Search

Objective: The aim of this study was to examine the processes involved in a guided self‐help (GSH) pre‐treatment intervention (RecoveryMANTRA) for patients with anorexia nervosa (AN), by measuring the levels of patient/mentor Language Style Matching (LSM). RecoveryMANTRA was supported by student mentors or peer mentors (recovered individuals) over six weekly chat‐based sessions. We examined whether LSM during RecoveryMANTRA predicted patients'working alliance with the clinic therapist, motivation, eating disorder (ED) and general psychopathology. A further aim was to examine differences in LSM between student mentors and peer mentors. Method: 87 AN adults received RecoveryMANTRA plus treatment as usual. The LSM algorithm was used to calculate verbal attunement between patient and mentor. Participants were assessed at baseline and at the end of the intervention. Results: Both early (1st session) and late (6th session) LSM predicted higher working alliance with the clinic therapist. Moreover, late LSM predicted lower EDs symptoms at the end of the RecoveryMANTRA intervention. Patient/peer mentor dyads showed higher late verbal attunement than patient/student mentor dyads. Conclusions: These findings suggests that in the early phase of treatment relational aspects can impact on engagement with treatment. Verbal attunement in a GSH for AN is associated with working alliance and better clinical outcome. Highlights: Language Style Matching (LSM) between patients and mentors in both the early and late phase of the RecoveryMANTRA intervention predicted higher working alliance with the clinic therapist.LSM in the last phase (6th session) was associated with lower eating disorders (EDs) symptoms at the end of the RecoveryMANTRA intervention.Patients/peer mentors dyads showed higher late LSM than patients/student mentors dyads. [ABSTRACT FROM AUTHOR]

Fibromyalgia (FM) is a chronic pain syndrome with an unclear etiology. In addition to pain, FM patients suffer from a diverse array of symptoms and comorbidities, encompassing fatigue, cognitive dysfunction, mood disorders, sleep deprivation, and dizziness. Due to the complexity of FM, the diagnosis and treatment of it are highly challenging. The aim of the present work was to investigate some clinical and psychological characteristics of FM patients, and to uncover possible correlations with pharmacological and non-pharmacological therapies. We conducted a cross-sectional, questionnaire-based study aimed at evaluating pain, psychological traits, and the self-perceived effectiveness of pharmacological and non-pharmacological treatments in an Italian population of FM patients. Descriptive statistics, correlation, and inference analyses were performed. We found a prevalence of a neuropathic/nociplastic type of pain, which correlated with psychological traits such as anxiety, low mood, psychophysical discomfort, and the inability to relax. The pain type and psychological traits proved to play a role in determining the self-perceived effectiveness of therapeutic interventions. Patients revealed a better response to non-pharmacological therapies, particularly dietary interventions, relaxation techniques, and psychotherapy rather than pharmacological interventions. The sum of our data indicates that for better outcomes, the type of pain and psychological traits should be considered for tailor-made treatments considering non-pharmacological protocols as a complement to the use of drugs. [ABSTRACT FROM AUTHOR]

Introduction: The Respiratory Disturbance Index (RDI) computed by an implantable cardioverter defibrillator (ICD) algorithm accurately identifies severe sleep apnea (SA). In the present analysis, we tested the hypothesis that RDI could also predict atrial fibrillation (AF) burden. Methods: Patients with ejection fraction ≤35% implanted with an ICD were enrolled and followed up for 24 months. One month after implantation, patients underwent a polysomnographic study. The weekly mean RDI value was considered, as calculated during the entire follow‐up period and over a 1‐week period preceding the sleep study. The endpoints were as follows: daily AF burden of ≥5 min, ≥6 h, ≥23 h. Results: Here, 164 patients had usable RDI values during the entire follow‐up period. Severe SA (RDI ≥ 30 episodes/h) was diagnosed in 92 (56%) patients at the time of the sleep study. During follow‐up, AF burden ≥ 5 min/day was documented in 70 (43%), ≥6 h/day in 48 (29%), and ≥23 h/day in 33 (20%) patients. Device‐detected RDI ≥ 30 episodes/h at the time of the polygraphy, as well as the polygraphy‐measured apnea hypopnea index ≥ 30 episodes/h, were not associated with the occurrence of the endpoints, using a Cox regression model. However, using a time‐dependent model, continuously measured weekly mean RDI ≥ 30 episodes/h was independently associated with AF burden ≥ 5 min/day (hazard ratio [HR]: 2.13, 95% confidence interval [CI]: 1.24–3.65, p =.006), ≥6 h/day (HR: 2.75, 95% CI: 1.37–5.49, p =.004), and ≥23 h/day (HR: 2.26, 95% CI: 1.05–4.86, p =.037). Conclusions: In heart failure patients, ICD‐diagnosed severe SA on follow‐up data review identifies patients who are from two‐ to three‐fold more likely to experience an AF episode, according to various thresholds of daily AF burden. [ABSTRACT FROM AUTHOR]

Background: Handwriting is an acquired complex cognitive and motor skill resulting from the activation of a widespread brain network. Handwriting therefore may provide biologically relevant information on health status. Also, handwriting can be collected easily in an ecological scenario, through safe, cheap, and largely available tools. Hence, objective handwriting analysis through artificial intelligence would represent an innovative strategy for telemedicine purposes in healthy subjects and people affected by neurological disorders. Materials and Methods: One-hundred and fifty-six healthy subjects (61 males; 49.6 ± 20.4 years) were enrolled and divided according to age into three subgroups: Younger adults (YA), middle-aged adults (MA), and older adults (OA). Participants performed an ecological handwriting task that was digitalized through smartphones. Data underwent the DBNet algorithm for measuring and comparing the average stroke sizes in the three groups. A convolutional neural network (CNN) was also used to classify handwriting samples. Lastly, receiver operating characteristic (ROC) curves and sensitivity, specificity, positive, negative predictive values (PPV, NPV), accuracy and area under the curve (AUC) were calculated to report the performance of the algorithm. Results: Stroke sizes were significantly smaller in OA than in MA and YA. The CNN classifier objectively discriminated YA vs. OA (sensitivity = 82%, specificity = 80%, PPV = 78%, NPV = 79%, accuracy = 77%, and AUC = 0.84), MA vs. OA (sensitivity = 84%, specificity = 56%, PPV = 78%, NPV = 73%, accuracy = 74%, and AUC = 0.7), and YA vs. MA (sensitivity = 75%, specificity = 82%, PPV = 79%, NPV = 83%, accuracy = 79%, and AUC = 0.83). Discussion: Handwriting progressively declines with human aging. The effect of physiological aging on handwriting abilities can be detected remotely and objectively by using machine learning algorithms. [ABSTRACT FROM AUTHOR]