Estimation of Shoulder Joint Rotation Angle Using Tablet Device and Pose Estimation Artificial Intelligence Model.

Traditionally, angle measurements have been performed using a goniometer, but the complex motion of shoulder movement has made these measurements intricate. The angle of rotation of the shoulder is particularly difficult to measure from an upright position because of the complicated base and moving axes. In this study, we attempted to estimate the shoulder joint internal/external rotation angle using the combination of pose estimation artificial intelligence (AI) and a machine learning model. Videos of the right shoulder of 10 healthy volunteers (10 males, mean age 37.7 years, mean height 168.3 cm, mean weight 72.7 kg, mean BMI 25.6) were recorded and processed into 10,608 images. Parameters were created using the coordinates measured from the posture estimation AI, and these were used to train the machine learning model. The measured values from the smartphone's angle device were used as the true values to create a machine learning model. When measuring the parameters at each angle, we compared the performance of the machine learning model using both linear regression and Light GBM. When the pose estimation AI was trained using linear regression, a correlation coefficient of 0.971 was achieved, with a mean absolute error (MAE) of 5.778. When trained with Light GBM, the correlation coefficient was 0.999 and the MAE was 0.945. This method enables the estimation of internal and external rotation angles from a direct-facing position. This approach is considered to be valuable for analyzing motor movements during sports and rehabilitation.

Long-Term Effect of a Tablet-Based Cognitive Behavioral Group Intervention on Step Count, Fatigue, Self-Efficacy, and Quality of Life in Older Adults With Arthritis: A Pilot Study.

PURPOSE: To evaluate the long-term effect of a tablet-based, cognitive-behavioral group intervention (Tab-G) to improve daily walking for older adults with arthritis. METHOD: Using an experimental pretest/posttest repeated measure design, long-term effects on step count, fatigue, self-efficacy, and quality of life (QOL) were investigated. RESULTS: Results of repeated measures analysis of variance showed significant improvement in step counts (F[1, 37] = 4.18, p = 0.048), fatigue (F[1, 36] = 9.971, p = 0.003), self-efficacy (F[1,28] = 4.645, p = 0.04), and QOL (F[1, 29] = 6.147, p = 0.019) in the Tab-G group compared to the control group. There were significant time effects across four time points (baseline and Weeks 4, 8, and 10) in fatigue (F[3, 108] = 5.43, p = 0.002), self-efficacy (F[3, 84] = 5.433, p = 0.002), and QOL (F[3, 87] = 3.673, p = 0.015), but not in step counts (F[3, 111] = 0.611, p = 0.609). CONCLUSION: Findings demonstrate positive long-term effects on fatigue in older adults with arthritis. [Journal of Gerontological Nursing, 50(5), 35-42.].

Measuring Visual Fields in Children With Glaucoma Using a Portable Tablet.

Purpose: To compare perimetric outcomes of an iPad perimetry app (Melbourne Rapid Fields [MRF]) with those of the Humphrey Field Analyser (HFA) testing children with glaucoma. Methods: Sixteen children diagnosed and treated for glaucoma were recruited to evaluate their perimetric performance over two visits. At each visit, they undertook visual field assessment using the MRF application as well as the HFA. The HFA test was part of their usual clinical work up and a clinical assistant judged which test format (24-2 SITA standard or SITA fast) might be suited to the testing of that child. The primary outcome measure was the association and repeatability of mean deviation (MD) for the MRF and HFA tests, by way of regression, intraclass correlation coefficient and Bland-Altman analysis. Secondary measures were comparisons of pattern deviation indices, test times as well as an indication of participant test preference. Summary data show means ± standard deviation. Results: The age for our cohort was 7 to 15 years of age (mean, 10.0 ± 2.4 years of age). The MRF MD was in close concordance to HFA MD with an intraclass correlation coefficient of 0.91 (95% confidence interval, 0.82-0.95). Bland-Altman analysis found little bias (-0.6 dB) and a 95% coefficient of repeatability of 2.1 dB in eyes having a normal HFA MD. In eyes with glaucomatous visual field defects the 95% coefficient of repeatability at retest was much larger for both the MRF (10.5 dB) as well as for the HFA (10.0 dB). Average MRF test times (5.6 ± 1.2 minutes) were similar to SITA Fast (5.4 ± 1.9 minutes) with both being significantly faster than SITA standard (8.6 ± 1.4 minutes; P < 0.001). All children chose testing with the MRF as their preference. Conclusions: MRF correlated strongly with HFA and was preferred by the children over the HFA. MRF is suitable for perimetric evaluation of children with glaucoma. Translational Relevance: This study finds that an iPad based visual field test can be used with children having glaucoma to yield outcomes similar to SITA-fast. Children indicate a preference for such testing.

