Background: Emotional behavior problems (EBP) are the most common and persistent mental health issues in early childhood. Early intervention programs are crucial in helping children with EBP. Parent–child interaction therapy (PCIT) is an evidence-based therapy designed to address personal difficulties of parent–child dyads as well as reduce externalizing behaviors. In clinical practice, parents consistently struggle to provide accurate characterizations of EBP symptoms (number, timing of tantrums, precipitating events) even from the week before in their young children. The main aim of the study is to evaluate feasibility of the use of smartwatches in children aged 3–7 years with EBP. Methods: This randomized double-blind controlled study aims to recruit a total of 100 participants, consisting of 50 children aged 3–7 years with an EBP measure rated above the clinically significant range (T-score ≥ 60) (Eyberg Child Behavior Inventory-ECBI; Eyberg & Pincus, 1999) and their parents who are at least 18 years old. Participants are randomly assigned to the artificial intelligence-PCIT group (AI-PCIT) or the PCIT-sham biometric group. Outcome parameters include weekly ECBI and Pediatric Sleep Questionnaire (PSQ) as well as Child Behavior Checklist (CBCL) obtained weeks 1, 6, and 12 of the study. Two smartphone applications (Garmin connect and mEMA) and a wearable Garmin smartwatch are used collect the data to monitor step count, sleep, heart rate, and activity intensity. In the AI-PCIT group, the mEMA application will allow for the ecological momentary assessment (EMA) and will send behavioral alerts to the parent. Discussion: Real-time predictive technologies to engage patients rely on daily commitment on behalf of the participant and recurrent frequent smartphone notifications. Ecological momentary assessment (EMA) provides a way to digitally phenotype in-the-moment behavior and functioning of the parent–child dyad. One of the study’s goals is to determine if AI-PCIT outcomes are superior in comparison with standard PCIT. Overall, we believe that the PISTACHIo study will also be able to determine tolerability of smartwatches in children aged 3–7 with EBP and could participate in a fundamental shift from the traditional way of assessing and treating EBP to a more individualized treatment plan based on real-time information about the child’s behavior. Trial registration: The ongoing clinical trial study protocol conforms to the international Consolidated Standards of Reporting Trials (CONSORT) guidelines and is registered in clinicaltrials.gov (ID: NCT05077722), an international clinical trial registry.

Dietary sugar reduction is one therapeutic strategy for improving nonalcoholic fatty liver disease (NAFLD), and the underlying mechanisms for this effect warrant further investigation. Here, we employed metabolomics and metagenomics to examine systemic biological adaptations associated with dietary sugar restriction and (subsequent) hepatic fat reductions in youth with NAFLD. Data/samples were from a randomized controlled trial in adolescent boys (11–16 years, mean ± SD: 13.0 ± 1.9 years) with biopsy-proven NAFLD who were either provided a low free-sugar diet (LFSD) (n = 20) or consumed their usual diet (n = 20) for 8 weeks. Plasma metabolomics was performed on samples from all 40 participants by coupling hydrophilic interaction liquid chromatography (HILIC) and C18 chromatography with mass spectrometry. In a sub-sample (n = 8 LFSD group and n = 10 usual diet group), 16S ribosomal RNA (rRNA) sequencing was performed on stool to examine changes in microbial composition/diversity. The diet treatment was associated with differential expression of 419 HILIC and 205 C18 metabolite features (p < 0.05), which were enriched in amino acid pathways, including methionine/cysteine and serine/glycine/alanine metabolism (p < 0.05), and lipid pathways, including omega-3 and linoleate metabolism (p < 0.05). Quantified metabolites that were differentially changed in the LFSD group, compared to usual diet group, and representative of these enriched metabolic pathways included increased serine (p = 0.001), glycine (p = 0.004), 2-aminobutyric acid (p = 0.012), and 3-hydroxybutyric acid (p = 0.005), and decreased linolenic acid (p = 0.006). Microbiome changes included an increase in richness at the phylum level and changes in a few genera within Firmicutes. In conclusion, the LFSD treatment, compared to usual diet, was associated with metabolome and microbiome changes that may reflect biological mechanisms linking dietary sugar restriction to a therapeutic decrease in hepatic fat. Studies are needed to validate our findings and test the utility of these “omics” changes as response biomarkers.

Background: Understanding the metabolic response to exercise may aid in optimizing stroke management. Therefore, the purpose of this pilot study was to evaluate plasma metabolomic profiles in chronic stroke survivors following aerobic exercise training. Methods: Participants (age: 62 ± 1 years, body mass index: 31 ± 1 kg/m2, mean ± standard error of the mean) were randomized to 6 months of treadmill exercise (N = 17) or whole-body stretching (N = 8) with preintervention and postintervention measurement of aerobic capacity (VO2peak). Linear models for microarray data expression analysis was performed to determine metabolic changes over time, and Mummichog was used for pathway enrichment analysis following analysis of plasma samples by high-performance liquid chromatography coupled to ultrahigh resolution mass spectrometry. Results: VO2peak change was greater following exercise than stretching (18.9% versus −.2%; P <. 01). Pathway enrichment analysis of differentially expressed metabolites results showed significant enrichment in 4 pathways following treadmill exercise, 3 of which (heparan-, chondroitin-, keratan-sulfate degradation) involved connective tissue metabolism and the fourth involve lipid signaling (linoleate metabolism). More pathways were altered in pre and post comparisons of stretching, including branched-chain amino acid, tryptophan, tyrosine, and urea cycle, which could indicate loss of lean body mass. Conclusions: These preliminary data show different metabolic changes due to treadmill training and stretching in chronic stroke survivors and suggest that in addition to improved aerobic capacity, weight-bearing activity, like walking, could protect against loss of lean body mass. Future studies are needed to examine the relationship between changes in metabolomic profiles to reductions in cardiometabolic risk after treadmill rehabilitation.