Smartphone Application Research Articles

Perceptions of the 2D short animated videos for literacy against chronic diseases among adults with diabetes and/or hypertension: a qualitative study in primary care clinics.

Animation has promise for teaching complex health content through smartphone applications. However, smartphones have had limited use in Thailand for health literacy improvement among adults with chronic diseases. This study aims to explore the perceptions of adults with diabetes and/or hypertension resulting from 2D short animated videos for literacy against chronic disease that are available via smartphones. Four animated videos were initially developed based on clinical practice guidelines and nursing experience. Physicians, an expert in health education, and an animation team developed and revised scripts and storyboards of the animated videos. Lastly, videos were validated by physicians and health educators for content breadth and depth and by media experts for motion graphics and illustration. Each video presents a different situation in diabetes, hypertension, missing appointments, and obesity, ranging from 2.18 to 4.14min in duration. The inclusion criteria were adults 35-70 years old with diabetes or hypertension who received care from primary care units. Thematic analysis was performed on the extracted data. Twenty participants with a mean age of 58.4 years (SD 6.7; ranged 41-68) were focus-group interviewed. Three animation elements were assessed, including presentation, impacts, and suggestions. Participants had positive responses regarding the presentation (suitability of images and smooth motion graphics, short length of videos, simple language, understandable content, and clear sound) and impacts (recalling information, enhancing engagement, and motivating health awareness and behavior change). Suggested improvements were for a bigger font size for subtitles and considering the use of spoken text instead of on-screen text for recommendations at the end of the animations. Animated videos are acceptable for delivering health information. Pilot testing animated videos for promoting literacy against chronic diseases in adults with diabetes and hypertension is needed for optimal utility.

Smart Phone Application Reminder for Patients with Fixed Orthodontic Appliance to Improve Oral Hygiene

INTRODUCTION: In light of the extensive use of mobile phones and their associated software applications (Apps), as well as the undisputed role of reminders and Apps in behavioral interventions, the goal of this study was to evaluate the usefulness of an application for smartphones (Brush DJ) to enhance oral hygiene compliance in individuals with fixed orthodontic therapy. MATERIALS AND METHODS: In this prospective study, 82 patients between the ages of 18 and 22 were divided into four groups, two of which (the experimental group and the control group) had just begun fixed orthodontic treatment (called New) and the other two (the experimental group and the control group) were have passed fifth months from the beginning of the treatment (called Old). Patients in the intervention group were asked to use the Brush DJ smartphone App after getting traditional oral hygiene training, compared to patients in the control group who got conventional oral hygiene instruction. At baseline (T0), 4 weeks, 8 weeks, and 12 weeks following the start of the study, the plaque index (PI) and marginal bleeding index (MBI) were measured. Only patients with the software completed a questionnaire to determine how frequently and how long they brushed their teeth each day. RESULTS: Both New and Old orthodontic cases in the intervention groups showed improvements in PI, whereas the parameter of bleeding showed no significant differences. During the follow-up period, application usage was associated with a higher brushing frequency and duration. CONCLUSION: Apps might significantly improve the oral hygiene compliance of orthodontic patients by acting as reminders and motivators.

Sleep prediction using data from oximeter, accelerometer and snoring for portable monitor obstructive sleep apnea diagnosis

The aim of this study was to build and validate an artificial neural network (ANN) algorithm to predict sleep using data from a portable monitor (Biologix system) consisting of a high-resolution oximeter with built-in accelerometer plus smartphone application with snoring recording and cloud analysis. A total of 268 patients with suspected obstructive sleep apnea (OSA) were submitted to standard polysomnography (PSG) with simultaneous Biologix (age: 56±11\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$56\\pm 11$$\\end{document} years; body mass index: 30.9±4.6\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$30.9\\pm 4.6$$\\end{document}kg/m2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\hbox {kg/m}^{2}$$\\end{document}, apnea-hypopnea index [AHI]: 35±30\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$35\\pm 30$$\\end{document} events/h). Biologix channels were input features for construction an ANN model to predict sleep. A k-fold cross-validation method (k=10) was applied, ensuring that all sleep studies (N=268; 246,265 epochs) were included in both training and testing across all iterations. The final ANN model, evaluated as the mean performance across all folds, resulted in a sensitivity, specificity and accuracy of 91.5%, 71.0% and 86.1%, respectively, for detecting sleep. As compared to the oxygen desaturation index (ODI) from Biologix without sleep prediction, the bias (mean difference) between PSG-AHI and Biologix-ODI with sleep prediction (Biologix-Sleep-ODI) decreased significantly (3.40 vs. 1.02 events/h, p<0.001). We conclude that sleep prediction by an ANN model using data from oximeter, accelerometer, and snoring is accurate and improves Biologix system OSA diagnostic precision.

