Artificial intelligence based discovery of the association between depression and chronic fatigue syndrome

Personalized Management of Fatigue in Individuals With Myalgic Encephalomyelitis/Chronic Fatigue Syndrome and Long COVID Using a Smart Digital mHealth Solution: Protocol for a Participatory Design Approach

BACKGROUND

Fatigue is the most common symptom in myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) and long COVID, impacting patients' quality of life; however, there is currently a lack of evidence-based context-aware tools for fatigue self-management in these populations.

OBJECTIVE

This study aimed to (1) address fatigue in ME/CFS and long COVID through the development of digital mobile health solutions for self-management, (2) predict perceived fatigue severity using real-time data, and (3) assess the feasibility and potential benefits of personalized digital mobile health solutions.

METHODS

The MyFatigue project adopts a patient-centered approach within the participatory health informatics domain. Patient representatives will be actively involved in decision-making processes. This study combines inductive and deductive research approaches, using qualitative studies to generate new knowledge and quantitative methods to test hypotheses regarding the relationship between factors like physical activity, sleep behaviors, and perceived fatigue in ME/CFS and long COVID. Co-design methods will be used to develop a personalized digital solution for fatigue self-management based on the generated knowledge. Finally, a pilot study will evaluate the feasibility, acceptance, and potential benefits of the digital health solution.

RESULTS

The MyFatigue project opened to enrollment in November 2023. Initial results are expected to be published by the end of 2024.

CONCLUSIONS

This study protocol holds the potential to expand understanding, create personalized self-management approaches, engage stakeholders, and ultimately improve the well-being of individuals with ME/CFS and long COVID.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

PRR1-10.2196/50157.

A synthetic data generation system for myalgic encephalomyelitis/chronic fatigue syndrome questionnaires

Artificial intelligence or machine-learning-based models have proven useful for better understanding various diseases in all areas of health science. Myalgic Encephalomyelitis or chronic fatigue syndrome (ME/CFS) lacks objective diagnostic tests. Some validated questionnaires are used for diagnosis and assessment of disease progression. The availability of a sufficiently large database of these questionnaires facilitates research into new models that can predict profiles that help to understand the etiology of the disease. A synthetic data generator provides the scientific community with databases that preserve the statistical properties of the original, free of legal restrictions, for use in research and education. The initial databases came from the Vall Hebron Hospital Specialized Unit in Barcelona, Spain. 2522 patients diagnosed with ME/CFS were analyzed. Their answers to questionnaires related to the symptoms of this complex disease were used as training datasets. They have been fed for deep learning algorithms that provide models with high accuracy [0.69-0.81]. The final model requires SF-36 responses and returns responses from HAD, SCL-90R, FIS8, FIS40, and PSQI questionnaires. A highly reliable and easy-to-use synthetic data generator is offered for research and educational use in this disease, for which there is currently no approved treatment.

Virtual Digital Psychotherapist App-Based Treatment in Patients With Methamphetamine Use Disorder (Echo-APP): Single-Arm Pilot Feasibility and Efficacy Study

BACKGROUND

Substance use disorder is one of the severe public health problems worldwide. Inequitable resources, discrimination, and physical distances limit patients' access to medical help. Automated conversational agents have the potential to provide in-home and remote therapy. However, automatic dialogue agents mostly use text and other methods to interact, which affects the interaction experience, treatment immersion, and clinical efficacy.

OBJECTIVE

The aim of this paper is to describe the design and development of Echo-APP, a tablet-based app with the function of a virtual digital psychotherapist, and to conduct a pilot study to explore the feasibility and preliminary efficacy results of Echo-APP for patients with methamphetamine use disorder.

METHODS

Echo-APP is an assessment and rehabilitation program developed for substance use disorder (SUD) by a team of clinicians, psychotherapists, and computer experts. The program is available for Android tablets. In terms of assessment, the focus is on the core characteristics of SUD, such as mood, impulsivity, treatment motivation, and craving level. In terms of treatment, Echo-APP provides 10 treatment units, involving awareness of addiction, motivation enhancement, emotion regulation, meditation, etc. A total of 47 patients with methamphetamine dependence were eventually enrolled in the pilot study to receive a single session of the Echo-APP-based motivational enhancement treatment. The outcomes were assessed before and after the patients' treatment, including treatment motivation, craving levels, self-perception on the importance of drug abstinence, and their confidence in stopping the drug use.

RESULTS

In the pilot study, scores on the Stages of Change Readiness and Treatment Eagerness Scale and the questionnaire on motivation for abstaining from drugs significantly increased after the Echo-APP-based treatment (P<.001, Cohen d=-0.60), while craving was reduced (P=.01, Cohen d=0.38). Patients' baseline Generalized Anxiety Disorder-7 assessment score (β=3.57; P<.001; 95% CI 0.80, 2.89) and Barratt Impulsiveness Scale (BIS)-motor impulsiveness score (β=-2.10; P=.04; 95% CI -0.94, -0.02) were predictive of changes in the patients' treatment motivation during treatment. Moreover, patients' baseline Generalized Anxiety Disorder-7 assessment score (β=-1.607; P=.03; 95% CI -3.08, -0.14), BIS-attentional impulsivity score (β=-2.43; P=.004; 95% CI -4.03, -0.83), and BIS-nonplanning impulsivity score (β=2.54; P=.002; 95% CI 0.98, 4.10) were predictive of changes in craving scores during treatment.