Mobile device screen time is associated with poorer language development among toddlers: results from a large-scale survey.

BACKGROUND: Despite young children's widespread use of mobile devices, little research exists on this use and its association with children's language development. The aim of this study was to examine the associations between mobile device screen time and language comprehension and expressive language skills. An additional aim was to examine whether three factors related to the domestic learning environment modify the associations. METHODS: The study uses data from the Danish large-scale survey TRACES among two- and three-year-old children (n = 31,125). Mobile device screen time was measured as time spent on mobile devices on a normal day. Measurement of language comprehension and expressive language skills was based on subscales from the Five to Fifteen Toddlers questionnaire. Multivariable linear regression was used to examine the association between child mobile device screen time and language development and logistic regression to examine the risk of experiencing significant language difficulties. Joint exposure analyses were used to examine the association between child mobile device screen time and language development difficulties in combination with three other factors related to the domestic learning environment: parental education, reading to the child and child TV/PC screen time. RESULTS: High mobile device screen time of one hour or more per day was significantly associated with poorer language development scores and higher odds for both language comprehension difficulties (1-2 h: AOR = 1.30; ≥ 2 h: AOR = 1.42) and expressive language skills difficulties (1-2 h: AOR = 1.19; ≥ 2 h: AOR = 1.46). The results suggest that reading frequently to the child partly buffers the negative effect of high mobile device screen time on language comprehension difficulties but not on expressive language skills difficulties. No modifying effect of parental education and time spent by the child on TV/PC was found. CONCLUSIONS: Mobile device screen time of one hour or more per day is associated with poorer language development among toddlers. Reading frequently to the child may have a buffering effect on language comprehension difficulties but not on expressive language skills difficulties.

iPad-Based Neurocognitive Testing (ImPACT-QT) in Acute Adult Mild Traumatic Brain Injury/Concussion: Study on Practicality and Bedside Cognitive Scores in a Level-1 Trauma Center.

BACKGROUND: There lacks rapid standardized bedside testing to screen cognitive deficits following mild traumatic brain injury (mTBI). Immediate Post-Concussion Assessment & Cognitive Testing-Quick Test (ImPACT-QT) is an abbreviated-iPad form of computerized cognitive testing. The aim of this study is to test ImPACT-QT utility in inpatient settings. We hypothesize ImPACT-QT is feasible in the acute trauma setting. METHOD: Trauma patients ages 12-70 were administered ImPACT-QT (09/2022-09/2023). Encephalopathic/medically unstable patients were excluded. Mild traumatic brain injury was defined as documented-head trauma with loss-of-consciousness <30 minutes and arrival Glasgow Coma Scale 13-15. Patients answered Likert-scale surveys. Bivariate analyses compared demographics, attention, motor speed, and memory scores between mTBI and non-TBI controls. Multivariable logistic regression assessed memory score as a predictor of mTBI diagnosis. RESULTS: Of 233 patients evaluated (36 years [IQR 23-50], 71% [166/233] female), 179 (76%) were mTBI patients. For all patients, mean test-time was 9.3 ± 2 minutes with 93% (73/76) finding the test "easy to understand." Mild traumatic brain injury patients than non-TBI control had lower memory scores (25 [IQR 7-100] vs 43 [26-100], P = .001) while attention (5 [1-23] vs 11 [1-32]) and motor score (14 [3-28] vs 13 [4-32]) showed no significant differences. Multivariable-regression (adjustment: age, sex, race, education level, ISS, and time to test) demonstrated memory score predicted mTBI positive status (OR .96, CI .94-.98, P = .004). DISCUSSION: Immediate Post-Concussion Assessment & Cognitive Testing-Quick Test is feasible in trauma patients. Preliminary findings suggest acute mTBIs have lower memory but not attention/motor scores vs non-TBI trauma controls.

