Interpretable artificial intelligence-based app assists inexperienced radiologists in diagnosing biliary atresia from sonographic gallbladder images.

BACKGROUND: A previously trained deep learning-based smartphone app provides an artificial intelligence solution to help diagnose biliary atresia from sonographic gallbladder images, but it might be impractical to launch it in real clinical settings. This study aimed to redevelop a new model using original sonographic images and their derived smartphone photos and then test the new model's performance in assisting radiologists with different experiences to detect biliary atresia in real-world mimic settings. METHODS: A new model was first trained retrospectively using 3659 original sonographic gallbladder images and their derived 51,226 smartphone photos and tested on 11,410 external validation smartphone photos. Afterward, the new model was tested in 333 prospectively collected sonographic gallbladder videos from 207 infants by 14 inexperienced radiologists (9 juniors and 5 seniors) and 4 experienced pediatric radiologists in real-world mimic settings. Diagnostic performance was expressed as the area under the receiver operating characteristic curve (AUC). RESULTS: The new model outperformed the previously published model in diagnosing BA on the external validation set (AUC 0.924 vs 0.908, P = 0.004) with higher consistency (kappa value 0.708 vs 0.609). When tested in real-world mimic settings using 333 sonographic gallbladder videos, the new model performed comparable to experienced pediatric radiologists (average AUC 0.860 vs 0.876) and outperformed junior radiologists (average AUC 0.838 vs 0.773) and senior radiologists (average AUC 0.829 vs 0.749). Furthermore, the new model could aid both junior and senior radiologists to improve their diagnostic performances, with the average AUC increasing from 0.773 to 0.835 for junior radiologists and from 0.749 to 0.805 for senior radiologists. CONCLUSIONS: The interpretable app-based model showed robust and satisfactory performance in diagnosing biliary atresia, and it could aid radiologists with limited experiences to improve their diagnostic performances in real-world mimic settings.

Development of a measuring app for systemic sclerosis-related digital ulceration (SALVE: Scleroderma App for Lesion VErification).

OBJECTIVES: To test the hypothesis that photographs (in addition to self-reported data) can be collected daily by patients with systemic sclerosis (SSc) using a smartphone app designed specifically for digital lesions, and could provide an objective outcome measure for use in clinical trials. METHODS: An app was developed to collect images and patient reported outcome measures (PROMS) including Pain score and the Hand Disability in Systemic Sclerosis-Digital Ulcers (HDISS-DU) questionnaire. Participants photographed their lesion(s) each day for 30 days and uploaded images to a secure repository. Lesions were analysed both manually and automatically, using a machine learning approach. RESULTS: 25 patients with SSc-related digital lesions consented of whom 19 completed the 30-day study, with evaluable data from 27 lesions. Mean (standard deviation [SD]) baseline Pain score was 5.7 (2.4) and HDISS-DU 2.2 (0.9), indicating high lesion and disease-related morbidity. 506 images were used in the analysis (mean number of used images per lesion 18.7, SD 8.3). Mean (SD) manual and automated lesion areas at day 1 were 11.6 (16.0) and 13.9 (16.7) mm2 respectively. Manual area decreased by 0.08mm2 per day (2.4mm2 over 30 days) and automated area by 0.1mm2 (3.0mm2 over 30 days). Average gradients of manual and automated measurements over 30 days correlated strongly (r = 0.81). Manual measurements were on average 40% lower than automated, with wide limits of agreement. CONCLUSION: Even patients with significant hand disability were able to use the app. Automated measurement of finger lesions could be valuable as an outcome measure in clinical trials.

Can mobile health apps with smartphones and tablets be the new frontier of cognitive rehabilitation in older individuals? A narrative review of a growing field.

INTRODUCTION: A recent interesting field of application of telemedicine/e-health involved smartphone apps. Although research on mHealth began in 2014, there are still few studies using these technologies in healthy elderly and in neurodegenerative disorders. Thus, the aim of the present review was to summarize current evidence on the usability and effectiveness of the use of mHealth in older adults and patients with neurodegenerative disorders. METHODS: This review was conducted by searching for recent peer-reviewed articles published between June 1, 2010 and March 2023 using the following databases: Pubmed, Embase, Cochrane Database, and Web of Science. After duplicate removal, abstract and title screening, 25 articles were included in the full-text assessment. RESULTS: Ten articles assessed the acceptance and usability, and 15 articles evaluated the efficacy of e-health in both older individuals and patients with neurodegenerative disorders. The majority of studies reported that mHealth training was well accepted by the users, and was able to stimulate cognitive abilities, such as processing speed, prospective and episodic memory, and executive functioning, making smartphones and tablets valuable tools to enhance cognitive performances. However, the studies are mainly case series, case-control, and in general small-scale studies and often without follow-up, and only a few RCTs have been published to date. CONCLUSIONS: Despite the great attention paid to mHealth in recent years, the evidence in the literature on their effectiveness is scarce and not comparable. Longitudinal RCTs are needed to evaluate the efficacy of mHealth cognitive rehabilitation in the elderly and in patients with neurodegenerative disorders.

