Suicide is the second leading cause of death among American adolescents (1). Nearly one in ten adolescents report having attempted suicide at least once (2), and one in seven report experiencing suicidal ideation (3). Additionally, there is a significant worry that current statistics underestimate suicidal attempts by adolescents (3). Adolescents who have attempted suicide show ongoing risk of death by suicide in adulthood and/or future mental health problems that affect functioning throughout adulthood (2).
Risk factors for adolescent suicide commonly include: prior suicidal behavior (3-6), exposure to death by suicide within ones peer group or family (3,5,6), hopelessness (7-11), low self-esteem (12,13), social isolation (8,14-17), and depression (3,5,11,18-20). Additionally, 21 percent of teenagers with symptoms of depression experience suicidal ideation, which is over three times the rate of adolescents without any symptoms of depression (21). These results indicate that while major depressive disorder is a major risk factor for suicide, individuals experiencing either early stages of the illness or lower levels of depressive symptoms are also at risk for suicidal thoughts and behaviors.
To date, very few studies have demonstrated decreased rates of deaths by suicide or suicide attempts by adolescents following intervention. Recent meta-analyses revealed a general difficulty in treating adolescent suicide at the population level (22-25). Interestingly, studies have shown that smartphone applications can reduce suicidal ideation in adult populations (26). Such apps along with internet sites are considered behavioral intervention technologies (BITs) (27) which can be implemented at a population level to reduce risk factors for suicide (28,29). Such implementation in a structured, efficient method, such as through Internet interventions, may reduce impractical costs associated with traditional methods of providing psychological support to large groups of individuals.
Internet interventions use digital and electronic technology to address cognitions and behaviors in an effort to improve mental and physical functioning and wellness (27). Internet interventions can be scaled up such that large numbers of users are engaging with the intervention simultaneously and can be disseminated globally at an extremely low cost (30,31). Interventions provided without human contact (via a facilitator, provider, or coach) are considered unsupported (31,32).
Competent Adulthood Transition with Cognitive-behavioral and Interpersonal Training (CATCH-IT) is an internet-based and minimally supported depression prevention program (31,32) that aims to provide a selective preventative intervention for adolescents with subclinical depression. CATCH-IT has been found to effectively reduce depressive symptoms at post-intervention and one-year follow-up (33-36) and yield high parental approval for the intervention (37). Additionally, it has been found to be cost-effective, costing about one third of comparable CBT groups targeting adolescent depression per client (38).
As part of a broader effort to curtail the rise of adolescent suicide, this study aimed to examine whether an online depression prevention program, CATCH-IT, can reduce risk factors for suicide such as suicidal ideation, hopelessness, social isolation, and low self-esteem in a cost-effective, broadly accessible manner.
Data was collected as part of a previously completed study that evaluated the effect of a brief motivational interview on completion of the CATCH-IT program, by comparing two groups of adolescents: (I) Group 1 received primary care physician motivational interview + CATCH-IT program and (II) Group 2 received brief advice + CATCH-IT program (33,35,36). In the present study, all analyses included the entire sample, without being separated by condition, to determine the overall efficacy of CATCH-IT on adolescent suicidal ideation and risk factors for suicide.
Participants were recruited from 13 primary care sites across four states in the United States South and Midwest regions. Youth between the ages of 14 and 21 were screened using the core depression symptoms items on the Patient Health Questionnaire-Adolescent (PHQ-A) (39), and those who expressed depressed mood, anhedonia, and/or irritability were contacted by phone for eligibility interviews that used the full PHQ-A.
Eligibility interviews were conducted 1 to 2 weeks following initial screening. Youth were given a monetary incentive (US $75–100, depending on the number of visits necessary to determine the presence of exclusion criteria) to participate. Because the initial data sample was collected to evaluate the effect of motivational interviews on CATCH-IT completion rates, participants were excluded if they reported frequent suicidal ideation or intent for safety reasons, or if they met full criteria for a depressive disorder. Past substance use was not an exclusionary criterion, as few adolescents endorsed use in the baseline questionnaire. Individuals who met criteria for a depressive disorder were excluded from the study and referred to treatment. In total, 83 youth aged 14–21 years [mean =17.5; standard deviation (SD) =2.04] were involved in this study, of whom 56.2% were female and 41% identified as an ethnicity other than Caucasian.
Materials and measures
The CATCH-IT program consists of 14 self-guided, online modules that use techniques from cognitive-behavioral therapy (CBT) and interpersonal psychotherapy to teach skills for increasing resiliency against depressive disorders and decreasing vulnerability to depression (31,33). The program did not specifically target aspects of depression or suicidality, instead focusing on CBT and interpersonal factors related to depression (e.g., pessimism, indirect communication). In focusing on reduction of behaviors related to depression, the program sought to decrease vulnerability to and increase protection from depression (33).
