Depression is a serious and burdensome psychiatric illness that contributes heavily to health expenditures. These costs are partly related to the observation that depression is often not limited to a single episode but can recur or follow a chronic pathway. In terms of risk factors, it is acknowledged that cognitive impairments play a crucial role in vulnerability to depression. Within this context, cognitive control training (CCT) has shown its effectiveness in reducing the risk for recurrence of depression. CCT is low cost intensive and can be provided as a web-based intervention, which makes it easy to disseminate. Despite increasing interest in the field, studies examining the cost-effectiveness of CCT in the context of depression are largely missing. Health economic evaluation (HEE) allows to inform decision makers with evidence-based insights about how to spend limited available (financial) resources in the most efficient way. HEE studies constitute a crucial step in the implementation of a new intervention in clinical practice. Approaching preventive measures for depression such as CCT from an HEE perspective is informative to health policy, fostering optimal use of health expenditures. The aim of this paper was to inform and guide researchers during the phase of designing HEE studies in the context of CCT for depression. A clear view of CCT’s cost-effectiveness is paramount for its clinical implementation.JMIR Ment Health 2023;10:e44679
Depression is a leading cause of disability worldwide, with devastating effects on individual well-being and functioning. Globally, >5% of the population is affected by depression . In terms of 12-month and lifetime prevalence of major depressive disorder (MDD), a recent American study reported 10.4% and 20.6% prevalence rates, respectively [ ].
Available and effective psychological treatments for depression are expensive and typically require extensive expertise, resulting in only a small portion of individuals with depression being able to profit from those. Despite significant investments in pharmacological and psychological interventions in the past decades, low-income and middle-income countries still have limited accessibility to treatment, resulting in 75% of people with mental health disorders worldwide not receiving adequate care .
The severity of depression, combined with the low accessibility of treatments, leads to important personal and societal costs [, ]. In 2006, Kessler et al [ ] estimated, among a sample of US workers, that a single episode of MDD was associated with an average of >5 weeks of lost productivity per worker, resulting in an annual capital loss of >US $36 billion to employers. As such, there is an unmet need for widely accessible and cost-effective treatments for depression. Recently, the use of various technologies to deliver treatments has shown potential, especially in its ability to reach a large proportion of individuals who would otherwise not have access to psychological interventions [ , ]. One such intervention that shows substantial promise is cognitive control training (CCT) [ , ].
This paper discusses the current state of the art regarding the role of cognitive control impairments as a risk factor for depression and, consequently, the relevance of CCT in the prevention of depression. Building on recent meta-analyses suggesting a need for high-quality and adequately powered health economic evaluation (HEE) studies , we argue that adopting a health economic perspective is paramount to comprehensively evaluate the dissemination potential of CCT for depression in an evidence-based way. For this purpose, we have provided theoretical and practical guidelines about how to design HEE studies that allow to address this specific question.
Cognitive Control Deficits as a Depressive Risk Factor
Identifying risk factors associated with recurrent depression has been the focus of extensive studies in the past decades, in which cognitive processes have shown to play an important role. Specifically, cognitive control—referring to the ability to flexibly adapt thoughts and behavior to achieve one’s goals —has been identified as an important cognitive risk factor in the context of depression [ ]. That is, cognitive control deficits have been observed in individuals with MDD [ ] and often persist following remission [ ], placing them at risk for the recurrence of depression [ ].
Individuals with remitted depression show, among other things, working memory impairments, diminished processing speed and attention, and executive functioning impairments. These deficits have major consequences on an individual’s daily functioning and provide an important source of disability . One of the main causes of costs associated with depression is loss of productivity owing to multiple or prolonged periods of sick leave or unemployment, where cognitive complaints interfere with occupational activities [ ].
Previous studies suggest a central role for rumination in linking cognitive control deficits to risk for recurrent depression. Rumination can be defined as a recurrent process of perseverative negative thinking about feelings, problems, or disturbing experiences [, ]. Elevated rumination, a key risk factor for depression, is associated with poor rates of recovery following pharmacological treatment for depression and slow response to psychotherapy [ , ]. In this context, it has been suggested that cognitive control is essential to be able to disengage from negative self-referential thoughts upon confrontation with stressors [ ] or to inhibit habitual ruminative responses [ ], where failure to do so may result in prolonged exposure to negative thoughts and affect. Consistent with this hypothesis, previous studies suggest that cognitive control deficits predict future increases in depressive symptomatology in patients with remitted depression, a relationship that appears to be mediated by rumination [ ].
Although current pharmacological or psychological treatments have proven to be effective in decreasing depressive symptomatology, existing interventions typically do not target cognitive impairments directly. Moreover, there is a problem of recurrence of depression after initial treatment, where perturbations at the level of cognitive functioning show a positive association with the number of previous depressive episodes . As such, it has been suggested that cognitive control deficits reflect increased vulnerability to recurrent depression [ ]. Together, these findings suggest the need for innovative interventions aimed at reducing these specific risk factors.
CCT for Depression
A particularly promising approach is CCT—a computerized psychological intervention during which operations and mental exercises aimed at improving cognitive control are performed . Within this context, cognitive training has shown to be a useful intervention to reduce vulnerability to depression [ - ].
Among the multiple CCT tasks used within the context of depression, the effects of the adaptive paced auditory serial addition task (aPASAT ; for a review, please see the paper by Koster et al [ ]) have been the most intensively studied. During this computer task, participants are presented with digits in a sequential fashion. They are asked to provide the sum of the 2 last heard digits. The aPASAT tailors the exercise based on individual performance by modifying the intertrial interval following every 4 consecutive correct (−100 milliseconds) or incorrect (+100 milliseconds) responses. Within the context of this task, individuals are continuously required to update information in working memory while preventing interference from previous responses.
Several studies suggest the beneficial effects of aPASAT training on depressive symptomatology. For instance, Siegle et al  investigated the impact of CCT on the neurobiological and cognitive mechanisms underlying depression in a sample of patients with clinical depression (N=31). In the first pilot study, the authors combined treatment as usual (TAU) with 6 sessions of aPASAT training and an attention training session during a period of 2 weeks. They observed beneficial effects of CCT on rumination and level of depressive symptomatology, in comparison with the TAU control group. In an extended sample (N=43), Siegle et al [ ] replicated the previous positive effects of CCT on rumination among patients with MDD and observed a decreased need for clinical care at 1-year follow-up, providing the first evidence for the long-term benefits of CCT. The clinical potential of cognitive training has been confirmed in other studies reporting the beneficial effects of CCT in patients with MDD (eg, the studies by Brunoni et al [ ], Iacoviello et al [ ], Morimoto et al [ ], and Sommer and Plewnia [ ], but see the studies by Ferrari [ ] and Moshier and Otto [ ]).
