MoodSensing: A smartphone app for digital phenotyping and assessment of bipolar disorder.

BACKGROUND: Daily life tracking has proven to be of great help in the assessment of patients with bipolar disorder. Although there are many smartphone apps for tracking bipolar disorder, most of them lack academic verification, privacy policy and long-term maintenance. METHODS: Our developed app, MoodSensing, aims to collect users' digital phenotyping for assessment of bipolar disorder. The data collection was approved by the Institutional Review Board. This study collaborated with professional clinicians to ensure that the app meets both clinical needs and user experience requirements. Based on the collected digital phenotyping, deep learning techniques were applied to forecast participants' weekly HAM-D and YMRS scale scores. RESULTS: In experiments, the data collected by our app can effectively predict the scale scores, reaching the mean absolute error of 0.84 and 0.22 on the scales. The statistical data also demonstrate the increase in user engagement. CONCLUSIONS: Our analysis reveals that the developed MoodSensing app can not only provide a good user experience, but also the recorded data have certain discriminability for clinical assessment. Our app also provides relevant policies to protect user privacy, and has been launched in the Apple Store and Google Play Store.

Systematic designs of single metal compounds synthesized using ammonia fluoride-based complex as structure directing agents for energy storage.

Tailoring morphology and composition of metal organic frameworks (MOF) can improve energy storage by establishing high surface area, large porosity and multiple redox states. Structure directing agents (SDA) is functional of designing surface properties of electroactive materials. Ammonium fluoride has functional abilities for designing MOF derivatives with excellent energy storage abilities. Systematic design of MOF derivatives using ammonia fluoride-based complex as SDA can essentially create efficient electroactive materials. Metal species can also play significant roles on redox reactions, which are the main energy storage mechanism for battery-type electrodes. In this work, 2-methylimidazole, two novel SDAs of NH4BF4 and NH4HF2, and six metal species of Al, Mn, Co, Ni, Cu and Zn are coupled to synthesize MOF derivatives for energy storage. Metal species-dependent compositions including hydroxides, oxides, and hydroxide nitrates are observed. The nickel-based derivative (Ni-HBF) shows the highest specific capacitance (CF) of 698.0F/g at 20 mV/s, due to multiple redox states and advanced flower-like surface properties. The diffusion and capacitive-control contributions of MOF derivatives are also analyzed. The battery supercapacitor hybrid with Ni-HBF electrode shows a maximum energy density of 27.9 Wh/kg at 325 W/kg. The CF retention of 170.9% and Coulombic efficiency of 93.2% are achieved after 10,000 cycles.

Ligand Incorporating Sequence-dependent ZIF67 Derivatives as Active Material of Supercapacitor: Competition between Ammonia Fluoride and 2-Methylimidazole.

The zeolitic imidazolate framework 67 (ZIF67) derivative is a potential active material of supercapacitors (SC), owing to high specific surface area and porosity and possible formation of cobalt compounds. A novel ZIF67 derivative is synthesized using a one-step solution process with cobalt precursor 2-methylimidazole (2-Melm) and ammonia fluoride in our previous work. Due to its facile synthesis and excellent electrocapacitive behavior, it is crucial to understand the competition between ammonia fluoride and 2-Melm on forming derivatives with cobalt ions and to create more efficient ZIF67 derivatives for charge storage. In this work, several ZIF67 derivatives are designed using a one-step solution process with 2-Melm and ammonia fluoride incorporated in different sequences. The reaction durations for a single ligand and two ligands are controlled. The largest capacity of 176.33 mAh/g corresponding to the specific capacitance of 1057.99 F/g is achieved for the ZIF67 derivative electrode prepared by reacting ammonia fluoride and a cobalt precursor for 0.5 h and then incorporating 2-Melm for another 23.5 h of reaction (NM0.5). This derivative composed of highly conductive CoF2, NiF2, Co(OH)F, and Ni(OH)F presents high specific surface area and porosity. The relevant SC presents a maximum energy density of 19.5 Wh/kg at 430 W/kg, a capacity retention of 92%, and Coulombic efficiency of 96% in 10000 cycles.

Associations among smartphone app-based measurements of mood, sleep and activity in bipolar disorder.

