Due to academic pressures and irregular schedules, university students often face challenges in maintaining healthy movement behaviours (including sleep, physical activity, and screen time), which are interrelated and influence both physical and mental health. Smartwatch- and smartphone-based ecological momentary assessments (EMAs) and ecological momentary interventions (EMIs) offer real-time, context-aware strategies to promote movement behaviours. This pilot study aims to assess the feasibility and preliminary effectiveness of a hybrid approach that combines continuous digital monitoring of movement behaviours with sequentially embedded randomised controlled trials (RCTs) evaluating EMIs. MOVE@NUS pilot study employed a five-month hybrid design that combines continuous passive monitoring (primarily via Apple Watches, supplemented by iPhones) with three embedded RCTs targeting sleep (RCT-1), physical activity (RCT-2), and screen time (RCT-3). For each RCT, participants are randomised on a 1:1:1 schedule (control, intervention 1, intervention 2). Eligible participants are first-year undergraduates at the National University of Singapore, aged 18-25 years, who own or regularly use an iPhone and an Apple Watch. EMIs, delivered via the study app, comprise standard health messages or personalised reminders based on HealthKit data or participants' self-reported behaviours and preferences. Self-reported measures include eight EMA bursts (three-day periods every two weeks) and online questionnaires at baseline, midway (2.5 months), and endpoint (5 months). All EMIs and EMAs are text-based and can be completed in under two minutes. Feasibility outcomes include recruitment, engagement, and user experience assessed through quantitative surveys and semi-structured interviews. Preliminary effectiveness will be explored separately for each RCT, comparing movement behaviours between intervention and control groups. Findings from this study will inform the development of scalable and longer-term digital intervention cohorts for promoting healthier lifestyles among university students. Furthermore, as university students soon transition to the workforce, insights gained will inform scalable digital health interventions for broader populations. ClinicalTrials.gov ID NCT06597890 First Posted: 19 September 2024.
We present a low-cost, fully reproducible software and hardware protocol for smartphone sensor battery cost tests. Our pipeline combines a rigorous hardware checklist and light-sealed enclosure, a software checklist for iOS devices, and a BatteryTest app to control sensor configurations and log battery state during tests. Methodologically, we applied this standardized protocol in 30 independent analyzed test runs using six iPhone 14 Pro and three iPhone 13 Pro devices, and compared battery-life outcomes across predefined sensor conditions (idle, TrueDepth, GPS, accelerometer, pedometer, gyroscope, and rear camera), sampling rates, and sensor-specific settings. Key findings include: (i) Baseline battery life was approximately 10% higher on the 14 Pro versus the 13 Pro models under idle conditions. Sensor activation substantially reduced battery life, with GPS and camera usage exhibiting the strongest impact. (ii) Software parameters matter: the sampling rate change from 27 s to 3 s led to significantly decreased battery life in several scenarios, while reducing the location accuracy in GPS tests increased battery life by up to 20 h on the 13 Pro devices. (iii) Cross-device-generation consistency is heterogeneous. The iPhone 14 Pro lasts up to 50% longer on GPS tests, yet drains about an hour faster than the 13 Pro in camera tests. This work introduces the first standardized, and fully reproducible protocol for quantifying sensor-specific battery consumption on iPhones, enabling consistent, comparable, and low-cost energy benchmarking across device generations.
Heart failure (HF) involves cycles of remission and exacerbation, which are poorly characterized by static disease measures. Consumer wearables have an understudied potential for daily monitoring of HF symptoms. Here we report results from an observational cohort of free-living patients over a median of 94.5 d with HF in the Ted Rogers Understanding Exacerbations of HF (TRUE-HF) study. The study measured the ability of Apple Watch data to predict peak oxygen uptake (pVO2) as measured using in-clinic cardiopulmonary exercise testing (CPET). A deep learning model was trained with data from 154 patients (46 women, 108 men) and validated on a held-out set of 63 patients (24 women, 39 men) for determining wearable-derived daily pVO2, which correlated strongly with CPET-measured pVO2 (Pearson's correlation = 0.85). Each 10% drop in wearable-derived daily pVO2 was associated with a 3.62-fold increased hazard ratio (HR) for unplanned healthcare events (95% confidence interval (CI), 1.37-9.55; P < 0.01), which occurred at a median of 7.4 d after the first 10% drop in wearable-derived pVO2. These findings were externally validated in an independent external cohort from the All of Us Research Program using a crossplatform model that accounted for the reduced-sensor capacities available in this external cohort. Using this reduced-sensor variant of the model, drops in wearable-derived daily pVO2 were associated with unplanned healthcare utilization (HR 1.32, 95% CI 1.03-1.69; P = 0.03), which occurred at a median of 21 d after the first 10% drop in wearable-derived pVO2. These results indicate that wearable-derived daily pVO2 provides earlier and improved risk discrimination compared with existing wearable fitness estimates and established clinical markers and offers a scalable and generalizable approach for longitudinal HF research and monitoring.
