Skip to main content
PMC home page
Journal for Person-Oriented Research logoLink to Journal for Person-Oriented Research
. 2019 Dec 30;5(2):1–8. doi: 10.17505/jpor.2019.10

Redefining Therapeutic Outcomes of Depression Treatment

Christien Slofstra a,✉, Sanne H Booij b,c, H J Rogier Hoenders b, Stynke Castelein a,b,d
PMCID: PMC7842646  PMID: 33569141

Abstract

Responses to evidence-based interventions for depression are divergent: Some patients benefit more than others during treatment and some do not benefit at all or even deteriorate. Tailoring interventions to the individual may improve outcomes. However, such personalization of evidence-based treatment in depression requires investigation of individual outcomes and the individual trajectories towards these outcomes. This theoretical paper provides a critical reflection on individual outcomes of depression treatment. First, it is argued that outcomes should be broadened, from a focus on mainly depressive symptomatology to recovery in different domains. It is acknowledged that recovery from depression reflects a personal journey that differs from person to person. Second, outcome measures should be lengthened beyond the acute treatment phase, taking a lifetime perspective on depression. The challenge then is to discover which trajectories of what measures during what interventions result in personalized sustainable recovery and for whom. Routine outcome monitoring systems may be used to inform this quest towards assessment of personalized sustainable therapeutic outcomes. Adaptations to broaden and lengthen measurements in routine outcome monitoring systems are proposed to identify predictors of personalized sustainable recovery. Routine outcome monitoring systems may eventually be used to implement personalized treatments for depression that result in personalized sustainable recovery.

Keywords: Depression, Routine Outcome Monitoring, Personalized sustainable recovery, Personalized treatment, Therapeutic outcomes

“Personalized treatment of depression is one of the most important challenges for mental health researchers in the next decades” (Cuijpers et al., 2016. p. 977)

The outcomes in depression research are traditionally compared between groups. If reduction of symptomatology is larger in an intervention condition than in a control group, the intervention is deemed effective. There are various evidence-based treatments for depression, including psychotherapeutic interventions (Cuijpers, Karyotaki, Reijnders, & Ebert, 2019), pharmacological interventions

(Cipriani et al., 2018), or the combination of both (Cuijpers, De Wit, Weitz, Andersson, & Huibers, 2015). However, an intervention that improves average outcomes in a group does not necessarily result in improved outcomes for each individual patient in that group.

Results from an individual patient data meta-analysis (N = 1700; Vittengl et al., 2016) demonstrated that some patients benefit more than others, while negative outcomes (deterioration during treatment or very high depression scores after treatment) were observed in around 13% of depressed patients receiving cognitive behavioural therapy or pharmacotherapy. In a large trial investigating the treatment steps required to achieve remission (N = 3,671; STAR*D; Rush et al., 2006), 37 percent of patients achieved remission after the first phase of (pharmacological) treatment, while approximately 30 percent of patients did not achieve remission even after four consecutive phases of various types of treatment. Thus, group- based outcomes fail to represent the large heterogeneity of treatment effects in depression.

Assessment of individual outcomes of depression treatment is needed to predict which intervention works best for whom. Personalizing interventions by matching patient characteristics to potential treatments is one avenue towards improving outcomes of depression treatment. Many studies have examined clinical moderators of treatment effects, such as pharmacogenetics, subtypes of depression or clinical stages. To date, these efforts to identify subgroups of patients that respond particularly well to a certain treatment have been disappointingly unproductive (Cuijpers & Christensen, 2017; Cuijpers, Ebert, Acarturk, Andersson, & Cristea, 2016). Combining several baseline variables, such as socio-demographics, childhood adversity and comorbid symptomatology, does hold promise for development of clinical decision tools, given that many of these characteristics predict differential treatment outcomes to some extent (Kessler et al., 2017).

Outcomes assessed at the individual level are also required for another manner in which interventions may be tailored to the individual depressed patient, namely using feedback tools. Based on repeated assessments of patient reported outcome measures during treatment, feedback tools are being developed that inform patients and clinicians on the trajectories during treatment, so that interventions for patients who are “not on track” can be adjusted (Kendrick et al., 2016; Shimokawa, Lambert, & Smart, 2010). To date, however, there is a lack of understanding of trajectories that are predictive of outcomes and the effects of these feedback tools are mixed (Kendrick et al., 2016).

In sum, identifying the patient characteristics at the start of treatment and change trajectories during treatment that predict individual outcomes of depression treatment has proven to be a great challenge. Crucially, predicting individual treatment outcomes requires assessment of these outcomes per individual. In the efforts described above, the same instruments used to assess outcomes in groups – typically a measure of depressive symptomatology – have been applied to the individual patient. This method of assessing individual outcomes may have hampered the quest for personalizing interventions. In this paper, we will argue that assessment of personalized sustainable recovery is needed. Possibilities for assessing these outcomes using routine outcome monitoring systems will be discussed.

Assessment of personalized sustainable recovery

Broader than symptomatic remission

The optimal treatment outcome for depression has long been symptomatic remission, defined as the (virtual) absence of depressive symptomatology for over eight weeks (Judd et al., 2016; Keller, 2003). Accordingly, depressive symptomatology is commonly used as outcome measure in the scientific literature (e.g. Cuijpers et al., 2016). However, how symptomatic remission should be defined is currently being debated (de Zwart, Jeronimus, & De Jonge, 2018). Moreover, as others have argued (e.g., Greer, Kurian, & Trivedi, 2010; Rush, Aaronson, & Demyttenaere, 2019), recovery entails more than the absence of depressive symptomatology.

