Quality Assessment of Smartphone Medication Management Apps in France: Systematic Search

BACKGROUND

Adherence to medication is estimated to be around 50% for chronically ill patients in high-income countries. Improving the effectiveness of adherence interventions could have a far greater impact on population health than any improvement in specific medical treatments. Mobile health (mHealth) is one of the most effective solutions for helping patients improve their medication intake, notably through the use of mobile apps with reminder systems. With more than 327,000 apps available in the mHealth field, it is difficult for health care professionals and patients alike to choose which apps to recommend and use.

OBJECTIVE

We aim to carry out a systematic search of medication management smartphone apps available in France that send reminders to patients and assess their quality using a validated scale.

METHODS

Mobile apps were identified in October and November 2022 after a systematic keyword search on the 2 main app download platforms: App Store (Apple Inc) and Google Play Store. Inclusion criteria were free availability, date of last update, and availability in French. Next, 2 health care professionals independently evaluated the included apps using the French version of the Mobile App Rating Scale (MARS-F), an objective scoring system validated for assessing the overall quality of apps in the mHealth field. An intraclass correlation coefficient was calculated to determine interrater reliability.

RESULTS

In total, 960 apps were identified and 49 were selected (25 from the App Store and 24 from the Google Play Store). Interrater reliability was excellent (intraclass correlation coefficient 0.92; 95% CI 0.87-0.95; P<.001). The average MARS-F score was 3.56 (SD 0.49) for apps on the App Store and 3.51 (SD 0.46) for those on the Google Play Store, with 10 apps scoring above 4 out of 5. Further, 2 apps were tested in at least one randomized controlled trial and showed positive results. The 2 apps with the highest ratings were Mediteo rappel de médicaments (Mediteo GmbH) and TOM rappel medicaments, pilule (Innovation6 GmbH), available on both platforms. Each app's MARS-F score was weakly correlated with user ratings on the App Store and moderately correlated on the Google Play Store.

CONCLUSIONS

To our knowledge, this is the first study that used a validated scoring system to evaluate medication management apps that send medication reminders. The quality of the apps was heterogeneous, with only 2 having been studied in a randomized controlled trial with positive results. The evaluation of apps in real-life conditions by patients is necessary to determine their acceptability and effectiveness. Certification of apps is also essential to help health care professionals and patients identify validated apps.

Mobile Apps for Common Noncommunicable Disease Management: Systematic Search in App Stores and Evaluation Using the Mobile App Rating Scale

Background

The success of mobile apps in improving the lifestyle of patients with noncommunicable diseases through self-management interventions is contingent upon the emerging growth in this field. While users of mobile health (mHealth) apps continue to grow in number, little is known about the quality of available apps that provide self-management for common noncommunicable diseases such as diabetes, hypertension, and obesity.

Objective

We aimed to investigate the availability, characteristics, and quality of mHealth apps for common noncommunicable disease health management that included dietary aspects (based on the developer's description), as well as their features for promoting health outcomes and self-monitoring.

Methods

A systematic search of English-language apps on the Google Play Store (Google LLC) and Apple App Store (Apple Inc) was conducted between August 7, 2022, and September 13, 2022. The search terms used included weight management, obesity, diabetes, hypertension, cardiovascular diseases, stroke, and diet. The selected mHealth apps' titles and content were screened based on the description that was provided. Apps that were not designed with self-management features were excluded. We analyzed the mHealth apps by category and whether they involved health care professionals, were based on scientific testing, and had self-monitoring features. A validated and multidimensional tool, the Mobile App Rating Scale (MARS), was used to evaluate each mHealth app's quality based on a 5-point Likert scale from 1 (inadequate) to 5 (excellent).

Results

Overall, 42 apps were identified. Diabetes-specific mHealth apps accounted for 7% (n=3) of the market, hypertension apps for 12% (n=5), and general noncommunicable disease management apps for 21% (n=9). About 38% (n=16) of the apps were for managing chronic diseases, while 74% (n=31) were for weight management. Self-management features such as weight tracking, BMI calculators, diet tracking, and fluid intake tracking were seen in 86% (n=36) of the apps. Most mHealth apps (n=37, 88%) did not indicate whether there was involvement of health professionals in app development. Additionally, none of the apps reported scientific evidence demonstrating their efficacy in managing health. The overall mean MARS score was 3.2 of 5, with a range of 2.0 to 4.1. Functionality was the best-rated category (mean score 3.9, SD 0.5), followed by aesthetics (mean score 3.2, SD 0.9), information (mean score 3.1, SD 0.7), and engagement (mean score 2.9, SD 0.6).

Conclusions

The quality of mHealth apps for managing chronic diseases was heterogeneous, with roughly half of them falling short of acceptable standards for both quality and content. The majority of apps contained scant information about scientific evidence and the developer's history. To increase user confidence and accomplish desired health outcomes, mHealth apps should be optimized with the help of health care professionals. Future studies on mHealth content analysis should focus on other diseases as well.

[Apheresis Techniques for the Treatment of Hyperbilirubinemia in the Nephrology Unit]

Therapeutic apheresis is an important hematological and nephrological method for conditions with altered plasma composition. It is also indicated for the removal of protein-bound molecules, such as bilirubin. Several techniques can remove these compounds, such as the extracorporeal circulation molecular adsorption system (MARS), plasma exchange (PEX), and plasma adsorption and perfusion (PAP). Here we report our experience in the comparison between MARS, PEX and PAP, since current guidelines do not specify which method is the most appropriate and under which circumstances it should be used. The choice of technique cannot be based on the desired plasma bilirubin concentration, since these three techniques show similar results with a similar final outcome (exitus). In fact, PAP, PEX and MARS significantly reduce bilirubin levels, but the degree of reduction is not different among the three. Furthermore, the three techniques do not differ in the rate of cholinesterase change, while less reduction of liver transaminases was found by using PAP. MARS should be preferred in the case of renal involvement (hepatorenal syndrome with hyperbilirubinemia). PAP has the advantage of being simple and inexpensive. PEX remains an option when emergency PAP is not available, but the risk of using blood products (plasma and albumin) must be considered.

Support for Chronic Pain Management for Breast Cancer Survivors Through Novel Digital Health Ecosystems: Pilot Usability Study of the PainRELife Mobile App

BACKGROUND

Chronic pain is one of the most common and critical long-term effects of breast cancer. Digital health technologies enhance the management of chronic pain by monitoring physical and psychological health status and supporting pain self-management and patient treatment decisions throughout the clinical pathway.

OBJECTIVE

This pilot study aims to evaluate patients' experiences, including usability, with a novel digital integrated health ecosystem for chronic pain named PainRELife. The sample included patients with breast cancer during survivorship. The PainRELife ecosystem comprises a cloud technology platform interconnected with electronic health records and patients' devices to gather integrated health care data.

METHODS

We enrolled 25 patients with breast cancer (mean age 47.12 years) experiencing pain. They were instructed to use the PainRELife mobile app for 3 months consecutively. The Mobile Application Rating Scale (MARS) was used to evaluate usability. Furthermore, pain self-efficacy and participation in treatment decisions were evaluated. The study received ethical approval (R1597/21-IEO 1701) from the Ethical Committee of the European Institute of Oncology.

RESULTS

The MARS subscale scores were medium to high (range: 3.31-4.18), and the total app quality score was 3.90. Patients with breast cancer reported reduced pain intensity at 3 months, from a mean of 5 at T0 to a mean of 3.72 at T2 (P=.04). The total number of times the app was accessed was positively correlated with pain intensity at 3 months (P=.03). The engagement (P=.03), information (P=.04), and subjective quality (P=.007) subscales were positively correlated with shared decision-making. Furthermore, participants with a lower pain self-efficacy at T2 (mean 40.83) used the mobile app more than participants with a higher pain self-efficacy (mean 48.46; P=.057).

CONCLUSIONS

The data collected in this study highlight that digital health technologies, when developed using a patient-driven approach, might be valuable tools for increasing participation in clinical care by patients with breast cancer, permitting them to achieve a series of key clinical outcomes and improving quality of life. Digital integrated health ecosystems might be important tools for improving ongoing monitoring of physical status, psychological burden, and socioeconomic issues during the cancer survivorship trajectory.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.2196/41216.

[Lung involvement in autoinflammatory diseases]

Genetic autoinflammatory diseases are now a recognized and rapidly expanding group. The lung involvement historically associated with autoinflammatory diseases is inflammatory seritis, primarily seen in familial Mediterranean fever and other interleukin-1 mediated diseases. Over the last ten years, pulmonary involvement has been the core presentation of two autoinflammatory diseases associated with constitutive type I interferon activation, i.e. SAVI and COPA syndrome. Most patients with these diseases usually develop early progression to pulmonary fibrosis, which is responsible for high rates of morbidity and mortality. Other rare autoinflammatory diseases are associated with alveolar proteinosis, particularly when related to MARS mutations. Additionally, in adults, VEXAS is frequently associated with pulmonary involvement, albeit without prognosis effect. A molecular approach to autoinflammatory diseases enables not only the definition of biomarkers for diagnosis, but also the identification of targeted treatments. Examples include JAK inhibitors in SAVI and COPA syndrome, even though this therapy does not prevent progression to pulmonary fibrosis. Another illustrative example is the efficacy of methionine supplementation in alveolar proteinosis linked to MARS mutations. Overall, in autoinflammatory diseases the lung is now emerging as a possible affected organ. Continuing discovery of new autoinflammatory diseases is likely to uncover further pathologies involving the lung. Such advances are expected to lead to the development of novel therapeutic perspectives.

Use of extracorporeal therapies to treat life-threatening intoxications

Toxic ingestions are a significant cause of pediatric morbidity and mortality, with some requiring extracorporeal removal for therapy. Given the emergent and life-threatening nature of such scenarios, it is paramount that clinicians caring for intoxicated children be familiar with the subject. This review summarizes the following: (a) the properties of a substance which lend it amenable to removal; (b) the current extracorporeal treatment modalities available for such removal (of which hemodialysis is typically the ideal choice); (c) an introduction and framework to use a quick reference guide from the Extrip organization, which has a website available to guide clinicians' rapid decisions; and (d) new membranes/approaches that may optimize clearance of certain intoxications.

A content-based review of mobile health applications for breast cancer prevention and education: Characteristics, quality and functionality analysis

Objective

Mobile Health apps could be a feasible and effective tool to raise awareness for breast cancer prevention and to support women to change their behaviour to a healthier lifestyle. The aim of this study was to analyse the characteristics and quality of apps designed for breast cancer prevention and education.