Importantly, the beneficial effects of aPASAT training on depressive symptomatology were also observed in individuals at risk for (recurrence of) depression. For instance, in a sample of 68 individuals with remitted depression, Hoorelbeke and Koster  observed beneficial effects of 10 sessions of aPASAT training on rumination and depressive symptomatology compared with an active control condition. Similarly, in a recent randomized controlled trial (N=92 individuals with remitted depression) in which CCT was combined with an intensive experience sampling procedure, Hoorelbeke et al [ ] observed beneficial effects of CCT on the deployment of rumination in daily life. Moreover, compared to an active control condition, CCT reduced the risk for recurrence of depression in individuals with remitted depression, as shown by lower recurrence rates in a period of one year following training [ ].
CCT has shown interesting preventive effects in the context of depression. Consistent with this finding, a recent meta-analysis suggested small to medium effects of aPASAT training on depressive symptomatology and rumination, both immediately after training and at follow-up . Interestingly, the authors explored the clinical relevance of their results by converting the pooled effect sizes into number needed to treat (NNT; ie, the number of patients who would need to be treated by an intervention to observe improvement in 1 patient). The results suggest that a similar number of patients should be treated with aPASAT training (NNT=6.15) to approximate the effects of monotherapeutic pharmacological (NNT=5.95) or psychotherapeutic interventions (NNT=7.13 [ ]). Despite these encouraging findings, several challenges remain, such as identifying the precise mechanisms of transfer and the optimal parameters of administration (eg, training dosage and use of therapist-supported vs unguided delivery), which have mostly been left unexplored so far [ ] (but see the study by Launder et al [ ]).
Taken together, CCT is a highly promising preventive intervention for recurrent depression. Given that recent findings suggest that CCT may reduce the risk for recurrence of depression , CCT is also likely to influence related factors, including work absenteeism and costs associated with depression. By targeting underlying vulnerability factors that are currently not targeted by traditional interventions for depression, CCT has multiple characteristics that make it an interesting choice from a clinical perspective: (1) it could be relatively low cost intensive for patients; (2) it is highly accessible as it can be provided as a web-based intervention; (3) it is easily disseminated [ ]; (4) it is a relatively inexpensive intervention, especially when provided on a large scale; and (5) compared with pharmacological treatments, limited side effects have been reported for CCT [ ].
Introducing a Health Economic Perspective
In recent decades, the importance of investing in health has been widely emphasized, resulting in a strong emphasis on the efficient use of budgets (eg, encouraging investments in preventive efforts) . Therefore, policy makers have been strongly encouraged to opt for the most efficient interventions to ensure population health [ ]. On the basis of effectiveness studies, digital health interventions seem to hold great promise as preventive interventions for depression [ , - ]. In particular, CCT is a potentially scalable preventive intervention for recurrent depression that would suit web-based dissemination [ , ]. However, it is clear that a sole focus on the efficacy and effectiveness of CCT is insufficient for policy making. This would require HEE studies, examining the value for money of CCT compared with alternative strategies, aiming to inform different stakeholders (eg, decision makers) with evidence-based insights [ ].
Health economics and HEE can be defined as “the study of how scarce resources are allocated among alternative uses for the care of sickness and the promotion, maintenance and improvement of health, including the study of how healthcare and health-related services, their costs and benefits, and health itself are distributed among individuals and groups in society” . [ , - ] provides an overview of the key health economic terms relevant to this paper.
Although multiple studies have explored the cost-effectiveness of other digital health interventions for depression [- ] (for a recent meta-analysis, refer to the paper by Rohrbach et al [ ]), the cost-effectiveness of CCT remains to be evaluated. If we aim to provide clear information to policy makers and governments, costs and health outcomes of CCT should be explored, compared with the available alternatives. As such, HEE forms a crucial step for the implementation of CCT.
To ensure that HEEs are methodologically correct, identifiable, interpretable, and useful for decision-making, several elements need to be considered, accounted for, and reported. The aim of this paper was to introduce HEE in the context of CCT and to discuss key considerations when planning an HEE. A focus will be placed on key methodological concepts when preparing an HEE of CCT for depression. This may guide researchers during the design phase of future studies.[ - ] provides an overview of these concepts and their translation to the context of CCT.
Evaluating the Cost-Effectiveness of CCT
HEE focuses on the comparative analysis of ≥2 alternative courses (intervention vs comparator) of action in terms of both their costs and effects. In the context of CCT, CCT can be considered as the intervention, whereas the comparator may be a waiting list condition, TAU, or a condition in which participants complete an alternative training procedure, with or without TAU. Overall, 4 main types of full HEE exist: cost-minimization analysis, cost-benefit analysis, cost-effectiveness analysis, and cost-utility analysis (CUA; for the definitions of these constructs, refer to[ ]). As one of the most common HEEs used, CUA allows for health-related quality of life (HRQoL) adjustments to a given set of treatment outcomes, while simultaneously providing a generic outcome measure for comparison of costs and effects between different intervention strategies. Therefore, this paper specifically focused on this type of economic evaluation.
HEE—Health Effects and Costs
As previously mentioned, in an HEE, both costs and effects are considered. Main cost categories include direct and indirect costs. Direct costs are those directly associated with the disease or condition (in this case, depression) that is considered. They can be divided into direct medical costs (eg, psychologist visit and hospitalization) and direct nonmedical costs (eg, transportation costs). Indirect costs include those associated with productivity losses owing to the disease or condition that is considered ([ ]).
The types of costs that are considered in an HEE depend on the perspective of the analysis. Every time an economic question is asked, it is crucial to carefully consider the analytic viewpoint. Commonly used perspectives include (1) patient perspective (only considering the costs for the patient), (2) health insurance perspective (considering the costs for the health insurer; ie, direct medical costs), (3) payer perspective (ie, the patient and the health insurer), and (4) societal perspective (considering direct costs and indirect costs). Importantly, decision makers must be informed about the viewpoint that has been taken in the HEE.
The effects of the intervention may be measured in natural units (eg, avoided recurrent episode of depression, avoided hospitalization, avoided complications, and avoided psychotherapy sessions) or in utilities. In a CUA, the effects are typically expressed in terms of quality-adjusted life years (QALYs;). QALYs are a standardized measure reflecting the extent to which interventions improve HRQoL [ ]. QALYs are calculated by multiplying a utility for a given condition (HRQoL weight) by the time an individual experiences the condition. These utilities are often derived from generic questionnaires such as the Short Form Health Survey (SF-36) or EuroQol–5 Dimension. The SF-36 is a 36-item patient-reported survey allowing to measure health status [ , ]. Similarly, the EuroQol–5 Dimension is a widely used measure of HRQoL, allowing to estimate utilities to calculate QALYs [ ].