Navigating the complexities of mobile medical app development from idea to launch, a guide for clinicians and biomedical researchers.

With today's pace of rapid technological advancement, many patient issues in modern medicine are increasingly solvable by mobile app solutions, which also have the potential to transform how clinical research is conducted. However, many critical challenges in the app development process impede bringing these translational technologies to patients, caused in large part by the lack of knowledge among clinicians and biomedical researchers of "what it takes" to design, develop, and maintain a successful medical app. Indeed, problems requiring mobile app solutions are often nuanced, requiring more than just clinical expertise, and issues such as the cost and effort required to develop and maintain a well-designed, sustainable, and scalable mobile app are frequently underestimated. To bridge this skill set gap, we established an academic unit of designers, software engineers, and scientists that leverage human-centered design methodologies and multi-disciplinary collaboration to develop clinically viable smartphone apps. In this report, we discuss major misconceptions clinicians and biomedical researchers often hold regarding medical app development, the steps we took to establish this unit to address these issues and the best practices and lessons learned from successfully ideating, developing, and launching medical apps. Overall, this report will serve as a blueprint for clinicians and biomedical researchers looking to better benefit their patients or colleagues via medical mobile apps.

A broad v. focused digital intervention for recurrent binge eating: a randomized controlled non-inferiority trial.

BACKGROUND: Empirically validated digital interventions for recurrent binge eating typically target numerous hypothesized change mechanisms via the delivery of different modules, skills, and techniques. Emerging evidence suggests that interventions designed to target and isolate one key change mechanism may also produce meaningful change in core symptoms. Although both 'broad' and 'focused' digital programs have demonstrated efficacy, no study has performed a direct, head-to-head comparison of the two approaches. We addressed this through a randomized non-inferiority trial. METHOD: Participants with recurrent binge eating were randomly assigned to a broad (n = 199) or focused digital intervention (n = 199), or a waitlist (n = 202). The broad program targeted dietary restraint, mood intolerance, and body image disturbances, while the focused program exclusively targeted dietary restraint. Primary outcomes were eating disorder psychopathology and binge eating frequency. RESULTS: In intention-to-treat analyses, both intervention groups reported greater improvements in primary and secondary outcomes than the waitlist, which were sustained at an 8-week follow-up. The focused intervention was not inferior to the broad intervention on all but one outcome, but was associated with higher rates of attrition and non-compliance. CONCLUSION: Focused digital interventions that are designed to target one key change mechanism may produce comparable symptom improvements to broader digital interventions, but appear to be associated with lower engagement.

Effectiveness of a Smartphone App (MINISTOP 2.0) integrated in primary child health care to promote healthy diet and physical activity behaviors and prevent obesity in preschool-aged children: randomized controlled trial.

BACKGROUND: Childhood overweight and obesity is a public health priority. We have previously reported the efficacy of a parent-oriented mobile health (mHealth) app-based intervention (MINISTOP 1.0) which showed improvements in healthy lifestyle behaviors. However, the effectiveness of the MINISTOP app in real-world conditions needs to be established. OBJECTIVE: To evaluate the real-world effectiveness of a 6-month mHealth intervention (MINISTOP 2.0 app) on children's intake of fruits, vegetables, sweet and savory treats, sweet drinks, moderate-to-vigorous physical activity, and screen time (primary outcomes), and on parental self-efficacy (PSE) for promoting healthy lifestyle behaviors, and children's body mass index (BMI) (secondary outcomes). METHODS: A hybrid type 1 effectiveness-implementation design was utilized. For the effectiveness outcomes, a two-arm, individually randomized controlled trial was conducted. Parents (n = 552) of 2.5-to-3-year-old children were recruited from 19 child health care centers across Sweden, and, randomized to either a control (standard care) or intervention group (MINISTOP 2.0 app). The 2.0 version was adapted and translated into English, Somali and Arabic to increase reach. All recruitment and data collection were conducted by the nurses. Outcomes were assessed at baseline and after six months, using standardized measures (BMI) and a questionnaire (health behaviors, PSE). RESULTS: Among the participating parents (n = 552, age: 34.1 ± 5.0 years), 79% were mothers and 62% had a university degree. Twenty-four percent (n = 132) of children had two foreign-born parents. At follow-up, parents in the intervention group reported lower intakes of sweet and savory treats (-6.97 g/day; p = 0.001), sweet drinks (-31.52 g/day; p < 0.001), and screen time (-7.00 min/day; p = 0.012) in their children compared to the control group. The intervention group reported higher total PSE (0.91; p = 0.006), PSE for promoting healthy diet (0.34; p = 0.008) and PSE for promoting physical activity behaviors (0.31; p = 0.009) compared to controls. No statistically significant effect was observed for children's BMI z-score. Overall, parents reported high satisfaction with the app, and 54% reported using the app at least once a week. CONCLUSION: Children in the intervention group had lower intakes of sweet and savory treats, sweet drinks, less screen time (primary outcomes) and their parents reported higher PSE for promoting healthy lifestyle behaviors. Our results from this real-world effectiveness trial support the implementation of the MINISTOP 2.0 app within Swedish child health care. TRIAL REGISTRATION: Clinicaltrials.gov NCT04147039; https://clinicaltrials.gov/ct2/show/NCT04147039.