Distractor filtering and task load in working memory training in healthy older adults.

Filtering efficiency (FE) has been suggested as a task-related process of working memory (WM) in older adults, but the interaction of distractors with WM when implemented as a training to improve WM capacity is unclear. To investigate the effect of manipulations of WM and/or FE load in a multicomponent model-based WM training in improving WM capacity. 205 healthy older adults (129 women; aged 64.0 ± 8.3 years) were randomised into three WM training groups with manipulations of WM and FE load (high WM; low FE load, high WM; high FE load and low WM; high FE load). All groups underwent three online cognitive testing sessions and twelve 40-min training sessions over three weeks using the SmartBrain smartphone application. WM capacity was measured with complex span tasks and FE using the Change Detection Task. Linear mixed-effect models adjusted for age, sex, education and pre-baseline performance showed increased WM capacity post-training, but no difference between groups. While both distractors and WM are needed to improve WM in the absence of distraction, manipulations of the FE and WM load do not influence improvements following training.

Analysis of the Validity and Reliability of the Photo Finish® Smartphone App to Measure Sprint Time

In athletic training and research, the evaluation of sprint speed is widely used, and its accurate measurement is especially demanding. High-cost photocells are the gold-standard system for sprint time assessment, although low-cost smartphone applications can be a suitable option. This study assesses the validity and reliability of an application to measure sprint time compared to photocells. Five physically active subjects completed six sprints of 10 m and 20 m at maximal speed and a 5 m go and return sprint to evaluate the validity of the Photo Finish® app (Version 2.30). To assess reliability, six trials of 5 m go and return sprints were measured by two smartphones. The validity results showed a mean bias of 0.012 s (95% CL: 0.000, 0.024) between the application and the photocells for the 10 m sprint, 0.007 s (95% CL: −0.007, 0.022) for the 20 m sprint and 0.005 s (95% CL: −0.005, 0.017) for the 5 m go and return test. The results also found R2 between both systems (R2= 0.9863, 0.990 and 0.958) for each distance (10 m, 20 m and 5 m go and return, respectively). As for reliability, the application showed outstanding consistency between two smartphones operating simultaneously (ICC 0.999; R2: 0.999). This study shows that the Photo Finish® app is an accurate and reliable tool to measure sprint time with an error of 0.09 s.

Advancements in automated external and wearable cardiac defibrillators.

Survival statistics for out-of-hospital cardiac arrests remain unsatisfactory. Prompt defibrillation of shockable rhythms, such as ventricular fibrillation and pulseless ventricular tachycardia, is crucial for improving survival. Automated external defibrillators (AEDs) and wearable cardiac defibrillators (WCDs) seek to improve the survival rates following out-of-hospital cardiac arrests. We aim to review the indications, utility, advancements, and limitations of AEDs and WCDs, as well as their role in contemporary and future clinical practice. Recent advancements in these technologies, such as smartphone applications and drone delivery of AEDs and less inappropriate shocks and decreased size of WCDs, have increased their ubiquity and efficacy. However, implementation of this technology remains limited due to lack of resources and suboptimal patient adherence. Out of hospital cardiac arrests continue to pose a significant public health challenge. Advancements in AEDs and WCDs aim to facilitate prompt defibrillation of shockable rhythms with the goal of improving survival rates. However, they remain underutilized due to limited resources and suboptimal patient adherence. As these technologies continue to evolve to become smaller, lighter and more affordable, their utilization and accessibility are expected to improve.

