As global population aging accelerates, preventing and treating cognitive impairment has become a major public health priority. Music therapy is a safe, well-tolerated non-pharmacological intervention with substantial potential to improve cognitive function. This review synthesizes the neurologic music therapy (NMT) framework, encompassing techniques targeting attention, memory, and executive function, delivered through both active and receptive approaches. Clinical investigations indicate that music therapy may improve cognition and neuropsychiatric symptoms in Alzheimer’s disease (AD), vascular cognitive impairment (VCI), Parkinson’s disease–related cognitive impairment, mild cognitive impairment (MCI), and traumatic brain injury (TBI); however, effects appear to vary by intervention duration and disease stage. This narrative review aims to provide a theoretical foundation and practical guidance for the non-pharmacological intervention of cognitive impairment by collating evidence on the neuroplasticity theoretical foundations, technical systems, and clinical applications of music therapy.
Transient multidomain functional improvement in advanced Alzheimer’s disease following high-dose psilocybin-containing mushroom administration: a case report
BackgroundAdvanced Alzheimer’s disease (AD) is generally regarded as a stage of irreversible functional decline. Psilocybin is known to transiently alter large-scale brain network dynamics and to induce plasticity-related mechanisms in preclinical models, yet clinical data in advanced dementia remain lacking.Case presentationWe report the case of an octogenarian Japanese-American woman with a 10-year history of Alzheimer’s disease, including 5 years of marked hypofunction and predominantly monosyllabic speech. Baseline features included chronic urinary incontinence, executive dysfunction, dysphagia, dependent mobility, flat affect, and severe reduction in spontaneous communication. The patient received 5 g of orally administered psilocybin-containing mushrooms (Enigma strain). The acute phase was marked by autonomic activation, clinically suspected hyperthermia, profuse sweating, and a prolonged deep sleep-like state. Approximately 19 h post-administration, spontaneous autobiographical speech emerged. Over subsequent days and weeks, functional improvements included restoration of urinary continence, improved ambulation, autonomous dressing, increased emotional responsiveness, sustained social interaction, contextual memory retrieval, preserved working memory for social context, and spontaneous conversational engagement.ConclusionThis case documents transient multidomain functional improvement in advanced Alzheimer’s disease following psilocybin administration. The findings do not imply disease reversal but suggest that residual functional capacity may persist in late-stage neurodegeneration and may become transiently accessible under specific neuromodulatory conditions.
When AI Colludes: Clinical Reliability of Training and Preference Data as a Trustworthy-AI Criterion
Research on artificial intelligence (AI) and mental health has focused largely on harms at deployment, including chatbot safety, sycophancy, and AI-associated delusions. Less attention has been paid to a prior question: whether the human-generated text and preference judgments that shape large language models are themselves clinically reliable, particularly when self-report may be distorted. This Viewpoint aims to develop the clinical psychiatric construct of collusion—the uncritical acceptance of an unreliable account—as an analytic lens for AI training and deployment, and to argue that the clinical reliability of training and preference data should be treated as an explicit trustworthy-AI criterion in mental-health–relevant systems. A conceptual synthesis of psychiatry, clinical psychology, and AI safety literature was undertaken. The analysis distinguishes three pipeline layers: pretraining corpora, preference data and posttraining methods, and deployment-time interaction. It maps the clinical construct of collusion against adjacent technical concepts, including sycophancy, reward overoptimization, grounding, refusal training, red-teaming, and live monitoring. The synthesis suggests that collusion-like dynamics are least applicable at the pretraining layer and most applicable at the preference-data and deployment layers, where unassessed user or labeler input can be reinforced without corroboration. Existing mitigations, including data curation, Constitutional AI, reward-model evaluation, grounded generation, refusal training, red-teaming, and postdeployment monitoring, address parts of this problem. However, these approaches are not yet organized around a clinically informed account of when self-report is unreliable. The central novelty is therefore not a generic claim about bias, but the proposal that clinical self-report reliability should be assessed as a distinct data-quality and governance dimension. Trustworthy-AI frameworks for mental-health–relevant applications should incorporate clinical expertise in self-report reliability into preference-data design, red-teaming, and postmarket surveillance. Adding the clinical reliability of training and preference data as an explicit criterion could complement existing technical safeguards while leaving empirical evaluation of clinician involvement as an open research agenda.
