Digital health applications and the fast-track pathway to public health coverage in Germany: Challenges and opportunities based on first results

doi:10.21203/rs.3.rs-1586622/v1

Objectives

Evidence-based decision-making is the sine qua non for safe and effective patient care and the long-term functioning of health systems. Since Digital Health Applications (DiHA) in Germany have been undergoing a systematic pathway to be reimbursed by statutory health insurance for more than one and a half years, the procedure is also attracting attention in other European countries. We therefore investigate current coverage decisions in Germany and the underlying evidence on care effects for DiHA.

Methods

Based on public available data of the German Institute for Medicines and Medical Devices and further literature, all DiHA that can be reimbursed in Germany were systematically investigated on the basis of predefined criteria (e.g. security, data protection, quality) and presented descriptively. Studies that are used as the evidence base for the decision on reimbursement were examined for their quality. Furthermore, a framework was used to determine potential evidence requirements to be used for DiHA assessments.

Results

By February 2022, 30 DiHA had been included in the DiHA register and can thus be reimbursed by the statutory health insurance. Most DiHA were therapeutic applications for mental illness and were based on cognitive behavioural therapy. One third of all DiHA were permanently included in the benefit basket while two thirds were initially included conditionally. For each of permanently included DiHA, a randomised controlled trial had been conducted to demonstrate positive care effects, with the majority of studies having a high risk of bias.

Conclusions

The so far unique system of evidence-based decision-making on coverage of DiHA in Germany leaves room for improvements both in terms of study reporting and the use of patient-relevant structure and process outcomes to demonstrate the effects of care in addition to the medical benefits. New study designs are needed, which at the same time open up the way for the use of real-world data. With appropriate evidence, DiHA can offers an opportunity in care as an adjunct to existing therapy. However, the risk of bias assessment raises doubts about the evidence and consequently also about the justification of their high costs averaging around € 150 per month.

digital health applications

digital health technology

evidence evaluation

evaluation concept

classification

Given limited health professional resources and limited (spatial) access in rural areas, DiHA is seen as having the potential to improve health care delivery (1–3). DiHA incorporate cooperative or interactive applications of modern information and communication technologies aiming to improve care and population health. However, especially in the light of evidence-based health care, assessing DiHA is crucial to ensure safe, efficacious and effective health care. In Europe, requirements to obtain market authorization (Conformité Européenne, CE) for DiHA, belonging to the scope of Medical Device Regulation (MDR), do not claim evidence on efficacy and effectiveness especially for lower risk devices. Uncertainty regarding benefits of DiHA is thus high, especially in the context of coverage decision. This may also lead to a partially reserved acceptance among physicians, for instance in Germany (4).

Even if Germany has been less advanced in terms of digitalisation compared to other countries so far (5), with digitalisation also deficient in the hospital sector (6), it has been the first country launched a DiHA “fast-track” pathway in the context of coverage decisions in ambulatory care. This has led to changes promoting development and evaluation of DiHA (7). Germany has been a pioneer for many countries but the use of DiHA is also increasingly supported by the governments of other European countries (8). France, for instance, will follow suit to foster innovation and provide access to DiHA for patients (9). Many countries are currently investing in national digital health strategies to include DiHA into the benefit basket of public health systems (10). Several approaches have been developed in European countries for categorisation and decision making whether to include DiHA in public benefit baskets. Examples for categorisation of DiHA include the concept of “Evidence standards framework for digital health technologies” developed by the National Institute for Health and Care Excellence (NHS) of England (11). Belgium built up a platform and a validation pyramid for mobile applications that received CE-mark in compliance with the European MDR 2017/745 (12). In France, there is a guide on clinical evaluation of a medical device in the context of eligibility decisions for reimbursement (13). Furthermore, the French Health Authority (HAS) introduced a system for classifying digital solutions according to their intended use, their ability to provide an individual response and their autonomy (14).