Peripheral facial palsy causes significant functional and psychosocial impairments, requiring precise assessment and patient engagement for effective rehabilitation. However, conventional clinician-graded scales (eg, House-Brackmann Scale, Sunnybrook Facial Grading System, and Stennert Index) are subjective and prone to interobserver variability, limiting their reliability for tracking recovery. Smartphone-based computer vision solutions offer objective, standardized facial movement grading, and interactive home-based training to improve adherence and outcomes. This pilot study evaluated a novel iOS smartphone app (Apple Inc.) for facial palsy management. The app uses the iPhone TrueDepth 3D camera and on-device computer vision to compute a Digital Facial Index (DFI) for objective facial movement analysis, and provides guided neuromuscular facial exercises with real-time biofeedback. The study aimed to validate DFI against standard clinical grading scales and assess patient-reported outcomes and usability. A 4-week single-arm pilot included 21 patients with unilateral facial palsy. Participants used the app at home for daily facial exercises and periodic self-assessments with DFI. Clinicians, blinded to DFI, rated facial function from standardized video exams at baseline and 4 weeks using the House-Brackmann Scale, the Sunnybrook Facial Grading System, and the Stennert Index. DFI concurrent validity was evaluated via correlation with these clinician scores. Patient-reported outcomes included pre- and postintervention Facial Disability Index (FDI) physical and social scores, the System Usability Scale, and a poststudy user feedback questionnaire. During the study period, strong correlations were observed between DFI and conventional clinical scores. FDI physical and social showed significant functional improvement. Mean System Usability Scale was 88.3 (SD 15.4), indicating excellent usability, and participants reported high satisfaction, preferring the app over traditional paper-based exercises. The app's DFI provided objective facial function grading that correlated well with standard clinical scales. Patients' FDI scores improved significantly over 4 weeks. High usability and patient preference support the app's feasibility for home-based rehabilitation. This digital approach is promising for facial palsy management, and controlled studies are needed to confirm efficacy and improve long-term engagement.
Smartphones are a pervasive feature of adolescents' daily lives, raising concern about how smartphones are used in contexts such as school that require sustained attention and self-regulation. To describe youths' smartphone use during each hour of the school day and examine whether smartphone use during school is associated with poorer cognitive control, a key developmental process underlying academic success. This cross-sectional study of youths aged 11 to 18 years from the Southeastern US objectively assessed smartphone use every hour for 14 consecutive days between April 8, 2021, and February 2, 2022 (cohort 1), and February 1, 2023, and December 11, 2024 (cohort 2), providing thousands of data points to capture actual engagement. The iPhone iOS (Apple) screen time report captured smartphone use at every hour. Cognitive control was measured in the older cohort using a go/no-go task, with the signal detection metric d' quantifying inhibitory control. A total of 79 participants (mean [SD] age, 15.10 [2.04] years; 41 [51.9%] female) participated in the study. Youths were using their smartphones during every hour of the school day, spending a total of 2.22 hours of the school day on their smartphones. Youths aged 15 to 18 years spent more time on their smartphones during school hours than those aged 11 to 14 years (mean [SD], 23.28 [18.34] vs 11.57 [16.83] min/h; F1,76 = 28.82, P < .001, η2 = 0.28). Youths spent a mean (SD) of 40.14 (39.56) minutes on social media and 13.85 (25.22) minutes on entertainment apps during school hours. Youths checked their smartphones a mean (SD) of 64.46 (32.83) times during school hours. More frequent smartphone checking was associated with lower d' values (F1,28 = 4.8, P = .04, η2 = 0.15), indicating poorer cognitive control. This cross-sectional study found that youths use smartphones approximately one-third of the school day; this use was associated with reduced cognitive control. These findings highlight the need for school-level policies and digital literacy programs that address not only overall screen time but also habitual smartphone-checking behaviors that fragment attention.
