Background: Medical and welfare facilities in the Noto region of Japan were severely affected by the 2024 Noto Peninsula earthquake and subsequent torrential rains. Staff working in these facilities were disaster survivors and frontline caregivers with limited psychological support. Nonverbal social robots may provide companionship and emotional comfort; however, their effects on the health-related quality of life (QoL) and well-being of care staff in disaster-affected settings remain unclear. Objective: This study explored whether introducing a nonverbal artificial intelligence communication robot was associated with changes in health-related QoL and well-being among care facility staff working under disaster conditions. Secondary objectives were to evaluate safety, acceptability, and intention to continue use. Methods: This pragmatic, exploratory pilot study used an ABAB design conducted between February 2025 and June 2025. After a 2-week baseline period, staff in dementia care, general care, and short-stay units underwent 2-week intervention, withdrawal, reintervention, and withdrawal phases. Questionnaires were administered at each phase end. The primary outcomes were health-related QoL (EQ-5D-5L), well-being (World Health Organization–5 Well‑Being Index), and positive mental health (Mental Health Continuum–Short Form). Friedman tests compared outcomes across the 5 phases, and effect sizes were expressed as Kendall . Safety, acceptability, and intention to continue use were compared between the first and second intervention phases using Wilcoxon signed rank tests with Bonferroni adjustment and rank-biserial correlations as effect sizes. Results: Of the 58 staff who completed the baseline assessment, 49 (84.5%) were included in the analytic sample (25 in dementia care, 12 in general care, and 12 in short-stay units). Among these participants, 40 (81.6%) were women, and 38 (77.6%) reported disaster-related damage to their homes or families. In the pooled analysis, no phase effect was observed for the EQ-5D-5L (=.10; Kendall =0.032, negligible), the World Health Organization–5 Well‑Being Index (=.70; Kendall =0.016, negligible), or the Mental Health Continuum–Short Form (=.44; Kendall =0.022, negligible). No robot-related adverse events were reported. In the dementia care unit, nominal unadjusted differences were observed for “made me feel calm” (=.045; rank-biserial correlation =0.571, large), “like” (=.03; =0.559, large), and “felt at peace” (=.02; =0.718, large); however, none remained statistically significant after Bonferroni correction. Conclusions: The short-term use of a nonverbal artificial intelligence communication robot did not measurably improve health-related QoL or well-being among staff in disaster-affected care facilities. Deployment appeared feasible and was not associated with reported adverse events, but efficacy as a mental health support intervention remains unproven. Exploratory acceptability and interaction signals may inform future adequately powered studies.
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Modeling Short-Term Symptom Changes and Behavioral Subtypes of Depression and Anxiety in the General Population: Observational Study Using Smartphone Data
Background: Smartphone-based digital phenotyping has emerged as a promising approach for monitoring mental health using passive behavioral data. Prior studies have linked smartphone-derived features to depression and anxiety severity; however, knowledge regarding whether short-term changes in symptoms can be captured using passive smartphone data in general population samples remains limited, as does the understanding of how such findings should be interpreted vis-à-vis behavioral patterns and demographic variability. Objective: This study aimed to model short-term changes in depression and anxiety severity using passive smartphone data, examine model performance across demographic subgroups, and identify behavioral patterns associated with symptom changes. Methods: We collected 2 weeks of smartphone usage data from 95 adults in the general population and assessed depressive and anxiety symptoms using the clinician-rated Hamilton Depression Rating Scale and Hamilton Anxiety Rating Scale, respectively. Behavioral features—including physical activity, app use, and screen usage metrics—were extracted and compressed using an autoencoder and principal component analysis. The resulting features—along with age, sex, and baseline Hamilton scores—were used to train random forest classifiers predicting symptom score changes (increase, decrease, or unchanged). Additionally, we examined whether model performance differed across demographic subgroups and whether models excluding baseline scores retained predictive performance, as baseline severity was expected to be a strong predictor. To add explanatory value beyond prediction, behavioral subtypes associated with symptom changes were identified by applying unsupervised clustering. Results: The model exhibited moderate performance in predicting changes in the