Mental disorders have a big impact on affected individuals as well as on the society. It is appraised to cause almost one-third of global years lived with disability and account for roughly 10% of all disability-adjusted life years, placing it in the top 3 causes of global burden worldwide [1]. When mental, neurological, and substance use disorders were taken together, the global economic costs in 2010 were estimated to be US $2.5 trillion, with projections for 2030 being around US $6 trillion [2]. While these numbers are serious and account for approximately 2.3% to 4.4% of the gross domestic product in high-income countries, most countries spend a disproportionally small amount of their health budget on mental health [3]. This not only warrants changes in policy but also stresses the need for effective and inexpensive interventions so that individuals can recover more swiftly from a mental disorder and the global burden is reduced.

Swift technological advancement brings the promise of new and effective interventions. Indeed, internet interventions have become a popular niche of research and treatment. A reason for attempting to create effective internet interventions is to reduce the treatment gap, which specifies the discrepancy between the proportion of people who need help for a particular disorder and the proportion of those individuals who actually receive care [4,5]. In other words, internet interventions can be used to reach an underserved population of people with a mental illness or a risk of developing a mental illness [6]. Internet interventions for people with symptoms of a mental disorder are increasingly confirmed in their effectiveness. There have been several meta-analyses on this topic covering various mental disorders such as depression, anxiety, posttraumatic stress, and eating disorders [7-11]. A recent umbrella review of meta-analyses shows that there is sufficient information to assume the effectiveness of internet interventions [12]. In general, guided internet interventions (most of which have a cognitive behavior underpinning) seem to be as effective in reducing symptomatology as face-to-face treatment and outperforming waitlist control conditions. Unguided internet interventions seem to be more effective than waitlist control conditions but less effective than guided internet or face-to-face interventions.

Internet interventions for mental disorders are often coined as a cost-effective alternative to established treatments [13], but the results on cost-effectiveness are tentative at best [14,15]. Individual studies show mixed results, and their heterogeneity in methods, outcomes, and comparators makes it difficult to draw conclusions [16,17], so no definitive assumptions can be established. In addition, cost-effectiveness studies conducted alongside randomized controlled trials (RCTs) are often not powered for economic evaluations, limiting their predictive ability [18]. Nevertheless, separate studies make an important contribution to the rapidly growing body of evidence, so that it becomes more feasible to make meaningful overviews in the form of systematic reviews and meta-analyses. Indeed, Naslund et al [17] performed a broad systematic review on cost outcomes for telemedicine interventions for mental disorders, and Donker et al [16] systematically reviewed RCTs on the cost-effectiveness of internet-based interventions for mental disorders. Both reviews concluded that internet interventions for mental disorders have the potential to be cost-effective compared with alternatives, but evidence is still circumstantial. Kolovos et al [19] performed an individual participant meta-analysis to investigate the cost-effectiveness of internet interventions compared with a control condition (eg, waiting list or care-as-usual) for depression. The authors cautiously concluded that the results showed no indication of cost-effectiveness and remarked that adding economic evaluations to trials more frequently would help to reach well-founded deductions.

To obtain a precise estimate of the cost-effectiveness of internet interventions for mental disorders compared with alternatives, it is desirable to pool the outcomes of individual studies in an aggregate data meta-analysis. As cost-effectiveness is expressed as a combination of 2 variables, the difference in costs and effects between an intervention and control condition, pooling cost-effectiveness outcomes is statistically complex. Fortunately, Crespo et al [20] developed a theoretical framework to perform these kinds of analyses. This method has been successfully applied by another research team in several studies in different areas of medicine [21-25]. Currently, no meta-analysis on the cost-effectiveness of internet interventions for mental disorders has been conducted. The individual participant data meta-analysis by Kolovos et al [19] examined depression only. The individual participant data approach is more reliable [26]; however, it is decreasingly feasible when more studies are included. Furthermore, the last overview of RCTs on the cost-effectiveness of internet-based interventions for mental disorders was conducted in 2015 [16], but the results were not pooled in a meta-analysis.

