Category: Statistics

Curr Opin Neurobiol. 2026 Apr 28;99:103206. doi: 10.1016/j.conb.2026.103206. Online ahead of print.

ABSTRACT

Cephalopod camouflage presents a longstanding paradox: many species appear to match the color of their surroundings, yet anatomical and behavioral evidence suggests they lack conventional color vision. Here I discuss behavioral, anatomical, optical, molecular, and ecological evidence bearing on this problem. I evaluate a range of proposed mechanisms that could reconcile color matching with monochromatic vision, including passive skin reflectance, chromatic aberration-based spectral inference, polarization vision, and extraocular photoreception. Each is found to be either insufficient or weakly supported by current data. I then consider the null hypothesis that cephalopods achieve effective color matching without color perception, drawing an analogy with machine vision systems that infer color from grayscale statistics. Cephalopod camouflage may rely on predictive mappings from luminance-based visual input to colored skin output, rather than on true color vision.

PMID:42054709 | DOI:10.1016/j.conb.2026.103206

JMIR Res Protoc. 2026 Apr 29;15:e83629. doi: 10.2196/83629.

ABSTRACT

BACKGROUND: Veterans have an increased risk of developing Alzheimer disease and related dementia (ADRD) due to military exposures such as traumatic brain injury. There is a lack of information on home- and community-based services (HCBS) use among Veterans who served in the post-9/11 era and their caregivers.

OBJECTIVE: This study aims to (1) quantify HCBS use among post-9/11 Veterans with or at higher risk of ADRD, (2) identify facilitators, barriers, and preferences for HCBS among Veterans and family caregivers, and (3) prioritize HCBS interventions with input from Veterans and family caregivers.

METHODS: This study will include post-9/11 Veterans aged 65 years or younger with early-onset Alzheimer disease or frontotemporal dementia (current ADRD), and Veterans at elevated ADRD risk due to traumatic brain injury or cognitive dysfunction. Veterans’ family caregivers will also be recruited. Secondary data will come from the Department of Veterans Affairs (VA), the Department of Defense, and a previous neurotrauma study. Using VA data augmented with Centers for Medicare and Medicaid Services data, we will characterize HCBS utilization. To address aim 1, we will calculate the crude and adjusted cumulative frequency of HCBS use and the proportion of Veterans using a service among Veterans with ADRD, and those at higher and lower risk for ADRD. We will compare groups using t tests for continuous measures (number of services) and chi-square tests for categorical measures (any service use). To address aim 2, we will interview Veterans and caregivers to identify facilitators and barriers to HCBS use. We will use descriptive content analysis, including rich descriptions, coding, and theme identification. Finally, to address aim 3, we will use a modified Delphi approach to identify and rank HCBS modifications that would increase use. Using the ranking data, we will consider items to have consensus on high importance if 70% or more respond “important” or “very important.” Participants for primary data collection will be recruited from prior studies, VA health systems data, VA clinics, and Veteran- and caregiver-serving organizations.

RESULTS: This study was reviewed by the institutional review boards of the University of Utah, Salt Lake City Veterans Affairs, and UT Health at San Antonio and classified as exempt. The 46,053 Veterans in the preliminary aim 1 cohort (903 with early-onset Alzheimer disease/frontotemporal dementia and 45,150 at-risk Veterans matched on age and index year) averaged 55 years old at the index date and were mostly male (38,842/46,053, 84%) and non-Hispanic White (28,016/46,053, 61%).

CONCLUSIONS: This study will quantify current HCBS use and identify barriers and needs of Veterans with or at higher risk of ADRD and their caregivers. It will identify HCBS modifications that have consensus for needed changes, which will be shared with health system leaders.

PMID:42054703 | DOI:10.2196/83629

Global Spine J. 2026 Apr 29:21925682261447888. doi: 10.1177/21925682261447888. Online ahead of print.

