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PubMed2026

Effectiveness of tobacco cessation interventions delivered in clinical settings in South Asia: a systematic review and meta-analysis.

BACKGROUND: Despite the burden of tobacco use, access to cessation support in South Asia remains scarce. OBJECTIVE: This review evaluates the effectiveness of tobacco cessation interventions delivered in clinical settings in South Asia. METHODS: Five relevant databases were searched from inception to February 2025. Eligibility criteria included randomized and non-randomized studies evaluating behavioral, pharmacotherapy, and multicomponent interventions delivered in clinical settings in South Asia. Data on study setting and design, participant information, intervention, comparator, and outcomes were extracted. Meta-analyses using random-effect models were conducted where possible. Certainty of evidence was assessed using GRADE. RESULTS: Thirty-seven studies were included (22 randomized and 15 non-randomized). Interventions involved pharmacotherapy (n = 6; 16.2%), nicotine replacement therapy (n = 7; 18.9%), behavioral counseling (n = 12; 32.4%), or combined/multicomponent interventions (n = 12; 32.4%). Most studies were conducted in India (n = 26; 70.3%), followed by Pakistan (n = 6; 16.2%), Nepal (n = 3; 8.1%), and two studies (5.4%) were multi-country in India, Pakistan, and Bangladesh. Pooled analyses demonstrated higher quit rates among intervention versus control for continuous abstinence at 0-3 months (RR: 1.21, 95%CI: 1.06 to 1.37) and >3 months (RR: 1.68, 95%CI: 1.1.4 to 2.47), and for point abstinence at >3 months post-intervention (RR: 2.03, 95%CI:1.35 to 3.08). Heterogeneity was high for all analyses (I2 range: 94% to 97%). Combined behavioral and pharmacotherapy interventions were most effective (RR: 1.70, 95%CI: 0.98 to 2.92), although not statistically significant (p = 0.06). CONCLUSION: Tobacco cessation interventions delivered in clinical settings in South Asia are effective, particularly when combining behavioral support with pharmacotherapy. However, evidence is limited by methodological weaknesses.

Humans↗

PubMed2026

Effectiveness and usability of artificial intelligence-powered assistive technologies in Supporting daily activities of children with cerebral palsy: a systematic review.

BACKGROUND: Cerebral Palsy (CP) is the main cause of motor disabilities in childhood, necessitating innovative approaches to rehabilitation and assistive technology (AT). Simultaneously, artificial intelligence (AI) is increasingly being integrated into devices to create more adaptive, personalized, and effective AT. This systematic review aimed to evaluate the effectiveness and usability of AI-powered assistive technologies designed to support daily activities and rehabilitation in children with CP. MATERIALS AND METHODS: Five databases, including Scopus, Web of Science, PubMed, Embase, and IEEE Xplore, were systematically searched, and 23 articles were included in the final analysis. Articles were identified, selected, and categorized into emerging thematic areas based on the primary function and application of the technology. RESULTS: Five key thematic topics were identified: 1) AI-driven motor rehabilitation and gait training for functional mobility; 2) intelligent assessment and monitoring systems for clinical decision support; 3) AI-supported communication, social interaction, and intention recognition tools; 4) gamified and virtual reality-based interventions to enhance engagement and usability; and 5) smart assistive systems supporting daily living and independent mobility. The findings demonstrate a strong trend toward the application of AI technologies in personalized, engaging, and data-driven interventions for children with CP. However, the field is predominantly in the proof-of-concept stage, with limitations including small sample sizes, lack of long-term clinical validation, challenges in user-centered design, and usability for children with CP. CONCLUSION: AI-powered assistive technologies hold significant potential for transforming the care of children with CP by enabling highly personalized and engaging interventions. To actualize this potential, future work must realize that practical application remains challenging owing to limited clinical validation, technological integration, and usability barriers for children with CP. Future research must prioritize user-centered design and multidisciplinary collaboration to ensure that AI and robotic advancements improve the usability and quality of life for children with CP.

Humans↗

PubMed2026

The efficacy of modified psychodynamic psychotherapy for patients with schizophrenia-spectrum disorders in Germany: a prospective, single-centre, assessor-blinded, parallel-group, randomised controlled trial.

