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Background It is well established that lack of physical activity is detrimental to the overall health of an individual. Modern-day activity trackers enable individuals to monitor their daily activities to meet and maintain targets. This is expected to promote activity encouraging behavior, but the benefits of activity trackers attenuate over time due to waning adherence. One of the key approaches to improving adherence to goals is to motivate individuals to improve on their historic performance metrics. Objective The aim of this work was to build a machine learning model to predict an achievable weekly activity target by considering (1) patterns in the user’s activity tracker data in the previous week and (2) behavior and environment characteristics. By setting realistic goals, ones that are neither too easy nor too difficult to achieve, activity tracker users can be encouraged to continue to meet these goals, and at the same time, to find utility in their activity tracker. Methods We built a neural network model that prescribes a weekly activity target for an individual that can be realistically achieved. The inputs to the model were user-specific personal, social, and environmental factors, daily step count from the previous 7 days, and an entropy measure that characterized the pattern of daily step count. Data for training and evaluating the machine learning model were collected over a duration of 9 weeks. Results Of 30 individuals who were enrolled, data from 20 participants were used. The model predicted target daily count with a mean absolute error of 1545 (95% CI 1383-1706) steps for an 8-week period. Conclusions Artificial intelligence applied to physical activity data combined with behavioral data can be used to set personalized goals in accordance with the individual’s level of activity and thereby improve adherence to a fitness tracker; this could be used to increase engagement with activity trackers. A follow-up prospective study is ongoing to determine the performance of the engagement algorithm.

Background: This study aimed to compare the shear bond strength (SBS) of ceramic and metal brackets bonded to the enamel using Grengloo color-change adhesive and Transbond XT conventional composite. Materials and Methods: In this in vitro, experimental study, 120 extracted human premolars were divided into four groups: (1) Grengloo with metal brackets, (2) Grengloo with ceramic brackets, (3) Transbond XT with metal brackets, and (4) Transbond XT with ceramic brackets. After bracket bonding, the teeth were subjected to 500 thermal cycles and incubated at 37°C for 24 h. The SBS was measured and the data were analyzed using two-way ANOVA. Mann–Whitney U test was used to compare the Adhesive Remnant Index (ARI) scores. was statistically significant (P < 0.001) Results: The effect of type of bracket (metal/ceramic) on SBS was not statistically significant (P = 0.368). However, the effect of type of composite on SBS was statistically significant (P < 0.001) and the SBS of Grengloo was higher than that of Transbond XT. No significant difference was found in ARI scores among the groups. Conclusion: Regarding the high SBS and the safe region of bond failure in Grengloo composite, this color-change adhesive can be a suitable alternative in cases that require high bond strength. Furthermore, the color contrast of Grengloo can make composite removal easier than Transbond XT with color match of composite and teeth.

In this study, we evaluated the diagnostic accuracy of shear wave elastography (SWE) for differentiating high-risk non-alcoholic steatohepatitis (hrNASH) from non-alcoholic fatty liver and low-risk non-alcoholic steatohepatitis (NASH). Patients with non-alcoholic fatty liver disease scheduled for liver biopsy underwent pre-biopsy SWE. Ten SWE measurements were obtained. Biopsy samples were reviewed using the NASH Clinical Research Network Scoring System and patients with hrNASH were identified. Receiver operating characteristic curves for SWE-based hrNASH diagnosis were charted. One hundred sixteen adult patients underwent liver biopsy at our institution for the evaluation of non-alcoholic fatty liver disease. The area under the receiver operating characteristic curve of SWE for hrNASH diagnosis was 0.73 (95% confidence interval: 0.61-0.84, p < 0.001). The Youden index-based optimal stiffness cutoff value for hrNASH diagnosis was calculated as 8.4 kPa (1.67 m/s), with a sensitivity of 77% and specificity of 66%. SWE may be useful for the detection of NASH patients at risk of long-term liver-specific morbidity and mortality.