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GGDA-net: geometry-guided deformable attention network for Alzheimer’s disease image classification
BackgroundConvolutional neural networks (CNNs) have achieved remarkable success in medical image analysis, including Alzheimer’s disease (AD) classification. However, conventional convolution operations rely on fixed sampling patterns, and most existing attention mechanisms primarily focus on feature responses while neglecting spatial sampling geometry, limiting their ability to capture structural variations in brain images.MethodsTo address these limitations, this paper proposes a Geometry-Guided Deformable Attention Network (GGDA-Net) for medical image classification. The proposed framework integrates Linear Deformable Convolution (LDConv) with a Geometry-Aware (GA) Attention mechanism to jointly model feature semantics and spatial geometry. Specifically, LDConv introduces adaptive spatial sampling through learnable offsets, enabling flexible modeling of geometric deformations in brain structures, while the GA attention exploits the resulting geometric cues to guide the network toward more informative anatomical regions.ResultsThe experimental results show that the accuracy rates on the two datasets reached 99.38 and 99.16% respectively, which are superior to the existing most advanced algorithms. At the same time, the model maintains a compact size and has a relatively low computational complexity. These results highlight the effectiveness of feature learning based on geometric perception in medical image analysis and Alzheimer’s disease diagnosis.
Task-state P300 and functional brain network abnormalities in adolescent major depressive disorder: a Stroop paradigm study
BackgroundCognitive control deficits are a core feature of adolescent major depressive disorder (MDD), yet the associated task-state neurophysiological mechanisms remain poorly characterized. This study investigated electrophysiological alterations in MDD using a Stroop color-word task.MethodsTwenty-two adolescents with MDD and fifteen age- and sex-matched healthy controls (HC) completed the task during 32-channel EEG recording. We analyzed P300 amplitude and latency, 1-30Hz power spectral density (PSD) in key cortical regions, and task-based functional connectivity using the phase locking value (PLV). A support vector machine (SVM) classifier with leave-one-out cross-validation was employed to assess the diagnostic utility of the multimodal features.ResultsRelative to HC, the MDD group exhibited significantly prolonged incongruent trial reaction times, reduced P300 amplitude at centro-parietal electrodes (Oz, PO7, O2), and enhanced alpha/beta-band PSD in occipitotemporal regions. Functional connectivity analysis revealed a task-state shift from a frontoparietal to an occipitotemporal network. The multimodal SVM model achieved 86.49% classification accuracy (AUC = 0.86).ConclusionTask-specific P300 hypoactivity, aberrant oscillatory dynamics, and functional network reorganization collectively distinguish adolescent MDD from HC. These findings provide convergent neurophysiological evidence for impaired cognitive control in MDD and highlight the potential of preliminary candidate EEG biomarkers for early identification, prognostic assessment, and monitoring treatment response in adolescent MDD.
A bibliometric analysis of neuroimaging studies on cognitive control in autism spectrum disorder (2000–2025)
ObjectiveThis study aims to systematically analyze neuroimaging research on cognitive control in Autism spectrum disorder (ASD) from 2000 to 2025 using bibliometric methods, in order to reveal the evolutionary trajectory, core knowledge base, research hotspots, and future frontiers of the field.MethodsA search was conducted on the Web of Science Core Collection and Scopus databases, resulting in the inclusion of 1,581 relevant articles. VOSviewer and the Bibliometrix package in R were utilized to conduct a comprehensive visualization and quantitative analysis of annual publication volume, country/institution/author collaboration networks, keyword co-occurrence, document co-citation, and thematic evolution.Results(1) The volume of research literature showed exponential growth, with an annual growth rate of 21.61%, entering a period of rapid development particularly after 2012, which is closely related to the popularization of functional magnetic resonance imaging (fMRI) technology. (2) “Functional connectivity,” “executive function,” and “default mode network” were the most central keywords. “Functional connectivity” rapidly became a hub connecting various themes after 2010, marking a paradigm shift from “functional localization” to “brain network dysregulation.” (3) The “Triple network model” proposed by Menon was the most cited document, laying the core theoretical foundation for understanding ASD as a disorder of large-scale brain network dysfunction. (4) “Transdiagnostic” research has emerged as a new hotspot, while “multimodal imaging,” “machine learning,” and “dynamic connectivity” represent highly promising future directions.ConclusionOver the past two decades, neuroimaging research on cognitive control in ASD has undergone a profound paradigm shift: from focusing on abnormal activation in isolated brain regions to exploring the static and dynamic dysregulation of large-scale brain networks. The research perspective has also expanded from a single-disorder model to a transdiagnostic framework that includes comparisons with other neurodevelopmental disorders (e.g., ADHD). Future research should focus on the fusion of multimodal data, the application of computational psychiatry methods, and the translation of basic research findings into personalized clinical interventions.