Rice Leaf Diseases Classification Using Deep Learning Algorithms for Smartphone Application Development: An Empirical Study

Rice is the most widely consumed grain across the world. The rice plants often suffer from diseases. Early detection of such diseases and adopting remedial measures can help the farmers to avoid major losses and can produce best-quality crops in large quantities. However, the conventional rice leaf disease detection techniques are often not accurate, time-consuming and sometimes require laboratory testing. In this context, automatic rice leaf disease detection techniques are presented based on the various deep learning classifiers (namely MobileNetV2, ResNet50, VGG16 and Le-Net5) and an Android application is also developed in order to instantly determine the possible rice diseases from the uploaded rice leaf images captured by the smartphone. The developed models are tested using the publicly available benchmark rice leaf dataset containing three types of rice leaf diseases, namely bacterial leaf blight, leaf smut and brown spot. Experimental results show that MobileNetV2 model performed better compared to other models in terms of classification accuracy, recall and [Formula: see text]-score. The results of statistical significance test also confirmed the superiority of the MobileNetV2 model over other compared deep learning models.

Is there innovation management of emotions or just the commodification of happiness? A sentiment analysis of happiness apps

PurposeThe aim of this research is to gain insight into the emotional impact and cognitive evaluations that these applications have on users from their own perspective.Design/methodology/approachSentiment analysis methodology is used to analyze users’ comments and ratings, assess perceived benefits and determine the emotional impact these applications have on them.FindingsThe results suggest a benefit for happiness scholars, professionals, users and organizations focused on employee well-being and provide valuable insights into the evolving landscape of happiness applications. While the study relies on publicly available user data, it complements, but does not replace, market research or product evaluation conducted by app developers and companies.Originality/valueAmong the myriad channels disseminating the assumption that the good life is attainable through personal means and psychological know-how, smartphone applications have emerged as accessible, cost-effective and attractive tools to help users become happier and alleviate feelings of distress and discomfort.

Use of Digital Smartphone Applications for Blood Pressure Monitoring: An Integrative Review

With the purpose of identifying in the literature and describing the scientific evidence for the use of smartphone applications for blood pressure monitoring, this study is an integrative literature review, employing a qualitative approach conducted with studies from 2014 to 2024, accessed via the Virtual Health Library (VHL) portal. The research resulted in 10 articles addressing the use of digital technology in smartphones and answering the following guiding question: What is the effectiveness of this technology for diagnostic purposes and blood pressure control? This study showed that digital smartphone applications are effective for blood pressure monitoring, providing important information for both patients and healthcare professionals. The evidence found indicates that digital applications are effective in monitoring blood pressure variations; however, they are not effective for diagnostic purposes but can be used to complement existing data.

DIGITIZATION DEVELOPMENT OPPORTUNITIES IN HUNTING MANAGEMENT IN HUNGARY

In the course of my scientific research so far, I have investigated many areas of professional administration from the point of view of how information communication devices (especially smartphones and drones) can improve and make the administration of the area more efficient. Of these, hunting management stands out for me and is particularly important, which is the main focus of this study. There are several reasons for this. On the one hand, I hunt myself, and as a senior official of the county's hunting civil organizations, I have a broader knowledge and network of contacts about the background of the area's administration and operating rules than the general citizen's perspective. On the other hand, in this area, I came to the formulation of a research result, which I hope will be able to be put into practice in the future, to convince the legislator of its raison d'etre. There are many opinions regarding what hunting can be considered: a leisure activity, a form of sport, or a way of life. I myself am a supporter of the latter definition, since we are talking about a complex activity that cannot be grasped solely from the aspect of one characteristic. Accordingly, the regulation of hunting is also a complex area of law covering several branches of law, which includes elements of civil law, criminal law and, of course, administrative law, but can also be approached from an environmental law point of view.[1] I believe that this fact also clearly shows that it is useful and by now even expected that the authorities can carry out better administrative processes in relation to the administration of the area with the help of the best possible technical support. In the following, I would like to support the applicability of drones and smartphones, especially by presenting the concept of the e-register of hunting

Developing biomimetic PVA/PAA hydrogels with cellulose nanocrystals inspired by tree frog structures for superior wearable sensor functionality