Methods

We conducted a systematic search for apps covering breast cancer prevention topics in the Google Play and Apple App Store accessible from Germany using search terms either in German or in English. Only apps with a last update after June 2020 were included. The apps identified were downloaded and evaluated by two independent researchers. App quality was analysed using the Mobile Application Rating Scale (MARS). Associations of app characteristics and MARS rating were analysed.

Results

We identified 19 apps available in the Google Play Store and seven apps available in the Apple App Store that met all inclusion criteria. The mean MARS score was 3.07 and 3.50, respectively. Functionality was the highest-scoring domain. Operating system, developer (healthcare), download rates and time since the last update were significantly associated with overall MARS score. In addition, the presence of the following app functions significantly influenced MARS rating: breast self-examination tutorial, reminder for self-examination, documentation feature and education about breast cancer risk factors.

Conclusions

Although most of the apps offer important features for breast cancer prevention, none of the analysed apps combined all functions. The absence of healthcare professionals' expertise in developing apps negatively affects the overall quality.

Mini Percutaneous Nephrolithotomy vs Standard Percutaneous Nephrolithotomy: A Perioperative Decision Support System for Surgical Success Comparison

Purpose

This study aimed to rank the features that are important in terms of safety and effectiveness in choosing the surgical method and providing appropriate care to the patient by using the variables examined before and after the surgery to evaluate the success of mini percutaneous nephrolithotomy and standard percutaneous nephrolithotomy surgeries.

Patients and Methods

The features evaluated before and after surgery were ranked according to their importance in the features considered, using Multivariate Adaptive Regression Splines (MARS), LASSO, Ridge, Elastic_net, and Random Forest algorithms as variable selection techniques. There are 278 samples in the relevant data set.

Results

Type of surgery (100%), intercostal access (97.75%), kidney opening procedure (94.25%), postoperative creatinine (59.22%), hydronephrosis (52.23%), the number of entries (41.61%), and pre- and post-operative hemoglobin difference (45.13%) were determined as the most critical variables. The MARS algorithm showed the most successful performance, with the lowest mean absolute error (MAE) value of 0.3622, the lowest root mean square error (RMSE) value of 0.3960, and the highest R2 value of 0.3405.

Conclusion

Clinical decision support systems can be helpful in eliminating errors and reducing costs. It can also improve the quality of healthcare and aid in the early diagnosis of diseases. Computer-aided decision-making systems can be developed using the results of such products. These systems can provide doctors with better information about their patient's treatment options and improve decision-making. It can contribute to patients being better informed about the surgery results and taking an active role. In conclusion, this study provides essential information that should be included in the surgical decision-making process for patients using medications and with a history of percutaneous nephrolithotomy.

The Most Popular Commercial Weight Management Apps in the Chinese App Store: Analysis of Quality, Features, and Behavior Change Techniques

BACKGROUND

Many smartphone apps designed to assist individuals in managing their weight are accessible, but the assessment of app quality and features has predominantly taken place in Western countries. Nevertheless, there is a scarcity of research evaluating weight management apps in China, which highlights the need for further investigation in this area.

OBJECTIVE

This study aims to conduct a comprehensive search for the most popular commercial Chinese smartphone apps focused on weight management and assess their quality, behavior change techniques (BCTs), and content-related features using appropriate evaluation scales. Additionally, the study sought to investigate the associations between the quality of various domains within weight management apps and the number of incorporated BCTs and app features.

METHODS

In April 2023, data on weight management apps from the iOS and Android app stores were downloaded from the Qimai Data platform. Subsequently, a total of 35 weight management apps were subjected to screening and analysis by 2 researchers. The features and quality of the apps were independently assessed by 6 professionals specializing in nutrition management and health behavioral change using the Mobile Application Rating Scale (MARS). Two registered dietitians, who had experience in app development and coding BCTs, applied the established 26-item BCT taxonomy to verify the presence of BCTs. Mean (SD) scores and their distributions were calculated for each section and item. Spearman correlations were used to assess the relationship between an app's quality and its technical features, as well as the number of incorporated BCTs.

RESULTS

The data set included a total of 35 apps, with 8 available in the Android Store, 10 in the Apple Store, and 17 in both. The overall quality, with a mean MARS score of 3.44 (SD 0.44), showed that functionality was the highest scoring domain (mean 4.18, SD 0.37), followed by aesthetics (mean 3.43, SD 0.42), engagement (mean 3.26, SD 0.64), and information (mean 2.91, SD 0.52), which had the lowest score. The mean number of BCTs in the analyzed apps was 9.17 (range 2-18 BCTs/app). The most common BCTs were "prompt review of behavioral goals" and "provide instruction," present in 31 apps (89%). This was followed by "prompt self-monitoring of behavior" in 30 apps (86%), "prompt specific goal setting" in 29 apps (83%), and "provide feedback on performance" in 27 apps (77%). The most prevalent features in the analyzed apps were the need for web access (35/35, 100%), monitoring/tracking (30/35, 86%), goal setting (29/35, 83%), and sending alerts (28/35, 80%). The study also revealed strong positive correlations among the number of BCTs incorporated, app quality, and app features. This suggests that apps with a higher number of BCTs tend to have better overall quality and more features.

CONCLUSIONS

The study found that the overall quality of weight management apps in China is moderate, with a particular weakness in the quality of information provided. The most prevalent BCTs in these apps were reviewing behavioral goals, providing guidance, self-monitoring of behavior, goal setting, and offering performance feedback. The most common features were the need for web access, monitoring and tracking, goal setting, and sending alerts. Notably, higher-quality weight management apps in China tended to incorporate more BCTs and features. These findings can be valuable for developers looking to improve weight management apps and enhance their potential to drive behavioral change in weight management.

Morphological characterization of the Polatli sheep in terms of live weight using data mining algorithms

The aim of this research is both to estimate the live weight (LW) of Polatli sheep (Ile de France × Akkaraman (G1)) by considering some body measurements (withers height (WH), rump height (RH), body length (BL), chest depth (CD), chest width (CD), chest girth (CG), cannon bone circumference (CBC)), age, and sex variables as independent variables using C&RT (Classification and Regression Tree), CHAID (Chi-square Automatic Interaction Detector), and MARS (Multivariate Adaptive Regression Splines) algorithms and to determine the significant independent variables in the estimation of live weight. For this purpose, a total of 210 sheep were used, including 180 females and 30 males of different ages, for the estimation of LW. The calculated Pearson correlation coefficients between LW and WH, RH, BL, CD, CW, CG, and CBC characteristics are 0.897, 0.896, 0.853, 0.948, 0.550, 0.914, and 0.798, respectively (p < 0.05). In the application of data mining algorithms as prediction models, a cross-validation of 10 was used, while for tree-based algorithms, the parent node was set to 10 and the child node to 5. While CHAID and C&RT algorithms each used 8 independent variables to explain the variation observed in LW, the MARS algorithm used 9 independent variables. In Polatli sheep, the sheep with the highest live weight was found in the node with age > 3 and CD > 36 cm cutting point in the CHAID algorithm (93.571 kg). In the C&RT algorithm, it was predicted to be (91.316 kg) when age > 0, CD > 36.5 cm, and CBC > 9.5 cm. When evaluated considering commonly used criteria, the prediction performances of CHAID, C&RT, and MARS algorithms were calculated as follows: RMSE (root mean square error) values are "5.788, 5.103, 4.005"; SDR (standard deviation ratio) values are "0.254, 0.224, 0.176"; MAPE (mean absolute percentage error) values are "7.555, 6.675, 5.682"; Adj-Rsq (adjusted R-squared) values are "0.935, 0.950, 0.969"; and AIC (Akaike information criterion) values are "741.436, 688.489, 582.792," respectively. In terms of prediction performance, among the tree algorithms (CHAID and C&RT), C&RT was found to be the best, while considering all performance measures, it was observed that the MARS algorithm exhibited the best performance. Consequently, it has been determined that C&RT and MARS algorithms can be safely employed in morphological characterization studies for the identification of indirect criteria and the formation of elite herds in terms of LW. This decision allows for the reliable use of these algorithms to facilitate the selection of indirect variables and the establishment of elite populations in breeding programs focusing on live weight.

Assessing the Usefulness of Mobile Apps for Noise Management in Occupational Health and Safety: Quantitative Measurement and Expert Elicitation Study

Background

Overexposure to occupational noise can lead to hearing loss. Occupational noise mapping is conventionally performed with a calibrated sound level meter (SLM). With the rise of mobile apps, there is a growing number of SLM apps available on mobile phones. However, few studies have evaluated such apps for accuracy and usefulness to guide those with occupational noise detection needs in selecting a quality app.

Objective

The purpose of this study was to evaluate the accuracy and usefulness of SLM mobile apps to guide workplace health and safety professionals in determining these apps' suitability for assessing occupational noise exposure.

Methods

The following three iOS apps were assessed: the NIOSH (National Institute for Occupational Safety and Health) Sound Level Meter, Decibel X, and SoundMeter X apps. The selected apps were evaluated for their accuracy in measuring sound levels in low-, moderate-, and high-noise settings within both simulated environments and real-world environments by comparing them to a conventional SLM. The usefulness of the apps was then assessed by occupational health specialists using the Mobile App Rating Scale (MARS).

Results

The NIOSH Sound Level Meter app accurately measured noise across a range of sound levels in both simulated settings and real-world settings. However, considerable variation was observed between readings. In comparison, the Decibel X and SoundMeter X apps showed more consistent readings but consistently underestimated noise levels, suggesting that they may pose a risk for workers. Nevertheless, none of the differences in sound measurements between the three apps and the conventional SLM were statistically significant (NIOSH Sound Level Meter: P=.78; Decibel X: P=.38; SoundMeter X: P=.40). The MARS scores for the three apps were all above 3.0, indicating the usefulness of these apps.

Conclusions

Under the conditions of this study, the NIOSH Sound Level Meter app had equivalent accuracy to the calibrated SLM and a degree of usefulness according to the MARS. This suggests that the NIOSH Sound Level Meter app may be suitable for mapping noise levels as part of a monitoring strategy in workplaces. However, it is important to understand its limitations. Mobile apps should complement but not replace conventional SLMs when trying to assess occupational noise exposure risk. Our outcomes also suggest that the MARS tool may have limited applicability to measurement-based apps and may be more suited to information-based apps that collect, record, and store information.

The Impact of Early Positive Studies on the Evolution of Extracorporeal Albumin Dialysis Literature: A Bibliometric Analysis

INTRODUCTION

Liver failure is a life-threatening condition characterized by the accumulation of metabolic toxins. Extracorporeal albumin dialysis (ECAD) has been promoted as a possible therapy.

