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Record W4390192103 · doi:10.1002/alz.080472

The Luci program: a coach‐based online intervention to reduce dementia risk factors

2023· article· en· W4390192103 on OpenAlexaff
Isabelle Lussier, Juliette Guillemont, Flavie Laroque, Simon Bacon, Michel Boivin, Guylaine Ferland, Kim Lavoie, Guy Paré, Sylvie Belleville

Bibliographic record

VenueAlzheimer s & Dementia · 2023
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsHEC MontréalUniversité du Québec à MontréalInstitut Universitaire de Gériatrie de MontréalUniversité de MontréalConcordia University
Fundersnot available
KeywordsPsychological interventionDementiaAttritionNeurocognitiveGerontologyCoachingMedicineCognitionCognitive declinePsychologyUsabilityIntervention (counseling)Physical therapyClinical psychologyPsychiatry

Abstract

fetched live from OpenAlex

Abstract Background A large body of evidence indicates that behavioural interventions on modifiable risk factors for dementia can reduce cognitive decline in at‐risk older individuals. There is growing interest in making such interventions more widely accessible using technology‐based solutions. However, online programs would benefit from incorporating coach‐based interventions, the feasibility of which needs to be demonstrated. Luci is an online, coach‐supported, multidomain lifestyle intervention aimed to improve cognitive health in older adults at risk of cognitive decline. A pilot study was conducted to establish the feasibility of the program. Method A web‐App was designed to allow 1‐1 coaching sessions, create and monitor personalized action plans, provide information on brain health, and collect outcome measures. 119 cognitively healthy, community‐dwelling individuals aged 50‐70 with ≥1 lifestyle risk factor (physical activity, diet, cognitive engagement) were recruited in this 24‐week, randomized (Luci vs. wait‐list controls), single‐blind pilot feasibility study. Main eligibility criteria included no severe health conditions (e.g., neurocognitive, psychiatric), to be French‐speaking, available for the duration of the program, and computer literate with access to a device and Internet. Adherence, retention rates, and satisfaction and usability were assessed. Secondary measures included change from baseline in lifestyle risk factors and cognitive performance. Result Participants had a mean age of 60.6 (SD = 5.6) and 16.7 (SD = 3.4) years of education; 84.9% were female. Retention and adherence rates were 92.4% and 84.5%, respectively, and a 3.7% difference in attrition rates between the intervention and control groups was observed. Program satisfaction and App usability rates were high, with over 95% agreeing that the coach helped them identify the benefits of and obstacles to changing their lifestyle and respected their opinion. A greater proportion of participants (1.3 to 2.7‐fold) in the intervention versus control group reached a clinically significant change in lifestyle risks. In contrast to the control group, the Luci intervention group showed improvements in cognitive performance. Conclusion Providing coach‐based online intervention programs might represent a powerful approach to support durable lifestyle changes in older adults at‐risk for dementia. The results from this study support the Luci program potential as a feasible intervention for effective and sustainable behavioural changes.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.055
GPT teacher head0.379
Teacher spread0.324 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNon-randomized trial
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".

Quick stats

Citations1
Published2023
Admission routes1
Has abstractyes

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