BACKGROUND: People with schizophrenia-spectrum disorders have difficulties in interpersonal functioning that remain insufficiently addressed by standard care. Despite long-standing clinical use, psychodynamic psychotherapy has little empirical support compared with other psychosocial treatments for people with schizophrenia-spectrum disorders. We evaluated the efficacy of Modified Psychodynamic Psychotherapy for Schizophrenia (MPP-S), a manualised treatment tailored to the interpersonal vulnerability characteristic of this population, plus treatment as usual (TAU), compared with TAU alone. METHODS: This prospective, single-centre, assessor-blinded, parallel-group, randomised controlled trial was conducted at the Psychiatric University Hospital of the Charité at St Hedwig Hospital in Berlin, Germany. Participants were outpatients aged 18-64 years who were diagnosed with schizophrenia or schizoaffective disorder and exclusion criteria included organic brain disorder, somatic illness affecting cerebral function, and current or past alcohol or illicit drug misuse requiring addiction-specific treatment. Participants were randomly assigned 1:1 in blocks of ten to MPP-S (minimum 30 sessions) plus TAU or TAU alone. Outcome assessors were masked, but participants and therapists were not. The primary outcome was psychosocial functioning, measured using the Mini International Classification of Functioning, Disability and Health Rating for Limitations of Activities and Participation in Psychological Disorders (Mini-ICF-APP) and evaluated at baseline and prespecified post-treatment (24 months) and follow-up (36 months) assessments. Analyses followed the intention-to-treat principle. Linear mixed models were used to analyse incomplete longitudinal data under a missing-at-random assumption. People with lived experience were not formally involved in the design, conduct, or reporting of this study. This trial was preregistered at ClinicalTrials.gov (NCT02576613) and is complete. FINDINGS: From Oct 12, 2015, to Dec 7, 2021, 130 participants (57 [44%] female and 73 [56%] male) were randomly assigned to either MPP-S plus TAU (n=65) or TAU alone (n=64). One participant withdrew consent to data analysis. Regarding the primary outcome of psychosocial functioning, linear mixed models showed significant group-by-time interactions favouring the intervention: estimated marginal means indicated adjusted between-group differences in Mini-ICF-APP scores of -3·45 (95% CI -5·51 to -1·40; p=0·0011) at 24 months and -4·07 (-6·19 to -1·94; p=0·0002) at 36 months. The frequency of adverse events was similar between groups. There were three serious adverse events: two participants died by suicide (one in the MPP-S plus TAU group who did not start psychotherapy and one in the TAU alone group) and one participant in the MPP-S plus TAU group was admitted to a forensic hospital. INTERPRETATION: MPP-S added to TAU could improve psychosocial functioning compared with TAU alone. Our findings suggest efficacy and possible long-term benefits of psychodynamic psychotherapy for schizophrenia-spectrum disorders and indicate its potential role alongside other psychotherapeutic and psychosocial treatments. FUNDING: Berlin Institute of Health, Deutsche Gesellschaft für Psychoanalyse, Psychotherapie, Psychosomatik und Tiefenpsychologie, International Psychoanalytic University Berlin, and Köhler-Stiftung.

Humans↗

PubMed2026

A Web-Based, Pedometer-Mediated Intervention Increases Amount and Intensity of Physical Activity in COPD: A Randomized Controlled Trial.

INTRODUCTION: Ground-based walking training is an aerobic exercise used in supervised pulmonary rehabilitation (PR). Physical activity (PA) interventions typically promote step counts, but it is unclear whether community-based walking intensity can be targeted as aerobic exercise. This randomized controlled trial evaluated a web-based, pedometer-mediated PA intervention designed to increase walking amount and intensity. MATERIAL AND METHODS: Participants with COPD who had never enrolled in PR were randomized 1:1 to control or intervention. The intervention included individualized step-count goals, iterative feedback, educational content, and an online community forum. The Fitbit Inspire Heart Rate objectively monitored daily step counts. Participants were instructed to achieve step-count goals with as many steps of moderate-intensity as possible guided by a modified Borg rating of 4-5 for dyspnea. The primary outcome was change in PA measured as average daily step count at 12 weeks. Aerobic intensity was assessed by the Rapid Assessment of PA Questionnaire which uses self-reported moderate or vigorous PA to categorize responders as underactive or active. Linear mixed-effects models (PROC MIXED, SAS v9.4), adjusting for group, time, group*time, FEV1%predicted, enrollment season, and study modality (eg, in-person, virtual, hybrid), assessed between-group change. RESULTS: Participants (57 intervention, 52 control) were 97% male, mean age 73±7 years, and baseline FEV1 73±23% predicted. Baseline daily steps were 4,222±1,929 (intervention) and 4,851±2,637 (control). Intervention participants increased average daily steps by 1,410 steps/day more than controls (p=0.005). The intervention group showed greater transitions from underactive to active intensity (between-group: p=0.025), with 20 (41%) moving to active status (within-group: p=0.001). CONCLUSION: Technology-mediated community-based walking increased PA amount and intensity. These findings support further evaluation of this intervention as a potential option for ground-based walking training with objective measurement of exercise intensity.

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PubMed2026

The Impact of Chatbot Type and Normative Messaging on Chatbot Usage Intention Based on the Health Technology Acceptance Model: Randomized Controlled Trial.

BACKGROUND: Digital health tools, such as health chatbots, may improve access to scalable health support, but adoption remains inconsistent. Existing models do not fully integrate technology acceptance factors with health motivation factors relevant to digital health use. OBJECTIVE: This study proposed and tested the health technology acceptance model and examined whether normative message framing and chatbot type were associated with health motivation, technology acceptance, and intention to use a health chatbot. METHODS: In October 2025, we conducted a 4 × 2 between-participants online experiment with 1000 US adults recruited from a nationally representative YouGov panel. Participants were randomized to 1 of 8 conditions varying norm message type (self-oriented, peer-oriented, expert-oriented, or family-oriented) and chatbot type (AI-powered or rule-based) in a cancer prevention and genetic risk information scenario. Outcomes included descriptive norms, injunctive norms, perceived susceptibility, perceived severity, perceived benefits, self-efficacy, perceived ease of use, trust, privacy concerns, and usage intention. Data were analyzed using a multivariate ANOVA with Bonferroni-adjusted post hoc tests and multiple linear regression. RESULTS: Peer-oriented and family-oriented messages produced higher usage intention than expert-oriented messages, and peer-oriented messages also increased descriptive norms, injunctive norms, self-efficacy, and trust. AI-powered chatbots were associated with higher usage intention (P=.02) and greater trust (P=.008) than rule-based chatbots. In regression analyses, the model explained 50.8% of the variance in usage intention. Usage intention was positively associated with descriptive norms (β=0.087; P=.003), injunctive norms (β=0.078; P=.009), perceived susceptibility (β=0.051; P=.03), perceived benefits (β=0.253; P<.001), and trust (β=0.33; P<.001), and negatively associated with perceived severity (β=-0.047; P=.049) and privacy concerns (β=-0.11; P<.001). Perceived ease of use and self-efficacy were not significant predictors. CONCLUSIONS: The health technology acceptance model was a useful framework for explaining the intention to use a health chatbot by combining technology acceptance and health motivation constructs. Both social design features and chatbot design features shaped adoption-related beliefs, with peer-oriented and family-oriented framing and AI-powered chatbots showing particular promise. Trust and privacy concerns remained central determinants of intended use.