This study developed a wearable sensor device featuring biomimetic structures inspired by tree frog toe pads to measure shoulder joint movements effectively. A PVA/PAA double-network hydrogel was formulated by mixing PVA and PAA in a 3:7 ratio, resulting in a Young's modulus of 7.2 kPa, closely matching human skin's mechanical properties. To enhance moisture retention, the hydrogel was supplemented with 20 mL glycerin, increasing its weight retention rate to 95 % after 28 days, a 5.6-fold improvement over glycerin-free hydrogels. Additionally, the incorporation of 0.2 wt% cellulose nanocrystals (CNC) increased the cross-linking density, reducing the swelling ratio from 124 % to 106 % and minimizing the impact of swelling cycles on mechanical properties from 22 % to 4 %. Treefrog-inspired microstructures were fabricated on the sensor surface using laser cutting and casting techniques, significantly enhancing adhesion under humid conditions, with normal, lateral, and peel-off adhesion forces improving by 500, 700, and 700 %, respectively. The sensor demonstrated accurate measurement of shoulder joint movements with an error margin of 6.3 % for polar angles (45°-135°) and 7.8 % for azimuth angles (0°-45°). The accompanying smartphone application provided real-time feedback, helping to prevent exercise injuries and improve user awareness and control of shoulder movements, making it suitable for both rehabilitation and athletic training.

Effects of an App-Based Visitation Program for Mothers of High-Risk Infants in the Neonatal Intensive Care Unit: A Quasi-Experimental Study.

This study established an app-based visitation program for mothers of infants admitted to the neonatal intensive care unit (NICU)-constrained by COVID-19 visitation restrictions-and assessed its impact on neonatal perception, maternal-infant attachment, and parental stress. High-risk infants in the NICU encounter heightened challenges, exacerbated by COVID-19 restrictions, leading to heightened maternal stress, impaired neonatal perception, and hindered mother-infant attachment. A quasi-experimental study was conducted with 40 mothers (20 in the experimental group and 20 in the control group) unable to visit the NICU of a tertiary general hospital in South Korea. The experimental group utilized the Dodam-Dodam smartphone application, while the control group received neonatal information through telephone calls and text messages. Data collection occurred in July 2022 (control) and September 2022 (experimental) using research instruments (parent's neonatal perception, maternal-infant attachment, and parental stressor scale: NICU, visiting program satisfaction). Descriptive statistics and tests (χ2, Fisher's exact, Shapiro-Wilk, parametric independent t and paired t tests, and nonparametric tests: Mann-Whitney and Wilcoxon's signed-rank) were employed for analysis. The Dodam-Dodam application significantly impacted maternal-infant attachment parental stress scores and program satisfaction in the experimental group, except for neonatal perception. The Dodam-Dodam application was more efficacious than traditional visiting programs in enhancing maternal-infant attachment, increasing maternal satisfaction and reducing parental stress within NICU settings amid restricted visitation. Recommendations include app-based NICU visitation, exploring app variations, studying diverse populations, and providing optimal information provision.

Design of an Automatic Trash Can Wheel Robot with Bluetooth Navigation Control Through Smartphone Application

Environmental cleanliness is an important aspect that must be maintained, especially in this modern era. The use of appropriate technology can help increase awareness of cleanliness, one of which is by developing automatic trash cans. This research designs and builds a mobile robot in the form of an automatic trash can that can be controlled via an Arduino Uno microcontroller using Bluetooth and controlled via a smartphone application. This research designs a mobile robot in the form of an automatic trash can that can be controlled via an Arduino Uno microcontroller with Bluetooth and a smartphone application. The system receives navigation commands from the app and uses ultrasonic sensors to detect objects in front of the trash can. The microcontroller processes the command and sensor data to drive the DC motor and servo motor. The servo motor opens and closes the trash can lid automatically when an object is detected at a certain distance. The test results show that this robot can move according to instructions from a smartphone and successfully open and close the trash can lid automatically when detecting objects.

Development of lateral acceleration prediction models and consistency evaluation criteria for horizontal curves with gradient on two lane rural highways

Objectives The challenge of combined horizontal and vertical highway alignments continues to be a concern in transportation design, particularly regarding truck stability. This study analyses the stability of trucks on horizontal curves combined with ascending or descending gradients using center of gravity (CoG) height and curve geometry as parameters. It also develops models that predict lateral stability of trucks and criteria for evaluating geometric design consistency based on vehicle stability. Method A smartphone application was used to collect real-time data on lateral acceleration experienced by two-axle trucks on curves with gradient. Results Findings show a lower safety margin against rollover on curves with descending gradient compared to ascending ones. Key factors affecting stability are identified as radius and length of horizontal curve, suggesting improvements in rollover stability by increasing these parameters. Geometric design consistency evaluation criteria were developed based on lateral acceleration, categorizing curves with gradient as good, fair, or poor. Predictive models were developed to quantify the impact of CoG height of truck and curve geometry on lateral acceleration, aiding in consistency assessment. Conclusion Outcomes of research will be useful for the design of safer horizontal curves with gradient by prioritizing vehicle stability

Evaluation of the Clinical Impact of a Smartphone Application for Cataract Detection.

