Social needs are sociocultural and economic factors influencing health and quality of life, including, for example, mobility limitations or financial difficulties. Multimorbidity - the presence of two or more long-term conditions (LTCs) - is an increasing public health challenge, with social needs often compounding the negative health outcomes associated with multimorbidity. In this study, we present a novel multidimensional framework for identifying and characterising social needs within a population-based cohort of adults with multimorbidity in England, utilising data from the Clinical Practice Research Datalink. The framework identifies eight critical domains of social needs: activities of daily living, mobility, financial constraints, disability, community care, housing status, social support, and bereavement. More than 100 related variables were captured in the dataset. Among 7,290,716 individuals with multimorbidity, 36.96% reported at least one social need, with the majority of affected individuals being older, female, and experiencing a higher burden of LTCs. The most prevalent social needs were related to community and social care services. Our findings underscore the significant association between social needs and multimorbidity, revealing a disproportionate burden of social needs in this population. This framework offers a systematic approach to quantifying and measuring social needs, providing a foundation for incorporating these factors into clinical care and interventions.

BACKGROUND:Social isolation is a critical social determinant of health that amplifies the significant treatment burden faced by community-dwelling adults with disabilities and multimorbidity. While an association between these factors is established, longitudinal evidence capturing their dynamic interplay is scarce, limiting the development of effective, equitable interventions. This study aimed to longitudinally explore how treatment burden evolves among this population and to elucidate the mechanisms through which social isolation appears to operate through these changes. METHODS:We conducted a longitudinal qualitative study using interpretive description in Hangzhou, China. Participants were adults with physician-diagnosed disabilities and ≥ 2 chronic conditions, recruited via purposive sampling from community health centres. Each participant completed three in-depth, semi-structured interviews over 12 months. We conceptualized treatment burden using Demain et al.'s adaptation of the Cumulative Complexity Model. Data analysis was an iterative process involving constant comparison to identify key themes regarding the interplay of social isolation and treatment burden over time. RESULTS:A total of 24 participants (13 were women; median age 67.5 years) completed the study. Our analysis revealed that social isolation was described by participants as dynamically contributing to increased treatment burden through four interconnected mechanisms: (1) Eroding autonomy, leading to passive healthcare decision-making; (2) Compromising emotional well-being, which depleted self-management capacity; (3) Straining relational networks, resulting in the loss of crucial informal support; and (4) Creating navigational barriers, which led to difficulties managing complex treatments. A key cross-cutting theme was the apparent role of depressive symptoms, which participants described as being exacerbated by isolation and, in turn, appearing to contribute to more negative illness perceptions and functional decline. This pattern was consistent with a progressive intensification of treatment burden as emotional and physical challenges fed into each other over time. CONCLUSION:Social isolation appeared to function not merely as a passive correlate but as a factor that longitudinally contributed to greater treatment burden, thereby exacerbating health inequities for adults with disabilities and multimorbidity. This pattern appeared to be further shaped by the intersection with depressive symptoms. To mitigate this, multi-level interventions are essential. Priorities should include addressing structural barriers through policies that foster community integration, strengthening mental health support within primary care, and redesigning services to be more relationally-centred and less burdensome. PATIENT OR PUBLIC CONTRIBUTION:Patients, care-givers, people with lived experience or members of the public were not involved in the study design, conduct, data analysis or preparation of the manuscript. However, preliminary findings were shared and discussed with two patient advisors who had lived experience of disability and multimorbidity but were not participants in the interviews. Their feedback helped refine the presentation and contextual relevance of the themes.

Background: Adaptive and master protocol clinical trials offer significant advantages for diabetes research, including enhanced efficiency and personalized treatment strategies. Purpose: This scoping review aimed to systematically map the use of adaptive and master protocol designs in interventional trials for type 1 and type 2 diabetes, identify research gaps and highlight opportunities for broader implementation. Data Sources: A systematic literature search was performed using MEDLINE, Embase, CENTRAL, Emcare, Global Health, Web of Science, and clinical trial registries. Grey literature searches complemented database findings. Study Selection: Studies using adaptive, platform, basket, or umbrella trial designs in people with type 1 or type 2 diabetes were included. Data Extraction: Data were charted using a standardized form. Extracted variables included diabetes type, trial design, adaptive features, interventions, endpoints, and key findings. Data Synthesis: Of 396 articles screened, six published adaptive trials met inclusion criteria: three in type 1 diabetes, one in type 2 diabetes, and two in diabetes-related neuropathy. Most used adaptive features for dose-finding, response-adaptive randomisation and sample size re-estimation. No published platform, basket, or umbrella trials were identified. Six ongoing adaptive trials in type 1 diabetes were identified through registry searches, four under an adaptive platform master protocol. Limitations: Despite a comprehensive search, some grey literature and unpublished studies may have been missed. Risk of bias was not assessed, consistent with scoping review methodology. Conclusions: Adaptive and master protocol trials remain rare in diabetes. Overcoming barriers through targeted training and awareness, robust regulatory frameworks, and strategic incentives could support broader adoption.