Online Learning State Evaluation Method Based on Face Detection and Head Pose Estimation.

In this paper, we propose a learning state evaluation method based on face detection and head pose estimation. This method is suitable for mobile devices with weak computing power, so it is necessary to control the parameter quantity of the face detection and head pose estimation network. Firstly, we propose a ghost and attention module (GA) base face detection network (GA-Face). GA-Face reduces the number of parameters and computation in the feature extraction network through the ghost module, and focuses the network on important features through a parameter-free attention mechanism. We also propose a lightweight dual-branch (DB) head pose estimation network: DB-Net. Finally, we propose a student learning state evaluation algorithm. This algorithm can evaluate the learning status of students based on the distance between their faces and the screen, as well as their head posture. We validate the effectiveness of the proposed GA-Face and DB-Net on several standard face detection datasets and standard head pose estimation datasets. Finally, we validate, through practical cases, that the proposed online learning state assessment method can effectively assess the level of student attention and concentration, and, due to its low computational complexity, will not interfere with the student's learning process.

Designing and Implementation of a Digitalized Intersectoral Discharge Management System and Its Effect on Readmissions: Mixed Methods Approach.

BACKGROUND: Digital transformation offers new opportunities to improve the exchange of information between different health care providers, including inpatient, outpatient and care facilities. As information is especially at risk of being lost when a patient is discharged from a hospital, digital transformation offers great opportunities to improve intersectoral discharge management. However, most strategies for improvement have focused on structures within the hospital. OBJECTIVE: This study aims to evaluate the implementation of a digitalized discharge management system, the project "Optimizing instersectoral discharge management" (SEKMA, derived from the German Sektorübergreifende Optimierung des Entlassmanagements), and its impact on the readmission rate. METHODS: A mixed methods design was used to evaluate the implementation of a digitalized discharge management system and its impact on the readmission rate. After the implementation, the congruence between the planned (logic model) and the actual intervention was evaluated using a fidelity analysis. Finally, bivariate and multivariate analyses were used to evaluate the effectiveness of the implementation on the readmission rate. For this purpose, a difference-in-difference approach was adopted based on routine data of hospital admissions between April 2019 and August 2019 and between April 2022 and August 2022. The department of vascular surgery served as the intervention group, in which the optimized discharge management was implemented in April 2022. The departments of internal medicine and cardiology formed the control group. RESULTS: Overall, 26 interviews were conducted, and we explored 21 determinants, which can be categorized into 3 groups: "optimization potential," "barriers," and "enablers." On the basis of these results, 19 strategies were developed to address the determinants, including a lack of networking among health care providers, digital information transmission, and user-unfriendliness. On the basis of these strategies, which were prioritized by 11 hospital physicians, a logic model was formulated. Of the 19 strategies, 7 (37%; eg, electronic discharge letter, providing mobile devices to the hospital's social service, and generating individual medication plans in the format of the national medication plan) have been implemented in SEKMA. A survey on the fidelity of the application of the implemented strategies showed that 3 of these strategies were not yet widely applied. No significant effect of SEKMA on readmissions was observed in the routine data of 14,854 hospital admissions (P=.20). CONCLUSIONS: This study demonstrates the potential of optimizing intersectoral collaboration for patient care. Although a significant effect of SEKMA on readmissions has not yet been observed, creating a digital ecosystem that connects different health care providers seems to be a promising approach to ensure secure and fast networking of the sectors. The described intersectoral optimization of discharge management provides a structured template for the implementation of a similar local digital care networking infrastructure in other care regions in Germany and other countries with a similarly fragmented health care system.

Cocreating an Automated mHealth Apps Systematic Review Process With Generative AI: Design Science Research Approach.