Dipping at home: is it better, easier, and more convenient? A feasibility and acceptability study of a novel home urinalysis using a smartphone application.

BACKGROUND: Monitoring proteinuria in patients with kidney disease is of crucial importance given its implications for long-term disease progression and clinical management. Leveraging digital health technology to provide a clinical grade urinalysis result from home holds the potential to greatly enhance the clinical experience and workflows for patients, caregivers, and providers. The goal of this study was to evaluate the acceptability and feasibility of a home-based urinalysis kit using a smartphone application. METHODS: This is a prospective cohort study of children and young adults (5-21 years of age) at a single pediatric center. The study received ethical board approval. Families performed a home urine test using the Healthy.io smartphone app. The app was compared with standard of care of either home dipstick monitoring or urinalysis performed in clinic or a local laboratory. Patient satisfaction was compared between the new app and current practice. RESULTS: A total of 103 children, 63 (61%) male and median age 10.9 years (inter-quartile range 7.8-14.2), were enrolled. Primary diagnosis included 47 (46%) glomerular disease, 48 (47%) non-glomerular kidney disease, and 8 (8%) kidney transplant recipients. One hundred and one (98%) patients reported being satisfied with the smartphone app compared to 41 (40%) patients who were satisfied with the current practice for urine protein monitoring (p < 0.0001). Positive themes identified included ease of use, convenience, and immediacy and accuracy of results. CONCLUSIONS: The Healthy.io home urine testing app received very high rates of satisfaction among patients and caregivers compared to current practice and holds great potential to enhance patient-centered care. A higher resolution version of the Graphical abstract is available as Supplementary information.

Investigating Individuals' Preferences in Determining the Functions of Smartphone Apps for Fighting Pandemics: Best-Worst Scaling Survey Study.

BACKGROUND: Smartphone apps have been beneficial in controlling and preventing the COVID-19 pandemic. However, there is a gap in research surrounding the importance of smartphone app functions from a user's perspective. Although the insights and opinions of different stakeholders, such as policymakers and medical professionals, can influence the success of a public health policy, any strategy will face difficulty in achieving the expected effect if it is not based on a method that users can accept. OBJECTIVE: This study aimed to assess the importance of a hypothetical smartphone app's functions for managing health during a pandemic based on the perspective of user preferences. METHODS: A cross-sectional and web-based survey using the best-worst scaling (BWS) method was used to investigate the general population's preferences for important smartphone app functions. Participants were recruited from a professional surveying company's web-based surveying panel. The attributes of the BWS questionnaire were developed based on a robust process, including literature review, interviews, and expert discussion. A balanced incomplete block design was used to construct the choice task to ensure the effectiveness of the research design. Count analysis, conditional logit model analysis, and mixed logit analysis were used to estimate preference heterogeneity among respondents. RESULTS: The responses of 2153 participants were eligible for analysis. Nearly 55% (1192/2153) were female, and the mean age was 31.4 years. Most participants (1765/2153, 81.9%) had completed tertiary or higher education, and approximately 70% (1523/2153) were urban residents. The 3 most vital functions according to their selection were "surveillance and monitoring of infected cases," "quick self-screening," and "early detection of infected cases." The mixed logit regression model identified significant heterogeneity in preferences among respondents, and stratified analysis showed that some heterogeneities varied in respondents by demographics and COVID-19-related characteristics. Participants who preferred to use the app were more likely to assign a high weight to the preventive functions than those who did not prefer to use it. Conversely, participants who showed lower willingness to use the app tended to indicate a higher preference for supportive functions than those who preferred to use it. CONCLUSIONS: This study ranks the importance of smartphone app features that provide health care services during a pandemic based on the general population's preferences in China. It provides empirical evidence for decision-makers to develop eHealth policies and strategies that address future public health crises from a person-centered care perspective. Continued use of apps and smart investment in digital health can help improve health outcomes and reduce the burden of disease on individuals and communities.

