Healthcare Apps Research Articles

Implementing cloud computing in drug discovery and telemedicine for quantitative structure-activity relationship analysis

This work aims to use cutting-edge machine learning methods to improve quantitative structure-activity relationship (QSAR) analysis, which is used in drug development and telemedicine. The major goal is to examine the performance of several predictive modeling approaches, including random forest, deep learning-based QSAR models, and support vector machines (SVM). It investigates the potential of feature selection techniques developed in chemoinformatics for enhancing model accuracy. The innovative aspect is using cloud computing resources to strengthen computational skills, allowing for managing massive amounts of chemical information. This strategy produces accurate and generalizable QSAR models. By using the cloud's scalability and constant availability, remote healthcare apps have a workable answer. The goal is to show how these methods may improve telemedicine and the drug development process. Utilizing cloud computing equips researchers with a flexible set of tools for precise and timely QSAR analysis, speeding up the discovery of bioactive chemicals for therapeutic use. This new method fits well with the dynamic nature of pharmaceutical study and has the potential to transform the way drugs are developed and delivered to patients via telemedicine.

Beyond Culture: Design Anthropology as a Tool of Social Design and Conflict-Resolution

Design Anthropology [DA] has gained significant prominence in recent years, along with increasing adoption of Design Thinking [DT] strategies, new human-centric approaches, and focused attention on the emotional and functional needs of the users. Both DA and DT have emerged during the last few decades as a result of a broader shift away from the profit-centered business models towards the development of value-driven paradigms that are placing the consumers at the center of research and production. This is a meaningful transformation that reflects the recognition of an urgent need for higher global sustainability and a new economy. The process of personalization, which is based on extensive study of consumers’ real needs rather continuous development of products, is the best strategic move to achieve these new global goals and set new social and individual values. Thus, business success in the contemporary world should aspire to merge values of sustainability with a good study of individual needs and lifestyle. DT is not a new way of business thinking and it has existed for decades. Yet, many of its advantages are still unknown, misunderstood, or are being used inefficiently due to lack of knowledge, strategic insights, and proper practices and experience. DT utilizes anthropological methods that provide the best strategies required to unfold hidden needs, heal pain points, and discover core cultural values. Anthropological tools include detailed observations of behavior and various activities, personal interviews, and immersion in the daily lives of individuals and communities studied in order to study their perspective and gain empathy and understanding as an insider. The project presented hereby demonstrates the uses and the professional advantages gained by using DT methods in finding new and creative pathways to complicated solutions in a case study that required the development of a healthcare app for the ASHA workers in India. The ASHA are healthcare activists who became a vital and crucial support system to many families in rural India, especially during the Covid19 pandemic. However, the low social caste to which they belong proved to be as big a challenge to face as the pandemic itself, and DT strategies combined with methodologies of anthropology supported the completion of the project on time with the best outcome possible.

User Experience and Extended Technology Acceptance Model in Commercial Health Care App Usage Among Patients With Cancer: Mixed Methods Study.

The shift in medical care toward prediction and prevention has led to the emergence of digital health care as a valuable tool for managing health issues. Aiding long-term follow-up care for cancer survivors and contributing to improved survival rates. However, potential barriers to mobile health usage, including age-related disparities and challenges in user retention for commercial health apps, highlight the need to assess the impact of patients' abilities and health status on the adoption of these interventions. This study aims to investigate the app adherence and user experience of commercial health care apps among cancer survivors using an extended technology acceptance model (TAM). The study enrolled 264 cancer survivors. We collected survey results from May to August 2022 and app usage records from the app companies. The survey questions were created based on the TAM. We categorized 264 participants into 3 clusters based on their app usage behavior: short use (n=77), medium use (n=101), and long use (n=86). The mean usage days were 9 (SD 11) days, 58 (SD 20) days, and 84 (SD 176) days, respectively. Analysis revealed significant differences in perceived usefulness (P=.01), interface satisfaction (P<.01), equity (P<.01), and utility (P=.01) among the clusters. Structural equation modeling indicated that perceived ease-of-use significantly influenced perceived usefulness (β=0.387, P<.01), and both perceived usefulness and attitude significantly affected behavioral intention and actual usage. This study showed the importance of positive user experience and clinician recommendations in facilitating the effective usage of digital health care tools among cancer survivors and contributing to the evolving landscape of medical care.

MHealth Apps in German Outpatient Mental Health Care: Protocol for a Mixed Methods Approach.

