10.1089/cyber.2015.0055
31. Kemp E, Sadeh N, Baskin-Sommers A. A latent profile analysis of affective triggers for risky and impulsive behavior. Front Psychol (2019) 9:2651.
32. Ahmed SP, Bittencourt-Hewitt A, Sebastian CL. Neurocognitive bases of emotion regulation development in adolescence. Dev Cogn Neurosci (2015) 15:11–25.
33. Garaigordobil M, Bernarás E. Self-concept, self-esteem, personality traits and psychopathological symptoms in adolescents with and without visual impairment. Span J Psychol (2009) 12(1):149–60. doi: 10.1017/S1138741600001566
34. Cosden C, Reynolds D. Photography as therapy. Arts Psychother (1982) 9(1):19–23. doi: 10.1016/0197-4556(82)90023-5
35. Gibson N. Therapeutic photography: enhancing patient communication. J Kidney Care (2017) 2(1):46–7. doi: 10.12968/jokc.2017.2.1.46
36. Werremeyer A, Skoy E, Burns W, Bach-Gorman A. Photovoice as an intervention for college students living with mental illness: A pilot study. Ment Heal Clin (2020) 10(4):237–43. doi: 10.9740/mhc.2020.07.237
37. Han CS, Oliffe JL. Photovoice in mental illness research: A review and recommendations. Heal (United Kingdom) (2016) 20(2):110–26. doi: 10.1177/1363459314567790
38. Brewster L, Cox AM. The daily digital practice as a form of self-care: Using photography for everyday well-being. Heal (United Kingdom) (2019) 23(6):621–38. doi: 10.1177/1363459318769465
39. Lee JA, Efstratiou C, Siriaraya P, Sharma D, Ang CS. SnapAppy: A positive psychology intervention using smartphone photography to improve emotional well-being. Pervasive Mob Comput (2021) 73:101369.
40. McKee LG, Algoe SB, Faro AL, O’Leary JL. What do daily reports add to the picture? Results from a photography intervention designed to increase positive emotion. J Posit Psychol (2020) 15:639–44.
41. Chen Y, Mark G, Ali S. Promoting positive affect through smartphone photography. Psychol Well Being (2016) 6(1):8. doi: 10.1186/s13612-016-0044-4
42. Zhang F, Pi Y, Li X. Photographic intervention effect on positive and negative affects during COVID-19: Mediating role of future self-continuity. Front Psychol (2023) 13:1085518. doi: 10.3389/fpsyg.2022.1085518
43. Breyer B, Bluemke M. Deutsche version der positive and negative affect schedule PANAS (GESIS panel). Zusammenstellung sozialwissenschaftlicher Items und Skalen (ZIS) (2016).
44. Watson D, Clark LA, Tellegen A. Development and validation of brief measures of positive and negative affect: The PANAS scales. J Pers Soc Psychol (1988) 54(6):1063–70. doi: 10.1037/0022-3514.54.6.1063
45. Schöne C, Stiensmeier J. SEKJ - Selbstwertinventar für Kinder und Jugendliche (Testmanual) Göttingen: Hogrefe (2016).
46. Schöne C, Tandler SS, Stiensmeier-Pelster J. Contingent self-esteem and vulnerability to depression: Academic contingent self-esteem predicts depressive symptoms in students. Front Psychol (2015) 6:1573.
47. Meerkerk G-J, Van Den Eijnden RJJM, Vermulst AA, Garretsen HFL. The compulsive internet use scale (CIUS): some psychometric properties. CyberPsychol Behav [Internet] (2009) 12(1):1–6. doi: 10.1089/cpb.2008.0181
48. Müller KW, Scherer L, Beutel ME, Wölfling K. Cognitive-behavioral strategies in the treatment of internet-related disorders: Diagnostics, motivation enhancement, and behavior modification | Verhaltenstherapeutische Ansätze bei internetbezogenen Störungen: Diagnostik, Motivation und Verhaltensmodifik. Verhaltenstherapie (2018).