QALYs are a recognized metric for evaluating treatments, allowing the comparison of intervention alternatives for different disorders. However, at the same time, the suitability of QALYs to assess changes in mental health has been questioned [, ]. That is, measurement tools used to estimate QALYs typically contain a limited focus on (specific aspects of) mental health [ ]. Therefore, measures such as the SF-36 have been criticized in terms of the extent to which they allow to capture the heterogeneous nature of depression. As depression is a complex construct to capture [ ], the key challenges to obtaining QALYs include describing this state and valuing it (eg, in terms of the morbidity and quality of life associated with living in this health state) [ ]. In this context, authors have highlighted the need for new conceptualizations of the QALYs, with the goal of basing it on a valid and comprehensive model of quality of life specific to mental illness. This would require an instrument that is more sensitive to detect expected changes in this context [ , ].
To overcome these challenges and increase efficiency in measurement, it has been suggested to combine QALYs with other outcomes. As such, we would advise the reader to consider different kinds of measurements, allowing a multimethod evaluation of cost-effectiveness (refer tofor an overview of relevant outcome measures in the context of CCT and how these can be operationalized). In this context, patient-reported outcomes (PRO) and PRO measures (PROMs) are increasingly used. PROs contain information from patients about their quality of life, their own health, and the functional status related to their treatment or the health care they receive. PROMs are the instruments or tools that are used to measure PROs [ ]. Considering both QALYs and PROMs in HEE could facilitate decision-making, improve the quality of health care, and stimulate improvements in services, essentially by enabling the comparison of providers’ performances [ , ].
The outcome of a CUA is usually expressed as the incremental cost-utility ratio (ICUR;). This is calculated by dividing the difference in costs (incremental costs) between the intervention and the comparator by the difference in their effects (incremental effects). The ICUR results in the following outcome: € per QALY. An ICUR as such does not allow to draw conclusions about whether an intervention can be considered as cost-effective.
To determine the cost-effectiveness of an intervention, a reference value above which the intervention is considered as not cost-effective can be used. Reference values can be based on a fixed amount threshold or calculated based on gross domestic product (GDP) per capita. This is the case in most low-income and middle-income countries. The World Health Organization promotes a threshold defined as 1 to 3 times the national annual GDP per capita as a guide for determining the cost-effectiveness of health interventions. We refer interested readers to the papers by Kazibwe et al  and Woods et al [ ] for a more detailed discussion about this topic.
The reference values used typically differ among countries. For example, in France, no explicit threshold is applied . This is similar to Belgium [ ], but a threshold of 1 time the GDP per capita, representing a value of €35,000 (US $38,080) to €40,000 (US $43,520) per QALY, is often used in HEEs [ - ]. In the Netherlands, the threshold is up to €80,000 (US $87,039) per QALY [ ], whereas in Spain, this is typically €30,000 (US $32,640) per QALY [ ]. The National Institute for Health and Care Excellence considers a threshold ranging between £20,000 (US $25,488) and £30,000 (US $38,232) for England and Wales [ ]. In the United States, a threshold between US $50,000 and US $100,000 is still often used by researchers, insurers, and public and private policy makers, but there is a debate about whether the threshold should be adjusted to inflation [ , ]. In India, researchers often use the World Health Organization per capita GDP as a cost-effectiveness threshold [ ].
HEE—Sources When Thinking About an HEE
Guidelines may be useful for researchers when planning an HEE. Well-known reporting guidelines include the Consolidated Health Economic Evaluation Reporting Standards statement that was recently updated, providing resources reflecting the most recent developments in HEE methods . This checklist includes 28 items and is primarily intended to guide researchers when reporting economic evaluations for peer-reviewed journals and peer reviewers and editors assessing these manuscripts for publication. Taking such guidelines into account in the phase of planning an HEE study is likely to add to the quality of the design.
Another checklist that has been widely used in this context is the Consensus Health Economic Criteria checklist. It has been designed for assessment of the methodological quality of economic evaluations in systematic reviews . These contain items that need to be included as good practice when reporting results of an HEE. Many countries have also developed specific national guidelines. For instance, Sharma et al [ ] identified 31 national HEE guidelines, published between 1997 and August 2020.
In addition to the guidelines mentioned previously, published HEEs within the field of CCT and depression prevention also provide valuable sources of information. We advise the reader to search appropriate literature for what is already known regarding the cost-effectiveness of a given intervention, preferentially by referring to peer-reviewed and evidence-based sources. To the best of our knowledge, there are currently no studies examining the cost-effectiveness of CCT for depression. Although not specific for CCT, a recent meta-analysis suggests that digital health interventions are slightly more cost-effective than usual care . Consistent with previous studies [ , , ], guided digital health interventions outperformed unguided interventions [ ]. A factor that may have contributed to this is that most unguided digital health interventions typically do not compensate for the lack of therapist support by including additional content or technological features (eg, to support adherence) [ ]. In this context, it should be noted that studies exploring the effects of CCT on indicators of vulnerability to depression strongly differ in the extent to which they have been delivered as a therapist-assisted or unguided web-based intervention. This, in addition to other factors related to the delivery mode of CCT [ ], remains as a parameter that requires further investigation and should be taken into account when considering HEE of CCT.
Overall, the current paucity of research on HEE of CCT for depression suggests the need for future CCT studies to include a focus on HEE. To advance such research in the context of interventions for depression, standardization of studies in terms of measurement and analytic approaches is paramount . For this purpose, provides an overview of methodological questions to consider when setting up such studies. In addition, for an overview of the optimal procedures to perform HEE (ie, data analysis), including step-by-step guidelines about how to interpret the results of such HEE, we refer to Briggs et al [ ].
Several challenges related to performing HEE should also be considered. For instance, HEE outcomes will always be context specific and country specific (eg, an intervention that is cost-effective in Belgium might not be cost-effective in Portugal). In addition, findings may also differ depending on the perspective taken. For instance, a recent meta-analysis found the cost-effectiveness of digital health interventions to be moderated by the economic perspective used . Studies using a societal perspective yielded a relatively lower incremental net monetary benefit ( ) than studies adopting a health care perspective, suggesting that the cost-effectiveness of internet interventions compared with control conditions cannot be assumed when maintaining a societal perspective [ ].