Selective Prevention of Depression in Workers Using a Smartphone App: Randomized Controlled Trial.

BACKGROUND: There is increasing evidence that depression can be prevented; however, universal approaches have had limited success. Appropriate targeting of interventions to at-risk populations has been shown to have potential, but how to selectively determine at-risk individuals remains unclear. Workplace stress is a risk factor for depression and a target for intervention, but few interventions exist to prevent depression among workers at risk due to heightened stress. OBJECTIVE: This trial aimed to evaluate the efficacy of a smartphone-based intervention in reducing the onset of depression and improving related outcomes in workers experiencing at least moderate levels of stress. METHODS: A randomized controlled trial was conducted with participants who were currently employed and reported no clinically significant depression and at least moderate stress. The intervention group (n=1053) were assigned Anchored, a 30-day self-directed smartphone app-based cognitive behavioral- and mindfulness-based intervention. The attention-control group (n=1031) were assigned a psychoeducation website. Assessment was performed via web-based self-report questionnaires at baseline and at 1-, 3-, and 6-month postbaseline time points. The primary outcome was new depression caseness aggregated over the follow-up period. The secondary outcomes included depressive and anxiety symptoms, stress, well-being, resilience, work performance, work-related burnout, and quality of life. Analyses were conducted within an intention-to-treat framework using mixed modeling. RESULTS: There was no significant between-group difference in new depression caseness (z score=0.69; P=.49); however, those in the Anchored arm had significantly greater depressive symptom reduction at 1 month (Cohen d=0.02; P=.049) and 6 months (Cohen d=0.08; P=.03). Anchored participants also showed significantly greater reduction in anxiety symptoms at 1 month (Cohen d=0.07; P=.04) and increased work performance at 1 month (Cohen d=0.07; P=.008) and 6 months (Cohen d=0.13; P=.01), compared with controls. Notably, for Anchored participants completing at least two-thirds of the intervention, there was a significantly lower rate of depression onset (1.1%, 95% CI 0.0%-3.7%) compared with controls (9.0%, 95% CI 6.8%-12.3%) at 1 month (z score=4.50; P<.001). Significant small to medium effect sizes for most secondary outcomes were seen in the highly engaged Anchored users compared with controls, with effects maintained at the 6-month follow-up for depressive symptoms, well-being, stress, and quality of life. CONCLUSIONS: Anchored was associated with a small comparative reduction in depressive symptoms compared with controls, although selective prevention of case-level depression was not observed in the intention-to-treat analysis. When users adequately engaged with the app, significant findings pertaining to depression prevention, overall symptom reduction, and functional improvement were found, compared with controls. There is a need for a greater focus on engagement techniques in future research. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry (ANZCTR) ACTRN12620000178943; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=378592.

The Effectiveness of Smartphone App-Based Interventions for Assisting Smoking Cessation: Systematic Review and Meta-analysis.

BACKGROUND: Smoking is a leading cause of premature death globally. Quitting smoking reduces the risk of all-cause mortality by 11%-34%. Smartphone app-based smoking cessation (SASC) interventions have been developed and are widely used. However, the evidence for the effectiveness of smartphone-based interventions for smoking cessation is currently equivocal. OBJECTIVE: The purpose of this study was to synthesize the evidence for the effectiveness of smartphone app-based interventions for smoking cessation. METHODS: We conducted a systematic review and meta-analysis of the effectiveness of smartphone interventions for smoking cessation based on the Cochrane methodology. An electronic literature search was performed using the Cochrane Library, Web of Science, PubMed, Embase, PsycINFO, China National Knowledge Infrastructure, and Wanfang databases to identify published papers in English or Chinese (there was no time limit regarding the publication date). The outcome was the smoking abstinence rate, which was either a 7-day point prevalence abstinence rate or a continuous abstinence rate. RESULTS: A total of 9 randomized controlled trials involving 12,967 adults were selected for the final analysis. The selected studies from 6 countries (the United States, Spain, France, Switzerland, Canada, and Japan) were included in the meta-analysis between 2018 and 2022. Pooled effect sizes (across all follow-up time points) revealed no difference between the smartphone app group and the comparators (standard care, SMS text messaging intervention, web-based intervention, smoking cessation counseling, or apps as placebos without real function; odds ratio [OR] 1.25, 95% CI 0.99-1.56, P=.06, I2=73.6%). Based on the subanalyses, 6 trials comparing smartphone app interventions to comparator interventions reported no significant differences in effectiveness (OR 1.03, 95% CI 0.85-1.26, P=.74, I2=57.1%). However, the 3 trials that evaluated the combination of smartphone interventions combined with pharmacotherapy compared to pharmacotherapy alone found higher smoking abstinence rates in the combined intervention (OR 1.79, 95% CI 1.38-2.33, P=.74, I2=7.4%). All SASC interventions with higher levels of adherence were significantly more effective (OR 1.48, 95% CI 1.20-1.84, P<.001, I2=24.5%). CONCLUSIONS: This systematic review and meta-analysis did not support the effectiveness of delivering smartphone-based interventions alone to achieve higher smoking abstinence rates. However, the efficacy of smartphone-based interventions increased when combined with pharmacotherapy-based smoking cessation approaches. TRIAL REGISTRATION: PROSPERO CRD42021267615; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=267615.

