Precise Recommendations Research Articles

Urban Heat Island Differentiation and Influencing Factors: A Local Climate Zone Perspective

With the acceleration of urbanization, the urban heat island (UHI) effect has become a major environmental challenge, severely affecting the quality of life of residents and the ecological environment. Quantitative analysis of the factors influencing urban heat island intensity (UHII) is crucial for precise urban planning. Although extensive research has investigated the causes of UHI effects and their spatial variability, most studies focus on macro-scale analyses, overlooking the spatial heterogeneity of thermal characteristics within local climate zones (LCZs) under rapid urbanization. To address this gap, this study took the central urban area of Chengdu, constructing a LCZ map using multisource remote sensing data. Moran’s Index was employed to analyze the spatial clustering effects of UHI across different LCZs. By constructing Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR) models, the study further explored the influencing factors within these climate zones. The results showed that: (1) Chengdu’s built and natural environments had comparable proportions, with the scattered building zone comprising the highest proportion at 22.12% in the built environment, and the low vegetation zone accounting for 21.8% in the natural environment. The UHII values in this study ranged from 10.2 °C to −1.58 °C, based on specific measurement conditions. Since UHII varied with meteorological conditions, time, seasons, and the selection of rural reference points, these values represented dynamic results during the study period and were not constant. (2) Chengdu’s urban spatial morphology and UHII exhibited significant spatial heterogeneity, with a global Moran’s I index of 0.734, indicating a high degree of spatial correlation. The highest local Moran’s I value was found in the proportion of impervious surfaces (0.776), while the lowest is in the floor area ratio (0.176). (3) The GWR model demonstrated greater explanatory power compared to the OLS model, with a fit of 0.827. The impact of spatial morphological factors on UHII varied significantly across different environments, with the most substantial difference observed in the sky view factor, which has a standard deviation of 13.639. The findings provide precise recommendations for ecological spatial planning, aiming to mitigate the UHI effect and enhance the quality of life for urban residents.

Effects of Resistance Training Volume on Physical Function, Lean Body Mass and Lower-Body Muscle Hypertrophy and Strength in Older Adults: A Systematic Review and Network Meta-analysis of 151 Randomised Trials.

The optimal prescription and precise recommendations of resistance training volume for older adults is unclear in the current literature. In addition, the interactions between resistance training volume and program duration as well as physical health status remain to be determined when assessing physical function, muscle size and hypertrophy and muscle strength adaptations in older adults. This study aimed to determine which resistance training volume is the most effective in improving physical function, lean body mass, lower-limb muscle hypertrophy and strength in older adults. Additionally, we examined whether effects were moderated by intervention duration (i.e. short term, < 20weeks; medium-to-long term, ≥ 20weeks) and physical health status (i.e. physically healthy, physically impaired, mixed physically healthy and physically impaired; PROSPERO identifier: CRD42023413209). CINAHL, Embase, LILACS, PubMed, Scielo, SPORTDiscus and Web of Science databases were searched up to April 2023. Eligible randomised trials examined the effects of supervised resistance training in older adults (i.e. ≥ 60years). Resistance training programs were categorised as low (LVRT), moderate (MVRT) and high volume (HVRT) on the basis of terciles of prescribed weekly resistance training volume (i.e. product of frequency, number of exercises and number of sets) for full- and lower-body training. The primary outcomes for this review were physical function measured by fast walking speed, timed up and go and 6-min walking tests; lean body mass and lower-body muscle hypertrophy; and lower-body muscle strength measured by knee extension and leg press one-repetition maximum (1-RM), isometric muscle strength and isokinetic torque. A random-effects network meta-analysis was undertaken to examine the effects of different resistance training volumes on the outcomes of interest. We included a total of 161 articles describing 151 trials (n = 6306). LVRT was the most effective for improving timed up and go [- 1.20 standardised mean difference (SMD), 95% confidence interval (95% CI): - 1.57 to - 0.82], 6-min walk test (1.03 SMD, 95% CI: 0.33-1.73), lean body mass (0.25 SMD, 95% CI: 0.10-0.40) and muscle hypertrophy (0.40 SMD, 95% CI: 0.25-0.54). Both MVRT and HVRT were the most effective for improving lower-limb strength, while only HVRT was effective in increasing fast walking speed (0.40 SMD, 95% CI: - 0.57 to 0.14). Regarding the moderators, our results were independent of program duration and mainly observed for healthy older adults, while evidence was limited for those who were physically impaired. A low resistance training volume can substantially improve healthy older adults' physical function and benefits lean mass and muscle size independently of program duration, while a higher volume seems to be necessary for achieving greater improvements in muscle strength. A low volume of resistance training should be recommended in future exercise guidelines, particularly for physically healthy older adults targeting healthy ageing.