Mental disorders are complex diseases that affect 28% (about 17.8 million people) of the adult population in Germany annually. Since 2020, certain mobile health (mHealth) apps, so-called digital health applications (DiGA), are reimbursable in the German statutory health insurance system. A total of 27 of the 56 currently available DiGA are approved for the treatment of mental and behavioral diseases. An indicator of existing problems hindering the use of DiGA is the rather hesitant prescribing behavior. This project aims to develop health policy recommendations for the optimal integration of DiGA into outpatient psychotherapeutic care. The project is funded by the Innovation Fund of the Joint Federal Committee (grant 01VSF22029). The current status quo of the use of DiGA will be analyzed. Furthermore, concepts for the integration of mHealth apps, as well as their transfer into the care process will be investigated. In addition, barriers will be identified, and existing expectations of different perspectives captured. The project will be based on a mixed methods approach. A scoping review and a qualitative analysis of focus groups and expert interviews will be carried out. Additionally, an analysis of claims data of the statutory health insurance will be conducted. This will be followed by a written survey of insured persons and health care providers. Finally, health policy recommendations will be derived in cooperation with stakeholders. The scoping reviews and qualitative analyses have been completed, and the quantitative surveys are currently being carried out. The target number of responses in the survey of insured persons has already been achieved. Furthermore, the analysis claims data of the statutory health insurance is currently being conducted. There is a need for research on how DiGA can be optimally integrated into the care process of patients with mental disorders as evidence regarding the topic is limited and prescribing behavior low. Although the potential of DiGA in mental health care has not yet fully unfolded, Germany serves as a model for other countries regarding reimbursable mHealth apps. This project aims to explore the potentials of DiGA and to describe the organizational, institutional, and procedural steps necessary for them to best support mental health care. DERR1-10.2196/56205.

A Secure Network with Minimization of Energy for E-healthcare Application in IoMT

Aims: Protect patient healthcare records. Background: The adaptability of the digital healthcare system is a major factor in its recent rise in popularity. Utilizing the digital healthcare system has resulted in an ever-increasing number of healthcare apps. The Internet of Medical Things-(IoMT) is a newly emerging digital healthcare system using various biomedical sensors and the cutting-edge capabilities of wireless systems and cloud computing. Since IoMT can exchange data between various connecting nodes thanks to the combination of other technologies, security and energy consumption provide the greatest challenge to the IoMT infrastructure. Objective: Reduce the cost of communication in order to strengthen defenses against unauthorized access and increase energy efficiency. Method: This study provides a protocol for protecting patients; medical records called the requesttype- based energy-aware framework (Re-EAF) based on patent. The primary goal is to reduce the cost of communication in order to strengthen defences against unauthorized access and increase energy efficiency. An identifying unit called a request-type energy aware framework has been proposed. The proposed method avoids treating all requests the same by instead characterizing them based on the identified criteria and characteristics. Using Constrained Application Protocol (CoAP), remote patient monitoring can increase the safety of gathered data. Results: Using Constrained Application Protocol (CoAP), remote patient monitoring can increase the safety of gathered data. Using a software-defined networking (SDN) framework, our research ensures that data and requests are sent and received as effectively and efficiently as possible while conserving energy. Conclusion: In this research, the transmitted healthcare data is encrypted via cipher Block-chaining. The experimental study demonstrates that the suggested Re-EAF consumes less energy while producing a higher throughput than conventional methods.

Software Level Security and Privacy Attacks and Challenges in Healthcare Systems

The widespread adoption of electronic healthcare technology holds immerse promise for improving clinical outcomes and transforming healthcare delivery. The COVID-19 pandemic further accelerated the use of Internet of things (IoT), Cloud Computing, Robotics and Artificial Intelligence (AI) for pandemic data analysis, remote diagnostics, use of wireless medical devices and public information services. This surge in usage and integration of smart devices, often connected through healthcare apps has also introduced new cyber-security and concerns. The vast amount of personal data, including health information, makes healthcare systems a prime target for cybercriminals who could exploit it for harassment and fraudulent purposes. Additionally, the increased reliance on anytime, anywhere connectivity creates vulnerabilities in wireless medical devices. Recent reports highlight these threats, with examples including privacy breaches, ransomware attacks, and disruptions in communication channels for medical devices, Such security breaches can erode patient trust, cripple healthcare systems and even endanger lives. to facilitate users also increases various cyber threats. In recent times, privacy attacks on This paper reviews the recent software-level security and privacy threats in smart healthcare systems, explores mitigation techniques proposed in the literature, and discuss the future challenges of software security in smart healthcare.