Concordantly, given the mixed evidence and limitations of individual studies and the rapid increase of novel research, a thorough and recent overview of the literature on the cost-effectiveness of internet interventions for individuals with a mental disorder compared with alternatives is warranted. A first step would be to establish whether internet interventions are cost-effective compared with control conditions such as a waiting list and care-as-usual. In this study, we aimed to investigate the cost-effectiveness of internet interventions for mental disorders compared with control conditions by conducting a systematic literature search and aggregate data meta-analysis of RCTs.

The inclusion criteria were studies that (1) were RCTs, (2) included participants (of all ages) who have symptoms of or a diagnosed mental disorder, and (3) investigated a telephone- or internet-based (that work via computer, tablet, or smartphone) intervention. All forms of internet-based interventions were considered, including fully automated (ie, unguided) interventions, guided interventions, and teleconferencing interventions. Guided interventions could contain asynchronous support (ie, a delay in the support such as with mail or forum services) or synchronous support (ie, no delay in the support such as with videoconferencing and chat). Smartphone apps with the purpose of elevating the symptoms of a mental disorder were included. In addition, studies were included in this review that (4) included a control condition in the form of (enhanced) treatment as usual, psychological placebo, waitlist control, or bibliotherapy; (5) included reported outcomes on both quality of life (quality-adjusted life years [QALYs]) and costs; and (6) were published in English. It is worth noting that studies were included if participants had somatic conditions (eg, cancer or diabetes) as long as the participants had comorbid symptoms of a mental disorder, and the investigated intervention had the primary aim to alleviate these symptoms. Studies were excluded if the main intervention relied exclusively on wearable devices or virtual reality, had only a face-to-face intervention as a control condition, or did not provide sufficient information on costs or QALYs.

The electronic databases PubMed (including MEDLINE), Embase, Emcare, PsycINFO, Web of Science, and the Cochrane Library were searched until March 1, 2021. A search string was created for PubMed and translated for the other databases. The PubMed search string contained Medical Subject Headings terms for the concepts of mental disorders, cost-benefit analysis, telemedicine, and internet, with all expressions under these headings included as free terms as well. Furthermore, other terms related to the 3 Medical Subject Headings terms were added to maximize the sensitivity of the search. The full PubMed search strategy is presented in Item 1 of Multimedia Appendix 2. By checking cross-references in the included studies, we minimized the chance of missing relevant data. During the screening phase, all relevant study protocols, conference abstracts, and trial registrations were identified, and the authors of unpublished studies with potentially relevant data were contacted.

The identified articles were screened in 3 steps by 2 researchers (PJR, AED, CE, MEVA-VM, IL, or FCC). First, titles and abstracts of the eligible articles were screened. Subsequently, full texts of all included abstracts were screened for eligibility. Finally, relevant data were extracted. If there was any disagreement between the 2 researchers in any of the 3 steps, a third researcher from the team made the final decision.

Risk of bias of the included RCTs was assessed by 2 researchers (PJR, AED, CE, IL, or FCC) using the Cochrane Risk of Bias assessment tool [28]. Specifically, data were gathered on the topics of (1) random sequence generation (selection bias), (2) allocation concealment (selection bias), (3) blinding of participants and personnel (performance bias), (4) blinding of outcome assessment (detection bias), (5) incomplete outcome data (attrition bias), (6) selective reporting (reporting bias), and (7) other sources of bias. Finally, for all these topics, the risk of bias was assessed (ie, low, unclear, or high risk of bias). Any disagreement between the researchers on each of the 7 areas of the risk of bias was resolved by means of a discussion between the 2 raters or by a third rater. A final rating of high, medium, or low bias was assigned to each study based on the revised tool for assessing the risk of bias [29]. Specifically, a high risk of bias was assigned to studies when a high risk of bias in any domain was present or unclear risk of bias was present in ≥2 domains, medium risk of bias was assigned in the case of 1 unclear rating across all domains, and low risk of bias was assigned when all domains were rated as low risk of bias. Exceptions were that blinding of participants and personnel was not considered in the final risk of bias rating as blinding was unfeasible in most studies, and unclear risk of bias on the selective reporting domain was considered as low risk of bias for the final risk of bias rating as the absence of a published protocol or trial preregistration is still common.