49. Guertler D, Rumpf HJ, Bischof A, Kastirke N, Petersen KU, John U, et al. Assessment of problematic internet use by the compulsive internet use scale and the internet addiction test: A sample of problematic and pathological gamblers. Eur Addict Res (2014) 20:75–81. doi: 10.1159/000355076
50. American Psychiatric Association APA. Diagnostic and statistical manual of mental disorders (5th ed.). Am J Psychiatry (2013). doi: 10.1176/appi.books.9780890425596
51. Wölfling K, Beutel ME, Müller KW. Geue K, Strauß B, Brähler E, editors. DiagnostischeVerfahren in der Psychotherapie (Diagnostik für Klinik undPraxis)[Diagnostic measures for psychotherapy], vol. p . Hogrefe, Göttingen, Germany (2016). p. 362–6.
52. Müller KW, Beutel ME, Wölfling K. A contribution to the clinical characterization of Internet addiction in a sample of treatment seekers: Validity of assessment, severity of psychopathology and type of co-morbidity. Compr Psychiatry [Internet] (2014) 55:770–7. doi: 10.1016/j.comppsych.2014.01.010
53. Achenbach TM. Manual for the youth self-report and 1991 profile. Dep Psychiatry Univ Vermont, Burlington (1991).
54. Achenbach T, Rescorla L. Manual for the ASEBA school-age forms & profiles: Child behavior checklist for ages 6-18, teacher’s report form, youth self-report. integrated system multi-informant assessment (2001). doi: 10.1037/t47452-000
55. Goldschmidt S, Berth H, Feel KJ. Fragebogen zur Erhebung der Emotionsregulation bei Kindern und Jugendlichen von Alexander Grob und Carola Smolenski (2005) Vol. 52. Bern: Huber, Testmappe komplett Euro 73.- (2006). Diagnostica.
57. Verduyn P, Ybarra O, Résibois M, Jonides J, Kross E. Do social network sites enhance or undermine subjective well-being? A Crit Review Soc Issues Policy Rev (2017) 11(1):274–302.
58. Wang M, Xu Q, He N. Perceived interparental conflict and problematic social media use among Chinese adolescents: The mediating roles of self-esteem and maladaptive cognition toward social network sites. Addict Behav (2021) 112:106601. doi: 10.1016/j.addbeh.2020.106601
59. Tiggemann M, Zaccardo M. ‘Strong is the new skinny’: A content analysis of #fitspiration images on Instagram. J Health Psychol (2018) 23(8):1003–11. doi: 10.1177/1359105316639436
60. Ghaznavi J, Taylor LD. Bones, body parts, and sex appeal: An analysis of #thinspiration images on popular social media. Body Image (2015) 14:54–61. doi: 10.1016/j.bodyim.2015.03.006
61. Wang Y, Xie X, Fardouly J, Vartanian LR, Lei L. The longitudinal and reciprocal relationships between selfie-related behaviors and self-objectification and appearance concerns among adolescents. New Media Soc (2021) 23(1):56–77. doi: 10.1177/1461444819894346
62. Wang Y, Chu X, Nie J, Gu X, Lei L. Selfie-editing, facial dissatisfaction, and cosmetic surgery consideration among Chinese adolescents: A longitudinal study. Curr Psychol (2022) 41(12):9027–37. doi: 10.1007/s12144-020-01280-4
63. Winds K, Aebi M, Plattner B. Problematic internet use among adolescent male and female psychiatric inpatients: A gender perspective. Child Psychiatry Hum Dev (2022). doi: 10.1007/s10578-022-01408-6
64. Fuchs M, Riedl D, Bock A, Rumpold G, Sevecke K. Pathological internet use—An important comorbidity in child and adolescent psychiatry: prevalence and correlation patterns in a naturalistic sample of adolescent inpatients. BioMed Res Int (2018) 2018:1–10.
65. Paulus FW, Joas J, Gerstner I, Kühn A, Wenning M, Gehrke T, et al. Problematic Internet Use among Adolescents 18 Months after the Onset of the COVID-19 Pandemic. Children (2022) 9(11). doi: 10.3390/children9111724
66. Wang Z, Hong B, Zhang Y, Su Y, Li M, Zhao L, et al. Children and adolescents’ positive youth development qualities and internet addiction during the COVID-19 pandemic: A longitudinal study in China. Front Psychiatry (2023) 13.
