Harriet Demnitz-King

Background: Previous studies have modelled long-term cost-effectiveness of dementia prevention but seldom within trials. APPLE-Tree (Active Prevention in People at risk of dementia through Lifestyle, bEhaviour change and Technology to build REsiliEnce) is a personalised, multi-domain, low-intensity dementia prevention intervention for adults experiencing subjective cognitive decline or mild cognitive impairment. Intervention receipt, relative to control, was associated with an adjusted mean difference of 0.06 (95%CI −0.001 to −0.128) in the Neuropsychological Test Battery (NTB). We evaluated cost-effectiveness from health- and social-care perspectives over 2 years. Methods: We recruited 746 older adults from English health and community settings and randomly assigned them (1:1) to APPLE-Tree plus usual care (UC) or UC plus written dementia prevention information. Quality-adjusted life-years (QALYs) were calculated from the EQ-5D-5L. Over 24-months, intention-to-treat analyses compared QALYs (primary) and incremental cost per NTB unit (one z-score) change (secondary). Results: Between October 2020 and December 2022, 374 participants were randomised to intervention and 372 to control. Mean adjusted QALYs were 1.511 (intervention) and 1.520 (control), an adjusted difference of −0.010 (95%CI −0.04 to −0.022). Mean adjusted (SD) costs were £2966 (3629) and £2551 (4439), respectively. Cost per 1-point NTB change z-score was £6809. At a £30 000 threshold, the probability of cost-effective was 12% and 96% based on QALYs and cognition, respectively. Post hoc analyses indicated higher cost-effectiveness for non-White participants (incremental costs: £374; 95%CI −590.751 to −1338.515) and socioeconomically disadvantaged participants (non-homeowners), who had higher QALYs (0.106) and lower costs (−£1830), yielding a 98% probability of cost-effectiveness at a £20 000 threshold. Conclusion: APPLE-Tree has a high probability of being cost-effective for a 1-point NTB gain, a difference previously associated with a three-fold reduction in 5-year dementia risk. QALY-based cost-effectiveness was not observed, but post-hoc analyses suggested it may be more effective in groups with greater needs. Findings highlight the potential of targeted, lower-intensity interventions to reduce dementia risk. Longer-term follow-up is required to determine whether cost-effectiveness is realised over time.

Background: Digital biomarkers are gaining interest as proxy markers for mental health, as they enable passive and continuous data collection. However, the association between digital biomarkers of health and anxiety, both generalized anxiety disorder and anxiety symptoms, remains unknown. Objective: This systematic review and meta-analysis examined the association between digital biomarkers of health obtained from wrist-worn wearables and anxiety in adults. Methods: Systematic literature searches were conducted across 6 databases, including unpublished gray literature. The final search was done on September 21, 2025. Cross-sectional or longitudinal studies investigating the association between digital biomarkers from wrist-worn wearables and anxiety were eligible. Studies using inferential statistics or machine learning methods were both eligible. Studies were excluded if participants received diagnoses of neurodegenerative disorders or physical health conditions. Two risk-of-bias tools were used: the National Heart, Lung, and Blood Institute assessment tool for inferential statistical studies, and the modified version of the Quality Assessment of Diagnostic Accuracy Studies-2 for machine learning studies. Whenever possible, effect sizes were combined across studies, for each digital biomarker of health separately, using random-effects meta-analyses. Sensitivity analyses were performed to assess whether results differed according to anxiety type (state or trait) and age group. Otherwise, studies were synthesized narratively. Results: A total of 44 studies from 42 articles were eligible. Among these, 36 studies used inferential statistical approaches for analysis (21 reporting sleep characteristics, 8 reporting physical activity, 2 reporting heart rate variability, and 5 reporting more than 1 type), and 8 studies used machine learning approaches. Sample size ranged from 17 to 170,320. Meta-analyses on 4 sleep metrics found no associations: sleep efficiency (Fisher z=–0.07, 95% CI –0.14 to 0.002; P=.06; PI –0.19 to 0.05), wake after sleep onset (Fisher z=0.13, 95% CI –0.04 to 0.30; P=.11; PI –0.15 to 0.41), total sleep time (Fisher z=0.009, 95% CI –0.01 to 0.03; P=.28; PI –0.02 to 0.03), and sleep onset latency (Fisher z=0.04, 95% CI –0.07 to 0.15; P=.08; PI –0.19 to 0.27). Qualitative syntheses revealed that lower physical activity levels and higher heart rate were associated with greater anxiety symptoms. Machine learning studies using wrist-worn wearable data alone showed varied performance, with predictive performance improving when wearable data were combined with other data sources. Conclusions: This is the first review to synthesize evidence from inferential statistical (mostly fair quality) and machine learning studies examining association between wearable-derived digital biomarkers and anxiety. Meta-analyses found no associations between sleep metrics and anxiety. Although based on limited studies, lower physical activity levels and elevated heart rate were associated with greater anxiety symptoms. Digital biomarkers may be more useful when integrated with other data sources (eg, self-report and clinical data) rather than used as stand-alone screening tools. Trial Registration: PROSPERO CRD42023409995; https://www.crd.york.ac.uk/PROSPERO/view/CRD42023409995