Remote Symptom Monitoring With Ecological Momentary Computerized Adaptive Testing: Pilot Cohort Study of a Platform for Frequent, Low-Burden, and Personalized Patient-Reported Outcome Measures.

BACKGROUND: Remote patient-reported outcome measure (PROM) data capture can provide useful insights into research and clinical practice and deeper insights can be gained by administering assessments more frequently, for example, in ecological momentary assessment. However, frequent data collection can be limited by the burden of multiple, lengthy questionnaires. This burden can be reduced with computerized adaptive testing (CAT) algorithms that select only the most relevant items from a PROM for an individual respondent. In this paper, we propose "ecological momentary computerized adaptive testing" (EMCAT): the use of CAT algorithms to reduce PROM response burden and facilitate high-frequency data capture via a smartphone app. We develop and pilot a smartphone app for performing EMCAT using a popular hand surgery PROM. OBJECTIVE: The aim of this study is to determine the feasibility of EMCAT as a system for remote PROM administration. METHODS: We built the EMCAT web app using Concerto, an open-source CAT platform maintained by the Psychometrics Centre, University of Cambridge, and hosted it on an Amazon Web Service cloud server. The platform is compatible with any questionnaire that has been parameterized with item response theory or Rasch measurement theory. For this study, the PROM we chose was the patient evaluation measure, which is commonly used in hand surgery. CAT algorithms were built using item response theory models derived from UK Hand Registry data. In the pilot study, we enrolled 40 patients with hand trauma or thumb-base arthritis, across 2 sites, between July 13, 2022, and September 14, 2022. We monitored their symptoms with the patient evaluation measure, via EMCAT, over a 12-week period. Patients were assessed thrice weekly, once daily, or thrice daily. We additionally administered full-length PROM assessments at 0, 6, and 12 weeks, and the User Engagement Scale at 12 weeks. RESULTS: The use of EMCAT significantly reduced the length of the PROM (median 2 vs 11 items) and the time taken to complete it (median 8.8 seconds vs 1 minute 14 seconds). Very similar scores were obtained when EMCAT was administered concurrently with the full-length PROM, with a mean error of <0.01 on a logit (z score) scale. The median response rate in the daily assessment group was 93%. The median perceived usability score of the User Engagement Scale was 4.0 (maximum possible score 5.0). CONCLUSIONS: EMCAT reduces the burden of PROM assessments, enabling acceptable high-frequency, remote PROM data capture. This has potential applications in both research and clinical practice. In research, EMCAT could be used to study temporal variations in symptom severity, for example, recovery trajectories after surgery. In clinical practice, EMCAT could be used to monitor patients remotely, prompting early intervention if a patient's symptom trajectory causes clinical concern. TRIAL REGISTRATION: ISRCTN 19841416; https://www.isrctn.com/ISRCTN19841416.

Automated Machine Learning Analysis of Patients With Chronic Skin Disease Using a Medical Smartphone App: Retrospective Study.