Echocardiography for Management of Cardiovascular Disease in Pregnancy.

Cardiovascular disease (CVD) continues to be a leading contributor to maternal mortality and morbidity. Echocardiography is an essential tool for patients with suspected and known CVD to establish symptom etiology, treatment strategy, and prognosis. We summarize the current status of conventional and novel techniques for assessment of CVD during pregnancy. Conventional techniques are still useful for evaluation of known or suspected CVD. Advanced technology using speckle tracking continues to evolve and is increasingly applied for diagnosis of subclinical disease including hypertensive disorders of pregnancy and left ventricular (LV) dysfunction. Precise recommendations on how frequently echocardiography should be performed and for whom remain in flux. However, a recently published consensus statement and new screening tool for pregnancy assessment of patients with valvular heart disease provide additional advice on using this modality. Echocardiography remains the diagnostic modality of choice for evaluation and risk stratification in pregnancy.

Predictive Model for Post-Transplant Renal Fibrosis Using Ultrasound Shear Wave Elastography.

BACKGROUND The aim of this study was to investigate the clinical utility of ultrasound shear wave elastography (SWE) for assessment of renal fibrosis in post-renal transplant patients. MATERIAL AND METHODS We selected 183 patients who underwent renal transplantation. The complete dataset was randomly partitioned into a training cohort (128 cases) and a validation cohort (55 cases). All patients were subjected to SWE and renal allograft biopsy. The baseline data was compared using t-test, Z-test, or chi-square test. Through univariate and multivariate analyses, we identified independent risk factors influencing renal fibrosis after transplantation, a predictive model for post-transplant renal fibrosis was developed, and calibration curves, decision curve analyses, and ROC curves were generated. RESULTS Age, TST, Scr, GFR, and Emean showed significant differences (P<0.05). The C-index of the nomogram was 0.85, and the calibration curve and Hosmer-Lemeshow test demonstrated accurate diagnosis of fibrosis in both the training and validation sets (P>0.05). DCA showed that the prediction model effectively improved the diagnostic accuracy of fibrosis. The highest AUC of the nomogram for combined prediction of renal fibrosis in transplant patients was 0.902 in the training group and 0.871 in the validation group. These values were significantly higher compared to the AUCs of individual predictors (P<0.05). CONCLUSIONS Ultrasound SWE allows for early evaluation of renal fibrosis following transplantation. The prediction model, constructed by amalgamating other indicators, augments the accuracy and reliability of the prediction, providing more precise and accurate diagnostic and therapeutic recommendations for clinical practitioners.

A Mobile Application for Anticoagulation Management in Patients After Heart Valve Replacement: A Usability Study.