Experiences of a digital health innovation for older adults living with long-term health problems: the SelfSTRENGTH application

ABSTRACT There is a national call for digital health innovations in the provision of health and social care, especially for older adults in the context of home. The Reflective STRENGTH-Giving Dialogue (STRENGTH) method is used to provide individual and holistic care using recurrent dialogues. The SelfSTRENGTH application (app) was developed to support reflection and stimulate activity that enables achievement of meaningful life projects. The objective of the study was to describe how the use of the SelfSTRENGTH app is perceived by older adults living with long-term health problems. A qualitative, descriptive and inductive design. Data, analyzed using a phenomenographic approach, consisted of individual qualitative interviews with older adults (age range = 74–96) conducted before (n = 34) and after (n = 27) participation in the STRENGTH intervention. The results consist of five categories of description: Resistance to use apps and digital tools; Health problems hinder its usefulness; Need of assistance is a prerequisite to be able to use the app; the app supports memory and reflection; and Using the app disperses thoughts and creates motivation. The outcome space thus contains descriptions of both barriers and possibilities regarding how the use of the SelfSTRENGTH app is perceived. The potential of using technology such as applications in health care is immense. However, long-term health problems in older adults, along with care providers’ attitudes and opportunities to support, must be taken into consideration when implementing apps in health and social care.

Secured Blockchain framework using deep learning to reduce the service latency in a 5G based distributed networking environment

The Internet of Things (IoT), which is also represented as Internet of Health Things (IoHT), has witnessed numerous progresses in recent existences, particularly in the management of healthcare-related data. Because those healthcare statistics ensure greater accuracy, security with improvedreliability, and higher data feature, supplying those data to those healthcare apps is a time-consuming difficulty this organisation is facing. To developstatisticsestablishing, data must be more secure and privacy-protected, as deep learning and privacy policies are enabled with 5G network. If the information is private, the information owner may be able to learn about it. The data process flow associated with IoHT is now connected to a variety of IoT devices as edge nodes, thanks to recent advancements. Learning at the nodes is more difficult because of the issue of a missing or insufficient amount of training data, which necessitates a completely distributed setting, as well as new, better datasets, their establishing, and their security. In this work, a Hy-FL-based Blockchain strategy is proposed since it is enabled with blockchain, which maintains trust ability and skilled data based on deep learning with enhanced validation. With the help of the suggested method, training information on the deep nodes can be encrypted and aggregated. In the analysis, data management is managed securely based on IoHT in terms of projected value, energy usage, and data correctness.

Using nominal group technique to compare patients’ and clinicians’ perspectives on symptoms in multiple myeloma to inform the development of a self management tool for patients with relapsed myeloma

Background The nominal group technique (NGT) allows stakeholders to directly generate items for needs assessment. The objective was to demonstrate the use of NGT to inform the development of a healthcare app in patients with relapsed myeloma. Healthcare professionals with experience in the care of patients with relapsed/refractory myeloma were invited to participate. Methods One NGT group was conducted. In the group, health care professionals working in haematology were asked to vote anonymously in order of highest priority, on symptoms previously highlighted by relapsed/refractory myeloma patients in four focus groups. Results A total of 18 healthcare professionals working in the area of haematology participated in the NGT discussion; consultants (n=6), haematology registrars (n=2), specialist nurses [Advanced Nurse Practitioner/Clinical Nurse Specialist] (haematology) (n=3), staff nurse (n=1), and “other” health care professionals (n=6). Participants ranged in experience of working with myeloma patients from 2 years to over 27 years. The symptoms voted in highest priority were: Pain, Fatigue, Peripheral Neuropathy, Infection Risk and Steroid Induced Side Effects. Conclusions The NGT was an efficient method for obtaining information to inform a healthcare app.

