Healthcare Apps Research Articles

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.

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.

Exploring Determinants of Successful Weight Loss with the Use of a Smartphone Healthcare Application: Secondary Analysis of a Randomized Clinical Trial.

Dietary and physical activity interventions through smartphone healthcare applications (apps) have recently surged in popularity as effective methods for weight loss. However, the specific factors contributing to successful weight loss remain uncertain. We conducted an analysis of baseline characteristics and app usage frequencies over three months among 68 Japanese adults with overweight and obesity who were assigned to the intervention group in a previous randomized controlled trial. Logistic regression analysis revealed a negative association (OR: 0.248; p = 0.018) between having a walking habit at baseline and successful weight loss, defined as a 3% reduction in initial weight. Additionally, slower walking speeds and family medical history were identified as potential predictors of successful weight loss. These findings offer insights into the profile of individuals who achieve success in weight loss through our smartphone app, providing valuable guidance for the development of future healthcare apps.

Development of the Huntington Support App (HD-eHelp study): a human-centered and co-design approach.

eHealth seems promising in addressing challenges in the provision of care for Huntington's disease (HD) across Europe. By harnessing information and communication technologies, eHealth can partially relocate care from specialized centers to the patients' home, thereby increasing the availability and accessibility of specialty care services beyond regional borders. Previous research on eHealth (development) in HD is however limited, especially when it comes to including eHealth services specifically designed together with HD gene expansion carriers (HDGECs) and their partners to fit their needs and expectations. This article describes the qualitative human-centered design process and first evaluations of the Huntington Support App prototype: a web-app aimed to support the quality of life (QoL) of HDGECs and their partners in Europe. Prospective end-users, i.e., HDGECs, their partners, and healthcare providers (HCPs), from different countries were involved throughout the development process. Through interviews, we captured people's experiences with the disease, quality of life (QoL), and eHealth. We translated their stories into design directions that were further co-designed and subsequently evaluated with the user groups. The resulting prototype centralizes clear and reliable information on the disease, HD-related news and events, as well as direct contact possibilities with HCPs via an online walk-in hour or by scheduling an appointment. The app's prototype was positively received and rated as (very) appealing, pleasant, easy to use and helpful by both HDGECs and partners. By involving end-users in every step, we developed a healthcare app that meets relevant needs of individuals affected by HD and therefore may lead to high adoption and retention rates. As a result, the app provides low-threshold access to reliable information and specialized care for HD in Europe. A description of the Huntington Support App as well as implications for further development of the app's prototype are provided.

Doctors' Personal Preference and Adoption of Mobile Apps to Communicate with Patients in China: Qualitative Study.

Different kinds of mobile apps are used to promote communications between patients and doctors. Studies have investigated patients' mobile app adoption behavior; however, they offer limited insights into doctors' personal preferences among a variety of choices of mobile apps. This study aimed to investigate the nuanced adoption behaviors among doctors in China, which has a robust adoption of mobile apps in health care, and to explore the constraints influencing their selection of specific mobile apps. This paper addressed 3 research questions: (1) Which doctors opt to adopt mobile apps to communicate with patients? (2) What types of mobile apps do they choose? (3) To what degree do they exercise personal choice in adopting specific mobile apps? We used thematic content analysis of qualitative data gathered from semistructured interviews with 11 doctors in Hangzhou, which has been recognized for its advanced adoption of mobile technology in social services, including health care services. The selection of participants was purposive, encompassing diverse departments and hospitals. In total, 5 themes emerged from the data analysis. First, the interviewees had a variety of options for communicating with patients via mobile apps, with the predominant ones being social networking apps (eg, WeChat) and medical platforms (eg, Haodf). Second, all interviewees used WeChat to facilitate communication with patients, although their willingness to share personal accounts varied (they are more likely to share with trusty intermediaries). Third, fewer than half of the doctors adopted medical platforms, and they were all from tertiary hospitals. Fourth, the preferences for in-person, WeChat, or medical platform communication reflected the interviewees' perceptions of different patient cohorts. Lastly, the selection of a particular kind of mobile app was significantly influenced by the doctors' affiliation with hospitals, driven by their professional obligations to fulfill multiple tasks assigned by the hospitals or the necessity of maintaining social connections with their colleagues. Our findings contribute to a nuanced understanding of doctors' adoption behavior regarding specific types of mobile apps for patient communication, instead of addressing such adoption behavior of a wide range of mobile apps as equal. Their choices of a particular kind of app were positioned within a social context where health care policies (eg, limited funding for public hospitals, dominance of public health care institutions, and absence of robust referral systems) and traditional culture (eg, trust based on social connections) largely shape their behavioral patterns.