BACKGROUND:Hypertension is the leading risk factor for death globally. Undiagnosed hypertension is common, but the incidence in hospitalised patients is unclear. There are calls for universal facility-based screening for hypertension among all attending patients. The hospital inpatient setting, where blood pressure (BP) is measured routinely and repeatedly, presents an ideal opportunity. However, international hypertension guidelines do not include inpatient BP thresholds for diagnostic or treatment purposes. We investigated the performance of current UK community BP thresholds for diagnosing hypertension in the hospital setting. OBJECTIVES:Investigate the diagnostic performance of the current UK ambulatory BP diagnostic thresholds for systolic and diastolic hypertension in the hospital setting against the reference test of community-based ambulatory BP monitoring (ABPM). DESIGN:A prospective diagnostic accuracy study. SETTING:Hospital inpatients admitted to three UK centres were approached. Follow-up ABPM was delivered in the community. PARTICIPANTS:Eligible patients were aged between 18 and 80 years, with no prior diagnosis of, or prescription for hypertension, and whose mean cumulative daytime BP was 120 mm Hg to 179 mm Hg systolic and ≤109 mm Hg diastolic from the 24th hour of their hospital admission. INTERVENTIONS:Participants received 24-hour ABPM 4-26 weeks post-discharge, as the reference test for hypertension, with UK diagnostic thresholds of an average daytime BP of ≥135 mm Hg systolic and ≥85 mm Hg diastolic applied. Participants found to be severely hypertensive at the ABPM fitting appointment were also considered reference-test positive but did not proceed with ABPM. PRIMARY AND SECONDARY OUTCOME MEASURES:The diagnostic performance of a mean daytime in-hospital BP of ≥135 mm Hg systolic or ≥85 mm Hg diastolic (index test) for the prediction of hypertension diagnosed on ABPM (reference test) was assessed using sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) as primary outcome measures. Additionally, we explored the accuracy of a range of alternative in-hospital systolic and diastolic BP thresholds against the same reference test. RESULTS:351 participants were enrolled and 206 completed the study protocol. The average age of the 206 participants was 53 years, 55% were male, and 91 (44%) had daytime community hypertension on ABPM reference testing. Of 107 participants with raised in-hospital daytime BP, 59 (55%) had daytime community hypertension. When assessing the performance of the index test for detecting daytime community hypertension, sensitivity was 65% (59/91, 54% to 75%) and specificity was 58% (67/115, 49% to 67%). The PPV was 55% (59/107, 45% to 65%) and NPV was 68% (67/99, 58% to 77%), respectively. A further 45/206 participants (23%) had night-time community hypertension when assessed using European diagnostic thresholds for nocturnal hypertension (120 mm Hg systolic or 70 mm Hg diastolic), while 25/107 of those with raised in-hospital daytime BP (23%) had night-time community hypertension. When assessing the performance of the index test for detecting either day or night-time community hypertension, sensitivity was 62% (84/135, 53% to 70%) and specificity was 68% (48/71, 55% to 78%). The PPV was 79% (84/107, 70% to 86%) and NPV was 48% (48/99, 38% to 59%). CONCLUSIONS:Undiagnosed hypertension is common in hospitalised patients, particularly those with raised in-hospital BP. While in-hospital BP alone is an imperfect predictor and should not be used as a stand-alone diagnostic test, this could serve as a trigger for further assessment of BP in the community after discharge. TRIAL REGISTRATION NUMBER:The study protocol was registered with the ISCTRN Registry (ISRCTN80586284).

Predictive modelling in healthcare has advanced rapidly, yet social care systems, despite their central role in supporting vulnerable populations, remain underexplored in this domain. In this study, we apply machine learning to a large, pseudonymised dataset of social care records from 27,590 adults in Oxfordshire, encompassing around 90% of individuals receiving care in the region. We developed models to predict three outcomes of interest: future care plan needs, hospital admissions, and all-cause mortality, evaluated across multiple prediction horizons. Our results show that hospital admission and mortality can be predicted with meaningful discriminative performance (AUROC up to 0.893), while care plan needs are more variable and harder to predict. Post-hoc exploratory analyses revealed distinct risk signatures across outcomes, and temporal evaluation showed that care needs often emerge earlier than clinical deterioration. These findings demonstrate the feasibility of social care-based risk modelling and suggest new opportunities to improve anticipatory care planning, health management, and resource allocation using routinely collected non-clinical data.