Furthermore, the 19-item Consensus Health Economic Criteria (CHEC) list [30] was used to assess the quality of the economic evaluation for all included studies. The expert on economic evaluations (MEVA-VM) scored all articles on the CHEC list, while other authors (PJR, AED, CE, IL, and FCC) divided included articles for the CHEC list as well, so articles were rated twice. All discrepancies between raters on the CHEC list were resolved through a discussion. Assigning an overall quality score for the CHEC list is not advocated, as cutoff scores are highly heterogeneous [31] and difficult to substantiate. As we wanted to group studies by the quality of the economic evaluation in analyses, we adopted an approach using a selection of items from the CHEC list. A study had to fulfill at least 8 specific items (ie, items 7-14, see Risk of Bias and Quality of Economic Evaluation below for a list of all items) to be deemed of high quality. These items were chosen because they contribute to the assessment of the costs and effects used in the current meta-analysis. Other items (ie, 1-4 and 15-19) were deemed less important for this study, as they aim at clarifying the text and the appropriateness of the used methods and analyses, which is also captured partly in the risk of bias assessment. Finally, items 5 and 6 regarding time horizon and perspective were not considered for the final quality rating because they were already explored in the moderator analyses.

Figure 1 illustrates the study selection flow. Tables 1 and 2 present a detailed overview of all the included studies [42-77] and their outcomes. In addition, an overview of all studies that were excluded after the full-text screening phase and the reason for exclusion can be found in Item 5 of Multimedia Appendix 2. A total of 6226 papers, conference abstracts, and trial registrations were identified. Full texts of 178 papers were examined, and data from 37 articles published between 1990 and March 2021 were extracted. From 200 relevant protocols, conference abstracts, and trial registrations, follow-up was needed for 93 records with 76 different corresponding authors. For those not needing follow-up, it was clear that data gathering was still ongoing or that data were already published and included in the screening. The 76 corresponding authors were approached via email to clarify whether the data were already available to implement in our meta-analysis. One reminder was sent within 2 weeks if there was no response. This yielded 4 additional articles from 3 different authors. In total, 70% (53/76) of authors responded, and for 94% (50/53) of those responses, cost-utility data were not yet available or authors were not willing to share data at this time.

For 7 studies, no measure of spread of delta QALY or delta costs could, directly or indirectly, be deduced from the article. Data from 1 study were available within our research team [42]. After enquiring with the authors of the other 6 studies (5 of which responded), data of only 3 comparisons were still missing. Two were solved using Webplot Digitizer on the presented cost-effectiveness plane, and the other was solved by mean imputation based on 2 comparisons within the same study.

All authors of the included studies were contacted to provide information on the covariance between delta QALY and delta costs. Authors responded for 84% (31/37) of the included studies; however, not all authors were able to provide information on the covariance. Specifically, for 12 of the studies, the covariance was based on data from the authors. The mean Pearson correlation (r) between delta QALY and delta costs based on these 12 studies was −0.12 (SD 0.16). For the remaining 25 studies, covariances were calculated using Webplot Digitizer (n=13) or using the estimated mean correlation calculated earlier (n=12).