At both baseline and post-intervention, participants completed a Likert-type questionnaire during phone interviews. The measure assessed symptoms of depression and anxiety, social functioning, family functioning, academic functioning, perceived coping ability, and other relevant mental health disorders (33).
At both baseline and post-intervention, participants completed the Center for Epidemiological Studies Depression Scale (CES-D) (40), a 20-item measure that assesses depression across several subscales (depressed mood, somatic, happy, and interpersonal). The CES-D has been validated on male and female adolescents with depression and demonstrated high sensitivity and specificity (41).
Risk factors for suicide variables
Risk factors for suicide included: suicidal ideation, hopelessness, low self-esteem and social isolation. These specific factors were selected due to opportunity for analysis in the sample—while others may also have been relevant in assessing risk for suicide (e.g., prior suicidal behavior), they occurred at a low rate within the sample, making analysis impossible. Risk factors for suicide were assessed by summing items at baseline and post-intervention questionnaires, along with responses to the CES-D, related to the relevant theoretical construct (i.e., suicidal ideation, hopelessness, low self-esteem, and social isolation). Items for suicidal ideation were selected with particular focus on desire to escape, in alignment with Wenzel and Beck’s (42) cognitive model of suicidal behavior, specifically cognitive processes associated with psychiatric disturbance and those associated with suicidal acts which combine to increase the likelihood of suicidal behavior (42). Hopelessness was developed using items associated with negative expectations of one’s capability to succeed in the future (7). Items used to create the sum score for low self-esteem include those focused on comparison of oneself to others and/or a sense of inadequacy or incompetence (12). Finally, items related to thwarted belongingness and disconnection to others made up the social isolation variable (43). The components of each sum score can be found in Table 1. Items were reverse coded when appropriate. Because items came from different Likert-type scales, they were standardized prior to being combined into sum score variables. Changes in risk factor variables were determined by subtracting post-intervention scores from baseline scores. Negative change scores reflected improvements in suicidal ideation and risk. Using guidelines from Gliem and Gliem (44), internal consistency of the risk factor variables was considered adequate for further analysis. See Table 2 for a complete list of Cronbach’s alpha values of the variables.
Due to the high rate of attrition among other BITs (45), previous studies using the CATCH-IT program defined intervention dosage as the time spent on the website (e.g., story or survey pages), number of modules completed, percentage of questions answered, and number of characters typed into response boxes (35,46). In the present study, intervention usage was measured with three variables—the total number of modules completed, the amount of time spent on the website, and the number of characters typed into response boxes—consistent with previous CATCH-IT studies.
Twenty-two participants (26.5%), after being recommended CATCH-IT by their primary care provider, did not complete any program modules. Twenty participants (24.1%) completed between 1 and 5 modules. Seventeen participants (20.5%) completed between 6 and 13 modules. Twenty-four participants (28.9%) completed the entire 14-module CATCH-IT program (33,46). To compare the full effect of CATCH-IT on risk factors for suicide, participants who finished all 14 modules were classified as “CATCH-IT completers” and all other participants as “CATCH-IT non-completers”.
Data was analyzed from a phase II clinical trial comparing brief advice versus motivational interviewing with CATCH-IT completion rates and related severity of depressive symptoms (33,46). Further description of the procedures and findings of the original study can be found in previous publications by the authors (33,35). The study found the program to be associated with lower scores on measures of depression following usage of CATCH-IT. In addition, those who received motivational interviewing by their primary care provider showed further improvements (33,35).
A paired samples t-test compared baseline suicidal ideation to post-intervention suicidal ideation. A multivariable regression analysis was conducted using change in suicidal ideation as the dependent variable and changes in hopelessness, low self-esteem, and social isolation as the independent variables, controlling for baseline values of risk factors for suicide, age, and gender. Lastly, a multivariable linear regression analysis was conducted using change in suicidal ideation as the dependent variable and three usage variables (number of modules completed, total time spent on the website in seconds, and number of characters typed into response boxes) as the independent variables. The effect of time was accounted for by controlling the equation for baseline values of risk factors for suicide, age, and gender. Because of the potential overlap of items in risk and usage variables, multicollinearity tolerance was calculated in each regression analysis as well.
The internal consistency of the variables of the risk factors for suicide were generally classified as acceptable, with a range between 0.67 (low self-esteem at baseline) and 0.87 (social isolation at baseline) (see Table 2).