BACKGROUND: The use of mobile devices for delivering health-related services (mobile health [mHealth]) has rapidly increased, leading to a demand for summarizing the state of the art and practice through systematic reviews. However, the systematic review process is a resource-intensive and time-consuming process. Generative artificial intelligence (AI) has emerged as a potential solution to automate tedious tasks. OBJECTIVE: This study aimed to explore the feasibility of using generative AI tools to automate time-consuming and resource-intensive tasks in a systematic review process and assess the scope and limitations of using such tools. METHODS: We used the design science research methodology. The solution proposed is to use cocreation with a generative AI, such as ChatGPT, to produce software code that automates the process of conducting systematic reviews. RESULTS: A triggering prompt was generated, and assistance from the generative AI was used to guide the steps toward developing, executing, and debugging a Python script. Errors in code were solved through conversational exchange with ChatGPT, and a tentative script was created. The code pulled the mHealth solutions from the Google Play Store and searched their descriptions for keywords that hinted toward evidence base. The results were exported to a CSV file, which was compared to the initial outputs of other similar systematic review processes. CONCLUSIONS: This study demonstrates the potential of using generative AI to automate the time-consuming process of conducting systematic reviews of mHealth apps. This approach could be particularly useful for researchers with limited coding skills. However, the study has limitations related to the design science research methodology, subjectivity bias, and the quality of the search results used to train the language model.

Association of mobile device proficiency and subjective cognitive complaints with financial management ability among community-dwelling older adults: a population-based cross-sectional study.

BACKGROUND: Mobile devices have been used by many older adults and have the potential to assist individuals with subjective cognitive complaints (SCCs) in daily living tasks. Financial management is one of the most complex daily activity for older adults, as it is easily impaired in the prodromal stage of dementia and cognitive impairment. AIM: To investigate financial management ability among older adults from SCCs and mobile device proficiency. METHODS: A self-administered questionnaire was sent to 529 participants who were ≥ 65 years and regularly use mobile devices. Participants were divided into four groups based on SCC prevalence and scores of the Mobile Device Proficiency Questionnaire (MDPQ-16). Financial management abilities were compared between groups using the Process Analysis of Daily Activities for Dementia subscale. Regression model and crosstabulation table were used to investigate associations in detail. RESULTS: A significant difference in financial management ability was observed among the four groups (p < 0.001), with the dual impairment group showed significantly lower than the robust and SCC groups (p < 0.001). Educational history, sex, and MDPQ-16 score were significantly associated with participants' financial management ability (p ≤ 0.01). The proportion of participants who could use ATMs and electronic money independently was significantly lower among those with low proficiency in mobile devices (LPM), regardless of SCC (p < 0.05). CONCLUSION: The LPM group showed an impaired ability to manage their finances, particularly in situations where they would use information devices. Therefore, healthcare professionals should assess not only the SCC but also their proficiency with mobile devices to predict their impairment in activities of daily living.

Accuracy and tracing time of cephalometric analyses on a tablet or desktop computer : A prospective study.

BACKGROUND: This prospective study aimed to evaluate the influence of the computer type (tablet or desktop) on accuracy and tracing time of cephalometric analyses. METHODS: Dental students used a web-based application specifically developed for this purpose to perform cephalometric analyses on tablet and desktop computers. Landmark locations and timestamps were exported to measure the accuracy, successful detection rate and tracing time. Reference landmarks were established by six experienced orthodontists. Statistical analysis included reliability assessment, descriptive statistics, and linear mixed effect models. RESULTS: Over a period of 8 semesters a total of 277 cephalometric analyses by 161 students were included. The interrater reliability of the orthodontists establishing the reference coordinates was excellent (ICC > 0.9). For the students, the mean landmark deviation was 2.05 mm and the successful detection rate for the clinically acceptable threshold of 2 mm suggested in the literature was 68.6%, with large variations among landmarks. No effect of the computer type on accuracy and tracing time of the cephalometric analyses could be found. CONCLUSION: The use of tablet computers for cephalometric analyses can be recommended.

[Optimizing radiological diagnostic management via mobile devices in trauma surgery]. / Anforderung von radiologischer Diagnostik in der Unfallchirurgie mittels mobiler Endgeräte.