Depending on the economic perspective taken, additional resources may be available. For instance, Powell and Torous  recently proposed a patient-centered method for estimating the economic value of clinical improvement following the use of digital health interventions. In particular, the authors suggest that an estimate of the economic value of clinical response to digital health interventions can be obtained by multiplying the country-specific willingness-to-pay (WTP) threshold per QALY, impact of the mental health condition in terms of QALYs, engagement level of digital health intervention users, effect size of the intervention (eg, in terms of percentage of improvement in symptoms), and duration of the impact of the intervention.
Extending the nation-specific example provided by Powell and Torous  to the context of CCT for depression, the economic value of aPASAT training for depression is estimated at US $555.61 per individual treated in the United States. That is, following Powell and Torous [ ], for the United States, we rely on WTP threshold values of US $175,000 per QALY and 0.159 QALYs lost per year of depression. We combine these estimates with data obtained from a recent study during which aPASAT training was used as a web-based digital health intervention targeting repetitive negative thinking [ ]. A total of 382 participants completed the baseline assessment of this study, of which 152 reported a level of depressive symptoms of at least moderate severity (≥14) based on the Depression Anxiety and Stress Scales [ ]. For this example, we consider these scores as potentially indicative for the presence of a depressive episode. Overall, 97 (64%) of the participants with elevated depressive symptoms completed the web-based intervention. Of the latter group, posttraining and follow-up data were available for 90 individuals, suggesting a median reduction in level of depressive symptoms of 33% and 39% immediately following the intervention and at 1 month follow-up, respectively. Using these parameters, we estimate the economic value of aPASAT training for depression as follows: US $175,000 per QALY × 0.159 QALYs lost per year of depression × 64% completing the intervention × 39% reduction in symptoms × 0.08 years of improvement (at least until 1 month follow-up)=US $555.61. This value could be compared with values obtained for other interventions and costs related to the use of the intervention (eg, subscription fees to obtain access to the digital health intervention) or be used to investigate the impact of country effects and context effects (eg, WTP threshold used).
Although informative to the patient, limitations of such methods include the imprecise nature of the estimation, owing to the input parameters used. For instance, in the example provided above, adherence and response rates were based on 1 study that relied on a convenience sample, in which evaluation of effects was limited to 1 month follow-up . It is possible that the context in which CCT was provided offers an overestimation of adherence rates and, related to this, economical gains. Vice versa, previous research suggests that effects of CCT likely extend beyond the duration included in this example [ ]. Other important limitations of this approach include the exclusive focus on the patient perspective and the lack of consideration of indirect benefits of the intervention (eg, increased productivity in a professional context). Such information can only be obtained from HEE studies designed to address the question of cost-effectiveness.
In this context, it is recommended for clinical trials in this area to systematically adopt measures that allow HEE based on input from various stakeholders. As CCT can be relatively inexpensive on a sufficiently large scale, we recommend researchers to cooperate with all the relevant stakeholders including patients, clinicians, health insurance companies, and policy makers. In addition, health economic researchers need to educate stakeholders about the relevance of cost-effectiveness analysis in policy and practice, how such analyses are performed, what uncertainties can be involved, and how the results should be interpreted. In addition to accurate communication with stakeholders, a clear view about CCT’s cost-effectiveness is paramount to its clinical implementation.
HEE—Dealing With Uncertainty
It is worth noting that HEEs are frequently characterized by a certain degree of uncertainty or methodological considerations related to input parameters (eg, utilities and costs). This uncertainty can be addressed by performing 1-way sensitivity analyses and probabilistic sensitivity analyses (for more information, refer to Drummond et al ).
As noted previously, HEE serves evidence-based decision-making processes in health care. As such, we recommend that researchers include an HEE in their studies as a crucial step toward implementation. More precisely, information about the cost-effectiveness of CCT for depression will provide an extra piece of evidence that can be considered as being highly relevant for different stakeholders (eg, researchers, clinicians, and policy makers). However, the accuracy of such evidence is highly dependent on the quality of HEE methodological approaches used. This requires thorough reflection about methodological choices when planning future studies, which may be guided by the tools provided previously.
Previous studies suggest the potential of CCT to be used as a scalable and effective preventive intervention for recurrent depression. Although previous studies have mainly focused on the effects of CCT in terms of cognitive and emotional transfer, so far, little attention has been given to the cost-benefit ratio in this context. As such, there is a strong need for investigation of the cost-effectiveness of CCT. This paper provided guidelines about how future studies can address this gap in the literature as a means to influence health care policy, paving the way toward clinical implementation of CCT.
The authors would like to thank Ruben Willems, PhD, for providing feedback about the manuscript. This study was supported by an Applied Biomedical (Toegepast Biomedisch onderzoek met een primair Maatschappelijke finaliteit [TBM]) grant from the Research Foundation–Flanders (Fonds Wetenschappelijk Onderzoek Vlaanderen [FWO]), awarded to the Prevention of Depression (PrevenD) project 2.0 (T000720N). KH is funded by FWO (12J9722N).