Nudging Data Privacy of Mobile Health Applications in Saudi Arabia

Mobile health apps are a digital era revolution, facilitating direct patient-physician communication, lab and test orders, and medication refills. Despite these benefits, security and privacy issues arise due to handling sensitive data. This paper assesses the security and privacy of Saudi Arabian mobile healthcare apps, gauging compliance with the Personal Data Protection Law (PDPL). Results highlight varied PDPL compliance, underscoring the imperative for enhanced security measures in the digital healthcare landscape.

Effectiveness of mHealth Apps for Maternal Health Care Delivery: Systematic Review of Systematic Reviews.

Globally, the use of mobile health (mHealth) apps or interventions has increased. Robust synthesis of existing systematic reviews on mHealth apps may offer useful insights to guide maternal health clinicians and policy makers. This systematic review aims to assess the effectiveness or impact of mHealth apps on maternal health care delivery globally. We systematically searched Scopus, Web of Science (Core Collection), MEDLINE or PubMed, CINAHL, and Cochrane Database of Systematic Reviews using a predeveloped search strategy. The quality of the reviews was independently assessed by 3 reviewers, while study selection was done by 2 independent raters. We presented a narrative synthesis of the findings, highlighting the specific mHealth apps, where they are implemented, and their effectiveness or outcomes toward various maternal conditions. A total of 2527 documents were retrieved, out of which 16 documents were included in the review. Most mHealth apps were implemented by sending SMS text messages with mobile phones. mHealth interventions were most effective in 5 areas: maternal anxiety and depression, diabetes in pregnancy, gestational weight management, maternal health care use, behavioral modification toward smoking cessation, and controlling substance use during pregnancy. We noted that mHealth interventions for maternal health care are skewed toward high-income countries (13/16, 81%). The effectiveness of mHealth apps for maternity health care has drawn attention in research and practice recently. The study showed that research on mHealth apps and their use dominate in high-income countries. As a result, it is imperative that low- and middle-income countries intensify their commitment to these apps for maternal health care, in terms of use and research. PROSPERO CRD42022365179; https://tinyurl.com/e5yxyx77.

Evaluating the Energy Efficiency of Popular US Smartphone Health Care Apps: Comparative Analysis Study Toward Sustainable Health and Nutrition Apps Practices.

The emergence of smartphones has sparked a transformation across multiple fields, with health care being one of the most notable due to the advent of mobile health (mHealth) apps. As mHealth apps have gained popularity, there is a need to understand their energy consumption patterns as an integral part of the evolving landscape of health care technologies. This study aims to identify the key contributors to elevated energy consumption in mHealth apps and suggest methods for their optimization, addressing a significant void in our comprehension of the energy dynamics at play within mHealth apps. Through quantitative comparative analysis of 10 prominent mHealth apps available on Android platforms within the United States, this study examined factors contributing to high energy consumption. The analysis included descriptive statistics, comparative analysis using ANOVA, and regression analysis to examine how certain factors impact energy use and consumption. Observed energy use variances in mHealth apps stemmed from user interactions, features, and underlying technology. Descriptive analysis revealed variability in app energy consumption (150-310 milliwatt-hours), highlighting the influence of user interaction and app complexity. ANOVA verified these findings, indicating the critical role of engagement and functionality. Regression modeling (energy consumption = β₀ + β₁ × notification frequency + β₂ × GPS use + β₃ × app complexity + ε), with statistically significant P values (notification frequency with a P value of .01, GPS use with a P value of .05, and app complexity with a P value of .03), further quantified these bases' effects on energy use. The observed differences in the energy consumption of dietary apps reaffirm the need for a multidisciplinary approach to bring together app developers, end users, and health care experts to foster improved energy conservation practice while achieving a balance between sustainable practice and user experience. More research is needed to better understand how to scale-up consumer engagement to achieve sustainable development goal 12 on responsible consumption and production.

