Efficacy of an Unguided Digital Intervention for Adults With Attention-Deficit/Hyperactivity Disorder

A Randomized Clinical Trial

Background: Psychotherapy for adults with ADHD is still underutilized in Germany and worldwide. Only a small percentage of these patients receive psychotherapy as recommended in the guidelines. Unguided digital interventions may help to lessen this treatment gap.

Methods: An open-label, exploratory randomized controlled trial was conducted (trial registration: DRKS00033320). Adults with confirmed ADHD were recruited at six study centers in Germany, as well as via social media, and were randomly allotted to receive either the intervention (treatment as usual + app) or the control condition (treatment as usual). Data were collected from 2 February to 5 June 2024. The primary endpoint was patients’ health-related quality of life (QOL) at 12 weeks. The secondary endpoints included changes in the severity of ADHD symptoms, functional impairment, and symptoms of anxiety or depression. The threshold for statistical significance was set at 10% in view of the exploratory nature of the trial.

Results: 307 subjects were randomized (intervention group: n = 155, control group: n = 152). 70% were women, 29% men, and 1% was gender diverse; their mean age was 36 years (SD, 9.5). The data were analyzed according to the intention-to-treat principle with a mixed-effects model. The improvement in QOL was greater in the intervention group than in the control group (p <0.001; d = 0.54; 90% confidence interval [0.33–0.74]), despite generally low adherence to the intervention in the intervention group. 40% of subjects in the intervention group attained clinically relevant improvement in QOL, compared to 27% of control subjects. Among the secondary endpoints, ADHD symptoms improved to a greater extent in the intervention group than in the control group.

Conclusion: Limitations include that no standardized diagnostics were conducted within the study and that no conclusions can be drawn regarding long-term effects.

Cite this as: Baumeister A, Schuurmans L, Schöttle D, Reif A, Kahl KG, Moritz S, Karow A, Lambert M, Betzler F, Philipsen A: Efficacy of an unguided digital intervention for adults with attention-deficit/hyperactivity disorder: A randomized clinical trial.
Dtsch Arztebl Int 2026; 123: 390–6. DOI: 10.3238/arztebl.m2026.0065

Attention-deficit/hyperactivity disorder (ADHD) is a common neurobiological developmental disorder that persists into adulthood in around 60% of cases, with a global prevalence of around 2.6% (1, 2). It impacts everyday functioning, emotional well-being, and social interactions, and is associated with health risks and impaired quality of life (2, 3). Combined pharmacotherapy, psychoeducation, and psychotherapy is more effective than medication alone (2, 3, 4). Only 3–5% of patients receive cognitive behavioral therapy (CBT) in accordance with the relevant guidelines; in Germany as a whole, the rate is 5% at 1 year post diagnosis (5). This low figure is due, among other factors, to:

• Long waiting times (6–24 months)
• Misdiagnosis
• Limited availability
• Shame or fear of stigmatization (3, 5, 6, 7, 8)

About 38% of those affected receive psychotherapeutic care of some kind within the first year; however, this treatment is usually not specific to ADHD (e.g., probatory sessions, training in skills such as progressive muscle relaxation, or psychodynamic therapy) (5). Therefore, low-threshold—easily accessible, flexible, and self-directed—evidence-based interventions are urgently needed. To be effective ADHD treatment must address both the core symptoms and the associated issues. CBT and dialectical behavior therapy (DBT) have proven effective both in reducing symptom severity and in enhancing quality of life (9, 10, 11).

Unguided digital interventions can help reduce the deficiencies in psychotherapeutic care. However, the available evidence is limited. Nasri and colleagues (12), for example, found a reduction in symptom severity after 12 weeks of a guided intervention compared with treatment as usual (TAU). Kenter et al. (13) demonstrated the efficacy of an unguided intervention regarding symptom severity and quality of life, but their study included participants with self-reported ADHD, limiting external validity.

The aim of the study reported here was to evaluate the efficacy of an unguided mobile psychological intervention (MiNDNET ADHD therapy) for adults with a confirmed diagnosis of ADHD with regard to quality of life as the primary endpoint together with secondary endpoints such as symptom severity and functional or emotional difficulties.