METHODS

We employed bibliometric analysis to scrutinize the conceptual, intellectual, and social structure of the ECAD literature including its co-citation network and thematic analysis to explore its evolution and organization.

RESULTS

We identified 784 documents with a mean of 30.25 citations per document in a corpus of 15,191 references. The average citation rate peaked in 1998 at 280.75 citations/year before a second 2013 peak of 54.81 citations/year and then progressively decreased to its nadir in 2022 (1.48 yearly citations). We identified four primary co-citation clusters, with the most impactful publications being small "positive" manuscripts by Mitzner et al. (2000) and Heemann et al. (2002) (Cluster 1). This first cluster had several relational citations with clusters 2 and 3, but almost no citation link with cluster 4 represented by Bañares et al. (2013), Saliba et al. (2013), and Larsen et al. (2016), with their three negative randomized controlled trials. Finally, the thematic map revealed a shift in focus over time, with inflammation and ammonia as recent emergent themes.

CONCLUSIONS

This bibliometric analysis provided a transparent and reproducible longitudinal assessment of ECAD literature and demonstrated how positive studies with low levels of evidence can dominate a research field and overshadow negative findings from higher quality studies. These insights hold significant implications for future research and clinical practice within this domain.

Parametric Optimization of Torsional Parameters of Ferrocement "U" Wrapped Beams Using Recent Meta-Heuristic Optimization Algorithms

Structural elements are subjected to different types of loads, one of which is a torsional load. Due to the complexity of the analysis, torsion was not given much importance in earlier days. With stringent updates in codal provisions and due to architectural modifications, torsion is now considered one of the major parameters for structural design. The main aim of this paper is to analyze distressed elements due to torsion. It highlights different approaches, such as destructive and non-destructive processes, to be adopted to estimate the torsional parameters of a ferrocement "U" wrapped beam. The destructive method is the experimental determination of parameters, which is absolutely necessary. The non-destructive method includes an analytical method based on a softened truss model as well as a soft computing method. The soft computing method is based on the regression coefficient analysis method along with two recent optimization algorithms, i.e., (1) ARO (artificial rabbits optimization) and (2) DAOA (dynamic arithmetic optimization algorithm). The predicted results are found to be in agreement with the experimental values (destructive method). Lastly, the obtained results from both proposed methods are analyzed, and it is found that both algorithms can be utilized in any engineering problem to determine the global optimum value with corresponding input optimal settings. As the experimental method is time-consuming and expensive, analytical, and soft computing methods can be preferred over the experimental method.

Identification and Evaluation of Mobile Applications for Self-Management of Diet and Lifestyle for Patients with Inflammatory Bowel Disease

Background

Mobile health applications (apps) providing diet and lifestyle self-management programs to patients with inflammatory bowel disease (IBD) are emerging. The objective of this study was to evaluate current apps available in the US and Canada based on app quality, perceived impact on diet and mental health and comprehensiveness to support self-management.

Methods

The Apple iOS and Google Play app stores were searched for terms related to IBD. Apps were included if they targeted diet and lifestyle behaviours for patients living with IBD and were available to the general public. Apps were excluded if they were not specific to IBD, not available in English, did not target diet or lifestyle therapy, were not available in the US and Canada, or did not offer stand-alone self-management programs. The Mobile App Rating Scale was used to assess mobile app quality.

Results

A total of 1,512 apps were identified through the app stores. Six apps met inclusion criteria. My IBD Care: Crohn's and Colitis received the highest quality rating and LyfeMD received the highest overall app rating. Only these two apps provided behaviour tracking over time, and three (50 percent) apps provided good-quality information.

Conclusions

While many IBD-related apps exist, few support self-management of diet and lifestyle behaviours. The My IBD Care and LyfeMD apps had the highest ratings and can be used to track lifestyle behaviours. The effectiveness of these apps to improve behaviours, and subsequently impact the disease course and quality of life, should be explored in future studies.

Comparing Different Chemometric Approaches to Detect Adulteration of Cold-Pressed Flaxseed Oil with Refined Rapeseed Oil Using Differential Scanning Calorimetry

Flaxseed oil is one of the best sources of n-3 fatty acids, thus its adulteration with refined oils can lead to a reduction in its nutritional value and overall quality. The purpose of this study was to compare different chemometric models to detect adulteration of flaxseed oil with refined rapeseed oil (RP) using differential scanning calorimetry (DSC). Based on the melting phase transition curve, parameters such as peak temperature (T), peak height (h), and percentage of area (P) were determined for pure and adulterated flaxseed oils with an RP concentration of 5, 10, 20, 30, and 50% (w/w). Significant linear correlations (p ≤ 0.05) between the RP concentration and all DSC parameters were observed, except for parameter h1 for the first peak. In order to assess the usefulness of the DSC technique for detecting adulterations, three chemometric approaches were compared: (1) classification models (linear discriminant analysis-LDA, adaptive regression splines-MARS, support vector machine-SVM, and artificial neural networks-ANNs); (2) regression models (multiple linear regression-MLR, MARS, SVM, ANNs, and PLS); and (3) a combined model of orthogonal partial least squares discriminant analysis (OPLS-DA). With the LDA model, the highest accuracy of 99.5% in classifying the samples, followed by ANN > SVM > MARS, was achieved. Among the regression models, the ANN model showed the highest correlation between observed and predicted values (R = 0.996), while other models showed goodness of fit as following MARS > SVM > MLR. Comparing OPLS-DA and PLS methods, higher values of R2X(cum) = 0.986 and Q2 = 0.973 were observed with the PLS model than OPLS-DA. This study demonstrates the usefulness of the DSC technique and importance of an appropriate chemometric model for predicting the adulteration of cold-pressed flaxseed oil with refined rapeseed oil.

Use of hepatic support with MARS in a patient with SARS-CoV-2 Pneumonia, in treatment with ECMO and CRRT therapies: Case Report

Extracorporeal Membrane Oxygenation (ECMO) therapy had an important role in the treatment of severe COVID-19 pneumonia, where invasive mechanical ventilation was not enough to provide correct oxygenation to various organ systems. However, there are other extracorporeal technologies, such as the Molecular Absorbent Recirculation System (MARS) and Continuous Renal Replacement Therapy (CRRT), that provide temporal support for any critical patient. The following case describes a 60-year-old man with severe Acute Respiratory Distress Syndrome (ARDS), who needed ECMO therapy. During the critical days of hospitalization, CRRT was used, but a sudden hyperbilirubinemia ensued. Consequently, MARS therapy was initiated; followed by an improvement of bilirubin levels. Additional studies are needed to establish the possible benefits of the combination of MARS therapy and ECMO; however, we detected that concomitantly, there was a decrease in other laboratory parameters such as acute phase reactants. Even though, no change in clinical course was observed, as shown in some studies.

Usage of the XGBoost and MARS algorithms for predicting body weight in Kajli sheep breed

This study aimed to utilize the XGBoost and MARS algorithms to predict present weight from body measurements. The algorithms have the potential to model nonlinear relationships between body measurements and weight, and this study attempted to find a model that provided the most accurate predictions of present weight. The current study was conducted with 152 animals in order to achieve a certain goal. To compare the model performances, goodness-of-fit criteria such as R2, r, RMSE, CV, SDratio, PI, MAPE, AIC were used. According to the results of this study, the XGBoost algorithm was the most reliable model for predicting present weight from body measurement. Even if the XGBoost algorithm was the most accurate model, the MARS algorithm was the reliable model for the same aim. In addition, it is hoped that the results of this study will help researchers and breeders better understand the relationship between body measurements and weight and ultimately be able to help individuals better manage their weight. As a conclusion, in the current study, the XGBoost algorithm is an effective, efficient, and reliable tool for accurately estimating present weight from body measurements. This makes it an invaluable tool in rural areas, where traditional weighing scales may not be available or reliable.

Scope, Characteristics, Behavior Change Techniques, and Quality of Conversational Agents for Mental Health and Well-Being: Systematic Assessment of Apps

BACKGROUND

Mental disorders cause substantial health-related burden worldwide. Mobile health interventions are increasingly being used to promote mental health and well-being, as they could improve access to treatment and reduce associated costs. Behavior change is an important feature of interventions aimed at improving mental health and well-being. There is a need to discern the active components that can promote behavior change in such interventions and ultimately improve users' mental health.

OBJECTIVE

This study systematically identified mental health conversational agents (CAs) currently available in app stores and assessed the behavior change techniques (BCTs) used. We further described their main features, technical aspects, and quality in terms of engagement, functionality, esthetics, and information using the Mobile Application Rating Scale.

METHODS

The search, selection, and assessment of apps were adapted from a systematic review methodology and included a search, 2 rounds of selection, and an evaluation following predefined criteria. We conducted a systematic app search of Apple's App Store and Google Play using 42matters. Apps with CAs in English that uploaded or updated from January 2020 and provided interventions aimed at improving mental health and well-being and the assessment or management of mental disorders were tested by at least 2 reviewers. The BCT taxonomy v1, a comprehensive list of 93 BCTs, was used to identify the specific behavior change components in CAs.

RESULTS

We found 18 app-based mental health CAs. Most CAs had <1000 user ratings on both app stores (12/18, 67%) and targeted several conditions such as stress, anxiety, and depression (13/18, 72%). All CAs addressed >1 mental disorder. Most CAs (14/18, 78%) used cognitive behavioral therapy (CBT). Half (9/18, 50%) of the CAs identified were rule based (ie, only offered predetermined answers) and the other half (9/18, 50%) were artificial intelligence enhanced (ie, included open-ended questions). CAs used 48 different BCTs and included on average 15 (SD 8.77; range 4-30) BCTs. The most common BCTs were 3.3 "Social support (emotional)," 4.1 "Instructions for how to perform a behavior," 11.2 "Reduce negative emotions," and 6.1 "Demonstration of the behavior." One-third (5/14, 36%) of the CAs claiming to be CBT based did not include core CBT concepts.

CONCLUSIONS

Mental health CAs mostly targeted various mental health issues such as stress, anxiety, and depression, reflecting a broad intervention focus. The most common BCTs identified serve to promote the self-management of mental disorders with few therapeutic elements. CA developers should consider the quality of information, user confidentiality, access, and emergency management when designing mental health CAs. Future research should assess the role of artificial intelligence in promoting behavior change within CAs and determine the choice of BCTs in evidence-based psychotherapies to enable systematic, consistent, and transparent development and evaluation of effective digital mental health interventions.