Laryngectomy is a life-changing procedure typically performed as a surgical treatment for patients with head and neck cancer. Voice conversion (VC) is a technology that converts one voice into another without changing the linguistic information. Recently, "Save the Voice Project" for patients planning to undergo laryngectomy using our VC technique has been initiated. This study evaluated the speech converted from EL speech using the VC technique in a patient with head and neck cancer who had undergone laryngectomy. Additionally, details of an iPhone/iPad application for voice recording were reported. VC technology would greatly improve the quality of life of patients planning to undergo laryngectomy, especially those with relatively well-preserved vocal function. To preserve the patient's original normal speech, medical staff should perform voice management before and after laryngectomy, and high-quality voice recordings are essential.
Human Activity Recognition (HAR) has numerous applications in healthcare, rehabilitation, athletics, and smart environments. Effective AI models rely on diverse and representative datasets to achieve robust generalization. However, the majority of existing HAR datasets are collected exclusively from non-disabled individuals, limiting their applicability in real-world healthcare scenarios involving the elderly or individuals with disabilities. To address this limitation, we introduce InclusiveHAR, a novel smartphone-based HAR dataset collected from 20 participants, including 10 non-disabled individuals and 10 individuals with disabilities, of whom five had a single disability and five had multiple distinct conditions. Participants performed six daily activities: walking, standing, sitting, jogging, ramp ascent, and ramp descent. The dataset captures a wide range of movement patterns and behavioural variability, with particular emphasis on differences in activity execution observed in individuals with disabilities. Data were collected using an iPhone 14 Pro at a sampling rate of 50 Hz (one sample every 20 ms). The SensorLog app was used to lock the rate at 50 Hz. To illustrate the potential use of the dataset, a baseline evaluation is provided under multiple training scenarios using the MLP machine learning model. In this paper, we report and evaluate the performance of dataset against K-NN, SVM, and XGBoost models. In addition, the dataset is accompanied by detailed feature descriptions and comprehensive documentation of the data collection protocol, enabling transparent analysis, reproducibility, and future comparative studies. The InclusiveHAR dataset offers a valuable resource for investigating activity recognition performance across diverse participant groups and for supporting the development of inclusive HAR systems in healthcare and assistive technology applications.
In aesthetic clinical trials, image self-capture using mobile devices may help reduce burden on clinic resources, increase data quality, and lower barriers to study participation. This study aimed to develop a mobile device app to help participants self-capture clinically usable images. The Allergan Aesthetic (an AbbVie Company) mobile image app was designed to auto-capture images while directing study participants on distance, head position, and expression to capture a high-quality clinical image. To assess resolution and optimal lighting conditions, images captured using the app in office, at home, and in outdoor settings were compared with those from a Canfield VISIA-CR system (Canfield Scientific). Objective image quality assessment of facial images captured using the app with an iPhone XR (Apple Inc) and iPhone 12 (Apple Inc), compared with images captured using the Canfield VISIA-CR with a digital single-lens reflex camera and the Canfield mobile image capture app with a variety of Android (Google) and iOS (Apple Inc) devices, was conducted using the Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE). Clinical utility was assessed by calculating inter- and intrarater variability for severity ratings of participants' lateral canthal lines (LCL) or forehead lines (FHL) obtained from app-captured images compared with ratings based on in-person evaluations performed by a physician. Usability was assessed according to the ISO (International Organization for Standardization)/IEC (International Electrotechnical Commission) 250101 standard. The Allergan Aesthetic mobile image app was found to perform best under natural light and had image resolution insufficient for assessing minor facial structures, but appropriate for larger structures (eg, FHL). A total of 3968 images were assessed using BRISQUE. Images captured with the Allergan Aesthetic mobile image app had better image quality than those captured using other modalities, as indicated by lower mean BRISQUE scores of 14.05-19.81 compared with Canfield VISIA-CR with a DSLR (34.47) and the Canfield mobile image capture app (23.43). LCL and FHL were rated both in person and digitally in 68 and 71 participants, respectively (median age 52-56 y; 48% to 52% female; 75% to 78% White). Interrater reliability between clinician live evaluations and independent photo review of self-captured photos based on intraclass correlation coefficients (ICCs) was substantial (0.61-0.80) to almost perfect (0.81-1.00) for all raters (LCL: ICC 0.75-0.91 at rest and 0.79-0.89 at maximum contraction; FHL: ICC 0.77-0.93 at rest and 0.70-0.89 at maximum contraction). After 2 iterations of improvements, mean usability ratings of the app experience (out of 5) were as follows: easy to complete=3.2, enjoyable=3.1, satisfied with the level of guidance provided=3.2, and likely to complete a full session without exiting=4.1. The Allergan Aesthetic mobile image app delivers consistent, high-quality images that allow for assessment of LCL and FHL in good agreement with in-person evaluation. Image self-capture using mobile devices may help reduce clinic costs and remove barriers to participation in aesthetic clinical trials.