A reality check on the AI jobs hysteria
Haven’t you heard? White-collar jobs are going away, decimated by AI. Waves of layoffs in the tech sector (most recently at Coinbase and Meta and Cisco) are said to presage what will soon come for all of us knowledge workers. But before you quit your job as a software developer or financial analyst—or tech journalist—and look to join the plumbers’ union, it’s worth considering today’s economic research on whether artificial intelligence has actually begun to devour white-collar work.
The short answer is: No.
Despite the warning by some of an imminent jobs apocalypse that will destroy much of if not most such work, or the rumblings about a “permanent underclass,” there’s scant evidence that AI has yet had any large-scale impact on the US labor market.
Analysis of the data gathered for the US Bureau of Labor Statistics (BLS) shows that the unemployment rate for the jobs potentially most affected by AI is actually lower than that for occupations less exposed to the technology. And, critically in the mind of economists, there are no signs that large numbers of people are shifting from jobs threatened by AI to supposedly safer ones, such as those involving mostly manual labor.
While the current labor statistics don’t preclude a sudden job upheaval in the coming years, they do throw doubt on the inevitability of the doomsday scenarios and the pace at which they’d unfold. Everyone in the AI community, it seems, is predicting that the technology will soon wipe out jobs, and everyone, it also seems, knows some young wannabe workers who can’t find one. Perhaps we haven’t seen any major disruption in the labor market statistics yet, people often say, but just wait.
But maybe we should pay attention to what the data is showing us. And right now, the numbers paint a picture of a relatively stable labor market in which AI disruptions remain largely speculative.
“It could be disruptive, but the data is telling us right now that disruption is not yet here, and we have time to plan.”
“All of the available evidence to date suggests that AI’s impact on current labor market conditions is likely small right now,” says Erika McEntarfer, a labor economist who headed the BLS until President Trump fired her last fall after a jobs report that displeased the administration. (Not surprisingly, BLS reports of sluggish job growth have continued since her dismissal.)
McEntarfer, who is now a fellow at the Stanford Institute for Economic Policy Research, says the relatively small impact that AI is having so far on today’s labor market “surprises many people, but it shouldn’t. What we know from history is that it takes time for innovations to work their way through changes in industries and changes in occupations. AI is unlikely to transform labor markets until it first transforms businesses.”
McEntarfer points to US Census data showing that only one in five companies are using AI in any business function. “The data are a great reality check on the fear that AI will be enormously disruptive,” she says. “It could be. It likely will be disruptive, but the data is telling us right now that disruption is not yet here, and that we have time to plan.”
Things ain’t great—but the question is why
The US job market, to be sure, sucks for many, especially younger would-be workers. Unemployment rates for recent college graduates stand at around 5.6%, well above the level for all workers. It’s a rate not seen since the pandemic and the years immediately after the 2008 recession. Even more troubling is that hiring rates have been particularly dismal during the post-covid economy, a trend that hits hard at young people trying to enter the workforce. If you’re a recent college graduate and looking for a tech job, no one, it can seem, is hiring.
There are signs that AI is contributing to the pain for the 22-to-25-year-olds seeking jobs in software development and other occupations that are feeling a big impact from AI. But these professions represent just a sliver of the overall labor market. What’s more, it’s uncertain how much blame AI should get for the job woes. Similarly unknown is whether the loss of entry-level jobs in AI-exposed occupations is a harbinger of what’s coming for others or simply an isolated symptom of what economists refer to as a “low-fire, low-hire” labor market caused by a variety of macroeconomic forces.