Background Effective self-management of type 2 diabetes (T2D) often requires changes to diet and physical activity behaviours. Text messaging is a promising way to deliver support for self-management. It has wide reach, low cost and potential for high acceptability among users. We have previously developed a library of text messages to help people with T2D change diet and physical activity, each based on a particular behaviour change technique (BCT) e.g. goal-setting. This paper aims to 1) assess the anticipated acceptability of these text messages before sending them to people with T2D, and 2) explore the experienced acceptability of these messages when sent to people with T2D. Methods Two studies were conducted sequentially to explore acceptability of evidence-based text messages. Study one: an online cross-sectional survey to evaluate the anticipated acceptability of 181 messages. People with T2D rated messages on understanding, liking, and usefulness and an overall acceptability score was calculated. Study two: a pilot feasibility study where the messages were sent to participants with T2D for 8 to 12 weeks and interviews were conducted to explore the experienced acceptability of the messages. Qualitative interviews were analysed using framework analysis. Results In study one (n = 60), all the messages scored above the mid-point for overall anticipated acceptability. In study two (n = 62) people with T2D received messages. Post-intervention interviews (n = 51) revealed that the messages were deemed acceptable by the majority of participants; aspects reported to have the most impact on acceptability were perceived effectiveness, self-efficacy, and affective attitude. Three themes were identified during mapping and interpretation: 'Right message, right time', 'novelty of information', and 'suggestions to improve acceptability'. Participants valued reassurance and a reminder of their progress, and the majority of participants reported experiencing benefits from the messages. Conclusions This study showed the acceptability of targeted text messages focusing on diet and physical activity to people with T2D, both hypothetically and when received. Text messages may provide a low burden and low-cost intervention, balancing relevance for a large range of people while maintaining engagement. Trial registration: Registered on clinicaltrials.gov 17/11/2022, ref: NCT05641402

Abstract RNA serves a central role in biology by converting genomic information into effector molecules, either as functional non-coding RNAs or as protein-coding mRNAs. While it has long been appreciated that complex RNA transcript profiles can be produced through alternative splicing, numerous discoveries have highlighted the essential role of alternative splicing in cell and developmental biology. Furthermore, aberrant splicing has recently been linked to diseases like cancer, neurodegeneration, and autoimmunity. Of particular interest, cancer-specific splice isoforms have emerged as a potential source of neo-antigens targetable by novel immune therapeutics. Thus, understanding the expression and function of RNA isoforms has become increasingly important in cancer biology research. A current limitation of long-read RNA sequencing (LR-RNA-seq) is the requirement of large amounts of input RNA, which can be unachievable for samples such as resected tumors or sorted single cells. Here, we describe SMART-Seq® mRNA Long Read kit, a new LR-RNA-seq library preparation technology that enables full-length RNA sequencing from single cells (∼10 pg RNA/cell) up to 100ng total RNA. In high-quality bulk RNA inputs, we demonstrate the ability to reliably sequence at an average read length (N50) of 2 kb and to detect full-length transcripts as long as 8 kb, enabling the discovery and quantification of novel mRNA isoforms in samples from both healthy tissues and cancer cells. Analysis of cancer cell lines with evolved resistance to targeted therapies identifies differential isoform usage associated with the evolution of cancer therapeutic resistance. We further describe an update to this technology, SMART-Seq® mRNA Long Read version 2, which expands the input range to 2µg, improves read-length performance, and enables UMI-based analysis. Comparison studies demonstrate that SMART-Seq mRNA Long Read technology substantially outperforms existing bulk and single-cell LR-RNA-seq methods. With PCR barcoding of up to 96 samples at a time, this technology will accelerate discovery as scientists catalog and study splice isoforms in both routine long read cDNA sequencing workflows and in settings where sample input is limited. Citation Format: Jackson Peterson, Yue Yun, Lisa Welter, Kazuo Tori, Alan Du, Yana Ryan, Ning Ma, Rachana Kumar, Shiyi Yin, Mike Covington, Shuwen Chen, Elena Shagisultanova, Mohammad Fallahi, Bryan Bell, Andrew Farmer. RNA isoform discovery and quantification with SMART-Seq® mRNA Long Read (v1 and v2) kits [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 1789.

