To determine whether a biopsychosocial model of suicidality, specifically sleep, nutrition, physical exercise, mindfulness, social connectedness, lower socioeconomic status (SES) and sex are uniquely associated with increased suicidal ideation, longitudinally over adolescence.

Longitudinal, prospective cohort study.

A structured self-report questionnaire was collected as part of the Longitudinal Adolescent Brain Study at the University of the Sunshine Coast’s Thompson Institute (Queensland, Australia) from July 2018 to January 2024.

159 Australian adolescents (n=91 female; 68 male) aged 12 to 17 years.

Self-reported suicidal ideation was measured longitudinally. Data were also collected on self-reported lifestyle factors (sleep, nutrition, physical exercise, mindfulness and social connectedness), psychological distress, SES and sex. All measures were collected at 4-monthly intervals for each participant for up to 5 years (maximum of 15 time points).

Significant relationships were identified between increased suicidal ideation and poor sleep (OR 2.6, 95% CI 1.4 to 4.6, p=0.002), socioeconomic disadvantage (SES quintile 1: OR 6.3, 95% CI, 1.8 to 21.8, p=0.004; SES quintile 2: OR 8.7, 95% CI 1.4 to 56.2, p=0.022), psychological distress (OR 5.7, 95% CI 2.1 to 15.6, p≤0.001) and eating habits (β –0.08, 95% CI –0.2 to –0.0).

Poor sleep, socioeconomic disadvantage, psychological distress and eating habits were all found to be significantly associated with increased adolescent suicidal ideation over time. These biopsychosocial factors should be considered in targeted interventions and policies for reducing adolescent suicidality. Further research should employ multilevel modelling to examine factor interactions and rigorously evaluate interventions targeting lifestyle factors and socioeconomic inequalities through randomised controlled trials and quasi-experimental designs.

We evaluated the performance of risk models that incorporate ambulatory ECG data and clinical information for prediction of healthcare expenditures related to heart failure (HF) and stroke events in treated and untreated patients.

A retrospective cohort study of Medicare patients who underwent Zio XT ambulatory monitoring in the USA was conducted between 2014 and 2020.

14-day ambulatory ECG data and claims data were evaluated in the study sample which included 89 923 patients in the HF hospitalisation group, 75 870 in the new-onset HF group and 90 159 in the stroke hospitalisation group. Predictive models for new-onset HF, HF hospitalisation and stroke hospitalisation were generated using LASSO Cox regression with ambulatory ECG variables and components of the CHA₂DS₂-VASc. For each outcome, we scored patients using standardised linear predictors from three composite risk models, and we evaluated the association between risk score and total Medicare cost.

The following hazard ratios per one SD increase in the new risk score were observed for the model that included all CHA₂DS₂-VASc components and ECG variables: HF hospitalisation in treated 2.94, 95% CI 2.75 to 3.15; new-onset HF in treated 1.84, 95% CI 1.75 to 1.93; HF hospitalisation in untreated 3.51, 95% CI 3.23 to 3.82; and new-onset HF in untreated 1.92, 95% CI 1.85 to 2.00. Risk scores generated by the model were also predictive of Medicare cost in both treated and untreated patients, with patients in the high-risk category for all outcomes having the greatest Medicare costs during 1 year of follow-up.

Integrating arrhythmia data from ambulatory ECG monitoring into clinical risk models allows for better prediction of healthcare utilisation and cost in both treated and untreated patients at high risk for HF and stroke events.