In total, the 37 included studies (Table 1) recruited 15,596 participants, ranging between 40 and 2116. Some study conditions were irrelevant for this meta-analysis (eg, an active intervention without an internet component), so the main analysis was based on 14,946 participants. Intention-to-treat analyses were conducted in most of the studies (32/37, 86%). Mental disorders that were targeted were depression (16/37, 43%), anxiety (7/37, 19%), alcohol or substance abuse (5/37, 13%), depression and anxiety simultaneously (5/37, 13%), obsessive compulsive disorder (2/37, 5%), posttraumatic stress disorder (1/37, 3%), and eating disorders (1/37, 3%). Experimental interventions were mostly cognitive behavior–based modules or websites that participants could engage with (29/37, 78%). Other interventions consisted of teleconferencing (2/37, 5%), chat or SMS text messaging (2/37, 5%), a web-based game (2/37, 5%), and telephone support (2/37, 5%). Some form of guidance within the intervention was available in 27 studies, whereas the intervention was self-guided in 10 studies. Guidance consisted of written feedback (11/27, 41%), telephone calls (5/27, 18%), face-to-face communication, including teleconferencing (4/27, 15%), chat (1/27, 4%), or a combination of these (6/27, 22%). Control conditions of the studies included waiting list or care-as-usual conditions (32/37, 86%) and psychological placebo or attention control conditions (5/37, 13%). The follow-up periods ranged from 8 weeks to 2 years, with most studies (20/37, 54%) maintaining a 12-month follow-up period.

Questionnaires used to calculate QALYs were EQ-5D (32/37, 86%), KIDSCREEN-10 (1/37, 3%), SF-6D or SF-36 (3/37, 8%), and Australian quality of life instrument (1/37, 3%). The pooled difference in effectiveness (intervention QALY gains minus control QALY gains) for the included studies was 0.004 QALY (SE 0.002; Hedges g=0.052, 95% CI 0.010-0.094; P=.02). Although the difference was statistically significant, likely because of the large sample size, the size of the difference was deemed negligible.

Main questionnaires used to measure health care use and costs in the included studies were the Treatment Inventory of Costs in Psychiatric Patients (n=14) and Client Service Receipt Inventory (n=7), but other or self-administered questionnaires, medical records, and diaries were also used. In total, 15 studies reported costs from a health care perspective and 22 presented a societal perspective. The pooled difference in costs (intervention costs minus control costs) when studies with a health care and societal perspective were taken together was US $49 (SE 40; Hedges g=0.002, 95% CI −0.080 to 0.84; P=.96). Considering the uncertainty in measurements of costs, the small difference indicates that internet interventions were equally expensive as control conditions. Results for studies with different economic perspectives were similar, with no difference in costs for studies with a health care (US $40, SE 44; Hedges g=−0.026, 95% CI −0.21 to 0.16; P=.78), and societal perspective (US $158, SE 76; Hedges g=0.034, 95% CI −0.012 to 0.080; P=.15).

Visual inspection of individual INBs and their 95% CIs (Figure 2) indicated substantial heterogeneity among the 37 included studies. Statistical measures of heterogeneity suggested otherwise (Cochran Q₃₆=37.12, P=.42; I²=3.0%, 95% CI 0.0%-42.8%) but are difficult to interpret because of the large 95% CI of I² and considerable within-study uncertainty. In other words, the between-study heterogeneity seemed to be overshadowed by the large within-study heterogeneity. Therefore, a random effects model was preferred over a fixed effects model to pool INBs. The INB was positive (more favorable balance of costs and effects in the internet intervention compared with the control condition) in 25 of the included RCTs. Furthermore, at a WTP of US $40,000 per QALY, the pooled INB was US $255 (95% CI US $91 to US $419; P=.002). The results suggest that internet interventions are slightly more cost-effective compared with a do-nothing or care-as-usual approach.

Pooled INB values were also calculated for the subgroups based on the 10 moderator variables. Outcomes for subgroups based on perspective, length of follow-up, and targeted mental disorder are presented in the text, and results for all moderator (including presence of guidance, intensity of guidance, type of guidance, recruitment strategy, diagnosis for inclusion, intervention duration, and control condition type) analyses are presented in Item 6 of Multimedia Appendix 2.

Pooling studies from an economic perspective influenced the results. Specifically, looking at studies with a health care perspective separately (n=15), the pooled INB was US $280 (95% CI US $109 to US $451; P=.001). For studies with a societal perspective (n=22), the pooled INB was substantially lower at US $161 (95% CI US $247 to US $569; P=.44). This suggests that the cost-effectiveness of internet interventions compared with control conditions cannot be assumed when maintaining a societal perspective.