Methods

Study design and participants

The study was an open-label, two-arm randomized controlled trial in which an intervention group (IG) was compared with a wait list control group (WCG). Both groups also had access to TAU, i.e., the participants remained free to take up all standard health care services, including psychotherapy and pharmacotherapy. Newly initiated psychotherapeutic treatment and medication were assessed at each measurement, including changes in dosing or the drugs used.

Assessments were conducted at baseline (T0), after 6 weeks (T1; during the intervention), and after 12 weeks (T2; post-intervention). Recruitment took place at six academic study centers in Germany and via social media. A detailed description of the recruitment and screening procedures can be found in the eMethods. The inclusion and exclusion criteria are presented in the Box. All participants gave electronic informed consent.

Box

Inclusion and exclusion criteria for participation

The study was approved by the local psychological ethics committee (LPEK–0709). The trial was pre-registered in the German Clinical Trials Register (DRKS00033320). The results are presented in accordance with the Consolidated Standards of Reporting Trials (CONSORT) recommendations. A fully completed CONSORT checklist can be found in the eChecklist.

Randomization and masking

Randomization was fully automated via the survey software Qualtrics, using a simple algorithm at the end of the baseline survey. No manual allocation occurred, ensuring allocation concealment until the time of randomization. Given the nature of the intervention, a self-managed app, no blinding of participants and investigators was possible.

Intervention

The digital ADHD therapy is an app for adults with ADHD. The app can be used in addition to the regular treatment (e.g., by primary care physicians, specialists, or psychotherapists). Users can employ the intervention independently.

The intervention aims at improving health-related quality of life. The app is based on evidence-based psychotherapeutic principles, specifically those of CBT, mindfulness-based cognitive therapy, DBT, and social skills training, together with psychoeducational content.

The intervention comprises 12 successive weekly modules for daily use. Each module contains psychoeducational videos, interactive exercises, and reflection tasks addressing topics such as attentiveness, impulsivity, organization, emotional regulation, and relapse prevention. Each module consists of seven units (i.e., daily sessions) that take 15–30 minutes to complete. Participants in the WCG were given access to the app after 12 weeks. The detailed content of the module is shown in Table 1.

Table 1

Content of the 12 weekly modules of the digital ADHD therapy app

eFigure

Flow chart of the participants through the study.

eTable 1

Frequencies of positive responses to the items on the patient satisfaction questionnaire by the intervention group participants

Measures

The primary endpoint was change in ADHD-associated quality of life (AAQoL) from T0 to T2 (14).

The secondary endpoints included changes in:

• ADHD symptoms (ASRS-1.1) (15)
• Subjective impairment (CGI-S) (16)
• Depression (PHQ-9) (17, 18)
• Anxiety (GAD-7) (19)
• Perceived stress (PSS-4) (20)
• Health competence (PHCS) (21)
• Medication adherence (MARS-D) (22)

All instruments were administered online at all three measurement times and are fully described in the eMethods.

Statistical analyses

All analyses followed a predetermined statistical analysis plan, available in the German Clinical Trials Register (DRKS00033320). Analyses followed the intention-to-treat principle (ITT) including all randomized participants (N = 307). The primary analysis used a mixed-effects model for repeated measures (MMRM) with fixed effects for group, time, and their interaction. Baseline scores were entered into the model as covariates. Within-participant correlations across repeated measurements were modeled using an unstructured covariance matrix. Results are reported as least squares mean differences (LSMD) with 90% confidence intervals (CI), reflecting the exploratory nature of this pilot trial (α = 0.10). A hierarchical testing procedure was employed to control the family-wise error rate across the primary and secondary endpoints. The sample size (n = 278; d = 0.30; 80% power) was calculated for two-sided α level of 0.10, allowing for attrition rates. To evaluate clinical relevance, thresholds for the minimal clinically important difference (MCID) were predefined and analyzed in the complete cases (CC) sample. An increase of ≥ 8 points on the AAQoL (23) and a reduction of ≥ 30% on the ASRS (12) were considered meaningful improvements. Group differences in responder status (clinically meaningfully improved vs. non-improved) were examined using χ² tests. Sensitivity analyses (CC and multiple imputation) and further information on assessment of clinical relevance are presented in the eMethods.

