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Numerous diabetes-management systems and programs for improving glycemic control to meet guideline targets have been proposed, using IT technology. But all of them allow only limited-or no-real-time interaction between patients and the system in terms of system response to patient input; few studies have effectively assessed the systems' usability and feasibility to determine how well patients understand and can adopt the technology involved. DialBetics is composed of 4 modules: (1) data transmission module, (2) evaluation module, (3) communication module, and (4) dietary evaluation module. A 3-month randomized study was designed to assess the safety and usability of a remote health-data monitoring system, and especially its impact on modifying patient lifestyles to improve diabetes self-management and, thus, clinical outcomes. Fifty-four type 2 diabetes patients were randomly divided into 2 groups, 27 in the DialBetics group and 27 in the non-DialBetics control group. HbA1c and fasting blood sugar (FBS) values declined significantly in the DialBetics group: HbA1c decreased an average of 0.4% (from 7.1 ± 1.0% to 6.7 ± 0.7%) compared with an average increase of 0.1% in the non-DialBetics group (from 7.0 ± 0.9% to 7.1 ± 1.1%) (P = .015); The DialBetics group FBS decreased an average of 5.5 mg/dl compared with a non-DialBetics group average increase of 16.9 mg/dl (P = .019). BMI improvement-although not statistically significant because of the small sample size-was greater in the DialBetics group. DialBetics was shown to be a feasible and an effective tool for improving HbA1c by providing patients with real-time support based on their measurements and inputs.

BackgroundThe recent rise in popularity and scale of social networking services (SNSs) has resulted in an increasing need for SNS-based information extraction systems. A popular application of SNS data is health surveillance for predicting an outbreak of epidemics by detecting diseases from text messages posted on SNS platforms. Such applications share the following logic: they incorporate SNS users as social sensors. These social sensor–based approaches also share a common problem: SNS-based surveillance are much more reliable if sufficient numbers of users are active, and small or inactive populations produce inconsistent results.ObjectiveThis study proposes a novel approach to estimate the trend of patient numbers using indirect information covering both urban areas and rural areas within the posts.MethodsWe presented a TRAP model by embedding both direct information and indirect information. A collection of tweets spanning 3 years (7 million influenza-related tweets in Japanese) was used to evaluate the model. Both direct information and indirect information that mention other places were used. As indirect information is less reliable (too noisy or too old) than direct information, the indirect information data were not used directly and were considered as inhibiting direct information. For example, when indirect information appeared often, it was considered as signifying that everyone already had a known disease, leading to a small amount of direct information.ResultsThe estimation performance of our approach was evaluated using the correlation coefficient between the number of influenza cases as the gold standard values and the estimated values by the proposed models. The results revealed that the baseline model (BASELINE+NLP) shows .36 and that the proposed model (TRAP+NLP) improved the accuracy (.70, +.34 points).ConclusionsThe proposed approach by which the indirect information inhibits direct information exhibited improved estimation performance not only in rural cities but also in urban cities, which demonstrated the effectiveness of the proposed method consisting of a TRAP model and natural language processing (NLP) classification.

Little is known about the relationship between mild cognitive impairment (MCI) and changes to language abilities. Here, we used the revised Hasegawa Dementia Scale (HDS-R) to identify suspected MCI in elderly individuals. We then analyzed written and spoken narratives to compare the language abilities between study participants with and without MCI in order to explore the relationship between cognitive and language abilities, and to identify a possible indicator for the early detection of MCI and dementia. We recruited 22 people aged 74 to 86 years (mean: 78.32 years; standard deviation: 3.36). The participants were requested to write and talk about one of the happiest events in their lives. Based on HDS-R scores, we divided the participants into 2 groups: the MCI Group comprised 8 participants with a score of 26 or lower, while the Healthy Group comprised 14 participants with a score of 27 or higher. The transcriptions of both written and spoken samples for each participant were used in the measurement of NLP-based language ability scores. Our analysis showed no significant differences in writing abilities between the 2 groups in any of the language ability scores. However, analysis of the spoken narrative showed that the MCI Group had a significantly larger vocabulary size. In addition, analysis of a metric that signified the gap in content between the spoken and written narratives also revealed a larger vocabulary size in the MCI Group. Individuals with early-stage MCI may be engaging in behavior to conceal their deteriorating cognition, thereby leading to a temporary increase in their active spoken vocabulary. These results indicate the possible detection of early stages of reduced cognition before dementia onset through the analysis of spoken narratives.

We propose a data-to-text generation model with two modules, one for tracking and the other for text generation. Our tracking module selects and keeps track of salient information and memorizes which record has been mentioned. Our generation module generates a summary conditioned on the state of tracking module. Our model is considered to simulate the human-like writing process that gradually selects the information by determining the intermediate variables while writing the summary. In addition, we also explore the effectiveness of the writer information for generation. Experimental results show that our model outperforms existing models in all evaluation metrics even without writer information. Incorporating writer information further improves the performance, contributing to content planning and surface realization.