During a depressive episode, the vast majority of individuals experience severe impairments in other domains besides their depressive symptomology (Kessler et al., 2003), including work functioning (Lagerveld et al., 2010; Lerner & Henke, 2008), social functioning (Hirschfeld et al., 2000; Kupferberg, Bicks, & Hasler, 2016), and reductions in quality of life (IsHak et al., 2011). Many patients consider improvements in these other domains to be at least as important as symptomatic remission (Zimmerman et al., 2006). When asked about what entails being cured from depression, patients place high importance on enjoying life, finding meaning in life and being satisfied with themselves. When comparing what patients and clinicians define as most important aspects of recovery from depression, patients focus more on improving the quality of life and increasing positive affect, whereas clinicians place relatively more emphasis on reductions of symptomatology and improvement of functioning (Demyttenaere et al., 2015, 2016).

Although symptomatic remission in depression is associated with improvements in other domains (Zimmerman et al., 2008), it appears to be neither sufficient nor necessary for recovery in other domains. Even after symptomatic remission, patients often experience impairments in diverse forms of functioning (de Vries, Koeter, Nieuwenhuijsen, Hees, & Schene, 2015; IsHak et al., 2016; Lerner & Henke, 2008; McKnight & Kashdan, 2009; Sarfati et al., 2017; Trivedi et al., 2013) and quality of life (IsHak et al., 2011). Improvements in other domains can lag behind compared to symptomatic recovery (McKnight & Kashdan, 2009) and vice versa (Solomon et al., 2008). Therefore, therapeutic outcome measures in the treatment of depression should be broadened to include other aspects of recovery than symptomatic remission alone (Romera et al., 2014).

This broadened view of recovery in depression (Demyttenaere et al., 2015) fits well within recent efforts of mental health organisations and policy makers to shift the focus from reducing mental illness to increasing mental health. Mental health or positive health should not be seen as the absence of mental illness, but rather as the ability to adapt and self-manage, and it can occur in the presence of mental illness (Huber et al., 2011). The road to recovery in depression can be described as a complex personal journey (Richardson & Barkham, 2017) and how recovery can best be defined may be an individual matter (Demyttenaere, 2016). Individual outcomes of depression treatment may thus be viewed as both reducing mental illness and increasing mental health.

Beyond post-treatment

It is increasingly recognized that depression should be viewed in a lifetime perspective (Bockting, Hollon, Jarrett, Kuyken, & Dobson, 2015). Depression is characterized by a highly recurring course (Hardeveld, Spijker, De Graaf, Nolen, & Beekman, 2010; Richards, 2011). Studies of recurrence in individuals who have been in psychiatric care for their problems reported a recurrence rate of 25% over 1 year up to 85% over 15 years (Holma, Holma, Melartin, Rytsala, & Isometsa, 2008; Mueller et al., 1999; Solomon et al., 2000). Therefore, treatment outcomes (at the end of treatment) should not only be reflective of recovery at that moment, but also predictive of sustainable recovery (after treatment).

How to evaluate the likelihood of personalized sustainable recovery during treatment is not yet known. There are several end-of-treatment factors that contribute to the prediction of depressive relapse, indicating symptomatic recovery was not sustainable. A first important predictor is the level of residual symptoms at the end of treatment: As the number of residual symptoms increases, so does the likelihood of relapse (e.g. Judd et al., 2016; Sakurai, Suzuki, Yoshimura, Mimura, & Uchida, 2017). Including other clinical information (e.g., symptoms from other domains of psychopathology, childhood adversity and abuse, and the number of previous episodes) adds to the prediction of relapse (van Loo, Aggen, Gardner, & Kendler, 2015; Wang et al., 2014). However, many of these variables are not modifiable during treatment and are therefore unsuitable as predictors of sustainable therapeutic recovery. Moreover, even the latest clinical prediction tool, based on the most consistent findings and taking several differences between previously depressed patients into account, only modestly predicts individual relapse (Klein, Holtman, Bockting, Heymans, & Burger, 2018). In sum, overall sustainability of therapeutic outcomes of depression treatment seems limited and individual assessment of this sustainability is poor.

Taking a lifetime perspective on depression further highlights the importance of a broadened view on recovery in depression, because the importance patients place on improving positive affect increases after treatment and is higher in patients with recurring depression (Demyttenaere et al., 2015). The literature to date has almost exclusively examined symptomatic relapse after treatment; how formerly depressed individuals fare in other domains of recovery is very much unclear (Rhebergen et al., 2010). Sustainable symptomatic recovery may not reflect recovery in all domains, given that the trajectories of recovery for the different domains may be largely independent, as described above. Therefore, it is important to evaluate sustainability of therapeutic outcomes in other domains longitudinally as well.

Furthermore, the sustainability of outcomes may benefit from a broader look on outcome measures in depression (Johnson & Wood, 2017; Rottenberg, Devendorf, Kashdan, & Disabato, 2018). For example, a lower risk of symptomatic relapse was found in individuals with higher psychosocial functioning (Solomon et al., 2004) and quality of life (IsHak, Greenberg, & Cohen, 2013). Moreover, early improvements in psychosocial functioning during treatment are predictive of long-term symptomatic remission (Jha et al., 2016), demonstrating that change in different recovery domains during treatment may be indicative of sustainable therapeutic recovery. Possibly, broader recovery post- treatment contributes to sustainability of therapeutic outcomes. However, this is a hypothesis that needs to be empirically tested.