Individualized anticoagulation therapy is a major challenge for patients after heart valve replacement. Mobile applications assisted by Artificial intelligence (AI) have great potential to meet the individual needs of patients. The study aimed to develop an AI technology-assisted mobile application (app) for anticoagulation management, understand patients' acceptance of such applications, and determine its feasibility. After using the mobile application for anticoagulation management for 2weeks, patients, doctors, and nurses rated its usability using the System Usability Scale (SUS). Additionally, semi-structured interviews were conducted with some patients, doctors, and nurses to gain insights about their thoughts and suggestions regarding the procedure. The study comprised 80 participants, including 38 patients, 18 doctors, and 24 nurses. The average SUS score for patients was 82.37±5.45; for doctors, it was 84.17±5.82; and for nurses, it was 81.88±6.44. This means the patients, physicians, and nurses rated the app highly usable. Semi-structured interviews were conducted on the app's usability with 18 participants (six nurses, three physicians, and nine patients). The interview results revealed that patients found the application of anticoagulation management simple and convenient, with high expectations for a precise dosage recommendation of anticoagulant drugs. Some patients expressed concerns regarding personal information security. Both doctors and nurses believed that elderly patients needed assistance from young family members to use the app and that it could improve patients' anticoagulant self-management ability. Some nurses also mentioned that the use of the app brought great convenience for transitional care. This study confirmed the feasibility of using an AI technology-assisted mobile application for anticoagulation management in patients after heart valve replacement. To further develop this application, challenges lie in continuously improving the accuracy of recommended drug doses, obtaining family support, and ensuring information security.

Trends In Tourism Recommendation Systems: A Review

Tourism Recommendation Systems (TRS) are increasingly important in the tourism industry to provide personalized recommendations based on diverse tourist preferences. Technology and big data have transformed TRS from traditional travel agencies to modern digital platforms, enabling the processing of vast amounts of user-generated data for precise recommendations. The study aims to identify strengths and weaknesses within existing TRS frameworks and techniques, propose recommendations to mitigate these weaknesses, and provide insights for practitioners and researchers. Key findings are the effectiveness of personalized and context-aware recommendations, the importance of multimodal data integration, the need for ethical and fair recommendation practices. Future directions in TRS research should focus on exploring and developing explainable AI and transparency, personalization at scale, enhancing multimodal recommendation capabilities, and ensuring ethical and fair recommendation. This review contributes to a deeper understanding of contemporary TRS methodologies and provides actionable insights for enhancing TRS performance. By addressing current trends and proposing recommendations for future research, this paper aims to advance the field of TRS and improve travel experiences for tourists.

Reducing Cluster Overlap in Movie Recommendations with IKSOM and Silhouette Clustering

The exponential growth of online material requires the implementation of effective and precise recommendation systems in order to optimize the user experience. Nevertheless, conventional approaches frequently encounter problems such as cluster overlap, which reduces the accuracy of suggestions. This paper presents a new method for minimising the overlap between clusters in movie recommendation systems. It achieves this by combining Improved Kohonen Self-Organizing Maps (IKSOM) with Silhouette Clustering. The proposed method utilises IKSOM to efficiently represent high-dimensional user-item interactions in a two-dimensional space, enabling the formation of distinct and meaningful clusters. Subsequently, Silhouette Clustering is utilised to enhance the separation and cohesion of clusters, hence reducing overlap. The experimental findings show that proposed hybrid model works much better than the baseline techniques, obtaining an RMSE of 0.423 and MAE of 0.216. Additionally, it improves precision (93.6%), recall (94.2%) and F1-score (93.4%). Additionally, the proposed technique demonstrates a high level of accuracy (97.3%) with a precision rate of 95.8%. These results emphasise the method's efficacy in minimising errors and enhancing the overall performance of the recommendation system. The results indicate that combining IKSOM with Silhouette Clustering can improve the precision and dependability of movie recommendation systems by resolving cluster overlap and offering more individualised user experiences. Subsequent research will investigate the implementation of this method in different fields and the integration of supplementary contextual information to enhance the accuracy of recommendations.