Mean suicidal ideation across all participants decreased by 3.3% [P<0.05; d =0.22 (paired observations statistic used), small effect size]. For a summary of change in scores for each of the risk factor variables, see Table 3. Additionally, when analyzing only those who completed all 14 modules (n=24), mean suicidal ideation decreased by 8.8% (P=0.01; d =0.60, moderate effect size). The risk factor model used to analyze predictors of change in suicidal ideation was significant and explained 42.12% of the variance in suicidal ideation change. The findings demonstrated that change in suicidal ideation was associated with changes in low self-esteem (P<0.05). Interestingly, hopelessness and social isolation were not associated with suicidal ideation (see Table 4 for specific values of the regression analysis). Regarding dosage and suicidal ideation, the regression model was significant (R2 =0.11; P=0.04), but no variables in the equation had significant standardized beta weights. These findings indicated that the number of modules completed (β=0.05, P=0.82), time on the website (β=−0.30, P=0.15), and number of characters typed (β=−0.08, P=0.64) were not related to changes in suicidal ideation.
This study evaluated the potential utility of an online depression prevention program in affecting adolescent risk factors for suicide. There was a significant change in suicidal ideation in adolescents at risk for depression after using CATCH-IT. Interestingly, for those who completed the entire program the effect size was moderate, but for CATCH-IT non-completers the effect size was small, and lower than the one reported in school-based adolescent suicide prevention programs (47). While Internet interventions reach many individuals, high attrition rates present a challenge (45). Attrition levels may reduce effect sizes found in this analysis and other studies. Following further research, CATCH-IT could be implemented as a universal intervention to reach many individuals who lack mental health care, producing a small impact for a large population and producing a more significant effect for individuals who complete the entire program.
Regarding the predictors of change in suicidal ideation, the model highlighted the importance of self-esteem in addressing adolescent suicide. Higher self-esteem often predicts lower suicidal ideation (13,48,49), which supports Baumeister’s (12) theory linking low self-esteem with adolescent suicide. This theory posits that low self-esteem results in a negative perception of one’s ability to manage problems that arise, leading to a desire to escape via suicide (12). With increased self-esteem, CATCH-IT participants may have felt more capable to manage thoughts and feelings related to depression or other problems in their lives, leading to a decreased desire to escape life.
Despite its well-established role in suicidal risk, it was surprising to see that changes in hopelessness were not significantly related to changes in suicidal ideation (5,7-9,11,50,51). Because change in hopelessness was measured concurrently with change in suicidal ideation in this study, potential delayed effects of changing cognitions would not have been measured. Additionally, the mean change in hopelessness was extremely small (baseline value not significant) and therefore made it more difficult to identify a related change in suicidal ideation. Previous researchers reported significant changes in hopelessness among CATCH-IT participants (35). However, that study measured hopelessness using a single item question and did not include the major components (i.e., affective, cognitive, and motivational components) that were included in this analysis (52-55). The lack of consistency between a single-item measure of hopelessness versus our more broad measure of hopelessness highlights the need for future research using full-scale measures of hopelessness [e.g., Beck Hopelessness Scale (BHS)] (56).
Although social isolation has been identified as a risk factor for adolescent suicide in previous studies (8,57,58), change was not significantly related to changes in suicidal ideation among CATCH-IT users. As social isolation among adolescents may be influenced by family relationships (59), analysis comparing both types of relationships among CATCH-IT participants may identify further effects of the program on adolescent suicide ideation. Other related variables for decreases in suicidal ideation were not present in the current analysis. Several authors have proposed the presence of significant mental illness (e.g., mood or schizophrenia spectrum disorders) and substance use as additional factors related to adolescent suicidal ideation (4,5,60-62), which were not measured in this study. Largely absent from analysis was the effect of an adolescent’s level of depression on suicidal ideation, and it may be valuable for inclusion in future research.
While utilization of the CATCH-IT program was significant in reducing suicidal ideation among participants, the proposed usage variables (number of modules completed, total time spent on the website, and number of characters typed into response boxes) were not individually significant in contributing to the regression model while controlling for the others. Interestingly, there was a moderate effect of the program among those who completed all 14 modules, but the number of modules completed was not significantly related to change in suicidal ideation. There may be other factors related to completion affecting change in suicidal ideation, such as variance in initial severity of risk. Previous analyses of the CATCH-IT sample found a significant effect of these variables on participant depression (35), indicating that increased participation in the study can lead to greater clinical effects.