Towards prediction of personalized sustainable recovery using routine outcome monitoring systems

To summarize, outcome measures in depression need to be broadened to include recovery in different domains and lengthened to include sustainability of recovery. Furthermore, individual characteristics and change trajectories during treatment need to be identified which are predictive of personalized sustainable recovery. In other words: For whom do which trajectories of which measures result in personalized sustainable recovery, and which interventions may be used to achieve these outcomes?

Identifying predictors of personalized sustainable recovery requires large datasets including information about individual differences at baseline, the treatment (or components thereof; Cuijpers, Cristea, Karyotaki, Reijnders, & Hollon, 2019), individual trajectories during treatment, and outcomes as assessed on different domains over a longer period of time (Kessler, 2017; 2018). These datasets could be generated in clinical practice (Kessler, 2018). Here, routine outcome monitoring systems are in place, in which patient-reported outcome measures are administered repeatedly: at the start, during, and at the end of treatment. These assessments differ between health care settings, but typically include measures of symptoms, patient satisfaction, functioning, and quality of life (Delespaul, 2015).

A broadening of measurements in routine outcome monitoring systems is needed. Outcome measures may for example be broadened by including measures of quality of life or positive mental health. A critical review of the outcome measures is needed to take the depressed patient’s perspective on recovery from depression into account (Demyttenaere, 2016). Assessments may be personalized using patient-defined outcomes measures or adaptive questionnaires to combine the strengths of validated questionnaires with personalized preferences.

Experience sampling methodology (Csikszentmihalyi & Larson, 2014) may be integrated in routine outcome monitoring systems to contribute to personalizing interventions in depression (Verhagen, Hasmi, Drukker, van Os, & Delespaul, 2016). This methodology has high ecological validity and provides a unique opportunity to assess potentially highly relevant processes, such as resilience when confronted with stressors (Waugh & Koster, 2015). Furthermore, trajectories of change during treatment can be assessed using experience sampling methodology, which may differ from person to person (Slofstra et al., 2018), and may be predictive of symptomatic relapse (Verhoeven, Wardenaar, Ruhé, Conradi, & de Jonge, 2018). Indeed, experience sampling tools with integrated feedback are already being developed and evaluated (Bastiaansen et al., 2018).

Most importantly, the lengthening of routine outcome monitoring systems is essential. Current routine outcome monitoring systems are almost always limited to measurements before, during, and at the end of treatment. However, investigating whether changes during treatment are predictive of sustainable recovery requires assessments of recovery after treatment, over a longer period of time. By relating between-individual differences and within-individual trajectories during treatment to these long-term outcomes, predictors of personalized sustainable recovery can be identified. It is the combination of between-individual differences and within-individual change that may improve the prediction of long-term depression treatment outcomes (e.g. Monden, Stegeman, Conradi, de Jonge, & Wardenaar, 2016) and, ultimately, the treatment outcomes themselves (Holmes et al., 2018). As long as these have not been identified and the prediction of sustainable recovery remains poor, such long-term follow-ups allow for monitoring of the course of recovery of all patients after treatment and, if necessary, application of timely interventions.

Potential for identifying working mechanisms and tailoring interventions

The understanding of working mechanisms is crucial for tailoring interventions to individuals and optimization of therapeutic outcomes (Kazdin, 2007). To date, the working mechanisms of treatments for depression are poorly understood (Kazdin, 2016; Lemmens, Müller, Arntz, & Huibers, 2016). It has been argued before (e.g. Slofstra et al., 2017) that trajectories during treatment as assessed using experience sampling methodology may provide new insights on working mechanisms, for example by uncovering differential effects of treatments (Bakker et al., 2016). No two individuals are the same, and the same mechanisms of treatment may not hold for every depressed individual. In sum, identifying individual trajectories of change during treatment that are predictive of personalized sustainable recovery hopefully contributes to personalizing interventions by elucidating how treatments work and for whom.

It is expected that some of these working mechanisms may be transdiagnostic, such as emotion regulation (e.g. Gratz, Weiss, & Tull, 2015), mental imagery (Holmes & Mathews, 2010), repetitive negative thinking (Ehring & Watkins, 2008), and generating hope (Schrank, Stanghellini, & Slade, 2008). The proposed broader outcome measures are perfectly suited to explore such potentially transdiagnostic processes. Targeting these mechanisms with transdiagnostic treatments is another potential avenue towards personalizing treatments and improving outcomes (Craske, 2012).