ABSTRACT

Study DesignNetwork Meta-Analysis.ObjectiveTo compare the efficacy and safety of osteobiologics used in posterior lumbar spinal fusion (LSF) for degenerative lumbar disorders, setting autologous iliac crest bone graft (AICBG) as the reference standard.MethodsA systematic search of randomized controlled trials (RCTs) evaluating osteobiologics in adult patients undergoing posterior LSF was performed. Primary outcomes were radiologic fusion and osteobiologic-related complications. Secondary outcomes included disability, low back pain, operative time, blood loss, and length of stay (LOS). A frequentist random-effects network meta-analysis (NMA) was performed. Meta-regression was employed to assess the influence of surgical technique on primary outcomes. Risk of bias was evaluated using the Cochrane RoB-2 tool, and certainty of evidence was assessed with the GRADE framework.ResultsThirty-five RCTs including 2298 patients were analyzed. Compared with AICBG, recombinant human bone morphogenetic protein-2 (rhBMP-2) showed significantly higher fusion rates (OR 3.86; 95% CI 2.60-5.74; P < 0.0001) and lower complication risk (OR 0.50; 95% CI 0.34-0.73; P = 0.0004). Disability and pain outcomes were comparable across treatments. rhBMP-2 (MD -21.8 minutes; 95% CI -28.0 to -15.7; P < 0.0001), autologous local bone (MD -12.0 minutes; 95% CI -21.5 to -2.5; P = 0.0133), and ABM/P-15 (MD -17.0 minutes; 95% CI -32.6 to -1.5; P = 0.0322) were associated with shorter operative time. Only rhBMP-2 significantly decreased blood loss (MD -72.6 mL; 95% CI -118.9 to -26.4; P = 0.002), while no treatment reduced LOS.ConclusionsAmong evaluated osteobiologics, rhBMP-2 demonstrated superior efficacy and safety compared to AICBG in posterior LSF. Other agents showed favourable trends without statistical significance, reflecting persistent uncertainty rather than confirmed equivalence.

PMID:42054700 | DOI:10.1177/21925682261447888

Age Ageing. 2026 Apr 4;55(4):afag109. doi: 10.1093/ageing/afag109.

ABSTRACT

AIM: To model projected trends in frailty prevalence, associated service use and costs in people aged 50 and over in England to 2040.

DESIGN: System dynamics simulation modelling.

SETTING: Adult population (aged 50 and over) of England.

PARTICIPANTS: Routine data from primary care patients aged 50 and over (2.2 million individuals) from participating practices from the Royal College of General Practitioners Research Surveillance Centre (RCGP RSC) database between 2006 and 2017.

OUTCOME MEASURES: Projected frailty prevalence, primary, secondary and urgent care service use and costs in those aged 50 and over between 2025 and 2040.

RESULTS: The population of England aged 50 and over is projected to increase from 23.1 million in 2025 to 24 million in 2040. Frailty prevalence in this group will rise from 70.2% to 76.1%, with associated service use costs increasing by £10 billion. Measures to reduce frailty incidence or progression could reduce costs by £310 million/annum and £644 million/annum, respectively.

CONCLUSIONS: Frailty prevalence and associated service use and costs will increase substantially in the ageing population. A shift in focus to prevention and slowing progression in middle age and the younger old would substantially reduce service use and costs by older people living with frailty.

PMID:42054699 | DOI:10.1093/ageing/afag109

J Med Internet Res. 2026 Apr 29;28:e83369. doi: 10.2196/83369.

