Sensitive visual detection of norfloxacin in water by smartphone assisted colorimetric method based on peroxidase-like active cobalt-doped Fe3O4 nanozyme.

Norfloxacin is widely used owing to its strong bactericidal effect on Gram-negative bacteria. However, the residual norfloxacin in the environment can be biomagnified via food chain and may damage the human liver and delay the bone development of minors. Present work described a reliable and sensitive smartphone colorimetric sensing system based on cobalt-doped Fe3O4 magnetic nanoparticles (Co-Fe3O4 MNPs) for the visual detection of norfloxacin. Compared with Fe3O4, Co-Fe3O4 MNPs earned more remarkably peroxidase-like activity and TMB (colorless) was rapidly oxidized to oxTMB (blue) with the presence of H2O2. Interestingly, the addition of low concentration of norfloxacin can accelerate the color reaction process of TMB, and blue deepening of the solution can be observed with the naked eye. However, after adding high concentration of norfloxacin, the activity of nanozyme was inhibited, resulting in the gradual fading of the solution. Based on this principle, a colorimetric sensor integrated with smartphone RGB mode was established. The visual sensor exhibited good linearity for norfloxacin monitoring in the range of 0.13-2.51 µmol/L and 17.5-100 µmol/L. The limit of visual detection was 0.08 µmol/L. In the actual water sample analysis, the spiked recoveries of norfloxacin were over the range of 95.7%-104.7 %. These results demonstrated that the visual sensor was a convenient and fast method for the efficient and accurate detection of norfloxacin in water, which may have broad application prospect.

Colorimetric detection of serum creatinine on a miniaturized platform using hue-saturation-value space analysis.

Chronic kidney disease (CKD) is a widespread condition with considerable health and economic impacts globally. However, existing methodologies for serum creatinine assessment often involve prolonged wait times and sophisticated equipment, such as spectrometers, hindering real-time diagnosis and care. Innovative solutions like point-of-care (POC) devices are emerging to address these challenges. In this context, there is a recognized need for remote, regular, automated, and low-cost analysis of serum creatinine levels, given its role as a critical parameter for CKD diagnosis and management. This study introduces a miniaturized system with integrated heater elements designed for precise serum creatinine measurement. The system operates based on the Jaffe method and accurate serum creatinine measurement within a microreservoir chip. Smartphone-based image processing using the hue-saturation-value (HSV) color space was applied to captured images of microreservoirs. The creatinine analyses were conducted in serum with a limit of detection of ~ 0.4 mg/dL and limit of quantification of ~ 1.3 mg/dL. Smartphone-based image processing employing the HSV color space outperformed spectrometric analysis for creatinine measurement conducted in serum. This pioneering technology and smartphone-based processing offer the potential for decentralized renal function testing, which could significantly contribute to improved patient care. The miniaturized system offers a low-cost alternative ($87 per device), potentially reducing healthcare expenditures (~ $0.5 per test) associated with CKD diagnosis and management. This innovation could greatly improve access to diagnosis and monitoring of CKD, especially in regions where access to sophisticated laboratory equipment is limited.

Mobile sensing-based depression severity assessment in participants with heterogeneous mental health conditions.

Mobile sensing-based depression severity assessment could complement the subjective questionnaires-based assessment currently used in practice. However, previous studies on mobile sensing for depression severity assessment were conducted on homogeneous mental health condition participants; evaluation of possible generalization across heterogeneous groups has been limited. Similarly, previous studies have not investigated the potential of free-living audio data for depression severity assessment. Audio recordings from free-living could provide rich sociability features to characterize depressive states. We conducted a study with 11 healthy individuals, 13 individuals with major depressive disorder, and eight individuals with schizoaffective disorders. Communication logs and location data from the participants' smartphones and continuous audio recordings of free-living from a wearable audioband were obtained over a week for each participant. The depression severity prediction model trained using communication log and location data features had a root mean squared error (rmse) of 6.80. Audio-based sociability features further reduced the rmse to 6.07 (normalized rmse of 0.22). Audio-based sociability features also improved the F1 score in the five-class depression category classification model from 0.34 to 0.46. Thus, free-living audio-based sociability features complement the commonly used mobile sensing features to improve depression severity assessment. The prediction results obtained with mobile sensing-based features are better than the rmse of 9.83 (normalized rmse of 0.36) and the F1 score of 0.25 obtained with a baseline model. Additionally, the predicted depression severity had a significant correlation with reported depression severity (correlation coefficient of 0.76, p < 0.001). Thus, our work shows that mobile sensing could model depression severity across participants with heterogeneous mental health conditions, potentially offering a screening tool for depressive symptoms monitoring in the broader population.