The recent popularization of smart technology presents new opportunities for continual, digital-monitoring of patient status. In this project, we used a smartphone app to track the mood, sleep, and activity levels of 159 outpatients with bipolar disorder (BD). The participants were asked to report their daily wake/sleep time and emotional status in the app, while daily activity data were automatically collected via GPS. We performed repeated-measures correlation analysis to examine possible correlations between the readouts. Mood, sleep and activity levels all showed intra-variable correlations with readings on the next day, in the next week, and in the next month. Furthermore, mood and sleep at the reference time were positively correlated with activity in subsequent weeks or months, and activity was positively correlated with mood and sleep in the same time ranges. Thus, our results were in line with previous studies, showing that mood, sleep, and activity levels are interdependent in patients with BD. With the association between mood on future activity level was most significant, and the correlations between each readout and the others were dependent on time frame. Our findings suggest our smartphone app has potential to provide an informative and reliable means for real-time tracking of BD status.

[Interdisciplinary Application of Smart Technology in Psychiatric Care].

The popularization of smart technology is a global phenomenon. The increasing ubiquity of smartphones offers the potential to apply smart technology in areas such as healthcare and behavioral change interventions. Mobile health services may enhance the effectiveness and resolve the shortcomings of traditional medical services, which cannot continuously and instantly track changes in disease symptoms. The popularity of mobile phones has led to the emergence of mobile health applications. Mobile health applications use active and passive methods to collect data and transmit information. Studies have confirmed the feasibility and acceptance of these applications in assessing and detecting diseases and in mental health interventions. In this article, the limitations of traditional psychiatric medical diagnosis and the opportunity to develop mobile health using information and communication technology are discussed, and related empirical research on using smart technology to evaluate and detect symptoms is explored using the example of bipolar disorders. In addition, the benefits and future prospects of onset alert and the development of healthcare models for action are highlighted. In the future, we look forward to developing mobile health applications that meet the needs of healthcare in Taiwan. Furthermore, we recommend more research and investment in related fields to accumulate more extensive empirical evidence.

Cell-Coupled Long Short-Term Memory With L -Skip Fusion Mechanism for Mood Disorder Detection Through Elicited Audiovisual Features.

In early stages, patients with bipolar disorder are often diagnosed as having unipolar depression in mood disorder diagnosis. Because the long-term monitoring is limited by the delayed detection of mood disorder, an accurate and one-time diagnosis is desirable to avoid delay in appropriate treatment due to misdiagnosis. In this paper, an elicitation-based approach is proposed for realizing a one-time diagnosis by using responses elicited from patients by having them watch six emotion-eliciting videos. After watching each video clip, the conversations, including patient facial expressions and speech responses, between the participant and the clinician conducting the interview were recorded. Next, the hierarchical spectral clustering algorithm was employed to adapt the facial expression and speech response features by using the extended Cohn-Kanade and eNTERFACE databases. A denoizing autoencoder was further applied to extract the bottleneck features of the adapted data. Then, the facial and speech bottleneck features were input into support vector machines to obtain speech emotion profiles (EPs) and the modulation spectrum (MS) of the facial action unit sequence for each elicited response. Finally, a cell-coupled long short-term memory (LSTM) network with an L -skip fusion mechanism was proposed to model the temporal information of all elicited responses and to loosely fuse the EPs and the MS for conducting mood disorder detection. The experimental results revealed that the cell-coupled LSTM with the L -skip fusion mechanism has promising advantages and efficacy for mood disorder detection.

Low-temperature preparation and characterization of iron-ion doped titania thin films.

Iron-ion doped titania thin films with an anatase phase were successfully synthesized in this study using the high-pressure crystallization (HPC) process. The crystallization temperature of Fe(3+)-doped TiO(2) thin films was markedly reduced to be as low as 125 degrees C. The films prepared via the HPC process have a more uniform microstructure and smaller grain sizes than the films prepared via the atmospheric-pressure annealing process. The films prepared via both processes were found to have photocatalytic properties under visible light. The films prepared via the HPC process exhibited enhanced photocatalytic activities in comparison with the films annealed via the conventional process. Increasing the annealing temperature in the HPC process resulted in an improvement in the photocatalytic properties because of an increase in the crystallinity of the prepared films. The HPC process was demonstrated to be a potential method for synthesizing visible-light driven titania thin films with enhanced photocatalytic activities at low temperatures.