Traditional assessments of functional recovery after spine surgery rely on patient-reported outcomes, which are prone to bias. Wearables and smartphone activity tracking offer objective monitoring but may be unreliable if devices are not carried continuously. Capacity-oriented measures, such as the 1-min walk test (1MWT) and 6-min walk test (6MWT), may be more reliable. This study evaluated smartphone-derived interval metrics after lumbar spine surgery retrospectively. iPhone Health exports from 41 patients were analyzed. A sliding-window algorithm parsed daily distances to simulate 1MWT and 6MWT. Step counts and active time were extracted. Activity was compared across four intervals: 6-month baseline, final 2 weeks preoperatively, early postoperative (0-2 weeks), and late postoperative (2-6 weeks). Paired t-tests or Wilcoxon signed-rank tests were used, with Simes-Hochberg adjustment for multiple comparisons. Day-to-day stability was summarized by the coefficient of variation (CV). Pearson correlations were calculated. Median 1MWT fell from 98 m at baseline to 82 m in the final two preoperative weeks (p < 0.05) and increased to 105 m by late recovery (p < 0.05 vs. preoperative). Median 6MWT declined from 403 to 345 m preoperatively, with this decline not reaching significance (p = 0.07), and increased to 407 m by late recovery (p < 0.05 vs. preoperative). Steps declined from 5030 to 3825 preoperatively (p < 0.05) and rose to 5538 at 2-6 weeks (p < 0.05 vs. preoperative). The 1MWT and 6MWT were strongly correlated. CV was lower for 1MWT and 6MWT than for steps. Smartphone-derived 1MWT and 6MWT improved significantly from the immediate preoperative period to late postoperative recovery, showed lower day-to-day variability than longitudinal activity metrics, and were strongly correlated with each other. These findings support smartphone-derived interval metrics as a feasible method to monitor recovery following lumbar spine surgery.
Polychromia remains one of the most reproducible dermoscopic indicators of melanoma, yet its clinical assessment is predominantly subjective. Shannon entropy has been proposed as an objective measure of color heterogeneity in pigmented skin lesions. However, global entropy derived from grayscale or composite RGB histograms may primarily capture luminance dispersion rather than true chromatic complexity. This proof-of-concept study evaluated whether global Shannon entropy quantifies polychromia and whether channel-specific entropy metrics more accurately reflect chromatic heterogeneity. Smartphone photographs (iPhone 13 Pro Max, Apple Inc.) of a histopathologically confirmed superficial melanoma, a benign junctional nevus, and their respective perilesional skin were analyzed using ImageJ (National Institutes of Health). Intensity histograms were generated in an 8-bit grayscale, composite RGB mode, and separately for the red, green, and blue channels. Shannon entropy (H, log₂), inter-channel entropy differences (ΔR-G, ΔR-B, and ΔG-B), red-channel asymmetry (Aᴿ), and a composite Polychromia Index (Iᴾ) were computed for each region of interest, with all metrics normalized to perilesional skin to control for illumination and baseline heterogeneity. Grayscale and RGB-composite histograms yielded nearly identical entropy values for both lesions, confirming that global entropy primarily reflects luminance contrast rather than chromatic structure. By contrast, channel-specific analysis revealed marked divergence in the melanoma, with normalized inter-channel entropy differences showing substantial residual chromatic heterogeneity (ΔG-B_residual = +12.31; ΔR-G_residual = +9.71), representing 600-4000% increases compared with the nevus. The normalized Polychromia Index (Iᴾ) demonstrated an 8.22-unit separation between the melanoma (+6.84) and the nevus (-1.38), closely aligning with the visual impression of color variegation. These findings indicate that global Shannon entropy does not meaningfully quantify polychromia under real-world smartphone imaging conditions. Channel-specific entropy and inter-channel metrics, however, reliably discriminate chromatically heterogeneous lesions from uniform ones. This low-cost, reproducible framework offers a physiologically interpretable approach to objective color heterogeneity assessment and holds potential for teledermatology and automated melanoma-detection systems.
