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BackgroundMobile applications and interactive websites are an increasingly used method of telemedicine, but their use lacks evidence in digestive diseases.AimThis study aims to explore digestive disease studies that use telemedicine to effectively manage disease activity, help monitor symptoms, improve compliance to the treatment protocol, increase patient satisfaction, and enhance the patient-to-provider communication.MethodsEBSCO, PubMed, and Web of Science databases were searched using Medical Subject Headings and other keywords to identify studies that utilized telemedicine in patients with digestive disease. The PRISMA guidelines were used to identify 20 research articles that had data aligning with 4 common overlapping themes including, patient compliance (n = 13), patient satisfaction (n = 11), disease activity (n = 15), and quality of life (n = 13). The studies focused on digestive diseases including inflammatory bowel disease (n = 7), ulcerative colitis (n = 4), Crohn’s Disease (n = 1), irritable bowel syndrome (n = 6), and colorectal cancer (n = 2).ResultsFrom the studies included in this systematic review, patient compliance and patient satisfaction ranged between 25.7–100% and 74–100%, respectively. Disease activity, measured by symptom severity scales and physiological biomarkers, showed improvements following telemedicine interventions in several, but not all, studies. Similar to disease activity, general and disease-specific quality of life showed improvements following telemedicine interventions in as little as 12 weeks in some studies.ConclusionTelemedicine and mobile health technology may be effective in managing disease activity and improving quality of life in digestive diseases. Future studies should explore both gastrointestinal and gastroesophageal diseases using these types of interventions.

The spatial distribution of a species assemblage is often determined by habitat and climate. In the marine environment, depth can become an important factor as declining light and water temperature leads to changes in the biological habitat structure. To date, much of the focus of ecological fish research has been based on reefs in less than 40 m with little research on the ecological role of mesophotic reefs. We deployed baited remote underwater stereo video systems (stereo-BRUVS) on temperate reefs in two depth categories: shallow (20-40 m) and mesophotic (80-120 m), off Port Stephens, Australia. Sites were selected using data collected by swath acoustic sounder to ensure stereo-BRUVS were deployed on reef. The sounder also provided rugosity, slope and relief data for each stereo-BRUVS deployment. Multivariate analysis indicates that there are significant differences in the fish assemblages between shallow and mesophotic reefs, primarily driven by Ophthalmolepis lineolatus and Notolabrus gymnogenis only occurring on shallow reefs and schooling species of fish that were unique to each depth category: Atypichthys strigatus on shallow reefs and Centroberyx affinis on mesophotic reefs. While shallow reefs had a greater species richness and abundance of fish when compared to mesophotic reefs, mesophotic reefs hosted the same species richness of fishery-targeted species. Chrysophrys auratus and Nemodactylus douglassii are two highly targeted species in this region. While C. auratus was numerically more abundant on shallow reefs, mesophotic reefs provide habitat for larger fish. In comparison, N. douglassii were evenly distributed across all sites sampled. Generalized linear models revealed that depth and habitat type provided the most parsimonious model for predicting the distribution of C. auratus, while habitat type alone best predicted the distribution of N. douglassii. These results demonstrate the importance of mesophotic reefs to fishery-targeted species and therefore have implications for informing the management of these fishery resources on shelf rocky reefs.

Predatory fishes on coral reefs continue to decline globally despite playing key roles in ecosystem functioning. Remote atolls and platform reefs provide potential refugia for predator populations, but quantitative information on their spatial distribution is required to establish accurate baselines for ongoing monitoring and conservation management. Current knowledge of predatory fish populations has been derived from targeted shallow diver-based surveys (<15 m). However, the spatial distribution and extent of predatory fishes on outer mesophotic shelf environments has remained under described. Middleton Reef is a remote, high-latitude, oceanic platform reef that is located within a no-take area in the Lord Howe Marine Park off eastern Australia. Here we used baited remote underwater stereo video to sample predatory fishes across lagoon and outer shelf habitats from depths 0–100 m, extending knowledge on use of mesophotic depths and habitats. Many predatory fish demonstrated clear depth and habitat associations over this depth range. Carcharhinid sharks and Carangid fishes were the most abundant predators sampled on Middleton Reef, with five predatory fishes accounting for over 90% of the predator fish biomass. Notably, Galapagos shark ( Carcharhinus galapagensis ) and the protected black rockcod ( Epinephelus daemelii ) dominated the predator fish assemblage. A higher richness of predator fish species was sampled on reef areas north and south of the lagoon. The more exposed southern aspect of the reef supported a different suite of predator fish across mesophotic habitats relative to the assemblage recorded in the north and lagoonal habitats, a pattern potentially driven by differences in hard coral cover. Biomass of predatory fishes in the more sheltered north habitats was twice that of other areas, predominantly driven by high abundances of Galapagos shark. This work adds to the growing body of literature highlighting the conservation value of isolated oceanic reefs and the need to ensure that lagoon, shallow and mesophotic habitats in these systems are adequately protected, as they support vulnerable ecologically and economically important predator fish assemblages.