BACKGROUND: Time is a scarce resource for physicians. One medical task is the request for radiological diagnostics. This process is characterized by high administrative complexity and sometimes considerable time consumption. Measures that lead to an administrative relief in favor of patient care have so far been lacking. AIM OF THE STUDY: Process optimization of the request for radiological diagnostics. As a proof of concept the request for radiological diagnostics was conducted using a mobile, smartphone and tablet-based application with dedicated voice recognition software in the Department of Trauma Surgery at the University Hospital of Würzburg (UKW). MATERIAL AND METHODS: In a prospective study, time differences and efficiency of the mobile app-based method (ukw.mobile based Application = UMBA) compared to the PC-based method (PC-based application = PCBA) for requesting radiological services were analyzed. The time from the indications to the completed request and the time required to create the request on the device were documented and assessed. Due to the non-normal distribution of the data, a Mann-Whitney U test was performed. RESULTS: The time from the indications to the completed request was significantly (p < 0.05) reduced using UMBA compared to PCBA (PCBA: mean ± standard difference [SD] 19.57 ± 33.24 min, median 3.00 min, interquartile range [IQR] 1.00-30.00 min vs. UMBA: 9.33 ± 13.94 min, median 1.00 min, IQR 0.00-20.00 min). The time to complete the request on the device was also significantly reduced using UMBA (PCBA: mean ± SD 63.77 ± 37.98 s, median 51.96 s, IQR 41.68-68.93 s vs. UMBA: 25.21 ± 11.18 s, median 20.00 s, IQR 17.27-29.00 s). CONCLUSION: The mobile, voice-assisted request process leads to a considerable time reduction in daily clinical routine and illustrates the potential of user-oriented, targeted digitalization in healthcare. In future, the process will be supported by artificial intelligence.

Making Digital Health Equitable.

Most agree that the current healthcare system is broken. Fortunately, technology is increasing at an exponential rate and provides a solution for the future. Digital Health is an integrator concept that has the potential to take advantage of technological advantages. Digital Health converges health, healthcare, research, and everyday life. It includes technologies, platforms, and systems that engage consumers in all aspects of life. It makes health and healthcare be people-centered and personalized. Digital health requires total interoperability - standards, common data elements, and the integration of data from all sources. It demands data sharing. Digital Health brings together a wide range of stakeholders for similar goals using the same resources. Digital Health uses mobile devices and wearable sensors and uses Artificial Intelligence and Machine Learning to handle the vast amount of data Digital Health engages. Finally, Digital Health has the potential to open the gap between the different social and economic classes that must be addressed.

Remote Multi-Person Heart Rate Monitoring with Smart Speakers: Overcoming Separation Constraint.

Heart rate is a key vital sign that can be used to understand an individual's health condition. Recently, remote sensing techniques, especially acoustic-based sensing, have received increasing attention for their ability to non-invasively detect heart rate via commercial mobile devices such as smartphones and smart speakers. However, due to signal interference, existing methods have primarily focused on monitoring a single user and required a large separation between them when monitoring multiple people. These limitations hinder many common use cases such as couples sharing the same bed or two or more people located in close proximity. In this paper, we present an approach that can minimize interference and thereby enable simultaneous heart rate monitoring of multiple individuals in close proximity using a commonly available smart speaker prototype. Our user study, conducted under various real-life scenarios, demonstrates the system's accuracy in sensing two users' heart rates when they are seated next to each other with a median error of 0.66 beats per minute (bpm). Moreover, the system can successfully monitor up to four people in close proximity.

Mobile Crowdsensing in Ecological Momentary Assessment mHealth Studies: A Systematic Review and Analysis.

As mobile devices have become a central part of our daily lives, they are also becoming increasingly important in research. In the medical context, for example, smartphones are used to collect ecologically valid and longitudinal data using Ecological Momentary Assessment (EMA), which is mostly implemented through questionnaires delivered via smart notifications. This type of data collection is intended to capture a patient's condition on a moment-to-moment and longer-term basis. To collect more objective and contextual data and to understand patients even better, researchers can not only use patients' input via EMA, but also use sensors as part of the Mobile Crowdsensing (MCS) approach. In this paper, we examine how researchers have embraced the topic of MCS in the context of EMA through a systematic literature review. This PRISMA-guided review is based on the databases PubMed, Web of Science, and EBSCOhost. It is shown through the results that both EMA research in general and the use of sensors in EMA research are steadily increasing. In addition, most of the studies reviewed used mobile apps to deliver EMA to participants, used a fixed-time prompting strategy, and used signal-contingent or interval-contingent self-assessment as sampling/assessment strategies. The most commonly used sensors in EMA studies are the accelerometer and GPS. In most studies, these sensors are used for simple data collection, but sensor data are also commonly used to verify study participant responses and, less commonly, to trigger EMA prompts. Security and privacy aspects are addressed in only a subset of mHealth EMA publications. Moreover, we found that EMA adherence was negatively correlated with the total number of prompts and was higher in studies using a microinteraction-based EMA (µEMA) approach as well as in studies utilizing sensors. Overall, we envision that the potential of the technological capabilities of smartphones and sensors could be better exploited in future, more automated approaches.