67. Bailen NH, Green LM, Thompson RJ. Understanding emotion in adolescents: A review of emotional frequency, intensity, instability, and clarity. Emot Rev (2019) 11(1):63–73. doi: 10.1177/1754073918768878
68. Guyer AE, Silk JS, Nelson EE. The neurobiology of the emotional adolescent: From the inside out. Neurosci Biobehav Rev (2016) 70:74–85.
69. Esteban-Gonzalo S, Esteban-Gonzalo L, Cabanas-Sánchez V, Miret M, Veiga OL. The investigation of gender differences in subjective wellbeing in children and adolescents: The up&down study. Int J Environ Res Public Health (2020) 17(8):2732. doi: 10.3390/ijerph17082732
70. Stapley JC, Haviland JM. Beyond depression: Gender differences in normal adolescents’ emotional experiences. Sex Roles (1989) 20(5–6):295–308. doi: 10.1007/BF00287726
71. Joiner TE, Blalock JA. Gender differences in depression: The role of anxiety and generalized negative affect. Sex Roles (1995) 33(1–2):91–108. doi: 10.1007/BF01547937
72. Hankin BL, Abramson LY. Development of gender differences in depression: an elaborated cognitive vulnerability-transactional stress theory. Psychol Bull (2001) 127(6):773–96. doi: 10.1037/0033-2909.127.6.773
73. Brooks-Gunn J, Warren MP. Biological and social contributions to negative affect in young adolescent girls. Child Dev (1989) 60. doi: 10.2307/1131069
74. Cyranowski JM, Frank E, Young E, Shear MK. Adolescent onset of the gender difference in lifetime rates of major depression. A theoretical model. Arch Gen Psychiatry (2000) 57(1):21–7. doi: 10.1001/archpsyc.57.1.21
75. Caprara GV, Steca P, Gerbino M, Paciello M, Vecchio GM. Looking for adolescents’ well-being: Self-efficacy beliefs as determinants of positive thinking and happiness. Epidemiol Psichiatr Soc (2006) 15(1):30–43. doi: 10.1017/S1121189X00002013
76. Lange S, Tröster H. Adaptive und maladaptive Emotionsregulationsstrategien im Jugendalter. Z fur Gesundheitspsychologie (2015) 23(3):101–11. doi: 10.1026/0943-8149/a000141
77. Cracco E, Goossens L, Braet C. Emotion regulation across childhood and adolescence: evidence for a maladaptive shift in adolescence. Eur Child Adolesc Psychiatry (2017) 26(8):909–21. doi: 10.1007/s00787-017-0952-8
78. Zimmermann P, Iwanski A. Emotion regulation from early adolescence to emerging adulthood and middle adulthood: Age differences, gender differences, and emotion-specific developmental variations. Int J Behav Dev (2014) 38(2):182–94. doi: 10.1177/0165025413515405
79. Brems C, Johnson ME. Problem-solving appraisal and coping style: The influence of sex-role orientation and gender. J Psychol Interdiscip Appl (1989) 123(2):187–94. doi: 10.1080/00223980.1989.10542975
80. Eschenbeck H, Kohlmann CW, Lohaus A. Gender differences in coping strategies in children and adolescents. J Individ Differ (2007) 28(1):18–26. doi: 10.1027/1614-0001.28.1.18
81. Schäfer JÖ, Naumann E, Holmes EA, Tuschen-Caffier B, Samson AC. Emotion regulation strategies in depressive and anxiety symptoms in youth: A meta-analytic review. J Youth Adolesc (2017) 46(2):261–76. doi: 10.1007/s10964-016-0585-0
82. Petersen AC, Sarigiani PA, Kennedy RE. Adolescent depression: Why more girls? J Youth Adolesc (1991) 20(2):247–71.
83. Graber JA. Internalizing problems during adolescence. In: Lerner RM, Steinberg L (Eds.), Handbook of adolescent psychology: Individual bases of adolescent development 3rd ed. John Wiley & Sons, Inc. (2009) 642–82.