Exploring medical students’ perceptions of telehealth in Pakistan: a cross-sectional study

IntroductionThis study aimed to investigate medical students’ perceptions of telehealth in Pakistan and assess the relationship between students’ proficiency in computers and technology, gender, and age.MethodsA cross-sectional study was conducted on 330 medical students from various universities in Karachi, Pakistan. Data on demographic characteristics, computer proficiency, and perceptions of telehealth were collected using a self-administered questionnaire. Descriptive statistics, chi-square tests, and Spearman’s rank correlation coefficient were utilized for data analysis.ResultsThe study included 330 participants, predominantly from public universities (83.3%), with a mean age of 21.40 ± 4.24 years. Most participants were pursuing MBBS (67%). In terms of computer proficiency, 41.8% fell into the middle category. Gender correlated significantly with proficiency (p < 0.05), with females more often in the middle category and males in the not-that-amateur category. Age demonstrated a small positive correlation with proficiency (p < 0.05). A majority (77.6%) expressed a general interest in technology. Regarding healthcare apps, 51.8% had 1–3 apps, and 33.6% had none. Most participants believed telehealth would enhance healthcare (72.1%) and make it cheaper (63.3%). Additionally, 56.7% agreed that Pakistan should invest in telehealth, while only 2.7% disagreed.ConclusionThe study revealed that medical students in Pakistan exhibit an interest in technology and perceive telehealth as beneficial. These findings suggest that telehealth holds the potential to enhance healthcare opportunities in Pakistan. Therefore, investing in and expanding telehealth services may represent a viable solution for improving access to healthcare in the country.

Using Pregnancy and Parenting Apps and Social Media During COVID-19: Absence and Sociality, Agency and Cultural Negotiations for South Asian–Origin Women in Australia

This article reports on and analyses data from a situated and in-depth project on the experiences of six cisgender South Asian-Australian women/people who gave birth during the COVID-19 pandemic. Prior to the pandemic, negatively racialized women experienced barriers to health care and a lack of social support, which were further exacerbated during the COVID-19 pandemic. International border closures in Australia combined with local mitigation strategies inhibited social and cultural support from families, impacting many migrant mothers/birthing people who gave birth for the first time in Australia. Many hospitals in the states of New South Wales and Victoria instituted restrictions to birthing services as a way of reducing exposure to the coronavirus during the pandemic. Our research suggests that pre-existing limitations of health care providers, digital platforms, and apps with regard to culturally and linguistically diverse (CALD) women in Australia have been amplified during the pandemic. Online Facebook groups from the mothers’ countries of origin or cultural backgrounds, or for mothers who had babies due in the same month, represented a significant source of information and support for the participants. This was particularly important at a time when women’s capacities to engage in traditional cultural practices, which provide practical, emotional, and informational support, were compromised by the inability to garner familial support. We situate these findings in the literature on “performing good motherhood” in neoliberal times and via reliance on digital devices and platforms and what it means for CALD women’s sociality, sense of agency, and negotiations with cultural practices.

Exploring Long-Term Determinants and Attitudes Toward Smartphone-Based Commercial Health Care Applications Among Patients With Cancer.

Our study explores how attitudes of patients with cancer toward smartphone-based commercial health care apps affect their use and identifies the influencing factors. Of the 960 patients with cancer who participated in a randomized controlled trial for a smartphone-based commercial health care app, only 264 participants, who completed a survey on app usage experiences conducted between May and August 2022, were included in this study. Participants were categorized into three groups: Positive Persistence (PP), Negative Nonpersistence (NN), and Neutral (NE) on the basis of their attitude and willingness to use smartphone-based commercial health care apps. The Health-Related Quality of Life (QOL) Instrument (8 Items), European QOL (5 Dimensions; 5 Levels), The Human Interaction and Motivation questionnaire, and open-ended questionnaires were used to examine factors potentially influencing extended utilization of digital interventions. Despite demographic similarities among the three groups, only the PP and NE groups showed similar app usage compared with the NN group. The combined group (positive persistence and neutral) exhibited significant improvement in depression (P = .02), anxiety (P = .03), and visual analog scale scores (P = .02) compared with the NN group. In addition, patient interaction (P < .01) and the presence of a chatbot/information feature on the app (P < .01) demonstrated a significant difference across the three groups, with the most favorable response observed among the PP group. Patients were primarily motivated to use the app owing to its health management functions, while the personal challenges they encountered during app usage acted as deterrents. These findings suggest that maintaining a non-negative attitude toward smartphone-based commercial health care apps could lead to an improvement in psychological distress. In addition, the social aspect of apps could contribute to extending patient's utilization of digital interventions.

State-of-the-Art Fast Healthcare Interoperability Resources (FHIR)-Based Data Model and Structure Implementations: Systematic Scoping Review.