Results

Sample characteristics

The characteristics of the sample are shown in Table 2 and described in more detail in the eResults. Table 3 shows the psychopathological scores of the endpoints in the groups at the measurement points T0, T1, and T2.

Table 2

Participants’ demographic characteristics and psychopathological variables at the beginning of the study

Table 3

Survey findings in the study group at all measurement times, expressed as means with standard deviations

eTable 2

Results of the sensitivity analyses by means of covariance analysis with complete data sets for the change at the post-intervention survey

eTable 3

Results of the sensitivity analyses with the intention-to-treat sample by means of covariance analysis with multiple imputation at the postintervention survey

Primary ITT analyses

The MMRM, with an α level of 10%, showed a statistically significantly greater improvement in the primary endpoint, ADHD-related quality of life (AAQoL), in the IG than in the WCG (LSMD = 5.3; 90% CI [3.3; 7.3]; p < 0.001; d = 0.54 [0.33; 0.74]). ADHD symptom severity (ASRS: p < 0.001; d = −0.58 [−0.79; −0.37]) and functional impairment (CGI-S: p < 0.001; d = −0.69 [−0.90; −0.48]) likewise improved in the IG compared with the WCG.

No difference between the groups was observed for stress symptoms (PSS-4: p = 0.95, d = −0.01 [−0.14; 0.13]) or medication adherence (MARS-D: p = 0.14, d = 0.21 [−0.03; 0.45]). For depressive symptoms (PHQ-9: p < 0.001, d = −0.42 [−0.61; −0.22]) and anxiety (GAD-7: p < 0.001, d = −0.41 [−0.60; −0.23]), as well as for improvement in health competence (PHCS: p = 0.055, d = 0.24 [0.04; 0.44]), greater improvement was observed than in the WCG. However, these differences cannot be considered statistically significant due to the test hierarchy.

Sensitivity analyses (complete-case and multiple imputation) are reported in detail in the eResults.

Clinical relevance

Clinically meaningful improvement, defined as an AAQoL increase of ≥ 8 points, was achieved by 40% of participants (40/100) in the IG and 22% (26/120) in the WCG (χ²(1) = 8.73; p = 0.003). The ADHD symptoms (ASRS) decreased by at least 30% in 39% of participants in the IG versus 15% in the WCG (χ²(1) = 16.37; p < 0.001) (Table 4).

Table 4

Results of primary intention-to-treat analyses with a mixed model for repeated measurements at the post-intervention time point*1

eTable 4

Results from the intention-to-treat analyses with a mixed model for repeated measures at the interim survey (a level set on 10%)

eTable 5

Results of the sensitivity analyses by means of covariance analysis with complete data sets *1 for the change at the interim survey

eTable 6

Results of the sensitivity analyses*1 by means of covariance analysis with multiple imputation for the change at the interim survey

Discussion

In this preliminary trial of the efficacy of the ADHD app, the ADHD-related quality of life improved in the IG versus TAU. There were additional benefits regarding symptom severity and functional impairment, indicating a consistent benefit across multiple clinical endpoints. The evaluation of clinical relevance showed that more persons in the IG than in the WCG experienced a clinically meaningful improvement in their quality of life. However, the proportion of participants experiencing a clinically important improvement was low overall, with 40 such persons in the IG. When interpreting these results, it must be noted that the effectiveness of a self-help intervention like the evaluated app may not be comparable with that of ADHD medication, which usually shows sizeable effects on ADHD symptoms (11).

The majority of participants (69%, 213/307) were taking ADHD medication and used the app as supplemental treatment; this may have limited the clinically meaningful improvement. Furthermore, adherence to the intervention was low. Given an assumed dose–response relationship, we interpret the relatively small proportion of participants with clinically meaningful improvement as being due to the incomplete adherence. As no further analyses were conducted, however, this interpretation remains speculative.