Enhancing response inhibition behavior through tDCS intervention in college students with smartphone addiction.

Smartphone addiction, emerging from excessive use of smartphones, poses a challenge to inhibitory control functions within society. This research employed transcranial direct current stimulation (tDCS) as an intervention alongside the stop signal task (SST) to explore behavioral distinctions between individuals with smartphone addiction and a non-addicted control group, focusing on the efficacy of tDCS intervention. The participant cohort comprised 80 individuals, divided into an addiction group (39 participants, with 19 receiving active tDCS and 20 receiving sham tDCS) and a control group (41 participants, with 20 receiving active tDCS and 21 receiving sham tDCS), with anodal stimulation applied over the right dorsolateral prefrontal cortex (dlPFC) and cathodal placement over the left arm. The findings indicate that university students struggling with smartphone addiction exhibit reduced inhibitory control compared to their non-addicted peers, while maintaining similar levels of general cognitive control. Remarkably, tDCS interventions were observed to enhance inhibitory control in both groups. Although the improvement in the addiction group appeared more pronounced numerically than in the control group, no significant interaction with group was noted. However, a higher percentage of participants in the smartphone addiction (SA) group exhibited enhanced response inhibition under active tDCS. This study demonstrates the inhibitory control deficits in individuals addicted to smartphones and underscores the potential of tDCS in enhancing response inhibition. It provides a valuable reference for future tDCS research targeting smartphone addiction and highlights the importance of developing healthier smartphone usage habits.

Precise Identification of Oral Cancer Lesions Using Artificial Intelligence.

Dentists, especially those who are not oral lesion specialists and live in rural areas, need an artificial intelligence (AI) system for accurately assisting them in screening for oral cancer that may appear in smartphone images. Not many literatures present a viable model that addresses the needs, especially in the context of oral lesion segmentation in smartphone images. This study demonstrates the use of a deep learning-based AI for simultaneously identifying types of oral cancer lesions as well as precisely outlining the boundary of the lesions in the images for the first time. The lesions of interest were oral potentially malignant disorders (OPMDs) and oral squamous cell carcinoma (OSCC) lesions. The model could successfully (1) detect if the images contained the oral lesions, (2) determine types of the lesions, and (3) precisely outline the boundary of the lesions. With future success of our project, patients will be diagnosed and treated early before the pre-cancer lesions can progress into deadly cancerous ones.

Adapting Gamification Strategies According to Targeted Health Behavior Change: Findings from a Focus Group.

In healthcare, improving lifestyle behaviors like medication adherence remains challenging, with an impact on treatment success and patient outcomes. Mobile health (mHealth) applications offer promising solutions by leveraging smartphone ubiquity and digital accessibility. However, sustaining user engagement and behavior change presents obstacles. This study explores the specificities of gamification-integrating game-like elements into non-game contexts-to improve mHealth applications for 3 health behaviors. During a focus group, 15 participants discussed the potential of 17 gamification strategies to improve medication adherence, physical activity, and diet within mHealth applications. Individual choices varied widely, with preferences influenced by targeted behaviors. Physical activity focused on self-monitoring and challenges, while dieting emphasized cooperation. Simulation models were particularly intriguing, projecting long-term benefits of behavior changes. Future research should explore personalized design options and consider individual characteristics. Iterative design cycles with end-users are crucial for refining concepts and sustaining user engagement. Overall, gamification presents an innovative avenue for empowering patients and transforming healthcare delivery.