Hamilton Depression Rating Scale (mean accuracy=0.70, mean area under the receiver operating characteristic curve=0.74) and Hamilton Anxiety Rating Scale (mean accuracy=0.65, mean area under the receiver operating characteristic curve=0.69) scores. Performance varied according to demographics, with reduced accuracy among younger adults and females, although these differences were not significant in permutation tests. Excluding baseline Hamilton scores diminished performance substantially, suggesting that baseline symptom severity accounted for a substantial proportion of the predictive performance. Clustering revealed 4 distinct behavioral subtypes according to smartphone usage patterns. A cluster characterized by structured, daytime-focused smartphone use and lower temporal entropy demonstrated greater improvement in depressive symptoms, whereas clusters with lower and irregular usage patterns exhibited minimal improvement or worsening. Conclusions: Passive smartphone-derived behavioral data demonstrated moderate ability to model short-term symptom changes in this predominantly nonclinical sample. However, a substantial proportion of the predictive performance was attributable to baseline symptom severity, underscoring that passive smartphone data may provide modest supplementary information rather than robust stand-alone predictive value. Nevertheless, clustering analyses indicated that passive data may still assist in identifying behaviorally distinct subtypes associated with different depressive symptom trajectories. These findings reflect a practical contribution to digital phenotyping research by elucidating both the potential and constraints of passive smartphone data for short-term symptom monitoring in small general population samples.
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Active Ingredients in Digital Cognitive Interventions: Integrating Dismantling Designs With Mechanistic Neuroscience
Digital cognitive interventions (DCIs) have emerged as scalable approaches for treating cognitive dysfunction across psychiatric, neurological, and aging populations. Despite growing evidence of efficacy, little is known about which intervention components drive therapeutic effects or through which neurocognitive mechanisms they operate. As a result, null findings are often difficult to interpret, making it unclear whether interventions failed to engage their intended targets, or whether the targets themselves are not causally related to meaningful outcomes. This limits intervention refinement, comparative evaluation, and precision personalization. Here, we argue that DCI research should shift from broad efficacy testing toward mechanistic trials designed to identify active ingredients—the intervention components responsible for engaging prespecified neurocognitive targets and producing clinically meaningful benefits. We propose adapting dismantling design methodology from psychotherapy research in order to integrate Research Domain Criteria constructs, mechanistic neuroscience, and high-resolution digital behavioral data to identify factors driving cognitive and functional outcomes. This approach aligns with the National Institute of Mental Health experimental therapeutics framework by explicitly linking target specification and target engagement with downstream clinical and functional outcomes. Mechanistic dismantling trials can determine whether specific DCI features, including adaptive difficulty, reward schedules, feedback contingencies, task variability, cognitive targets, and human support, are necessary, sufficient, or synergistic for engaging neural circuitry and producing durable and clinically meaningful transfer. Beyond optimizing intervention design, such studies may transform null or negative trials into mechanistically interpretable findings, while clarifying disease mechanisms and supporting the development of personalized, optimized, and usable DCIs.
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STAT+: FTC settles lawsuit with CVS Caremark over charges it manipulated insulin prices, impeded access
The Federal Trade Commission settled a lawsuit against CVS Caremark, one of the largest pharmacy benefit managers in the U.S., over allegations that the company artificially inflated the price of insulin and impeded access to the lifesaving diabetes treatment.
As part of the deal, which the agency maintained will save Americans up to $8.5 billion in out-of-pocket costs over 10 years, CVS Caremark, which is owned by CVS Health, must make several changes to its dealings with employers, health plans, and pharmacies. The FTC also estimated the deal will unlock up to $4.5 billion in further savings for patients through pharmacy counter rebates.
In its complaint, the FTC alleged that CVS Caremark — as well as Cigna’s Express Scripts and UnitedHealth’s Optum Rx — created a “perverse” system of rebates that favored insulin, which was then sold at higher list prices in order to “line their pockets” at the expense of patients who were forced to pay more for the medication.