ABSTRACT

Synthetic data generation (SDG) structured health data is increasingly promoted as a solution to longstanding barriers in health data access. It is offering the promise of privacy-preserving data reuse for research, innovation, and policy. Despite rapid technical advances, the adoption of synthetic health data in real-world settings remains limited. Shaped by challenges around data quality, representativeness, infrastructure readiness, trust, and legal uncertainty, this viewpoint draws on experiences from 7 European research initiatives within the HealthData4EU cluster to reflect on how SDG is being operationalized in practice. It synthesizes cross-project insights to highlight recurring methodological and governance tensions and to examine their implications for trust and responsible use. The analysis argues that trustworthy SDG cannot be achieved through technical optimization alone but requires alignment between evaluation practices, upstream data stewardship, regulatory clarity, and sustained stakeholder engagement. Addressing these conditions is essential for moving synthetic data from experimental pilots toward a credible and sustainable component of European health research ecosystems.

PMID:42054696 | DOI:10.2196/83369

JMIR Mhealth Uhealth. 2026 Apr 29;14:e71509. doi: 10.2196/71509.

ABSTRACT

BACKGROUND: Wearable devices with real-time feedback (WRFs) provide increasing opportunities to enhance physical activity and improve rehabilitation through collecting and processing health-related data. Real-time feedback (RTF) from the device is expected to result in a more dynamic, coordinated, and synchronous rhythmic activity, defined as step-by-step movements mediated by the real-time heart rate feedback. However, age-specific characteristics in the user engagement with WRFs integrating real-time audio feedback have largely remained unexplored.

OBJECTIVE: This study explores the lived experiences of older adults using wearables with RTF to uncover motivations, aspirations, and hindering factors in their engagement with WRFs in rhythmic activity. The study explores narratives that older adults articulate in their previous use of wearables for physical activity, their experiences with WRFs during rhythmic activity, and their meaning-making of the interactive features enhancing the synchronization of the movement during rhythmic activity.

METHODS: The study was conducted as a qualitative interview study with 18 older adults who used a WRF for rhythmic activity during a 3-week period in their home environment. The wearable used in the study is a chest-band sensor device that helps users to synchronize their steps with their heartbeat through the provision of real-time audio feedback. The material consists of semistructured interviews before and after using the device. Material from the semistructured interviews was analyzed with an interpretative phenomenological analysis method.

RESULTS: The study identified four main themes characterizing older adults’ lived experiences with wearables, which are (1) use of wearable technologies without RTF in daily life, (2) embodied rhythmic negotiation with RTF, (3) interpretation of health data with RTF, and (4) temporal trajectories of device engagement with RTF. Older adults demonstrated intentional distancing from wearable technologies rather than simple disuse, prioritizing authentic bodily experiences over external validation. Their engagement was fundamentally relational, mediated through trusted social networks, and required dialogical support for data interpretation. Device-guided movement synchronization created contextually situated challenges that varied significantly based on environmental demands, individual bodily capacity, and exercise routines. Extended temporal engagement transformed participants’ relationships with the technology from initial disruption to potential integration, with RTF serving as a bridge toward enhanced embodied awareness when carefully designed.

CONCLUSIONS: The study concludes that RTF from the device can enhance synchronization and bodily awareness, but meaningful engagement requires adaptive designs that respect older adults’ authentic movement practices, accommodate their relational approach to technology validation, and allow sufficient time for embodied competency development.

PMID:42054677 | DOI:10.2196/71509

JMIR Res Protoc. 2026 Apr 29;15:e73060. doi: 10.2196/73060.

ABSTRACT

BACKGROUND: Veterans face an increased risk of common mental disorders when compared to civilian groups. However, veteran disengagement from treatment is a concern among health care providers, resulting in a need to explore novel ways of managing veteran mental health. Wearable devices, such as fitness trackers and smartwatches, have been explored for their potential to assess, monitor, and predict mental health outcomes in the general population. Such devices provide continuous data on metrics including physical activity, heart rate, sleep quality, and stress levels, offering a comprehensive view of the lifestyle and physiological factors influencing mental health.

OBJECTIVE: This study aims to explore the feasibility of using wearable technology as a data collection and potential health monitoring tool among UK veterans. It also aims to explore the associations between mental health, physical activity, and functioning factors among UK veterans.

