Using Postmarket Surveillance to Assess Safety-Related Events in a Digital Rehabilitation App (Kaia App): Observational Study

Digital Therapeutics for Improving Effectiveness of Pharmaceutical Drugs and Biological Products: Preclinical and Clinical Studies Supporting Development of Drug + Digital Combination Therapies for Chronic Diseases

Limitations of pharmaceutical drugs and biologics for chronic diseases (e.g., medication non-adherence, adverse effects, toxicity, or inadequate efficacy) can be mitigated by mobile medical apps, known as digital therapeutics (DTx). Authorization of adjunct DTx by the US Food and Drug Administration and draft guidelines on "prescription drug use-related software" illustrate opportunities to create drug + digital combination therapies, ultimately leading towards drug-device combination products (DTx has a status of medical devices). Digital interventions (mobile, web-based, virtual reality, and video game applications) demonstrate clinically meaningful benefits for people living with Alzheimer's disease, dementia, rheumatoid arthritis, cancer, chronic pain, epilepsy, depression, and anxiety. In the respective animal disease models, preclinical studies on environmental enrichment and other non-pharmacological modalities (physical activity, social interactions, learning, and music) as surrogates for DTx "active ingredients" also show improved outcomes. In this narrative review, we discuss how drug + digital combination therapies can impact translational research, drug discovery and development, generic drug repurposing, and gene therapies. Market-driven incentives to create drug-device combination products are illustrated by Humira® (adalimumab) facing a "patent-cliff" competition with cheaper and more effective biosimilars seamlessly integrated with DTx. In conclusion, pharma and biotech companies, patients, and healthcare professionals will benefit from accelerating integration of digital interventions with pharmacotherapies.

Impact of an AI app-based exercise program for people with low back pain compared to standard care: A longitudinal cohort-study

Background

Low Back Pain (LBP) is a common health problem worldwide. In recent years, the use of mobile applications for the treatment of various diseases has increased, due to the Corona pandemic.

Objective

The aim of this study is to investigate the extent to which artificial intelligence (AI)-assisted exercise recommendations can reduce pain and pain-related impairments in daily life for patients with LBP, compared to standard care.

Methods

To answer the research question, an 8-week app-based exercise program was conducted in the intervention group. To measure the influence of the exercise program, pain development and pain-related impairment in daily life have been evaluated. A so-called rehabilitation sports group served as the control group. The main factors for statistical analysis were factor time and group comparison. For statistical calculations, a mixed analysis of variance for pain development was conducted. A separate check for confounders was made. For pain impairment in daily life nonparametric tests with the mean of change between the time points are conducted.

Results

The intervention group showed a reduction in pain development of 1.4 points compared to an increase of 0.1 points in the control group on the numeric rating scale. There is a significant interaction of time and group for pain development. Regarding pain-related impairments in daily life, the intervention group has a reduction of the oswestry disability index scores by 3.8 points compared to an increase of 2.3 in the control group. The biggest differences become apparent 8 weeks after the start of treatment. The significant results have a medium to strong effect.

Conclusion

The results shown here suggest that the use of digital AI-based exercise recommendations in patients with LBP leads to pain reduction and a reduction in pain-related impairments in daily living compared to traditional group exercise therapy.