Humans↗

PubMed2026

Diagnostic Performance of Machine Learning for Systemic Lupus Erythematosus: Systematic Review and Meta-Analysis.

BACKGROUND: Early and accurate diagnosis of systemic lupus erythematosus (SLE) and its organ involvement is essential. Previous reviews of machine learning (ML) in SLE combined heterogeneous tasks and validation strategies and may have overinterpreted model performance. OBJECTIVE: This study evaluated the diagnostic performance of ML and deep learning (DL) models for 3 clinically distinct SLE-related tasks: SLE classification or diagnosis, lupus nephritis (LN) diagnosis, and neuropsychiatric systemic lupus erythematosus (NPSLE) discrimination. We also assessed methodological quality and certainty of evidence. METHODS: PubMed, Embase, Cochrane Library, Web of Science, and IEEE Xplore were searched from January 2014 to April 2026. Eligible peer-reviewed diagnostic accuracy studies developed or validated ML or DL models for 1 of the 3 prespecified tasks, used an accepted reference standard, and provided data for a 2×2 contingency table. Bivariate random-effects meta-analyses with the Hartung-Knapp-Sidik-Jonkman adjustment were used to pool sensitivity and specificity. We reported 95% prediction intervals (PIs), assessed risk of bias using the Quality Assessment of Diagnostic Accuracy Studies for Artificial Intelligence tool (QUADAS-AI; Viknesh Sounderajah [Imperial College London]), and evaluated certainty of evidence using the Grading of Recommendations Assessment, Development, and Evaluation framework for diagnostic test accuracy. RESULTS: Twenty-nine studies were included: 17 for SLE classification, 5 for LN diagnosis, and 7 for NPSLE discrimination. In the primary task-stratified analysis, pooled sensitivity was 0.91 (95% CI 0.86-0.94; 95% PI 0.56-0.99), and pooled specificity was 0.94 (95% CI 0.91-0.96; 95% PI 0.69-0.99), with low heterogeneity (I²=23.9% and 22.9%, respectively). DL models showed a sensitivity of 0.93 and specificity of 0.95, compared with 0.88 and 0.94 for traditional ML models. Certainty of evidence was high for most analyses but low for LN diagnosis because of inconsistency and imprecision. All studies were retrospective, and only 9 of 29 (31%) performed independent external validation. Overall risk of bias was high or unclear in 22 of 29 (75.9%) studies. No study reported model calibration, decision-curve analysis, or net clinical benefit. CONCLUSIONS: ML models showed promising diagnostic accuracy across 3 distinct SLE-related tasks, but wide PIs, limited external validation, and pervasive risk of bias restrict conclusions about real-world generalizability. Prospective multicenter studies with standardized tasks and reference standards, independent external validation, and formal assessment of calibration and clinical utility are required before clinical implementation.

Humans↗

PubMed2026

Multidisciplinary mHealth Rehabilitation for Patients With Abdominal Cancer Who Are Receiving Chemoradiotherapy: Randomized Phase II Trial.

BACKGROUND: Concurrent chemoradiotherapy (CCRT) for abdominal cancer frequently induces muscle loss, weight loss, and malnutrition. OBJECTIVE: This exploratory randomized phase II trial evaluated whether a multidisciplinary, mobile health (mHealth)-based multimodal rehabilitation program could preserve handgrip strength and muscle mass in patients with abdominal cancer undergoing CCRT. METHODS: In this prospective, multicenter, randomized, open-label phase II trial (NCT05325554), 111 eligible patients with abdominal malignancies scheduled for CCRT were randomly assigned (1:1) to receive either multidisciplinary mHealth rehabilitation care (MRC; n=57) or standard care (SC; n=54). The MRC program was delivered by a dedicated multidisciplinary team using the AiNST mHealth platform and wearable heart rate monitors. The primary end point was handgrip strength at the end of CCRT (analyzed with analysis of covariance adjusting for baseline). Secondary end points were exploratory and analyzed without multiplicity adjustment; sensitivity analysis using false discovery rate (FDR) correction was performed. RESULTS: Between February 2022 and April 2023, 111 patients were enrolled. Adherence was high (n=93, 83.9% achieved exercise targets). After adjusting for baseline handgrip strength, the MRC group had significantly higher handgrip strength at the end of CCRT than the SC group (adjusted mean difference 4.87 kg, 95% CI 3.36-6.38; P<.001). Exploratory analyses of secondary end points (without multiplicity adjustment) showed that the MRC group also had better preservation of body weight (P=.005), skeletal muscle mass (P<.001), serum albumin (P=.009), prealbumin (P=.02), and lower rates of hematological toxicity (P<.05), as well as improved psychological status (distress thermometer [DT] and Hospital Anxiety and Depression Scale [HADS]) and nutritional scores (Nutritional Risk Screening 2002 [NRS-2002] and Patient-Generated Subjective Global Assessment [PG-SGA]) at the end of CCRT (all P<.05). All nominally significant secondary end points remained significant after FDR correction (q<.05). These findings are preliminary and should be interpreted with caution due to the open-label design, population heterogeneity, and exploratory secondary analyses. CONCLUSIONS: In this exploratory phase II trial, a multidisciplinary, mHealth-based multimodal rehabilitation program was associated with better preservation of handgrip strength, muscle mass, and nutritional status, as well as lower rates of certain treatment toxicities, compared with SC. However, definitive conclusions are limited by the open-label design, heterogeneity of tumor types, and short follow-up. Larger, blinded phase III trials are needed to confirm these findings.

