2026-04-01
James Patrick Moon, Khalid Abdul Jabbar, Tony Chin Ian Tay, Laura Tay, Rathi Mahendran, Tze Pin Ng, Shian Ming Tan, Wilbur Zhi Hao Koh, Weng Yan Ying, Ah-Hwee Tan, Tih-Shih Lee, Iris Rawtaer
Background: As the global population continues to age, the prevalence of geriatric conditions, including dementia and frailty, is also increasing. Early identification of individuals at an elevated risk of these conditions, such as those presenting with mild cognitive impairment (MCI) or prefrailty, can provide a critical window for prompt intervention aimed at preventing or reversing disease progression. To promote such early identification, there is a burgeoning interest in the use of digital sensor technology and predictive modeling. Objective: This study aimed to use a continuous, home-based monitoring sensor system for older adults to distinguish those exhibiting normal aging from those with MCI, early dementia, prefrailty, or frailty, and to predict their transition from normal aging to one of these conditions. Methods: This longitudinal cohort study will recruit 200 community-dwelling adults aged ≥65 years with normal cognition or MCI at baseline. A multi-sensor system will be installed in participants’ homes, including passive infrared motion sensors, door contact sensors, bed sensors, medication box sensors, wearable activity bands, and Bluetooth proximity beacons. These devices will continuously capture spatiotemporal activity patterns, mobility indicators, sleep behaviors, and medication-taking routines. Annual assessments will include standardized cognitive tests (eg, Montreal Cognitive Assessment, Mini-Mental State Examination, Rey Auditory-Verbal Learning Test, digit span, Color Trails Test, semantic fluency, Stroop), frailty measures (modified Fried phenotype, gait speed, grip strength), mental health scales, sleep quality, and psychosocial indicators. Sensor-derived features—such as gait variability, activity regularity, sleep fragmentation, and medication adherence patterns—will be integrated with clinical data to develop supervised machine learning models. Planned approaches include logistic regression, random forests, gradient boosting, and deep learning. Model performance will be evaluated using cross-validation and independent test sets. Primary metrics will include area under the receiver operating characteristic curve, sensitivity, specificity, precision, recall, and -score. Models will be benchmarked against gold-standard clinical diagnoses and validated using temporal subsets of the dataset. Results: Enrollment for this study started in November 2019 and will continue until March 2030. As of June 2025, we have enrolled 138 participants. Full data analysis has yet to begin. Conclusions: We aim to develop a reliable and effective sensor system for in-home use that will facilitate the early detection of cognitive and physical decline. In so doing, it will add to our current understanding of digital biomarkers. It is common for older adults to seek clinical intervention only when their cognitive impairment has already reached an advanced stage. The implementation of readily deployable sensor systems within community settings presents us with opportunities for prompt intervention, which holds the potential for delaying or reversing disease progression and allowing for a greater number of functional and meaningful years. International Registered Report Identifier (IRRID): DERR1-10.2196/79490
2026-03-31
Lara Stone, Lili Ding, Meron Hirpa Kassa, Beakal Amsalu, Bruktawit Getnet, Hermella Eshete, Tesfaye B Mersha, Lisa M Vaughn
2026-03-31
Yoshiko Doi, Hiroshi Aikata, Yumi Kosaka, Takashi Nakahara, Michiyo Kodama, Masashi Hieda, Masakazu Hashimoto, Hideki Nakahara, Ippei Takahashi, Hideaki Kakizawa, Nami Mori, Keiji Tsuji, Nobuki Imano, Yuji Murakami, Saki Sueda, Tomokazu Kawaoka, Masataka Tsuge, Shiro Oka
Background: Systemic therapy, including immune checkpoint inhibitors, has improved survival in advanced hepatocellular carcinoma (HCC); however, its efficacy remains limited in patients with macroscopic vascular invasion (MVI), a subgroup with an extremely poor prognosis. Although combining immunotherapy with local treatments such as hepatic arterial infusion chemotherapy (HAIC) and radiation therapy (RT) is considered a promising approach, robust supportive evidence from routine clinical practice is lacking. Objective: This study aims to evaluate the safety and therapeutic effectiveness of a multidisciplinary treatment strategy involving RT after HAIC, followed by immunotherapy, in patients with MVI-positive HCC, using real-world clinical data from Japan. Methods: This is a prospective, multicenter registry study conducted at 3 hospitals in Hiroshima Prefecture, Japan. Eligible patients will have unresectable MVI-positive HCC confirmed by dynamic computed tomography. The treatment protocol follows a standardized sequence: 1 session of HAIC (cisplatin), RT targeting the MVI site (25 Gy in 5 fractions), and subsequent systemic immunotherapy. The primary end point is safety, which will be evaluated using the Common Terminology Criteria for Adverse Events (version 5.0). The secondary end points include progression-free survival at 12 and 24 weeks, tumor