This study adds to the limited number of high-quality trials evaluating digital interventions for ADHD (13). The moderate effects observed on quality of life and symptoms post intervention are consistent with the findings of other recent trials of digital CBT-based interventions for ADHD, including the studies by Nasri et al. (12) and Kenter et al. (13), both of which reported effects comparable with inactive control conditions. These results strengthen the evidence for the potential of low-threshold, scalable digital interventions in this population. The fact that the present trial showed efficacy without guidance is particularly advantageous in that this offers further benefits regarding availability and accessibility compared to guided web-based CBT.

On perceived stress, the IG showed no greater improvement. This may be explained by the only partial adherence to the app. On average, four modules were started in the intervention period. The recommended approach was to work through one module per week. Stress was not addressed until module 8, so most participants in the IG did not work on this topic. Low adherence is a known problem: in a meta-analysis by Karyotaki et al. (24) with over 2700 participants, almost 60% dropped out before completing half of the intervention content. While the overall attrition rate in our study was acceptable (28%, 87/307), the recommended “dosage” was not achieved. Only 20% of the participants (20/100) completed more than half of the 12 modules.

The effects found in this study could therefore be interpreted as a placebo effect. However, it must be noted that each module has extensive content. Completing the average of four modules still provided substantial therapeutic exposure, especially since the initial modules addressed core ADHD symptoms. The exploratorily observed dose–response relationship in this sample indicates that patients show greater improvement in quality of life when they engage with the app more intensively. Although this simple correlation provides only a descriptive indication of a possible dose–response relationship, it underscores the potential importance of adherence to the intervention.

Efforts should be made to further increase adherence, e.g., through automated reminders about app usage. The recommended treatment plan of one module per week might not be optimal—especially for a population with ADHD, who typically have difficulties with task management, avoidance behavior, and organizational skills. Despite the low adherence, the digital therapy was effective overall, indicating that the full “dose” of the treatment is not essential to achieve an effect. This is supported by the results of the interim analyses after 6 weeks.

No effect was found on medication adherence, which may be explained by the high medication adherence at baseline (mean MARS-D score > 20, with a maximum of 25).

Limitations

When interpreting the results presented here, it should be noted that the sample was not fully balanced in terms of sociodemographic characteristics. The high proportion of women (70%) diverges from epidemiological data in Germany, which suggest a balanced or slightly male-dominated distribution (25, 26). However, rising ADHD diagnoses among adult women (27), who are more likely to seek psychotherapy (28), may explain this overrepresentation.

Additionally, persons with ADHD often encounter difficulties at school and in further education, lowering their likelihood of attaining a higher-education qualification compared with those without ADHD (29). However, the sociodemographic pattern in this trial is not unusual in the context of studies evaluating psychotherapeutic interventions, particularly in samples of adults with ADHD. The prevalence of ADHD decreases with age (1), and gender differences in help-seeking behavior further affect clinical samples. Moreover, other studies focusing on interventions for adults with ADHD report similar levels of education (30, 31), suggesting that the observed distribution is in line with the existing research.

Nevertheless, the skewed gender and education distribution may limit generalizability of the results, particularly for older, male, or less educated subgroups, who may show different symptom courses, treatment effects, or adherence. Therefore, future studies should aim to achieve more balanced samples.

Furthermore, the participants were observed only over a 12-week period, without long-term follow-up. This limits the evaluation of lasting effects. Therefore, no claims regarding long-term effects can be made. Moreover, the assessments were based solely on the patients’ own reports; no active control group (e.g., with a sham intervention) was used. ADHD diagnoses were not reassessed using a standardized diagnostic procedure within the study. Instead, inclusion relied on previously established clinical diagnoses as documented in medical records and verified by study staff. The majority of diagnoses were relatively recent, however, and the current symptoms were ascertained using the ASRS cutoff.

Although the control group with TAU permits realistic comparison, active controls could better address nonspecific and expectancy effects (e.g., nocebo). Adverse events were not assessed systematically with standardized measures but only recorded if directly reported.

The use of a high significance threshold (α = 0.10) reflects the exploratory objectives of this study, but increases the risk of a type I error. The results should therefore be interpreted with caution and confirmed in future studies with more conservative methods (e.g., α = 0.05; active control groups).