Humans↗

PubMed2026

Compliance With Ecological Momentary Assessment Among Patients With Cancer: Systematic Review and Meta-Analysis.

BACKGROUND: Patients with cancer often experience substantial fluctuations in psychological states during disease management. Traditional research tools are limited in capturing these dynamic changes in real time, constraining clinicians' understanding of patients' true conditions. Ecological momentary assessment (EMA) enables high-frequency, real-time data collection, providing patient-reported data with greater ecological validity. However, the effectiveness of EMA studies critically depends on patient compliance, and reported compliance rates vary widely, with a lack of systematic quantitative synthesis. OBJECTIVE: This study aims to systematically review and quantitatively analyze compliance with EMA among patients with cancer, and to examine whether EMA design characteristics were associated with compliance. METHODS: Web of Science, PubMed, Embase, Cochrane Library, CINAHL, PsycINFO, CNKI, and Wanfang databases were searched for literature published up to April 30, 2026. Compliance was defined as completed prompts divided by delivered prompts. Single-group proportions were pooled using logit transformation and random-effects models with the Hartung-Knapp-Sidik-Jonkman adjustment. Prediction intervals were calculated to describe the expected distribution of compliance in future comparable settings. Subgroup analyses, univariable meta-regressions, leave-one-out sensitivity analyses, and tests for small-study effects were performed. Risk of bias was assessed using the Joanna Briggs Institute Critical Appraisal Checklist for Studies Reporting Prevalence Data, methodological reporting quality was assessed using a modified Checklist for Reporting EMA Studies, and certainty of evidence was evaluated using the Grading of Recommendations Assessment, Development, and Evaluation approach. RESULTS: Twenty-three studies involving 13,565 participants were included. The pooled compliance rate was 78.55% (95% CI 73.48%-82.87%), with a prediction interval of 48.59%-93.41%. Subgroup analyses identified no robust differences across study characteristics. Although study length showed a statistically significant subgroup test, the result was not stable after excluding singleton categories. Meta-regression analyses similarly found no significant linear associations for study length, prompts per day, items per prompt, or assessment window. Leave-one-out analyses showed that no single study drove the pooled estimate. Regarding the risk of bias, 2 studies were judged as low, while 21 were judged as moderate risk. Quality scores ranged from 6.5 to 9.0, and the certainty of evidence for the pooled compliance rate was rated as very low according to the Grading of Recommendations Assessment, Development, and Evaluation approach. CONCLUSIONS: Overall compliance with EMA among patients with cancer was moderate to high, suggesting that repeated real-world assessment may be feasible in oncology research settings. Nevertheless, the very high heterogeneity, wide prediction interval, and very low certainty of evidence indicate that compliance is context-dependent. The pooled estimate should therefore be interpreted as an approximate benchmark rather than a universal expected rate. Future oncology EMA studies should use standardized compliance denominators, report missing prompts transparently, and prospectively evaluate patient-centered design strategies that reduce burden while preserving data quality.

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PubMed2026

Comparative Efficacy of Different AI Systems for Polyp Detection by Size During Colonoscopy: Systematic Review and Network Meta-Analysis.

