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It remains unclear which individuals with subthreshold depression benefit most from psychological intervention, and what long-term effects this has on symptom deterioration, response and remission.
Aims
To synthesise psychological intervention benefits in adults with subthreshold depression up to 2 years, and explore participant-level effect-modifiers.
Method
Randomised trials comparing psychological intervention with inactive control were identified via systematic search. Authors were contacted to obtain individual participant data (IPD), analysed using Bayesian one-stage meta-analysis. Treatment–covariate interactions were added to examine moderators. Hierarchical-additive models were used to explore treatment benefits conditional on baseline Patient Health Questionnaire 9 (PHQ-9) values.
Results
IPD of 10 671 individuals (50 studies) could be included. We found significant effects on depressive symptom severity up to 12 months (standardised mean-difference [s.m.d.] = −0.48 to −0.27). Effects could not be ascertained up to 24 months (s.m.d. = −0.18). Similar findings emerged for 50% symptom reduction (relative risk = 1.27–2.79), reliable improvement (relative risk = 1.38–3.17), deterioration (relative risk = 0.67–0.54) and close-to-symptom-free status (relative risk = 1.41–2.80). Among participant-level moderators, only initial depression and anxiety severity were highly credible (P > 0.99). Predicted treatment benefits decreased with lower symptom severity but remained minimally important even for very mild symptoms (s.m.d. = −0.33 for PHQ-9 = 5).
Conclusions
Psychological intervention reduces the symptom burden in individuals with subthreshold depression up to 1 year, and protects against symptom deterioration. Benefits up to 2 years are less certain. We find strong support for intervention in subthreshold depression, particularly with PHQ-9 scores ≥ 10. For very mild symptoms, scalable treatments could be an attractive option.
To assess influenza symptoms, adherence to mask use recommendations, absenteesm and presenteeism in acute care healthcare workers (HCWs) during influenza epidemics.
Methods:
The TransFLUas influenza transmission study in acute healthcare prospectively followed HCWs prospectively over 2 consecutive influenza seasons. Symptom diaries asking for respiratory symptoms and adherence with mask use recommendations were recorded on a daily basis, and study participants provided midturbinate nasal swabs for influenza testing.
Results:
In total, 152 HCWs (65.8% nurses and 13.2% physicians) were included: 89.1% of study participants reported at least 1 influenza symptom during their study season and 77.8% suffered from respiratory symptoms. Also, 28.3% of HCW missed at least 1 working day during the study period: 82.6% of these days were missed because of symptoms of influenza illness. Of all participating HCWs, 67.9% worked with symptoms of influenza infection on 8.8% of study days. On 0.3% of study days, symptomatic HCWs were shedding influenza virus while at work. Among HCWs with respiratory symptoms, 74.1% adhered to the policy to wear a mask at work on 59.1% of days with respiratory symptoms.
Conclusions:
Respiratory disease is frequent among HCWs and imposes a significant economic burden on hospitals due to the number of working days lost. Presenteesm with respiratory illness, including influenza, is also frequent and poses a risk for patients and staff.
Spanish–English bilinguals rarely code-switch in the perfect structure between the Spanish auxiliary haber (“to have”) and the participle (e.g., “Ella ha voted”; “She has voted”). However, they are somewhat likely to switch in the progressive structure between the Spanish auxiliary estar (“to be”) and the participle (“Ella está voting”; “She is voting”). This phenomenon is known as the “auxiliary phrase asymmetry”. One hypothesis as to why this occurs is that estar has more semantic weight as it also functions as an independent verb, whereas haber is almost exclusively used as an auxiliary verb. To test this hypothesis, we employed a connectionist model that produces spontaneous code-switches. Through simulation experiments, we showed that i) the asymmetry emerges in the model and that ii) the asymmetry disappears when using haber also as a main verb, which adds semantic weight. Therefore, the lack of semantic weight of haber may indeed cause the asymmetry.
The output of many healthy physiological systems displays fractal fluctuations with self-similar temporal structures. Altered fractal patterns are associated with pathological conditions. There is evidence that patients with bipolar disorder have altered daily behaviors.
Methods
To test whether fractal patterns in motor activity are altered in patients with bipolar disorder, we analyzed 2-week actigraphy data collected from 106 patients with bipolar disorder type I in a euthymic state, 73 unaffected siblings of patients, and 76 controls. To examine the link between fractal patterns and symptoms, we analyzed 180-day actigraphy and mood symptom data that were simultaneously collected from 14 patients.
Results
Compared to controls, patients showed excessive regularity in motor activity fluctuations at small time scales (<1.5 h) as quantified by a larger scaling exponent (α1 > 1), indicating a more rigid motor control system. α1 values of siblings were between those of patients and controls. Further examinations revealed that the group differences in α1 were only significant in females. Sex also affected the group differences in fractal patterns at larger time scales (>2 h) as quantified by scaling exponent α2. Specifically, female patients and siblings had a smaller α2 compared to female controls, indicating more random activity fluctuations; while male patients had a larger α2 compared to male controls. Interestingly, a higher weekly depression score was associated with a lower α1 in the subsequent week.
Conclusions
Our results show sex- and scale-dependent alterations in fractal activity regulation in patients with bipolar disorder. The mechanisms underlying the alterations are yet to be determined.
Current approaches to learning semantic representations of sentences often use prior word-level knowledge. The current study aims to leverage visual information in order to capture sentence level semantics without the need for word embeddings. We use a multimodal sentence encoder trained on a corpus of images with matching text captions to produce visually grounded sentence embeddings. Deep Neural Networks are trained to map the two modalities to a common embedding space such that for an image the corresponding caption can be retrieved and vice versa. We show that our model achieves results comparable to the current state of the art on two popular image-caption retrieval benchmark datasets: Microsoft Common Objects in Context (MSCOCO) and Flickr8k. We evaluate the semantic content of the resulting sentence embeddings using the data from the Semantic Textual Similarity (STS) benchmark task and show that the multimodal embeddings correlate well with human semantic similarity judgements. The system achieves state-of-the-art results on several of these benchmarks, which shows that a system trained solely on multimodal data, without assuming any word representations, is able to capture sentence level semantics. Importantly, this result shows that we do not need prior knowledge of lexical level semantics in order to model sentence level semantics. These findings demonstrate the importance of visual information in semantics.
Prior language input is not lost but integrated with the current input. This principle is demonstrated by “reservoir computing”: Untrained recurrent neural networks project input sequences onto a random point in high-dimensional state space. Earlier inputs can be retrieved from this projection, albeit less reliably so as more input is received. The bottleneck is therefore not “Now-or-Never” but “Sooner-is-Better.”