Randomized controlled trials with longer follow-up periods and more conservative methodological approaches are necessary to strengthen the evidence for the effectiveness of self-guided digital psychotherapy for adults with ADHD. Additionally, efforts must be made to improve adherence and therefore increase the proportion of patients who experience a clinically meaningful change. Moreover, future work should apply more sophisticated dose–response analyses to explore the relationship between use and treatment effect. Furthermore, evaluation of health economic data would be helpful in drawing conclusions regarding cost effectiveness compared with standard treatment.

Conclusion

Despite some limitations, the pilot trial presented here provides preliminary but promising evidence for the efficacy of a new app for ADHD, thus addressing the urgent need for a novel intervention for adults with ADHD. Overall, the results are consistent with previous studies on web-based interventions for ADHD (12, 13). Self-guided interventions have advantages over conventional psychotherapy and guided web-based CBT in terms of availability and accessibility and therefore could greatly improve the care of adults with ADHD.

Funding
This study was funded by MiNDNET E-Health Solutions AG, Weinbergstrasse 29, 8006 Zürich, Switzerland. MiNDNET E-Health Solutions AG is the developer of the application evaluated in this study. 

Data sharing statement
Upon publication, anonymized participant data and a data dictionary will be made available to researchers on request, provided the proposed use of the data has been approved.

Conflict of interest statement
AB and SM have received honoraria for lectures and/or serving on advisory boards from Boehringer Ingelheim.

DS has, during the past 3 years, been a consultant to and/or has received honoraria from Janssen Cilag GmbH, Otsuka/Lundbeck Pharma GmbH, Laboratorios Farmacéuticos Rovi, Takeda Pharma Vertrieb GmbH & Co. KG, Medice GmbH, Boehringer Ingelheim, Recordati, MiNDNET AG and GmbH, Roche, Beiersdorf, and MedTriX GmbH, and has received authorship payments (books) from Elsevier, Thieme, Kohlhammer, Penguin Books, and Kösel.

AR has received honoraria for lectures and/or serving on advisory boards from Janssen, Boehringer Ingelheim, COMPASS, SAGE/Biogen, LivaNova, Medice, Shire/Takeda, MSD, and Cyclerion. AR has also received research grants from Medice and Janssen.

KGK has received funding from the German Federal Ministry of Education and Research (BMBF), JobCenter Hannover, HannoverPLUS Foundation, and the German Research Foundation (DFG). He reports serving on advisory boards for Takeda, Servier, Eli Lilly, and Johnson & Johnson. He has given lectures sponsored by Takeda, Eli Lilly, Idorsia, and Johnson & Johnson.

AK has, during the past 3 years, been a consultant and/or has received honoraria from Lilly Deutschland GmbH, Janssen Cilag GmbH, Otsuka Pharma GmbH, Laboratorios Farmacéuticos Rovi, and Takeda Pharma Vertrieb GmbH & Co. KG, and has shares in MiNDNET AG and GmbH.

ML has, during the past 3 years, been a consultant to and/or has received honoraria from Janssen Cilag GmbH, Otsuka Pharma GmbH, Laboratorios Farmacéuticos Rovi, Takeda Pharma Vertrieb GmbH & Co. KG, and TEVA Pharma, and has shares in MiNDNET AG and GmbH.

FB has received honoraria for lectures and/or serving on advisory boards and/or research funding from Forum für medizinische Fortbildung (FOMF; Forum for Advanced Medical Training), Takeda, Medice, and MiNDNET.

AP has received funding from the BMBF, Horizon2020, Medice, the DFG, and the National Institute for Health and Care Research (NIHR); serves on advisory boards for Takeda, Medice, and Boehringer, the scientific advisory board of ADHD Germany (ADHS Deutschland e.V.), and the steering group of the German Central ADHD Network (Centrales ADHS-Netz); has delivered lectures sponsored by Medice and Takeda; and is author of books and articles on psychotherapy.

AK and ML declare that no conflict of interest exists.