BACKGROUND: Colorectal cancer remains a leading cause of death despite being largely preventable through polypectomy. AI systems designed to enhance polyp detection during colonoscopy have shown promise, but the extent to which they improve detection of different-sized polyps remains unclear. OBJECTIVE: This study compared the size-stratified efficacy of AI-assisted colonoscopy vs standard colonoscopy using the Hartung-Knapp-Sidik-Jonkman (HKSJ) method, and generated exploratory rankings while acknowledging all cross-platform comparisons are indirect. METHODS: This systematic review and network meta-analysis (NMA) searched PubMed, Embase, Cochrane CENTRAL, and Web of Science from inception to July 25, 2026, supplemented by citation searching. We included randomized controlled trials (RCTs) comparing AI-assisted vs standard colonoscopy in adults (≥18 years of age), reporting mean polyp detection counts stratified by size (≤5 mm, 6-9 mm, and ≥10 mm). Two reviewers screened studies, extracted data, and assessed risk of bias using the Cochrane Risk of Bias 2.0. We conducted frequentist NMA using the HKSJ method with restricted maximum likelihood estimation, calculated 95% prediction intervals (PIs), and assessed heterogeneity using I2 and τ2. Certainty of evidence was rated using the GRADE (Grading of Recommendations Assessment, Development, and Evaluation) framework. RESULTS: A total of 13 RCTs (4156 participants) compared 8 AI systems to standard colonoscopy, forming a network without direct AI comparisons. For diminutive polyps (≤5 mm), AI showed a modest advantage (standardized mean difference [SMD] 0.21, 95% CI 0.07 to 0.35, 95% PI -1.12 to 1.54), but substantial heterogeneity (I2=86.6%) and wide PI crossing the null indicated high uncertainty. EndoScreener showed the most consistent evidence (SMD 0.36, 95% CI 0.18-0.54). For small and large polyps, effects were minimal (SMD 0.02, 95% CI -0.02 to 0.06, 95% PI -0.03 to 0.07; SMD 0.01, 95% CI 0.00-0.02, 95% PI -0.01 to 0.03). GRADE certainty was very low for diminutive polyps and low for small and large polyps. Sensitivity analysis excluding Tianjin YuJin did not materially change findings. CONCLUSIONS: AI may modestly enhance diminutive polyp detection, but effects on small and large polyps are minimal, with no platform superiority. Given very low to low certainty, findings are hypothesis-generating. This exploratory NMA provides size-stratified comparisons that can inform future head-to-head trial design. Unlike prior reviews aggregating all polyp sizes, we show the overall AI benefit is driven by diminutive polyp detection, providing a framework for targeted deployment-prioritizing AI for diminutive polyp screening, with limited value for larger lesions. Head-to-head trials are urgently needed. TRIAL REGISTRATION: PROSPERO International Prospective Register of Systematic Reviews CRD420251266932; https://www.crd.york.ac.uk/PROSPERO/view/CRD420251266932.

Colonoscopy↗

PubMed2026

Cost-Effectiveness of Electronic Patient-Reported Outcome Measure Interventions in Cancer: Systematic Review and Parameter Extraction for Economic Modeling.

BACKGROUND: Complex digital interventions that integrate electronic patient-reported outcome measures (ePROM) into clinical practice in cancer have the potential to improve quality of life, increase survival, and reduce health resource use and costs. Such systems can help patients with cancer self-manage chemotherapy symptoms, reduce clinicians' workloads through automated decision support, and resolve problems earlier. However, more research on the cost-effectiveness of ePROM monitoring is needed. OBJECTIVE: This paper comprises two complementary components: (1) a systematic literature review summarizing and evaluating the quantitative and qualitative evidence related to the cost-effectiveness of ePROM monitoring and (2) a health economic model parameter extraction. We also conducted supplementary targeted searches and scoping to provide context to our findings. METHODS: We searched Ovid (including MEDLINE and Embase), Scopus, and the International Health Technology Assessment Database for original English-language papers published on or before March 2025 using search strings that combined terms related to ePROMs, health economics, and cancer/oncology. We included papers reporting health economic-related outcomes for ePROM interventions designed for adult cancer populations and excluded screening tools and conference abstracts. RESULTS: We included 34 publications from 27 unique studies and identified and analyzed 26 ePROM-integrated interventions within these. Most (23/26) of the included interventions explicitly described some form of alert handling and automated decision support based on remote ePROM monitoring. Of the 34 publications, 5 presented full cost-effectiveness analysis results, of which 3 were highly uncertain and lacked clear differences in costs and health outcomes between ePROMs and standard care; conversely, 2 presented strong evidence of cost-effectiveness due to quality-of-life improvements, reduced hospitalizations, and potentially more autonomy in health-related travel (eg, ePROM-monitored patients can drive or walk to the hospital instead of using taxis or ambulances). A further 5 publications reported partial health economic results (eg, cost-consequence and budget impact), of which 1 detected no difference in strategies; in contrast, 4 reported lower health resource use and costs of ePROMs, mainly due to hospitalization reductions. Overall, 12 of the 27 studies included a qualitative component but mostly focused on user experience and design-related themes; only 2 of these addressed economic-specific themes (eg, changes in workflow and resource use due to ePROM implementation and integration), indicating some potential for time saving due to ePROM monitoring. CONCLUSIONS: Some ePROM-integrated interventions demonstrated cost-effectiveness in cancer care, but the evidence base remains limited. Where evidence does exist, cost-effectiveness appears driven by reduced hospitalization and improved quality of life. Qualitative research within the included studies rarely addressed economic questions. We provide a detailed parameter extraction for use in future economic modeling and recommend research priorities, including quantitative mapping of ePROM symptom data onto health resource use patterns, and qualitative work exploring how ePROM implementation affects clinical workloads and patient-perspective costs.

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PubMed2026

Extended Reality Interventions for Osteoarthritis of the Knee and Recovery After Total Knee Arthroplasty: Systematic Review and Meta-Analyses.