The evidence base for coverage decisions is a frequently and highly discussed topic. Increasingly, models are being developed to evaluate DiHA (15–17) also due to methodological gaps and difficulties in assessing the value of DiHA, including the question for an adequate control group to be included in studies and the level of evidence to be used (18). In a recent study, a classification for DiHA was developed based on criteria to be used for evaluation and final determination of DiHA coverage and pricing (19). This can be used by countries to develop policies for evidence-based implementation of DiHA in publicly financed health systems.

Although a process of assessing DiHA with regard to coverage decisions was set up in 2019 in Germany, there is no study analysing the findings of the policy. This study investigates the first results of the fast-track pathway while especially focusing on the evidence assessment of DiHA. Furthermore, we tested the classification developed for digital health interventions to determine potential evidence requirements. We then compared the evidence level used for assessing DiHA in the current fast-track pathway in Germany and the evidence level that should have been used according to the overall classification framework (19). Additionally, a quality assessment of studies used for assessing positive care effects within the German fast-track pathway was performed.

Procedure and reimbursement of DiHA in Germany

In Germany, the Digital Health Care Act (Digitale-Versorgung-Gesetz, DVG) was passed in November 2019 (20). It describes the formalisation of the DiHA, which can be prescribed by physicians and psychotherapists or reimbursed directly by sickness funds upon request of insured persons provided the corresponding diagnoses was made by the physician. DiHA may include standard software, software as a service, mobile as well as browser-based applications. Insureds are entitled to certain DiHA, which must meet certain requirements in order to be covered by the German statutory health insurance (SHI).

DiHA-manufacturers must apply to the German Institute for Medicines and Medical Devices (Bundesinstitut für Arzneimittel und Medizinprodukte, BfArM) to be considered for coverage through SHI. Their application must meet requirements for safety, quality, functionality, privacy, and data security. Furthermore, evidence of positive care effects must be shown. The latter include medical benefits and patient-relevant structure and process improvements to be demonstrated by corresponding endpoints in comparative studies. The manufacturer must decide between two possibilities of the fast-track pathway: an application to be considered (1) permanently or (2) conditionally into SHI directory to be eligible for reimbursement. (1) If evidence on positive care effects is available at the time of application, the DiHA can be included in the official DiHA directory of SHI. (2) For DiHA following the application process to be conditionally included into the official directory for a limited time period (usually 12 months, max. 24 months), manufacturers have to submit plausible justification of DiHA’s contribution to positive care effects and a scientific evaluation concept prepared by a manufacturer-independent institution to demonstrate care effects. Following the concept of coverage with evidence development, manufacturers must generate respective evidence of positive care effects, while the DiHA is conditionally reimbursed (Fig. 1). After inclusion in the directory, manufacturers are basically free to set their own prices for the first year. As of month 13 after inclusion into the DiHA directory, the price negotiated between the manufacturer and Federal Association of Sickness Funds applies.

Figure 2 gives an overview on the methods of the study. We identified all DiHA and respective decision documents of BfArM (21) used during the fast-track pathway between September 2020, when the first decisions were taken, to February 2022. First, all DiHA permanently and conditionally included in the German DiHA directory were investigated descriptively according to predefined criteria, e.g. general information, such as clinical condition, and costs. The studies on the permanently included DiHA that were crucial for decision-making and listed in the DiHA directory were analyzed with regard to evidence of care effects.

Second, permanently and conditionally included DiHA (n = 30) were classified according to the classification scheme developed to determine potential evidence requirements for DiHA (Lantzsch et al., 2022). With regard to n = 10 permanently included DIHA, evidence level of DiHA assessments were compared between the German fast-track pathway and the evidence level that should have been required according to the overall classification scheme (19).

Third, the risk of bias in the identified randomised controlled trials (RCT) was assessed independently by two researchers (HL, HE) using the revised Cochrane risk of bias tool for randomised trials (RoB II) (22). Disagreements during the assessment were discussed until agreement was reached.