Data models are crucial for clinical research as they enable researchers to fully use the vast amount of clinical data stored in medical systems. Standardized data and well-defined relationships between data points are necessary to guarantee semantic interoperability. Using the Fast Healthcare Interoperability Resources (FHIR) standard for clinical data representation would be a practical methodology to enhance and accelerate interoperability and data availability for research. This research aims to provide a comprehensive overview of the state-of-the-art and current landscape in FHIR-based data models and structures. In addition, we intend to identify and discuss the tools, resources, limitations, and other critical aspects mentioned in the selected research papers. To ensure the extraction of reliable results, we followed the instructions of the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist. We analyzed the indexed articles in PubMed, Scopus, Web of Science, IEEE Xplore, the ACM Digital Library, and Google Scholar. After identifying, extracting, and assessing the quality and relevance of the articles, we synthesized the extracted data to identify common patterns, themes, and variations in the use of FHIR-based data models and structures across different studies. On the basis of the reviewed articles, we could identify 2 main themes: dynamic (pipeline-based) and static data models. The articles were also categorized into health care use cases, including chronic diseases, COVID-19 and infectious diseases, cancer research, acute or intensive care, random and general medical notes, and other conditions. Furthermore, we summarized the important or common tools and approaches of the selected papers. These items included FHIR-based tools and frameworks, machine learning approaches, and data storage and security. The most common resource was "Observation" followed by "Condition" and "Patient." The limitations and challenges of developing data models were categorized based on the issues of data integration, interoperability, standardization, performance, and scalability or generalizability. FHIR serves as a highly promising interoperability standard for developing real-world health care apps. The implementation of FHIR modeling for electronic health record data facilitates the integration, transmission, and analysis of data while also advancing translational research and phenotyping. Generally, FHIR-based exports of local data repositories improve data interoperability for systems and data warehouses across different settings. However, ongoing efforts to address existing limitations and challenges are essential for the successful implementation and integration of FHIR data models.

Predicting Long-Term Engagement in mHealth Apps: Comparative Study of Engagement Indices.

Digital health care apps, including digital therapeutics, have the potential to increase accessibility and improve patient engagement by overcoming the limitations of traditional facility-based medical treatments. However, there are no established tools capable of quantitatively measuring long-term engagement at present. This study aimed to evaluate an existing engagement index (EI) in a commercial health management app for long-term use and compare it with a newly developed EI. Participants were recruited from cancer survivors enrolled in a randomized controlled trial that evaluated the impact of mobile health apps on recovery. Of these patients, 240 were included in the study and randomly assigned to the Noom app (Noom Inc). The newly developed EI was compared with the existing EI, and a long-term use analysis was conducted. Furthermore, the new EI was evaluated based on adapted measurements from the Web Matrix Visitor Index, focusing on click depth, recency, and loyalty indices. The newly developed EI model outperformed the existing EI model in terms of predicting EI of a 6- to 9-month period based on the EI of a 3- to 6-month period. The existing model had a mean squared error of 0.096, a root mean squared error of 0.310, and an R2 of 0.053. Meanwhile, the newly developed EI models showed improved performance, with the best one achieving a mean squared error of 0.025, root mean squared error of 0.157, and R2 of 0.610. The existing EI exhibited significant associations: the click depth index (hazard ratio [HR] 0.49, 95% CI 0.29-0.84; P<.001) and loyalty index (HR 0.17, 95% CI 0.09-0.31; P<.001) were significantly associated with improved survival, whereas the recency index exhibited no significant association (HR 1.30, 95% CI 1.70-2.42; P=.41). Among the new EI models, the EI with a menu combination of menus available in the app's free version yielded the most promising result. Furthermore, it exhibited significant associations with the loyalty index (HR 0.32, 95% CI 0.16-0.62; P<.001) and the recency index (HR 0.47, 95% CI 0.30-0.75; P<.001). The newly developed EI model outperformed the existing model in terms of the prediction of long-term user engagement and compliance in a mobile health app context. We emphasized the importance of log data and suggested avenues for future research to address the subjectivity of the EI and incorporate a broader range of indices for comprehensive evaluation.

Effectiveness of Home-Based Cardiac Rehabilitation with Optimized Exercise Prescriptions Using a Mobile Healthcare App in Patients with Acute Myocardial Infarction: A Randomized Controlled Trial.