BACKGROUND: Nonpharmacologic interventions are important for treating knee pain due to osteoarthritis or after total knee arthroplasty (TKA), and extended reality (XR) technology may enhance treatments for these indications. OBJECTIVE: This systematic review aimed to evaluate XR interventions for pain due to knee osteoarthritis (KOA) or for recovery after TKA. METHODS: Databases were searched through May 2023 and updated in December 2025. Eligible trials evaluated XR interventions to treat KOA pain or after TKA. We classified interventions by depth of immersion and clinical mechanism. We used the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) criteria to determine the certainty of evidence for prioritized outcomes. Meta-analyses were performed when ≥3 studies evaluated similar comparisons, outcomes, and time points. RESULTS: Eligible trials addressed KOA (k=12) or recovery after TKA (k=9). Sample sizes ranged from 36 to 306 participants, and most studies had a follow-up of ≤3 months. Nineteen studies assessed pain-related functioning and pain intensity, and 5 assessed adverse events (AEs). For KOA, 10 studies examined interactive digital rehabilitation (IDR), and 2 examined virtual reality (VR)-digitally augmented exercise (DAE). IDR for KOA may result in better pain-related functioning (low certainty of evidence [COE]; pooled standardized mean difference [SMD] -0.59, 95% CI -1.11 to -0.06; prediction interval [PI] -1.72 to 0.55; k=5) and lower pain intensity at 6-8 weeks (low COE; pooled SMD -0.46, 95% CI -0.92 to 0.00; PI -1.39 to 0.47; k=4). VR-DAE for KOA (k=2) produced inconsistent results (very low COE). For post-TKA studies, 5 examined IDR, 2 examined VR-DAE, 1 examined VR-distraction, and 1 examined VR-psychoeducation. Post-TKA IDR may result in better pain-related functioning (low [k=4] and moderate COE [k=1]) but little to no difference in pain intensity (low-moderate COE; pooled SMD at 3-4 months -0.12, 95% CI -0.75 to 0.52; PI -1.63 to 1.27; k=3). VR-psychoeducation probably results in lower pain at 4 weeks (moderate COE; k=1), and VR-distraction may result in 6 months (low COE; k=1), whereas VR-DAE produced mixed findings (k=2; very low COE). IDR was not associated with AEs, and VR may not be associated with AEs for KOA (high and low COE), though AE reporting was uncommon (k=5) and evidence was very uncertain for post-TKA. CONCLUSIONS: IDR may augment treatment for KOA and post-TKA recovery, and VR may benefit post-TKA rehabilitation. This review is the first to stratify by level of immersion, clinical mechanism, and follow-up duration and to systematically evaluate AEs. IDR may be ready for integration into KOA care, while use after TKA needs more evidence. Randomized controlled trials with implementation outcomes could determine how XR interventions can be used for KOA, whereas trials evaluating efficacy and AEs are needed before their use for post-TKA.

Humans↗

PubMed2026

Performance of AI-Based Screening Tools for Obstructive Sleep Apnea Across Apnea-Hypopnea Index Thresholds: Systematic Review and Meta-Analysis.

BACKGROUND: Obstructive sleep apnea (OSA) is highly prevalent but remains substantially underdiagnosed. Polysomnography (PSG) is the reference standard, but its cost and limited availability constrain large-scale case identification. AI-based screening tools may support risk stratification and referral prioritization, but their diagnostic accuracy across apnea-hypopnea index (AHI) thresholds remains uncertain. OBJECTIVE: This review aimed to systematically evaluate the diagnostic accuracy of AI-based OSA screening tools at AHI thresholds of ≥5, ≥15, and ≥30 events/hour, with emphasis on models using non-PSG-derived inputs. METHODS: PubMed, Embase, Scopus, and Web of Science were searched for studies published from January 1, 2016, to May 3, 2026. Eligible studies included adults evaluated for suspected OSA or recruited from population-based cohorts, assessed AI-based models intended or interpretable for OSA screening, risk prediction, or screening-oriented severity classification, used PSG as the reference standard, and reported sufficient data to construct or reconstruct 2×2 contingency tables. Diagnostic accuracy was synthesized separately by AHI threshold and input source using bivariate random-effects models, with 95% CIs and prediction intervals (PIs). Risk of bias and certainty of evidence were assessed using QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies 2) and GRADE (Grading of Recommendations Assessment, Development, and Evaluation), respectively. RESULTS: A total of 60 studies were included, of which 47 contributed data to the meta-analysis. At AHI thresholds of ≥5, ≥15, and ≥30 events/hour, pooled sensitivities were 0.94 (95% CI 0.92-0.96; 95% PI 0.71-0.99), 0.87 (95% CI 0.84-0.89; 95% PI 0.66-0.96), and 0.83 (95% CI 0.79-0.87; 95% PI 0.61-0.94), respectively; the corresponding specificities were 0.77 (95% CI 0.69-0.84; 95% PI 0.30-0.96), 0.81 (95% CI 0.75-0.85; 95% PI 0.39-0.96), and 0.91 (95% CI 0.87-0.94; 95% PI 0.55-0.99), respectively. The corresponding areas under the summary receiver operating characteristic curves were 0.943, 0.907, and 0.920. For non-PSG-derived tools, sensitivities were 0.92, 0.85, and 0.81, and specificities were 0.70, 0.74, and 0.85 at the 3 thresholds, respectively. For PSG-derived models, sensitivities were 0.96, 0.90, and 0.85, and specificities were 0.82, 0.88, and 0.96, respectively. Exploratory subgroup analyses suggested performance variation across selected study and model characteristics, including region, algorithmic framework, data source, and validation method. CONCLUSIONS: AI-based tools showed generally favorable screening performance for OSA across clinically relevant AHI thresholds, although wide PIs suggest variable performance across future comparable populations and settings. By synthesizing diagnostic accuracy across 3 AHI thresholds and distinguishing non-PSG-derived from PSG-derived models, this review extends previous broad or modality-specific reviews and offers a clinically interpretable, pathway-specific basis for linking model performance to intended use. The findings may clarify potential roles for non-PSG-derived tools in front-end screening and referral prioritization and for PSG-derived models in reduced-channel assessment and sleep-laboratory workflow support. Given substantial heterogeneity, limited external validation, and low or very low certainty of evidence, prospective validation is needed before routine implementation.