Background Approximately 10 million people in India suffer from bilateral blindness, with cataracts accounting for roughly 70% of these cases. However, there is a severe scarcity of ophthalmologists in India (12,000 across the country), which makes routine cataract screening very difficult, particularly in rural areas. To tackle this problem, we investigated the use of an artificial intelligence (AI)-based application for cataract screening at All India Institute of Medical Sciences (AIIMS), Bibinagar, that can be used by nursing officers and other healthcare professionals as a primary screening tool. Ophthalmologists from AIIMS Bibinagar additionally validate the results of this application. Purpose The aim of this study was to assess the clinical performance of a smartphone-based cataract screening application that uses an AI module to identify cataracts in photos taken with the device's camera. The study compared the application's results with diagnoses made by ophthalmologists using a slit lamp. Methods At AIIMS Bibinagar, 495 patients participated in a prospective clinical trial. The AI-based screening solution examined smartphone images that were taken in accordance with a set protocol to identify whether cataracts were present. The results of the application were then compared with the diagnoses made by ophthalmologists based on slit-lamp tests. Results The study included 990 eye images. The AI screening application demonstrated an overall accuracy of 90.01% for cataract detection. Specific metrics include a sensitivity of 89.50%, specificity of 89.73%, precision of 91.43%, and an F1 score of 90.36%. The positive predictive value (PPV) was approximately 91.3%, based on 485 true positives and 46 false positives. The negative predictive value (NPV) was approximately 87.6%, based on 402 true negatives and 57 false negatives. Conclusions The smartphone-based cataract screening application proves to be an effective tool for community-level cataract screening in remote areas where access to expensive equipment and specialized ophthalmic care is limited. Its high accuracy and efficiency make it a valuable option for low-resource settings and suitable for home screening, particularly in the post-COVID era.

Smartphone application-based intervention to lower blood pressure: a systematic review and meta-analysis.

Nowadays, the mHealth market is flooded with smartphone applications (apps) lacking validation for blood pressure (BP)-lowering effects and BP measurement accuracy. This systematic review for Guidelines for BP control using digital technologies of the Japanese Society of Hypertension aimed to assess the validation studies of apps. We searched eligible studies in Ovid MEDLINE, Cochrane Library, and Ichushi, focusing on randomized controlled trials and observational studies comparing the effects of smartphone app-based interventions with non-digital healthcare. Random effects models of meta-analysis were employed to estimate the pooled effects of mean BP change and 95% confidence intervals (CIs). Out of 7385 studies screened, 76 studies with 46,459 participants were included. The interventions were significantly associated with a reduction in office systolic and diastolic BP at six months (systolic BP, -2.76 mmHg, 95% CI: -3.94 to -1.58; diastolic BP, -1.23 mmHg, -1.80 to -0.67). Normotensives saw a significant reduction in office systolic BP at three-month (-4.44 mmHg, -6.96 to -1.92), diminishing afterward (six-month, 0.86 mmHg, -2.81 to 4.52; twelve-month, 0.86 mmHg, -2.81 to 4.52). Conversely, hypertensive participants experienced a significant reduction in office systolic BP at both three- and six-month (three-month, -7.71 mmHg, -10.63 to -4.79; six-month, -1.88 mmHg, -3.41 to -0.35), albeit with limited evidence thereafter. A larger BP reduction was observed among participants using apps with wireless transmission of BP measurements (P = 0.047 for interaction), while there was no clear difference in BP reduction according to the presence of other functions. Smartphone app-based interventions may hold the potential to improve BP levels.

The role of patient reported symptom data in co-producing meaning in rheumatoid arthritis.