For the second step of the methodological approach, a classification scheme was used to determine the level of evidence required to demonstrate DiHA’s care effects (requirement level). This scheme was developed in parallel to the DVG legislative process to enable decision-makers to link DiHA to the required level of evidence (19). Differentiating between three evidence requirement levels “low, medium or high”, the requirement level for each DiHA group is determined by the attributes “target group” and “function”. The darker the blue shade (Fig. 3), the more rigorous the study design required to provide evidence of care effects. The grayed-out fields represent possible functionalities of DiHA as medical devices according to Regulation (EU) 2017/745, which are, however, not among those meeting the requirements of the 2020 iteration of the DVG for prescribability. The highest level assigned to either category determines the final level of evidence requirements for each group of DiHA.

A control group (before-after) on the basis of non-comparative study designs would be required for a low requirement level. With a medium requirement level, a control group and intention-to-treat evaluation from (quasi-)experimental studies would be required. At the highest level, a randomisation would be required in addition to the requirements for the medium requirement level (Table 1).

Overview of DiHA included in the German DiHA directory

As of February 2022, 30 digital health applications were listed in the DiHA directory of BfArM. The majority of the applications (n = 20) had been conditionally included while ten DiHA were listed permanently (23). Additional file 1 gives an overview on DiHA listed in the directory. Two-thirds of DiHA were admitted in 2021 (n = 18), one-third in 2020 (n = 10) and two DiHA in early 2022. The manufacturers are most often start-up companies, small to partly medium-sized companies. Employee numbers range up to 140. Almost half of the DiHA are web-only apps, 13 Apple iOS and Google Android apps only and 3 are accessible via all platforms.

Although most DiHA were in the therapeutic field of mental illness, with the help of cognitive behavioral therapy, the clinical conditions that can be treated with DiHA were diverse and ranged from tinnitus therapy, self-management for diabetes to therapies for obesity (Fig. 4).

No additional equipment external to the application itself was necessary for most (n = 28) DiHA. This is important as additional equipment has to be paid out of pocket by patients. For the Rehappy DiHA there is an activity tracker as an additional device, and for the zanadio DiHA there are some optional additional devices. Two thirds of the DiHA (n = 22) were only available in German language; only some in one additional language (n = 4) and several languages (n = 4).

The costs of DiHA covered by SHI are listed in the directory. There are always start-up costs (costs for start-up package) and partly costs for a use for 90 days. In some cases, there are cost for an additional 90 days of use (n = 4). Overall, the average price is € 443.80 per quarter with a price spectrum ranging from € 119.00 to € 743.75. The most expensive DiHA thus cost 6.3 times as much as the cheapest. The average cost of a DiHA permanently listed is € 469.82 per quarter (min = € 203.97, max = € 743.75). The average cost of the DiHA listed conditionally is € 430.80 per quarter (min = € 119.00, max = € 718.20). Figure 5 shows the cost of all DiHA in time trend by half-year. The prices are quite far apart in all half-years and increase slightly.

Evidence on care effects for DiHA conditionally included in the registry

With the requirement to provide evidence of positive care effects, 20 DiHA were provisionally listed (Additional file 1). For the DiHA CANKADO PRO-React Onco, an RCT is ongoing (NCT03220178). For the DiHA Novego, three RCTs were already published (24–26) (Moritz et al. 2016; Beiwinkel et al. 2017; Miegel et al. 2019) and another RCT is currently ongoing. For the other DiHA, the current evidence is limited to efficacy data, systematic data evaluations, preliminary studies, and “data from a sample”. For all DiHA, an assessment of the medical benefit is planned. For five DiHA (CANKADO PRO-React Onco, Cara Care für Reizdarm, Kranus Edera, Mindable, Rehappy), an additional demonstration of patient-relevant structure and process improvement such as health literacy, patient sovereignty, adherence, and patient satisfaction is planned. The manufacturers of the DiHA provisionally included state that they want to conduct studies such as prospective RCT (n = 19), or a multicentre, prospective, two-arm study (Cankado) (n = 1).

