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To investigate the reciprocal relationship between unhealthy eating behaviours and depressive symptoms from childhood to adolescence.
Unhealthy eating behaviours were measured by the frequencies of eating foods with excess salt, sugar or fat in the past week. Depressive symptoms in the past two weeks were measured using a seven-item scale. Hierarchical linear growth models were used to analyse longitudinal associations between unhealthy eating behaviours and depressive symptoms. Time-fixed variables (sex, parents’ education level and household monthly income) and time-varying variables (parents’ marital status, family activities, body weight, vegetable or fruit consumption, exercising and smoking) were controlled for.
The Child and Adolescent Behaviors in Long-Term Evolution study, which commenced in 2001 and has annual follow-up.
Students (n 2630) followed from 2nd grade (8 years old in 2002) to 11th grade.
The frequency of unhealthy eating behaviours in the previous year and the difference between the frequency in the previous and successive year were positively associated with the initiation and growth rate of depressive symptoms. Depressive symptoms in the previous year and the difference in depressive symptoms between the previous and successive year were positively associated with the initial state and growth rate of unhealthy eating behaviours.
Our results suggest a reciprocal relationship between depressive symptoms and unhealthy eating behaviours. This relationship should be considered when developing programmes targeting depressive symptoms and unhealthy diet in children and adolescents.
This work proposes a query-by-singing (QBS) content-based music retrieval (CBMR) system that uses Approximate Karbunen–Loeve transform for noise reduction. The proposed QBS-CBMR system uses a music clip as a search key. First, a 51-dimensional matrix containing 39-Mel-frequency cepstral coefficients (MFCCs) features and 12-Chroma features are extracted from an input music clip. Next, adapted symbolic aggregate approximation (adapted SAX) is used to transform each dimension of features into a symbolic sequence. Each symbolic sequence corresponding to each dimension of MFCCs is then converted into a structure called advanced fast pattern index (AFPI) tree. The similarity between the query music clip and the songs in the database is evaluated by calculating a partial score for each AFPI tree. The final score is obtained by calculating the weighted sum of all partial scores, where the weighting of each partial score is determined by its entropy. Experimental results show that the proposed music retrieval system performs robustly and accurately with the entropy weighting mechanism.
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