Liver-Support Therapies in Critical Illness-A Comparative Analysis of Procedural Characteristics and Safety

Extracorporeal liver-support therapies remain controversial in critically ill patients, as most studies have failed to show an improvement in outcomes. However, heterogeneous timing and inclusion criteria, an insufficient number of treatments, and the lack of a situation-dependent selection of available liver-support modalities may have contributed to negative study results. We retrospectively investigated the procedural characteristics and safety of the three liver-support therapies CytoSorb, Molecular Adsorbent Recirculating System (MARS) and therapeutic plasma exchange (TPE). Whereas TPE had its strengths in a shorter treatment duration, in clearing larger molecules, affecting platelet numbers less, and improving systemic coagulation and hemodynamics, CytoSorb and MARS were associated with a superior reduction in particularly small protein-bound and water-soluble substances. The clearance magnitude was concentration-dependent for all three therapies, but additionally related to the molecular weight for CytoSorb and MARS therapy. Severe complications did not appear. In conclusion, a better characterization of disease-driving as well as beneficial molecules in critically ill patients with acute liver dysfunction is crucial to improve the use of liver-support therapy in critically ill patients. TPE may be beneficial in patients at high risk for bleeding complications and impaired liver synthesis and hemodynamics, while CytoSorb and MARS may be considered for patients in whom the elimination of smaller toxic compounds is a primary objective.

Mobile applications in the Philippines during the COVID-19 pandemic: systematic search, use case mapping, and quality assessment using the Mobile App Rating Scale (MARS)

Background

In the Philippines, various mobile health apps were implemented during the COVID-19 pandemic with very little knowledge in terms of their quality. The aims of this paper were 1) to systemically search for mobile apps with COVID-19 pandemic use case that are implemented in the Philippines; 2) to assess the apps using Mobile App Rating Scale (MARS); and 3) to identify the critical points for future improvements of these apps.

Methods

To identify existing mobile applications with COVID-19 pandemic use case employed in the Philippines, Google Play and Apple App Stores were systematically searched. Further search was conducted using the Google Search. Data were extracted from the app web store profile and apps were categorized according to use cases. Mobile apps that met the inclusion criteria were independently assessed and scored by two researchers using the MARS-a 23-item, expert-based rating scale for assessing the quality of mHealth applications.

Results

A total of 27 apps were identified and assessed using MARS. The majority of the apps are designed for managing exposure to COVID-19 and for promoting health monitoring. The overall MARS score of all the apps is 3.62 points (SD 0.7), with a maximum score of 4.7 for an app used for telehealth and a minimum of 2.3 for a COVID-19 health declaration app. The majority (n = 19, 70%) of the apps are equal to or exceeded the minimum "acceptable" MARS score of 3.0. Looking at the categories, the apps for raising awareness received the highest MARS score of 4.58 (SD 0.03) while those designed for managing exposure to COVID-19 received the lowest mean score of 3.06 (SD 0.6).

Conclusions

There is a heterogenous quality of mHealth apps implemented during the COVID-19 pandemic in the Philippines. The study also identified areas to better improve the tools. Considering that mHealth is expected to be an integral part of the healthcare system post-pandemic, the results warrant better policies and guidance in the development and implementation to ensure quality across the board and as a result, positively impact health outcomes.

Supplementary Information

The online version contains supplementary material available at 10.1186/s44247-023-00007-2.

Quality Assessment of the Road Traffic Health and Safety Apps with a Focus on the Five Rights of Information Management

Objective

The expansion of mobile applications as a tool for road traffic health and safety may develop several issues from the perspective of information management. Quality assessment of these apps, especially from an information system management perspective, appears inevitable, as their possible low quality may cause irreversible injury or fatal consequences. This study aimed to evaluate the quality of the apps in the three subcategories of road traffic safety apps (including Accident Record and Report (ARR), Distraction Management (DM), and Vehicle Operating, Fixing, and Maintenance (VOFM)) using the Mobile Application Rating Scale (MARS), which rates 23 evaluation criteria organized in five domains (Engagement, Esthetics, Information, and Subjective Quality) with particular attention to the five rights framework of health information system.

Method

The researchers retrieved road traffic health and safety mobile apps from Google Play. First, the domain expert panel (n= 7) (from disciplines of HIM and medical informatics) was formed. They scrutinized and discussed the MARS items and mapped them into the five rights framework of information quality. Moreover, the researchers assigned the apps to the information system or decision support system category. Two researchers independently reviewed the apps and conducted the qualitative content analysis to categorize them into ARR, DM, and VOFM classes. Finally, the quality of the apps was assessed using the MARS rating scale (max=5) in terms of 1) app classification category with a descriptive aim; 2) app subjective and objective quality categories comprised of engagement, functionality, esthetics, and information sections; and 3) an optional app-specific section. The mean scores for the subjective quality, objective quality, and app-specific sections were calculated separately for each mobile app. A score ≥ 3.0 was considered acceptable.

Results

A total number of 42 apps met the criteria for the assessment. The average objective quality scores were computed as 2.6, 2.2, and 3.0 for the ARR, DM, and VOFM apps, respectively. Therefore, the quality of the apps in the ARR and DM subgroups was not acceptable. Moreover, the quality of the apps in the VOFM subcategory was considered moderate. Furthermore, the subjective quality and app-specific sections of apps in the ARR and DM categories were less than moderate. Most apps had the potential of an information system or decision support system. Also, the criteria measured by MARS could be mapped to the five rights framework of information management.

Conclusion

The findings of this study revealed the existing gaps in three subcategories of road traffic safety apps. Considering the multiple criteria of the MARS and having in mind the framework of five rights, developers of the apps may develop better products in road traffic health and safety.

The development of dissolved oxygen forecast model using hybrid machine learning algorithm with hydro-meteorological variables

Dissolved oxygen (DO) forecasting is essential for aquatic managers responsible for maintaining ecosystem health and the management of water bodies affected by water quality parameters. This paper aims to forecast dissolved oxygen (DO) concentration using a multivariate adaptive regression spline (MARS) hybrid model coupled with maximum overlap discrete wavelet transformation (MODWT) as a feature decomposition approach for Surma River water using a set of water quality hydro-meteorological variables. The proposed hybrid model is compared with numerous machine learning methods, namely Bayesian ridge regression (BNR), k-nearest neighbourhood (KNN), kernel ridge regression (KRR), random forest (RF), and support vector regression (SVR). The investigational results show that the proposed model of MODWT-MARS has a better prediction than the comparing benchmark models and individual standalone counter parts. The result shows that the hybrid algorithms (i.e. MODWT-MARS) outperformed the other models (r = 0.981, WI = 0.990, RMAE = 2.47%, and MAE = 0.089). This hybrid method may serve to forecast water quality variables with fewer predictor variables.

Daily precipitation performances of regression-based statistical downscaling models in a basin with mountain and semi-arid climates

The impacts of climate change on current and future water resources are important to study local scale. This study aims to investigate the prediction performances of daily precipitation using five regression-based statistical downscaling models (RBSDMs), for the first time, and the ERA-5 reanalysis dataset in the Susurluk Basin with mountain and semi-arid climates for 1979-2018. In addition, comparisons were also performed with an artificial neural network (ANN). Before achieving the aim, the effects of atmospheric variables, grid resolution, and long-distance grid on precipitation prediction were holistically investigated for the first time. Kling-Gupta efficiency was modified and used for holistic evaluation of statistical moments parameters at precipitation prediction comparison. The standard triangular diagram, quite new in the literature, was also modified and used for graphical evaluation. The results of the study revealed that near grids were more effective on precipitation than single or far grids, and 1.50° × 1.50° resolution showed similar performance to 0.25° × 0.25° resolution. When the polynomial multivariate adaptive regression splines model, which performed slightly higher than ANN, tended to capture skewness and standard deviation values of precipitations and to hit wet/dry occurrence than the other models, all models were quite well able to predict the mean value of precipitations. Therefore, RBSDMs can be used in different basins instead of black-box models. RBSDMs can also be established for mean precipitation values without dry/wet classification in the basin. A certain success was observed in the models; however, it was justified that bias correction was required to capture extreme values in the basin.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s00477-022-02345-5.

Mobile Apps for the Management of Gastrointestinal Diseases: Systematic Search and Evaluation Within App Stores

Background

Gastrointestinal diseases are associated with substantial cost in health care. In times of the COVID-19 pandemic and further digitalization of gastrointestinal tract health care, mobile health apps could complement routine health care. Many gastrointestinal health care apps are already available in the app stores, but the quality, data protection, and reliability often remain unclear.

Objective

This systematic review aimed to evaluate the quality characteristics as well as the privacy and security measures of mobile health apps for the management of gastrointestinal diseases.

Methods

A web crawler systematically searched for mobile health apps with a focus on gastrointestinal diseases. The identified mobile health apps were evaluated using the Mobile Application Rating Scale (MARS). Furthermore, app characteristics, data protection, and security measures were collected. Classic user star rating was correlated with overall mobile health app quality.

Results

The overall quality of the mobile health apps (N=109) was moderate (mean 2.90, SD 0.52; on a scale ranging from 1 to 5). The quality of the subscales ranged from low (mean 1.89, SD 0.66) to good (mean 4.08, SD 0.57). The security of data transfer was ensured only by 11 (10.1%) mobile health apps. None of the mobile health apps had an evidence base. The user star rating did not correlate with the MARS overall score or with the individual subdimensions of the MARS (all P>.05).

Conclusions

Mobile health apps might have a positive impact on diagnosis, therapy, and patient guidance in gastroenterology in the future. We conclude that, to date, data security and proof of efficacy are not yet given in currently available mobile health apps.

Promoting Adolescent Sexual and Reproductive Health in North America Using Free Mobile Apps: Environmental Scan

BACKGROUND

Neglecting adolescents' sexual and reproductive health (SRH) can affect multiple domains of development. Promoting healthy adolescent SRH is increasingly done using mobile phone apps. Providing SRH information via mobile phones can positively influence SRH outcomes including improving knowledge, reducing sexual risk behavior, and increasing the use of health services. A systematic approach to establishing and evaluating the quality of adolescent SRH mobile apps is urgently needed to rigorously evaluate whether they are a viable and effective strategy for reaching adolescents and improving adolescent SRH knowledge and behaviors in particular.

OBJECTIVE

This study aimed to conduct an environmental scan to produce an inventory of adolescent SRH-specific mobile apps with descriptions of their purpose, structure, operations, and quality of evidence.

METHODS

We used a literature review to develop 15 search terms for adolescent SRH-related apps in the Canadian and US Apple and Google app stores. After generating the search results, inclusion and exclusion criteria were applied. Using the remaining apps, we built an evidence table of app information, and app reviewers assessed each included app using the Mobile App Rating Scale. App assessments were then used to highlight trends between apps and identify gaps in app quality.