CATCH-IT’s efficacy could be better understood by analyzing the manner in which adolescents completed the intervention as intended by investigating program fidelity, which is measured by participant adherence to the CATCH-IT program and can be moderated by several factors (63). A moderating factor such as participant responsiveness to the intervention may explain the non-significance of the traditional CATCH-IT dosage measures. Additionally, there may be aspects of clinical care that are effective in reducing symptoms of depression that may not be effective with adolescent suicidal ideation (24). While CATCH-IT and other interventions may be effective in reducing depressive symptoms (46,64), renewed efforts in providing support for adolescent suicide and dismantling research when comparing depression to suicide interventions are warranted. These efforts would align with current need for the development of suicide-specific online- and computer-based interventions recognized by others (65,66). Ethical concerns have been identified by several researchers (67) and addressed in several promising intervention studies that primarily target adolescent suicidality (66).
Adolescents expressing frequent or severe suicidal ideation or intent were excluded from the original CATCH-IT study. While this decision was clinically appropriate, removing those at extreme risk of suicidal behavior produces an artificial ceiling effect for the variable of interest. Future studies may find increased significance of findings by including those deemed at heightened risk for suicide along with increased safety measures (e.g., greater use of telephonic safety check-ins). In doing so, such a study may be able to provide further evidence supporting the use of BITs in reducing risk factors for suicide in adolescents.
Main variables of interest were created by combining items from existing measures, which may not have adequately covered the intended construct. Despite adequate internal reliability, it was impossible to measure concurrent and construct validity through comparison with known measures of suicidal ideation, hopelessness, self-esteem, and social isolation. Additionally, using intent-to-treat to reduce bias in the data may have been overly conservative, especially in the case of studies with high rates of attrition (68). This secondary analysis attempted to control for confounding variables to the extent that it was possible, but several could not be controlled (e.g., academic achievement, quality of familial relationships, etc.). While change in low self-esteem was found to have a significant relationship with changes in suicidal ideation, it is not possible to ensure causality at this time. Additionally, the findings in these analyses may have been impacted by low power. With a sample size of 83, only 24 participants completed all 14 modules of CATCH-IT, meaning unusual reactions to the program could have drastically affected the results. Power analysis indicates that a sample size of 129 would be needed to detect an effect in future studies. Finally, because the data used in this study did not include a control condition, it is impossible at this time to determine interactional effects of time within the regression analysis. Further controlled research with a larger sample size will be needed to determine with more confidence the effect of CATCH-IT on suicidal ideation. Despite these limitations, this study makes a contribution to the literature by identifying a novel manner for addressing adolescent suicide which can be flexibly applied over a broad population.
The potential for widespread rollout of depression prevention programs that can help to prevent adolescent suicide exists. This study provides initial evidence for and serves as a stepping stone to the development of future studies as well as the refinement and dissemination of new online programs. The potential value of mental health online prevention programs for adolescent suicide risk should be considered by service developers and providers. Current adolescent suicide prevention programs are focused either on individual risk factors or suicide as a whole, and population suicide rates have not decreased (1,69,70). Future research with online depression prevention interventions for adolescents should include standardized suicide risk measures (e.g., Columbia-Suicide Severity Rating Scale; Posner et al., 2011) that specifically address suicidal ideation, hopelessness, self-esteem, and interpersonal well-being in its design. Follow-up studies with these measures would provide additional evidence for the value of CATCH-IT and similar interventions. Because CATCH-IT showed initial evidence for a significant decrease in suicidal ideation among adolescents receiving minimal support, further research examining the impact of variables levels of support for Internet interventions is needed.
Participants who completed CATCH-IT yielded a moderate reduction on suicidal ideation and partial CATCH-IT completers yielded a small reduction in suicidal ideation. These findings provide initial evidence that an online depression prevention programs can reduce risk factors for suicide. Furthermore, different levels of implementation could be used to approach those at risk for experiencing depression. Low-risk individuals may benefit from this online depression prevention program as a standalone intervention, and those with higher levels of risk could benefit from human support aiming to increase completion of CATCH-IT, and consequently increasing the potential effect of the intervention.
The authors would like to acknowledge both Nathan Bradford and Blake Fagan for their hard and important work in creating the dataset used for this paper.
Conflicts of Interest: The authors have no conflicts of interest to declare.
Ethical Statement: Original data collection was approved by IRB at the University of Chicago (ID: 13240B). Current analysis was approved by IRB at Palo Alto University (ID: 17-016-H). Written informed consent was obtained from all parent or legal guardians of participants by study staff.
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Cite this article as: Dickter B, Bunge EL, Brown LM, Leykin Y, Soares EE, Van Voorhees B, Marko-Holguin M, Gladstone TR. Impact of an online depression prevention intervention on suicide risk factors for adolescents and young adults. mHealth 2019;5:11.