Adaptive multi-criteria decision making for electric vehicles: a hybrid approach based on RANCOM and ESP-SPOTIS

In today’s globalized technological area, aligning decisions with customer preferences is crucial yet challenging due to the complexities and uncertainties involved. Multi-Criteria Decision Analysis (MCDA) serves as a vital tool for constructing support systems that cater to customer-centric trends. While existing MCDA methods vary in their calculation concepts, some prioritize ideal solutions, while others accommodate personalized preferences within dynamic decision contexts. Moreover, determining the relevance of criteria based on expert knowledge adds another layer of personalization to the evaluation process, further individualizing decision-making. However, current decision models often fail to integrate these concepts, leaving a gap in how recommendations can be enhanced when both are combined. To address these challenges, this paper introduces an innovative approach integrating Ranking Comparison and Expected Solution Point Stable Preference Ordering Towards Ideal Solution methods. This hybrid model incorporates personalization into multi-criteria evaluation, catering to individual preferences. By representing customer preferences through two distinct measures, the proposed approach ensures personalized recommendations aligned with decision-makers’ needs. The efficacy of the hybrid model was validated through its application to the electric vehicle selection problem. The verification process highlighted potential disparities compared to other multi-criteria approaches, establishing a consumer preference-based Decision Support System approach for more precise and personalized selection recommendations.

Component diagnosis of food allergy: precise identification of allergenic components in egg and milk

Food allergy are triggered by an abnormal immune response to specific food components, with milk and eggs being the most common food allergens, especially in children. Food allergy can cause various symptoms such as rashes, difficulty breathing, and digestive issues. Allergen component diagnostics is a technique used to identify specific allergenic proteins, aiding doctors in providing more precise treatment and management recommendations for patients. This article analyzes the latest research developments and clinical significance of milk and egg allergen components based on the " Molecular Allergology User's Guide 2.0 (MAUG 2.0)" issued by the European Academy of Allergy and Clinical Immunology (EAACI), including their applications in clinical diagnosis, treatment, and management. This article aims to enhance healthcare professionals' understanding of milk and egg allergies, offering new perspectives and practical guidelines for research and clinical practice to promote accurate diagnosis and personalized treatment strategies.

Update of the Spanish clinical practice guideline for urinary tract infection in infants and children. Summary of recommendations for diagnosis, treatment and follow-up

The management of urinary tract infection (UTI) in infants and children has changed significantly over the past few decades based on scientific evidence that questioned the efficacy of strategies used to prevent kidney injury and subsequent progression to chronic kidney disease, which is very unlikely in most paediatric cases. However, there is still substantial heterogeneity in its management and uncertainty regarding the diagnosis, indication of imaging tests, treatment or follow-up in these patients. The Spanish clinical practice guideline has been updated through the review of the literature published since 2009 and a rigorous evaluation of current clinical practice aspects, taking into account the evidence on the benefits of each intervention in addition to its risks and drawbacks to attempt to provide more precise recommendations.

Effect of cinnamon supplementation on blood pressure, oxidative stress, and inflammatory biomarkers in adults: An umbrella review of the meta-analyses of randomized controlled trials

AimsCinnamon has positive effects on markers of cardiometabolic health, including blood pressure (BP), oxidative stress, and inflammation. Despite the evidence accumulated from meta-analysis studies on the effects of cinnamon on these markers, the reported findings are still controversial. This umbrella review was conducted to evaluate the evidence and provide a definitive clarification. Data synthesisWe conducted a systematic search in four scientific databases, including PubMed, Scopus, Web of Science, and Embase electronic databases, up to March 2024 to identify systematic reviews and meta-analyses of randomized clinical trials investigating the impact of cinnamon on blood pressure, oxidative stress, and inflammation. The findings revealed that cinnamon might exert favorable effects on systolic blood pressure (SBP) (ES = −2.36 mmHg; 95% CI: 3.86, −1.40), diastolic blood pressure (DBP) (ES = −1.65 mmHg; 95% CI: 2.41, −0.90), total antioxidant capacity (TAC) (WMD = 0.34; 95% CI: 0.04, 0.64), and interleukin-6 (IL-6) (WMD = −1.48; 95% CI: 2.96, −0.01). However, the results did not show any significant effect of cinnamon on malondialdehyde (MDA) (WMD = −0.47; 95% CI: 0.99, 0.05), C-reactive protein (CRP) (WMD = −1.33; 95% CI: 2.66, 0.00), and intercellular adhesion molecule 1 (ICAM-1) (WMD= 1.53, 95% CI: 12.03, 15.10). ConclusionsThe results of the studies included in this umbrella review support the usefulness of cinnamon consumption in modulating BP as well as improving TAC and IL-6 in metabolic disorders. Due to the limited number of studies, clinical diversity, and other limitations, more high-quality studies must be conducted to provide more precise and comprehensive recommendations. Registration numberPROSPERO, CRD42023487350.