Insights into these uncertainties will tell us much about our working fates in the transition to an AI economy. There are no shortage of confident assertions and predictions about what is about to happen; while some people forecast the end of work, others say economic history teaches us that technology advances always lead to more and better jobs eventually.
The honest answer is that no one knows for sure what AI will bring and whether this time will be different. To help figure it out, we need better and far more comprehensive data.
The statistics gleaned from the federal government’s monthly survey of 60,000 households for the BLS provide a broad overview of the changes to the labor market, while academics and even some AI companies have begun trying to gain a more granular view of specific jobs that are being affected. But the existing data-gathering tools don’t adequately explain how AI is affecting the huge and diverse US labor market.
There’s a long list of questions that we don’t have the data to fully answer. How is AI being used in the workplace? Does the increased use of AI mean the technology will replace workers, or will it make them more productive and valuable? Which occupations and skills are most affected? Who is in most peril from the changes? As David Deming, a professor of economics at Harvard University, puts it: “We’re sort of flying blind.”
To gather more insight into some of these questions, Deming and his colleagues have been surveying several thousand people every three months since 2024, asking them basic questions: Do you use generative AI, and how often? Does it save you time at work? Tracking the answers over time gives the economists important clues (it’s used by a little over 40% of workers but adoption varies by sectors) and allows them to estimate productivity gains (they’ve found some, but nothing economy-shaking). It has also helps document how quickly AI has been adopted in the workplace and how it compares with earlier technologies such as the PC and the internet (the pace has been faster but roughly in the same ballpark).
It’s far from a complete picture of how AI is changing work. But it provides some intriguing results; for example, a fair number of workers in manufacturing and other industrial sectors have tried AI. Deming’s results show that while businesses in general might be relatively slow to formally adopt the technology, lots of their employees are using it.
Getting a picture of these early adopters and how they’re using AI provides a “crystal ball for the future of the labor market,” Deming says. “It gives you important clues about how it’s going to be used tomorrow, and who’s going to be affected, and who’s going to be harmed and how do we need to get ready for it. It’s a diagnostic of what’s coming down the road.”
But what it doesn’t tell you is the fate of various jobs.
The young are most vulnerable
Analysis of how AI will affect jobs typically begins with identifying so-called exposure of various occupations to the technology. This approach is based on the idea that any given job is a collection of tasks. By evaluating which tasks can be performed by, say, the latest large language model, researchers gauge an occupation’s overall exposure. A small army of economists have created a slew of such studies, meticulously ranking hundreds of jobs and scrambling to update the results as the capabilities of generative AI keep exploding.
The results have often triggered a panic, with graphics showing the growing vulnerability of different jobs to AI.
But by themselves the exposure results are not a true predictor of which jobs will be lost to AI. That depends on the kinds of tasks done by the technology, the extent to which the AI is adopted, various business calculations about the value of workers, and even the costs of deploying AI. But the exposure findings are a valuable starting point.
In a working paper called “Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of Artificial Intelligence,” researchers at the Stanford Digital Economy Lab looked at 950 jobs, placing the occupations into five categories from least exposed to most. Then they used a vast data set from ADP, the world’s largest payroll provider, to look at employment growth in each of the categories. Their exclusive access to the ADP data set, which is far larger than the one available through the BLS, allows the researchers to better spot impacts by demographic. When they examined what was happening to different age groups, says Erik Brynjolfsson, the director of the lab who led the effort, “it was extremely striking.”
They spotted the drop in head count for 22-to-25-year-olds in the most exposed occupations, such as software development and customer service, beginning in late 2022, when ChatGPT was first publicly released. Other researchers reported evidence that the decline in these jobs began well before ChatGPT and questioned whether the labor market could react so quickly to the introduction of AI technology.
But while the Stanford researchers acknowledge that other factors in addition to AI probably contributed to the early declines, they say that after controlling for those factors, they saw convincing evidence of a significant effect from AI after 2024 and growing in 2025 to a 16% decline in entry-level jobs in AI-exposed occupations. In contrast, head count grew for older workers in the same occupations, as did the number of jobs in the less exposed occupations.
Digging deeper into the data, the researchers found another important clue, though one that wasn’t totally unexpected. The impact on head counts depended on how AI was being used. It was specifically the jobs where tasks could be automated (that is, AI could do them “with minimal human involvement”) that accounted for the decrease in employment—jobs for people like software developers. In jobs where AI was mainly used but to augment human work, head counts grew faster than the average for entry-level workers.