Citri Reticulatae Pericarpium (CRP), the dried peel of citrus fruits, holds notable dietary and medicinal value. Its quality and price largely depend on origin and aging. Lower-grade CRP is often adulterated to imitate premium products, making accurate authentication of region and vintage essential for quality assurance and fair market valuation. Existing methods for vintage classification are limited due to complex equipment and high operational costs, restricting their scalability in practical applications. To address these issues, a convenient method for the accurate identification of Citri Reticulatae Pericarpium using image and multi-stream is proposed. The method comprises three main stages. Firstly, an object detection network with bounding box refinement localizes exocarp and albedo regions from whole CRP images. Secondly, a three-stream feature extractor processes the whole images along with exocarp and albedo patches to capture complementary visual details. A channel-level feature interaction module further enhances robustness through cross-region feature integration. Thirdly, a meta-learning module enables rapid adaptation to images captured under varying conditions by different consumer-grade devices. Experimental results demonstrate that the proposed method achieves an accuracy of 95.5% on iPhone-captured images. In addition, for images captured by different devices, the proposed method achieves a relative accuracy improvement of more than 34% over the direct transfer method, mainly owing to the meta-learning adaptation to different devices.
Mobile applications may offer an accessible alternative for objective gingival colour measurement; however, their accuracy compared with spectrophotometry remains unclear. This study evaluated the accuracy and agreement between seven mobile applications and a reference spectrophotometer for gingival colour measurement using CIELAB coordinates and CIEDE2000 colour differences across different gingival regions. It also assessed whether discrepancies varied according to the region evaluated. A SpectroShade Micro was used as the reference device. Seven applications were tested: four iOS apps using an iPhone 12 Pro Max and three Android apps using a Samsung Galaxy A52s 5G. Gingival colour was recorded in 250 participants with healthy gingiva in five regions: mesial papilla, distal papilla, free gingival margin, attached gingiva, and mucogingival line. CIELAB values were recorded in triplicate. Differences in L*, a*, b*, and ΔE00 were calculated and analysed statistically. All mobile applications showed relevant discrepancies compared with the spectrophotometer. The greatest differences were mainly observed in L*, with most applications recording higher lightness values. Android Color Picker AR and Android Colorimeter showed the largest chromatic discrepancies, particularly for a* and b*. OptiShade showed the smallest differences, although its results were still clinically unacceptable in more than 65% (n=163) of cases. Android applications exceeded 98% (n=246) unacceptable differences in all regions CONCLUSIONS: None of the evaluated applications achieved accuracy comparable to spectrophotometry for gingival colour assessment. Although OptiShade performed best, mobile applications cannot currently be considered reliable standalone tools. These applications may be useful as complementary tools, but clinical judgement and validated dental-specific technologies remain essential.