BACKGROUND: Rapid digitalization in health care has led to the adoption of digital technologies; however, limited trust in internet-based health decisions and the need for technical personnel hinder the use of smartphones and machine learning applications. To address this, automated machine learning (AutoML) is a promising tool that can empower health care professionals to enhance the effectiveness of mobile health apps. OBJECTIVE: We used AutoML to analyze data from clinical studies involving patients with chronic hand and/or foot eczema or psoriasis vulgaris who used a smartphone monitoring app. The analysis focused on itching, pain, Dermatology Life Quality Index (DLQI) development, and app use. METHODS: After extensive data set preparation, which consisted of combining 3 primary data sets by extracting common features and by computing new features, a new pseudonymized secondary data set with a total of 368 patients was created. Next, multiple machine learning classification models were built during AutoML processing, with the most accurate models ultimately selected for further data set analysis. RESULTS: Itching development for 6 months was accurately modeled using the light gradient boosted trees classifier model (log loss: 0.9302 for validation, 1.0193 for cross-validation, and 0.9167 for holdout). Pain development for 6 months was assessed using the random forest classifier model (log loss: 1.1799 for validation, 1.1561 for cross-validation, and 1.0976 for holdout). Then, the random forest classifier model (log loss: 1.3670 for validation, 1.4354 for cross-validation, and 1.3974 for holdout) was used again to estimate the DLQI development for 6 months. Finally, app use was analyzed using an elastic net blender model (area under the curve: 0.6567 for validation, 0.6207 for cross-validation, and 0.7232 for holdout). Influential feature correlations were identified, including BMI, age, disease activity, DLQI, and Hospital Anxiety and Depression Scale-Anxiety scores at follow-up. App use increased with BMI >35, was less common in patients aged >47 years and those aged 23 to 31 years, and was more common in those with higher disease activity. A Hospital Anxiety and Depression Scale-Anxiety score >8 had a slightly positive effect on app use. CONCLUSIONS: This study provides valuable insights into the relationship between data characteristics and targeted outcomes in patients with chronic eczema or psoriasis, highlighting the potential of smartphone and AutoML techniques in improving chronic disease management and patient care.

Repeatability, Reproducibility, and Concurrent Validity of a Stethoscope and Health App System for the Quantification of Breath Rate in Healthy Adults: Repeatability and Validity Study.

BACKGROUND: Apps for smartphones that can measure the breathing rate easily can be downloaded. OBJECTIVE: The aim of this study was to demonstrate agreement in measuring breath rates between the stethoscope and Breath Counter health app. METHODS: We performed a repeatability study with 56 healthy volunteers. The patient's demographic data and breathing rates per minute were collected. Breathing rates were measured via two methods: (1) using a stethoscope placed in the upper area of the right lung and (2) a Breath Counter app developed by Vadion on a Samsung Fold smartphone. RESULTS: This study demonstrated high repeatability and validity with respect to the breathing rate parameter of healthy adults using the aforementioned 2 systems. Intrasession repeatability measure using the intraclass correlation coefficient was >0.962, indicating excellent repeatability. Moreover, the intraclass correlation coefficient between methods was 0.793, indicating good repeatability, and coefficients of variation of method errors values were 1.83% with very low values in terms of other repeatability parameters. We found significant correlation coefficients and no systematic differences between the app and stethoscope methods. CONCLUSIONS: The app method may be attractive to individuals who require repeatability in a recreational setting.

Smartphone Apps for Smoking Cessation: Systematic Framework for App Review and Analysis.

BACKGROUND: Cigarette smoking is a leading cause of preventable death, and identifying novel treatment approaches to promote smoking cessation is critical for improving public health. With the rise of digital health and mobile apps, these tools offer potential opportunities to address smoking cessation, yet the functionality of these apps and whether they offer scientifically based support for smoking cessation are unknown. OBJECTIVE: The goal of this research was to use the American Psychiatric Association app evaluation model to evaluate the top-returned apps from Android and Apple app store platforms related to smoking cessation and investigate the common app features available for end users. METHODS: We conducted a search of both Android and iOS app stores in July 2021 for apps related to the keywords "smoking," "tobacco," "smoke," and "cigarette" to evaluate apps for smoking cessation. Apps were screened for relevance, and trained raters identified and analyzed features, including accessibility (ie, cost), privacy, clinical foundation, and features of the apps, using a systematic framework of 105 objective questions from the American Psychiatric Association app evaluation model. All app rating data were deposited in mindapps, a publicly accessible database that is continuously updated every 6 months given the dynamic nature of apps available in the marketplace. We characterized apps available in July 2021 and November 2022. RESULTS: We initially identified 389 apps, excluded 161 due to irrelevance and nonfunctioning, and rated 228, including 152 available for Android platforms and 120 available for iOS platforms. Some of the top-returned apps (71/228, 31%) in 2021 were no longer functioning in 2022. Our analysis of rated apps revealed limitations in accessibility and features. While most apps (179/228, 78%) were free to download, over half had costs associated with in-app purchases or full use. Less than 65% (149/228) had a privacy policy addressing the data collected in the app. In terms of intervention features, more than 56% (128/228) of apps allowed the user to set and check in on goals, and more than 46% (106/228) of them provided psychoeducation, although few apps provided evidence-based support for smoking cessation, such as peer support or skill training, including mindfulness and deep breathing, and even fewer provided evidence-based interventions, such as acceptance and commitment therapy or cognitive behavioral therapy. Only 12 apps in 2021 and 11 in 2022 had published studies supporting the feasibility or efficacy for smoking cessation. CONCLUSIONS: Numerous smoking cessation apps were identified, but analysis revealed limitations, including high rates of irrelevant and nonfunctioning apps, high rates of turnover, and few apps providing evidence-based support for smoking cessation. Thus, it may be challenging for consumers to identify relevant, evidence-based apps to support smoking cessation in the app store, and a comprehensive evaluation system of mental health apps is critically important.