That’s consistent with one explanation for the woes of many young would-be workers. It could be, according to the Stanford paper, that entry-level jobs depend more on the types of knowledge that people acquire through education but that can readily be mimicked by AI; the authors call this codified knowledge. It might be particularly easy to automate such tasks as entry-level coding. In contrast, older workers have more so-called tacit knowledge, the type based on their experience. That type of wisdom is harder for AI to replace.
Despite the findings about AI’s impact on young workers, Bharat Chandar, an economist at Stanford and one of the authors (along with Brynjolfsson and Ruyu Chen), stresses that it’s still early when it comes to understanding how the technology will affect jobs in the future. It could be that the job loss will spread to older workers and to less AI-exposed occupations, he says. But Chandar says it is also possible that firms and workers will adjust to shifting labor demands, and the effects will level off or even disappear.
To track how it plays out, the Stanford Digital Economy Lab is about to launch a regularly updated project providing data on how AI is transforming the economy.
The Stanford research and other work has put a particular spotlight on coding, a task at which AI is getting extremely adept.
A recent paper by economists at the Federal Reserve Board found, not surprisingly, that annual employment growth for coders has slowed significantly—by about 3%—since the introduction of ChatGPT. But here’s a critical detail: Overall employment for coders continues to grow. Employment in coding jobs is still rising, they noted, just more slowly than before 2022.
In short, coding jobs are not going away, at least not anytime soon. But it’s an occupation that is clearly being transformed by AI.
One of the somewhat surprising wrinkles uncovered by recent research is that wages in sectors highly exposed to AI have risen relatively fast since the introduction of ChatGPT. One explanation is that employers are still willing to pay for the kinds of knowledge and experience that are, at least for now, hard to replace with AI. If true, this suggests not the end of work in AI-exposed jobs but, more specifically, the demise of the typical career model in which young graduates are hired to do software tasks that can be automated and are slowly trained to gain that valuable tacit experience. The earn-while-you-learn model might finally be broken—at least for some occupations.
The simple truth could be that coding skills are no longer a guarantee of a job. That may help to explain the drop-off of computer science majors at schools around the country. Future canaries in the cubicles are sniffing out the dangers of looking for a job when their skills can be matched by AI.
But a closer look at the data shows that students are not necessarily turning away from AI-related careers. Rather, they appear to be tailoring their skills to the changes they see underway as AI becomes increasingly important for various disciplines. Interest is rising in AI-adjacent fields like data science and cybersecurity. One fast-growing major: artificial intelligence itself (a recent addition to many college offerings).
Is this time different?
Anxiety over the potential of AI to replace workers is nothing new. I wrote “How Technology Is Destroying Jobs” in 2013, describing how a slew of new digital technologies, including AI, were beginning to threaten white-collar work. I wasn’t alone. It was a popular theme at a time when the labor market was sluggish and jobs were scarce.
In one of his last days in office in late 2016, President Obama issued a report written by his top economic and science advisors warning that AI was threatening workers. Among the findings was that automated vehicles—especially driverless trucks—could eliminate 2.2 million to 3.1 million existing US jobs. Around the same time, one of the pioneers of AI, Geoffrey Hinton, said that “people should stop training radiologists” because it was “completely obvious” the occupation was soon to be replaced by AI.
None of these predictions came true, of course (nor did so-called technological unemployment occur during several earlier tech-related job panics). The forecasts were often wrong about the pace of the technological advances—we’re still waiting for fleets of driverless trucks on the highways—and failed to understand the complex portfolio of tasks that make up many jobs. AI has indeed become a tool for screening radiology images, but there are more radiologists than ever. It turns out that human radiologists perform a multitude of valuable tasks, including interpreting results and interacting with patients, that can’t be accomplished with AI (yet).
Perhaps this time is different, and we can put aside the lessons of economic history. Certainly, AI has gained unimaginable powers to do humanlike tasks. Perhaps it will devour jobs in ways that we’ve never seen before. And perhaps that will happen abruptly, without a warning buried in the labor statistics. But the previous bouts of AI job anxiety still hold a prescient lesson: Our real focus needs to be less on the dystopian fears and more on the very real transitions in the workplace that will likely affect millions of people.