The functioning and richness of marine systems (and biological interactions such as parasitism) are continuously influenced by a changing environment. Using hierarchical modelling of species communities (HMSC), the presence and abundance of multiple parasite species of the black-spotted croaker, Protonibea diacanthus (Sciaenidae), was modelled against environmental measures reflecting seasonal change. Protonibea diacanthus were collected in three seasons across 2019–2021 from four locations within the waters of the Northern Territory, Australia. The length of P. diacanthus proved to have a strong positive effect on the abundance of parasite taxa and overall parasitic assemblage of the sciaenid host. This finding introduces potential implications for parasitism in the future as fish body size responds to fishing pressure and climate changes. Of the various environmental factors measured during the tropical seasons of northern Australia, water temperature and salinity changes were shown as potential causal factors for the variance in parasite presence and abundance, with changes most influential on external parasitic organisms. As environmental factors like ocean temperature and salinity directly affect parasite–host relationships, this study suggests that parasite assemblages and the ecological functions that they perform are likely to change considerably over the coming decades in response to climate change and its proceeding effects.

The purpose of this study was to examine the relationship between parental BMI and the family environment and determine if differences exist in child diet and physical activity related parenting behaviors by parental BMI in a community sample of families recruited through elementary schools in a local school district. We found an association between parental BMI category and family nutrition and physical activity (FNPA) score. Families with an underweight or normal weight parent had a larger proportion (64.3%) of high (indicating a healthier family environment) FNPA scores and families with an overweight or obese parent had a smaller proportion (45.2%) of high FNPA scores (χ² = 5.247, P = 0.022). Families with a parent who was overweight or obese had 2.18 times the odds (95% CI 1.11–4.27) of being in the low FNPA (“less healthy” environment) group. Further, underweight/normal weight parents reported higher levels of monitoring of child diet (Z = –3.652, P < 0.0001), higher levels of parental monitoring of child physical activity (Z = –3.471, p < 0.001), and higher levels of parental limit setting related to child sedentary activities compared to overweight/obese parents (Z = –2.443, P = 0.01). Parent BMI and parenting behaviors are known to have a major impact on childhood obesity. In this study, lower parent BMI and authoritative parenting behaviors were associated with a less obesogenic home environment and a positive parenting style related to child eating and physical activity behaviors.

BackgroundThe Stopping Elderly Accidents, Deaths and Injuries (STEADI) screening algorithm aligns with current fall prevention guidelines and is easy to administer within clinical practice. However, the stratification into low, moderate and high risk categories limits the meaningful interpretation of the fall-related risk factors.MethodsBaseline measures from a modified STEADI were used to predict self-reported falls over 4 years in 3170 respondents who participated in the 2011–2015 National Health and Aging Trends Study. A point method was then applied to find coefficient-based integers and 4-year fall risk estimates from the predictive model. Sensitivity and specificity estimates from the point method and the combined moderate and high fall risk STEADI categories were compared.ResultsThere were 886 (27.95%) and 387 (12.21%) respondents who were classified as moderate and high risk, respectively, when applying the stratification method. Falls in the past year (OR: 2.16; 95% CI: 1.61 to 2.89), multiple falls (OR: 2.94; 95% CI: 1.89 to 4.55) and a fear of falling (OR: 1.77; 95% CI: 1.45 to 2.16) were among the significant predictors of 4-year falls in older adults. The point method revealed integers that ranged from 0 (risk: 27.21%) to 44 (risk: 99.71%) and a score of 10 points had comparable discriminatory capacity to the combined moderate and high STEADI categories.ConclusionCoefficient-based integers and their risk estimates can provide an alternative interpretation of a predictive model that may be useful in determining fall risk within a clinical setting, tracking changes longitudinally and defining the effectiveness of an intervention.