Insomnia may play a causal role in type 2 diabetes (T2D). Addressing insomnia through cognitive behavioural therapy (CBTi) in people with non-diabetic hyperglycaemia could potentially reduce the risk of progression to T2D. To inform a future randomised trial, we performed a feasibility study of digital CBT (dCBTi) in individuals at increased risk of T2D. Participants were identified from 10 primary care practices in the UK and given access to dCBTi. Outcomes were evaluated at baseline (Week-0) and post-treatment (Week-11). Primary feasibility outcomes were ability to recruit and treatment engagement. We also quantified within-group mean change (95% CI) in insomnia severity (Insomnia Severity Index), health-related quality of life (EQ-5D-3L), depression (Center for Epidemiologic Studies Depression Scale), chronotype (reduced Morningness-Eveningness Questionnaire), sleep (7-day actigraphy and diary), continuous glucose monitoring (7-days) and fasting blood metabolites (insulin, lipids, glucose and C-reactive protein). The recruitment target was 20. Of 242 people completing screening, 36 were eligible and 24 were enrolled (age 65.5 ± 12.4 years, 70.8% female). Twenty-three (96%) completed post-intervention assessments. Treatment engagement was excellent (83.3% completed ≥ 4 sessions). The intervention was associated with a large reduction in insomnia severity [-4.7 (95% CI: -6.2 to -3.2), d = -1.4] and medium reduction in depressive symptoms [-2.7 (95% CI: -5.1 to -0.2), d = -0.5]. Sleep diary parameters tended to show greater improvement following intervention relative to actigraphy. There was evidence of a reduction in serum lactate, glycerol and triglycerides but no clear change in glucose or insulin. Results suggest a full trial is likely feasible and that people with NDH find the intervention acceptable and beneficial. Trial Registration: This trial was prospectively registered on the UKs clinical study registry, the ISRCTN (ISRCTN19682964, https://doi.org/10.1186/ISRCTN19682964).

Objective To examine how variation in clinical coding systems and the number of conditions included under different study criteria influence estimates of multimorbidity prevalence in a nationally representative adult population in England.Methods and analysis We conducted a cross-sectional analysis of anonymised records from 7.2 million adults in the Clinical Practice Research Datalink, linked to Hospital Episode Statistics, covering the period from 1987 to 2020. Adults were included if they had at least two recorded health conditions. Multimorbidity was defined as >= 2 health conditions selected from a list of 54 conditions. Prevalence was estimated separately using general practice (GP) data, hospital data and combined sources, and stratified by age, sex, ethnicity and deprivation. A stepwise inclusion approach assessed the impact of expanding the number of conditions included after different study criteria. Gradient boosting (XGBoost) with Shapley Additive Explanations values identified predictors of multimorbidity recorded only in GP data. Directionality was examined using Pearson correlations.Results Multimorbidity prevalence was 92.3% using GP data, 63.2% using hospital data and 100% when both sources were combined. Prevalence increased consistently as more conditions were included under different study criteria and was always higher in GP data. Discrepancies were most pronounced among younger adults and ethnic minority groups. GP-only coding was associated with younger age, female sex, shorter hospital stays, absence of Accident & Emergency use, no palliative care coding and lower deprivation.Conclusion Estimates of multimorbidity prevalence are highly sensitive to both the clinical coding system used and the number of conditions included under different study criteria. Standardised approaches to condition selection and the integration of data sources are essential to ensure accurate measurement and equitable representation.

BACKGROUND:Previous longitudinal studies have linked multimorbidity to loneliness (feeling alienated) and social isolation (having reduced social contact). However, the nature of these associations over time is unclear. OBJECTIVE:To examine bidirectional associations of multimorbidity with loneliness and social isolation over a 14-year follow-up in a nationally representative cohort of adults aged ≥ 50 years. METHODS:This retrospective cohort study used seven waves of data (collected between 2004/2005 and 2018/2019) from adults in the English Longitudinal Study of Ageing. Multimorbidity was defined as the presence of ≥2 long-term conditions. Loneliness was measured using the 3-item University of California Los Angeles (UCLA) scale. Social isolation was derived based on cohabitation status, frequency of contact with children, relatives, and friends, and social organisation membership. We used Cox proportional hazards models adjusted for social isolation or loneliness, demographic and health behaviour variables. RESULTS:The cohort consisted of 6031 adults with baseline and follow-up data on loneliness, social isolation, multimorbidity, and other covariates. Loneliness was associated with increased risk of incident multimorbidity [aHR (95 % CI): 1.38 (1.15-1.65)], whereas social isolation was not [aHR (95 % CI): 0.97 (0.81-1.16)]. Multimorbidity was associated with increased risk of incident loneliness [aHR (95 % CI): 1.55 (1.30-1.84)], but not significantly associated with subsequent risk of incident social isolation [aHR (95 % CI): 1.09 (0.92-1.28)]. CONCLUSIONS:An independent bidirectional association exists between loneliness and multimorbidity. Interventions targeting loneliness may prevent or delay multimorbidity and also improve wellbeing for people with multimorbidity.