Aim and objectives Accurate shade selection is critical for achieving optimal esthetic outcomes in restorative dentistry. Digital photography has emerged as an alternative to conventional visual shade matching; however, the accuracy of smartphone (SP) cameras compared with digital single-lens reflex (DSLR) cameras remains uncertain. This study aimed to compare Commission Internationale de l'Éclairage L*a*b* (CIELAB) color coordinates obtained from SP and DSLR images with manufacturer-provided values of the VITA 3D-Master shade guide (VITA Zahnfabrik, Bad Säckingen, Germany) and to evaluate the reliability of smartphone photography for shade selection. Methods An in vitro study was conducted using a commercial VITA 3D-Master shade guide. Images of 26 shade tabs were captured using an iPhone 13 smartphone (Apple Inc., Cupertino, CA, USA) and a Canon EOS 700D DSLR (Canon Inc., Tokyo, Japan). Two images per shade tab were obtained under standardized daylight conditions (4000-5000 K) at a fixed distance of 18 cm against a neutral gray background, yielding a total of 104 images. Image selection was standardized based on predefined criteria of focus and exposure consistency. CIELAB color values (L*, a*, b*) were extracted using digital image analysis software. Color differences (ΔE) between photographic values and manufacturer reference values were calculated using the CIEDE2000 formula. Statistical analysis was performed using the Wilcoxon signed-rank test, with significance set at P < 0.05. Results Statistically significant differences were observed between SP and DSLR images in deviations of L*, a*, and b* values (P < 0.05). DSLR images demonstrated greater color accuracy, achieving 75% agreement with manufacturer reference values, whereas SP images showed 55% agreement. Mean ΔE values were lower for DSLR images, indicating improved color fidelity. Conclusions Within the limitations of this in vitro study, DSLR photography demonstrated greater accuracy in shade selection compared to smartphone photography. Although smartphones may serve as an accessible adjunct, they currently exhibit lower color accuracy than DSLR systems for shade determination.
The rising incidence of breast cancer and increasing demand for esthetic breast reconstruction have led to a higher frequency of nipple reconstruction procedures. Methods for nipple reconstruction vary; however, the assessment of surgical outcomes through nipple volume measurement often relies on imprecise or inconsistent techniques. Conventional approaches can be time-consuming, uncomfortable for patients, and depend on expensive equipment. We retrospectively analyzed 37 reconstructed nipples in 32 female patients aged >19 years who underwent evaluation at Chungnam National University Hospital between December 1, 2023, and May 30, 2025. Traditional measurement methods include mold-based water displacement using Archimedes' principle, and geometric calculations based on physical dimensions that serve as the reference standard. Computed tomography (CT) and Antera 3D®, two more recent modalities, were also used. In this study, we introduced a novel, noninvasive three-dimensional (3D) reconstruction method using the Gaussian splatting technique. The proposed method demonstrated a mean squared error of ±0.128 cc compared with ±0.023 cc for Antera 3D®. The 95% confidence interval for the difference between the methods (0.005-0.012 cc) remained within acceptable limits. Bland-Altman analysis confirmed agreement between both methods and the reference standard, with Antera 3D® showing narrower limits of agreement. The proposed Gaussian splatting method provides an accurate, low-cost, non-contact, and patient-friendly alternative to conventional nipple volume measurement techniques.
The integration of markerless motion capture systems such as OpenCap with force platforms expands the possibilities of biomechanical analysis in low-cost environments; however, it requires robust temporal synchronization procedures in the absence of shared hardware triggers. To develop and validate an automatic synchronization algorithm based on heel kinematic events to align OpenCap data with force platform signals during lower-limb functional exercises. Thirty normal-weight adult women (18-45 years) were evaluated while performing between 11 and 14 functional tasks (60° and 90° squats, lunges, sliding variations, and step exercises), yielding 330 motion records. Kinematics were estimated using OpenCap (four iPhone 12 cameras at 60 Hz), and kinetics were recorded using BTS P6000 force platforms synchronized with an OptiTrack system (Gold Standard). The algorithm detected heel contact from the filtered vertical coordinate and aligned this event with the initial rise in vertical ground reaction force. Validation against the Gold Standard was performed in 20 squat repetitions (10 at 60° and 10 at 90°) using Pearson correlation, RMSE, and MAE of the time-normalized and amplitude-normalized (0-1) vertical ground reaction force (vGRF). The algorithm successfully synchronized 92.5% of the 330 records; the remaining cases showed kinematic noise or additional steps that prevented robust event detection. During validation, correlations were r = 0.85 (60°) and r = 0.81 (90°), with Root Mean Square Error (RMSE) < 0.17 and Mean Absolute Error (MAE) < 0.14, values representing less than 0.1% of the peak force. The heel-contact-based algorithm allows accurate synchronization of OpenCap and force platform signals during lower-limb functional exercises, achieving performance comparable to hardware-synchronized systems. This approach facilitates the integration of markerless motion capture in clinical, sports, and occupational settings where advanced dynamic analysis is required with limited infrastructure.