RISKOCA: A Smartphone-Based Digital Platform for Oral Cancer Self-Examination.

We pioneered a smartphone-based digital platform for oral cancer self-examination, namely RISKOCA. It enabled anyone to self-submit their own oral images to evaluate the potential risk of oral lesions. Integrative artificial intelligence (AI) could immediately report if the image might have a type of oral cancer as well as the precise locations of the lesions. Participating specialist dentists would have to re-evaluate and confirm the results before sending back recommendation to the patients. High participation and satisfaction indicated the success of this pilot study. This project aims to promote oral public health and health surveillance, both nationally and globally.

A Development of a Sound Recognition-Based Cardiopulmonary Resuscitation Training System.

The objective of this study was to develop a sound recognition-based cardiopulmonary resuscitation (CPR) training system that is accessible, cost-effective, easy-to-maintain and provides accurate CPR feedback. Beep-CPR, a novel device with accordion squeakers that emit high-pitched sounds during compression, was developed. The sounds emitted by Beep-CPR were recorded using a smartphone, segmented into 2-second audio fragments, and then transformed into spectrograms. A total of 6,065 spectrograms were generated from approximately 40 minutes of audio data, which were then randomly split into training, validation, and test datasets. Each spectrogram was matched with the depth, rate, and release velocity of the compression measured at the same time interval by the ZOLL X Series monitor/defibrillator. Deep learning models utilizing spectrograms as input were trained using transfer learning based on EfficientNet to predict the depth (Depth model), rate (Rate model), and release velocity (Recoil model) of compressions. Results: The mean absolute error (MAE) for the Depth model was 0.30 cm (95% confidence interval [CI]: 0.27-0.33). The MAE of the Rate model was 3.6/min (95% CI: 3.2-3.9). For the Recoil model, the MAE was 2.3 cm/s (95% CI: 2.1-2.5). External validation of the models demonstrated acceptable performance across multiple conditions, including the utilization of a newly-manufactured device, a fatigued device, and evaluation in an environment with altered spatial dimensions. We have developed a novel sound recognition-based CPR training system, that accurately measures compression quality during training. Significance: Beep-CPR is a cost-effective and easy-to-maintain solution that can improve the efficacy of CPR training by facilitating decentralized at-home training with performance feedback.

Application of smartphone-based infrared thermography devices for ocular surface thermal imaging.

Infrared thermography (IRT) is a well-known imaging technique that provides a non-invasive displaying of the ocular surface temperature distribution. Currently, compact smartphone-based IRT devices, as well as special software for processing thermal images, have become available. The study aimed to determine the possible use of smartphone-based IRT devices for real-time ocular surface thermal imaging. This study involved 32 healthy individuals (64 eyes); 10 patients (10 eyes) with proliferative diabetic retinopathy (PDR) and absolute glaucoma; 10 patients (10 eyes) with PDR, who underwent vitreoretinal surgery. In all cases, simultaneous ocular surface IRT of both eyes was performed. In healthy individuals, the ocular surface temperature (OST) averaged 34.6 ± 0.8 °C and did not differ substantially between the paired eyes, in different age groups, and after pupil dilation. In our study, high intraocular pressure was accompanied by a decrease in OST in all cases. After vitreoretinal surgery in cases with confirmed subclinical inflammation, the OST was higher than the baseline and differed from that of the paired eye by more than 1.0 °C. These results highlight that smartphone-based IRT imaging could be useful for the non-invasive detection of OST asymmetry between paired eyes due to increased intraocular pressure or subclinical inflammation.

Microplate-based 3D-printed image box for urea determination in milk by digital image colorimetry.