Translation of evidence about dementia risk and its reduction into effective, equitable public health policy is a major challenge. To address this challenge, the National Institute for Health and Care Research Policy Research Unit in Dementia and Neurodegeneration at Queen Mary University of London (DeNPRU-QM) convened a multidisciplinary panel of 40 experts from across England, with diverse lived, academic, clinical, policy and advocacy experience, at various career stages, and of diverse gender and ethnicity, to develop actionable policy recommendations for dementia risk reduction. Through a 2-day in-person workshop and a subsequent three-round modified Delphi survey, the panel evaluated and refined statements on dementia prevention. The panel achieved consensus on 56 recommendations in four domains: public health messaging, individual-level interventions, population-level interventions and research commissioning. A key priority across all domains was the need to consider and address health inequalities so that prevention efforts do not exacerbate existing disparities. Our recommendations provide policymakers with a robust foundation for designing and implementing an evidence-based dementia prevention strategy in England and provide guidance that can inform approaches in other countries and contexts. By prioritizing clear communication, targeted intervention and sustained research investment, the recommendations can help to address structural inequities and advance dementia risk reduction. Ongoing cross-sector advocacy will be crucial in driving policy adoption and implementation.

Background: In the absence of a cure or widely accessible, effective treatments, prevention efforts are critical to mitigating the future burden of dementia. Achieving this requires coordinated efforts across health and social care systems, alongside engagement from other government sectors and key stakeholders. However, translating evidence into effective and equitable policies presents a significant challenge. To address this, the NIHR Dementia and Neurodegeneration Policy Research Unit at Queen Mary University of London (DeNPRU‐QM) launched an expert consensus initiative aimed at developing actionable public health policy recommendations for dementia prevention. Method: The study was pre‐registered on Open Science Framework ( https://osf.io/5c4mh ). A diverse, multidisciplinary panel of 40 experts balanced across lived, academic, clinical, policy and advocacy experience, career stages, gender and ethnicity met in October 2024 at Nottingham University for a two‐day in‐person workshop. They shared perspectives regarding how public health policy should respond to/account for current dementia prevention research, co‐creating preliminary statements to reflect views discussed and receiving support across stakeholder groups. This was followed by a three‐round online modified‐Delphi survey where panellists rated statements on a 5‐point Likert scale, provided feedback, and suggested new statements. In rounds 2 and 3, statements were revised based on feedback, and panellists could adjust ratings after reviewing group comments and prior results. Consensus was defined as ≥67% agreement, with statements achieving this threshold graded as ‘U’ (100% agreement), ‘A’ (90%–99% agreement), ‘B’ (78%–89% agreement), or ‘C’ (67%–77% agreement). Result: 38 (95%) panellists completed all three survey rounds. Consensus was achieved on 56 recommendations spanning four domains: public health messaging, individual‐level interventions, population‐level interventions, and research commissioning. Six statements achieved unanimous agreement; these highlighted the need for consistent, evidence‐based messaging, equitable access to interventions, systemic approaches to reduce health disparities, and improved data infrastructure for tracking dementia outcomes. Conclusion: This framework provides a robust foundation for policymakers seeking to implement evidence‐based dementia prevention. By prioritising clear communication, targeted interventions, and sustained investment in research, the recommendations can guide efforts to address structural inequities and advance dementia risk reduction. Ongoing cross‐sector advocacy will be crucial to drive policy adoption and implementation.