Humans↗

PubMed2026

A full review of online education resources available on antifungal stewardship.

BACKGROUND AND OBJECTIVES: Antifungal resistance represents an increasing global threat, driven by the rising burden of fungal disease. Antifungal stewardship (AFS) is a critical component of broader antimicrobial resistance (AMR) efforts, but education in this area remains less established than antibacterial stewardship initiatives. The scope and characteristics of the current landscape of online AFS resources have not yet been systematically described. To identify and evaluate online educational resources focused on fungal disease management and AFS, and assess their accessibility, format, educational design and implementation focus. METHODS: A structured search of internet search engines, distribution platforms and organizational websites was conducted to identify English-language web-based resources related to fungal disease management and stewardship. Resources were evaluated using predefined criteria including access model, format, length, educational design, interactivity and AFS content. An overall educational value score (1-10) was assigned. RESULTS: Twenty-three educational resources were identified. Most were delivered as online unfacilitated courses (11, 48%) and were short (<4 h) (12, 52%). Most focused on guidelines and syndromic management (18, 78%) and targeted doctors and/or nurses/midwives (22, 96%). Limited interactivity was reported in nine (39%) courses. Five courses (22%) had either a substantial or comprehensive focus on AFS. CONCLUSIONS: Online AFS educational resources are available and support awareness and knowledge development. However, they remain relatively few in number. Greater emphasis on implementation-focused learning, behaviour change components and broader global representation may enhance their impact.

Journal Article↗

PubMed2026

Smoking Cue Reactivity in Relation to Uncertain-Threat and Reward-Anticipation Networks: A Coordinate-Based fMRI Meta-Analysis.

BACKGROUND: Smoking is a concerning medical and social problem, yet how the brain links stress to continued smoking is still not well understood. This coordinate-based meta-analysis identified convergent activations for smoking cues, uncertain threat, and reward anticipation, and examined co-activation patterns to clarify whether smoking-cue activity in smokers relates to threat and reward activity in non-addicted controls. METHODS: We conducted a coordinate-based activation likelihood estimation (ALE) meta-analysis of 102 fMRI studies (N = 3,068), including 30 studies on smoking cue reactivity (n = 945), 48 on reward anticipation (n = 1,413), and 24 on uncertain threat processing (n = 1,621). We performed single, conjunction, and contrast analyses, followed by meta-analytic connectivity modeling (MACM) of key regions. RESULTS: Single analysis revealed smoking engaged bilateral ACC (-1.1, 46.2, -1.1), uncertain threat engaged bilateral insula (left = -33.6, 22, 4.3, right = 41.3, 22, 1), reward anticipation activated thalamus (0.9, 1.3, -3.1) and medial frontal gyrus (2.7, 6.6, 52.1). Conjunction and contrast analyses showed shared or unique activation for each task in its respective regions. MACM showed ACC co-activation with thalamus and medial frontal gyrus, while insula co-activated with ACC and inferior frontal gyrus. CONCLUSIONS: Each process converged in a separate region, with no overlap between the smoking-cue map and either the threat or reward map. The ACC nonetheless co-activated with reward-related regions and shared network membership with the threat-related insula. On this basis we hypothesize a shift in motivation from stress-driven reward toward cue-driven craving, to be tested within subjects, and identify candidate neuromodulation targets for preventing stress-precipitated relapse.

fMRI↗

PubMed2026

Self-selected goals outperform assigned goals in reducing mobile phone usage: Evidence from a randomized controlled trial.

Excessive smartphone use is increasingly recognized as a public-health concern, yet scalable approaches to help individuals regulate daily use remain limited. We examine whether allowing individuals to self-select reduction goals improves behavioral and psychological outcomes when incentives and average goal levels are held constant across conditions. In a twelve-week randomized controlled trial, (N = 149; over 9000 person-day observations), participants were assigned to (i) a self-selected condition (choosing a 10%, 20%, or 30% reduction in daily phone use), (ii) an assigned condition (assigned a 14% reduction goal), or (iii) a no-goal control condition. Participants who selected their own goals reduced phone use by 26 min more per day (73% larger reduction) and achieved their goals 11 percentage points more often than those assigned goals, despite identical incentives and average goal levels. Reductions in phone use and higher goal achievement were associated with improvements in perceived addiction, depressive, and anxiety symptoms. These psychological outcomes were secondary endpoints. Although the between-group estimates generally followed the same directional pattern as the behavioral outcomes, the sample size for these analyses was limited and the between-group differences were not statistically significant. These findings should therefore be interpreted with caution. Overall, the results provide causal field evidence that self-selection under this goal-setting design can improve behavioral outcomes. Allowing individuals to choose their own goals may strengthen engagement and support healthier digital behavior. Incorporating opportunities for goal-selection may represent a simple addition to digital-health and public-health interventions aimed at helping individuals moderate smartphone use and improve well-being.