RESULTS

In total, 2761 apps were identified by our searches, of which 1515 were duplicates. Of the 1246 remaining apps, 15 met the criteria for further assessment. Across all subdomains, on a scale of 1-5, the mean app score was 3.4/5. The Functionality subdomain had the highest mean score of 4.1/5, whereas the Engagement subdomain had the lowest score of 2.9/5. The top 4 apps were Tia: Female Health Advisor (4.7/5), Under the Stethoscope (4.2/5), Condom Credit Card (4.1/5), and Shnet (3.7/5).

CONCLUSIONS

This environmental scan aimed to provide a comprehensive overview of the mobile apps developed to promote adolescent SRH knowledge and outcomes. Of the 15 mobile apps available to provide information related to adolescent SRH, few provided comprehensive, reliable, and evidence-based SRH information. Areas of strength included the apps' gestural design, performance, ease of use, and navigation. Areas of weakness included app goals, evidence base, and app customization options. These results can be used to conduct future studies evaluating the use and efficacy of mobile apps on health knowledge and behaviors and promote adolescent SRH.

Molecular adsorbent recirculating system treatment in patients with post-hepatectomy liver failure: Long-term results of a pilot study

BACKGROUND

Post-hepatectomy liver failure (PHLF) is the leading cause of postoperative mortality following major liver resection. Between December 2012 and May 2015, 10 consecutive patients with PHLF (according to the Balzan criteria) following major/extended hepatectomy were included in a prospective treatment study with the molecular adsorbent recirculating system (MARS). Sixty- and 90-day mortality rates were 0% and 10%, respectively. Of the nine survivors, four still had liver dysfunction at 90 days postoperatively. One-year overall survival (OS) of the MARS-PHLF cohort was 50%. The present study aims to assess long-term outcome of this cohort compared to a historical control cohort.

METHODS

To compare long-term outcome of the MARS-PHLF treatment cohort with PHLF patients not treated with MARS, the present study includes all 655 patients who underwent major hepatectomy at Karolinska University Hospital between 2010 and 2018. Patients with PHLF were identified according to the Balzan criteria.

RESULTS

The cohort was split into three time periods: pre-MARS period (n = 192), MARS study period (n = 207), and post-MARS period (n = 256). The 90-day mortality of patients with PHLF was 55% (6/11) in the pre-MARS period, 14% during the MARS study period (2/14), and 50% (3/6) in the post-MARS period (p = 0.084). Median OS (95% confidence interval (CI)) was 37.8 months (29.3-51.7) in the pre-MARS cohort, 57 months (40.7-75.6) in the MARS cohort, and 38.8 months (31.4-51.2) in the post-MARS cohort. The 5-year OS of 10 patients included in the MARS study was 40% and the median survival 11.6 months (95% CI: 3 to not releasable). In contrast, for the remaining 21 patients fulfilling the Balzan criteria during the study period but not treated with MARS, the 5-year OS and median survival were 9.5% and 7.3 months (95% CI, 0.5-25.9), respectively (p = 0.138)).

CONCLUSIONS

MARS treatment may contribute to improved outcome of patients with PHLF. Further studies are needed.The initial pilot study was registered at ClinicalTrials.gov (NCT03011424).

Usability Evaluation and Classification of mHealth Applications for Type 2 Diabetes Mellitus Using MARS and ID3 Algorithm

The rapid growth of mHealth applications for Type 2 Diabetes Mellitus (T2DM) patients’ self-management has motivated the evaluation of these applications from both the usability and user point of view. The objective of this study was to identify mHealth applications that focus on T2DM from the Android store and rate them from the usability perspective using the MARS tool. Additionally, a classification of these mHealth applications was conducted using the ID3 algorithm to identify the most preferred application. The usability of the applications was assessed by two experts using MARS. A total of 11 mHealth applications were identified from the initial search, which fulfilled our inclusion criteria. The usability of the applications was rated using the MARS scale, from 1 (inadequate) to 5 (excellent). The Functionality (3.23) and Aesthetics (3.22) attributes had the highest score, whereas Information (3.1) had the lowest score. Among the 11 applications, “mySugr” had the highest average MARS score for both Application Quality (4.1/5) as well as Application Subjective Quality (4.5/5). Moreover, from the classification conducted using the ID3 algorithm, it was observed that 6 out of 11 mHealth applications were preferred for the self-management of T2DM.

Functional and Technical Aspects of Self-management mHealth Apps: Systematic App Search and Literature Review

BACKGROUND

Although the past decade has witnessed the development of many self-management mobile health (mHealth) apps that enable users to monitor their health and activities independently, there is a general lack of empirical evidence on the functional and technical aspects of self-management mHealth apps from a software engineering perspective.

OBJECTIVE

This study aims to systematically identify the characteristics and challenges of self-management mHealth apps, focusing on functionalities, design, development, and evaluation methods, as well as to specify the differences and similarities between published research papers and commercial and open-source apps.

METHODS

This research was divided into 3 main phases to achieve the expected goal. The first phase involved reviewing peer-reviewed academic research papers from 7 digital libraries, and the second phase involved reviewing and evaluating apps available on Android and iOS app stores using the Mobile Application Rating Scale. Finally, the third phase involved analyzing and evaluating open-source apps from GitHub.

RESULTS

In total, 52 research papers, 42 app store apps, and 24 open-source apps were analyzed, synthesized, and reported. We found that the development of self-management mHealth apps requires significant time, effort, and cost because of their complexity and specific requirements, such as the use of machine learning algorithms, external services, and built-in technologies. In general, self-management mHealth apps are similar in their focus, user interface components, navigation and structure, services and technologies, authentication features, and architecture and patterns. However, they differ in terms of the use of machine learning, processing techniques, key functionalities, inference of machine learning knowledge, logging mechanisms, evaluation techniques, and challenges.

CONCLUSIONS

Self-management mHealth apps may offer an essential means of managing users' health, expecting to assist users in continuously monitoring their health and encourage them to adopt healthy habits. However, developing an efficient and intelligent self-management mHealth app with the ability to reduce resource consumption and processing time, as well as increase performance, is still under research and development. In addition, there is a need to find an automated process for evaluating and selecting suitable machine learning algorithms for the self-management of mHealth apps. We believe that these issues can be avoided or significantly reduced by using a model-driven engineering approach with a decision support system to accelerate and ameliorate the development process and quality of self-management mHealth apps.

Prediction of the live weight at breeding age from morphological measurements taken at weaning in indigenous Honamli kids using data mining algorithms

The purpose of this study was to predict live weight at breeding age (LW) based on weaning morphological traits, which birth weight (BW), weaning weight (WW), withers height (WH), back height (BH), rump height (RH), chest depth (CD), body length (BL), tail length (TL), chest girth (CG), leg girth (LG), front shark circumference (FSC), head length (HL), head width (HW), nose length (NL), ear length (EL), and ear width (EW). For this purpose, measurements were taken from 84 Honamli kids born in 2018. The study also included sex, birth type (BT), and ear type as the nominal predictors. For this purpose, two MARS (Multivariate Adaptive Regression Splines), which are interaction (MARS2) and non-interaction (MARS1), and based-tree algorithms, such as CART (Classification and Regression Tree), CHAID (Chi-Square Automatic Interaction Detector), and Exhaustive CHAID, were used by cross-validation 5 and compared with each other considering the predictive performance by taking into account nine predictive performances criteria. LW has a significantly positive and high linear relationship with WH (0.770), BH (0.770), RH (0.750), BL (0.750), and CG (0.770), respectively (p < 0.01). According to these criteria, second-order interaction MARS2 model had the best performance among all data mining algorithms. Also, the CHAID algorithm was the best predictor of LW among regression tree-based algorithms. The CHAID algorithm predicted that the Honamli goat with 14.426 < WW < 15.575 kg and HW > 16.464 cm had the heaviest LW at 56.268 kg. The MARS2 model showed that the heaviest LW could be produced by WW > 16.10 kg, HW > 17 cm, Sex-Male × BL > 60 cm, WW × BL < 50 cm, BT-twin × WW < 15.60 kg, BL > 50 cm × CG > 62.4 cm and male goats. Also, CHAID and MARS2 algorithms explain 92.00% and 94.50% of the variation in LW, respectively. According to the results, it can be concluded that the CHAID and MARS algorithms used in the prediction of LW at breeding age could give an idea to reveal the breed standards examined for breeding purposes. While determining that there are important statistical methods in defining body characteristics at weaning in a complex way, the body characteristics determined by these models can be used as indirect selection criteria.

Medication Adherence in Chronic Older Patients: An Italian Observational Study Using Medication Adherence Report Scale (MARS-5I)

Background: the world population is aging, and the prevalence of chronic diseases is increasing. Chronic diseases affect the quality of life of patients and contribute toward increased healthcare costs if patients do not adhere to treatment. This study defines the medication adherence levels of patients with chronic diseases. Methods: an observational cross-sectional study was carried out. Patients aged 65 years and older with chronic diseases were included in this study. The medication adherence report scale was used. Results: overall, 98 patients aged 65 years and older were included. The mean age of responders was 78.65 years. Study population: 71.43% were always adherent; 9.79% often adherent; 14.89% sometimes adherent; 3.87% rarely adherent; and 1% never adherent. The internal consistency of the MARS-5I was good: Cronbach’s alfa value of 0.77. Conclusions: the MARS-5I is an effective self-report instrument to measure the medication adherence of patients. However, further studies are needed to explore factors affecting medication adherence to avoid clinical consequences for patients and high healthcare costs for healthcare facilities. Healthcare communication could be improved to ensure better transitional care.

Japanese Version of the Mobile App Rating Scale (MARS): Development and Validation

Background

The number of mobile health (mHealth) apps continues to rise each year. Widespread use of the Mobile App Rating Scale (MARS) has allowed objective and multidimensional evaluation of the quality of these apps. However, no Japanese version of MARS has been made available to date.

Objective

The purposes of this study were (1) to develop a Japanese version of MARS and (2) to assess the translated version’s reliability and validity in evaluating mHealth apps.

Methods

To develop the Japanese version of MARS, cross-cultural adaptation was used using a universalist approach. A total of 50 mental health apps were evaluated by 2 independent raters. Internal consistency and interrater reliability were then calculated. Convergent and divergent validity were assessed using multitrait scaling analysis and concurrent validity.

Results

After cross-cultural adaptation, all 23 items from the original MARS were included in the Japanese version. Following translation, back-translation, and review by the author of the original MARS, a Japanese version of MARS was finalized. Internal consistency was acceptable by all subscales of objective and subjective quality (Cronbach α=.78-.89). Interrater reliability was deemed acceptable, with the intraclass correlation coefficient (ICC) ranging from 0.61 to 0.79 for all subscales, except for “functionality,” which had an ICC of 0.40. Convergent/divergent validity and concurrent validity were also considered acceptable. The rate of missing responses was high in several items in the “information” subscale.