METHODS: This is an observational feasibility study measuring mental health via validated questionnaires completed at baseline (T0), day 28 (T1), day 56 (T2), and day 84 (T3), and physiological metrics measured continuously via wrist-worn fitness trackers (Garmin vívosmart-5 watches) over 3 months (84 days). UK veterans will be recruited through convenience sampling methods. Statistical analysis will be exploratory, and machine learning models will be trained to detect changes in mental health and well-being outcomes.

RESULTS: Data collection was conducted between February 2025 and October 2025, and data analysis is scheduled to begin in January 2026.

CONCLUSIONS: This study will provide information on the feasibility of using wearable technology devices within a UK veteran population and may inform potential future interventions seeking to integrate wearable-derived data alongside the management of common mental disorders in veterans experiencing mental health difficulties. Findings would also enhance understanding of the relationship between mental health and physiological factors (eg, physical activity and sleep) in UK veterans.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/73060.

PMID:42054675 | DOI:10.2196/73060

JMIR Cancer. 2026 Apr 29;12:e80909. doi: 10.2196/80909.

ABSTRACT

BACKGROUND: Myeloid neoplasms (MNs) are most frequently diagnosed among adults aged 60 years and older. Cancer and chemotherapy can cause gait disturbances and increase fall risk in older adults with MNs. Exercise may improve gait, but there is a lack of research among older adults with MNs undergoing active chemotherapy.

OBJECTIVE: We explored gait changes following a home-based mobile health exercise intervention during 2 cycles of outpatient chemotherapy (8-12 weeks).

METHODS: In a single-arm pilot study, we included adults aged 60 years and older with MNs undergoing chemotherapy. Geriatric Oncology-Exercise for Cancer Patients intervention integrates a progressive aerobic walking and resistance exercise program with a mobile app. We assessed gait by using a waist-worn G-Walk motion sensor during a 6-minute walk at the preintervention and postintervention time points. Spatiotemporal outcomes included cadence (steps per minute), velocity (meters per minute), normalized stride (stride length normalized over height), and swing duration (percentage of the gait cycle during which a foot is in the air when walking). Regularity outcomes that measure gait rhythm included variability of normalized stride and variability of swing duration. Variability for both outcomes was quantified as the SD across all gait cycles. We calculated Cohen d effect sizes (ESs) for change in gait outcomes and used the Spearman rank correlation to correlate changes in daily steps and resistance exercise duration with gait outcomes.

RESULTS: We included 13 patients (mean age 71, SD 4.8 years); most were male (n=8, 61.5%), White individuals (n=12, 92.3%), and non-Hispanic individuals (n=13, 100%). Average daily steps were 3084 (SD 1765.5) at the preintervention time point and 3757 (SD 2623.6) at the postintervention time point. Patients performed resistance exercises for 25 minutes per day, 4 days per week at low intensity (mean rating of perceived exertion 3/10, SD 1.3). At the postintervention time point, we observed numerical changes in gait outcomes, including increased cadence (mean +4.6, SD 14.6 steps per minute; P=.24; ES=0.38) and decreased variability in normalized stride (mean -1.4%, SD 8.5%; P=.34; ES=-0.18) and swing duration (mean -0.1%, SD 1.1%; P=.54; ES=-0.15), although these improvements were not statistically significant. Increased daily steps significantly correlated with decreased swing duration variability (r=-0.72; P=.01). Resistance exercise duration significantly correlated with increased cadence (r=0.54; P=.06) and velocity (r=0.56; P=.05).

CONCLUSIONS: In our exploratory analyses, better adherence to exercise correlated with improved gait outcomes. Our ongoing pilot randomized controlled trial (ClinicalTrials.gov identifier: NCT04981821) will further examine the effects of the Geriatric Oncology-Exercise for Cancer Patients intervention on gait outcomes in this population.

PMID:42054674 | DOI:10.2196/80909

JMIR Hum Factors. 2026 Apr 29;13:e80734. doi: 10.2196/80734.