Eventually, treatment selection models and feedback systems may be integrated in the routine outcome monitoring systems to inform clinicians and patients on the best course of treatment (Kendrick et al., 2016; Shimokawa et al., 2010). Routine outcome monitoring systems have been implemented in clinical practice worldwide over the last decades (Roe, Drake, & Slade, 2015) to optimize treatment, to provide data for scientific research, and to compare mental health care institutions (i.e. benchmarking) (Boswell, Kraus, Miller, & Lambert, 2015; de Beurs et al., 2011; Delespaul, 2015). Most clinicians and researchers agree that it provides valuable data for evaluating and optimizing individual treatments, although there are debates on the validity and reliability of benchmarking using routine outcome monitoring data (e.g. Hoenders et al., 2014). From a patient’s perspective, routine outcome monitoring is valued as a tool to facilitate shared decision making and regular evaluation of the goals (Groeneweg, 2019). In our opinion, routine outcome monitoring may be an ideal vehicle for moving towards assessment of personalized sustainable recovery and implementation of personalized interventions in depression (Hallgren, Bauer, & Atkins, 2017).

Conclusion

Measures of individual therapeutic outcomes in depression are needed that take into account the long-term effects of treatment on different domains of recovery. Adaptations to broaden and lengthen measurements in routine outcome monitoring systems are proposed to identify which trajectories of what measures result in sustainable recovery and for whom. Investigation is needed into which (components of) interventions result in such personalized sustainable recovery and for whom. Finally, routine outcome monitoring systems may be used to implement personalized treatments for depression based on the assessment of personalized sustainable recovery.

Acknowledgments

We gratefully acknowledge Wycher van den Bremen, who has inspired this manuscript by aiming for personalized sustainable recovery for previously depressed individuals in the Steadymood project.

Declaration of conflicting interests

The authors declare that there is no conflict of interest.

Author contribution

All authors contributed to the ideas presented in this paper. CS and SB drafted the first version of the manuscript and SC and RH were actively involved in revising the manuscript. All authors read and approved the final manuscript.

Action editor

Lars-Gunnar Lundh served as action editor for this article.