Manuscript submitted on 23 October 2025, revised version accepted on 15 April 2026

Corresponding author
Dipl.-Psych. Anna Baumeister

References

1. Song P, Zha M, Yang Q, Zhang Y, Li X, Rudan I: The prevalence of adult attention-deficit hyperactivity disorder: A global systematic review and meta-analysis. J Glob Health 2021; 11: 04009

2. Faraone SV, Banaschewski T, Coghill D, et al.: The World Federation of ADHD International Consensus Statement: 208 evidence-based conclusions about the disorder. Neurosci Biobehav Rev 2021; 128: 789–818

3. Canadian Attention Deficit Hyperactivity Disorder Resource Alliance (CADDRA): Canadian ADHD Practice Guidelines. Third Edition. Toronto ON: CADDRA 2011: https://caddra.ca/pdfs/caddraGuidelines2011.pdf (last accessed on 6 June 2025) .

4. Kooij JJS, Bijlenga D, Salerno L, et al.: Updated European Consensus Statement on diagnosis and treatment of adult ADHD. Eur Psychiatry 2019; 56: 14–34

5. Damm K, Kreis K, de Jong Lea et al.: VASOB – Versorgung von Patienten mit ADHS: Vom Status quo zur Optimierung der Behandlung – Ergebnisbericht 2023: https://innovationsfonds.g-ba.de/downloads/beschluss-dokumente/460/2022-10-16_VASOB_Ergebnisbericht.pdf (last accessed on 6 June 2025).

6. Kessler RC, Adler L, Barkley R, et al.: The prevalence and correlates of adult ADHD in the United States: Results from the national comorbidity survey replication. Am J Psychiatry 2006; 163: 716–23

7. Deutsche Gesellschaft für Kinder- und Jugendpsychiatrie, Psychosomatik und Psychotherapie (DGPPN), Deutsche Gesellschaft für Kinder- und Jugendpsychiatrie, Psychosomatik und Psychotherapie (DGKJP), Deutsche Gesellschaft für Sozialpädiatrie und Jugendmedizin e. V. (DGSPJ): S3-Leitlinie ADHS bei Kindern, Jugendlichen und Erwachsenen 2017 (Langfassung) – AWMF-Register-Nr. 028-045.

8. Young S, Adamo N, Ásgeirsdóttir BB, et al.: Females with ADHD: An expert consensus statement taking a lifespan approach providing guidance for the identification and treatment of attention-deficit/hyperactivity disorder in girls and women. BMC Psychiatry 2020; 20: 404

9. López-Pinar C, Martínez-Sanchís S, Carbonell-Vayá E, Sánchez-Meca J, Fenollar-Cortés J: Efficacy of nonpharmacological treatments on comorbid internalizing symptoms of adults with attention-deficit/hyperactivity disorder: A meta-analytic review. J Atten Disord 2020; 24: 456–78

10. Liu CI, Hua MH, Lu ML, Goh KK: Effectiveness of cognitive behavioural-based interventions for adults with attention-deficit/hyperactivity disorder extends beyond core symptoms: A meta-analysis of randomized controlled trials. Psychol Psychother 2023; 96: 543–59

11. Ostinelli EG, Schulze M, Zangani C, et al.: Comparative efficacy and acceptability of pharmacological, psychological, and neurostimulatory interventions for ADHD in adults: A systematic review and component network meta-analysis. Lancet Psychiatry 2025; 12: 32–43

12. Nasri B, Cassel M, Enhärje J, et al.: Internet delivered cognitive behavioral therapy for adults with ADHD—a randomized controlled trial. Internet Interv 2023; 33: 100636

13. Kenter RMF, Lundervold AJ, Nordgreen T: A self-guided internet-delivered intervention for adults with ADHD: A protocol for a randomized controlled trial. Internet Interv 2023; 32: 100614

14. Brod M, Johnston J, Able S, Swindle R: Validation of the adult attention-deficit/hyperactivity disorder quality-of-life scale (AAQoL): A disease-specific quality-of-life measure. Qual Life Res 2006; 15: 117–29

15. Kessler RC, Adler L, Ames M, et al.: The World Health Organization Adult ADHD Self-Report Scale (ASRS): A short screening scale for use in the general population. Psychol Med 2005; 35: 245–56