In this study, we describe a rapid and high-throughput smartphone-based digital colorimetric method for determining urea in milk. A compact and cost-effective 3D-printed image box microplate-based system was designed to measure multiple samples simultaneously, using minimal sample and reagent volumes. The apparatus was applied for the quantification of urea in milk based on its reaction with p-dimethylaminobenzaldehyde (DMAB). The predictive performance of calibration was evaluated using RGB and different colour models (CMYK, HSV, and CIELAB), with the average blue (B) values of the RGB selected as the analytical signal for urea quantification. Under optimized conditions, a urea concentration linear range from 50 to 400 mg L-1 was observed, with a limit of detection (LOD) of 15 mg L-1. The values found with the smartphone-based DIC procedure are in good agreement with spectrophotometric (spectrophotometer and microplate treader) and reference method (mid-infrared spectroscopy) values. This proposed approach offers an accessible and efficient solution for digital image colorimetry, with potential applications for various target analytes in milk and other fields requiring high-throughput colorimetric analysis.

Bimetallic Single-Atom Nanozyme-Based Electrochemical-Photothermal Dual-Function Portable Immunoassay with Smartphone Imaging.

Rapid and accurate detection of human epidermal growth factor receptor 2 (HER2) is crucial for the early diagnosis and prognosis of breast cancer. In this study, we reported an iron-manganese ion N-doped carbon single-atom catalyst (FeMn-NCetch/SAC) bimetallic peroxidase mimetic enzyme with abundant active sites etched by H2O2 and further demonstrated unique advantages of single-atom bimetallic nanozymes in generating hydroxyl radicals by density functional theory (DFT) calculations. As a proof of concept, a portable device-dependent electrochemical-photothermal bifunctional immunoassay detection platform was designed to achieve reliable detection of HER2. In the enzyme-linked reaction, H2O2 was generated by substrate catalysis via secondary antibody-labeled glucose oxidase (GOx), while FeMn-NCetch/SAC nanozymes catalyzed the decomposition of H2O2 to form OH*, which catalyzed the conversion of 3,3',5,5'-tetramethylbenzidine (TMB) to ox-TMB. The ox-TMB generation was converted from the colorimetric signals to electrical and photothermal signals by applied potential and laser irradiation, which could be employed for the quantitative detection of HER2. With the help of this bifunctional detection technology, HER2 was accurately detected in two ways: photothermally, with a linear scope of 0.01 to 2.0 ng mL-1 and a limit of detection (LOD) of 7.5 pg mL-1, and electrochemically, with a linear scope of 0.01 to 10 ng mL-1 at an LOD of 3.9 pg mL-1. By successfully avoiding environmental impacts, the bifunctional-based immunosensing strategy offers strong support for accurate clinical detection.

A Novel Approach for Improving Gait Speed Estimation Using a Single Inertial Measurement Unit Embedded in a Smartphone: Validity and Reliability Study.

Background: Gait speed is a valuable biomarker for mobility and overall health assessment. Existing methods to measure gait speed require expensive equipment or personnel assistance, limiting their use in unsupervised, daily-life conditions. The availability of smartphones equipped with a single inertial measurement unit (IMU) presents a viable and convenient method for measuring gait speed outside of laboratory and clinical settings. Previous works have used the inverted pendulum model to estimate gait speed using a non-smartphone-based IMU attached to the trunk. However, it is unclear whether and how this approach can estimate gait speed using the IMU embedded in a smartphone while being carried in a pants pocket during walking, especially under various walking conditions. Objective: This study aimed to validate and test the reliability of a smartphone IMU-based gait speed measurement placed in the user's front pants pocket in both healthy young and older adults while walking quietly (ie, normal walking) and walking while conducting a cognitive task (ie, dual-task walking). Methods: A custom-developed smartphone application (app) was used to record gait data from 12 young adults and 12 older adults during normal and dual-task walking. The validity and reliability of gait speed and step length estimations from the smartphone were compared with the gold standard GAITRite mat. A coefficient-based adjustment based upon a coefficient relative to the original estimation of step length was applied to improve the accuracy of gait speed estimation. The magnitude of error (ie, bias and limits of agreement) between the gait data from the smartphone and the GAITRite mat was calculated for each stride. The Passing-Bablok orthogonal regression model was used to provide agreement (ie, slopes and intercepts) between the smartphone and the GAITRite mat. Results: The gait speed measured by the smartphone was valid when compared to the GAITRite mat. The original limits of agreement were 0.50 m/s (an ideal value of 0 m/s), and the orthogonal regression analysis indicated a slope of 1.68 (an ideal value of 1) and an intercept of -0.70 (an ideal value of 0). After adjustment, the accuracy of the smartphone-derived gait speed estimation improved, with limits of agreement reduced to 0.34 m/s. The adjusted slope improved to 1.00, with an intercept of 0.03. The test-retest reliability of smartphone-derived gait speed was good to excellent within supervised laboratory settings and unsupervised home conditions. The adjustment coefficients were applicable to a wide range of step lengths and gait speeds. Conclusions: The inverted pendulum approach is a valid and reliable method for estimating gait speed from a smartphone IMU placed in the pockets of younger and older adults. Adjusting step length by a coefficient derived from the original estimation of step length successfully removed bias and improved the accuracy of gait speed estimation. This novel method has potential applications in various settings and populations, though fine-tuning may be necessary for specific data sets.