Background: Large‐scale trials of multidomain interventions indicate that modifying lifestyle and psychological risk factors can slow cognitive decline. This study evaluated whether a lower‐intensity, personally tailored secondary dementia prevention programme, informed by behaviour change theory, could reduce cognitive decline over two years in adults with subjective cognitive decline (SCD) or mild cognitive impairment (MCI). Method: We conducted a multi‐site, single‐blind randomised controlled trial (ISRCTN17325135) in England, recruiting 747 older adults with SCD or MCI between 05/10/2020 and 31/12/2022. Participants were randomised 1:1 to the 12‐month APPLE‐Tree intervention or control (usual care plus brief written dementia prevention information). The intervention focused on promoting healthy lifestyles, enhancing social connections and enjoyable activities, and improving self‐management of long‐term conditions. Shifting online due to COVID‐19, it comprised ten 1‐hour group video sessions over six months, delivered by two non‐clinical facilitators, supervised by a clinical psychologist. Sessions were supplemented with alternating video‐call “tea breaks” for informal interaction and biweekly individual goal‐setting calls. From months 6–12, participants continued with monthly “tea breaks”. The primary outcome was cognition, measured by NTB composite score at 24 months. An intention‐to‐treat analysis was conducted using a three‐level mixed effects model including treatment arm, time, treatment‐by‐time interaction, baseline Neuropsychological Test Battery (NTB) score, and site as fixed effects. Random effects accounted for intervention arm group clustering and repeated measures at 12 and 24 months. Result: The trial recruited above target, with high intervention adherence (305/374 [82%] attending ≥5 main sessions). In the primary analysis that included 635/746 (85%) of participants randomised, the mean adjusted NTB scores increased over time in both arms (Table 1 and Figure 1), but the increment was higher in the intervention compared with the control arm (adjusted mean difference at 24 months: 0.06 [95% Confidence Interval ‐0.00–0.13], p =0.055). Conclusion: APPLE‐Tree provides an accessible, scalable model for secondary dementia prevention. The small effect size is comparable with previous successful, more intensive dementia prevention interventions with similar participant groups. Leveraging remote delivery and non‐clinical facilitators could enable wide‐scale implementation to support older adults with memory concerns. Secondary outcomes (e.g., quality of life, intervention‐targeted factors) available May 2025.

Background – Current pathways for assessment and treatment of memory disorders are designed for people with dementia but are also accessed by people with subjective cognitive decline without objective deficits (SCD), or with cognitive deficits likely to be of psychological causation without neurodegeneration (Functional Cognitive Disorders, FCDs). We aimed to identify how frequently adults meeting SCD/FCD criteria, arguably better served by different service models, present to health services. Methods – Updating a previous review, we searched MEDLINE, Embase, and PsycINFO for studies reporting SCD/FCD prevalence within representative clinical samples, published between 2019 and 2025. We assessed study quality using the RoB-PrevMH tool and synthesised data using similar methods to the previous review. Results – We included 5/21 high- and 16/21 moderate-quality studies. The pooled mean age was 73.3 years (Standard Deviation = 7.0; N = 12,785) and 55.1% of participants were female. Across studies reporting SCD (k = 20; n = 13,155) and FCD (k = 3; n = 4,551), pooled prevalences were 17.3% and 5.6% respectively. Most studies (k = 11; 52.4%) did not explicitly exclude pseudodementia related to psychiatric illnesses. In high quality studies that did, SCD prevalence was 11.4% ( k = 3, n = 3,295; 10.2–14.1%) in memory clinic attendees and 16.5% ( k = 1, n = 91) among primary care attendees aged 50 + presenting with cognitive complaints without pre-existing dementia. Conclusions – At least 10% of people presenting to memory services are diagnosed with SCD and fewer with FCD. We need consistent guidelines about how these conditions are defined and diagnosed. An integrated, multidisciplinary, neighbourhood health service would be well positioned to treat these groups.

Introduction: In a mixed-methods process evaluation embedded within a randomised controlled trial, we aimed to investigate how the APPLE-Tree secondary dementia prevention intervention might support behavioural and lifestyle goal attainment, through determining contexts influencing engagement and testing intervention theoretical assumptions. Methods: We measured intervention reach and dose. We selected interviewees for setting (urban/rural, NHS/non-NHS), gender and ethnic diversity, from the 374 APPLE-Tree trial participants randomised to the intervention-arm. We interviewed 25 intervention participants (including six who withdrew), 12 facilitators and three study partners (family members or friends). Additionally, we analysed 11 interviews previously conducted during or after intervention delivery for an ethnography and 208 (55.6%) facilitator-completed participant goal records. We thematically analysed data, combining inductive/deductive approaches informed by the COM-B (Capability, Opportunity, and Motivation) behaviour change model. We audio-recorded a randomly-selected tenth of sessions and rated fidelity. Results: 346/374 (92.5%) intervention-arm participants received some intervention (reach), and 305/374 (81.6%) attended ≥5 main sessions (predefined as adhering: dose). According to facilitator records, participants met a mean of 5.1/7.5 (68.3%) goals set. We generated three themes: (1) Building Capability and Motivation: Increased capability and reflective motivation altered automatic motivation; (2) Connecting with other participants and facilitators helped increase social opportunity, motivation and capability; (3) A flexible, tailored approach increased capability, motivation, and opportunity for engagement. Conclusion: The intervention supported behaviour change, through increasing knowledge and providing space to plan, implement and evaluate new strategies and make social connections. Feedback indicated that the intervention was flexible and inclusive of diverse preferences and needs.