Conflicts of Interest
Key health economic constructs and definitions of basic concepts.PDF File (Adobe PDF File), 147 KB
Key considerations when designing health economic studies in the context of cognitive control training for depression.PDF File (Adobe PDF File), 185 KB
- Depressive disorder (depression). World Health Organization. 2023 Mar. URL: https://www.who.int/news-room/fact-sheets/detail/depression [accessed 2023-05-08]
- Hasin DS, Sarvet AL, Meyers JL, Saha TD, Ruan WJ, Stohl M, et al. Epidemiology of adult DSM-5 major depressive disorder and its specifiers in the United States. JAMA Psychiatry 2018 Apr 01;75(4):336-346 [https://europepmc.org/abstract/MED/29450462] [CrossRef] [Medline]
- Kazdin AE, Blase SL. Rebooting psychotherapy research and practice to reduce the burden of mental illness. Perspect Psychol Sci 2011 Jan;6(1):21-37 [CrossRef] [Medline]
- Kessler RC. The costs of depression. Psychiatr Clin North Am 2012 Mar;35(1):1-14 [https://europepmc.org/abstract/MED/22370487] [CrossRef] [Medline]
- Kessler RC, Akiskal HS, Ames M, Birnbaum H, Greenberg P, Hirschfeld RM, et al. Prevalence and effects of mood disorders on work performance in a nationally representative sample of U.S. workers. Am J Psychiatry 2006 Sep;163(9):1561-1568 [https://europepmc.org/abstract/MED/16946181] [CrossRef] [Medline]
- Kim J, Aryee LM, Bang H, Prajogo S, Choi YK, Hoch JS, et al. Effectiveness of digital mental health tools to reduce depressive and anxiety symptoms in low- and middle-income countries: systematic review and meta-analysis. JMIR Ment Health 2023 Mar 20;10:e43066 [https://mental.jmir.org/2023//e43066/] [CrossRef] [Medline]
- Koster EH, Hoorelbeke K, Onraedt T, Owens M, Derakshan N. Cognitive control interventions for depression: a systematic review of findings from training studies. Clin Psychol Rev 2017 Apr;53:79-92 [CrossRef] [Medline]
- Siegle GJ, Ghinassi F, Thase ME. Neurobehavioral therapies in the 21st century: summary of an emerging field and an extended example of cognitive control training for depression. Cognit Ther Res 2007 Mar 23;31(2):235-262 [https://link.springer.com/article/10.1007/s10608-006-9118-6] [CrossRef]
- Rohrbach PJ, Dingemans AE, Evers C, Van Furth EF, Spinhoven P, Aardoom JJ, et al. Cost-effectiveness of internet interventions compared with treatment as usual for people with mental disorders: systematic review and meta-analysis of randomized controlled trials. J Med Internet Res 2023 Jan 05;25:e38204 [https://www.jmir.org/2023//e38204/] [CrossRef] [Medline]
- Cohen JD. Cognitive control: core constructs and current considerations. In: Egner T, editor. The Wiley Handbook of Cognitive Control. Chichester, UK: John Wiley & Sons; 2017:1-28
- Joormann J, Stanton CH. Examining emotion regulation in depression: a review and future directions. Behav Res Ther 2016 Nov;86:35-49 [CrossRef] [Medline]
- Levens SM, Gotlib IH. Updating positive and negative stimuli in working memory in depression. J Exp Psychol Gen 2010 Nov;139(4):654-664 [https://europepmc.org/abstract/MED/21038984] [CrossRef] [Medline]
- Levens SM, Gotlib IH. Updating emotional content in recovered depressed individuals: evaluating deficits in emotion processing following a depressive episode. J Behav Ther Exp Psychiatry 2015 Sep;48:156-163 [https://europepmc.org/abstract/MED/25889375] [CrossRef] [Medline]
- De Raedt R, Koster EH. Understanding vulnerability for depression from a cognitive neuroscience perspective: a reappraisal of attentional factors and a new conceptual framework. Cogn Affect Behav Neurosci 2010 Mar;10(1):50-70 [CrossRef] [Medline]
- Knight MJ, Air T, Baune BT. The role of cognitive impairment in psychosocial functioning in remitted depression. J Affect Disord 2018 Aug 01;235:129-134 [CrossRef] [Medline]
- Clark M, DiBenedetti D, Perez V. Cognitive dysfunction and work productivity in major depressive disorder. Expert Rev Pharmacoecon Outcomes Res 2016 Aug;16(4):455-463 [CrossRef] [Medline]
- Nolen-Hoeksema S, Wisco BE, Lyubomirsky S. Rethinking rumination. Perspect Psychol Sci 2008 Sep;3(5):400-424 [CrossRef] [Medline]
- Watkins ER. Constructive and unconstructive repetitive thought. Psychol Bull 2008 Mar;134(2):163-206 [https://europepmc.org/abstract/MED/18298268] [CrossRef] [Medline]
- Watkins E. Psychological treatment of depressive rumination. Curr Opin Psychol 2015 Aug;4:32-36 [https://www.sciencedirect.com/science/article/abs/pii/S2352250X15000901] [CrossRef]
- Jones NP, Siegle GJ, Thase ME. Effects of rumination and initial severity on remission to cognitive therapy for depression. Cognit Ther Res 2008 Aug 01;32(4):591-604 [https://europepmc.org/abstract/MED/24353355] [CrossRef] [Medline]
- Koster EH, De Lissnyder E, Derakshan N, De Raedt R. Understanding depressive rumination from a cognitive science perspective: the impaired disengagement hypothesis. Clin Psychol Rev 2011 Feb;31(1):138-145 [CrossRef] [Medline]
- Watkins ER, Roberts H. Reflecting on rumination: consequences, causes, mechanisms and treatment of rumination. Behav Res Ther 2020 Apr;127:103573 [CrossRef] [Medline]
- Demeyer I, De Lissnyder E, Koster EH, De Raedt R. Rumination mediates the relationship between impaired cognitive control for emotional information and depressive symptoms: a prospective study in remitted depressed adults. Behav Res Ther 2012 May;50(5):292-297 [https://core.ac.uk/reader/186985150?utm_source=linkout] [CrossRef] [Medline]
- Vanderhasselt MA, De Raedt R. Impairments in cognitive control persist during remission from depression and are related to the number of past episodes: an event related potentials study. Biol Psychol 2009 Jul;81(3):169-176 [CrossRef] [Medline]
- Launder N, Minkov R, Davey C, Finke C, Malmberg GH, Lampit A. Computerized cognitive training in people with depression: a systematic review and meta-analysis of randomized clinical trials. medRxiv. Preprint posted online March 26, 2021 [https://www.medrxiv.org/content/10.1101/2021.03.23.21254003v1] [CrossRef]
- Motter JN, Pimontel MA, Rindskopf D, Devanand DP, Doraiswamy PM, Sneed JR. Computerized cognitive training and functional recovery in major depressive disorder: a meta-analysis. J Affect Disord 2016 Jan 01;189:184-191 [CrossRef] [Medline]