Innovative approaches in imaging photoplethysmography for remote blood oxygen monitoring.

Peripheral Capillary Oxygen Saturation (SpO2) has received increasing attention during the COVID-19 pandemic. Clinical investigations have demonstrated that individuals afflicted with COVID-19 exhibit notably reduced levels of SpO2 before the deterioration of their health status. To cost-effectively enable individuals to monitor their SpO2, this paper proposes a novel neural network model named "ITSCAN" based on Temporal Shift Module. Benefiting from the widespread use of smartphones, this model can assess an individual's SpO2 in real time, utilizing standard facial video footage, with a temporal granularity of seconds. The model is interweaved by two distinct branches: the motion branch, responsible for extracting spatiotemporal data features and the appearance branch, focusing on the correlation between feature channels and the location information of feature map using coordinate attention mechanisms. Accordingly, the SpO2 estimator generates the corresponding SpO2 value. This paper summarizes for the first time 5 loss functions commonly used in the SpO2 estimation model. Subsequently, a novel loss function has been contributed through the examination of various combinations and careful selection of hyperparameters. Comprehensive ablation experiments analyze the independent impact of each module on the overall model performance. Finally, the experimental results based on the public dataset (VIPL-HR) show that our model has obvious advantages in MAE (1.10%) and RMSE (1.19%) compared with related work, which implies more accuracy of the proposed method to contribute to public health.

Enhancing efficiency in detection of COVID-19 through AI-driven colorimetric isothermal detection with multiplex primers.

COVID-19 has afflicted millions of lives worldwide. Although there are many rapid methods to detect it based on colorimetric loop-mediated isothermal amplification, there remains room for improvement. This study aims to 1) integrate multiple primers into a singleplex assay to enhance the diagnostic sensitivity, and 2) utilize a high-throughput smartphone-operatable AI-driven color reading tool to enable a rapid result analysis. This setup can improve the sensitivity by 10-100 times and can analyze approximately 6700 samples per minute. The assay is simpler than RT-qPCR, with a turnaround time of less than 75 min. It can detect various types of SARS-CoV-2 by targeting 3 genes, increasing the likelihood that it will remain effective even if the virus undergoes mutations in any single target gene. In summary, it affords potential for adaptation to detection of new/re-emerging diseases with the visual readout for maximum assay simplicity and AI-operated mode for large-scale testing.

Age related changes in skin sensitivity assessed with smartphone vibration testing.