Conclusions

A Japanese version of MARS was developed and shown to be reliable and valid to a degree that was comparable to the original MARS. This Japanese version of MARS can be used as a standard to evaluate the quality and credibility of mHealth apps.

Designing and developing an app to perform Hofstee cut-off calculations

Determining a Hofstee cut-off point in medical education student assessment is problematic: traditional methods can be time-consuming, inaccurate, and inflexible.  To counter this, we developed a simple Android app that receives raw, unsorted student assessment data in .csv format, allows for multiple judges' inputs, mean or median inputs, calculates the Hofstee cut-off mathematically, and outputs the results with other guiding information. The app contains a detailed description of its functionality.

Prediction of rainfall time series using soft computing techniques

Past studies indicate that increasing temperatures would accelerate the Earth's water cycle and in turn would increase the evaporation rate. Increased evaporation will result in more frequent and intense storms; hence, most researchers focus on climate change and its effect on Earth, particularly the precipitation. In the last two decades, the Udaipur district, India, faces water scarcity and flooding situations twice. The present study focuses on the prediction of rainfall using the most advanced soft computing techniques (SCT) such as multivariate adaptive regression splines (MARS), classification and regression trees (CART), and gene expression programming (GEP) in India's Udaipur district. The performance of these SCT was evaluated to test the capability to predict the rainfall. Results showed that the MARS model for rainfall prediction showed better performance than the GEP model.

10-Year Evaluation of Adherence and Satisfaction with Information about Tolvaptan in ADPKD: A Single-Center Pilot Study

PURPOSE

Tolvaptan is the only approved drug for the treatment of autosomal dominant polycystic kidney disease (ADPKD) and causes significant polyuria with secondary polydipsia. Up to now, there is no study that examines tolvaptan adherence and satisfaction with information received about tolvaptan in ADPKD patients 10 years after starting tolvaptan therapy.

PATIENTS AND METHODS

This pilot study includes 12 ADPKD patients that were formerly enrolled in the tolvaptan registration trials and have continued to use tolvaptan thereafter. Data were collected once via questionnaires on patients' self-reported adherence (MARS-D: Medication Adherence Report Scale - German version) and satisfaction with the information received about tolvaptan (SIMS-D: Satisfaction with Information about Medicines Scale - German version) at the time of the present study. In addition, serum creatinine levels and clinical data were evaluated.

RESULTS

The MARS-D demonstrated strong adherence to tolvaptan (range of possible score: 5-25; median: 23.5; range of individual results: 5). The SIMS-D showed a high level of satisfaction with the information received about the action and usage of tolvaptan (SIMS-D AU subscale; range of possible score: 0-9; median: 9, range of individual results: 1), but also revealed dissatisfaction regarding the information received about potential problems of tolvaptan in 42% of the participants (SIMS-D PP subscale; range of possible score: 0-8; median: 8, range of individual results: 6). During treatment with tolvaptan, the eGFR decreased from 78.8 ± 15.9 mL/min/1.73 m2 to 48.3 ± 19.4 mL/min/1.73 m2 (P < 0.0001).

CONCLUSION

Although patients reported strong adherence to tolvaptan, there was still dissatisfaction with the information received about potential problems with tolvaptan. Therefore, our data suggest conduction of at least one patient survey on adherence and satisfaction with the information received about tolvaptan during any tolvaptan treatment to improve patient education regarding the use of tolvaptan in slowing down of ADPKD.

Promoting Health via mHealth Applications Using a French Version of the Mobile App Rating Scale: Adaptation and Validation Study

Background

In the recent decades, the number of apps promoting health behaviors and health-related strategies and interventions has increased alongside the number of smartphone users. Nevertheless, the validity process for measuring and reporting app quality remains unsatisfactory for health professionals and end users and represents a public health concern. The Mobile Application Rating Scale (MARS) is a tool validated and widely used in the scientific literature to evaluate and compare mHealth app functionalities. However, MARS is not adapted to the French culture nor to the language.

Objective

This study aims to translate, adapt, and validate the equivalent French version of MARS (ie, MARS-F).

Methods

The original MARS was first translated to French by two independent bilingual scientists, and their common version was blind back-translated twice by two native English speakers, culminating in a final well-established MARS-F. Its comprehensibility was then evaluated by 6 individuals (3 researchers and 3 nonacademics), and the final MARS-F version was created. Two bilingual raters independently completed the evaluation of 63 apps using MARS and MARS-F. Interrater reliability was assessed using intraclass correlation coefficients. In addition, internal consistency and validity of both scales were assessed. Mokken scale analysis was used to investigate the scalability of both MARS and MARS-F.

Results

MARS-F had a good alignment with the original MARS, with properties comparable between the two scales. The correlation coefficients (r) between the corresponding dimensions of MARS and MARS-F ranged from 0.97 to 0.99. The internal consistencies of the MARS-F dimensions engagement (ω=0.79), functionality (ω=0.79), esthetics (ω=0.78), and information quality (ω=0.61) were acceptable and that for the overall MARS score (ω=0.86) was good. Mokken scale analysis revealed a strong scalability for MARS (Loevinger H=0.37) and a good scalability for MARS-F (H=0.35).

Conclusions

MARS-F is a valid tool, and it would serve as a crucial aid for researchers, health care professionals, public health authorities, and interested third parties, to assess the quality of mHealth apps in French-speaking countries.

What do people want in a smoking cessation app? An analysis of user reviews and app quality

INTRODUCTION

Mobile smoking cessation (mCessation) apps have the potential to complement and enhance existing interventions, but many are of low quality. Exploring app reviews can provide a broader understanding of user experiences and engagement, to enhance the quality, acceptability and effectiveness of future developments.

METHODS

Publicly available user reviews and ratings of smoking cessation apps were mined from Google Play TM and the App Store ® via a targeted two-stage search strategy. English language smoking cessation apps with at least 20 consumer reviews between 2011 and 2020 were included. User reviews were thematically analysed using Braun & Clarke's framework. Apps were independently scored using the Mobile Apps Rating Scale (MARS) and compared to average user star ratings.

RESULTS

Forty-eight versions of 42 apps, encompassing 1,414 associated reviews, met eligibility criteria. Inductive coding of reviews produced 1,084 coding references including reviews coded across multiple nodes. Themes generated included: (1) supportive characteristics/tools; (2) useability; (3) influence on smoking behaviour; (4) benefits of quitting; and (5) role as a supplementary tool for quitting. The mean MARS score of 36 free and accessible apps was 3.10 (SD 0.71) with mean scores ranging from 2.00 to 4.47. An inverse relationship between MARS scores and average user star ratings was observed .

CONCLUSIONS

App personalisation, relationality, functionality and credibility were important to users, and should be considered as key design components for future apps. Differences between user star ratings and MARS scores may illustrate competing priorities of consumers and researchers, and the importance of a co-design development method.

IMPLICATIONS

This is the first study to use unsolicited user reviews from a large population to understand the general mCessation user experience in relation to making a quit attempt. Our findings highlight specific features favoured and disliked by users, including their influence on engagement, and supports previous findings that mCessation applications need to be highly tailorable, functional, credible and supportive. We recommend a consumer-driven, co-design approach for future mCessation app developments to optimise user acceptability and engagement.

Characterisation and assessment of the most popular mobile apps designed for neck pain self-management: A systematic search in app stores

PURPOSE

This study aimed to assess commonly downloaded apps targeting neck pain (NP) using the Mobile Apps Rating Scale (MARS), to characterise the interventions described in the apps and to correlate the ratings of quality retrieved from app stores against the ratings of quality assessed using MARS.

MATERIALS AND METHODS

A systematic search was conducted at the Apple App Store, Google Play, Windows Phone Store and Huawei App Gallery. Mobile apps with 100 or more ratings, a user rating of 4 or higher and which were available for free were retrieved for assessment.

RESULTS

A total of 17 apps met the inclusion criteria. The mean user rating was 4.4 ± 0.28, the mean MARS score was 4.0 ± 0.58 and the correlation between user ratings and MARS total scores and subdomains varied between a maximum of 0.41 (p > 0.05) and a minimum of -0.11 (p > 0.05). All apps included exercise but only 5 (29%) included an educational component.

CONCLUSIONS

The most downloaded apps on NP available for free in app stores are, generally, of acceptable quality as assessed by the MARS total score, target exercise, but fail to include an appropriate educational component. The user rating available in app stores is not an indicator of the quality of the apps. Therefore, health professionals should carefully assess the mobile apps before recommending them to the patients.

Mobile Apps for Drug-Drug Interaction Checks in Chinese App Stores: Systematic Review and Content Analysis

Background

As a computerized drug–drug interaction (DDI) alert system has not been widely implemented in China, health care providers are relying on mobile health (mHealth) apps as references for checking drug information, including DDIs.

Objective

The main objective of this study was to evaluate the quality and content of mHealth apps supporting DDI checking in Chinese app stores.

Methods

A systematic review was carried out in November 2020 to identify mHealth apps providing DDI checking in both Chinese iOS and Android platforms. We extracted the apps’ general information (including the developer, operating system, costs, release date, size, number of downloads, and average rating), scientific or clinical basis, and accountability, based on a multidimensional framework for evaluation of apps. The quality of mHealth apps was evaluated by using the Mobile App Rating Scale (MARS). Descriptive statistics, including numbers and percentages, were calculated to describe the characteristics of the apps. For each app selected for evaluation, the section-specific MARS scores were calculated by taking the arithmetic mean, while the overall MARS score was described as the arithmetic mean of the section scores. In addition, the Cohen kappa (κ) statistic was used to evaluate the interrater agreement.

Results

A total of 7 apps met the selection criteria, and only 3 included citations. The average rating score for Android apps was 3.5, with a minimum of 1.0 and a maximum of 4.9, while the average rating score for iOS apps was 4.7, with a minimum of 4.2 and a maximum of 4.9. The mean MARS score was 3.69 out of 5 (95% CI 3.34-4.04), with the lowest score of 1.96 for Medication Guidelines and the highest score of 4.27 for MCDEX mobile. The greatest variation was observed in the information section, which ranged from 1.41 to 4.60. The functionality section showed the highest mean score of 4.05 (95% CI 3.71-4.40), whereas the engagement section resulted in the lowest average score of 3.16 (95% CI 2.81-3.51). For the information quality section, which was the focus of this analysis, the average score was 3.42, with the MCDEX mobile app having the highest score of 4.6 and the Medication Guidelines app having the lowest score of 1.9. For the overall MARS score, the Cohen interrater κ was 0.354 (95% CI 0.236-0.473), the Fleiss κ was 0.353 (95% CI, 0.234-0.472), and the Krippendorff α was 0.356 (95% CI 0.237-0.475).