Despite the effectiveness of cardiac rehabilitation (CR), the actual participation rate in CR is low. While home-based CR offers a viable alternative, it faces challenges in participation due to factors such as a lack of self-motivation and fear of exercising without supervision. Utilizing a mobile healthcare application (app) during counseling may be an effective strategy for patients. Therefore, the aim of this study was to assess whether 6 weeks of home-based CR with exercise readjustment using a mobile app is an effective therapy for patients with acute myocardial infarction (AMI). Post-AMI patients eligible for home-based CR were randomized into the intervention group (CR-Mobile) and the control group, which followed the usual home-based CR protocol (CR-Usual). Both groups participated in a 6-week home-based CR program, with exercise readjustment and encouragement carried out every 2 weeks. The CR-Mobile group was supervised using data recorded in the mobile app, while the CR-Usual group was supervised via phone consultations. The primary outcome measured was maximal oxygen consumption (VO2max). Within-group comparisons showed significant improvements in VO2max (PCR-Mobile = 0.011 vs. PCR-Usual = 0.020) and METs (PCR-Mobile = 0.011 vs. PCR-Usual = 0.011) for both groups. These findings suggest that a 6-week home-based CR program with exercise readjustment using a mobile app can potentially enhance exercise capacity as effectively as verbal supervision.

Health-care app detection using optimized clustering

Medical health-care apps have become ubiquitous in today&amp;rsquo;s world, enhancing health-care quality at affordable costs. The continuous development of new apps underscores their high acceptance and popularity. Machine learning techniques offer effective app identification owing to their high prediction accuracy, particularly with a training dataset of known apps. Although machine learning techniques provide high detection accuracy for known apps, they exhibit abysmal accuracy in detecting unknown and novel apps. This research proposes a novel approach to optimizing the K-means clustering algorithm for detecting zero-day apps. The proposed technique integrates a perceptron feed-forward neural network to determine the coordinates of the centroids of the clusters in K-means clustering. Experimental evaluations demonstrate the efficacy of the proposed approach in enhancing the performance of K-means clustering, providing improved detection for both known and unknown medical health-care apps. A total of 30 health-care apps was utilized in this evaluation. This research enhances the detection accuracy of medical healthcare apps, particularly zero-day apps. The intercluster similarity of the benign class improved to 0.99, and that of the malicious class improved to 0.91, highlighting the improved classification of the apps. The major contribution of this work is achieving an intercluster similarity of 0.89 for detecting novel apps.

Mental health app boost my mood (BMM) as preventive early intervention for adolescents with (sub)clinical depressive symptoms

BackgroundDepression is a common mental disorder. Detecting (sub)clinical depressive symptoms in adolescents at an early stage and offering a low-threshold early intervention can minimize the risk of serious and/or long-term depression. As such, a digital intervention can be a low-threshold preventive and early intervention. This study aims to examine whether the Boost My Mood (BMM)-app is a suitable preventive early intervention for adolescents with (sub)clinical depressive symptoms.MethodsThis naturalistic single-arm evaluation study (N = 50) was conducted in adolescents aged 16–21 with (sub)clinical depressive symptoms. Furthermore, the BMM-app was studied in relation to anxiety, worrying, stress, and sleeping problems. An exploratory objective was to determine whether positive expectations and social support are related to app use.ResultsThe study showed a significant decrease in not only depressive symptoms, but also anxiety, worrying and stress while using the BMM-app. Sleeping problems did not significantly decrease over time while using the BMM-app. The degree of use of the BMM-app and telling significant others about using the BMM-app were both not related to a decrease in depressive symptoms. The BMM-app was used significantly more when the adolescent had told relatives about their depressive symptoms.ConclusionsA digital intervention, such as the BMM-app, can be a low-threshold preventive and early intervention for adolescents with (sub)clinical depressive symptoms. Beneficial effects of the BMM-app were reported on depressive symptoms as well as other aspects of quality of life, such as anxiety, worrying, and stress. Whereas several factors may have played a role in the current findings on depressive symptoms, there are reasons to assume that part of the reduction in symptoms could be attributed to the BMM-app. Although no causality can be assumed, this study is a first step in the implementation of preventive apps in mental health care.