Background Total joint replacements are high-volume and high-cost procedures that should be monitored for cost and quality control. Models that can identify patients at high risk of readmission might help reduce costs by suggesting who should be enrolled in preventive care programs. Previous models for risk prediction have relied on structured data of patients rather than clinical notes in electronic health records (EHRs). The former approach requires manual feature extraction by domain experts, which may limit the applicability of these models. Objective This study aims to develop and evaluate a machine learning model for predicting the risk of 30-day readmission following knee and hip arthroplasty procedures. The input data for these models come from raw EHRs. We empirically demonstrate that unstructured free-text notes contain a reasonably predictive signal for this task. Methods We performed a retrospective analysis of data from 7174 patients at Partners Healthcare collected between 2006 and 2016. These data were split into train, validation, and test sets. These data sets were used to build, validate, and test models to predict unplanned readmission within 30 days of hospital discharge. The proposed models made predictions on the basis of clinical notes, obviating the need for performing manual feature extraction by domain and machine learning experts. The notes that served as model inputs were written by physicians, nurses, pathologists, and others who diagnose and treat patients and may have their own predictions, even if these are not recorded. Results The proposed models output readmission risk scores (propensities) for each patient. The best models (as selected on a development set) yielded an area under the receiver operating characteristic curve of 0.846 (95% CI 82.75-87.11) for hip and 0.822 (95% CI 80.94-86.22) for knee surgery, indicating reasonable discriminative ability. Conclusions Machine learning models can predict which patients are at a high risk of readmission within 30 days following hip and knee arthroplasty procedures on the basis of notes in EHRs with reasonable discriminative power. Following further validation and empirical demonstration that the models realize predictive performance above that which clinical judgment may provide, such models may be used to build an automated decision support tool to help caretakers identify at-risk patients.

Background It is well reported that tracking physical activity can lead to sustained exercise routines, which can decrease disease risk. However, most stop using trackers within a couple months of initial use. The reasons people stop using activity trackers can be varied and personal. Understanding the reasons for discontinued use could lead to greater acceptance of tracking and more regular exercise engagement. Objective The aim of this study was to determine the individualistic reasons for nonengagement with activity trackers. Methods Overweight and obese participants (n=30) were enrolled and allowed to choose an activity tracker of their choice to use for 9 weeks. Questionnaires were administered at the beginning and end of the study to collect data on their technology use, as well as social, physiological, and psychological attributes that may influence tracker use. Closeout interviews were also conducted to further identify individual influencers and attributes. In addition, daily steps were collected from the activity tracker. Results The results of the study indicate that participants typically valued the knowledge of their activity level the activity tracker provided, but it was not a sufficient motivator to overcome personal barriers to maintain or increase exercise engagement. Participants identified as extrinsically motivated were more influenced by wearing an activity tracker than those who were intrinsically motivated. During the study, participants who reported either owning multiple technology devices or knowing someone who used multiple devices were more likely to remain engaged with their activity tracker. Conclusions This study lays the foundation for developing a smart app that could promote individual engagement with activity trackers.

Introduction: Documentation of patient care in medical record formats is always emphasized. These documents are used as a means to go on treating the patients, staff in their own defense, assessment, care, any legal proceedings and medical science education. Therefore, in this study, each of the data elements available in patients’ records are important and filling them indicates the importance put by the documenting teams, so it has been dealt with the documentation the patient records in the hospitals of Mazandaran province. Method: This cross-sectional study aimed to review medical records in 16 hospitals of Mazandaran University of Medical Sciences (MazUMS). In order to collection data, a check list was prepared based on the data elements including four forms of the admission, summary, patients’ medical history and progress note. The data recording was defined as “Yes” with the value of 1, lack of recording was defined as “No” with the value of 2, and “Not applied” with the value of 0 for the cases in which the mentioned variable medical records are not applied. Results: The overall evaluation of the documentation was considered as 95-100% equal to “good”, 75-94% equal to “average” and below -75% equal to “poor”. Using the stratified random sample volume formula, 381 cases were reviewed. The data were analyzed by the SPSS version 19 and descriptive statistics. Results: The results showed that %62 of registration and all the four forms were in the “poor” category. There was no big difference in average registration among the hospitals. Among the educational groups Gynecology and Infectious were equal and had the highest average of documentation of %68. In the data categories, the highest documentation average belonged to the verification, %91. Conclusion: According to the overall assessment in which the rate of documentation was in the category “week”, we should make much more efforts to reach better conditions. Even if a data element is recognized meaningless, unnecessary and repetitive by the in charge of documentation, it should not be neglected and skipped. In order to solve the problems of these types, it is suggested to discuss the medical records forms and elements that seem unnecessary in the related committees.