Evidence on care effects for DiHA permanently included in the registry

Table 2 gives an overview on permanently listed DiHA (n = 10), and the evidence used in the fast-track pathway. This includes the level of evidence of studies, intervention and control group, and results of outcomes split into the evidence on medical benefit, patient relevant structure and process outcomes. RCTs were conducted for all DiHA with two studies still in the process of publication (Kalmeda and Vivira).

Medical benefit was investigated and demonstrated for all DiHA. Outcomes mostly include morbidity as reduction of the disease symptoms, and partly quality of life. Further effects on care were demonstrated in studies of two DiHA, including reduced therapy-related costs and burdens for patients and their relatives, and increased patient sovereignty. The outcomes were assessed with different survey instruments. These were partly validated instruments, such as Patient Health Questionnaire-9 (deprexis, Kalmeda) or the General Depression Scale (HelloBetter Diabetes and Depression) or the Hospital Anxiety and Depression Scale (Elevida).

The intervention in the studies was the app with and without e-mail-support, the app with and without scheduled e-mail contact with a therapist, a web-based therapy, or care as usual and the app. The control group was either “Waitlist control” or “Care as usual”. Intention to treat analyses were performed to investigate effects for eight DiHA. Randomised group allocation was performed in all studies of the ten DiHA (Table 2).

Classification of DiHA

In Fig. 6, DiHA are assigned in the classification scheme, which is intended to determine the required level of evidence for DiHA to be evaluated (19). In some cases, multiple assignments are available if a DiHA could not be clearly assigned. According to the classification scheme, most DiHA were assigned to the target group "chronically ill, stable health" and "high risk/unstable health", one to the target group "Healthy with risk factors". DiHA for people with mental illnesses, such as depression, anxiety or panic disorders, were assigned to the group "chronically ill, stable state of health", but could also be assigned to the group "highly vulnerable/unstable health status".

Regarding the functions, the classification is not always completely clear-cut. Double assignments are possible in some cases. Decisions on classification were made based on the priority function. In most cases, the function of the DiHA was “treatment” and, more specifically, "indirect intervention (self-management)". This function is present 22 times, while simple monitoring applies to two DiHA.

According to the classification, 63% (n = 19) of the permanently and conditionally included DiHA fall into a medium requirement level requiring a control group and intention-to-treat analysis, but not randomisation (Fig. 5 and Additional file 1), while 37% (n = 11) fall into the high requirement level necessitating randomisation. Regarding permanently included DiHA (n = 10), for six DiHA the highest requirement level would have been required.

The categories "Diagnosis", "Complex monitoring" and "Direct intervention", which all correspond to a high requirement level, are not yet occupied. Nevertheless, manufacturers of all (n = 10) DiHA permanently included in the directory conducted a randomised controlled trial.

Study assessment

Risk of bias in 11 published RCTs of 8 permanently included DiHA was assessed based on the RoB II tool (22). The agreement rate between the two assessors (HL, HE) across all studies was 93.4 %.Studies of two DiHA were at the time of assessment in publication process (Kalmeda and Vivira). A total of 9 out of 11 RCTs were judged to be at high risk of bias and two studies raise some concerns. For graphical representations of overall risk of bias per assessment category in included studies see Fig. 7. A justification of the evaluation can be found in the Additional file 2.

Randomisation

The randomisation process was associated with a low risk of bias in three studies. These studies showed random allocation sequence generation and concealed allocation. About two-thirds (7/11) of the studies were associated with some concerns as no information on the allocation sequence was available. One study (27) resulted in a high risk of bias, as baseline differences between intervention groups suggest a problem with the randomisation process.

Deviation from the intended intervention

For deviations from intended interventions, almost all studies (10/11) had low risk of bias, as no deviation from the intended intervention was observed. Almost all authors conducted an intention-to-treat analysis. The authors of one study (28) did not provide information on the analyses conducted to estimate the effect of assignment to intervention.

