Results of the Study
A key quality feature of this project lies in its methodological transparency and the disclosure of training parameters and algorithms. Unlike many proprietary neurofeedback systems, this project clearly documents
- which frequency bands are trained,
- how the feedback is delivered, and
- which individual adjustments can be made throughout the training process.
This allows for a verifiable, replicable, and scientifically grounded application within the framework of indicated prevention.
The collected data are being systematically analyzed and serve as the basis for the Master’s thesis of Lulu Jiang (MBA Digital Health), which evaluates the results from the training groups and contributes to the economic assessment of neurofeedback in health education.
Symptoms Included in the Study
The study included clients presenting with early symptoms of stress-related or attention-related dysregulation (indicated prevention).
The following symptom categories were recorded:
- Concentration and attention difficulties
- Inner restlessness, nervousness, and sleep disturbances
- Emotional dysregulation (e.g., irritability, fatigue)
- Psychosomatic stress symptoms (e.g., tension, headaches, vegetative imbalance)
- Reduced self-regulation capacity (e.g., in burnout risk)
Comparison of Training Systems
1. Home Training with Myndlift (16)
App-based EEG neurofeedback using Beta/SMR or frequency band training.
Self-directed training with online supervision.
Observed improvements:
Approximately 70% of participants reported a marked reduction in stress-related symptoms, Improved concentration and sleep quality, Enhanced emotional stability and sense of self-efficacy.
2. Home Therapy with Atlantis Neurofeedback (7)
Computer-based EEG neurofeedback using Beta/SMR or frequency band training.
Self-directed training with online supervision.
Observed improvements:
Approximately 70% symptom improvement, with somewhat stronger effects among chronically stressed clients. Improved stress resilience and reduction of psychosomatic complaints.
Limitation:
In the research setting, around 70% of the training sessions with the Atlantis system were discontinued due to the complex handling of the therapy cap, whereas no such difficulties occurred with the Myndlift system (0%).
Scientific and Preventive Context:
A central question for future research is whether the positive effect of approximately 70% symptom improvement observed in the Remote-Health projects can be replicated in a placebo-controlled, double-blind study with a larger sample size, and whether this effect remains stable after six months.
This line of inquiry builds on existing meta-analyses (e.g., Arns et al.), which have demonstrated sustained and reproducible effects of neurofeedback for attention disorders.
Relevance for Indicated Prevention:
These findings are particularly relevant to the field of indicated prevention, where symptoms are already present but full-blown disorders have not yet developed.
A 70% success rate represents a significant public health and economic potential, as neurofeedback at this stage could effectively prevent the onset of chronic conditions and reduce long-term healthcare costs.
Conclusion
For individuals already engaged in in-person neurofeedback therapy, it may be economically beneficial to consider a home-based training option, adapted to their individual circumstances. Overall, home-based neurofeedback offers a more cost-effective alternative with comparable therapeutic effects, provided appropriate supervision and individualized adjustments are ensured.
It does not appear meaningful to implement neurofeedback within the framework of general health education, as the economic and personnel requirements are disproportionately high. However, there is significant potential in the field of indicated prevention, where early intervention can prevent the development of manifest disorders.
Further placebo-controlled, double-blind studies are needed to confirm these findings and to illustrate the economic impact of neurofeedback within public health policy frameworks.
M.A. Lulu Jiang (cand. MBA Digital Health)