The capacity to perceive tactile input at the fingertips, referred to as tactile sensitivity, is known to diminish with age due to regressive changes to mechanoreceptor density and morphology. Sensitivity is measured as perceptual responses to stimuli of varying intensity. Contrary to traditional sensitivity monitoring instruments, smartphones are uniquely suited for remote assessment and have shown to deliver highly calibrated stimuli along a broad spectrum of intensity, which may improve test reliability. The aim of this study was to evaluate a vibration-emitting smartphone application, the Vibratus App, as a mode of estimating tactile sensory thresholds in the aging adult. The peripheral nerve function of 40 neurologically healthy volunteers (ages 18-71) was measured using monofilaments, a 128-Hz tuning fork, the Vibratus App, and nerve conduction studies (NCS). Between group differences were analyzed to determine each measurement's sensitivity to age. Spearman correlation coefficients depicted the associative strength between hand-held measurements and sensory nerve action potential (SNAP) amplitude. Inter-rater reliability of traditional instruments and the software-operated smartphone were assessed by intraclass correlation coefficient (ICC2,k). Measurements taken with Vibratus App were significantly different between age groups (p < 0.001). The inter-rater reliability of monofilament, smartphone vibration, and tuning fork testing was moderate to good (ICC2,k = 0.65, 0.69, and 0.79, respectively). The findings of this study support further investigation of smartphones as sensitivity monitoring devices for at home monitoring of skin sensitivity.

The chain mediating role of rumination and social withdrawal in the relationship between smartphone distraction and depression among Chinese nursing students.

Smartphone distraction (SD) is closely related to depression, and the prevalence of SD among nursing students is gradually increasing. However, the potential mechanism of the effect of SD on nursing students' depression is unclear. A total of 574 nursing students were assessed using Smartphone Distraction Scale, Ruminative Response Scale, Hikikomori Questionnaire, and Patient Health Questionnaire-9. The results indicated that SD among nursing students had an impact on depression through four pathways: (1) SD was positively associated with depression (ß = 0.353, P < 0.001); (2) Rumination (ß = 0.199, 95% CI: 0.081 to 0.162) and social withdrawal (ß = 0.061, 95% CI: 0.034 to 0.091) mediated the effects of SD on depression, respectively; and (3) Rumination and social withdrawal played a chain mediating role in the effect of SD on nursing students' depression (ß = 0.027, 95% CI: 0.015 to 0.042). The negative impact of SD on nursing students' mental health should not be taken lightly. Schools and hospitals should guide nursing students to use smartphones correctly, including providing mental health education and professional psychological counselling; families could play a supervisory role and communicate regularly to understand the psychological state and learning of nursing students. These measures can help nursing students cope with stress and reduce the risk of depression.

Smartphone-Based Smoking Cessation Intervention (OKquit) for Oklahoma Tobacco Helpline Users: Protocol for a Randomized Controlled Trial.

BACKGROUND: Tobacco quitlines provide effective resources (eg, nicotine replacement therapy, smoking cessation counseling, and text and web-based support) for those who want to quit smoking in the United States. However, quitlines reach approximately only 1%-3% of people who smoke each year. Novel, smartphone-based, and low-burden interventions that offer 24/7 access to smoking cessation resources that are tailored to current readiness to quit may increase appeal, reach, and effectiveness of smoking cessation interventions. OBJECTIVE: This study will examine the efficacy of OKquit, a low-burden smartphone-based app for smoking cessation. METHODS: Approximately 500 people who smoke cigarettes and access the Oklahoma Tobacco Helpline (OTH) will be randomized to receive standard OTH care (SC) or SC plus the novel OKquit smartphone app for smoking cessation (OKquit). All participants will use a smartphone app to complete study surveys (ie, baseline, 27 weekly surveys, brief daily check-ins, and 27-week follow-up). Upon completion of daily check-ins and weekly surveys, participants will receive either trivia type messages (SC) or messages that are tailored to current readiness to quit smoking and currently experienced lapse triggers (OKquit). In addition, those assigned to receive the OKquit app will have access to on-demand smoking cessation content (eg, quit tips, smoking cessation medication tips). It is hypothesized that participants assigned to OKquit will be more likely to achieve biochemically verified 7-day point prevalence abstinence than those assigned to SC at 27 weeks post enrollment. In addition, participants who use more OTH resources (eg, more cessation coaching sessions completed) or more OKquit resources (eg, access more quit tips) will have greater biochemically verified smoking cessation rates. RESULTS: Data collection began in September 2022 and final follow-ups are expected to be completed by May 2025. CONCLUSIONS: Data from this randomized controlled trial will determine whether the OKquit smartphone app combined with OTH care will increase smoking cessation rates over standard OTH care alone. If successful, OKquit could provide tailored intervention content at a fraction of the cost of traditional interventions. Furthermore, this type of low-burden intervention may offer a way to reach underserved populations of adults who smoke and want to quit. TRIAL REGISTRATION: ClinicalTrials.gov NCT05539209; https://clinicaltrials.gov/study/NCT05539209. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/56827.