Conclusions

This study systematically reviewed the mHealth apps in China with a DDI check feature. The majority of investigated apps demonstrated high quality with accurate and comprehensive information on DDIs. However, a few of the apps that had a massive number of downloads in the Chinese market provided incorrect information. Given these apps might be used by health care providers for checking potential DDIs, this creates a substantial threat to patient safety.

Methionyl-tRNA synthetase and aminoacyl-tRNA synthetases interacting multi-functional protein-lacking exon 2 as potential diagnostic biomarkers for lung cancer

Cytological specimens from computed tomography (CT)-guided needle aspiration biopsy (CT-NAB) have relatively low sensitivity for lung cancer diagnosis. This study evaluated the usefulness of the dual immunofluorescence (IF) staining method using methionyl-tRNA synthetase (MARS), aminoacyl-tRNA synthetases interacting multi-functional protein-lacking exon 2 (AIMP2-DX2), and pan-cytokeratin (pan-CK) obtained from clinical specimens. One-hundred forty-five cytology specimens were prospectively collected from patients who underwent CT-NAB under the suspicion of lung cancer. The results of two combinations of MARS, AIMP2-DX2, and pan-CK dual IF staining were compared with those of conventional cytology by calculating the area under the curve (AUC). The results of combining dual IF with conventional cytology showed higher AUC than conventional cytology alone: cytology/MARS/AIMP2-DX2 (0.891 vs. 0.829, P = 0.003), cytology/MARS/pan-CK (0.916 vs. 0.829, P < 0.001), and cytology/AIMP2-DX2/pan-CK (0.877 vs. 0.829, P = 0.005). In specimens with non-diagnostic results in conventional cytology, MARS/AIMP2-DX2 dual IF staining showed sensitivity, specificity, and AUC of 60.0%, 86.4%, and 0.79, respectively. The dual IF staining method using two combinations of MARS, AIMP2-DX2, and pan-CK is an effective diagnostic tool that can improve the lung cancer diagnostic yield by complementing conventional cytology.

Role of liver support systems in the management of post hepatectomy liver failure: A systematic review of the literature

Backgrounds/Aims

Post-hepatectomy liver failure (PHLF) is a serious complication following liver resection, with limited treatment options, and is associated with high mortality. There is a need to evaluate the role of systems that support the function of the liver after PHLF.

Aims

The aim of this study was to review the literature and summarize the role of liver support systems (LSS) in the management of PHLF. Publications of interest were identified using systematically designed searches. Following screening, data from the relevant publications was extracted, and pooled where possible.

Findings

Systematic review identified nine studies, which used either Plasma Exchange (PE) or Molecular Adsorbent Recirculating System (MARS) as LSS after PHLF. Across all studies, the pooled 90-day mortality rate was 38% (95% CI: 9-70%). However, there was substantial heterogeneity, likely since studies used a variety of definitions for PHLF, and had different selection criteria for patient eligibility for LSS treatment.

Conclusions

The current evidence is insufficient to recommend LSS for the routine management of severe PHLF, with the current literature consisting of only a limited number of studies. There is a definite need for larger, multicenter, prospective studies, evaluating the conventional and newer modalities of support systems, with a view to improve the outcomes in this group of patients.

Factors Associated With Obesity and Abdominal Obesity Among Malaysian Older Adults

Obesity is a rising concern globally. This study investigated the prevalence and factors associated with obesity and abdominal obesity (AO) among 5613 Malaysians aged 40 years and older via computer-assisted personal interviewing and anthropometric measurements. Obesity and AO prevalence were 37.8% and 63.1%, respectively. Significant associations were observed between: obesity with higher risk in patients aged between 40 and 49 years, Indians, females, income ≥RM 2000, and health status, and AO with higher risk in patients aged between 50 and 59 years, Indians, females, never married, income ≥RM 2000, and vigorous physical activity. Hence, age, sex, income, and ethnicity are associated with both obesity and AO. Promoting healthy body mass index and waist circumference is essential for healthy ageing.

Prediction of live body weight based on body measurements in Thalli sheep under tropical conditions of Pakistan using cart and mars

Thalli sheep is a significant breed reared under tropical region of Punjab province of Pakistan. The present study was conducted to predict live body weight (LBW) by means of from some body measurements, i.e., chest girth (CG), belly girth (BG), rump height (RH), withers height (WH), neck girth (NG), and body length (BL) taken from 155 Thalli indigenous sheep of Pakistan. Age factor is determined to be a significant source of variation for BL, BG, CG, BG, WH, and NG (p < 0.05). LBW is correlated significantly with BL (0.850), CG (0.825), BG (0.849), RH (0.579), WH (0.547), and NG (0.7760), respectively (p < 0.01). For LBW prediction, CART and MARS data mining algorithms were comparatively used based on ten cross-validation method. Among 185 candidate MARS models with 1-5 degrees of interaction and 2-38 terms, the MARS model with 7 terms and no interaction effect in R software was the best model for LBW prediction on the basis of the smallest cross-validated RMSE value. Also, the optimal CART tree structure was obtained with 9 terminal nodes for the smallest cross-validated RMSE value. MARS algorithm outperformed CART in LBW prediction and explained 90.3 (%) of variability in LBW of Thalli sheep. Results of the optimal CART structure reflected that Thalli sheep with BL > 75 cm, RH > 83 cm, and NG > 55 cm has the heaviest LBW of 72 kg. The optimal MARS model displays that the heaviest LBW can be produced by Thalli sheep with BL > 71.12 cm, BG > 106.68 cm, WH > 76.2 cm, NG > 50.8 cm in 5^th age group. In conclusion, it coud be recommended that MARS predictive modeling may enable animal breeders to obtain elite Thalli sheep population and to detect body measurement positively influencing LBW as indirect selection criteria for not only describing breed characterization and developing flock management standards, but also ensuring sustainable meat production and rural development in Pakistan.

Measuring the Quality of Clinical Skills Mobile Apps for Student Learning: Systematic Search, Analysis, and Comparison of Two Measurement Scales

BACKGROUND

Mobile apps are widely used in health professions, which increases the need for simple methods to determine the quality of apps. In particular, teachers need the ability to curate high-quality mobile apps for student learning.

OBJECTIVE

This study aims to systematically search for and evaluate the quality of clinical skills mobile apps as learning tools. The quality of apps meeting the specified criteria was evaluated using two measures-the widely used Mobile App Rating Scale (MARS), which measures general app quality, and the Mobile App Rubric for Learning (MARuL), a recently developed instrument that measures the value of apps for student learning-to assess whether MARuL is more effective than MARS in identifying high-quality apps for learning.

METHODS

Two mobile app stores were systematically searched using clinical skills terms commonly found in medical education and apps meeting the criteria identified using an approach based on PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. A total of 9 apps were identified during the screening process. The apps were rated independently by 2 reviewers using MARS and MARuL.

RESULTS

The intraclass correlation coefficients (ICCs) for the 2 raters using MARS and MARuL were the same (MARS ICC [two-way]=0.68; P<.001 and MARuL ICC [two-way]=0.68; P<.001). Of the 9 apps, Geeky Medics-OSCE revision (MARS Android=3.74; MARS iOS=3.68; MARuL Android=75; and MARuL iOS=73) and OSCE PASS: Medical Revision (MARS Android=3.79; MARS iOS=3.71; MARuL Android=69; and MARuL iOS=73) scored highly on both measures of app quality and for both Android and iOS. Both measures also showed agreement for the lowest rated app, Patient Education Institute (MARS Android=2.21; MARS iOS=2.11; MARuL Android=18; and MARuL iOS=21.5), which had the lowest scores in all categories except information (MARS) and professional (MARuL) in both operating systems. MARS and MARuL were both able to differentiate between the highest and lowest quality apps; however, MARuL was better able to differentiate apps based on teaching and learning quality.

CONCLUSIONS

This systematic search and rating of clinical skills apps for learning found that the quality of apps was highly variable. However, 2 apps-Geeky Medics-OSCE revision and OSCE PASS: Medical Revision-rated highly for both versions and with both quality measures. MARS and MARuL showed similar abilities to differentiate the quality of the 9 apps. However, MARuL's incorporation of teaching and learning elements as part of a multidimensional measure of quality may make it more appropriate for use with apps focused on teaching and learning, whereas MARS's more general rating of quality may be more appropriate for health apps targeting a general health audience. Ratings of the 9 apps by both measures also highlighted the variable quality of clinical skills mobile apps for learning.

Assessing the Quality of Mobile Health-Related Apps: Interrater Reliability Study of Two Guides

Background

There is a huge number of health-related apps available, and the numbers are growing fast. However, many of them have been developed without any kind of quality control. In an attempt to contribute to the development of high-quality apps and enable existing apps to be assessed, several guides have been developed.

Objective

The main aim of this study was to study the interrater reliability of a new guide — the Mobile App Development and Assessment Guide (MAG) — and compare it with one of the most used guides in the field, the Mobile App Rating Scale (MARS). Moreover, we also focused on whether the interrater reliability of the measures is consistent across multiple types of apps and stakeholders.

Methods

In order to study the interrater reliability of the MAG and MARS, we evaluated the 4 most downloaded health apps for chronic health conditions in the medical category of IOS and Android devices (ie, App Store and Google Play). A group of 8 reviewers, representative of individuals that would be most knowledgeable and interested in the use and development of health-related apps and including different types of stakeholders such as clinical researchers, engineers, health care professionals, and end users as potential patients, independently evaluated the quality of the apps using the MAG and MARS. We calculated the Krippendorff alpha for every category in the 2 guides, for each type of reviewer and every app, separately and combined, to study the interrater reliability.

Results

Only a few categories of the MAG and MARS demonstrated a high interrater reliability. Although the MAG was found to be superior, there was considerable variation in the scores between the different types of reviewers. The categories with the highest interrater reliability in MAG were “Security” (α=0.78) and “Privacy” (α=0.73). In addition, 2 other categories, “Usability” and “Safety,” were very close to compliance (health care professionals: α=0.62 and 0.61, respectively). The total interrater reliability of the MAG (ie, for all categories) was 0.45, whereas the total interrater reliability of the MARS was 0.29.

Conclusions

This study shows that some categories of MAG have significant interrater reliability. Importantly, the data show that the MAG scores are better than the ones provided by the MARS, which is the most commonly used guide in the area. However, there is great variability in the responses, which seems to be associated with subjective interpretation by the reviewers.