Prototyping Apps for the Management of Sleep, Fatigue, and Behavioral Health in Austere Far-Forward Environments: Development Study.

BACKGROUND: Military service inherently includes frequent periods of high-stress training, operational tempo, and sustained deployments to austere far-forward environments. These occupational requirements can contribute to acute and chronic sleep disruption, fatigue, and behavioral health challenges related to acute and chronic stress and disruption of team dynamics. To date, there is no centralized mobile health platform that supports self- and supervised detection, monitoring, and management of sleep and behavioral health issues in garrison and during and after deployments. OBJECTIVE: The objective of this study was to adapt a clinical decision support platform for use outside clinical settings, in garrison, and during field exercises by medics and soldiers to monitor and manage sleep and behavioral health in operational settings. METHODS: To adapt an existing clinical decision support digital health platform, we first gathered system, content, and context-related requirements for a sleep and behavioral health management system from experts. Sleep and behavioral health assessments were then adapted for prospective digital data capture. Evidence-based and operationally relevant educational and interventional modules were formatted for digital delivery. These modules addressed the management and mitigation of sleep, circadian challenges, fatigue, stress responses, and team communication. Connectivity protocols were adapted to accommodate the absence of cellular or Wi-Fi access in deployed settings. The resulting apps were then tested in garrison and during 2 separate field exercises. RESULTS: Based on identified requirements, 2 Android smartphone apps were adapted for self-monitoring and management for soldiers (Soldier app) and team supervision and intervention by medics (Medic app). A total of 246 soldiers, including 28 medics, received training on how to use the apps. Both apps function as expected under conditions of limited connectivity during field exercises. Areas for future technology enhancement were also identified. CONCLUSIONS: We demonstrated the feasibility of adapting a clinical decision support platform into Android smartphone-based apps to collect, save, and synthesize sleep and behavioral health data, as well as share data using adaptive data transfer protocols when Wi-Fi or cellular data are unavailable. The AIRE (Autonomous Connectivity Independent System for Remote Environments) prototype offers a novel self-management and supervised tool to augment capabilities for prospective monitoring, detection, and intervention for emerging sleep, fatigue, and behavioral health issues that are common in military and nonmilitary high-tempo occupations (eg, submarines, long-haul flights, space stations, and oil rigs) where medical expertise is limited.

A pilot study for a smartphone app for the prevention of depression in non-professional caregivers.

OBJECTIVES: The main objective of the current study was to evaluate the feasibility and acceptability of a cognitive-behavioral prevention intervention administered through a smartphone app in non-professional caregivers with symptoms of depression. The secondary objective was to make a preliminary evaluation of its effectiveness either alone or supplemented with telephone conference calls. METHODS: Eighty-seven participants (Mage = 51.8 years) were randomly assigned to an app-based cognitive-behavioral intervention (CBIA; n = 29), CBIA supplemented with telephone conference calls (CBIA + CC; n = 28), or an attention control group (ACG; n = 30). The participants for both interventions received five cognitive-behavioral modules through the app, and those in CBIA + CC an additional 30-minute phone call in each module. RESULTS: 3.4% of caregivers dropped out. In all groups, the number of modules completed was high. Participants completed a high percentage of the homework and were highly satisfied with both CBIA and CBIA + CC. At post-intervention, there was a lower incidence of depression and depressive symptoms for CBIA + CC compared with CBIA, and for CBIA and CBIA + CC compared with ACG. CONCLUSION: The results supported the feasibility and acceptability of the cognitive-behavioral intervention, and demonstrated that telephone contact improves its effectiveness.

System Based on an Inertial Measurement Unit for Accurate Flight Time Determination in Vertical Jumps.

