Wearable devices have attracted much attention from the market in recent years for their fitness monitoring and other health-related metrics; however, the accuracy of fitness tracking results still plays a major role in health promotion. The aim of this study was to evaluate the accuracy of a host of latest wearable devices in measuring fitness-related indicators under various seminatural activities. A total of 44 healthy subjects were recruited, and each subject was asked to simultaneously wear 6 devices (Apple Watch 2, Samsung Gear S3, Jawbone Up3, Fitbit Surge, Huawei Talk Band B3, and Xiaomi Mi Band 2) and 2 smartphone apps (Dongdong and Ledongli) to measure five major health indicators (heart rate, number of steps, distance, energy consumption, and sleep duration) under various activity states (resting, walking, running, cycling, and sleeping), which were then compared with the gold standard (manual measurements of the heart rate, number of steps, distance, and sleep, and energy consumption through oxygen consumption) and calculated to determine their respective mean absolute percentage errors (MAPEs). Wearable devices had a rather high measurement accuracy with respect to heart rate, number of steps, distance, and sleep duration, with a MAPE of approximately 0.10, whereas poor measurement accuracy was observed for energy consumption (calories), indicated by a MAPE of up to 0.44. The measurements varied for the same indicator measured by different fitness trackers. The variation in measurement of the number of steps was the highest (Apple Watch 2: 0.42; Dongdong: 0.01), whereas it was the lowest for heart rate (Samsung Gear S3: 0.34; Xiaomi Mi Band 2: 0.12). Measurements differed insignificantly for the same indicator measured under different states of activity; the MAPE of distance and energy measurements were in the range of 0.08 to 0.17 and 0.41 to 0.48, respectively. Overall, the Samsung Gear S3 performed the best for the measurement of heart rate under the resting state (MAPE of 0.04), whereas Dongdong performed the best for the measurement of the number of steps under the walking state (MAPE of 0.01). Fitbit Surge performed the best for distance measurement under the cycling state (MAPE of 0.04), and Huawei Talk Band B3 performed the best for energy consumption measurement under the walking state (MAPE of 0.17). At present, mainstream devices are able to reliably measure heart rate, number of steps, distance, and sleep duration, which can be used as effective health evaluation indicators, but the measurement accuracy of energy consumption is still inadequate. Fitness trackers of different brands vary with regard to measurement of indicators and are all affected by the activity state, which indicates that manufacturers of fitness trackers need to improve their algorithms for different activity states.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: Vocabulary gap between consumers and professionals in the medical domain hinders information seeking and communication. Consumer health vocabularies (CHVs) have been developed to aid such informatics applications. This purpose is best served if the vocabulary evolves with consumers’ language. Objective: Our objective is to develop a method for identifying and adding new terms to CHV, so it can keep up with the constantly evolving medical knowledge and language use. Methods: A consumer health terms finding framework based on distributed word vector space model is proposed. We first learn word vectors from large-scale text corpus and then adopt a supervised method with existing CHVs for learning vector representation of words, which can provide additional supervised fine tuning after unsupervised word embedding learning. With a fine-tuned word vector space, we identify pairs of professional terms and their consumer variants by their semantic distance in the vector space. A subsequent manual review of the extracted and labeled pairs of entities was conducted to validate the results generated by the proposed approach. Results: The results are evaluated using mean reciprocal rank (MRR). After manual evaluation, results show that it is feasible to identify alternative medical concepts by using professional or consumer concepts as queries in the word vector space without fine tuning, and the results are more promising in final fine-tuned word vector space. The MRR values indicate that on average, a professional or consumer concept is about 14th closest to its counterpart in word vector space without fine tuning, and the MMR in final fine-tuned word vector space is 8. Furthermore, the results demonstrate that our method is able to collect abbreviations and common typos frequently used by consumers. Conclusions: By integrating a large amount of text information and existing CHVs, our method outperformed several baseline ranking methods and is effective for generating a list of candidate terms for human review during CHV development.

The role of microbes in diseases, especially cancer, has garnered significant attention. However, research on the oral microbiota in oral potentially malignant disorders (OPMDs) remains limited. Our study investigates microbial communities in OPMDs. Oral biopsies from19 oral leukoplakia (OLK) patients, 19 proliferative verrucous leukoplakia (PVL) patients, 19 oral lichen planus (OLP) patients, and 19 oral lichenoid lesions (OLL) patients were obtained. 15 SCC specimens were also collected from PVL patients. Healthy individuals served as controls, and DNA was extracted from their paraffin-embedded tissues. 2bRAD-M sequencing generated taxonomic profiles. Alpha and beta diversity analyses, along with Linear Discriminant Analysis effect size analysis, were conducted. Our results showed the microbial richness and diversity were significantly different among groups, with PVL-SCC resembling controls, while OLK exhibited the highest richness. Each disease group displayed unique microbial compositions, with distinct dominant bacterial species. Noteworthy alterations during PVL-SCC progression included a decline in Fusobacterium periodonticum and an elevation in Prevotella oris. Different disease groups exhibited distinct dominant bacterial species and microbial compositions. These findings offer promise in elucidating the underlying mechanisms of this disease.

A thick periodontal phenotype with thick gingiva and alveolar bone volume is required for safe orthodontic tooth movement and long-term stability. A high incidence of dehiscence and fenestration in the labial aspect of mandibular anterior teeth may limit the correction of deformity and orthodontic treatment, especially when the lower anterior teeth are needed to have a large range of movement. This study reports a combination of periodontal therapy and orthodontic therapy with periodontal corticotomy regenerative surgery (PCRS) in a 25-year-old patient suffering from skeletal Class II malocclusion and periodontitis. The patient received periodontal therapy 5 years ago and commenced orthodontic treatment 4.5 years ago. During the 4 years of follow-up for PCRS, the clinical and radiographic evaluations revealed significant improvements in the periodontal phenotype of the mandibular anterior region. The periodontal phenotypes in the mandibular incisors region were all modified from thin to thick. Supplementing orthodontic treatment with labial PCRS could be a promising treatment strategy to maintain long-term periodontal health in adult patients with alveolar deficiency and thin gingiva tissue.

Saliva blood mixed liquid (SBML) appears in oral surgery, such as scaling and root planning, and it affects surgical vision and causes discomfort to the patient. However, removing SBML, i.e. frequent aspiration of the mixed liquid, is a routine task involving heavy workload and interruption of oral surgery. Therefore, it is valuable to alternate the manual mode by autonomous robotic technique. The robotic system is designed consisting of an RGB-D camera, a manipulator, a disposable oral aspirator. An algorithm is developed for detection of SBML. Path planning method is also addressed for the distal end of the aspirator. A workflow for removing SBML is presented. 95% of the area of the SBML in the oral cavity was removed after liquid aspiration among a group of ten SBML aspiration experiments. This study provides the first result of the autonomous aspirating robot (AAR) for removing SBML in oral surgery, demonstrating that SBML can be removed by the autonomous robot, freeing stomatology surgeon from tedious work.