Effectiveness of smartphone app-based interventions after surgery on quality of recovery among cancer patients: a systematic review and meta-analysis.

BACKGROUND: Postoperative recovery in patients with cancer is a complex process that influences quality of life, functional recovery, and mental well-being. Smartphone app-based interventions have emerged as potential tools for improving various aspects of health and well-being in cancer patients. However, the existing literature lacks a consensus on the efficacy of these interventions, leading to conflicting outcomes. METHODS: We searched multiple databases, including PubMed, Web of Science, Cochrane Library, Scopus, EMBASE, and MEDLINE Complete (EBSCO). We exclusively selected randomized controlled trials meeting the inclusion criteria for our systematic review and meta-analysis. Utilizing a random-effects model, we derived the pooled effect size estimates for the meta-analysis. Where applicable, we calculated the pooled standardized mean difference (SMD) with 95% confidence interval (CI). The Cochrane Collaboration tool (Cochrane ROB) was used to evaluate bias in randomized trials. The primary outcome was the quality of life. The secondary outcomes were psychological symptoms, health conditions, satisfaction, and self-efficacy. RESULTS: Of 731 screened articles, 15 were included, comprising 1,831 participants. Our meta-analysis revealed that app-based interventions potentially improved quality of life (SMD =  -0.58, 95% CI -1.00 to -0.16), alleviated psychological symptoms (SMD =  -0.43, 95% CI -0.72,-0.15; p = .003), and enhanced self-efficacy (SMD = 0.90, 95% CI 0.26 to 1.53; p  =  0.001). However, there was no statistically significant effect on satisfaction (SMD = 1.25, 95% CI-1.06 to 3.57; p  =  0.23). CONCLUSIONS: Our findings suggest that mobile health apps hold promise in improving the well-being of cancer patients after surgery by enhancing their quality of life, health status, and self-efficacy, while also reducing anxiety and depression.

Many smartphone apps focus on managing health, particularly for activities such as exercise and preventing diseases such as obesity, diabetes, and mental health; however, there is a noticeable absence of specialized health management apps tailored for cancer patients after surgery. Smartphone app-based interventions have the potential to enhance quality of life, health status, self-efficacy, and decrease feelings of anxiety and depression in adult cancer patients after surgery.

Hybrid-resolution facial and dental scanning: a cost-effective approach to utilizing 3D facial data in anterior dental implant restoration.

As digital technology becomes more prevalent in the practice of dental medicine, methods to fully replace 2-dimensional photography and analog devices such as the facebow are still in their infancy. As more practices adopt 3-dimensional (3D) intraoral scanners, effective digital communication of the relationships between the teeth and the face becomes essential. With the high cost of intraoral scanners, the additional expense of a face scanner is not a feasible investment for many practices. This article explores a technique for meshing (lower resolution) facial data obtained from a smartphone-based scanner with high-resolution intraoral scan data. In this approach, the data from a free 3D scanning application on a smartphone and a traditional intraoral scanner are meshed so that high-resolution data are available for intraoral features and lower resolution data are used to capture the gross contours of the face. In this way, a hybrid-resolution composite scan that incorporates all of the data needed to simulate the face and accurately reproduce the teeth is generated without the cost of additional scanning equipment. This article defines a new term, the facial registration scan, for use alongside the familiar digital bite registration obtained with an intraoral scanner. To illustrate the clinical use of the hybrid-resolution scan concept, this article presents a case in which this method was used for the restoration of maxillary anterior implants.