Quality, Features, and Presence of Behavior Change Techniques in Mobile Apps Designed to Improve Physical Activity in Pregnant Women: Systematic Search and Content Analysis

Background

Physical activity during pregnancy is associated with several health benefits for the mother and child. However, very few women participate in regular physical activity during pregnancy. eHealth platforms (internet and mobile apps) have become an important information source for pregnant women. Although the use of pregnancy-related apps has significantly increased among pregnant women, very little is known about their theoretical underpinnings, including their utilization of behavior change techniques (BCTs). This is despite research suggesting that inclusion of BCTs in eHealth interventions are important for promoting healthy behaviors, including physical activity.

Objective

The aim of this study was to conduct a systematic search and content analysis of app quality, features, and the presence of BCTs in apps designed to promote physical activity among pregnant women.

Methods

A systematic search in the Australian App Store and Google Play store using search terms relating to exercise and pregnancy was performed. App quality and features were assessed using the 19-item Mobile App Rating Scale (MARS), and a taxonomy of BCTs was used to determine the presence of BCTs (26 items). BCTs previously demonstrating efficacy in behavior changes during pregnancy were also identified from a literature review. Spearman correlations were used to investigate the relationships between app quality, app features, and number of BCTs identified.

Results

Nineteen exercise apps were deemed eligible for this review and they were accessed via Google Play (n=13) or App Store (n=6). The MARS overall quality scores indicated moderate app quality (mean 3.5 [SD 0.52]). Functionality was the highest scoring MARS domain (mean 4.2 [SD 0.5]), followed by aesthetics (mean 3.7 [SD 0.6]) and information quality (mean 3.16 [SD 0.42]). Subjective app quality (mean 2.54 [SD 0.64]) and likelihood for behavioral impact (mean 2.5 [SD 0.6]) were the lowest scoring MARS domains. All 19 apps were found to incorporate at least two BCTs (mean 4.74, SD 2.51; range 2-10). However, only 11 apps included BCTs that previously demonstrated efficacy for behavior change during pregnancy, the most common being provide opportunities for social comparison (n=8) and prompt self-monitoring of behavior (n=7). There was a significant positive correlation between the number of BCTs with engagement and aesthetics scores, but the number of BCTs was not significantly correlated with functionality, information quality, total MARS quality, or subjective quality.

Conclusions

Our findings showed that apps designed to promote physical activity among pregnant women were functional and aesthetically pleasing, with overall moderate quality. However, the incorporation of BCTs was low, with limited prevalence of BCTs previously demonstrating efficacy in behavior change during pregnancy. Future app development should identify and adopt factors that enhance and encourage user engagement, including the use of BCTs, especially those that have demonstrated efficacy for promoting physical activity behavior change among pregnant women.

Modeling and predicting the growth of indigenous Harnai sheep in Pakistan: non-linear functions and MARS algorithm

Five non-linear functions, i.e. Gompertz, Logistic, Negative exponential, Brody and Bertalanffy, and multivariate adaptive regression splines (MARS) data mining algorithm were implemented with the objective to describe the body weight-age relationship of Harnai sheep of Balochistan, Pakistan. The data comprised of 1317 records of body weight from birth to 1 year were provided from Multi-Purpose Sheep Research Station Loralai, Balochistan. Each non-linear function and MARS algorithm were fitted to the data of male and female, single and twin and all lambs. Comparison among different non-linear models was based using the adjusted coefficient of determination ([Formula: see text]), Durbin-Watson statistic (DW), root mean square error (RMSE), Akaike's and Bayesian information criteria (AIC and BIC) and the coefficient of correlation (r) between observed and fitted live body weight. The best fit was provided by the Brody model in terms of the highest [Formula: see text] and r values and lowest RMSE, AIC and BIC values in male and female, single and twin and all lambs followed by Bertalanffy, Gompertz, Negative exponential and Logistic model in order of their goodness. The negative correlation between asymptotic weight and maturing rate inferred that animals with smaller mature weight mature fast. Though males and singles were found heavier at mature weight than females and twins, respectively, they mature more slowly. The results of the study suggested the use of the Brody model to accurately describe the weight-age relationship of Harnai sheep. The present study also showed a very high predictive performance of the MARS data mining algorithm for describing the growth of sheep. In conclusion, MARS algorithm may be a good alternative for breeders aiming at describing the weight-age relationship of Harnai sheep.

Systematic evaluation of content and quality of English and German pain apps in European app stores

BACKGROUND AND OBJECTIVE

Pain spans a broad spectrum of diseases and types that are highly prevalent and cause substantial disease burden for individuals and society. Up to 40% of people affected by pain receive no or inadequate treatment. Providing a scalable, time-, and location-independent way for pain diagnostic, management, prevention and treatment mobile health applications (MHA) might be a promising approach to improve health care for pain. However, the commercial app market is rapidly growing and unregulated, resulting in an opaque market. Studies investigating the content, privacy and security features, quality and scientific evidence of the available apps are highly needed, to guide patients and clinicians to high quality MHA.Contributing to this challenge, the present study investigates the content, quality, and privacy features of pain apps available in the European app stores.

METHODS

An automated search engine was used to identify pain apps in the European Google Play and Apple App store. Pain apps were screened and checked for systematic criteria (pain-relatedness, functionality, availability, independent usability, English or German). Content, quality and privacy features were assessed by two independent reviewers using the German Mobile Application Rating Scale (MARS-G). The MARS-G assesses quality on four objectives (engagement, functionality, aesthetics, information quality) and two subjective scales (perceived impact, subjective quality).

RESULTS

Out of 1034 identified pain apps 218 were included. Pain apps covered eight different pain types. Content included basic information, advice, assessment and tracking, and stand-alone interventions. The overall quality of the pain apps was average M = 3.13 (SD = 0.56, min = 1, max = 4.69). The effectiveness of less than 1% of the included pain apps was evaluated in a randomized controlled trial. Major problems with data privacy were present: 59% provided no imprint, 70% had no visible privacy policy.

CONCLUSION

A multitude of pain apps is available. Most MHA lack scientific evaluation and have serious privacy issues, posing a potential threat to users. Further research on evidence and improvements privacy and security are needed. Overall, the potential of pain apps is not exploited.

Diet-Related Mobile Apps to Promote Healthy Eating and Proper Nutrition: A Content Analysis and Quality Assessment

Dietary mobile applications (apps) continue to hold promise for facilitating a healthy diet and managing nutrition. However, few studies have objectively evaluated the content and quality of such apps in Korea. The present study assessed the content and quality of dietary mobile apps using the Mobile App Rating Scale (MARS). We selected 29 dietary apps based on keywords and eligibility criteria for inclusion in the analyses. We conducted regression analyses to examine the association between app content and MARS scores. Most of the apps featured a tracking tool, while few featured rewards or follow-up management. Our quality assessment revealed that the top-rated apps have distinct levels of quality in terms of MARS scores. The regression analyses showed that the ways in which the apps provide information and motivate the users are statistically significant predictors of app quality. Our findings may facilitate the selection of dietary apps in Korea and provide guidelines for app developers regarding potential improvements in terms of content and quality.

Use of MARS algorithm for predicting mature weight of different camel (Camelus dromedarius) breeds reared in Pakistan and morphological characterization via cluster analysis

Mature weight is a significant trait that can be influenced by age, sex, breed, production system, and climate conditions in camels. In camel breeding, it is essential to describe breed standards of the studied camel breeds as part of morphological characterization and to determine morphological traits positively influencing mature weight within the scope of indirect selection criteria. This study was to find the best one among candidate models in prediction of mature weight from several morphological traits measured for eight camel breeds (Bravhi, Kachi, Kharani, Kohi, Lassi, Makrani, Pishin, and Rodbari) raised under Pakistan conditions. The morphological measurements taken from the camels in the study were birth weight (BW), weaning weight (WW), mature weight (MW), age of ridding (ARD), face length (FL), face width (FW), head length (HL), head width (HW), ear length (EL), ear width (EW), neck length (NL), neck width (NW), hump length (HL), hump width (HuW), heart girth (HG), withers height (WH), body length (BL), fore leg length (FLL), and hind leg length (HLL), respectively. In the prediction of mature body weight as a response variable, the optimal MARS predictive model with 15 terms selected by train function of the caret package produced very high predictive performance without encountering overfitting problem. Goodness of fit criteria were estimated to measure predictive quality of the MARS model using ehaGoF package available in R environment. Morphological characterization of the camel breeds was performed with hierarchical cluster analysis (HCA) on the basis of Euclidean distance-Single linkage. At the first step of hierarchical cluster analysis, the similarity level of Bravhi and Kachi camel breeds was the highest with 85.3569 (%). At the second step, Makrani joined to new cluster of Bravhi and Kachi camels found at the first step, and the similarity level of the new cluster comprising Bravhi, Kachi, and Makrani breeds was found as 84.5562 (%). MW was significantly correlated with BW (0.677), WW (0.536), HL (0.524), HuW (0.529), and ARD (0.375) at P < 0.01, and there was the highest correlation of 0.994 between HHL and FLL (P < 0.01). As a result, it could be suggested that results of MARS modeling may help camel breeders to reproduce the elite camel populations and to describe characteristics associated positively with MW within the scope of indirect selection criteria.

Mobile Apps for Older Adults: Systematic Search and Evaluation Within Online Stores

Background

Through the increasingly aging population, the health care system is confronted with various challenges such as expanding health care costs. To manage these challenges, mobile apps may represent a cost-effective and low-threshold approach to support older adults.

Objective

This systematic review aimed to evaluate the quality, characteristics, as well as privacy and security measures of mobile apps for older adults in the European commercial app stores.

Methods

In the European Google Play and App Store, a web crawler systematically searched for mobile apps for older adults. The identified mobile apps were evaluated by two independent reviewers using the German version of the Mobile Application Rating Scale. A correlation between the user star rating and overall rating was calculated. An exploratory regression analysis was conducted to determine whether the obligation to pay fees predicted overall quality.

Results

In total, 83 of 1217 identified mobile apps were included in the analysis. Generally, the mobile apps for older adults were of moderate quality (mean 3.22 [SD 0.68]). Four mobile apps (5%) were evidence-based; 49% (41/83) had no security measures. The user star rating correlated significantly positively with the overall rating (r=.30, P=.01). Obligation to pay fees could not predict overall quality.

Conclusions

There is an extensive quality range within mobile apps for older adults, indicating deficits in terms of information quality, data protection, and security precautions, as well as a lack of evidence-based approaches. Central databases are needed to identify high-quality mobile apps.