Background Quality Adjusted Life Years (QALYs) are often used in economic evaluations, yet utility weights for deriving them are rarely directly measured in mental health services.Objectives We aimed to: (i) identify the best Transfer To Utility (TTU) algorithms and predictors for adolescent weighted Assessment of Quality of Life - six dimensions (AQoL-6D) health utility and (ii) assess ability of TTU algorithms to predict longitudinal change.Methods We recruited 1107 young people attending Australian primary mental health services, collecting data at two time points, three months apart. Five linear and three generalised linear models were explored to identify the best TTU algorithm. Forest models were used to assess predictive ability of six candidate measures of psychological distress, depression and anxiety and linear / generalised linear mixed effect models were used to construct longitudinal predictive models for AQoL-6D change.Results A depression measure (Patient Health Questionnaire-9) was the strongest independent predictor of health utility. Linear regression models with complementary log-log transformation of utility score were the best performing models. Between-person associations were slightly larger than within-person associations for most of the predictors.Conclusions Adolescent AQoL-6D utility can be derived from a range of psychological distress, depression and anxiety measures. TTU algorithms estimated from cross-sectional data can approximate longitudinal change but may slightly bias QALY predictions.Toolkits The TTU models produced by this study can be searched, retrieved and applied to new data to generate QALY predictions with the Youth Outcomes to Health Utility (youthu) R package - https://ready4-dev.github.io/youthu.Competing Interest StatementThe authors have declared no competing interest.Funding StatementThis study was funded by the National Health and Medical Research Council (NHMRC, APP1076940), Orygen and headspace.Author DeclarationsI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.YesThe details of the IRB/oversight body that provided approval or exemption for the research described are given below:The study was reviewed and granted approval by the University of Melbourne Human Research Ethics Committee, and the local Human Ethics and Advisory Group (1645367.1).All necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived.YesI 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).YesI have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.YesDetailed results in the form of catalogues of the Transfer to Utility (TTU) models produced by this study and other supporting information are available in the results repository https://doi.org/10.7910/DVN/DKDIB0. Tools for finding and using the TTU models appropriate for use with new prediction datasets are available as part of the youthu R package (https://ready4-dev.github.io/youthu/). The youthvars R package (https://ready4- dev.github.io/youthvars/) provides a number of tools helpful for replicating this study (including a synthetic dataset) while TTU (https://ready4-dev.github.io/TTU/) has tools for both replicating the study and generalising our algorithms to develop TTU algorithms with other utility measures and predictors. https://doi.org/10.7910/DVN/DKDIB0 https://ready4-dev.github.io/TTU https://ready4-dev.github.io/youthu https://ready4-dev.github.io/youthvars