To evaluate the accuracy, test-retest reliability, and time-efficiency of Apple's Hearing Test Feature (HTF) compared to reference standard pure-tone audiometry (PTA). Cross-sectional validation study. Single-center study at a university clinic. PTA was performed in a sound-treated booth. HTF testing occurred in a quiet room. A sample of 25 adults (mean age 50.1 years [SD 14.2]; 68% female) with self-reported mild-to-moderate hearing loss participated. Each contributed 16 thresholds, yielding 400 comparisons. Participants underwent PTA by an audiologist, followed by two independent HTF assessments (start and end of session) using Apple AirPods Pro 2 paired with an iPhone 13. Outcomes included threshold accuracy versus PTA, test-retest reliability, and test duration. Across 400 comparisons, 86.5% of HTF thresholds were within 10 dB HL of PTA. Root mean square deviation (RMSD) values ranged from 3.3 to 7.9 dB HL (left ear) and 5.8 to 9.7 dB HL (right ear), meeting minimally acceptable accuracy (≤10 dB HL). Test-retest was reliable, 84.1% of thresholds within 5 dB HL and 96.6% within 10 dB HL. Desired reliability (≤6 dB HL) was met at all frequencies except 250 Hz (left ear), which met minimum acceptable level. HTF was significantly faster (median 5.5 minutes) than PTA (10.0 minutes; P < .001). Apple's HTF demonstrated clinically acceptable accuracy and reliability, with improved time-efficiency compared to PTA. Findings support its potential for consumer-led hearing monitoring and OTC hearing aid self-fitting. Further research should assess inter-device reliability and integration with Apple's Hearing Aid Feature.
The canalith repositioning maneuver (CRM) is an effective method for treating benign paroxysmal positional vertigo (BPPV). Classical CRM is designed mainly for a single semicircular canal (SC). A mixed maneuver (MM) hybridized by classical maneuvers was proposed for otoconia of three SCs on the same side. Simulations of otoconia movement induced by classical CRMs and MM were performed on a 3-axis servo motors platform (SMP) with an iPhone containing the aVOR app. MM was performed on BPPV VIEWER to show the otoconia trajectory. The Epley and Barbecue maneuver were effective for the otoconia of posterior semicircular canal (PSC) and horizontal semicircular canal (HSC), respectively. Similarly, the supine roll test + Gufoni maneuver was effective for otoconia of HSC, and the Dix-Hallpike test + Yacovino maneuver was effective for otoconia of ASC. Furthermore, the MM was effective for free-floating otoconia in HSC, ASC and long arm of PSC on the same side. The SMP-aVOR model and BPPV VIEWER provide methods for studying otoconia repositioning. The MM showed potential capability to reposition the common types of otoconia in the three SCs on the same side.
Decentralized clinical trials using direct-to-participant recruitment can potentially engage large, representative participant pools. The objective of the study was to share insights on multichannel strategies for participant recruitment in the decentralized Heartline Study, a randomized trial testing the impact of a mobile application-based heart health program with the electrocardiogram and Irregular Rhythm Notification features on an Apple Watch for early diagnosis, treatment, and outcomes of atrial fibrillation. Eligible participants were U.S. adults aged ≥65 years with an iPhone and Medicare coverage. Multiple pathways for broad outreach were explored, including digital (eg, email, social media) and traditional channels (eg, direct mail, community outreach). Recruitment efforts were assessed and refined throughout the study to maximize reach. Across multiple channels, 321,272 Heartline Study applications were installed, with 34,244 participants (11%) completing enrollment (February 2020-December 2022) and 82% (28,155/34,244) completing baseline demographic assessments. Women accounted for 54.2% (15,258/28,155) of study participants; 93.0% identified as White (26,192/28,155), 2.8% Asian (781/28,155), 2.7% Black (747/28,155), and 2.5% Hispanic (699/28,155). Broad geographic representation throughout the United States was achieved. The Heartline Study demonstrated the ability to recruit large numbers of participants aged ≥65 years. A direct-to-participant approach across multiple channels achieved excellent gender and geographic diversity, enrolling a higher percentage of women than typical cardiology trials and participation from rural areas. However, less racial and ethnic representation was achieved, highlighting the need for additional strategies to meet this goal. Future trials may consider such multichannel recruitment approaches to support decentralized clinical trials. (A Study to Investigate if Early Atrial Fibrillation [AF] Diagnosis Reduces Risk of Events Like Stroke in the Real-World; NCT04276441).