Psychiatric consultation record retrieval using scenario-based representation and multilevel mixture model.

Psychiatric consultation record retrieval attempts to help people to efficiently and effectively locate the consultation records relevant to their depressive problems. Consultation records can also make people aware that they are not alone, because many individuals have suffered from the same or similar problems. Additionally, people can understand how to alleviate their depressive symptoms according to recommendations from health professionals. To achieve this goal, this paper proposes the use of a scenario-based representation, i.e., a symptom-based structural representation, to capture the depressive symptoms and their semantic relations, such as cause-effect and temporal relations, for understanding users' queries clearly. The symptoms and relations are identified from semantic mining and analysis of consultation records. The multilevel mixture model is adopted to estimate the relevance of queries and consultation records based on the structural information. Experimental results show that the proposed approach achieves higher precision than does a term-based flat representation. An experiment is also conducted to examine the effect of error propagation resulting from incorrect identification of symptoms and relations. Experimental results demonstrate that combining different approaches can improve the retrieval robustness.

Joint optimization of word alignment and epenthesis generation for Chinese to Taiwanese sign synthesis.

This work proposes a novel approach to translate Chinese to Taiwanese sign language and to synthesize sign videos. An aligned bilingual corpus of Chinese and Taiwanese Sign Language (TSL) with linguistic and signing information is also presented for sign language translation. A two-pass alignment in syntax level and phrase level is developed to obtain the optimal alignment between Chinese sentences and Taiwanese sign sequences. For sign video synthesis, a scoring function is presented to develop motion transition-balanced sign videos with rich combinations of intersign transitions. Finally, the maximum a posteriori (MAP) algorithm is employed for sign video synthesis based on joint optimization of two-pass word alignment and intersign epenthesis generation. Several experiments are conducted in an educational environment to evaluate the performance on the comprehension of sign expression. The proposed approach outperforms the IBM Model 2 in sign language translation. Moreover, deaf students perceived sign videos generated by the proposed method to be satisfactory.

Text generation from Taiwanese Sign Language using a PST-based language model for augmentative communication.

This paper proposes a novel approach to the generation of Chinese sentences from ill-formed Taiwanese Sign Language (TSL) for people with hearing impairments. First, a sign icon-based virtual keyboard is constructed to provide a visualized interface to retrieve sign icons from a sign database. A proposed language model (LM), based on a predictive sentence template (PST) tree, integrates a statistical variable n-gram LM and linguistic constraints to deal with the translation problem from ill-formed sign sequences to grammatical written sentences. The PST tree trained by a corpus collected from the deaf schools was used to model the correspondence between signed and written Chinese. In addition, a set of phrase formation rules, based on trigger pair category, was derived for sentence pattern expansion. These approaches improved the efficiency of text generation and the accuracy of word prediction and, therefore, improved the input rate. For the assessment of practical communication aids, a reading-comprehension training program with ten profoundly deaf students was undertaken in a deaf school in Tainan, Taiwan. Evaluation results show that the literacy aptitude test and subjective satisfactory level are significantly improved.

Error-tolerant sign retrieval using visual features and maximum a posteriori estimation.

This paper proposes an efficient error-tolerant approach to retrieving sign words from a Taiwanese Sign Language (TSL) database. This database is tagged with visual gesture features and organized as a multilist code tree. These features are defined in terms of the visual characteristics of sign gestures by which they are indexed for sign retrieval and displayed using an anthropomorphic interface. The maximum a posteriori estimation is exploited to retrieve the most likely sign word given the input feature sequence. An error-tolerant mechanism based on mutual information criterion is proposed to retrieve a sign word of interest efficiently and robustly. A user-friendly anthropomorphic interface is also developed to assist learning TSL. Several experiments were performed in an educational environment to investigate the system's retrieval accuracy. Our proposed approach outperformed a dynamic programming algorithm in its task and shows tolerance to user input errors.