“Even if there is not mass or even increased unemployment, the transition could still be very difficult,” says Jed Kolko, senior fellow at the Peterson Institute for International Economics and former undersecretary of commerce in the Biden administration. “And what does a difficult transition period mean? It means people losing jobs, or people’s jobs being redefined in ways that make those jobs pay worse or be less meaningful. And some people whose jobs are threatened may not be able to adapt.”
The more we understand this transition, the better prepared we’ll be to deal with it. And for that we’ll need better and more complete data.
For McEntarfer, the former commissioner of the BLS, the real question is the speed of any disruption. “If it happens at the normal pace of technological change, labor markets will have time to adapt. If there is a sudden and severe disruption, then that will be a big challenge for policymakers,” she says. “That’s really the most important question facing us right now: how rapid this transformation is going to be.” And, she adds, “we’ll know by watching the data.”
Two decades ago, the country was caught flat-footed by the so-called China shock as free-trade policies led to an influx of imports and the devastation of manufacturing jobs in many parts of the country. It took years for researchers to understand the data showing how the trade policies, generally welcomed by economists, were destroying communities. Today the threat of an economic transformation brought on by AI is far larger and points to potentially far more damage for huge groups of workers.
To head off another devastating labor transition, we will need well-timed government and business policies, especially programs to train and reskill workers. If McEntarfer and other labor economists are correct, we probably have time to design deliberate and effective strategies to manage the transition. But first we need to better understand what is going on—and how fast.
It’s hard to find an economist who is more enthusiastic about AI’s future than Stanford’s Brynjolfsson, who believes that we’re likely on the brink of a huge boost that will transform the economy. “Perhaps the best productivity growth of my lifetime is coming up,” he says.
But Brynjolfsson also warns that a lack of data is severely limiting our visibility into the economic and societal impacts that are coming. At a time when hundreds of billions are being spent on rolling out the technology, he says, “we’re not investing even 1% of that on understanding the transition.”
Role of psychological resilience and psychological distress in linking fear of disease progression to quality of life in chronic heart failure: a cross-sectional serial mediation analysis
ObjectiveTo examine whether psychological resilience and psychological distress serially mediate the association between fear of disease progression and quality of life (QoL) in patients with chronic heart failure (CHF).MethodsThis cross-sectional study enrolled 212 patients with CHF admitted between June 2023 and June 2025. Assessment tools included a demographic questionnaire, the Fear of Progression Questionnaire (FoP-Q), the Connor–Davidson Resilience Scale (CD-RISC), the Depression Anxiety Stress Scales-21 (DASS-21), and the Minnesota Living with Heart Failure Questionnaire (MLHFQ). Correlation and serial mediation analyses were performed using IBM SPSS Statistics for Windows, version 22.0, and the PROCESS macro, with the bootstrap method (5,000 resamples) used to test the mediation effects.ResultsThe mean scores were 43.60 ± 8.32 for FoP-Q, 52.71 ± 14.28 for CD-RISC, 44.29 ± 10.68 for DASS-21, and 48.63 ± 10.85 for MLHFQ. Correlation analysis indicated that FoP was negatively correlated with psychological resilience (r = −0.775) and positively correlated with psychological distress and MLHFQ scores (r = 0.868 and 0.773, respectively; all P < 0.05). Psychological resilience was negatively correlated with both psychological distress and MLHFQ scores (r = −0.728 and −0.744, respectively), while psychological distress was positively correlated with MLHFQ scores (r = 0.745; all P < 0.05). The mediation model revealed a direct effect of FoP on QoL (effect = 0.629, 41.14%), along with three indirect pathways: via psychological resilience alone (effect = 0.508, 33.22%), via psychological distress alone (effect = 0.344, 22.50%), and via the serial pathway from psychological resilience to psychological distress (effect = 0.048, 3.14%).ConclusionPatients with CHF exhibited elevated levels of FoP and generally reduced QoL. Psychological resilience and psychological distress served as significant serial mediators in the relationship between FoP and QoL. FoP could directly reduce QoL in patients with CHF and indirectly affect it by decreasing psychological resilience and exacerbating psychological distress. Clinical attention should be directed toward assessing the psychological status of patients with CHF, improving psychological resilience, alleviating negative emotions, reducing the adverse impact of FoP, and enhancing patients’ QoL.