Background Data to support individualised choice of optimal glucose-lowering therapy are scarce for people with type 2 diabetes. We aimed to establish whether routinely available clinical features can be used to predict the relative glycaemic effectiveness of five glucose-lowering drug classes. Methods We developed and validated a five-drug class model to predict the relative glycaemic effectiveness, in terms of absolute 12-month glycated haemoglobin (HbA1c), for initiating dipeptidyl peptidase-4 inhibitors, glucagon-like peptide-1 receptor agonists, sodium-glucose co-transporter-2 inhibitors, sulfonylureas, and thiazolidinediones. The model used nine routinely available clinical features of people with type 2 diabetes at drug initiation as predictive factors (age, duration of diabetes, sex, and baseline HbA1c, BMI, estimated glomerular filtration rate, HDL cholesterol, total cholesterol, and alanine aminotransferase). The model was developed and validated with observational data from England (Clinical Practice Research Datalink [CPRD] Aurum), in people with type 2 diabetes aged 18-79 years initiating one of the five drug classes between Jan 1, 2004, and Oct 14, 2020, with holdback validation according to geographical region and calendar period. The model was further validated in individual-level data from three published randomised drug trials in type 2 diabetes (TriMaster three-drug crossover trial and two parallel-arm trials [NCT00622284 and NCT01167881]). For validation in CPRD, we assessed differences in observed glycaemic effectiveness between matched (1:1) concordant and discordant groups receiving therapy that was either concordant or discordant with model-predicted optimal therapy, with optimal therapy defined as the drug class with the highest predicted glycaemic effectiveness (ie, lowest predicted 12-month HbA1c). Further validation involved pairwise drug class comparisons in all datasets. We also evaluated associations with long-term outcomes in model-concordant and model-discordant groups in CPRD, assessing 5-year risks of glycaemic failure (confirmed HbA1c >= 69 mmol/mol), all-cause mortality, major adverse cardiovascular events or heart failure (MACE-HF) outcomes, renal progression, and microvascular complications using Cox proportional hazards regression adjusting for relevant demographic and clinical covariates. Findings The five-drug class model was developed from 100 107 drug initiations in CPRD. In the overall CPRD cohort (combined development and validation cohorts), 32 305 (152%) of 212 166 drug initiations were of the model-predicted optimal therapy. In model-concordant groups, mean observed 12-month HbA1c benefit was 53 mmol/mol (95% CI 49-57) in the CPRD geographical validation cohort (n=24 746 drug initiations, n=12 373 matched pairs) and 50 mmol/mol (43-56) in the CPRD temporal validation cohort (n=9682 drug initiations, n=4841 matched pairs) compared with matched model-discordant groups. Predicted HbA1c differences were well calibrated with observed HbA1c differences in the three clinical trials in pairwise drug class comparisons, and in pairwise comparisons of the five drug classes in CPRD. 5-year risk of glycaemic failure was lower in model-concordant versus model-discordant groups in CPRD (adjusted hazard ratio [aHR] 062 [95% CI 059-064]). For long-term non-glycaemic outcomes, model-concordant versus model-discordant groups had a similar 5-year risk of all-cause mortality (aHR 095 [083-109]) and lower risks of MACE-HF outcomes (aHR 085 [076-095]), renal progression (aHR 071 [064-079]), and microvascular complications (aHR 086 [078-096]). Interpretation We have developed a five-drug class model that uses routine clinical data to identify optimal glucose- lowering therapies for people with type 2 diabetes. Individuals on model-predicted optimal therapy had lower 12-month HbA1c , were less likely to need additional glucose-lowering therapy, and had a lower risk of diabetes complications than individuals on non-optimal therapy. With setting-specific optimisation, the use of routinely collected parameters means that the model is easy to introduce to clinical care in most countries worldwide. Copyright (c) 2025 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license.

Background:People living with MLTCs attending primary care often have unmet social care needs (SCNs), which can be challenging to identify and address. Artificial intelligence (AI) derived clusters could help to identify patients at risk of SCNs. Evidence is needed on views about the use of AI-derived clusters, to inform acceptable and meaningful implementation within interventions. Method:Qualitative semi-structured interviews (online and telephone), including a description of AI-derived clusters and a tailored vignette, with 24 people living with MLTCs and 20 people involved in the care of MLTCs (carers and health care professionals). Interviews were analysed using Reflexive and Codebook Thematic Analysis. Results:Primary care was viewed as an appropriate place to have conversations about SCNs. However, participants felt health care professionals lack capacity to have these conversations and to identify support. AI was perceived as a tool that could potentially increase capacity but only when supplemented with effective, clinical conversations. Interventions harnessing AI should be brief, be easy to use and remain relevant over time, to ensure no additional burden on clinical capacity. Interventions must allow flexibility to be used by multidisciplinary teams within primary care, frame messages positively and facilitate conversations that remain patient centered. Conclusion:Our findings suggest that implementing AI-derived clusters to identify and support SCNs in primary care is perceived as valuable and can be used as a tool to inform and prioritse effective clinical conversations. But concerns must be addressed, including how AI-derived clusters can be used in a way that considers personal context.

