In this systematic review and meta-analysis, we evaluated the association between Attention-Deficit/Deficit Hyperactivity (ADHD) and atypical Sensory Processing Patterns (SPPs). We searched PubMed, Embase, and Web of Science from their inception until June 30^th, 2024. We included studies examining SPP using questionnaires in participants with a diagnosis of ADHD compared with healthy controls. The study risk of bias was assessed using a modified Newcastle-Ottawa Scale. A random-effects meta-analysis was conducted using R software (Version 4.3.1), considering sensory modulation atypicities severity as the primary outcome. We explored the risk of publication bias and conducted sensitivity analyses to test the robustness of the findings. After screening 10,750 references, we included 30 studies (reported in 32 publications) encompassing a total of 5,374 participants, with 23 studies focusing on children, and seven on adults. Patients with ADHD presented significantly more severe sensory atypicities than the control group in multiple domains: sensory sensitivity (k=13; SMD=1.17; 5% CI [0.75;1.59]; I²=87%), sensory avoiding (k=11; SMD=1.15; 95% CI [0.66;1.64]; I²=92%), low sensory registration (k=23; SMD=1.22; 95%CI [0.89;1.56]; I²=92%), and sensory seeking (k=15; SMD=1.23; 95%CI [0.48;1.97]; I²=97%). Heterogeneity was high in all analyses. Only nine studies were deemed at overall low risk of bias. Even though available ADHD clinical guidelines do not specifically mention the need to assess sensory processing in ADHD, our meta-analysis suggests that this should be systematically explored in the evaluation of children and adults referred for ADHD.

Background and aims: Ecological momentary assessment (EMA) studies have previously demonstrated a prospective influence of craving on substance use in the following hours. Conceptualizing substance use as a dynamic system of causal elements could provide valuable insights into the interaction of craving with other symptoms in the process of relapse. The aim of this study was to improve the understanding of these daily life dynamic inter‐relationships by applying dynamic networks analyses to EMA data sets. Design, setting and participants: Secondary analyses were conducted on time‐series data from two 2‐week EMA studies. Data were collected in French outpatient addiction treatment centres. A total of 211 outpatients beginning treatment for alcohol, tobacco, cannabis, stimulants and opiate addiction took part. Measurements: Using mobile technologies, participants were questioned four times per day relative to substance use, craving, exposure to cues, mood, self‐efficacy and pharmacological addiction treatment use. Multi‐level vector auto‐regression models were used to explore contemporaneous, temporal and between‐subjects networks. Findings: Among the 8260 daily evaluations, the temporal network model, which depicts the lagged associations of symptoms within participants, demonstrated a unidirectional association between craving intensity at one time (T0) and primary substance use at the next assessment (T1, r = 0.1), after controlling for the effect of all other variables. A greater self‐efficacy at T0 was associated with fewer cues ( r = −0.04), less craving ( r = −0.1) and less substance use at T1 ( r = −0.07), and craving presented a negative feedback loop with self‐efficacy ( r = −0.09). Conclusions: Dynamic network analyses showed that, among outpatients beginning treatment for addiction, high craving, together with low self‐efficacy, appear to predict substance use more strongly than low mood or high exposure to cues.

To establish an overarching definition of what constitutes a sleep disorder, it is essential to know which health conditions should be included in the classifications of sleep disorders and to better distinguish the normal from the pathological in sleep medicine. This would bring together several professional organizations in their understanding of this hitherto heterogeneous concept. However, no consensus regarding a general definition of a sleep disorder currently exists. We explore two of the main frameworks used in medical epistemology that could be used to define sleep disorders rigorously: harmful dysfunction analysis (HDA) and symptom network theory. For each framework, we detail the conceptual background, the method for applying it to the establishment of a general definition of a sleep disorder, the application to main sleep disorders of the ICSD-3-TR (with an emphasis on insomnia disorder) and a discussion of the main aspects of these two general definitions in the context of sleep medicine. The complementarity between the two definitions could then be considered as an "epistemic hub" which could serve to clarify the debate about what sleep disorders are, so that our understanding of them and their clinical management improve.

Sleep disorders are defined on the basis of diagnostic criteria presented in medical classifications. However, no consensus has emerged on the exact list of operational symptoms that should be systematically investigated in the field of sleep medicine. We propose a systematic analysis of sleep symptoms that figure in a set of self-reported multiple sleep disorder screening questionnaires for adult populations, to identify the content overlap of symptoms that probe the presence of central sleep symptoms, and to highlight the potential level of heterogeneity among sleep disorder questionnaires. The method comprises three steps: (i) the selection of self-reported multiple sleep disorder screening questionnaires; (ii) item extraction and selection; (iii) the extraction of symptoms from items. Frequency of sleep symptoms and content overlap (Jaccard Index) are analyzed. We extracted 469 items that provide 60 different symptoms from 12 questionnaires. Insomnia, somnolence, and sleep-related breathing symptoms were found in all the questionnaires. The mean overlap among all questionnaires evaluated with the Jaccard Index is 0.44, i.e., moderate similarity. Despite limitations related to the selection of questionnaires and the symptom extraction and harmonization, this study underlines the need to standardize sleep symptom contents for sleep medicine in order to enhance the practicability, reliability, and validity of sleep disorder diagnoses.

Introduction: Major depression episode (MDE) and postpartum depression (PPD) have the same diagnosis criteria, but dissimilarities may be present regarding the frequency and structure of depressive symptoms.Methods: We used data from the IGEDEPP Cohort (France) to examine DSM-5 depressive symptoms in two groups of women: 486 with PPD and 871 with a history of non-perinatal MDE. We compare (i) the frequency of each depressive symptom adjusted for the severity of depression, (ii) the global structure of depressive symptom networks, and (iii) the centrality of each symptom in the two networks.Results: Women with PPD were significantly more likely to have appetite disturbance, psychomotor symptoms, and fatigue than those with MDE, while sadness, anhedonia, sleep disturbance, and suicidal ideation were significantly less common. There were no significant differences in the global structure of depressive symptoms of MDE and PPD. However, the most central criterion of the MDE network was “Sadness” while it was “Suicidal ideations” for the PPD network. “Sleep” and “Suicidal ideations” criteria were more central for PPD network, whereas “Culpability” was more important for MDE network than for PPD network.Conclusion: We found differences in depressive symptoms expression between PPD and MDE, which justify continuing to clinically distinguish PPD from MDE.