CONCLUSIONS

Echo-APP is a practical, accepted, and promising virtual digital psychotherapist program for patients with methamphetamine dependence. The preliminary findings lay a good foundation for further optimization of the program and the promotion of large-scale randomized controlled clinical studies for SUD.

Occupational Fatigue: Relationship With Personality Traits and Decent Work

Psychology of working theory (PWT) and psychology of working framework (PWF) offered a psychological view of decent work. The present study examined the associations among personality traits, decent work and Occupational Fatigue Exhaustion Recovery Scale (OFER). Two hundred and thirty four participants filled out the Big Five Questionnaire, the Italian version of the Decent Work Scale, and the Italian version of the OFER Scale. Hierarchical regressions showed that decent work explained incremental variance beyond personality traits with respect to OFER both considering total score and its three dimensions (chronic fatigue, acute fatigue, persistent fatigue). The present study underlined the value of decent work in relation to occupational fatigue beyond the contributions of personality, in particular in relation to the dimensions of Adequate compensation and Free time and rest for less occupational fatigue (both as total and as dimensions).

Induced pluripotent stem cells as suitable sensors for fibromyalgia and myalgic encephalomyelitis/chronic fatigue syndrome

BACKGROUND

Fibromyalgia (FM) and myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) are devastating metabolic neuroimmune diseases that are difficult to diagnose because of the presence of numerous symptoms and a lack of specific biomarkers. Despite patient heterogeneity linked to patient subgroups and variation in disease severity, anomalies are found in the blood and plasma of these patients when compared with healthy control groups. The seeming specificity of these “plasma factors”, as recently reported by Ron Davis and his group at Stanford University, CA, United States, and observations by our group, have led to the proposal that induced pluripotent stem cells (iPSCs) may be used as metabolic sensors for FM and ME/CFS, a hypothesis that is the basis for this in-depth review.

AIM

To identify metabolic signatures in FM and/or ME/CFS supporting the existence of disease-associated plasma factors to be sensed by iPSCs.

METHODS

A PRISMA (Preferred Reported Items for Systematic Reviews and Meta-analysis)-based systematic review of the literature was used to select original studies evaluating the metabolite profiles of FM and ME/CFS body fluids. The MeSH terms “metabolomic” or “metabolites” in combination with FM and ME/CFS disease terms were screened against the PubMed database. Only original studies applying omics technologies, published in English, were included. The data obtained were tabulated according to the disease and type of body fluid analyzed. Coincidences across studies were searched and P-values reported by the original studies were gathered to document significant differences found in the disease groups.

RESULTS

Eighteen previous studies show that some metabolites are commonly altered in ME/CFS and FM body fluids. In vitro cell-based assays have the potential to be developed as screening platforms, providing evidence for the existence of factors in patient body fluids capable of altering morphology, differentiation state and/or growth patterns. Moreover, they can be further developed using approaches aimed at blocking or reversing the effects of specific plasma/serum factors seen in patients. The documented high sensitivity and effective responses of iPSCs to environmental cues suggests that these pluripotent cells could form robust, reproducible reporter systems of metabolic diseases, including ME/CFS and FM. Furthermore, culturing iPSCs, or their mesenchymal stem cell counterparts, in patient-conditioned medium may provide valuable information to predict individual outcomes to stem-cell therapy in the context of precision medicine studies.

CONCLUSION

This opinion review explains our hypothesis that iPSCs could be developed as a screening platform to provide evidence of a metabolic imbalance in FM and ME/CFS.

Hericium erinaceus Improves Mood and Sleep Disorders in Patients Affected by Overweight or Obesity: Could Circulating Pro-BDNF and BDNF Be Potential Biomarkers?

Epidemiological data indicate that subjects affected by obesity have an increased risk of developing mood disorders. The relationship between obesity and mood disorders is bidirectional. We assessed whether a Hericium erinaceus treatment improved depression, anxiety, sleep, and binge eating disorders after 8 weeks of supplementation in subjects affected by overweight or obesity under a low calorie diet regimen. Looking for a possible clinical biomarker, we assessed the serum balance between brain-derived neurotrophic factor (BDNF) and its precursor pro-BDNF before and after H. erinaceus supplementation. Seventy-seven volunteers affected by overweight or obesity were recruited at the offices of the Department of Preventive Medicine, Luigi Devoto Clinic of Work, Obesity Centre, at the IRCCS Foundation Policlinico Hospital of Milan (Italy). Patients were recruited only if they had a mood and/or sleep disorder and/or were binge eating as evaluated through self-assessment questionnaires. We used two different enzyme-linked immunosorbent assays kits to discriminate circulating levels of pro-BDNF and BDNF. Eight weeks of oral H. erinaceus supplementation decreased depression, anxiety, and sleep disorders. H. erinaceus supplementation improved mood disorders of a depressive-anxious nature and the quality of the nocturnal rest. H. erinaceus increased circulating pro-BDNF levels without any significant change in BDNF circulating levels.