Mixed Method Approach towards Continuous Use of Chatbots in Healthcare Apps: An Integration of Humanizing Experience Theory

The unprecedented adoption of apps has transformed consumer interactions. Chatbots are revolutionizing healthcare apps, but more than their integration is needed to guarantee a seamless user experience. This study advances the technology acceptance model by including anthropomorphic elements that build trust in technology for continuous chatbot use in mobile health apps. It leverages the humanizing experience theory to comprehensively examine the boundary conditions that explain the effects of humanizing digital experiences, such as self-representation and intimacy, on utilitarian and hedonic conversational cues. This research contributes to interactive marketing and mobile commerce by offering insights into user behavior for continued usage of health apps.

Digital Self-Management Platform for Adult Asthma: Randomized Attention-Placebo Controlled Trial.

Asthma is one of the most common chronic conditions worldwide, with a substantial individual and health care burden. Digital apps hold promise as a highly accessible, low-cost method of enhancing self-management in asthma, which is critical to effective asthma control. We conducted a fully remote randomized controlled trial (RCT) to assess the efficacy of juli, a commercially available smartphone self-management platform for asthma. We conducted a pragmatic single-blind, RCT of juli for asthma management. Our study included participants aged 18 years and older who self-identified as having asthma and had an Asthma Control Test (ACT) score of 19 or lower (indicating uncontrolled asthma) at the beginning of the trial. Participants were randomized (1:1 ratio) to receive juli for 8 weeks or a limited attention-placebo control version of the app. The primary outcome measure was the difference in ACT scores after 8 weeks. Secondary outcomes included remission (ACT score greater than 19), minimal clinically important difference (an improvement of 3 or more points on the ACT), worsening of asthma, and health-related quality of life. The primary analysis included participants using the app for 8 weeks (per-protocol analysis), and the secondary analysis used a modified intention-to-treat (ITT) analysis. We randomized 411 participants between May 2021 and April 2023: a total of 152 (37%) participants engaged with the app for 8 weeks and were included in the per-protocol analysis, and 262 (63.7%) participants completed the week-2 outcome assessment and were included in the modified ITT analysis. Total attrition between baseline and week 8 was 259 (63%) individuals. In the per-protocol analysis, the intervention group had a higher mean ACT score (17.93, SD 4.72) than the control group (16.24, SD 5.78) by week 8 (baseline adjusted coefficient 1.91, 95% CI 0.31-3.51; P=.02). Participants using juli had greater odds of achieving or exceeding the minimal clinically important difference at 8 weeks (adjusted odds ratio 2.38, 95% CI 1.20-4.70; P=.01). There were no between group differences in the other secondary outcomes at 8 weeks. The results from the modified ITT analyses were similar. Users of juli had improved asthma symptom control over 8 weeks compared with users of a version of the app with limited functionality. These findings suggest that juli is an effective digital self-management platform that could augment existing care pathways for asthma. The retention of patients in RCTs and real-world use of digital health care apps is a major challenge. International Standard Randomised Controlled Trial Number (ISRCTN) registry ISRCTN87679686; https://www.isrctn.com/ISRCTN87679686.

User Preferences for AI-based Healthcare Apps: an Association Mining Analysis

User Preferences for AI-based Healthcare Apps: an Association Mining Analysis