Fingerprint Recognition in Forensic Scenarios.

Fingerprints are unique patterns used as biometric keys because they allow an individual to be unambiguously identified, making their application in the forensic field a common practice. The design of a system that can match the details of different images is still an open problem, especially when applied to large databases or, to real-time applications in forensic scenarios using mobile devices. Fingerprints collected at a crime scene are often manually processed to find those that are relevant to solving the crime. This work proposes an efficient methodology that can be applied in real time to reduce the manual work in crime scene investigations that consumes time and human resources. The proposed methodology includes four steps: (i) image pre-processing using oriented Gabor filters; (ii) the extraction of minutiae using a variant of the Crossing Numbers method which include a novel ROI definition through convex hull and erosion followed by replacing two or more very close minutiae with an average minutiae; (iii) the creation of a model that represents each minutia through the characteristics of a set of polygons including neighboring minutiae; (iv) the individual search of a match for each minutia in different images using metrics on the absolute and relative errors. While in the literature most methodologies look to validate the entire fingerprint model, connecting the minutiae or using minutiae triplets, we validate each minutia individually using n-vertex polygons whose vertices are neighbor minutiae that surround the reference. Our method also reveals robustness against false minutiae since several polygons are used to represent the same minutia, there is a possibility that even if there are false minutia, the true polygon is present and identified; in addition, our method is immune to rotations and translations. The results show that the proposed methodology can be applied in real time in standard hardware implementation, with images of arbitrary orientations.

AMDDLmodel: Android smartphones malware detection using deep learning model.

Android is the most popular operating system of the latest mobile smart devices. With this operating system, many Android applications have been developed and become an essential part of our daily lives. Unfortunately, different kinds of Android malware have also been generated with these applications' endless stream and somehow installed during the API calls, permission granted and extra packages installation and badly affected the system security rules to harm the system. Therefore, it is compulsory to detect and classify the android malware to save the user's privacy to avoid maximum damages. Many research has already been developed on the different techniques related to android malware detection and classification. In this work, we present AMDDLmodel a deep learning technique that consists of a convolutional neural network. This model works based on different parameters, filter sizes, number of epochs, learning rates, and layers to detect and classify the android malware. The Drebin dataset consisting of 215 features was used for this model evaluation. The model shows an accuracy value of 99.92%. The other statistical values are precision, recall, and F1-score. AMDDLmodel introduces innovative deep learning for Android malware detection, enhancing accuracy and practical user security through inventive feature engineering and comprehensive performance evaluation. The AMDDLmodel shows the highest accuracy values as compared to the existing techniques.

Test-retest evaluation of a notched-noise test using consumer-grade mobile audio equipment.

OBJECTIVE: The aim of this study was to investigate whether consumer-grade mobile audio equipment can be reliably used as a platform for the notched-noise test, including when the test is conducted outside the laboratory. DESIGN: Two studies were conducted: Study 1 was a notched-noise masking experiment with three different setups: in a psychoacoustic test booth with a standard laboratory PC; in a psychoacoustic test booth with a mobile device; and in a quiet office room with a mobile device. Study 2 employed the same task as Study 1, but compared circumaural headphones to insert earphones. STUDY SAMPLE: Nine and ten young, normal-hearing participants completed studies 1 and 2, respectively. RESULTS: The test-retest accuracy of the notched-noise test on the mobile implementation did not differ from that for the laboratory setup. A possible effect of the earphone design was identified in Study 1, which was corroborated by Study 2, where test-retest variability was smallest when comparing results from experiments conducted using identical acoustic transducers. CONCLUSIONS: Results and test-retest repeatability comparable to standard laboratory settings for the notched-noise test can be obtained with mobile equipment outside the laboratory.