Craving fullness: a fullness-seeking phenotype that blurs the line between binge eating disorder and food addiction
Food addiction and binge eating disorder show striking clinical overlap that current diagnostic frameworks do not fully capture. In binge eating disorder samples, Yale Food Addiction Scale-defined food addiction has been reported in roughly half of participants. Higher symptom severity is associated with greater impairment. Within this overlap, clinicians frequently observe a subset of patients whose compulsive eating is organized around the pursuit of extreme fullness rather than palatability for specific trigger foods. Compulsive high-volume eating (CHVE) refers to recurrent, distressing episodes of consuming dangerously large volumes of food, often to the point of marked gastric distension. In some cases, the food is low-caloric or non-hedonic, and the dominant motivator is the interoceptive target state of extreme fullness rather than taste. This Perspectives article focuses on fullness-seeking as a high-volume pattern within binge eating disorder phenomenology that has received little attention in the food addiction literature. It extends prior work that framed volume addiction within compulsive high-volume eating as a public health issue. The focus here is clinical formulation, not the public health case. Evidence from reward learning, gut-brain signaling, gastrointestinal physiology, and neuroendocrinology suggests that this pattern may be rooted in binge eating disorder. It may identify a subset of patients with addiction-like dynamics. These cases may be better addressed when addiction-informed concepts and tools are integrated. It offers a physiologically grounded lens for combining eating disorder and addiction frameworks in a more coherent clinical approach.
Older Adults’ Motivators and Barriers to Using Mindfulness Apps for Stress Management in Brain Health Interventions: Interview Study
<strong>Background:</strong> Population aging is driving a rapid rise in dementia cases worldwide, posing a major challenge for health care systems around the globe, including in the Netherlands. Digital multidomain lifestyle interventions, which target multiple lifestyle domains simultaneously, can protect against cognitive decline in at-risk older adults but struggle to sustain engagement. Addressing stress in these interventions is crucial, as it can directly increase dementia risk and may promote unhealthy behaviors in other domains targeted in these interventions, including physical activity, diet, and sleep. <strong>Objective:</strong> This study explores the motivators and barriers for Dutch adults aged 60 years and older to use mindfulness apps for stress management within digital multidomain lifestyle interventions. Despite their potential, it remains unclear which stress-related needs would motivate older adults to use mindfulness apps in these interventions and whether these apps effectively address those needs. Moreover, little is known about how older adults practice mindfulness independently in everyday settings alongside other lifestyle priorities. <strong>Methods:</strong> We conducted 15 semistructured interviews with former participants of a 26-week multidomain lifestyle intervention study (the “HELI” [Hersenfuncties na Leefstijlinterventie] study) that included a mindfulness app for stress management. Participants (8 females; age range 61-73 years) were first invited to describe their practices to improve brain health to see whether stress and mindfulness emerged spontaneously. Follow-up questions and scenarios explored experiences with stress and mindfulness before these concepts were explicitly introduced midway through the interview, providing insight into their roles in participants’ lives free from associations with the terms. Data were analyzed using template analysis. <strong>Results:</strong> Older adults reported experiencing fewer minor daily stressors than earlier in life and having developed effective coping strategies with age. These strategies often included elements associated with mindfulness, such as acceptance and deliberate attention to the present moment. However, many frequently worried at night about major concerns, including personal health, the well-being of loved ones, and global issues. These findings suggest that older adults may be more motivated to use mindfulness apps to cope with nighttime worry than with minor daily stressors. At the same time, older adults reported barriers to using mindfulness apps, including negative associations with the term “mindfulness” (eg, perceived as too spiritual) and challenges in maintaining focus during exercises. <strong>Conclusions:</strong> We discuss how older adults develop mindfulness skills with age and how these skills help older adults cope with daily stressors but not with nighttime rumination. Specifically, we argue that diminished cognitive resources at night, fewer distractions, and metacognitive patterns may sustain worry before sleep. This study highlights the importance of tailoring multidomain lifestyle interventions to the unique challenges of older adults. We also offer recommendations to present mindfulness in ways that help older adults stay focused during practice.
