Objective: The aim of this study was to assess how different groups of health professionals evaluated the usability of a new electronic health record (EHR) and to investigate the association between the usability and burnout, insomnia and turnover intention. Methods: This cross-sectional study included 1424 health professionals who worked at a Norwegian University Hospital. The usability was measured with the System Usability Scale (SUS) 6 months after the previous electronic record was replaced with a more comprehensive, sector-wide, patient-centred EHR in 2022. Results: The median SUS score was 25 (IQR 12.5–37.5) out of 100 and ranged from 15 (IQR 7.5–25.0) among medical doctors to 40 (IQR 27.6–55.0) among laboratory technicians. Nurses reported a score of 25 (IQR 12.5–40.0). In clinical contexts, the median SUS score ranged from 15 (IQR 10.0–30.0) within radiology to 27.5 (IQR 15.0–42.5) within internal medicine, whereas laboratory medicine reported a score of 37.5 (IQR 27.5–55.0). In multivariable analyses using health professionals in the highest quarter of the SUS as the reference, those in the lowest quarter were more likely to report burnout (OR 3.05, 95% CI 1.86 to 5.00), insomnia (OR 1.72, 95% CI 1.18 to 2.50) and turnover intention (OR 2.35, 95% CI 1.53 to 3.64). Conclusion: Most health professionals across all occupational groups and clinical contexts reported low usability of a new EHR 6 months after go-live. Those who reported the lowest usability were more likely to report burnout, insomnia and turnover intention.

Human Activity Recognition models have potential to contribute to valuable and detailed knowledge of habitual physical activity for typically developing children and children with Cerebral Palsy. The main objective of the present study was to develop and validate two Human Activity Recognition models. One trained on data from typically developing children (n = 63), the second also including data from children with Cerebral Palsy (n = 16), engaging in standardised activities and free play. Our data was collected using accelerometers and ground truth was established with video annotations. Additionally, we aimed to investigate the influence of window settings on model performance. Utilizing the Extreme gradient boost (XGBoost) classifier, twelve sub-models were created, with 1-,3- and 5-seconds windows, with and without overlap. Both Human Activity Recognition models demonstrated excellent predictive capabilities (>92%) for standardised activities for both typically developing and Cerebral Palsy. From all window sizes, the 1-second window performed best for all test groups. Accuracy was slightly lower (>75%) for the Cerebral Palsy test group performing free play activities. The impact of window size and overlap varied depending on activity. In summary both Human Activity Recognition models effectively predict standardised activities, surpassing prior models for typically developing and children with Cerebral Palsy. Notably, the model trained on combined typically developing children and Cerebral Palsy data performed exemplary across all test groups. Researchers should select window settings aligned with their specific research objectives.

SelfBACK is an artificial intelligence based self-management app for low back pain (LBP) recently reported to reduce LBP-related disability. The aim of this study was to examine if age, gender, or education modify the effectiveness of theselfBACK intervention using secondary analysis of theselfBACKrandomized controlled trial. Persons seeking care for LBP were recruited from primary care in Denmark and Norway and an outpatient clinic (Denmark). The intervention group (n = 232) received theselfBACKapp adjunct to usual care. The control group (n = 229) received usual care only. Analyses were stratified by age (18–34, 35–64, ≥65 years), gender (male, female), and education (≤12, >12 years) to investigate differences in effect at three and nine months follow-up on LBP-related disability (Roland-Morris Disability Questionnaire [RMDQ]), LBP intensity and pain self-efficacy. Overall, there was no effect modification for any of the sociodemographic factors. However, data on LBP-related disability suggest that the effect of the intervention was somewhat more beneficial in older than in younger participants. The difference between the intervention and control group due to interaction was 2.6 (95% CI: 0.4 to 4.9) RMDQ points for those aged ≥65 years as compared to those aged 35–64 years. In conclusion, age, gender, or education did not influence the effect of theselfBACKintervention on LBP-related disability. However, older participants may have an additional long-term positive effect compared to younger participants.Trial registration: ClinicalTrials.gov Identifier:NCT03798288.