- Legemaat AM, Semkovska M, Brouwer M, Geurtsen GJ, Burger H, Denys D, et al. Effectiveness of cognitive remediation in depression: a meta-analysis. Psychol Med 2021 Apr 14;52(16):1-16 [https://europepmc.org/abstract/MED/33849674] [CrossRef] [Medline]
- Siegle GJ, Price RB, Jones NP, Ghinassi F, Painter T, Thase ME. You gotta work at it: pupillary indices of task focus are prognostic for response to a neurocognitive intervention for rumination in depression. Clin Psychol Sci 2014 Jul;2(4):455-471 [https://journals.sagepub.com/doi/10.1177/2167702614536160] [CrossRef]
- Brunoni AR, Boggio PS, De Raedt R, Benseñor IM, Lotufo PA, Namur V, et al. Cognitive control therapy and transcranial direct current stimulation for depression: a randomized, double-blinded, controlled trial. J Affect Disord 2014 Jun;162:43-49 [https://core.ac.uk/reader/55705010?utm_source=linkout] [CrossRef] [Medline]
- Iacoviello BM, Wu G, Alvarez E, Huryk K, Collins KA, Murrough JW, et al. Cognitive-emotional training as an intervention for major depressive disorder. Depress Anxiety 2014 Aug;31(8):699-706 [CrossRef] [Medline]
- Morimoto SS, Altizer RA, Gunning FM, Hu W, Liu J, Cote SE, et al. Targeting cognitive control deficits with neuroplasticity-based computerized cognitive remediation in patients with geriatric major depression: a randomized, double-blind, controlled trial. Am J Geriatr Psychiatry 2020 Sep;28(9):971-980 [CrossRef] [Medline]
- Sommer A, Plewnia C. Depression treatment by tDCS-enhanced cognitive control training: a test of two stimulation intensities. Brain Stimul 2021 Jul;14(4):987-989 [https://linkinghub.elsevier.com/retrieve/pii/S1935-861X(21)00123-6] [CrossRef] [Medline]
- Ferrari GR, Vanderhasselt M, Rinck M, Demeyer I, De Raedt R, Beisel S, et al. A cognitive control training as add-on treatment to usual care for depressed inpatients. Cogn Ther Res 2021 Feb 27;45(5):929-943 [https://link.springer.com/article/10.1007/s10608-020-10197-y] [CrossRef]
- Moshier SJ, Otto MW. Behavioral activation treatment for major depression: a randomized trial of the efficacy of augmentation with cognitive control training. J Affect Disord 2017 Mar 01;210:265-268 [CrossRef] [Medline]
- Hoorelbeke K, Koster EH. Internet-delivered cognitive control training as a preventive intervention for remitted depressed patients: evidence from a double-blind randomized controlled trial study. J Consult Clin Psychol 2017 Feb;85(2):135-146 [CrossRef] [Medline]
- Hoorelbeke K, Van den Bergh N, De Raedt R, Wichers M, Albers CJ, Koster EH. Regaining control of your emotions? Investigating the mechanisms underlying effects of cognitive control training for remitted depressed patients. Emotion 2023 Feb;23(1):194-213 [CrossRef] [Medline]
- Hoorelbeke K, Van den Bergh N, De Raedt R, Wichers M, Koster EH. Preventing recurrence of depression: long-term effects of a randomized controlled trial on cognitive control training for remitted depressed patients. Clin Psychol Sci 2021 Jul;9(4):615-633 [https://journals.sagepub.com/doi/abs/10.1177/2167702620979775] [CrossRef]
- Vander Zwalmen Y, Liebaert E, Hoorelbeke K, de Mévergnies CN, Baeken C, Verhaeghe N, et al. Treatment response following adaptive PASAT training for depression vulnerability: a systematic review and meta-analysis. Neuropsychol Rev (Forthcoming) 2023 Feb 28 [CrossRef] [Medline]
- Cuijpers P, Turner EH, Mohr DC, Hofmann SG, Andersson G, Berking M, et al. Comparison of psychotherapies for adult depression to pill placebo control groups: a meta-analysis. Psychol Med 2014 Mar;44(4):685-695 [CrossRef] [Medline]
- Vander Zwalmen Y, Hoorelbeke K, Liebaert E, Nève de Mévergnies C, Koster EH. Cognitive remediation for depression vulnerability: Current challenges and new directions. Front Psychol 2022 Jul 22;13:903446 [https://europepmc.org/abstract/MED/35936259] [CrossRef] [Medline]
- Littlejohns P, Weale A, Chalkidou K, Faden R, Teerawattananon Y. Social values and health policy: a new international research programme. J Health Organ Manag 2012;26(3):285-292 [CrossRef] [Medline]
- Drummond MF, Sculpher MJ, Claxton K, Stoddart GL, Torrance GW. Methods for the Economic Evaluation of Health Care Programmes. 4th edition. New York, NY: Oxford University Press; 2015.
- Sander L, Rausch L, Baumeister H. Effectiveness of internet-based interventions for the prevention of mental disorders: a systematic review and meta-analysis. JMIR Ment Health 2016 Aug 17;3(3):e38 [https://mental.jmir.org/2016/3/e38/] [CrossRef] [Medline]
- Välimäki M, Anttila K, Anttila M, Lahti M. Web-based interventions supporting adolescents and young people with depressive symptoms: systematic review and meta-analysis. JMIR Mhealth Uhealth 2017 Dec 08;5(12):e180 [https://mhealth.jmir.org/2017/12/e180/] [CrossRef] [Medline]
- Linardon J, Cuijpers P, Carlbring P, Messer M, Fuller-Tyszkiewicz M. The efficacy of app-supported smartphone interventions for mental health problems: a meta-analysis of randomized controlled trials. World Psychiatry 2019 Oct;18(3):325-336 [https://europepmc.org/abstract/MED/31496095] [CrossRef] [Medline]
- Glossary of frequently encountered terms in health economics. National Information Center on Health Services Research and Health Care Technology. URL: https://wayback.archive-it.org/org-350/20210820174512/https://www.nlm.nih.gov/nichsr/edu/healthecon/glossary.html [accessed 2023-05-08]
- A glossary of health economic terms. York Health Economics Consortium. 2016. URL: https://yhec.co.uk/resources/glossary/ [accessed 2023-03-21]
- Cleemput I, Neyt M, Van de Sande S, Thiry N. Belgian guidelines for economic evaluations and budget impact analyses: second edition. Belgian Health Care Knowledge Centre. URL: https://kce.fgov.be/sites/default/files/2021-11/KCE_183_economic_evaluations_second_edition_Report_update.pdf
- Rice DP. Cost of illness studies: what is good about them? Inj Prev 2000 Sep;6(3):177-179 [https://injuryprevention.bmj.com/lookup/pmidlookup?view=long&pmid=11003181] [CrossRef] [Medline]
- Health-related quality of life (HRQOL). Centers for Disease Control and Prevention. 2021. URL: https://www.cdc.gov/hrqol/index.htm [accessed 2023-04-10]