Synergizing Random Forest and K Means Algorithms: An Analytical Study for Precise Crop Recommendation in Southeast Asia

Synergizing Random Forest and K Means Algorithms: An Analytical Study for Precise Crop Recommendation in Southeast Asia

A Lung Nodule Dataset with Histopathology-based Cancer Type Annotation

Recently, Computer-Aided Diagnosis (CAD) systems have emerged as indispensable tools in clinical diagnostic workflows, significantly alleviating the burden on radiologists. Nevertheless, despite their integration into clinical settings, CAD systems encounter limitations. Specifically, while CAD systems can achieve high performance in the detection of lung nodules, they face challenges in accurately predicting multiple cancer types. This limitation can be attributed to the scarcity of publicly available datasets annotated with expert-level cancer type information. This research aims to bridge this gap by providing publicly accessible datasets and reliable tools for medical diagnosis, facilitating a finer categorization of different types of lung diseases so as to offer precise treatment recommendations. To achieve this objective, we curated a diverse dataset of lung Computed Tomography (CT) images, comprising 330 annotated nodules (nodules are labeled as bounding boxes) from 95 distinct patients. The quality of the dataset was evaluated using a variety of classical classification and detection models, and these promising results demonstrate that the dataset has a feasible application and further facilitate intelligent auxiliary diagnosis.

Calcium- and Sodium-Rich Food Intake among Koreans with and without Metabolic Syndrome: Cross-Sectional Analysis of the Korean Genome and Epidemiology Study.

The prevalence of metabolic syndrome (MetS) is increasing. While calcium and sodium are known nutritional factors used for managing MetS, few studies have focused on food-based analyses. This cross-sectional study examined the distribution of calcium- and sodium-rich food intake among Koreans with MetS. This cross-sectional analysis evaluated 130,423 participants from the Health Examinees-Gem cohort study. Foods contributing up to 90% of the calcium and sodium intake were selected from the semi-quantitative food frequency questionnaire, and consumption levels were calculated. MetS was defined as satisfying three or more criteria from the National Cholesterol Education Program Adult Treatment Panel III. The results are presented as odds ratios (OR) with an interquartile range (ORIQR) and 95% confidence interval. Participants with MetS showed a low calcium intake (ORIQR = 0.95 and 0.92 for men and women, respectively), low consumption of dairy products (ORIQR = 0.92 and 0.89), beverages except for coffee or green tea (ORIQR = 0.97 and 0.96), and bread (ORIQR = 0.96 and 0.94). Men with MetS consumed high total sodium (ORIQR = 1.04), and large amounts of Kimchi (ORIQR = 1.03), fermented paste (ORIQR = 1.04), and noodles (ORIQR = 1.07). Women with MetS consumed more Kimchi than those without MetS (ORIQR = 1.04). The odds ratio for the low calcium and high sodium group compared to the high calcium and low sodium group was 1.26. The MetS group consumed less calcium-rich foods and more sodium-rich foods than those without MetS. Patients with MetS might benefit from precise recommendations of high calcium-rich and low sodium-rich foods.