Studies with a short (<12 months) follow-up (n=14) had a pooled INB of US $112 (95% CI US $194 to US $418; P=.47) and studies with a long (≥12 months) follow-up (n=23) had a pooled INB of US $270 (95% CI US $14 to US $554; P=.06). For RCTs with a long follow-up period, statistical significance was likely not attained because the sample size decreased compared with the main analysis. Nevertheless, the studies with a long follow-up, which are usually better able to capture all relevant costs and effects than those with a short time horizon [78,79], had a pooled estimate comparable with the estimate of all studies taken together. This strengthens the idea that internet interventions are likely to be cost-effective compared with control conditions in the long term.

Concerning targeted mental disorders, a significant positive INB was found for internet interventions targeting anxiety (n=7; US $644, 95% CI US $227 to US $1062; P=.002) and depression (n=16; US $387, 95% CI US $156 to US $618, P=.001). Similarly, the INB of studies with internet interventions targeting depression and anxiety simultaneously (n=5), pooled INB of US $580 (95% CI US $584 to US $1744; P=.33), and obsessive compulsive disorder (n=2), pooled INB of US $253 (95% CI US $544 to US $1051; P=.53), was positive. The size of the INB for these 2 groups also indicated that cost-effectiveness compared with control conditions was likely, but statistical significance was not attained. Internet interventions were unlikely to be cost-effective when targeting alcohol or substance abuse (n=5), with a pooled INB of US $129 (95% CI US $448 to US $191; P=.43). It must be noted that of the 2 studies regarding obsessive compulsive disorder, one targeted children, further obscuring the interpretability for this subgroup. Only 1 study was available for eating disorders and posttraumatic stress disorder, rendering it impossible to pool.

Table 3 presents details on the risk of bias, and Table 4 presents details on the quality ratings of the economic analysis for the included studies. The overall risk of bias of the included RCTs was considered high, with a low risk of bias for 8, medium risk of bias for 8, and high risk of bias for 21 studies. The economic appraisal of the included studies, based on the CHEC list, suggested moderate quality, with 24 studies receiving a high-quality rating and 13 receiving a low-quality rating. The level of agreement between the raters was considered low for the risk of bias assessment (55% agreement) and high for the CHEC list ratings (89% agreement).

A funnel plot of the included studies is shown in Figure 3. Visual inspection of the plot suggested some evidence for publication bias. The Egger test did not indicate asymmetry in the funnel plot (z=−0.026, 1-tailed P=.80).

Results of all sensitivity analyses are presented in Item 7 of Multimedia Appendix 2. First, the main analysis was repeated for studies with a high-quality rating on the CHEC list (n=24), resulting in a pooled INB of US $253 (95% CI US $43 to US $463; P=.02). This suggests cost-effectiveness of internet interventions was maintained when looking at high-quality studies alone and that results from the main analysis were not dependent on low-quality studies. Second, the pooled INB for studies with a low risk of bias (n=8) was US $244 (95% CI US $555 to US $1042; P=.55). A similar result was found when medium risk of bias was considered as a low risk of bias (n=16), with a pooled INB of US $216 (95% CI US $182 to US $615; P=.29). The pooled INB for RCTs with low risk of bias was comparable with that of the main analysis, suggesting that the pooled result of all 37 studies was not critically biased. However, for high risk of bias studies, the pooled INB was slightly higher, US $272 (95% CI US $68 to US $475; P=.009). This finding suggests a small overestimation in the overall pooled estimate. Third, when 1 QALY was valued at US $20,000 instead of US $40,000, the pooled INB of the 37 studies was US $145 (95% CI US $56 to US $234; P=.001). At a WTP of US $80,000 for 1 QALY, the pooled INB was US $431 (95% CI US $115 to US $747; P=.008). The 2 analyses show that if society’s WTP for 1 additional QALY for an individual is lower (US $20,000) or higher (US $80,000) than US $40,000, the cost-effectiveness of internet interventions compared with care-as-usual is still expected. Finally, the pooled INB of the studies for which the covariance could be calculated directly from author data (n=12) was US $264 (95% CI US $167 to US $694; P=.23). This was similar to the pooled estimate of all 37 studies, indicating that the way the covariance was calculated did not greatly influence the results of the meta-analysis.