- Kim DD, Wilkinson CL, Pope EF, Chambers JD, Cohen JT, Neumann PJ. The influence of time horizon on results of cost-effectiveness analyses. Expert Rev Pharmacoecon Outcomes Res 2017 Dec;17(6):615-623 [CrossRef] [Medline]
- Buntrock C, Berking M, Smit F, Lehr D, Nobis S, Riper H, et al. Preventing depression in adults with subthreshold depression: health-economic evaluation alongside a pragmatic randomized controlled trial of a web-based intervention. J Med Internet Res 2017 Jan 04;19(1):e5 [https://www.jmir.org/2017/1/e5/] [CrossRef] [Medline]
- Kolovos S, Kenter RM, Bosmans JE, Beekman AT, Cuijpers P, Kok RN, et al. Economic evaluation of internet-based problem-solving guided self-help treatment in comparison with enhanced usual care for depressed outpatients waiting for face-to-face treatment: a randomized controlled trial. J Affect Disord 2016 Aug;200:284-292 [CrossRef] [Medline]
- Warmerdam L, Smit F, van Straten A, Riper H, Cuijpers P. Cost-utility and cost-effectiveness of internet-based treatment for adults with depressive symptoms: randomized trial. J Med Internet Res 2010 Dec 19;12(5):e53 [https://www.jmir.org/2010/5/e53/] [CrossRef] [Medline]
- Ware Jr JE, Sherbourne CD. The MOS 36-item short-form health survey (SF-36). I. Conceptual framework and item selection. Med Care 1992 Jun;30(6):473-483 [Medline]
- Ware JE. SF-36 health survey. In: Maruish ME, editor. The Use of Psychological Testing for Treatment Planning and Outcomes Assessment. 2nd edition. Mahwah, NJ: Lawrence Erlbaum Associates Publishers; 1999:1227-1246
- Rabin R, de Charro F. EQ-5D: a measure of health status from the EuroQol group. Ann Med 2001 Jul;33(5):337-343 [CrossRef] [Medline]
- Reilly MC, Zbrozek AS, Dukes EM. The validity and reproducibility of a work productivity and activity impairment instrument. Pharmacoeconomics 1993 Nov;4(5):353-365 [CrossRef] [Medline]
- Manual iMTA Medical Cost Questionnaire (iMCQ). institute for Medical Technology Assessment. 2018. URL: https://www.imta.nl/questionnaires/ [accessed 2023-07-10]
- Sheehan DV, Lecrubier Y, Sheehan KH, Amorim P, Janavs J, Weiller E, et al. The Mini-International Neuropsychiatric Interview (M.I.N.I.): the development and validation of a structured diagnostic psychiatric interview for DSM-IV and ICD-10. J Clin Psychiatry 1998;59 Suppl 20:22-57 [Medline]
- Zimmerman M, Martinez JH, Attiullah N, Friedman M, Toba C, Boerescu DA, et al. A new type of scale for determining remission from depression: the remission from depression questionnaire. J Psychiatr Res 2013 Jan;47(1):78-82 [CrossRef] [Medline]
- Beck AT, Steer RA, Brown G. Beck depression inventory–II. APA PsycTests. 1996. URL: https://psycnet.apa.org/doiLanding?doi=10.1037%2Ft00742-000 [accessed 2023-05-08]
- Kroenke K, Spitzer RL, Williams JB. The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med 2001 Sep;16(9):606-613 [https://europepmc.org/abstract/MED/11556941] [CrossRef] [Medline]
- Ehring T, Zetsche U, Weidacker K, Wahl K, Schönfeld S, Ehlers A. The Perseverative Thinking Questionnaire (PTQ): validation of a content-independent measure of repetitive negative thinking. J Behav Ther Exp Psychiatry 2011 Jun;42(2):225-232 [https://linkinghub.elsevier.com/retrieve/pii/S0005-7916(10)00114-X] [CrossRef] [Medline]
- Nolen-Hoeksema S, Morrow J. A prospective study of depression and posttraumatic stress symptoms after a natural disaster: the 1989 Loma Prieta Earthquake. J Pers Soc Psychol 1991 Jul;61(1):115-121 [CrossRef] [Medline]
- Fried EI, Flake JK, Robinaugh DJ. Revisiting the theoretical and methodological foundations of depression measurement. Nat Rev Psychol 2022 Apr 14;1(6):358-368 [https://www.nature.com/articles/s44159-022-00050-2] [CrossRef]
- Briggs A, Sculpher M, Claxton K. Decision Modelling for Health Economic Evaluation. Oxford: Oxford University Press; 2006.
- Lokkerbol J, Wijnen B, Ruhe HG, Spijker J, Morad A, Schoevers R, et al. Design of a health-economic Markov model to assess cost-effectiveness and budget impact of the prevention and treatment of depressive disorder. Expert Rev Pharmacoecon Outcomes Res 2021 Oct;21(5):1031-1042 [https://europepmc.org/abstract/MED/33119427] [CrossRef] [Medline]
- Prieto L, Sacristán JA. Problems and solutions in calculating quality-adjusted life years (QALYs). Health Qual Life Outcomes 2003 Dec 19;1:80 [https://hqlo.biomedcentral.com/articles/10.1186/1477-7525-1-80] [CrossRef] [Medline]
- Knapp M, Mangalore R. "The trouble with QALYs...". Epidemiol Psichiatr Soc 2007 Oct;16(4):289-293 [CrossRef] [Medline]
- Luyten J, Naci H, Knapp M. Economic evaluation of mental health interventions: an introduction to cost-utility analysis. Evid Based Ment Health 2016 May;19(2):49-53 [CrossRef] [Medline]
- Brazier J. Measuring and valuing mental health for use in economic evaluation. J Health Serv Res Policy 2008 Oct;13 Suppl 3:70-75 [CrossRef] [Medline]
- Brazier J. Is the EQ-5D fit for purpose in mental health? Br J Psychiatry 2010 Nov;197(5):348-349 [CrossRef] [Medline]
- Black N. Patient reported outcome measures could help transform healthcare. BMJ 2013 Jan 28;346:f167 [https://core.ac.uk/reader/13116393?utm_source=linkout] [CrossRef] [Medline]
- Nelson EC, Eftimovska E, Lind C, Hager A, Wasson JH, Lindblad S. Patient reported outcome measures in practice. BMJ 2015 Feb 10;350:g7818 [CrossRef] [Medline]
- Kazibwe J, Gheorghe A, Wilson D, Ruiz F, Chalkidou K, Chi YL. The use of cost-effectiveness thresholds for evaluating health interventions in low- and middle-income countries from 2015 to 2020: a review. Value Health 2022 Mar;25(3):385-389 [https://linkinghub.elsevier.com/retrieve/pii/S1098-3015(21)01731-9] [CrossRef] [Medline]
- Woods B, Revill P, Sculpher M, Claxton K. Country-level cost-effectiveness thresholds: initial estimates and the need for further research. Value Health 2016 Dec;19(8):929-935 [https://linkinghub.elsevier.com/retrieve/pii/S1098-3015(16)00064-4] [CrossRef] [Medline]
- Massetti M, Aballéa S, Videau Y, Rémuzat C, Roïz J, Toumi M. A comparison of HAS and NICE guidelines for the economic evaluation of health technologies in the context of their respective national health care systems and cultural environments. J Mark Access Health Policy 2015 Mar 12;3:24966 [https://europepmc.org/abstract/MED/27123190] [CrossRef] [Medline]