Missing outcome data

For around two-thirds of the studies (7/11), there was substantial amount of missing outcome data. There is reason to suggest that the missingness in the outcome is related to its true value, which is why the potential for bias in this category was estimated to be high. Two studies (29, 30) performed sensitivity analyses and had a low risk of bias in the domain. In two studies (31, 24), it was not likely that missingness in the outcome depended on its true value - this led to some concerns.

Measurement of the outcome

All eleven studies have some concerns as participants' knowledge of their assignment to the intervention or control could theoretically lead them to over- or understate their outcome measurements; however, there is no evidence that such bias has occurred.

Selection of the reported results

More than half of the studies (6/11) had a low risk of bias in the selection of the reported results, as a pre-specified protocol was provided, and data produced was analysed in accordance with the pre‐specified analysis plan. Almost one third of the studies (3/11) raised some concerns as it remains unclear if selective reporting occurred. One third of the studies (3/11) had a high risk of bias as outcomes that were supposed to be investigated according to the protocol were not mentioned in the study or were not evaluated.

European countries want to advance the field of digital health. Germany is the first country in Europe where DiHA have been included into the benefit basket of SHI and therefore can be prescribed by office-based physicians and psychotherapists. With the fast-track pathway aiming at systematic evidence-based inclusion of DiHA into SHI’s benefit basket, DiHA are available to respective patient groups German-wide instead of being available only for patients through certain selective programmes of individual sickness funds. Germany can thus serve as an example for other countries on how to advance the adoption of DiHA with a simultaneous focus on improving care effects (7).

About one third of all DiHA listed in the German DiHA directory are permanently listed. A comparative study must be conducted to prove positive care effects (21). Although the manufacturer is free in selecting the study design, the study design depends on the type of DiHA and the positive care effects to be evaluated (21). Our study shows that permanently included DiHA in the directory are based on RCTs without exception to evaluate care effects. However, a high risk of bias could be shown for eleven studies (seven DiHA) and some concerns for two studies. In particular, the studies did not score well in terms of missing outcome data and measurement of the outcomes. Regarding to the control group, the Federal Association of Sickness Funds criticises that often there is no active control group, but instead a waiting list (32). Therefore, it is not possible to determine a treatment advantage of the DiHA compared to other DiHA or conventional treatment. Manufacturers argue that a control group that does not receive guideline-compliant therapy would reflect the common care reality in Germany (33). It is questionable with regard to the comparison group whether an application plus standard treatment or standard treatment alone would be sufficient (34). A comparison group could be a waiting group approach, a guideline-conform face-to-face treatment or a regular treatment approach, which represents the most promising approach as the other two approaches are ethically less justifiable or represent a clear difference to the DiHA (35). It would be conceivable to evaluate continuously changing variants of the same DiGA that are constantly compared with each other in a randomised way (36).

It is remarkable that all studies of the permanently admitted DiHA always demonstrated a medical benefit for the positive care effects, but few a patient-relevant structure and process improvement. This could be due to the fact that the studies were conducted or started before the DiHA legislation, and the operationalisation is partly less easy. For provisionally listed DiHA, more manufacturers plan to prove patient-relevant structure and process improvement.

According to the overall classification scheme developed to determine the study design requirements for DiHA (19), slightly different results emerged at first glance. No randomisation would have been necessary for about half of the DiHA permanently included in SHI´s benefit basket. Using real-world-data and alternative study designs in evaluating care effects of DiHA would have been also possible, although RCTs are the most common study design used to evaluate DiHA. However, an early exchange with the BfArM is suggested to plan the evaluation concept accordingly (34). New study designs are not yet widely used, but will be relevant in future (37). There is a need for appropriate study characteristics to support the generation of evidence (18). Advantages in pragmatic randomized trials can be seen because this study design is feasible and suitable for the characteristics of DiGA (36, 38).