Importance Rapid digitalization of health care and a dearth of digital health education for medical students and junior physicians worldwide means there is an imperative for more training in this dynamic and evolving field. Objective To develop an evidence-informed, consensus-guided, adaptable digital health competencies framework for the design and development of digital health curricula in medical institutions globally. Evidence Review A core group was assembled to oversee the development of the Digital Health Competencies in Medical Education (DECODE) framework. First, an initial list was created based on findings from a scoping review and expert consultations. A multidisciplinary and geographically diverse panel of 211 experts from 79 countries and territories was convened for a 2-round, modified Delphi survey conducted between December 2022 and July 2023, with an a priori consensus level of 70%. The framework structure, wordings, and learning outcomes with marginal percentage of agreement were discussed and determined in a consensus meeting organized on September 8, 2023, and subsequent postmeeting qualitative feedback. In total, 211 experts participated in round 1, 149 participated in round 2, 12 participated in the consensus meeting, and 58 participated in postmeeting feedback. Findings The DECODE framework uses 3 main terminologies: domain, competency, and learning outcome. Competencies were grouped into 4 domains: professionalism in digital health, patient and population digital health, health information systems, and health data science. Each competency is accompanied by a set of learning outcomes that are either mandatory or discretionary. The final framework comprises 4 domains, 19 competencies, and 33 mandatory and 145 discretionary learning outcomes, with descriptions for each domain and competency. Six highlighted areas of considerations for medical educators are the variations in nomenclature, the distinctiveness of digital health, the concept of digital health literacy, curriculum space and implementation, the inclusion of discretionary learning outcomes, and socioeconomic inequities in digital health education. Conclusions and Relevance This evidence-informed and consensus-guided framework will play an important role in enabling medical institutions to better prepare future physicians for the ongoing digital transformation in health care. Medical schools are encouraged to adopt and adapt this framework to align with their needs, resources, and circumstances.

In medicine and public health, the randomised controlled trial (RCT) is generally considered the key generator of ‘gold standard’ evidence. However, basic and clinical research and trials are often unrepresentative of real-world populations. Recruiting insufficiently diverse cohorts of participants in trials (e.g. in terms of socioeconomic status, racial and ethnic background, or sex and gender) may not only overstate the general effectiveness of a technology; it may also actively increase health inequalities. We highlight some general issues in this domain, before discussing several specific illustrative examples in the context of medical devices. High quality evidence on factors that would improve trial recruitment is extremely limited. There is a clear need for research on candidate strategies for improving recruitment of under-represented groups in RCTs. These could include, for example, offering various forms of financial incentives; non-monetary incentives, such as preferential access to the technologies that are being tested if they are found to be effective; and various types of informational messages and nudges; as well as involvement of community partners and champions in the recruitment process. Ideally, recruitment practices should ultimately be based on evidence generated from RCTs. Studies Within a Trial (SWAT), where randomised experiments are built into the actual recruitment processes in RCTs, are an ideal way to gain this evidence. SWAT studies are seeing an increase in traction, as indicated by funding streams in bodies such as the UK-based NIHR. Making greater funding available for studies of this kind is needed to improve the evidence base on how best to improve diversity in trial recruitment.

