AIM: To investigate the utility of a Traffic Light Control (TLC) system as a measurement/assessment of self-perceived eczema control.
METHODS: This is a prospectively study of all Chinese children (aged 6 to 18 years old) with eczema attending the paediatric dermatology clinic of a tertiary hospital from Jan to June 2020. Eczema control, eczema severity, quality of life and biophysical skin condition of consecutive patients at the paediatric dermatology clinic of a teaching hospital were evaluated with the validated Chinese versions of Depressive, Anxiety, Stress Scales (DASS-21), Patient Oriented Eczema Measure (POEM), transepidermal water loss (TEWL), and stratum corneum skin hydration (SH), respectively. With a visual TLC analogy, patients were asked if their eczema is under control (green light), worsening (yellow) or in flare-up (red light).
RESULTS: Among AE patients (n = 36), self-perceived TLC as green (under control), amber (worsening) and red (flare up) reflected acute and chronic severity (SCORAD, NESS, POEM) and quality of life (CDLQI) (p< 0.0001), but not SH, TEWL or Depression, anxiety and stress.
CONCLUSIONS: Eczema control can be semi-quantified with a child-friendly TLC self-assessment system. AE patients reporting worse eczema control have worse acute and chronic eczema severity, more impairment of quality of life; but not the psychologic symptoms of depression, anxiety and stress or skin hydration or transepidermal water loss. TLC can be linked to an eczema action plan to guide patient management.
DATA SOURCE: Six major databases were searched from inception till June 2015: MEDLINE, CINAHL, EMBASE, PsychInfo, SPORTDiscus, and Cochrane Center Register of Controlled Trials.
STUDY APPRAISAL AND SYNTHESIS METHODS: Two reviewers independently rated methodological quality using the modified Downs and Black Scale and extracted and synthesized key findings (i.e., participant characteristics, study design, physical function and fitness outcomes, and adverse events).
RESULTS: Eight of 276 studies met the inclusion criteria, of which none showed high research quality. Four studies assessed physical function outcomes and 4 studies evaluated aerobic fitness as outcome measures. Significant improvements on these 2 outcomes were generally found. Other physical or fitness outcomes including body composition, muscular strength, and balance were rarely reported.
CONCLUSIONS AND IMPLICATIONS OF KEY FINDINGS: There is weak evidence supporting aquatic exercise training to improve physical function and aerobic fitness among adults with spinal cord injury. Suggestions for future research include reporting details of exercise interventions, evaluating other physical or fitness outcomes, and improving methodological quality.
MATERIALS AND METHODS: We propose a mixed-method study of mental health assessment that combines psychological questionnaires with facial emotion analysis to comprehensively evaluate the mental health of students on a large scale. The Depression Anxiety and Stress Scale-21(DASS-21) is used for the psychological questionnaire. The facial emotion recognition model is implemented by transfer learning based on neural networks, and the model is pre-trained using FER2013 and CFEE datasets. Among them, the FER2013 dataset consists of 48 × 48-pixel face gray images, a total of 35,887 face images. The CFEE dataset contains 950,000 facial images with annotated action units (au). Using a random sampling strategy, we sent online questionnaires to 400 college students and received 374 responses, and the response rate was 93.5%. After pre-processing, 350 results were available, including 187 male and 153 female students. First, the facial emotion data of students were collected in an online questionnaire test. Then, a pre-trained model was used for emotion recognition. Finally, the online psychological questionnaire scores and the facial emotion recognition model scores were collated to give a comprehensive psychological evaluation score.
RESULTS: The experimental results of the facial emotion recognition model proposed to show that its classification results are broadly consistent with the mental health survey results. This model can be used to improve efficiency. In particular, the accuracy of the facial emotion recognition model proposed in this paper is higher than that of the general mental health model, which only uses the traditional single questionnaire. Furthermore, the absolute errors of this study in the three symptoms of depression, anxiety, and stress are lower than other mental health survey results and are only 0.8%, 8.1%, 3.5%, and 1.8%, respectively.
CONCLUSION: The mixed method combining intelligent methods and scales for mental health assessment has high recognition accuracy. Therefore, it can support efficient large-scale screening of students' psychological problems.