References

  1. Bakker J. M., Lieverse R., Geschwind N., Peeters F., Myin-Germeys I., & Wichers M. (2016). The two-sided face of antidepressants: The impact of their use on real-life affective change during mindfulness-based cognitive therapy. Psychotherapy and Psychosomatics , 85(3), 180-182. 10.1159/000443333 [DOI] [PubMed] [Google Scholar]
  2. Bastiaansen J. A., Meurs M., Stelwagen R., Wunderink L., Schoevers R. A., Wichers M., & Oldehinkel A. J. (2018). Self-monitoring and personalized feedback based on the experiencing sampling method as a tool to boost depression treatment: A protocol of a pragmatic randomized controlled trial (ZELF-i). BMC Psychiatry , 18(1), 276 10.1186/s12888-018-1847-z [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Bockting C. L., Hollon S. D., Jarrett R. B., Kuyken W., & Dobson K. (2015). A lifetime approach to major depressive disorder: The contributions of psychological interventions in preventing relapse and recurrence. Clinical Psychology Review , 41, 16-26. 10.1016/j.cpr.2015.02.003 [DOI] [PubMed] [Google Scholar]
  4. Boswell J. F., Kraus D. R., Miller S. D., & Lambert M. J. (2015). Implementing routine outcome monitoring in clinical practice: Benefits, challenges, and solutions. Psychotherapy Research, 25(1), 6-19. 10.1080/10503307.2013.817696 [DOI] [PubMed] [Google Scholar]
  5. Cipriani A., Furukawa T. A., Salanti G., Chaimani A., Atkinson L. Z., Ogawa Y., . . . Geddes J. R. (2018). Comparative efficacy and acceptability of 21 antidepressant drugs for the acute treatment of adults with major depressive disorder: A systematic review and network meta-analysis. The Lancet, 391(10128), 1357-1366. 10.1016/S0140-6736(17)32802-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Craske M. G. (2012). Transdiagnostic treatment for depression and anxiety. Depression and Anxiety, 29(9), 749-753. 10.1002/da.21992 [DOI] [PubMed] [Google Scholar]
  7. Csikszentmihalyi M., & Larson R. (2014). Validity and reliability of the experience-sampling method. In M. Csikszentmihalyi (Ed.), Flow and the foundations of positive psychology: The collected works of Mihaly Csikszentmihalyi (pp. 35-54). Dordrecht: Springer Netherlands; 10.1007/978-94-017-9088-8_3 [DOI] [Google Scholar]
  8. Cuijpers P., De Wit L., Weitz E., Andersson G., & Huibers M. J. (2015). The combination of psychotherapy and pharmacotherapy in the treatment of adult depression: A comprehensive meta-analysis. Journal of Evidence-Based Psychotherapies, 15(2), 147-168. [Google Scholar]
  9. Cuijpers P., & Christensen H. (2017). Are personalised treatments of adult depression finally within reach? Epidemiology and Psychiatric Sciences, 26(1), 40-42. 10.1017/S204579601600007X [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Cuijpers P., Karyotaki E., Reijnders M., & Ebert D. D. (2019). Was Eysenck right after all? A reassessment of the effects of psychotherapy for adult depression. Epidemiology and Psychiatric Sciences, 28(1), 21-30. 10.1017/S2045796018000057 [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Cuijpers P., Cristea I. A., Karyotaki E., Reijnders M., & Hollon S. D. (2019). Component studies of psychological treatments of adult depression: A systematic review and meta-analysis. Psychotherapy Research, 29(1), 15-29. 10.1080/10503307.2017.1395922 [DOI] [PubMed] [Google Scholar]
  12. Cuijpers P., Ebert D. D., Acarturk C., Andersson G., & Cristea I. A. (2016). Personalized psychotherapy for adult depression: A meta-analytic review. Behavior Therapy, 47(6), 966-980. 10.1016/j.beth.2016.04.007 [DOI] [PubMed] [Google Scholar]
  13. de Beurs E., den Hollander‐Gijsman M. E., van Rood Y. R., Van der Wee, Nic JA, Giltay E. J., van Noorden M. S., . . . Zitman F. G. (2011). Routine outcome monitoring in the netherlands: Practical experiences with a web‐based strategy for the assessment of treatment outcome in clinical practice. Clinical Psychology & Psychotherapy, 18(1), 1-12. 10.1002/cpp.696 [DOI] [PubMed] [Google Scholar]
  14. de Vries G., Koeter M. W. J., Nieuwenhuijsen K., Hees H. L., & Schene A. H. (2015). Predictors of impaired work functioning in employees with major depression in remission. Journal of Affective Disorders, 185, 180-187. 10.1016/j.jad.2015.07.013 [DOI] [PubMed] [Google Scholar]
  15. de Zwart P., Jeronimus B., & De Jonge P. (2018). Empirical evidence for definitions of episode, remission, recovery, relapse and recurrence in depression: A systematic review. Epidemio- logy and Psychiatric Sciences, October 28(5), 544-562. 10.1017/S2045796018000227 [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Delespaul P. (2015). Routine outcome measurement in the Netherlands – A focus on benchmarking. International Review of Psychiatry, 27(4), 320-328. 10.3109/09540261.2015.1045408 [DOI] [PubMed] [Google Scholar]
  17. Demyttenaere K. (2016). Taking the depressed "person" into account before moving into personalized or precision medicine. World Psychiatry: Official Journal of the World Psychiatric Association (WPA), 15(3), 236-237. 10.1002/wps.20362 [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Demyttenaere K., Donneau A., Albert A., Ansseau M., Constant E., & van Heeringen K. (2015). What is important in being cured from depression? discordance between physicians and patients (1). Journal of Affective Disorders, 174, 390-396. 10.1016/j.jad.2014.12.004 [DOI] [PubMed] [Google Scholar]
  19. Ehring T., & Watkins E. R. (2008). Repetitive negative thinking as a transdiagnostic process. International Journal of Cognitive Therapy, 1(3), 192-205. 10.1521/ijct.2008.1.3.192 [DOI] [Google Scholar]