ABSTRACT

BACKGROUND: The rising popularity of apps that sync with continuous glucose monitors (CGMs) reflects growing interest in on-demand, personalized care. These platforms combine real-time glucose biofeedback with self-monitored behaviors to optimize metabolic health among individuals with and without diabetes. However, little is known about user characteristics, engagement patterns, or factors associated with sustained use of CGM-integrated digital health apps in real-world settings.

OBJECTIVE: This study aimed to describe user demographics, CGM usage patterns, and food logging behaviors among Vively app users and to identify characteristics of sustained engagement with CGM wear and food tracking.

METHODS: We conducted a retrospective observational study of Vively app users between August 2021 and February 2025. Vively is a commercial digital health app that integrates with Abbott FreeStyle Libre CGMs to deliver personalized nutrition guidance. Users with at least 1 day of CGM wear were included. Primary outcomes were CGM wear duration (total days) and food logging engagement. Factors associated with engagement were identified using negative binomial regression for CGM wear and hurdle negative binomial models for food logging, adjusting for age, sex, BMI, baseline glucose, and device connectivity; the food logging model additionally adjusted for CGM wear category.

RESULTS: The analytical sample included 7647 users (4782/6905, 69.3% female, mean age 44.4, SD 10.9 years, mean BMI 27.8, SD 6.1 kg/m²). Users wore CGMs for a median of 15 (IQR 14-30) days, with 42.7% (3263/7647) completing one full wear period (13-15 days) and 30.3% (2315/7647) completing 2 or more wear periods (≥28 days). Most users (7013/7647, 91.7%) logged food at least once, with a median of 47 (IQR 18-91) food entries over 12 days. Food logging declined progressively during CGM wear (mean 63.2%, SD 8) and dropped sharply after sensor removal (mean 2.4%, SD 1.1). In multivariate models, higher baseline glucose was associated with longer CGM wear (incidence rate ratio [IRR] 1.15, 95% CI 1.13-1.17) but fewer food logging days (IRR 0.96, 95% CI 0.94-0.98). Connected device syncing showed the strongest association for both CGM wear (IRR 1.32, 95% CI 1.28-1.37) and food logging (IRR 1.45, 95% CI 1.39-1.51). Older age and female sex were associated with higher engagement in both behaviors.

CONCLUSIONS: This large-scale analysis reveals distinct engagement patterns with CGM-integrated digital health applications. Food logging was largely concurrent with active CGM wear, dropping dramatically in CGM-free periods. The divergent associations of baseline glucose levels, with longer CGM wear but reduced food logging, may reflect different motivational drivers for passive monitoring versus active behavior tracking. These findings have important implications for designing sustainable digital health interventions that maintain user engagement beyond periods of biological feedback, though replication in more diverse samples and studies accounting for diabetes status and socioeconomic factors is needed.

PMID:42054670 | DOI:10.2196/80734

J Med Internet Res. 2026 Apr 29;28:e86769. doi: 10.2196/86769.

ABSTRACT

This study explores the role of open-source large language models (LLMs) in promoting artificial intelligence (AI) health equity from the perspective of the health service triangle model. First, it defines AI health, categorizes AI-supported decision-making patterns, and assesses the status quo of AI health inequalities. Second, by comparing open-source and closed-source LLMs in terms of patient privacy, data security, accessibility, and use, it demonstrates the distinct advantages of open-source LLMs for AI-enabled health services. Finally, based on the health service triangle model, this study demonstrates how open-source LLMs drive the democratization of AI-enabled health services-particularly benefiting low-resource regions-by expanding service types, improving accessibility, enhancing quality, and reducing costs. This study concludes that, while open-source LLMs must address challenges such as hallucination risks and ethical responsibilities, they ultimately enable AI health equity through technological sharing.

PMID:42054668 | DOI:10.2196/86769