1. Background
Depression has diverse adverse effect to people. Adverse effects range from unhealthy eating habits to death. One of the groups of individuals who are most prone to depression is the adolescents Accordingly, approximately 20% of adolescents experience depression (Bansal, Goval, and Srivastava, 2009). There are numerous published studies on the effects of depression and possible mitigation strategies, but there is still a need for studies with regards to its prevention. Note that preventing depression is better than treating it, as its adverse effects are also prevented and the patient`s well-being is of the higher assurance. This research, therefore, delves on collecting important information on the prevention of depression among adolescents. This will be done by reviewing past studies which tackle this topic. Only recent (2009 to 2013), peer reviewed journal articles will be used in this study in order to make sure that the information presented are up to date and therefore have high validity and reliability.
2. Discussion
2.1 Models of Depression, behavioral, and neurological correlates of depression
Psychologists have developed different models in order to describe how depression occurs, its effects, and cures. Some of these models include the Beck`s cognitive model of depression, Hopelessness Theory of Depression, and Response Styles Theory. All these three models are currently used as conceptual road maps for predicting the onset, severity, and reoccurrence of depression. Nevertheless, these models still lacks one essential element – they are not able to explain or consider the behavioral and neurological mechanisms that contribute or underlie primary cognitive vulnerability factors. Note that understanding the behavioral indicators as well as the biomarkers of depression will greatly improve the effectiveness of early detection and prevention of depression. Auerbach, Webb, Gardiner, & Pechtel (2013) have studied the correlates between the occurrence of depression with behavioral indicators and neurological mechanisms. Accordingly, they have identified volunteers in the study, which are within the adolescents` period. They have then subjected these samples on diagnostic interviews which aim to determine the behavioral patterns before and after they have acquired depression. They have also subjected the samples to Electron Encephalography (EEG) during resting state ( task) and during active or event related potentials (ERPs) while performing probabilistic reward tasks which probe and hedonic processes. Findings of their study showed that common behavioral indicators of depression among adolescents is the avoidance or withdrawal from or not experiencing interest or pleasure from usual recreational activities, such as sports, etc. The neurological assessment through EEG also showed that depressed patients have lesser left frontal lobe activity than those who have not experienced depression.
2.2 Predicting recurrent Major Depressive Disorder (rMDD) among adolescents
Although using neurological correlates in order to predict and monitor depression can be highly accurate, there are some easier methods – although less reliable methods – of predicting the recurrence of major depressive disorders. In a research conducted by Pettit, Hartley, Lewinsohn, Seeley and Klein (2013) they have shown how the first onset of major Depressive Disorder (MDD) is correlated with the likelihood of acquiring rMDD among adolescents. They have also shown that before an MDD occur, several minor depressions occur to individuals. With this findings, they have recommended on their study that in order to prevent the occurrence of rMDD, psychologists should always make a routine to determine the historical occurrence of past minor depressions. Accordingly, they have shown that 72.88% of adolescents who have experience a first onset of MDD are prone to develop rMDD. They have also explained on their study that hereditary factors play a significant role in the susceptibility of individuals to acquire depression during adolescents. Their research used diagnostic interviews in order to obtain the necessary information from their samples, which are all high school, adolescent students.
2.3 Preventing Chronic Depression from recurring
A similar study to that of Pettit et al (2013) was also conducted by Steidtmann, Manber, Blasey, Markowitz, Klein, Rothbaum, Thase & Kocsis (2013). In their study they have studied the relationship of the efficiency of previous intervention against depression to its reoccurrence. Accordingly, they have employed randomized clinical trials on chronic depression. They were able to obtain 352 research participants who have previously received 12 weeks of cognitive behavioral system of psychotherapy (CBASP) and antidepressant drugs. They have also employed the use of operating curves in the analysis of the data which they have obtained. These curves reflect the efficient percentage of symptom reduction cut points from the data gathered through a survey form called that Depressive Symptoms-Self-Report (IDS-SR). They have also monitored the levels of depression of the research respondents using the Hamilton Rating Scale for Depression (HRSD). They have shown in their research that patients who have undergone the cognitive behavioral treatments and religious intake of drugs are less prone by almost twice to have recurring depression.
2.4 Eating habits and weight as indicators of depression
Still another easier way of predicting depression is the careful monitoring of a person`s changes in weight and eating habit or appetite. Cole, Cho, Martin, Youngstrom, March, Finding, Compas, Goodyear, Rohde, Weissman, Essex, Hyde, Forehand, Slattery, Felton, & Maxwell. (2012) have studied the correlation between changes on adolescents` weight and eating habit or appetite. They have pointed out on their study that physiological, behavioral, and psychological processes affect weight gain or loss, and increase or decrease in appetite. They have further explained that the popular knowledge was that both weight gain and weight, as well as decrease or increase in appetite can be used as indicators for depression among adolescents. Nevertheless, they have shown on their study, that weight loss and decrease in appetite are better and are more usual indicators or depression among adolescents over increase in appetite and weight gain. They have selected a method called “Multi group, Multidimensional Item Response Theory” in order to assess over 2000 research respondents. This theory used IRT analysis, KSADS analysis, and multidimensional factor analysis.
3. Conclusion
There are numerous ways to prevent the occurrence and re-occurrence of depression among adolescents. Nevertheless, these ways or methods have their varying degrees of accuracy and applicability. Psychologists should perform multiple types or kinds of diagnosis on order to properly predict and then prevent the occurrence of depression among adolescents.
4. Bibliography
Auerbach, R.P., Webb, C.A., Gardiner, C.K. and Pechtel, P. (2013). Behavioral and Neural Mechanisms Underlying Cognitive Vulnerability Models of Depression, Journal of Psychotherapy Integration, 23(3): 222 – 235.
Bansal, V., Goval, S. and Srivastava, K. (2009). Study of prevalence of depression in adolescent students of a public school, Indian Psychiatry Journ, 18(1): 43 – 46.
Cole, D.A., Cho, S-J., Martin, N.C., Youngstrom, E.C., March, J.S., Finding, R.L., Compas, B.E., Goodyear, I.M., Rohde, P., Weissman, M., Essex, M.J., Hyde, J.S., Forehand, R., Slattery, M.J., Felton, J.W., and Maxwell, M.A. (2012). Are Increased Weight and Appetite Useful Indicators of Depression in Children and Adolescents?, Journal of Abnormal Psychology, 121(4): 838 – 851.
Pettit, J.W., Hartley, C., Lewinsohn, P.M., Seeley, J.R. and Klein, D.N. (2013). Is Liability to Recurrent Major Depressive Disorder Present Before First Episode Onset in Adolescence or Acquired After the Initial Episode?, Journal of Abnormal Psychology, 122(2): 353 – 358.
Steidtmann, D., Manber, R., Blasey, C., Markowitz, J.C., Klein, D.N., Rothbaum, B.O., Thase, M.E., and Kocsis, J.H. (2013). Detecting Critical Decision Points in Psychotherapy and Psychotherapy + Medication for Chronic Depression, Journal of Consulting and Clinical Psychology, 81(5): 783 – 790.

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