  20. Gratz K. L., Weiss N. H., & Tull M. T. (2015). Examining emotion regulation as an outcome, mechanism, or target of psychological treatments. Current Opinion in Psychology, 3, 85-90. 10.1016/j.copsyc.2015.02.010 [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Greer T. L., Kurian B. T., & Trivedi M. H. (2010). Defining and measuring functional recovery from depression. CNS Drugs, 24(4), 267-284. 10.2165/11530230-000000000-00000 [DOI] [PubMed] [Google Scholar]
  22. Groeneweg B. (2019). The all-encompassing perspective on the mental health care patient. Wold Psychiatry, 18(3), 296-297. 10.1002/wps.20670 [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Hallgren K. A., Bauer A. M., & Atkins D. C. (2017). Digital technology and clinical decision making in depression treatment: Current findings and future opportunities. Depression and Anxiety, 34(6), 494-501. doi: 10.1002/da.22640 [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Hardeveld F., Spijker J., De Graaf R., Nolen W., & Beekman A. (2010). Prevalence and predictors of recurrence of major depressive disorder in the adult population. Acta Psychiatrica Scandinavica, 122(3), 184-191. 10.1111/j.1600-0447.2009.01519.x [DOI] [PubMed] [Google Scholar]
  25. Hirschfeld R. M. A., Montgomery S. A., Keller M. B., Kasper S., Schatzberg A. F., Möller H., . . . Bourgeois M. (2000). Social functioning in depression: A review. The Journal of Clinical Psychiatry, 61(4), 268-275. 10.4088/JCP.v61n0405 [DOI] [PubMed] [Google Scholar]
  26. Hoenders R. H. J., Bos E. H., Bartels-Velthuis A., Vollbehr N. K., van d. P., de Jonge P., & de Jong, Joop T. V. M. (2014). Pitfalls in the assessment, analysis, and interpretation of routine outcome monitoring (ROM) data: Results from an outpatient clinic for integrative mental health. Administration and Policy in Mental Health and Mental Health Services Research, 41(5), 647-659. 10.1007/s10488-013-0511-7 [DOI] [PubMed] [Google Scholar]
  27. Holma K. M., Holma I. A., Melartin T. K., Rytsälä H. J., & Isometsä E. T. (2008). Long-term outcome of major depressive disorder in psychiatric patients is variable. The Journal of Clinical Psychiatry, 69(2), 196-205. 10.4088/jcp.v69n0205 [DOI] [PubMed] [Google Scholar]
  28. Holmes E. A., Ghaderi A., Harmer C. J., Ramchandani P. G., Cuijpers P., Morrison A. P., . . . Craske M. G. (2018). The lancet psychiatry commission on psychological treatments research in tomorrow's science. The Lancet Psychiatry, 5(3), 237-286. https://doi.org/S2215-0366(17)30513-8 [DOI] [PubMed] [Google Scholar]
  29. Holmes E. A., & Mathews A. (2010). Mental imagery in emotion and emotional disorders. Clinical Psychology Review, 30(3), 349-362. 10.1016/j.cpr.2010.01.001 [DOI] [PubMed] [Google Scholar]
  30. Huber M., Knottnerus J. A., Green L., van d. H., Jadad A. R., & Kromhout D. (2011). How should we define health? BMJ, 343 10.1136/bmj.d4163 [DOI] [PubMed] [Google Scholar]
  31. IsHak W. W., Greenberg J. M., Balayan K., Kapitanski N., Jeffrey J., Fathy H., . . . Rapaport M. H. (2011). Quality of life: The ultimate outcome measure of interventions in major depressive disorder. Harvard Review of Psychiatry, 19(5), 229-239. 10.3109/10673229.2011.614099 [DOI] [PubMed] [Google Scholar]
  32. IsHak W. W., James D. M., Mirocha J., Youssef H., Tobia G., Pi S., . . . Cohen R. M. (2016). Patient-reported functioning in major depressive disorder. Therapeutic Advances in Chronic Disease, 7(3), 160-169. 10.1177/2040622316639769 [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. IsHak W. W., Greenberg J. M., & Cohen R. M. (2013). Predicting relapse in major depressive disorder using patient-reported outcomes of depressive symptom severity, functioning, and quality of life in the individual burden of illness index for depression (IBI-D). Journal of Affective Disorders, 151(1), 59-65. 10.1016/j.jad.2013.05.048 [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Jha M. K., Minhajuddin A., Greer T. L., Carmody T., Rush A. J., & Trivedi M. H. (2016). Early improvement in psychosocial function predicts longer-term symptomatic remission in depressed patients. PLOS ONE, 11(12), e0167901 10.1371/journal.pone.0167901 [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Johnson J., & Wood A. M. (2017). Integrating positive and clinical psychology: Viewing human functioning as continua from positive to negative can benefit clinical assessment, interventions and understandings of resilience. Cognitive Therapy and Research, 41(3), 335-349. 10.1007/s10608-015-9728-y [DOI] [Google Scholar]
  36. Judd L. L., Schettler P. J., Rush A. J., Coryell W. H., Fiedorowicz J. G., & Solomon D. A. (2016). A new empirical definition of major depressive episode recovery and its positive impact on future course of illness. The Journal of Clinical Psychiatry, 77(8), 1065-1073. 10.4088/JCP.15m09918 [DOI] [PubMed] [Google Scholar]
  37. Kazdin A. E. (2007). Mediators and mechanisms of change in psychotherapy research. Annual Review of Clinical Psychology, 3, 1-27. 10.1146/annurev.clinpsy.3.022806.091432 [DOI] [PubMed] [Google Scholar]
  38. Kazdin A. E. (2016). Evidence-based psychosocial treatment: Advances, surprises, and needed shifts in foci. Cognitive and Behavioral Practice, 23(4), 426-430. 10.1016/j.cbpra.2015.11.003 [DOI] [Google Scholar]
  39. Keller M. B. (2003). Past, present, and future directions for defining optimal treatment outcome in depression: Remission and beyond. JAMA, 289(23), 3152-3160. 10.1001/jama.289.23.3152 [DOI] [PubMed] [Google Scholar]