Importance:Rapid digitalization of health care and a dearth of digital health education for medical students and junior physicians worldwide means there is an imperative for more training in this dynamic and evolving field. Objective:To develop an evidence-informed, consensus-guided, adaptable digital health competencies framework for the design and development of digital health curricula in medical institutions globally. Evidence Review:A core group was assembled to oversee the development of the Digital Health Competencies in Medical Education (DECODE) framework. First, an initial list was created based on findings from a scoping review and expert consultations. A multidisciplinary and geographically diverse panel of 211 experts from 79 countries and territories was convened for a 2-round, modified Delphi survey conducted between December 2022 and July 2023, with an a priori consensus level of 70%. The framework structure, wordings, and learning outcomes with marginal percentage of agreement were discussed and determined in a consensus meeting organized on September 8, 2023, and subsequent postmeeting qualitative feedback. In total, 211 experts participated in round 1, 149 participated in round 2, 12 participated in the consensus meeting, and 58 participated in postmeeting feedback. Findings:The DECODE framework uses 3 main terminologies: domain, competency, and learning outcome. Competencies were grouped into 4 domains: professionalism in digital health, patient and population digital health, health information systems, and health data science. Each competency is accompanied by a set of learning outcomes that are either mandatory or discretionary. The final framework comprises 4 domains, 19 competencies, and 33 mandatory and 145 discretionary learning outcomes, with descriptions for each domain and competency. Six highlighted areas of considerations for medical educators are the variations in nomenclature, the distinctiveness of digital health, the concept of digital health literacy, curriculum space and implementation, the inclusion of discretionary learning outcomes, and socioeconomic inequities in digital health education. Conclusions and Relevance:This evidence-informed and consensus-guided framework will play an important role in enabling medical institutions to better prepare future physicians for the ongoing digital transformation in health care. Medical schools are encouraged to adopt and adapt this framework to align with their needs, resources, and circumstances.

BACKGROUND:Recommendations to prevent diabetes ulceration and amputation include an annual foot check, primarily screening for sensation and circulation. Using these simple, evidence-based components is vital to identifying complications early, assessing risk, and managing care to prevent or delay amputations. However, routine implementation of these assessments is suboptimal and approaches to their integration remain poorly understood. AIM:We aimed to identify and synthesize information on the factors affecting implementation of simple evidence-based diabetes foot screening. METHODS:We reviewed published and grey literature using a blinded two-stage process by two independent reviewers. Included studies were primary research that implemented or improved foot screening for adults with type 1 or 2 diabetes, assessing at least one of the following: 10-g monofilament sensitivity, pedal pulse palpation, or history of ulceration or amputation. A thematic synthesis approach was used. RESULTS:We screened 5133 titles and abstracts, reviewed 102 full-text articles, and included 26 studies in the final analysis. We identified four key themes: (1) Existing diabetes screening (i.e. retinal screening) or treatment interventions (i.e. medication collection) provide opportunities for synergistic integration; (2) Annual event-based foot screening (e.g. on World Diabetes Day) in lower resource settings provides community-focused preventative care; (3) Further opportunities to increase access to foot screening include self-administered screening and screening in complex residential settings; (4) Healthcare provider champions are essential for local foot screening implementation in primary and secondary care. CONCLUSION:Further research should evaluate the issues identified in these four themes, in different contexts, and with support of implementation frameworks.

Summary Background Research on multimorbidity, commonly defined as the presence of two or more long term conditions, has predominantly focused on biological and clinical needs. However, unmet social care needs (SCNs), including, for example, mobility limitations, financial hardship, and social isolation, can also adversely affect health outcomes. We aimed to identify distinct clusters of individuals with multimorbidity and SCNs, and to examine their associations with 10 year all cause mortality. Methods We analysed data from the Clinical Practice Research Datalink (CPRD) Gold and Aurum databases (Jan 1, 1987, to Dec 31, 2020) to identify adults in England living with multimorbidity. Latent class analysis was used to derive clusters based on long term conditions and eight predefined domains of SCNs. Threshold criteria were applied to determine meaningful representation of long-term conditions and SCNs within each cluster. Associations with 10-year all cause mortality were assessed using Cox proportional hazards models, adjusted for age, sex, ethnicity, deprivation, multimorbidity burden, SCNs, and year of multimorbidity diagnosis. Findings Amongst 7.2 million individuals with multimorbidity (mean age 54.4 years [SD 18.2]; 55% female), 37% had at least one SCN, with community care needs being the most common (28%). Four distinct clusters were identified: Cluster 1 (mean age 37.3 years [SD 14.5]) comprised individuals with predominantly mental and behavioural conditions, no SCNs, high deprivation, and the lowest mortality risk (reference group); Cluster 2 (mean age 57.9 years [SD 16.3]) included those with cardiovascular, musculoskeletal, and mental and behavioural conditions, no SCNs, and a higher mortality risk (adjusted hazard ratio [aHR] 1.47 [95% CI 1.45 to 1.48]);Cluster 3 (mean age 62.6 years [SD 16.4]) had similar clinical profiles to cluster 2 but the highest SCN burden, lower deprivation, and the greatest mortality risk (aHR 2.83 [2.75 to 2.92]); Cluster 4 (mean age 62.6 years [SD 15.2]) exhibited the highest burden of long-term conditions, two SCNs, high deprivation, and elevated mortality (aHR 1.49 [1.47 to 1.51]). Interpretation We identified four distinct clusters of individuals with varying profiles of multimorbidity, social care needs, and mortality risk. These findings underscore the importance of personalised, integrated care approaches and support the development of targeted interventions to address the complex and intersecting needs of the most vulnerable populations. Keywords: multiple long term conditions, social care needs, clustering, mortality, artificial intelligence ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement HDM has received funding from the National Institute for Health and Care Research - the Artificial Intelligence for Multiple Long-Term Conditions, or AIM. The development and validation of population clusters for integrating health and social care: A mixed-methods study on multiple long-term conditions (NIHR202637); receives funding from the National Institute for Health and Care Research Multiple Long-Term Conditions (MLTC) Cross NIHR Collaboration (CNC) (NIHR207000); and receives funding from the National Institute for Health and Care Research Developing and optimising an intervention prototype for addressing health and social care need in multimorbiditY(NIHR206431). The views expressed in this publication are those of the author(s) and not necessarily those of the NHS, the National Institute for Health Research or the Department of Health and Social Care. AF was supported by the National Institute of Health Research (NIHR) Oxford Biomedical Research Centre (BRC). ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes For this study, data were obtained from the Clinical Practice Research Datalink (CPRD) under a full license agreement, which does not permit data sharing outside the research team. However researchers interested in replicating the study may apply directly to CPRD (enquiries{at}cprd.com) to gain access to the dataset. Details of the READ and SNOMED codes used in the analysis are available on GitHub.