With aging, slight changes in some cognitive processes can be observed. Therefore, we aimed to assess if meditation expertise is associated with preserved cognition in key domains affected in aging. We used data from two older-adult samples of the Medit-Ageing Age-well Study: 135 non-meditators and 27 expert meditators. We examined group differences in four objective cognitive domains (attention, executive functioning, episodic memory, and global cognition) and three subjective scores: Cognitive Difficulties Scale (CDS) total score as well as Attentional Style Questionnaire (ASQ) internal and external scores using generalized mixed effect models, controlling for age, sex, and education. We did not observe group differences on attentional, executive and global cognitive scores or on ASQ internal score and CDS total score. However, meditators reported less external distraction (ASQ external score) and had better memory than non-meditators. These cross-sectional results indicate a better management of external stimuli and higher memory performance in expert meditators. Memory difficulties and distractions being the main complaints of older people, prolonged meditation practice could lead them to greater cognitive capacities important for healthy aging. Trial registration: NCT02977819 (ClinicalTrials.gov)

BACKGROUND Digital biomarkers are gaining interest as proxy markers for mental health, as they enable passive and continuous data collection. However, the association between digital biomarkers of health and anxiety, both generalized anxiety disorder and anxiety symptoms, remains unknown. OBJECTIVE This systematic review and meta-analysis examined the association between digital biomarkers of health obtained from wrist-worn wearables and anxiety in adults. METHODS Systematic literature searches were conducted across 6 databases, including unpublished gray literature. The final search was done on September 21, 2025. Cross-sectional or longitudinal studies investigating the association between digital biomarkers from wrist-worn wearables and anxiety were eligible. Studies using inferential statistics or machine learning methods were both eligible. Studies were excluded if participants received diagnoses of neurodegenerative disorders or physical health conditions. Two risk-of-bias tools were used: the National Heart, Lung, and Blood Institute assessment tool for inferential statistical studies, and the modified version of the Quality Assessment of Diagnostic Accuracy Studies-2 for machine learning studies. Whenever possible, effect sizes were combined across studies, for each digital biomarker of health separately, using random-effects meta-analyses. Sensitivity analyses were performed to assess whether results differed according to anxiety type (state or trait) and age group. Otherwise, studies were synthesized narratively. RESULTS A total of 44 studies from 42 articles were eligible. Among these, 36 studies used inferential statistical approaches for analysis (21 reporting sleep characteristics, 8 reporting physical activity, 2 reporting heart rate variability, and 5 reporting more than 1 type), and 8 studies used machine learning approaches. Sample size ranged from 17 to 170,320. Meta-analyses on 4 sleep metrics found no associations: sleep efficiency (Fisher z=–0.07, 95% CI –0.14 to 0.002; P=.06; PI –0.19 to 0.05), wake after sleep onset (Fisher z=0.13, 95% CI –0.04 to 0.30; P=.11; PI –0.15 to 0.41), total sleep time (Fisher z=0.009, 95% CI –0.01 to 0.03; P=.28; PI –0.02 to 0.03), and sleep onset latency (Fisher z=0.04, 95% CI –0.07 to 0.15; P=.08; PI –0.19 to 0.27). Qualitative syntheses revealed that lower physical activity levels and higher heart rate were associated with greater anxiety symptoms. Machine learning studies using wrist-worn wearable data alone showed varied performance, with predictive performance improving when wearable data were combined with other data sources. CONCLUSIONS This is the first review to synthesize evidence from inferential statistical (mostly fair quality) and machine learning studies examining association between wearable-derived digital biomarkers and anxiety. Meta-analyses found no associations between sleep metrics and anxiety. Although based on limited studies, lower physical activity levels and elevated heart rate were associated with greater anxiety symptoms. Digital biomarkers may be more useful when integrated with other data sources (eg, self-report and clinical data) rather than used as stand-alone screening tools. CLINICALTRIAL PROSPERO CRD42023409995; https://www.crd.york.ac.uk/PROSPERO/view/CRD42023409995