  40. Kendrick T., El‐Gohary M., Stuart B., Gilbody S., Churchill R., Aiken L., . . . de Jong K. (2016). Routine use of patient reported outcome measures (PROMs) for improving treatment of common mental health disorders in adults. Cochrane Database of Systematic Reviews, 7 10.1002/14651858.CD011119.pub2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Kessler R. C. (2018). The potential of predictive analytics to provide clinical decision support in depression treatment planning. Current Opinion in Psychiatry, 31(1), 32-39. 10.1097/YCO.0000000000000377 [DOI] [PubMed] [Google Scholar]
  42. Kessler R. C., Berglund P., Demler O., Jin R., Koretz D., Merikangas K. R., . . . Wang P. S. (2003). The epidemiology of major depressive disorder: results from the national comorbidity survey replication (NCS-R). JAMA, 289(23), 3095-3105. 10.1001/jama.289.23.3095 [DOI] [PubMed] [Google Scholar]
  43. Kessler R. C., van Loo H. M., Wardenaar K. J., Bossarte R. M., Brenner L. A., Ebert D. D., . . . Zaslavsky A. M. (2017). Using patient self-reports to study heterogeneity of treatment effects in major depressive disorder. Epidemiology and Psychiatric Sciences, 26(1), 22-36. 10.1017/S2045796016000020 [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Klein N. S., Holtman G. A., Bockting C. L. H., Heymans M. W., & Burger H. (2018). Development and validation of a clinical prediction tool to estimate the individual risk of depressive relapse or recurrence in individuals with recurrent depression. Journal of Psychiatric Research, 104, 1-7. 10.1016/j.jpsychires.2018.06.006 [DOI] [PubMed] [Google Scholar]
  45. Kupferberg A., Bicks L., & Hasler G. (2016). Social functioning in major depressive disorder. Neuroscience and Biobehavioral Reviews, 69, 313-332. 10.1016/j.neubiorev.2016.07.002 [DOI] [PubMed] [Google Scholar]
  46. Lagerveld S., Bültmann U., Franche R., Van Dijk F., Vlasveld M., Van der Feltz-Cornelis C., . . . Van Der Klink J. (2010). Factors associated with work participation and work functioning in depressed workers: A systematic review. Journal of Occupational Rehabilitation, 20(3), 275-292. 10.1007/s10926-009-9224-x [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Lemmens L. H., Müller V. N., Arntz A., & Huibers M. J. (2016). Mechanisms of change in psychotherapy for depression: An empirical update and evaluation of research aimed at identifying psychological mediators. Clinical Psychology Review, 50, 95-107. 10.1016/j.cpr.2016.09.004 [DOI] [PubMed] [Google Scholar]
  48. Lerner D., & Henke R. M. (2008). What does research tell us about depression, job performance, and work productivity? Journal of Occupational and Environmental Medicine, 50(4), 401-410. 10.1097/JOM.0b013e31816bae50 [DOI] [PubMed] [Google Scholar]
  49. McKnight P. E., & Kashdan T. B. (2009). The importance of functional impairment to mental health outcomes: A case for reassessing our goals in depression treatment research. Clinical Psychology Review, 29(3), 243-259. 10.1016/j.cpr.2009.01.005 [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Monden R., Stegeman A., Conradi H. J., de Jonge P., & Wardenaar K. J. (2016). Predicting long-term depression outcome using a three-mode principal component model for depression heterogeneity. Journal of Affective Disorders, 189, 1-9. 10.1016/j.jad.2015.09.018 [DOI] [PubMed] [Google Scholar]
  51. Mueller T. I., Leon A.C., Keller M. B., Solomon D. A., Edicott J., Coryell W., Warshaw M., & Maser J. D. (1999). Recurrence after recovery from major depressive disorder during 15 years of observational follow-up. American Journal of Psychiatry, 156, 1000-1006. 10.1176/ajp.156.7.1000 [DOI] [PubMed] [Google Scholar]
  52. Rhebergen D., Beekman A. T. F., de Graaf R., Nolen W. A., Spijker J., Hoogendijk W. J., & Penninx B. W. J. H. (2010). Trajectories of recovery of social and physical functioning in major depression, dysthymic disorder and double depression: A 3-year follow-up. Journal of Affective Disorders, 124(1-2), 148-152. 10.1016/j.jad.2009.10.029 [DOI] [PubMed] [Google Scholar]
  53. Richards D. (2011). Prevalence and clinical course of depression: A review. Clinical Psychology Review, 31(7), 1117-1125. 10.1016/j.cpr.2011.07.004 [DOI] [PubMed] [Google Scholar]
  54. Richardson K., & Barkham M. (2017). Recovery from depression: A systematic review of perceptions and associated factors. Journal of Mental Health, September 6, 1-13. 10.1080/09638237.2017.1370629 [DOI] [PubMed] [Google Scholar]
  55. Roe D., Drake R. E., & Slade M. (2015). Routine outcome monitoring: An international endeavour. International Review of Psychiatry, 27(4), 257-260. 10.3109/09540261.2015.1070552 [DOI] [PubMed] [Google Scholar]
  56. Romera I., Pérez V., Quail D., Berggren L., Lenox-Smith A., & Gilaberte I. (2014). Individual residual symptoms and functional impairment in patients with depression. Psychiatry Research, 220(1), 258-262. 10.1016/j.psychres.2014.07.042 [DOI] [PubMed] [Google Scholar]
  57. Rottenberg J., Devendorf A. R., Kashdan T. B., & Disabato D. J. (2018). The curious neglect of high functioning after psychopathology: The case of depression. Perspectives on Psycho- logical Science, 13(5), 549-566. 10.1177/1745691618769868 [DOI] [PubMed] [Google Scholar]
  58. Rush A. J., Trivedi M. H., Wisniewski S. R., Nierenberg A. A., Stewart J. W., Warden D., . . . Fava M. (2006). Acute and longer-term outcomes in depressed outpatients requiring one or several treatment steps: A STAR*D report. AJP, 163(11), 1905-1917. 10.1176/ajp.2006.163.11.1905 [DOI] [PubMed] [Google Scholar]
  59. Rush A. J., Aaronson S. T., & Demyttenaere K. (2019). Difficult-to-treat depression: A clinical and research roadmap for when remission is elusive. Aust N Z J Psychiatry, 53(2), 109-118. 10.1177/0004867418808585 [DOI] [PubMed] [Google Scholar]