16. Busner J, Targum SD: The clinical global impressions scale: Applying a research tool in clinical practice. Psychiatry (Edgmont) 2007; 4: 28–37

17. Gräfe K, Zipfel S, Herzog W, Löwe B: Screening psychischer Störungen mit dem „Gesundheitsfragebogen für Patienten (PHQ-D)“. Diagnostica 2004; 50: 171–81

18. Kroenke K, Spitzer RL, Williams JB: The PHQ-9: Validity of a brief depression severity measure. J Gen Intern Med 2001; 16: 606–13

19. Spitzer RL, Kroenke K, Williams JBW, Löwe B: A brief measure for assessing generalized anxiety disorder: The GAD-7. Arch Intern Med 2006; 166: 1092–7

20. Schäfer SK, von Boros L, Göritz AS, et al.: The perceived stress scale 2&2: A two-factorial German short version of the perceived stress scale. Front Psychiatry 2023; 14: 1195986

21. Smith MS, Wallston KA, Smith CA: The development and validation of the perceived health competence scale. Health Educ Res 1995; 10: 51–64

22. Mahler C, Hermann K, Horne R, et al.: Assessing reported adherence to pharmacological treatment recommendations. Translation and evaluation of the Medication Adherence Report Scale (MARS) in Germany. J Eval Clin Pract 2010; 16: 574–9

23. Tanaka Y, Brod M, Lane JR, Upadhyaya H: What is a clinically relevant improvement in quality of life in adults with ADHD? J Atten Disord 2019; 23: 65–75

24. Karyotaki E, Kleiboer A, Smit F, et al.: Predictors of treatment dropout in self-guided web-based interventions for depression: An „individual patient data“ meta-analysis. Psychol Med 2015; 45: 2717–26

25. de Zwaan M, Gruss B, Müller A, et al.: The estimated prevalence and correlates of adult ADHD in a german community sample. Eur Arch Psychiatry Clin Neurosci 2012; 262: 79–86

26. Müller M, Turner D, Barra S, Rösler M, Retz W: ADHD and associated psychopathology in older adults in a german community sample. J Neural Transm (Vienna) 2023; 130: 313–23

27. Ivanova M, Holstiege J, Akmatov MK, Müller D, Kohring C: The incidence of AD(H)D spectrum disorders in adults: An analysis of nationwide claims data of the statutory health insurance system in Germany, 2015–2024. Dtsch Arztebl Int 2025; 122: 697–8

28. Prütz F, Rommel A, Thom J, Du Y, Sarganas G, Starker A: Inanspruchnahme ambulanter medizinischer Leistungen in Deutschland – Ergebnisse der Studie GEDA 2019/2020-EHIS. J Health Monit 2021 6: 49–71.

29. Murphy KR, Barkley RA, Bush T: Young adults with attention deficit hyperactivity disorder: Subtype differences in comorbidity, educational, and clinical history. J Nerv Ment Dis 2002; 190: 147–57

30. Selaskowski B, Reiland M, Schulze M, et al.: Chatbot-supported psychoeducation in adult attention-deficit hyperactivity disorder: Randomised controlled trial. BJPsych Open 2023; 9: e192

31. Schönenberg M, Wiedemann E, Schneidt A, et al.: Neurofeedback, sham neurofeedback, and cognitive-behavioural group therapy in adults with attention-deficit hyperactivity disorder: A triple-blind, randomised, controlled trial. Lancet Psychiatry 2017; 4: 673–84

e1. Attkisson CC, Zwick R: The client satisfaction questionnaire: Psychometric properties and correlations with service utilization and psychotherapy outcome. Eval Program Plann 1982; 5: 233–7

e2. Schmidt J, Lamprecht F, Wittmann WW: [Satisfaction with inpatient management. Development of a questionnaire and initial validity studies]. Psychother Psychosom Med Psychol 1989; 39: 248–55

e3. Faul F, Erdfelder E, Lang AG, Buchner A: G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behav Res Methods 2007; 39: 175–91

e4. Cohen J: Statistical Power Analysis for the Behavioral Sciences. Routledge (New York) 2013