Atrial fibrillation (AF) can lead to significant cardiovascular events and health care utilization. Continuous monitoring via insertable cardiac monitors (ICMs) and data analytics have the potential to improve care delivery. The DEFINE Atrial Fibrillation (DEFINE AFib) study was designed to develop and evaluate novel algorithms using ICM data to predict AF-associated clinical actions (AFCAs) and guide AF management. DEFINE AFib enrolled patients with an ICM (Reveal LINQ/LINQ II; Medtronic) and a history of AF. An Apple iPhone application collected AF-related quality of life (AFEQT) and EQ-5D data. ICM daily AF burden and Apple Watch (AW) irregular rhythm notification (IRN) data were also collected. AFCA was defined as an AF-related procedure or initiation of rate/rhythm control medication. Mutivariable logistic regression was used to identify ICM features in the last 30 days associated with first occurrence of AFCA in the next 30 days in a train and test approach (70%/30%). Among 864 patients (mean 69 ± 10 years; 56% male) meeting inclusion criteria, there were 8963 30-day evaluation windows that included 151 AFCAs. Area under the receiver operating characteristic curve (AUC) was 76% (train) and 70% (test). The model placed participants into high- vs low-risk AFCA groups. At the patient-level, 21% of participants crossing the high-risk threshold for their first time experienced an AFCA at a mean time of 195 ± 164 days compared with 5% in the low-risk group (AUC 65%). Increasing daily mean AF burden was associated with lower quality of life: <6 minutes (reference), 6 minutes to 5.5 hours (AFEQT -7.69; P < .001), and 5.5-12 hours (AFETQ -13.97, P < .001; ≥5.5 hours EQ-5D -0.03, P = .007). In a subanalysis of individuals with smart watch data (n = 53), the ICM model signaled high risk before AFCAs 85% of the time (AUC 57%) compared with 23% for AW IRN (P = .005; AUC 55%). DEFINE AFib transformed ICM diagnostic data to predict risk of AFCA with good discrimination, particularly compared with wearable data. These results highlight the potential advantages of ICM-based continuous monitoring for AF management and the utility of ICM prediction models that could help inform pre-emptive therapeutic strategies.
The aim of this study was for the first time to evaluate of intraoral digital photography in assessing buccal surface of anterior composite restorations using a smartphone (iPhone 14 Pro), a smartphone with a lens (2IN1 Phone Macro Lens), and a digital camera with a macro lens (Canon Rebel XTi), compared to clinical examination, based on World Dental Federation (FDI) criteria. A total of 185 anterior composite restorations were evaluated by calibrated restorative dentistry specialists. Restorations were scored according to the FDI criteria as intact, requiring repair, or needing replacement. Clinical examination was considered the gold standard. Photographs were taken under standardized conditions, and inter-observer and inter-method agreement were analyzed using Cohen’s Kappa and intraclass correlation coefficient (ICC). High inter-observer agreement was observed across all methods (Kappa = 0.928–1.0). Good to excellent agreement was found between clinical examination and digital photography methods for FDI final scores (Kappa = 0.775–0.973, p < 0.001). Photographs taken with the lens-equipped smartphone and macro camera showed higher agreement with clinical examination (Kappa = 0.973). Digital photography, particularly with a lens-equipped smartphone and macro camera, offers effectiveness comparable to clinical examination in evaluating anterior composite restorations. This study demonstrates that intraoral digital photography, particularly with a lens-equipped smartphone and macro camera, achieves diagnostic outcomes closely aligned with clinical examination using FDI criteria. While smartphones alone are practical, lens-assisted imaging enhances accuracy, supporting its use as a cost-effective alternative to professional macro cameras.