The world of elite sports has always been characterized by intense competition, where victories are often determined by minimal differences. This means that every little detail in the preparation of top-level athletes is crucial to their performance at the highest level. One of the most significant aspects to monitor is the jumping capacity, as it enables the measurement of performance, progression, and helps prevent injuries. Herein, we present the development of a system capable of measuring the flight time and height reached by the user, reporting the results through a smartphone using an Android ad-hoc application, which handles all the data processing. The system consists of an affordable and portable circuit based on an accelerometer. It communicates with the smartphone via UART using a Bluetooth module, and its battery provides approximately 9 h of autonomy, making it suitable for outdoor operations. To evaluate the system's precision, we conducted performance tests (counter-movement jumps) with seven subjects. The results confirmed the system's potential for monitoring high-level sports training sessions, as the average deviation obtained was only 2.1% (~0.01 s) in the analysis of flight time and 4.6% (~0.01 m) in jump height.

Effectiveness of an oral health education programme using a mobile application for older adults: A randomised clinical trial.

OBJECTIVE: To investigate the effectiveness of an oral health education programme using a mobile app for adults aged 65 years or older. METHODS: Participants aged 65 or older were randomly allocated into non-app use (n = 25), app use (n = 22) and control (n = 26) groups. The non-app use group received lecture-based oral health education using PowerPoint presentations and participated in workbook activities twice a week for five weeks, whereas the app use group underwent oral health education using a smartphone app and workbook activities for five weeks. Before and after the intervention, a questionnaire survey and oral health examination were performed to examine changes in oral health-related indices. RESULTS: The non-app use group showed significant changes, with a 2.1 increase in oral health knowledge score, 0.2 decrease in the O'Leary index and 0.6 decrease in tongue coating. The app use group showed significant changes, with a 3.1 increase in oral health knowledge score, a 2.5 increase in oral health perception, a 0.3 decrease in the O'Leary index and a 1.4 decrease in tongue coating. Repeated-measures ANOVA showed that interaction between time and group was significant only in tongue coating variable. CONCLUSION: The smartphone app developed in this study carries the possibility to convey informative content for oral health education among older adults.

An Acceptance and Commitment Therapy Smartphone Application for Erectile Dysfunction: A Feasibility Study.

Background: Erectile dysfunction (ED) is a multifactorial disorder with both psychogenic and organic components, but psychosocial factors are usually neglected. Objective: The purpose of this study was to develop a smartphone application targeting psychosocial factors of ED and to examine its feasibility, acceptability, and treatment response to determine the parameters for a larger clinical trial. Methods: In this single-arm feasibility study, 8 participants with situational ED were enrolled. Dr. App, a newly developed smartphone treatment application for patients with psychogenic ED consisting of 8 weekly modules based on Acceptance and Commitment Therapy, was delivered. The primary outcome was comparison of the International Index of Erectile Function-15 domain scores measured pre- and post-intervention. Results: Six out of 8 participants completed the Dr. App and the post-intervention measures. The Wilcoxon signed-rank test showed a significant change in erectile function (P < 0.05; r = -0.65) and a significant trend in intercourse satisfaction (P < 0.10; r = -0.47) and overall satisfaction (P < .10; r = -0.47). Additionally, the reliable change index values were used to calculate the number of participants for whom a clinically meaningful difference occurred. The results showed that 33.30% of the participants had clinically meaningful differences in erectile function and 66.70% in intercourse satisfaction and overall satisfaction. On the other hand, no significant differences were shown in orgasmic function and sexual desire. Conclusions: Findings from this study support the feasibility, acceptability, and potential usefulness of the smartphone application targeting psychosocial factors of ED and warrant a larger randomized clinical trial to confirm the results.

Accuracy of flight time and countermovement-jump height estimated from videos at different frame rates with MyJump.

Recent improvements in smartphone video technology may provide sufficient accuracy for estimation of jump height via flight time determined from video recordings of vertical-jump tests. The aim of this study is to evaluate the accuracy of jump height estimated from videos at different frame rates. High-definition videos of 10 young adults (6 males, 4 females) performing 5 countermovement jumps were recorded at a frame rate of 1000 Hz and transcoded to frame rates of 120, 240, and 480 Hz. Flight time in the videos was assessed independently by three observers at each of the four frame rates with MyJump. Flight time and jump height were analyzed with mixed models for estimation of means and of standard deviations representing technical error of measurement (free of within-subject jump-to-jump variability) at each frame rate. The four frame rates and three observers produced practically identical estimates of mean jump height. The technical errors at 120, 240, 480 and 1000 Hz were respectively 3.4, 1.8, 1.2 and 0.8 ms for flight time, and 1.4%, 0.7%, 0.5% and 0.3% for jump height. Assessed relative to either differences in jump height between elite football players (standard deviation of ~12%) or the smallest expected test-retest variability (typical error of ~3%), the technical error was substantial at 120 Hz but negligible at 240 Hz or higher. In conclusion, use of frame rates above 240 Hz to estimate jump height with MyJump will not improve accuracy substantially.