- Vandepitte S, Putman K, Van Den Noortgate N, Verhaeghe N, Annemans L. Cost-effectiveness of an in-home respite care program to support informal caregivers of persons with dementia: a model-based analysis. Int J Geriatr Psychiatry 2020 Jun;35(6):601-609 [CrossRef] [Medline]
- Werbrouck A, Schmidt M, Putman K, Annemans L, Benhalima K, Simoens S, et al. Cost-utility analysis of lifestyle interventions to prevent type 2 diabetes in women with prior gestational diabetes. Eur J Public Health 2020 Jun 01;30(3):473-478 [CrossRef] [Medline]
- Pil L, Hoorens I, Vossaert K, Kruse V, Tromme I, Speybroeck N, et al. Cost-effectiveness and budget effect analysis of a population-based skin cancer screening. JAMA Dermatol 2017 Feb 01;153(2):147-153 [CrossRef] [Medline]
- Schurer M, Matthijsse SM, Vossen CY, van Keep M, Horscroft J, Chapman AM, et al. Varying willingness to pay based on severity of illness: impact on health technology assessment outcomes of inpatient and outpatient drug therapies in The Netherlands. Value Health 2022 Jan;25(1):91-103 [https://linkinghub.elsevier.com/retrieve/pii/S1098-3015(21)01690-9] [CrossRef] [Medline]
- Piera-Jiménez J, Etzelmueller A, Kolovos S, Folkvord F, Lupiáñez-Villanueva F. Guided internet-based cognitive behavioral therapy for depression: implementation cost-effectiveness study. J Med Internet Res 2021 May 11;23(5):e27410 [https://www.jmir.org/2021/5/e27410/] [CrossRef] [Medline]
- Updated guide to the methods of technology appraisal. National Institute for Health and Clinical Excellence. 2013. URL: https://www.nice.org.uk/process/pmg9/resources/guide-to-the-methods-of-technology-appraisal-2013-pdf-2007975843781 [accessed 2023-05-08]
- Menzel P. How should willingness-to-pay values of quality-adjusted life-years be updated and according to whom? AMA J Ethics 2021 Aug 01;23(8):E601-E606 [https://journalofethics.ama-assn.org/article/how-should-willingness-pay-values-quality-adjusted-life-years-be-updated-and-according-whom/2021-08] [CrossRef] [Medline]
- Ubel PA, Hirth RA, Chernew ME, Fendrick AM. What is the price of life and why doesn't it increase at the rate of inflation? Arch Intern Med 2003 Jul 28;163(14):1637-1641 [CrossRef] [Medline]
- Prinja S, Sundararaman T, Muraleedharan V. Cost-effectiveness threshold and health opportunity cost achieving universal health coverage. Economic & Political Weekly EPW. 2020 Jan. URL: https://hss.iitm.ac.in/ctap/wp-content/uploads/2022/05/CM_LV_1_110120_Shankar_Prinja.pdf [accessed 2023-05-08]
- Husereau D, Drummond M, Augustovski F, de Bekker-Grob E, Briggs AH, Carswell C, CHEERS 2022 ISPOR Good Research Practices Task Force. Consolidated health economic evaluation reporting standards 2022 (CHEERS 2022) statement: updated reporting guidance for health economic evaluations. Int J Technol Assess Health Care 2022 Jan 11;38(1):e13 [CrossRef] [Medline]
- Evers S, Goossens M, de Vet H, van Tulder M, Ament A. Criteria list for assessment of methodological quality of economic evaluations: consensus on health economic criteria. Int J Technol Assess Health Care 2005;21(2):240-245 [https://core.ac.uk/reader/15461749?utm_source=linkout] [Medline]
- Sharma D, Aggarwal AK, Downey LE, Prinja S. National healthcare economic evaluation guidelines: a cross-country comparison. Pharmacoecon Open 2021 Sep;5(3):349-364 [https://dx.doi.org/10.1007/s41669-020-00250-7] [CrossRef] [Medline]
- Donker T, Blankers M, Hedman E, Ljótsson B, Petrie K, Christensen H. Economic evaluations of internet interventions for mental health: a systematic review. Psychol Med 2015 Dec;45(16):3357-3376 [CrossRef] [Medline]
- Kählke F, Buntrock C, Smit F, Ebert DD. Systematic review of economic evaluations for internet- and mobile-based interventions for mental health problems. NPJ Digit Med 2022 Nov 23;5(1):175 [https://doi.org/10.1038/s41746-022-00702-w] [CrossRef] [Medline]
- Burger F, Neerincx MA, Brinkman WP. Technological state of the art of electronic mental health interventions for major depressive disorder: systematic literature review. J Med Internet Res 2020 Jan 20;22(1):e12599 [https://www.jmir.org/2020/1/e12599/] [CrossRef] [Medline]
- Powell A, Torous J. A patient-centered framework for measuring the economic value of the clinical benefits of digital health apps: theoretical modeling. JMIR Ment Health 2020 Oct 30;7(10):e18812 [https://mental.jmir.org/2020/10/e18812/] [CrossRef] [Medline]
- Hoorelbeke K, Vervaeke J, Siegle GJ, Baeken C, Koster EH. Individual differences associated with treatment adherence and transfer effects following gamified web-based cognitive control training for repetitive negative thinking. Internet Interv 2022 Feb 12;27:100507 [https://linkinghub.elsevier.com/retrieve/pii/S2214-7829(22)00014-8] [CrossRef] [Medline]
- Lovibond PF, Lovibond SH. The structure of negative emotional states: comparison of the Depression Anxiety Stress Scales (DASS) with the beck depression and anxiety inventories. Behav Res Ther 1995 Mar;33(3):335-343 [CrossRef] [Medline]
|aPASAT: adaptive paced auditory serial addition task|
|CCT: cognitive control training|
|CUA: cost-utility analysis|
|GDP: gross domestic product|
|HEE: health economic evaluation|
|HRQoL: health-related quality of life|
|ICUR: incremental cost-utility ratio|
|MDD: major depressive disorder|
|NNT: number needed to treat|
|PRO: patient-reported outcome|
|PROM: patient-reported outcome measure|
|QALY: quality-adjusted life year|
|SF-36: Short Form Health Survey|
|TAU: treatment as usual|
Edited by J Torous; submitted 29.11.22; peer-reviewed by A Peckham, J Torous; comments to author 04.03.23; revised version received 22.05.23; accepted 10.06.23; published 18.08.23Copyright
©Constance Nève de Mévergnies, Nick Verhaeghe, Ernst H W Koster, Chris Baeken, Yannick Vander Zwalmen, Kristof Hoorelbeke. Originally published in JMIR Mental Health (https://mental.jmir.org), 18.08.2023.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Mental Health, is properly cited. The complete bibliographic information, a link to the original publication on https://mental.jmir.org/, as well as this copyright and license information must be included.