STAT+: Sales from controversial U.S. drug discount program rose to $100 billion last year
Prescription medicines purchased in the U.S. under a controversial government discount program amounted to $100 billion in 2025, a 22.8% increase from the previous year, according to the Health Resources and Services Administration, which oversees the program.
Expensive medicines represented an increasing proportion of spending in the 340B Drug Discount Program, accounting for $61.9 billion, or nearly 62% of all prescription drugs purchased through the program. Nearly $8.9 billion was spent on Merck’s Keytruda immunotherapy treatment, followed by more than $4.47 billion on Biktarvy, an HIV medicine sold by Gilead Sciences.
The data mark a steady rise in sales under the 340B program, which requires drugmakers to offer discounts that are typically estimated to be 25% to 50% — but could be higher — off all outpatient drugs to hospitals and clinics that primarily serve lower-income patients.
As cyclospora illnesses surge to a record, Michigan officials eye lettuce as a possible cause
NEW YORK — Infections from the diarrhea-causing parasite cyclospora are surging, with state-level data suggesting that 2026 is already the nation’s worst year for reported cases.
More than 30 states have reported infections this year, and current data from them shows the number of infections surpassing the record U.S. mark of about 4,700 set in 2019. The illness is not usually life-threatening and is typically treated with antibiotics.
STAT+: Top Democrats question RFK Jr.’s health care advisory panel
WASHINGTON — Two high-ranking Democratic senators are questioning the credentials and financial conflicts of members of an advisory committee established by health secretary Robert F. Kennedy Jr. that aims to improve and modernize the health care system.
In a letter sent Tuesday to Kennedy and Centers for Medicare and Medicaid Services Administrator Mehmet Oz, Oregon Sens. Jeff Merkley, ranking member of the budget committee, and Ron Wyden, ranking member of the finance committee, write that they have “serious concerns” about credibility and conflicts of interest among those on the Healthcare Advisory Committee, which was announced in March.
At the time, Kennedy and Oz said they had reviewed 400 candidates before settling on 18, including motivational coach Tony Robbins, a venture capitalist who previously worked alongside Kennedy’s son, and a number of health care executives.
STAT+: Hospital chain HCA warns of lower profits as more patients go uninsured
The country’s biggest hospital chain lowered its 2026 profit outlook on Tuesday after treating more uninsured patients than expected in the second quarter.
Many of those uninsured patients had dropped their Affordable Care Act plans after losing enhanced subsidies, HCA Healthcare said, an early indicator of the fallout from the expiration of ACA enhanced premium tax credits in January.
All in, HCA now expects the increase in uninsured patients stemming from the end of those subsidies to lower its income by between $1 billion and $1.2 billion this year, up from an earlier projection of a $600 million to $900 million hit.
Humanized APOE mouse brain volume increases over age irrespective of sex and APOE genotype: implications for translational validity to the human
BackgroundHumanized APOE mouse models are widely used to study late-onset Alzheimer’s disease (LOAD) risk, yet it remains unclear whether they reproduce the macrostructural brain changes observed in human aging and disease.MethodsWe performed ex vivo magnetic resonance imaging to quantify total and voxelwise brain volumes in male and female mice across APOE genotypes (ε3/ε3, ε3/ε4, ε4/ε4) and ages spanning 6–25 months.ResultsTotal brain volume increased with age (cross-sectional estimate: 2.12 mm3/month) and was greater in APOE-ε4 carriers, with no effect of sex. Voxelwise analyses revealed regionally specific changes independent of total volume, characterized by cortical volume decreases and subcortical preservation or increases, as well as sex-dependent spatial patterns. No localized volumetric effects of APOE genotype were detected.ConclusionThese findings indicate that, despite incorporating a major genetic risk factor for LOAD, this model does not reproduce the atrophy phenotype characteristic of human aging and Alzheimer’s disease. Instead, the observed pattern is more consistent with non-pathological or vulnerable aging, suggesting that humanized APOE alone is insufficient to induce MRI-detectable macrostructural atrophy within this cross-sectional comparison, though this finding does not preclude other APOE-dependent pathological mechanisms.