Patients with rheumatoid arthritis (RA) experience a range of symptoms including joint pain and inflammation, stiffness, fatigue, anxiety, and low mood. Similar to patients with other long-term conditions, they may have periods of time when their disease is under control, and times when their condition is less stable, requiring treatment adjustments. The REMORA2 feasibility study explored the implementation of an integrated symptom-tracking system using a smartphone application (app), enabling patients to track day-to-day symptoms. The data was available in the electronic health record to be viewed at subsequent consultations. This paper explores patients' comments on living with RA, and how patient-reported symptom data supports informed interactions as patients and clinicians work together to coproduce meaning from the data. Individual semi-structured interviews were conducted with 21 patients and 7 clinicians, supplemented by nonparticipant observations of 5 clinical appointments. Thematic analysis was used to analyse data from the interviews, with an ethnographic approach used to analyse the observational data. Both clinicians and patients reported the benefits of reviewing the data in the clinic together. This helped inform decisions about pain management and identified patients who might otherwise have dismissed symptoms such as pain, because of their natural inclination to be stoical. Improved insights on the care of RA were generated as patients and clinicians discuss symptom tracking data. This can assist the patient-clinician dyad in the process of two-way learning and shared decision-making on the management of a long-term condition.

Low-profile MIMO antenna for sub-6G smartphone applications with minimal footprint: an SVM-guided approach

Low-profile MIMO antenna for sub-6G smartphone applications with minimal footprint: an SVM-guided approach

Collecting population-representative bike-riding GPS data to understand bike-riding activity and patterns using smartphones and Bluetooth beacons

Bike-riding GPS data offers detailed insights and individual-level mobility information which are critical for understanding bike-riding travel behaviour, enhancing transportation safety and equity, and developing models to estimate bike route choice and volumes at high spatio-temporal resolution. Yet, large-scale bicycling-specific GPS data collection studies are infrequent, with many existing studies lacking robust spatial and/or temporal coverage, or have been influenced by sampling biases leading to these data lacking representativeness. Additionally, accurately detecting bike-riding trips from continuously collected raw GPS data without human intervention remains a challenge. This study presents a novel GPS data collection approach by leveraging the combination of a smartphone application with a Bluetooth beacon attached to a participant’s bike. Aided by minimal heuristic post-processing, our method limits data collection to trips taken by bike without the need for participant intervention, carefully optimising between survey participation, privacy challenges, participant workload, and robust bike-riding trip detection. Our method is applied to collect 19,782 bike trips from 673 adults spanning eight months and three seasons in Greater Melbourne, Australia. The collected dataset is shown to represent the underlying adult bike-riding population in terms of demographics (sex, occupation and employment type), temporal and spatial patterns. The average trip length (median = 4.8 km), duration (median = 20.9 min), and frequency of bicycling trips (median = 2.7 trips/week) were greater among men, middle-aged and older adults. The ‘Interested but Concerned’ riders (classified using Geller typology) rode more frequently, while the ‘Strong and Fearless’ and ‘Enthused and Confident’ groups rode greater distances and for longer. Participants rode on roads/streets without bike infrastructure for more than half of their trips by distance, while spending 24% and 17% on off-road paths and bike lanes respectively. This population-representative dataset will be key in the context of urban planning and policymaking.

Long-Term Study on the Fluctuation of Self-Reported Awake Bruxism in a Cohort of Healthy Young Adults.

The introduction of a smartphone-based ecological momentary assessment (EMA) approach has allowed achieving data on the frequency of different awake bruxism (AB) behaviours (i.e., teeth contact, teeth clenching, teeth grinding, and mandible bracing) reported by an individual in the natural environment. The fluctuation of AB reports over time has a certain degree of variability that has never been investigated. Therefore, the aim of this investigation was to assess the long-term fluctuation of AB behaviours in a population of young adults. A smartphone application was used to assess a real-time report on five specific oral conditions related to AB in a sample of 77 young adults, aged 24.0 ± 0.8 years. Data were recorded over three periods of 7 days, with a three-month interval for a total of 6 months. The average frequency of the relaxed condition was 72.9%, 78.2%, and 80.8% at the end of the first, second, and third sessions, respectively. On average, teeth contact and mandible bracing were the most frequently reported conditions, with a mean prevalence of 12.9% and 7%, respectively, whilst the frequency of teeth clenching and teeth grinding was less than 3%. The ANOVA test showed an absence of significant differences (p < 0.05) between the three recording periods, and the frequency was in general only moderately variable from day-to-day (e.g., the coefficient of variation (CV) for the condition "relaxed jaw muscles" was 0.3). No gender differences were detected either. Findings suggest that in a population of healthy individuals, the frequency of AB behaviours over a six-month monitoring period is quite constant. This investigation represents a standpoint for future comparisons on the study of natural fluctuations of AB behaviours as well as on AB frequency in populations with risk/associated factors and possible clinical consequences.