84. Keyes KM, Gary D, O’Malley PM, Hamilton A, Schulenberg J. Recent increases in depressive symptoms among US adolescents: trends from 1991 to 2018. Soc Psychiatry Psychiatr Epidemiol (2019) 54(8):987–96. doi: 10.1007/s00127-019-01697-8
85. Collishaw S. Annual research review: Secular trends in child and adolescent mental health. J Child Psychol Psychiatry Allied Discip (2015) 56(3):370–93. doi: 10.1111/jcpp.12372
86. Cosgrove VE, Rhee SH, Gelhorn HL, Boeldt D, Corley RC, Ehringer MA, et al. Structure and etiology of co-occurring internalizing and externalizing disorders in adolescents. J Abnorm Child Psychol (2011) 39(1):109–23. doi: 10.1007/s10802-010-9444-8
87. Nolen-Hoeksema S, Girgus JS. The emergence of gender differences in depression during adolescence. Psychol Bull (1994) 115(3):424–43. doi: 10.1037/0033-2909.115.3.424
88. Farrington DP. Conduct disorder, aggression, and delinquency. In: Lerner RM, Steinberg L (Eds.), Handbook of adolescent psychology: Individual bases of adolescent development 3rd ed. John Wiley & Sons, Inc. (2009) 683–722.
89. Keyes KM, Gary DS, Beardslee J, Prins SJ, O’Malley PM, Rutherford C, et al. Joint effects of age, period, and cohort on conduct problems among american adolescents from 1991 through 2015. Am J Epidemiol (2018) 187(3):548–57. doi: 10.1093/aje/kwx268
90. Moss SL, Santaella-Tenorio J, Mauro PM, Keyes KM, Martins SS. Changes over time in marijuana use, deviant behavior and preference for risky behavior among US adolescents from 2002 to 2014: testing the moderating effect of gender and age. Addiction (2019) 114(4):674–86. doi: 10.1111/add.14506
91. Bányai F, Zsila Á, Király O, Maraz A, Elekes Z, Griffiths MD, et al. Problematic social media use: Results from a large-scale nationally representative adolescent sample. PloS One (2017) 12:10–4.
92. Andreassen CS, Billieux J, Griffiths MD, Kuss DJ, Demetrovics Z, Mazzoni E, et al. The relationship between addictive use of social media and video games and symptoms of psychiatric disorders: A large-scale cross-sectional study. Psychol Addict Behav (2016) 30(2):252–62. doi: 10.1037/adb0000160
93. Krause HV, Baum K, Baumann A, Krasnova H. Unifying the detrimental and beneficial effects of social network site use on self-esteem: a systematic literature review. Media Psychol (2021) 24(1):10–47. doi: 10.1080/15213269.2019.1656646
94. Orth U, Robins RW. The development of self-esteem. Curr Dir Psychol Sci (2014) 23(5):381–7. doi: 10.1177/0963721414547414
95. Kernis MH. Measuring self-esteem in context: The importance of stability of self-esteem in psychological functioning. J Pers (2005) 73(6):1569–605 . doi: 10.1111/j.1467-6494.2005.00359.x
96. Gorrese A, Ruggieri R. Peer attachment and self-esteem: A meta-analytic review. Pers Individ Dif (2013) 55(5):559–68. doi: 10.1016/j.paid.2013.04.025
97. Josephs RA, Markus HR, Tafarodi RW. Gender and self-esteem. J Pers Soc Psychol (1992) 63:391–402.
98. Mayor E. Gender roles and traits in stress and health. Front Psychol (2015) 6. doi: 10.3389/fpsyg.2015.00779
99. Li CE, DiGiuseppe R, Froh J. The roles of sex, gender, and coping in adolescent depression. Adolescence (2006) 41(163):409–15.
100. Hossain MA. Effects of uses and gratifications on social media use: The Facebook case with multiple mediator analysis. PSU Res Rev (2019) 3(1):16–28. doi: 10.1108/PRR-07-2018-0023
101. Ma CMS. Relationships between social networking sites use and self-esteem: the moderating role of gender. Int J Environ Res Public Health (2022) 19:11462.