BackgroundPoor adherence to oral medications for type 2 diabetes can increase the risk of health complications. Digital interventions can affect people’s experiences of self-managing a chronic condition, and SMS text messages may provide an effective delivery method for an intervention. The Support Through Mobile Messaging and Digital Health Technology for Diabetes (SuMMiT-D) intervention uses evidence-based SMS text messages to support people with type 2 diabetes with regular and consistent use of diabetes medication. ObjectiveThis process analysis, conducted alongside a randomized controlled trial of SuMMiT-D, aimed to explore (1) the contextual factors that may interact with the SuMMiT-D intervention and (2) the self-reported mechanisms through which change in behavior or attitude might occur. MethodsA nested qualitative process study was conducted within primary care in England. A total of 43 trial participants diagnosed with type 2 diabetes were assigned to receive the SuMMiT-D intervention, were undergoing oral glucose-lowering treatment, blood pressure-lowering treatment or lipid-lowering treatment, either alone or in combination, and had access to a mobile phone, took part in semistructured telephone interviews. Data were analyzed using inductive thematic analysis. ResultsIn total, 2 overarching themes were developed exploring relevant contextual factors and potential mechanisms of change. The system exerted a range of holistic benefits and supported the cognitions, beliefs, and behaviors necessary for longitudinal self-management. The perceived value of the messages was fluid and linked to contextual need. Appraisal of the system was influenced by existing routines, lifestyle disruption, people’s understanding of type 2 diabetes, relationships with other people, and subjective attitudes toward living with type 2 diabetes in contemporary society. ConclusionsThis work demonstrates the value of engaging people longitudinally in thinking about their general health, the importance of interrogating context, and the holistic benefit of health messaging. Many people perceived wide-ranging and unexpected benefits from using the intervention over time, challenging assumptions about who might be expected to appraise the system more positively and who should be offered access to it.

People living with multimorbidity often face complex social care needs that significantly affect their health and wellbeing. Despite growing recognition of the importance of addressing these needs in primary care, practical and systemic barriers, such as time constraints, unclear professional roles, and fragmented service pathways, limit effective support. Using the Person-Based Approach, we developed a dual-component intervention: (1) a brief screening tool for primary care professionals (PCPs) to identify patients with potential social care needs, and (2) a patient-facing self-navigation tool to help individuals recognise, prioritise, and plan responses to their needs. Sixteen patients and fifteen PCPs participated in think-aloud interviews. Data were analysed using a Table of Changes to inform real-time optimisation. Participants appreciated the autonomy-supportive, personalised tone of the self-navigation tool and its journey-based framing. Managing expectations was essential to avoid misinterpretation, and acknowledging prior negative healthcare experiences helped build trust. PCPs valued the screening tool’s brevity and practicality but raised concerns about role clarity and integration into workflows. Both groups highlighted the importance of accessibility, digital inclusion, and the burden of multimorbidity. Findings informed a practical checklist for future intervention design, including: (1) prioritising control and choice, (2) setting clear expectations, (3) minimising burden, (4) acknowledging past experiences, (5) clarifying roles, and (6) embedding tools within existing systems. This study offers actionable insights into person-centred intervention development and highlights the need for further evaluation to assess effectiveness and scalability in improving outcomes and system efficiency.