Humans↗

PubMed2025

Preoperative Patient Education on Opioid Use and Pain After Surgery: A Randomized Trial.

OBJECTIVE: To evaluate the impact of preoperative analgesic education on postoperative opioid consumption, pain scores, and patient satisfaction with analgesia. BACKGROUND: Effective postoperative pain management is crucial for patient recovery and satisfaction, yet opioid use poses risks of tolerance and addiction. Preoperative patient education offers a potential avenue to mitigate opioid reliance and improve pain management outcomes. METHODS: This single-center randomized trial was conducted at the Cleveland Clinic Main Campus between October 2021 and October 2023. Adult patients scheduled for hip arthroplasty or laparoscopic-assisted abdominal surgery with an ASA physical status of 1 to 4 were eligible. Patients with a history of prolonged opioid use, planned regional block or epidural analgesia, or limited English fluency were excluded. Participants were randomized 1:1 to receive either an analgesic educational video or a generic video about surgery and hospitalization. The primary outcome was opioid consumption during the initial 72 postoperative hours. Secondary outcomes included time-weighted average pain scores and patient satisfaction with analgesia. RESULTS: Among 957 analyzed patients, preoperative analgesic education did not significantly reduce opioid consumption (adjusted ratio of geometric means, 1.01; 95% CI, 0.86-1.18; P =0.890) or improve pain scores (adjusted mean difference, -0.1; 95% CI, -0.3 to 0.2; P =0.617). Patient satisfaction scores also did not differ significantly between groups (adjusted mean difference, -0.1; 95% CI, -0.3 to 0.2; P = 0.611). CONCLUSIONS: Preoperative analgesic education did not result in clinically meaningful reductions in opioid consumption or improvements in pain management outcomes. Further research may explore more intensive educational interventions to optimize postoperative pain management strategies.

Humans↗

PubMed2026

Decoding the spatiotemporal patterns of food spoilage microbial communities: Integrating multi-omics and artificial intelligence to enable precision preservation.

In the global food supply chain, food wastage caused by spoilage has resulted in significant economic losses, food shortages, and environmental pressure. This process is fundamentally driven by the spatiotemporal dynamics of microbial communities. However, traditional research methods struggle to elucidate the complex mechanisms of spatial heterogeneity, interspecies interactions, and functional succession. This limits the development of effective preservation strategies. This review systematically reviews the cutting-edge progress of integrating multi-omics technologies and artificial intelligence (AI) to study food spoilage microbial communities, breaking through this bottleneck. We propose an intelligent theoretical framework that could potentially analyze microbial metabolic activities and predict dynamic shelf life if implemented. The conceptual framework integrates multidimensional data, including spatial metabolomics, temporal metatranscriptomics, single-cell transcriptomics, and longitudinal metagenomics. It can also be combined with AI models, such as graph neural networks. The article elaborates on the principles and applications of spatio-temporal monitoring technologies, such as nano secondary ion mass spectrometry, hyperspectral imaging, and the Internet of Things sensing. Through illustrative cases of typical perishable foods, it also explores how such a multi-omics - AI system might be applied to spoilage warning and precise intervention. Additionally, the article addresses the current challenges in data coverage, model generalization, and federated learning implementation. Then the research further explores emerging areas such as engineered probiotics, edge AI, and microfluidic sensing. These areas are targeted at transforming food preservation from an empirical control approach to a data-driven, precise regulatory framework. This transformation provides theoretical support and technical approaches for developing a smart, sustainable food preservation system.

Multiomics↗

PubMed2026

Indigenous body image amid rapid social and economic change: A reflexive thematic analysis of Wayuu narratives.

The Wayuu, Colombia's largest Indigenous group, are experiencing rapid social, economic, and technological change, including expanding internet access, educational opportunities, and increasing exposure to globalised media. Though the harmful effects of appearance-idealised media imagery on body image are well documented, indigenous body image research remains unevenly distributed across global contexts, with Latin American Indigenous communities particularly underrepresented. This study explored how Wayuu people understand and experience appearance ideals and body image in the context of expanding digital media exposure and rapid sociocultural and economic change. Five focus groups of up to 9 participants were conducted with 29 Wayuu participants (18-68 years; 23 women, 6 men). Using reflexive thematic analysis, three overarching themes were identified: (1) Intersecting sources of appearance pressure: encompassing influences from social media, Wayuu family expectations, and discrimination from non-Indigenous peers; (2) Negotiating and resisting appearance ideals: from body dissatisfaction and restrictive eating to affirmation of cultural identity as protection; and (3) The changing role of appearance in today's Wayuu culture: participants linked globalised appearance ideals to expanding educational opportunities, migration, digital connectivity, and broader socioeconomic transformations occurring within Wayuu territories. While exposure to global ideals fostered comparison and dissatisfaction, cultural pride and collective belonging appeared to buffer against internalised colonial values. Culturally grounded media literacy and education initiatives co-developed with Wayuu communities could foster critical reflection while strengthening heritage. These results highlight the need for decolonial, community-based approaches to body image research and intervention in Indigenous contexts.

Adolescent↗