response, median progression-free survival, overall survival, and objective response rate at 12 and 24 weeks, assessed according to the Response Evaluation Criteria in Solid Tumors criteria. Data will be collected prospectively and analyzed according to the intention-to-treat principle. Results: Patient enrollment began in March 2025, and data collection and analysis are ongoing as participants continue to be followed. Conclusions: This prospective registry study will generate real-world evidence on the safety and effectiveness of a multidisciplinary strategy combining HAIC, RT, and immunotherapy in patients with MVI-positive HCC. Given that all components are covered under Japan’s national health insurance, this approach could be readily implemented in clinical practice and may inform future treatment guidelines for MVI-positive HCC. International Registered Report Identifier (IRRID): DERR1-10.2196/82992
2026-03-31
Kylie Boyd, Lydia Bliss, Tingting Fan, Kayla Kern, Caitlin M Carlson, Megan A Moreno, Christopher N Cascio, Ellen Selkie
Background: Adolescent social media research has primarily focused on frequency of platform use and self-report measures. There has been limited focus on the self-generated content posted by adolescents and how this might relate to their well-being. Objective: This study describes a researcher-observed codebook for characterizing adolescents’ self-generated content in a longitudinal sample. Methods: Participants in the study provided informed assent (and parental informed consent) for researchers to follow them and passively observe their self-generated content on Instagram (Meta Platforms), TikTok (ByteDance Ltd), Facebook (Meta Platforms), and X (formerly known as Twitter; X Corp). Guided by Bronfenbrenner’s social ecological biopsychosocial model, the research team created a codebook incorporating prior cyberethnographic observation of self-generated social media content. After codebook refinement, coders (research staff and student research assistants) were trained through multiple rounds of test coding, and the codebook was applied to participant data with periodic quality control measures to ensure interrater reliability. Results: This study was funded in early 2023 and began data collection in March 2023 and will conclude in 2027. So far, the interrater reliability agreement scores (AC1) between coders have shown strong interrater reliability. For Year 1, scores were Facebook 0.89, Instagram 0.89, TikTok 0.88, X 0.87, and combined 0.88; for Year 2, Facebook 0.95, Instagram 0.96, TikTok 0.96, X 0.96, and combined 0.96. This project provides replicable guidance to categorize social media data from adolescent participants using human coders who can contextualize content through longitudinal observation. The method that our team chose and followed paved the way for many strengths to be recognized as well as lessons learned by our team that allowed for adaptation and growth to occur while this study has been ongoing. Conclusions: Cyberethnography, the chosen method for this research protocol, has allowed this research project to collect self-generated content for adolescent social media in a comprehensive manner. Thus, allowing our team to be able to cross-analyze this data with the well-being data that are being collected under the grander project for patterns. Sharing our protocol through this paper will also allow other researchers to draw from our methodology for future projects to aid in social media and adolescent understanding. International Registered Report Identifier (IRRID): DERR1-10.2196/84461
2026-03-31
Bailey Bruckner, Rachael Taylor, Carmen Parata, Jillian Haszard, Barry Taylor, Samantha Bevin, Kassidy Gooding, Anne-Louise Heath, Ioanna Katiforis, Lisa Daniels
Background: It is well known that breastfeeding provides favorable health outcomes for both mother and baby. However, many mothers struggle to meet global recommendations to exclusively breastfeed for 6 months. Of those who cease breastfeeding early, one third attribute this to perceived insufficient milk supply. Currently, it is uncertain how the perception of insufficient milk supply relates to physiological milk volume or nutrient composition. Objective: The Māmā and Baby Breastfeeding Study aims to estimate human milk volumes produced by a diverse sample of breastfeeding mothers at 3 months post partum with differing perceptions of milk supply and to investigate human milk composition in relation to milk volume. Methods: We plan to recruit a sample of 150 mother–infant dyads in this observational study in Dunedin, Aotearoa New Zealand. Participants will represent a diverse range of ethnicities and socioeconomic backgrounds. Human milk volume will be assessed using the dose-to-mother stable isotope (deuterium oxide) technique. Mother participants will consume an accurate dose (30 g) of deuterium oxide after baseline saliva samples are collected from both mother and infant. Subsequent postdose samples will be collected over 3 time points to determine deuterium enrichment over a 14-day period using Fourier transform infrared spectrometry. Human milk macronutrient (energy, fat, carbohydrate, crude protein, and true protein) and mineral and trace element (sodium, magnesium, phosphorus, potassium, calcium, iron, copper, zinc, selenium, and iodine) composition of 1 full milk expression from 1 breast will be analyzed using the MIRIS Human Milk Analyzer (Miris AB) and inductively coupled plasma mass spectrometry, respectively. Potential predictors and maternal perception of milk supply will be assessed via questionnaire. Infant BMI will be calculated from measures of weight and length at 3 different time points over 4 weeks, using standard techniques. These, alongside anthropometric measurements collected at the infant’s Well Child Tamariki Ora visits, will be used to assess infant growth trajectory in the first 6 months of life. Regression models will be used to assess the associations between maternal perception of milk supply, human milk volumes, and composition. Results: This study was funded in April 2023 by the Health Research Council of New Zealand (grant 23/461). Recruitment for this study began in February 2025 and is anticipated to conclude in June 2026, with analysis expected to be completed by February 2027. As of January 17, 2026, a total of 91 participants have been enrolled. Final results are anticipated to be disseminated in late 2027 following completion of data analysis. Conclusions: This research will provide new knowledge on whether maternal perception of milk supply aligns with actual human milk volume or nutrient composition. Such information will be extremely useful for health professionals working with breastfeeding mothers with milk supply concerns and for informing the design of breastfeeding support programs and resources. Trial Registration: Australian and New Zealand Clinical Trial Registry ACTRN12625000180415; https://www.anzctr.org.au/ACTRN12625000180415.aspx
2026-03-31
Rija Mir, Shoba Poduval, Jessica Sheringham, Fiona Louise Hamilton
Background: Type 2 diabetes (T2D) currently has no cure. However, extensive evidence suggests that addressing key risk factors through lifestyle changes can help individuals effectively self-manage their condition. Diabetes self-management primarily involves patients engaging in self-monitoring behaviors and adopting coping strategies to manage their long-term illness. In recent years, health coaching interventions have gained recognition as a valuable approach for providing personalized support, enabling patients to take an active role in managing their health. By fostering behavior change through goal setting, active learning, accountability, and empowerment, health coaching equips patients with the tools to proactively manage their condition over time. This approach is especially important for researchers and policymakers, as it underscores the need for acceptable, engaging, and personalized care interventions that have a lasting positive impact, promoting self-sufficiency and improved quality of life for individuals with T2D. Objective: The objective of this study is to systematically review published, peer-reviewed, primary research studies to evaluate the effectiveness of health coaching interventions for T2D self-management, with glycated hemoglobin (HbA) as the primary outcome. It aims to examine how intervention components, delivery modalities, and health coach characteristics relate to effectiveness, acceptability, and engagement. Methods: This study protocol outlines the procedure for a systematic review that follows the methodology recommended by PRISMA-P (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols) guidelines. The review will include randomized controlled trials, quasi-experimental studies, and qualitative studies published in English up to December 1, 2024. The following databases will be used to conduct searches: MEDLINE, PsycINFO, Embase, CINAHL, Web of Science, and the Cochrane Central Register of Controlled Trials, with additional studies identified through citation searching and reference list screening. Included studies will consist of health coaching interventions delivered one-to-one through face-to-face, remote or digital, or artificial intelligence–supported formats. The primary outcome of interest is HbA, alongside additional clinical outcomes and qualitative data related to acceptability and engagement. Study selection will involve independent screening by reviewers, with disagreements resolved by consensus. Quantitative data will be synthesized using narrative synthesis, and qualitative findings will be analyzed using thematic and framework analysis within a staged, segregated synthesis approach. Results: As of September 2025, database searches have been conducted, and decisions have been made related to the included publications. Data extraction and analysis are currently ongoing. Results are expected to be published in summer 2026. The review is underway and is anticipated to be completed by May 2026. Conclusions: This review will synthesize evidence on the effectiveness and implementation of health coaching interventions for T2D and is expected to inform the design of effective and sustainable coaching models for diabetes self-management. Trial Registration: PROSPERO CRD42025637862; https://www.crd.york.ac.uk/PROSPERO/view/CRD42025637862 International Registered Report Identifier (IRRID): PRR1-10.2196/71383