Past mobile health (mHealth) efforts to empower type 2 diabetes (T2D) self-management include portals, text messaging, collection of biometric data, electronic coaching, email, and collection of lifestyle information. The primary objective was to enhance patient activation and self-management of T2D using the US Department of Defense's Mobile Health Care Environment (MHCE) in a patient-centered medical home setting. A multisite study, including a user-centered design and a controlled trial, was conducted within the US Military Health System. Phase I assessed preferences regarding the enhancement of the enabling technology. Phase II was a single-blinded 12-month feasibility study that randomly assigned 240 patients to either the intervention (n=123, received mHealth technology and behavioral messages tailored to Patient Activation Measure [PAM] level at baseline) or the control group (n=117, received equipment but not messaging. The primary outcome measure was PAM scores. Secondary outcome measures included Summary of Diabetes Self-Care Activities (SDSCA) scores and cardiometabolic outcomes. We used generalized estimating equations to estimate changes in outcomes. The final sample consisted of 229 patients. Participants were 61.6% (141/229) male, had a mean age of 62.9 years, mean glycated hemoglobin (HbA ) of 7.5%, mean BMI of 32.7, and a mean duration of T2D diagnosis of 9.8 years. At month 12, the control group showed significantly greater improvements compared with the intervention group in PAM scores (control mean 7.49, intervention mean 1.77; P=.007), HbA (control mean -0.53, intervention mean -0.11; P=.006), and low-density lipoprotein cholesterol (control mean -7.14, intervention mean 4.38; P=.01). Both groups showed significant improvement in SDSCA, BMI, waist size, and diastolic blood pressure; between-group differences were not statistically significant. Except for patients with the highest level of activation (PAM level 4), intervention group patients exhibited significant improvements in PAM scores. For patients with the lowest level of activation (PAM level 1), the intervention group showed significantly greater improvement compared with the control group in HbA (control mean -0.09, intervention mean -0.52; P=.04), BMI (control mean 0.58, intervention mean -1.22; P=.01), and high-density lipoprotein cholesterol levels (control mean -4.86, intervention mean 3.56; P<.001). Significant improvements were seen in AM scores, SDSCA, and waist size for both groups and in diastolic and systolic blood pressure for the control group; the between-group differences were not statistically significant. The percentage of participants who were engaged with MHCE for ≥50% of days period was 60.7% (68/112; months 0-3), 57.4% (62/108; months 3-6), 49.5% (51/103; months 6-9), and 43% (42/98; months 9-12). Our study produced mixed results with improvement in PAM scores and outcomes in both the intervention and control groups. Structural design issues may have hampered the influence of tailored behavioral messaging within the intervention group. ClinicalTrials.gov NCT02949037; https://clinicaltrials.gov/ct2/show/NCT02949037. RR2-10.2196/resprot.6993.

Youth mental health issues have been recognized as a pressing crisis in the United States in recent years. Effective, evidence-based mental health research and interventions require access to integrated datasets that consolidate diverse and fragmented data sources. However, researchers face challenges due to the lack of centralized, publicly available datasets, limiting the potential for comprehensive analysis and data-driven decision-making. This paper introduces a curated directory of publicly available datasets focused on youth mental health (less than 18 years old). The directory is designed to serve as critical infrastructure to enhance research, inform policymaking, and support the application of artificial intelligence and machine learning in youth mental health research. Unlike a systematic review, this paper offers a brief overview of open data resources, addressing the challenges of fragmented health data in youth mental health research. We conducted a structured search using 3 approaches: targeted searches on reputable health organization websites (eg, National Institutes of Health [NIH] and Centers for Disease Control and Prevention [CDC]), librarian consultation to identify hard-to-find datasets, and expert knowledge from prior research. Identified datasets were curated with key details, including name, description, components, format, access information, and study type, with a focus on freely available resources. A curated list of publicly available datasets on youth mental health and school policies was compiled. While not exhaustive, it highlights key resources relevant to youth mental health research. Our findings identify major national survey series conducted by organizations such as the NIH, CDC, Substance Abuse and Mental Health Services Administration (SAMHSA), and the U.S. Census Bureau, which focus on youth mental health and substance use. In addition, we include data on state and school health policies, offering varying scopes and granularities. Valuable health data repositories such as ICPSR, Data.gov, Healthdata.gov, Data.CDC.gov, OpenFDA, and Data.CMS.gov host a wide range of research data, including surveys, longitudinal studies, and individual research projects. Publicly accessible health data are essential for improving youth mental health outcomes. Compiling and centralizing these resources streamlines access, enhances research impact, and informs interventions and policies. By improving data integration and accessibility, it encourages interdisciplinary collaboration and supports evidence-based interventions.