Design of experiments with sequential randomizations on multiple timescales: the hybrid experimental design.

Psychological interventions, especially those leveraging mobile and wireless technologies, often include multiple components that are delivered and adapted on multiple timescales (e.g., coaching sessions adapted monthly based on clinical progress, combined with motivational messages from a mobile device adapted daily based on the person's daily emotional state). The hybrid experimental design (HED) is a new experimental approach that enables researchers to answer scientific questions about the construction of psychological interventions in which components are delivered and adapted on different timescales. These designs involve sequential randomizations of study participants to intervention components, each at an appropriate timescale (e.g., monthly randomization to different intensities of coaching sessions and daily randomization to different forms of motivational messages). The goal of the current manuscript is twofold. The first is to highlight the flexibility of the HED by conceptualizing this experimental approach as a special form of a factorial design in which different factors are introduced at multiple timescales. We also discuss how the structure of the HED can vary depending on the scientific question(s) motivating the study. The second goal is to explain how data from various types of HEDs can be analyzed to answer a variety of scientific questions about the development of multicomponent psychological interventions. For illustration, we use a completed HED to inform the development of a technology-based weight loss intervention that integrates components that are delivered and adapted on multiple timescales.

A Scoping Review of Varying Mobile Electrocardiographic Devices.

The electrocardiogram (ECG) can now be measured using mobile devices. Mobile ECG devices, which are defined as devices capable of recording and transmitting non-standard ECGs, offer numerous advantages such as cost-effectiveness and being user-friendly. Mobile ECG can also extend recording lengths (e.g., 2 days, 14 days), which is necessary to capture important intermittent events (e.g., cardiac arrhythmias) and evaluate prognostic risk markers (e.g., prolonged corrected QT (QTc) interval). Some mobile ECG devices can even connect to broadband networks allowing patients to remotely transmit their ECG to a clinician. This article systematically examines different mobile ECG devices used in prior studies and provides a detailed assessment of five diverse yet commonly used mobile ECG devices: AliveCor KardiaMobile; AliveCor KardiaMobile 6L; iRhythm ZioPatch; Apple Smartwatch ECG; and CardioSecur System. These mobile ECG devices are diverse in the number of leads measured and the duration of monitoring. Similar to their diversity, there has been a wide range of clinical applications of mobile ECG devices. Despite significant progress, questions regarding data quality, and clinican and patient acceptance and compliance persist.

Using mobile technology to influence alcohol-impaired driving risk perceptions and decisions.

OBJECTIVE: Despite significant individual and societal risk, alcohol-impaired driving (AID) remains prevalent in the United States. Our aim was to determine whether breathalyzer-cued warning messages administered via mobile devices in the natural drinking environment could influence real-world AID cognitions and behaviors. METHOD: One hundred twenty young adults (53% women; mean age = 24.7) completed 6 weeks of ecological momentary assessment (EMA) and provided breathalyzer samples using a BACtrack Mobile Pro linked to their mobile device. On mornings after drinking episodes, participants reported their driving activities from the previous evening (787 episodes). Participants were randomly assigned to receive warning messages if they reached a breath alcohol concentration (BrAC) ≥ .05, or no messages. Participants in the warnings condition reported their willingness to drive and perceived danger of driving at EMA prompts (1,541 reports). RESULTS: We observed a significant effect of condition, such that the association between cumulative AID engagement and driving after reaching a BrAC of .05 was dampened among individuals in the warnings condition, compared to those in the no warnings condition. Receiving a warning message was associated with increased momentary perceived danger of driving and decreased willingness to drive. CONCLUSIONS: We found that BrAC-cued warning messages reduced the probability of AID and willingness to drive while impaired, and increased the perceived danger of driving after drinking. These results serve as proof-of-concept for the use of mobile technology to deliver an adaptive just-in-time intervention to reduce the probability of AID. (PsycInfo Database Record (c) 2024 APA, all rights reserved).