Evidence map›Paper›PMID 42328805›Full record

Trial reportAge and ageing2026

Cost utility and cost-effectiveness of the APPLE-Tree programme: Active Prevention in People at risk of dementia through Lifestyle, bEhaviour change and Technology to build REsiliEnce: economic evaluation embedded within a randomised controlled trial.

Harriet Demnitz-King, Claudia Cooper, Michaela Poppe, Jessica Budgett, Sweedal Alberts, Larisa Duffy, Mariam Adeleke, Julie Barber, Elisa Aguirre, Henry Brodaty and 14 more

Abstract readRandomized Controlled Trial
PubMed Full text at PMC Publisher
In one paragraph

Trial report in Age and ageing, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

24 authors.

Harriet Demnitz-KingCentre for Psychiatry and Mental Health, Queen Mary University of London, London, UK.ORCID 0000-0002-7421-7101
Claudia CooperCentre for Psychiatry and Mental Health, Queen Mary University of London, London, UK.ORCID 0000-0002-2777-7616
Michaela PoppeDivision of Psychiatry, University College London, London, UK.
Jessica BudgettCentre for Psychiatry and Mental Health, Queen Mary University of London, London, UK.ORCID 0000-0003-1095-5065
Sweedal AlbertsDivision of Psychology and Language Sciences, University College London, London, UK.
Larisa DuffyDivision of Psychiatry, University College London, London, UK.
Mariam AdelekeDepartment of Statistical Science, University College London, London, UK.
Julie BarberDepartment of Statistical Science, University College London, London, UK.
Elisa AguirreFacultad de Ciencias Biomédicas y de la Salud, Universidad Europea, Madrid, Spain.
Henry BrodatyCentre for Healthy Brain Ageing, Discipline of Psychiatry and Mental Health, University of New South Wales, Sydney, New South Wales, Australia.ORCID 0000-0001-9487-6617
Alexandra BurtonCentre for Psychiatry and Mental Health, Queen Mary University of London, London, UK.
Paul HiggsDivision of Psychiatry, University College London, London, UK.ORCID 0000-0003-0077-0710
Jonathan HuntleyFaculty of Health and Life Sciences, University of Exeter, Exeter, UK.
Helen C KalesDepartment of Psychiatry and Behavioral Sciences, University California Davis, Davis, California, USA.
Iain A LangNational Institute for Health and Care Research (NIHR) Applied Research Collaboration the South West Peninsula (PenARC), University of Exeter, Exeter, Devon, UK.
Natalie L MarchantDivision of Psychiatry, University College London, London, UK.
Sarah Morgan-TrimmerFaculty of Medicine, University of Southampton, Southampton, UK.
Anne Marie MinihaneNorwich Medical School, University of East Anglia, Norwich, UK.
Penny RapaportDivision of Psychiatry, University College London, London, UK.
Miguel RioDepartment of Electronic and Electrical Engineering, University College London, London, UK.
Karen RitchieInserm, Unit 1061, Neuropsychiatry: Epidemiological and Clinical Research, La Colombière Hospital, University of Montpellier, Montpellier, France.
Zuzana WalkerDivision of Psychiatry, University College London, London, UK.
Kate WaltersDepartment of Statistical Science, University College London, London, UK.
Rachael M HunterPriment Clinical Trials Unit, Research Department of Primary Care and Population Health, University College London, London, UK.ORCID 0000-0002-7447-8934

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPrevious 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.

methodsWe 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).

resultsBetween 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.

conclusionAPPLE-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.

Indexed as

DementiaResilience, PsychologicalAgedAged, 80 and overCost-Benefit AnalysisCost-Effectiveness AnalysisFemaleHumansLife StyleMaleQuality-Adjusted Life YearsRisk Reduction Behaviordementiahealth economicsnon-pharmacologicalolder peopleprevention

Identifiers

PMID42328805
PMCPMC13284706

What Socratic holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.