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Record W7134818711 · doi:10.3310/pplhg1141

Digital intervention to support cancer survivors: the CLASP research programme

2025· article· en· W7134818711 on OpenAlexaff
P. S. Little, Katherine Bradbury, B. Stuart, Jane Barnett, Adele Krusche, Mary Steele, Elena Heber, Steph Easton, Kirsten A. Smith, Joanna Slodowska-Barabasz, Liz Payne, Teresa Corbett, Sebastien Pollet, Jazzine Smith, Judith Joseph, Megan Lawrence, Dankmar Böhning, Tara Cheetham-Blake, Diana Eccles, Claire Foster, Adam WA Geraghty, Geraldine Leydon, André Müller, R. Neal, Richard Osborne, Shanaya Rathod, Chloe Grimmett, Geoffrey Sharman, Roger Bacon, Lesley Turner, Richard Stephens, Tamsin Burford, Laura Wilde, Megan Liddiard, Kirsty Rogers, James Raftery, Shihua Zhu, Karmpal Singh, Frances Webley, Gareth Griffiths, Trudie Chalder, Clare Wilkinson, Eila Watson, Lucy Yardley

Bibliographic record

VenueProgramme Grants for Applied Research · 2025
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsUniversity of Calgary
FundersProgramme Grants for Applied ResearchNational Institute for Health Research Southampton Biomedical Research CentreNational Institute for Health and Care Research
KeywordsIntervention (counseling)Psychological interventionQuality of life (healthcare)Qualitative researchDigital healthBespokeDistressCancerRandomized controlled trial

Abstract

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Background There are increasing numbers of cancer survivors who have finished their primary treatment, but whose quality of life remains consistently poor over years. There is limited evidence for pragmatic, brief interventions to support cancer survivors in primary care, where most patients are managed. Objective To develop, trial and assess the effectiveness and cost-effectiveness of a digital intervention to support cancer survivors (named ‘Renewed’) designed to require minimal health service resources. Design Qualitative development of the intervention, then open randomised controlled trial, with a process analysis and health economic analysis. Setting United Kingdom primary care Interventions: Development of the intervention We systematically reviewed the relevant qualitative and quantitative literature to inform initial intervention planning, intervention content and design features of a digital intervention. This was followed by iterative development and optimisation of intervention content and the human support component – in qualitative studies of the views of cancer survivor, and of National Health Service, volunteer and charity workers. Main trial: Participants People who had finished primary treatment for colorectal, breast or prostate cancer with lower quality of life (European Organization for Research and Treatment of Cancer QLQ-C30 score < 85) within the last 10 years. Participants were randomised to one of three groups: (1) ‘generic’ advice: detailed digital National Health Service support for healthier living (‘Living Well’), (2) a bespoke digital intervention (‘Renewed’) addressing symptom management, physical activity, diet, weight, distress and/or fear of recurrence, or (3) ‘Renewed’ plus support (additional brief support by e-mail, telephone, or face to face) Main outcome measures Primary outcome: European Organization for Research and Treatment of Cancer QLQ-C30 (overall score). Secondary outcomes: subscales of European Organization for Research and Treatment of Cancer QLQ-C30 (global self-rated health; functional subscales; symptom subscales), EuroQol-5 Dimensions, five-level version, psychological measures and costs. Results At the primary time point of 6 months, there were clinically important improvements in European Organization for Research and Treatment of Cancer QLQ-C30 score contrary to the expected trajectory of quality of life in this population, but with no evidence of differences between groups. By 12 months, the Renewed plus support group had continued to improve and was better than generic advice (1.42, 95% confidence intervals 0.33 to 2.51), with the largest differences in the prostate cancer subgroup. 13 of the 14 subscales also improved compared to generic advice, statistically significant for self-rated global health (Renewed: 3.06, 1.39 to 4.74; Renewed plus support: 2.78, 1.08 to 4.48), dyspnoea, constipation and enablement. For Renewed plus support, there were also statistically significant differences for physical, cognitive and emotional functioning and fatigue. Renewed and Renewed plus support were dominant given improved effectiveness combined with and lower mean primary care National Health Service costs per patient (respectively −£141, −153 to −128; −£77, −90 to −65). Limitations Of those sent invitation letters, 14% (7883/59,295) were assessed for eligibility and 35% (2732/7883) of those assessed were eligible and agreed to participate – which is normal with the ‘cold calling’ method of invitation. The digital intervention would not suit people who find technology or the internet difficult to access, but only 25% (2649/10,697) of those who gave reasons for declining did so due to lack of internet access. The extensive generic advice available to participants in the National Health Service limited the ability to assess the specific benefits of Renewed in the short term, but nevertheless longer-term benefit and lower National Health Service costs are likely to be achieved with the bespoke intervention. Conclusions Cancer survivors with lower quality of life given detailed generic online support improve significantly. Providing robustly developed, low-cost, bespoke digital support can provide further modest long-term improvements in enablement, symptom management and self-rated global health, with substantially lower National Health Service costs. Future work The cost-effectiveness and benefits for symptom management on self-rated health suggest a more widespread implementation study should be undertaken. Trial registration This trial is registered as Current Controlled Trials ISRCTN 96374224. Funding This award was funded by the National Institute for Health and Care Research (NIHR) Programme Grants for Applied Research programme (NIHR award ref: RP-PG-0514-20001) and is published in full in Programme Grants for Applied Research ; Vol. 14, No. 4. See the NIHR Funding and Awards website for further award information.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.965
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.183
GPT teacher head0.474
Teacher spread0.292 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2025
Admission routes1
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