Prototipe Sistem Rekomendasi Film Indonesia Menggunakan Pendekatan Content Based Filtering dan Metode Vector Space Model

Film represents a combination of narrative and cinematographic aspects in an audio-visual form. Films also provide an engaging visual, auditory, and emotional experience. The film industry in Indonesia has shown significant growth. In 2015, cinema audiences numbered 16.2 million, increasing to 51.2 million in 2019. The COVID-19 pandemic caused a drastic decline in the number of viewers, with only 4.5 million in 2021. However, the industry quickly recovered, reaching 54.07 million viewers in 2022 and 55 million in 2023, and is projected to reach 60 million in 2024. With the increasing availability of films and streaming platforms, finding movies that match user preferences often becomes a challenge. This study proposes a film recommendation system using the Content-Based Filtering method and the Vector Space Model to help users more easily find movies that match their preferences. Content-Based Filtering recommends films based on content similarity, while the Vector Space Model measures content similarity using metrics such as cosine similarity or euclidean distance. Based on experiments using 20 sample films, the results show that the designed system can provide accurate and precise movie recommendations based on keywords provided by users. The film "Pengabdi Setan 2: Communion" has the highest similarity score of 0.3018, followed by "Nana," "Menjelang Magrib," "Jailangkung: Sandekala," and "Qorin," with similarity scores of 0.0865, 0.0138, 0.0136, and 0.0125, respectively

Precision Nutrition Management in Continuous Care: Leveraging AI for User-Reported Dietary Data Analysis.

A As health technology advances, this study aims to develop an innovative nutritional intake management system that integrates artificial intelligence technology and social media software to achieve precise analysis of patient-generated data and comprehensive management in continuous care. Our system is built on the Line Bot platform, allowing users to easily and intuitively obtain detailed analyses of their individual nutritional intake by reporting dietary information. While users report their dietary habits through the Line Bot, our AI model conducts real-time analysis of nutrient intake, providing personalized nutritional recommendations. This instantaneous feedback not only enhances user engagement in nutritional management but also aids in establishing healthy habits. Additionally, through integration with social media software, our system facilitates information sharing and community support among users, promoting the exchange of nutritional knowledge and mutual assistance. This study further explores the specific needs of patients with chronic diseases, collecting individual data on chronic conditions and total nutritional intake. Based on the nutritional intake guidelines proposed by the Health Promotion Administration in Taiwan, more precise nutritional management recommendations are provided to meet the unique health needs of each patient. This study introduces a comprehensive, patient-generated data-based approach for precision nutrition management in continuous care. By integrating artificial intelligence, social media software, and data analysis, our system not only offers effective tools for monitoring and managing patients' nutritional intake but also fosters interaction and support among patients, driving the implementation of continuous care practices.

Pretreatments to bonding on enamel and dentin disorders: a systematic review.

This systematic review focuses on structural anomalies of enamel and dentin such as fluorosis (F), molar-incisor hypomineralization (MIH), amelogenesis imperfecta (AI), dentinogenesis imperfecta (DI), osteogenesis imperfecta (OI), and X-linked hypophosphatemia (XLH). These pathologies affect up to 31% of the population, posing challenges in the adhesion of direct restorations. The primary objective of this analysis is to examine the survival rate and/or bonding resistance of direct restorations on tissues affected by enamel and dentin disorders in humans. We aim to provide precise clinical recommendations for dentists to choose the appropriate bonding pretreatment for various enamel and dentin disorders. We systematically searched the medical literature to identify abstracts of interest indexed between 1993 and May 2024, from 4 databases (PubMed, PMC-PubMed, Web of Science and Cochrane Library). The results are reported following the PRISMA statement. The GRADE approach was used to assess the risk of bias. The two authors included 27 studies out of the 600 identified. The data extracted from these studies are highly heterogeneous, and the mentioned bonding protocols are all different. 15 articles pertain to F, 7 to MIH, 5 to AI, and none focus on HSPM, dentin disorders and XLH. We identified 12 pretreatments of enamel or dentin (7 for F, 2 for MIH, and 3 for AI), including 3 strong recommendations. Additional studies are necessary to confirm the effectiveness of certain pretreatments. In developmental enamel defects, the main recommendation we collected is to bond on enamel with an etch-and-rinse technique on F and MIH. Considerations with lower strength of evidence include a deproteinization step on enamel on F, MIH and AI and modifications of the etching parameters on F and AI. The research strategy was registered on the Prospero platform (CRD42023447502).