Improving Performance of Customer Relationship Management to Leverage Customer Loyalty (Case Study: MILKY CRM Loyalty Program)

Customer Relationship Management (CRM) plays a crucial role for businesses as technology advances and consumer demands continue to rise. However, CRM implementation faces challenges from both external and internal factors within an organization. Ineffective CRM implementation can lead to customer dissatisfaction, decreased loyalty, reduced profitability, and missed business opportunities. This research aims to improve the MILKY CRM loyalty program by identifying the key root causes and proposing alternative solutions, ultimately determining the best alternative to enhance the program. The methodology includes in-depth interviews and analysis using the Kepner-Tregoe framework, which involves problem analysis, decision analysis, and potential problem analysis. Supported by external and internal analyses such as Porter’s Five Forces, Customer Journey Mapping, and an Integrated CRM Scorecard, the results are then utilized for a SWOT analysis. The problem analysis identified 21 attributes as the root causes affecting MILKY CRM loyalty program performance. These attributes include Consumer Behaviour Change, Aggressiveness of Competitors, CRM Agency Capability, Healthcare Apps Strategic Partnership, Potential New Segments generated by New Entrants, CRM Differentiation, Strong Brand Reputation, Content Management, Ability Services Program, Customer Engagement, Customer Experience Improvement, Win Back Lapsed Customers, Incomplete metrics tracking on CRM scorecard, Financial Budget Constraints, Personalization Services &amp; Rewards, Digital Acquisition Transition, Segmentation &amp; Tiering Review, Balancing Strategy on Customer Acquisition &amp; Retention, IT System Capabilities, and Human Capability on CRM. In the decision analysis section, alternative solutions were generated through TOWS analysis, and the best alternative was determined using the Analytical Hierarchy Process (AHP) tool. The top chosen strategy, Strategy #2: Optimizing CRM &amp; Organizational Strategies for Enhanced Customer Engagement &amp; Operational Efficiency (W/O Strategies), is recommended for future improvement. Focus areas include communication, resources, technology, and KPI metrics to mitigate potential problems. Overall, this research provides valuable insights and recommendations for enhancing the performance of the MILKY CRM loyalty program, ensuring its effectiveness in meeting customer needs and driving business success.

Experiences of patients with endometriosis with a digital health application: a qualitative analysis

BackgroundEndometriosis is a frequent disease in women of reproductive age in which the endometrium occurs outside the uterine cavity. Multimodal treatment approaches are necessary due to loss of quality of life and the chronic nature of the disease. Digital health applications (DiGa) are becoming increasingly important. This research project investigates how a healthcare app can influence the subjective experience of illness in patients with endometriosis.MethodsEmpiric data were collected through semi-structured interviews. Data analysis was carried out using qualitative focussed interview analysis. Reliability was ensured by joint interdisciplinary and interprofessional evaluation of the interviews by experts and those affected.ResultsTen patients with endometriosis and the prescribed healthcare app Endo-App© were examined. Categories were defined from the superordinate categories “Factors influencing the experience of illness” and “Evaluation of the app”. The app provided reliable information, promoted self-efficacy through exercises and strengthened the perception of the individuality of the illness. It helped to minimise nocebo effects from internet research and enabled a positive change of perspective. Patients criticised the time required for data input and had data protection concerns. The educational elements were often seen as redundant. Some patients only used the app briefly, or not at all.ConclusionOnce a DiGa has been prescribed, it may be useful to explain its use on an outpatient basis and validate regular use. Blind re-prescribing of DiGas should be avoided. Younger patients with a recent diagnosis or patients following rehabilitation may benefit more from prescribing.

Persian Mobile Apps for Diabetic Patients: App Review and Evaluation Study

Background: The development of mobile apps in healthcare facilities helps users perform self-care effectively. Objectives: This study aimed to evaluate the quality, functionality, and features of Persian language diabetes mobile apps. Methods: Persian language diabetes self-management mobile apps were searched in the Android and iOS App Stores using relevant keywords such as "diabetes" and "blood sugar." Twelve apps met the inclusion criteria. Nine evaluators assessed the apps' quality based on the Mobile App Rating Scale (MARS), their functionality according to the IMS institute for healthcare informatics app functionality scoring system, and their features and content using a checklist. Results: The mean MARS score of the apps was 3.02 out of 5. Among the four dimensions of engagement, aesthetics, functionality, and information quality, functionality had the highest mean ± SD score (3.64 ± 1.13). Most of the apps used 5 - 7 out of the 11 defined functionalities. All self-management apps included the functionality of monitoring blood glucose levels, and the majority had health status monitoring features. Conclusions: The mobile apps had several drawbacks, including a lack of engagement strategies, insufficient evidence-based information for patients, limited guideline-based self-management functionalities, failure to evaluate clinical effectiveness in trials, and an absence of mutual communication with healthcare providers.