With regard to reimbursement prices, there is a wide price range for DiHA and there have been disagreements between the SHI and the DiHA manufacturers about the reimbursement prices. The Federal Association of Sickness Funds criticises that the legal conditions place too little emphasis on a current benefit and that this leads to excessive prices. Accordingly, a reform of the reimbursement of DiHA is sought. Since every fifth app has not yet been activated after prescription, sickness funds might pay too much for nothing. In addition, especially with regard to conditionally listed DiHA undergoing the process of coverage with evidence development, prices of the DiHA in the trial phase have to be financed by the SHI (32). With the new framework on the benchmarks for agreements on remuneration amounts set by an arbitration board (39), DiHA will in future be grouped on the basis of the indication and their positive care effect. In addition, maximum amounts that apply to cost coverage by SHI will be calculated and there will be thresholds. Although this might be a similar approach to the pricing of pharmaceuticals and medical aids, no co-payments for patients are envisaged so far.

Overall, some limitations of the studies need to be mentioned. The quality assessment included those studies that were included in the decision-making process for inclusion in the DiHA directory. It is possible that these are not all studies that exist on the DiHA. The classification scheme, in which the DiHA were assigned, was developed based on the regulations and particularities of the German health system; this may be considered a limitation. However, the system can be helpful for countries to build up a system of evidence-based decision-making for the integration of DiHA into the benefit basket of public health systems, especially with regard to sufficient evidence that should be considered in this process.

This is the first study to provide both a descriptive overview of first results of the fast-track pathway to include DiHA into SHI's benefit basket, to examine the evidence on care effects and to assess the quality of the studies. Results show that primarily the medical benefit was evaluated to show care effects. However, in the future, an increased evaluation of patient-relevant structure and process improvement is to be expected, necessitating new study designs and opening up the way for the use of real-world data. As the results of the present study are rather sobering regarding study quality, improvements are required also with respect to good reporting in the studies. Economic evaluation of DiHA can offer an added value, since costs of DiHA are partly very high. In the future it is important that the price and benefit of DiHA are in reasonable proportion to each other. The presented classification scheme, developed for DiHA to determine potential evidence requirements, can serve as a valuable orientation for other countries to build up a framework on how to classify and assess DiHA for inclusion into the benefit basket of public health insurance. Further research is needed regarding the utilization of DiHA under real world conditions.

Ethics approval and consent to participate

No ethics committee review was required as this manuscript used prior published data only. Data used in the study includes (1) data from published studies and (2) data with regard to characteristics of Digital Health Applications. The latter one was public available at the German Institute for Medicines and Medical Devices (Bundesinstitut für Arzneimittel und Medizinprodukte, BfArM) (23) . No administrative permission was required to access data. All methods were carried out in accordance with relevant guidelines and regulations.

Consent for publication

Not applicable.

Availability of data and materials

All data generated or analysed during this study are included in this published article [and its supplementary information files].

Competing interests

The authors declare that they have no competing interests.

Funding

There was no funding. The Department of Health Care Management receives research funding from a multitude of public or non-profit institutions, which are listed on the Department’s website. Open Access funding enabled and organized by Projekt DEAL.

Authors' contributions

HL and CH conceptualized the manuscript. HL and AC extracted the data and made the assignment to the classification scheme. RB, HL and external authors designed the categorization. Risk of bias was assessed by HL and HE. All authors read and approved the final manuscript.

Acknowledgements

Not applicable.

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Pöttgen J, Moss-Morris R, Wendebourg J-M, Feddersen L, Lau S, Köpke S et al. Randomised controlled trial of a self-guided online fatigue intervention in multiple sclerosis. J Neurol Neurosurg Psychiatry 2018; 89(9):970–6.
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Table 1 Study design requirements per requirement level (adapted to Lantzsch et al., 2022)

	Control group	Intention-to-treat-analysis	Randomisation
Low requirement level		-	-
Medium requirement level			-
High requirement level

Table 2 Current overview of the evidence of the permanently included DiHA (based on information of DiHA directory, studies, and study registers)