102. Andreassen CS, Pallesen S, Griffiths MD. The relationship between addictive use of social media, narcissism, and self-esteem: Findings from a large national survey. Addict Behav (2017) 64:287–93. doi: 10.1016/j.addbeh.2016.03.006
103. Andreassen CS. Online social network site addiction: A comprehensive review. Curr Addict Rep (2015) 2:175–84.
104. Torstveit L, Sütterlin S, Lugo RG. Empathy, guilt proneness, and gender: Relative contributions to prosocial behaviour. Eur J Psychol (2016) 12. doi: 10.5964/ejop.v12i2.1097
105. Kuss DJ, Lopez-Fernandez O. Internet addiction and problematic Internet use: A systematic review of clinical research. World J Psychiatry (2016) 6(1):143–76. doi: 10.5498/wjp.v6.i1.143
106. Van Deursen AJAM, Bolle CL, Hegner SM, Kommers PAM. Modeling habitual and addictive smartphone behavior: The role of smartphone usage types, emotional intelligence, social stress, self-regulation, age, and gender. Comput Hum Behav (2015) 45:411–20.
107. Siegel JM. Body image change and adolescent depressive symptoms. J Adolesc Res (2002) 17(1):27–41. doi: 10.1177/0743558402171002
108. Vandenbosch L, Eggermont S. The interrelated roles of mass media and social media in adolescents’ Development of an objectified self-concept: A longitudinal study. Communic Res (2016) 43(8):1116–40. doi: 10.1177/0093650215600488
109. Morrison TG, Kalin R, Morrison MA. Body-image evaluation and body-image investment among adolescents: A test of sociocultural and social comparison theories. Adolescence (2004) 39(155):571–92.
110. Taylor SE, Lobel M. Social comparison activity under threat: downward evaluation and upward contacts. Psychol Rev (1989) 96(4):569–75. doi: 10.1037/0033-295X.96.4.569
111. Wills TA. Downward comparison principles in social psychology. Psychol Bull (1981) 90(2):245–71. doi: 10.1037/0033-2909.90.2.245
112. Rounsefell K, Gibson S, McLean S, Blair M, Molenaar A, Brennan L, et al. Social media, body image and food choices in healthy young adults: A mixed methods systematic review. Nutr Dietetics (2020) 77(1):19–40. doi: 10.1111/1747-0080.12581
113. Bäzner E, Brömer P, Hammelstein P, Meyer TD. Current and former depression and their relationship to the effects of social comparison processes. Results of an internet based study. J Affect Disord (2006) 93(1-3):97–103.
114. Pyszczynski T, Greenberg J, LaPrelle J. Social comparison after success and failure: Biased search for information consistent with a self-serving conclusion. J Exp Soc Psychol (1985) 21(2):195–211. doi: 10.1016/0022-1031(85)90015-0
115. Rousseau A, Eggermont S, Frison E. The reciprocal and indirect relationships between passive Facebook use, comparison on Facebook, and adolescents’ body dissatisfaction. Comput Hum Behav (2017) 73:336–44.
116. Thorisdottir IE, Sigurvinsdottir R, Asgeirsdottir BB, Allegrante JP, Sigfusdottir ID. Active and passive social media use and symptoms of anxiety and depressed mood among Icelandic adolescents. Cyberpsychol Behav Soc Netw (2019) 22(8):535–42. doi: 10.1089/cyber.2019.0079
117. Frison E, Eggermont S. Browsing, posting, and liking on instagram: the reciprocal relationships between different types of instagram use and adolescents’ Depressed mood. Cyberpsychol Behav Soc Netw (2017) 20(10):603–9. doi: 10.1089/cyber.2017.0156
Keywords: children and adolescent psychiatry, self-esteem, state emotions, photo intervention, gender differences, social media, social networking sites
Citation: Winds K, Marka T, Salcher B, Rieser N, Skrivanek C, Hochrainer M, Trost-Schrems J, Rainer LJ, Hitzl W, Augner C and Plattner B (2024) Glow up: does a professional photoshoot intervention affect self-esteem and emotions among adolescent psychiatric patients?—A longitudinal intervention study. Front. Psychiatry 15:1310252. doi: 10.3389/fpsyt.2024.1310252
Received: 09 October 2023; Accepted: 06 February 2024;
Published: 23 February 2024.
Edited by:
David Cohen, Sorbonne Universités, FranceReviewed by:
Doron Amsalem, Columbia University, United StatesPaolo Meneguzzo, University of Padua, Italy
Copyright © 2024 Winds, Marka, Salcher, Rieser, Skrivanek, Hochrainer, Trost-Schrems, Rainer, Hitzl, Augner and Plattner. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Kornelius Winds, ay53aW5kc0BzYWxrLmF0