An individual participant data meta-analysis was not achievable, so moderator analyses based on age, gender, and symptom severity were not possible or feasible. In addition, a moderator analysis with subgroups based on whether the study was conducted by developers of the interventions was planned, but this information proved too difficult to find for many of the studies.

This systematic review and meta-analysis aimed to research the pooled evidence of the cost-effectiveness of internet interventions for mental disorders compared with control conditions. The results indicated that internet interventions were negligibly more effective in terms of QALY gains and equally costly compared with control conditions but might be cost-effective. Internet interventions had an INB of US $255 (95% CI US $91 to US $419) compared with control conditions when society is willing to pay US $40,000 for 1 QALY improvement for an individual. INBs were still positive when WTP for 1 additional QALY was lower (US $20,000) or higher (US $80,000). The results suggest that internet interventions for mental disorders are likely to be cost-effective compared with a do-nothing approach, especially when they target depression or anxiety and when some form of guidance is added to the intervention. This is especially interesting considering that internet interventions are now frequently and successfully being implemented [80] and can be used to serve populations that need mental health care but do not yet receive it [81]. Financing such scalable and, in some cases, anonymous interventions comes with difficulties, as they often do not fit in the funding possibilities for traditional treatments. Nevertheless, findings from this meta-analysis indicate that doing so might ultimately help reduce the global burden of mental disorders.

Moderator analyses revealed that cost-effectiveness of internet interventions compared with control conditions was maintained for studies with a health care perspective but not for those with a societal perspective. An explanation could be that indirect costs included in a societal perspective are usually higher and measured with more uncertainty than direct health care costs. Indeed, studies with a health care perspective included in the analyses had both a smaller cost range and smaller pooled SE compared with studies with a societal perspective. A relatively small difference in health care costs might then be obscured by large indirect costs. This reflects a larger problem that sample size calculations for RCTs are almost exclusively based on detecting differences in disorder-specific effectiveness while neglecting QALYs and costs, rendering the trial unlikely to be adequately powered for an economic evaluation [18]. Furthermore, compared with control conditions, internet interventions were likely to be cost-effective for symptoms of anxiety, depression, and obsessive compulsive disorder. This was not the case for alcohol and substance abuse. The finding that internet interventions for alcohol and substance abuse were not found to be cost-effective compared with controls might be because of a lack of power in that subgroup. Alternatively, it could relate to the absence of guidance. Indeed, guided interventions were found to be cost-effective compared with care-as-usual, whereas self-guided interventions were not, and none of the alcohol and substance abuse studies incorporated guidance. Adding guidance to an internet intervention might have a positive effect on cost-effectiveness. However, self-guided interventions have fewer functions than guided ones and do not compensate for this by higher levels of technical sophistication (ie, responsiveness) at least for those targeting depression [82], perhaps partly explaining the positive influence of guidance in internet interventions. Another result was that studies with a follow-up period of ≥12 months generally showed internet interventions for mental disorders to be efficient compared with a control condition, while this was not the case for studies with shorter follow-up periods. It is possible that some factors that influence the efficiency of internet interventions become apparent after a certain time only. For example, effects of an intervention concerning health care visits or work productivity may take a while to attain. This substantiates the idea that follow-up periods of at least 12 months are important when conducting an economic evaluation of such interventions [78,79]. Several other findings of subgroup analyses are worth mentioning. First, recruiting participants through (social) media rather than by clinical referral was more likely to yield a positive INB. Speculatively, studies that used an open recruitment system included participants who were less severely ill or more strongly motivated for this type of intervention, thereby benefitting more. Comparing study subgroups based on the severity of symptoms, motivation to change, or related participant characteristics directly was not feasible for this meta-analysis but could be an interesting avenue for future research. Second, shorter interventions were likely to be more efficient compared with controls, whereas this was not true for longer interventions. It might be that longer internet interventions cost more but do not perform better compared with shorter interventions in terms of QALYs or health care or societal costs. Third, cost-effectiveness of internet interventions for mental disorders was not likely when control conditions included an active component (ie, attention control). Accordingly, activating participants seems to be an important component for an efficient intervention, while the content may be of lesser importance. Moderator analyses should be considered as exploratory because of the small number of studies in some subgroups.