  60. Sakurai H., Suzuki T., Yoshimura K., Mimura M., & Uchida H. (2017). Predicting relapse with individual residual symptoms in major depressive disorder: A reanalysis of the STAR* D data. Psychopharmacology, 234(16), 2453-2461. 10.1007/s00213-017-4634-5 [DOI] [PubMed] [Google Scholar]
  61. Sarfati D., Stewart K., Woo C., Parikh S. V., Yatham L. N., & Lam R. W. (2017). The effect of remission status on work functioning in employed patients treated for major depressive disorder. Annuals of Clinical Psychiatry, 29(1), 11-16. [PubMed] [Google Scholar]
  62. Schrank B., Stanghellini G., & Slade M. (2008). Hope in psychiatry: A review of the literature. Acta Psychiatrica Scandinavica, 118(6), 421-433. 10.1111/j.1600-0447.2008.01271.x [DOI] [PubMed] [Google Scholar]
  63. Shimokawa K., Lambert M. J., & Smart D. W. (2010). Enhancing treatment outcome of patients at risk of treatment failure: Meta- analytic and mega-analytic review of a psychotherapy quality assurance system. Journal of Consulting and Clinical Psycho- logy, 78(3), 298-311. 10.1037/a0019247 [DOI] [PubMed] [Google Scholar]
  64. Slofstra C., Nauta M. H., Bringmann L. F., Klein N. S., Albers C. J., Batalas N., . . . Bockting C. L. H. (2018). Individual negative affective trajectories can be detected during different depressive relapse prevention strategies. Psychotherapy and Psychosomatics, 87(4), 243-245. 10.1159/000489044 [DOI] [PMC free article] [PubMed] [Google Scholar]
  65. Slofstra C., Klein N. S., Nauta M. H., Wichers M., Batalas N., & Bockting C. L. H. (2017). Imagine your mood: Study design and protocol of a randomized controlled micro-trial using app-based experience sampling methodology to explore processes of change during relapse prevention interventions for recurrent depression. Contemporary clinical trials communication, 7, 172-178. 10.1016/j.conctc.2017.07.003 [DOI] [PMC free article] [PubMed] [Google Scholar]
  66. Solomon D. A., Keller M. B., Leon A. C., Mueller T. I., Lavori P. W., Shea M. T., Coryell W., Warshaw M., Turvey C., Maser J. D., & Edicott J. (2000). Multiple recurrences of major depressive disorder. American Journal of Psychiatry, 157, 229-233. 10.1176/appi.ajp.157.2.229 [DOI] [PubMed] [Google Scholar]
  67. Solomon D. A., Leon A. C., Coryell W., Mueller T. I., Posternak M., Endicott J., & Keller M. B. (2008). Predicting recovery from episodes of major depression. Journal of Affective Disorders, 107(1-3), 285-291. 10.1016/j.jad.2007.09.001 [DOI] [PMC free article] [PubMed] [Google Scholar]
  68. Solomon D. A., Leon A. C., Endicott J., Mueller T. I., Coryell W., Shea M. T., & Keller M. B. (2004). Psychosocial impairment and recurrence of major depression. Comprehensive Psychiatry, 45(6), 423-430. 10.1016/j.comppsych.2004.07.002 [DOI] [PubMed] [Google Scholar]
  69. Trivedi M. H., Morris D. W., Wisniewski S. R., Lesser I., Nierenberg A. A., Daly E., . . . Rush A. J. (2013). Increase in work productivity of depressed individuals with improvement in depressive symptom severity. American Journal of Psychiatry, 170(6), 633-641. 10.1176/appi.ajp.2012.12020250 [DOI] [PubMed] [Google Scholar]
  70. van Loo H. M., Aggen S. H., Gardner C. O., & Kendler K. S. (2015). Multiple risk factors predict recurrence of major depressive disorder in women . Journal of Affective Disorders, 180, 52-61. 10.1016/j.jad.2015.03.045 [DOI] [PMC free article] [PubMed] [Google Scholar]
  71. Verhagen S., Hasmi L., Drukker M., van Os J., & Delespaul P. (2016). Use of the experience sampling method in the context of clinical trials. Evidence-Based Mental Health, 19(3), 86-89. 10.1136/ebmental-2016-102418 [DOI] [PMC free article] [PubMed] [Google Scholar]
  72. Verhoeven F. E., Wardenaar K. J., Ruhé H. G., Conradi H. J., & de Jonge P. (2018). Seeing the signs: Using the course of residual depressive symptomatology to predict patterns of relapse and recurrence of major depressive disorder. Depression and Anxiety, 35(2), 148-159. [DOI] [PubMed] [Google Scholar]
  73. Vittengl J. R., Jarrett R. B., Weitz E., Hollon S. D., Twisk J., Cristea I., … & Cuijpers P. (2016). Divergent outcomes in cognitive-behavioral therapy and pharmacotherapy for adult depression. American Journal of Psychiatry, 173(5), 481-190. 10.1176/appi.ajp.2015.15040492 [DOI] [PMC free article] [PubMed] [Google Scholar]
  74. Wang J. L., Patten S., Sareen J., Bolton J., Schmitz N., & MacQueen G. (2014). Development and validation of a prediction algorithm for use by health professionals in prediction of recurrence of major depression. Depression and Anxiety, 31(5), 451-457. 10.1002/da.22215 [DOI] [PMC free article] [PubMed] [Google Scholar]
  75. Waugh C. E., & Koster E. H. (2015). A resilience framework for promoting stable remission from depression. Clinical Psycho- logy Review, 41, 49-60. 10.1016/j.cpr.2014.05.004 [DOI] [PubMed] [Google Scholar]
  76. Zimmerman M., McGlinchey J. B., Posternak M. A., Friedman M., Attiullah N., & Boerescu D. (2006). How should remission from depression be defined? the depressed patient's perspective. AJP, 163(1), 148-150. 10.1176/appi.ajp.163.1.148 [DOI] [PubMed] [Google Scholar]
  77. Zimmerman M., McGlinchey J. B., Posternak M. A., Friedman M., Boerescu D., & Attiullah N. (2008). Remission in depressed outpatients: More than just symptom resolution? Journal of Psychiatric Research, 42(10), 797-801. 10.1016/j.jpsychires.2007.09.004 [DOI] [PubMed] [Google Scholar]

Articles from Journal for Person-Oriented Research are provided here courtesy of Scandinavian Society for Person-Oriented Research

RESOURCES