Classifying Stand Compositions in Clover Grass Based on High-Resolution Multispectral UAV Images

In organic farming, clover is an important basis for green manure in crop rotation systems due to its nitrogen-fixing effect. However, clover is often sown in mixtures with grass to achieve a yield-increasing effect. In order to determine the quantity and distribution of clover and its influence on the subsequent crops, clover plants must be identified at the individual plant level and spatially differentiated from grass plants. In practice, this is usually done by visual estimation or extensive field sampling. High-resolution unmanned aerial vehicles (UAVs) offer a more efficient alternative. In the present study, clover and grass plants were classified based on spectral information from high-resolution UAV multispectral images and texture features using a random forest classifier. Three different timestamps were observed in order to depict the phenological development of clover and grass distributions. To reduce data redundancy and processing time, relevant texture features were selected based on a wrapper analysis and combined with the original bands. Including these texture features, a significant improvement in classification accuracy of up to 8% was achieved compared to a classification based on the original bands only. Depending on the phenological stage observed, this resulted in overall accuracies between 86% and 91%. Subsequently, high-resolution UAV imagery data allow for precise management recommendations for precision agriculture with site-specific fertilization measures.

Fertilizer and Crop Yield Prediction using Machine Learning

The agricultural sector is indispensable to feeding the growing global population, making efficient crop management and yield prediction imperative. Traditional farming practices often rely on subjective decision-making and generalized fertilizer application methods, leading to suboptimal resource utilization and yield outcomes. In this research, we introduce an innovative method Utilizing the capability the bunch of algorithms introduced for machine learning tasks to precise fertilizer recommendation and crop yield prediction. The developed system provides farmers with personalized fertilizer recommendations tailored to their specific soil and crop requirements, thereby minimizing waste and maximizing yield potential. Additionally, real-time monitoring and feedback mechanisms enable adaptive adjustments throughout the growing season, ensuring timely interventions to mitigate adverse outcomes and optimize productivity

User-centered design of a mobile app for physical activity advice in cancer care: preliminary study in Morocco at the National Institute of Oncology

ContextUtilizing mobile apps to increase physical activity levels is now standard practice in cancer care. The study’s objective was to provide patients with precise physical activity recommendations and/or initiate self-directed exercise programs.MethodsA multidisciplinary group directed the design procedure. We adhered to the self-determination approach,and accompanying behavior modification strategies, as well as international guidelines in the field of physical activity in cancer care. The design process was user-centered and involved three steps: Understanding the user and the context, conceptualization, and evaluation of the prototype. The testing was carried out by the beneficiaries, healthcare professionals, and two experts. Three criteria were evaluated in the designed solution: usability, quality, and potential for behavior change.ResultsThe evaluation results of the designed solution showed good perception by both beneficiaries and healthcare professionals. The average system usability scale score was 90,6 (SD: 7.33) for beneficiaries, and 92.75 (SD: 3.83) for healthcare professionals. The first expert received a mean overall quality score of 4.51 on the mobile app rating scale, while the second received a score of 4.27. The results of the app behavior change scale gave the first expert a score of 17/21 and the second a score of 15/21.ConclusionBased on the criteria of usability, quality, and potential for behavior change, the designed app demonstrated positive results. This is a preliminary evaluation, which will be followed by additional, in-depth evaluations with larger sample sizes.