Interestingly, internet interventions were found to be only marginally more effective than control conditions in terms of QALY gain. It is surprising that internet interventions produced practically the same amount of QALYs as a do-nothing approach, given the substantial evidence for the effectiveness of such interventions [12]. However, it is likely that the internet interventions were more effective in terms of symptom reduction and other areas of well-being, but these improvements were not captured well by the generic health-related quality-of-life measures (eg, EQ-5D and SF-36) used in economic evaluations. Such instruments capture only a selective number of domains of quality of life and use an almost exclusive focus on people’s current functional abilities with little emphasis on coping capabilities and resources [83]. Consequently, they might not be suitable in contexts outside of health, such as chronic illness, older adult care, general well-being, and mental illness [84]. Future economic analyses of internet interventions for mental disorders should consider using other instruments, such as the ICECAP-A [85], which measures well-being beyond physical health, to complement generic health questionnaires [86,87].

To our knowledge, meta-analyses on cost-effectiveness studies have only been attempted in 5 studies in different fields of medicine by one other research team [21-25] but not yet in the area of mental health interventions. The theoretical method by Crespo et al [20] has been practically applied and explained by Bagepally et al [21]. This study built on this method by improving the precision of estimating covariances between delta costs and delta QALYs. In general, much data are necessary to pool cost-effectiveness outcomes in a meta-analysis, some of which are often not reported. Enhancing the quality of economic evaluations by clearly reporting costs, QALYs, and indicators of spread (eg, SD or SE) for all studied conditions or open availability of study data makes conducting meta-analyses on cost-effectiveness data more feasible. In addition to these practical challenges, the unavoidable and substantial heterogeneity between cost-effectiveness studies alongside RCTs might make statistically pooling outcomes undesirable [88]. Therefore, careful planning and cautious interpretation of the findings are warranted when considering a meta-analysis on cost-effectiveness data. Nevertheless, bearing the limitations in mind, several considerations corroborated the choice for pooling cost-effectiveness data. First, by adhering to clear predefined inclusion and exclusion criteria throughout the search procedure, the included studies were comparable across many characteristics. For example, the interventions were often similar in terms of content, mode of delivery, duration, and frequency. In addition, in most studies, the control conditions consisted of a do-nothing approach (ie, participants did not receive the internet intervention but were allowed to continue receiving the care they received before the study). Similarly, 21 of the included studies used the Treatment Inventory of Costs in Psychiatric Patients or Client Service Receipt Inventory to measure service use, suggesting that these studies considered the same costs, and almost all studies used the EQ-5D for calculating QALYs. Second, modeling studies were excluded to avoid additional variation between methods. Third, characteristics that were thought to have a large influence on the outcomes were tested in moderator and sensitivity analyses. Overall, these analyses were in line with the overall pooled estimate, further substantiating the robustness of the finding that internet interventions are likely to be cost-effective compared with control conditions. Finally, the variance of the INB of individual studies was often large. Consequently, a meta-analysis was important because it offered insight beyond individual studies, which are rarely powered to detect differences in QALYs and costs [18].