No.	DiHA	Author (Year)	LoE (Study Design)	Number of study participants (Intervention and Control)	Intervention	Control	Follow-up (Max in months)	ITT	Evidence of positive care effects
									Medical benefit (outcomes)	Patient-relevant structure and process improvement
1	deprexis	Klein et al. (2016) + Klein et al. (2013) (31, 40)	Ib (RCT)	1.013	Online self-help App; in addition, therapeutic follow-up by email for patients with moderate depressive symptoms	Care as usual	6	x	Reduction of depressive symptoms
		Meyer et al. (2015) (41)	Ib (RCT)	163	Online self-help App	Waitlist control + care as usual	6	x	Reduction of depressive symptoms
		Berger et al. (2011) (42)	Ib (RCT)	236	Two intervention arms: a) Deprexis without any support from a therapist during the 10-week treatment period b) Deprexis plus scheduled e-mail contact with a therapist during the 10-week treatment period	Waitlist control	6	x	Reduction of depressive symptoms
		Moritz et al., (2012) (24)	Ib (RCT)	210	Behavioral: Deprexis (10 sessions) delivered online	Waitlist control	NA	x	Reduction of depressive symptoms
2	elevida	Pöttgen et al. (2018) (30)	Ib (RCT)	275	Elevida-App	Waitlist control + care as usual	6	x	Primary: Reduction of fatigue Secondary: MS-specific motor and cognitive fatigue, anxiety and depression, quality of life, self-reported cognitive difficulties, activities of daily living
3	HelloBetter Diabetes und Depression	Nobis et al (2016) + Nobis et al. (2013) (29, 43)	Ib (RCT)	256	Six-week online training with supporting emails	Waitlist control + Materials for psychoeducation in the field of depression	1	x	Primary: Reduction of depressive symptoms Secondary: Behavioural activation, problem solving, worry, diabetes-related emotional stress, quality of life
4	HelloBetter Stress und Burnout	Heber et al. (2016) (44)	Ib (RCT)	264	Standard medical care and additional access to HelloBetter stress and burnout	Waitlist control	12	x	Primary: Reduction of stress exposure Secondary: Depressive symptoms, anxiety symptoms, emotional exhaustion, sleep (severity of insomniac symptoms), work engagement, self-assessment of emotional competencies, psychological distancing from work, worries
5	HelloBetter Vaginismus Plus	Zarski et al. (2021) + Zarski et al. (2018) (45, 27)	Ib (RCT)	200	Online-training (DiHA)	Waitlist control	6	x	Primary: Improvement of vaginal penetration during sexual intercourse Secondary: Non-coital vaginal penetration ability, sexual functioning, sexuality-related anxiety, penetration-related cognitions
6	Kalmeda	In publication process	Ib (RCT)	187	Kalmeda Tinnitus-App (immediate use)	Waitlist control	3	NA	Reduction of tinnitus exposure
7	somnio	Lorenz et al. (2018) (28)	Ib (RCT)	56	Web-Based Therapy	Waitlist control	12	NA	Reduction of insomnia symptoms
8	velibra	Berger et al. (2017) (46)	Ib (RCT)	139	Care as usual + DiHA	Waitlist control	6	x	Reduction of anxiety and depressive symptoms; quality of life	Reduction of therapy-related expenses and burdens for patients and their relatives
9	Vivira	In publication process	Ib (RCT)	213	DiHA Vivira; therapeutic home exercise programme	Physiotherapy; physiotherapeutic treatment, home exercise instruction	3	x	Reduction of back pain
10	vorvida	Zill et al. (2019) + Zill et al. (2016) (47, 48)	Ib (RCT)	608	Care as usual + DiHA	Waiting list control + care as usual	6	x	Primary: Reduction of alcohol consumption / Alcohol consumption Secondary: Fall drinking days/month, being drunk days/month	Autonomous health action of the patient (patient sovereignty)

ITT=intention to treat; RCT=randomised controlled trial; NA=Not available; source (23)

No competing interests reported.

Digital health applications and the fast-track pathway to public health coverage in Germany: Challenges and opportunities based on first results

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