Although the main, moderator, and sensitivity analyses point in the direction of internet interventions being cost-effective compared with control conditions, the findings should be interpreted with considerable caution and are difficult to generalize beyond the current sample of included studies. The first limitation was that the studies were heterogeneous in terms of included costs and follow-up duration, which warrants careful interpretation of the overall pooled results. Second, variances of the cost-effectiveness outcomes were large to such an extent that they overshadowed the differences between studies, making it difficult to understand and quantify between-study heterogeneity. Third, publication bias possibly shifted results in favor of internet interventions because economic analyses are often a last step in effectiveness research and some results might never get published. For example, some contacted authors who published a protocol mentioning an economic analysis replied that such an analysis was ultimately not attempted, as the internet intervention was not found to be effective. To counter this problem, researchers are encouraged to designate in their RCT study protocol whether an economic analysis will be attempted and perform it regardless of the results on effectiveness. A fourth reason to interpret the results with caution is that the risk of bias in most RCTs was high, possibly leading to a slight overestimation of the efficiency of internet interventions compared with control conditions. However, when quality was assessed using the CHEC list, which is arguably more suitable in the case of economic evaluations, high-quality studies showed a positive INB, whereas low-quality studies did not. This confirms the idea that internet interventions might be cost-effective compared with controls only when the economic evaluation is of adequate quality. The subjectively chosen, though theory driven, cutoff points for the risk of bias and CHEC list should be considered when considering the importance of these sensitivity analyses. Indeed, studies designated as high quality based on the CHEC might not have had an appropriate time horizon or economic perspective.

This study provides an overview of published articles on the cost-effectiveness of internet interventions for mental disorders and their findings and provides insights for future research and implementation steps. First, it must be noted that the results should be replicated in other meta-analyses before the clinical and policy implications can be reliably stated. To accomplish this, standardization of economic evaluations in the area of mental health, as has been done in other sectors [89,90], would be helpful. Consequently, included studies in similar meta-analyses would be more homogeneous and easier to compare. Nevertheless, the pooled estimates of the main and subgroup analyses complement the results of the individual studies. They suggest that policy makers, insurance companies, and subsidy providers should invest in internet interventions for mental disorders, as they appear to be an efficient way of improving quality of life compared with not offering them. For clinical practice, often facing financial pressure and waiting lists, this might involve a transition where internet interventions are increasingly embraced and become an integral part of the treatment options. The results of this meta-analysis also suggest that some components of internet interventions for mental disorders, such as recruitment strategy, symptom severity, guidance, and length of the intervention, are worth considering upon implementation. This is the first study to pool cost-effectiveness outcomes in the area of psychiatry, paving the way for other researchers to apply this method to new meta-analyses to further enhance the understanding of the cost-effectiveness of internet interventions compared with alternatives. For example, this study only considered control conditions, as internet interventions are often used for early detection and underserved populations, for which care-as-usual and a waiting list are realistic comparators. Future work could compare internet interventions with active comparators, such as face-to-face treatment, to clarify the difference in effects and costs between these forms of treatment. In addition, this study looked broadly at the topic of cost-effectiveness of internet interventions for mental disorders and should be considered as a starting point. It might be valuable to investigate a study sample with more homogeneous interventions or designs to obtain more precise estimates of cost-effectiveness. Relatedly, modeling studies were not considered in this meta-analysis, as pooling the outcomes from such studies with those obtained from RCTs was undesirable. Performing a meta-analysis on modeling studies is feasible, however, and might be an interesting extension. Importantly, no study from a non-Western culture or low-income country was included in the study, while especially low-income countries or people in areas where health care is not paid for by the government might benefit from internet interventions. Initiating internet interventions in such contexts might be a challenge but can help to reduce health care costs in the long term. Conducting research on the cost-effectiveness of internet interventions in low-income countries is therefore highly recommended.

Pooling outcomes of 37 studies revealed a small benefit of internet interventions for mental disorders compared with control conditions. The perspective of the economic evaluation targeted mental disorder and WTP for 1 QALY moderated the results. Generalizability to new studies is poor given the large variance of the outcome of interest and heterogeneity between studies. The findings show that cost-effectiveness of internet interventions for mental disorders compared with a do-nothing or care-as-usual approach is likely but not guaranteed. The continuation of high-quality and adequately powered economic evaluations is necessary. This is the first study in the field of psychiatry to pool cost-effectiveness outcomes in an aggregate data meta-analysis, paving the way for other researchers to use and expand this method.