Personal Mobile Health Research Articles

Portable Electrochemical System and Platform with Point-of-Care Determination of Urine Albumin-to-Creatinine Ratio to Evaluate Chronic Kidney Disease and Cardiorenal Syndrome.

The urine albumin (Alb)-to-creatinine (Crn) ratio (UACR) is a sensitive and early indicator of chronic kidney disease (CKD) and cardiorenal syndrome. This study developed a portable and wireless electrochemical-sensing platform for the sensitive and accurate determination of UACR. The developed platform consists of a carbon nanotube (CNT)-2,2'-azino-bis(3-ethylbenzothiazoline-6-sulphonic acid)(ABTS)-based modified UACR sensor, a miniaturised potentiostat, a cup holder embedded with a magnetic stirrer and a smartphone app. The UACR sensing electrode is composed of two screen-printed carbon working electrodes, one screen-printed carbon counter electrode and a screen-printed AgCl reference electrode. The miniaturised potentiostat, which is controlled by the developed app, performs cyclic voltammetry and amperometry to detect Alb and Crn, respectively. Clinical trials of the proposed system by using spot urine samples from 30 diabetic patients indicate that it can accurately classify all three CKD risk statuses within 30 min. The high accuracy of our proposed sensing system exhibits satisfactory agreement with the commercial biochemical analyser TBA-25FR (Y = 0.999X, R2 = 0.995). The proposed UACR sensing system offers a convenient, reliable and affordable solution for personal mobile health monitoring and point-of-care urinalysis.

Factors associated with the use of the Indonesia COVID-19 mobile app: What needs to be improved for the future personal mobile health app?

Background: Personal mobile health applications, including the COVID-19 mobile app, offer various benefits for enhancing the effectiveness of health programs. Objective: This study aimed to investigate and compare the factors associated with the utilization of the Peduli Lindungi COVID-19 mobile application, employing the behavioral theory of the Health Belief Model (HBM). Methods: This cross-sectional survey study, conducted at the Department of Public Health in two universities in South Jakarta, Indonesia, involved 744 respondents. The independent variables were derived from the Health Belief Model (HBM), including components such as perceived vulnerability, severity, benefits, obstacles, and self-efficacy. The dependent variable was the use of the COVID-19 mobile application. Results: The majority of respondents, with an average age of 19.9 years, were female. Out of the total, 51.9% (386 individuals) used the PL application in the past seven days, albeit not on a daily basis, and 86.6% utilized the application to access public facilities. Non-users primarily cited a lack of necessity due to not having traveled in the last seven days. The variable of perceived usefulness was significantly associated with application use (p <0.001). Conclusion: There is a need for increased public education regarding the application's benefits. Furthermore, the safe entrance system feature of the app, utilized for accessing public spaces, could be consistently employed to monitor and prevent the transmission of infectious diseases, including COVID-19.

StepAdd: A personalized mHealth intervention based on social cognitive theory to increase physical activity among type 2 diabetes patients

ObjectiveInvestigate the preliminary efficacy and feasibility of a personalized mobile health (mHealth) intervention based on social cognitive theory (SCT) to promote physical activity among type 2 diabetes patients via self-monitoring, goal setting, and automatic feedback. MethodsWe conducted a pilot study involving 33 type 2 diabetes patients attending Mitsui Memorial Hospital in Japan using a pre-post evaluation design over 12 weeks. Participants measured daily step count, body weight, and blood pressure at home, with the measurements synchronized with the StepAdd application (app) automatically. Participants used the app to review daily results, update personalized step goals, identify individualized barriers to achieving the step goals, find coping strategies to overcome each barrier, and implement these strategies, thereby building effective coping skills to meet the goals. Pharmacists examined the usage of the app and provided coaching on lifestyle modifications. Ultimately, patients established skills to enhance diabetes self-care by using the app. ResultsDaily step count increased dramatically with high statistical significance (p < 0.0001), from a mean of 5436 steps/day to 10,150 steps/day, an 86.7 % increase. HbA1c (p = 0.0001) and BMI (p = 0.0038) also improved. Diabetes self-care in diet, exercise, and foot care as well as self-management behavior, self-regulation, and self-efficacy in achieving daily step goals showed significant improvements. The retention rate of the study was very high, at 97.0 % (n = 32). ConclusionsA personalized smartphone-based mHealth intervention based on SCT is feasible and effective at promoting physical activity among type 2 diabetes patients. The methodology of the intervention could be readily applied to other patient populations.

Patient Monitoring for Personalized Mobile Health (PMH) based on Medical Virtual Instruments

One of the newest technologies, mobile health, has the potential to support the provision of care for older adults and offer them individualised treatment. This study’s goal is to evaluate the benefits and challenges of personalised mobile health (PMH) for elderly residential care. Virtual worlds are quickly integrating into the landscape of instructional technologies. One of the most well-known of these settings is Second Life (SL). Despite the potential of SL for health professions education, there aren’t many official SL applications for this purpose, and the effectiveness of these applications hasn’t been evaluated to the fullest extent possible. Similarly, it appears that nothing is known about the use of virtual worlds for continuing medical education. In order to better grasp the fundamentals of the aid of MVIs for personal health monitoring (PHM), we were able to pinpoint the key disease regions, sensors, channels, calculations and communication protocols. The main obstacles limiting MVIs’ degree of integration into the international health care system were also identified. The analysis demonstrates that MVIs offer an excellent possibility for the creation of affordable, personalised health systems that meet the unique equipment requirements of a certain field of medicine.

Personalized Mobile Health for Elderly Home Care: A Systematic Review of Benefits and Challenges.

Mobile health as one of the new technologies can be a proper solution to support care provision for the elderly and provide personalized care for them. This study is aimed at reviewing the benefits and challenges of personalized mobile health (PMH) for elderly home care. With a systematic review methodology, 1895 records were retrieved by searching four databases. After removing duplicates, 1703 articles remained. Following full-text examination, 21 articles that met the inclusion criteria were studied in detail, and the output was presented in different tables. The results indicated that 25% of the challenges were related to privacy, cybersecurity, and data ownership (10%), technology (7.5%), and implementation (7.5%). The most frequent benefits were related to cost-saving (17.5%), nurse engagement improvement (10%), and caregiver stress reduction (7.5%). In general, the number of benefits in this study was slightly higher than the challenges, but in order to use PMH technologies, the challenges presented in this study must be carefully considered and a suitable solution must be adopted. Benefits can also be helpful in persuading individuals and health-care providers. This study shed light on those points that need to be highlighted for further work in order to convert the challenges toward benefits.

Feasibility and usability of a personalized mHealth app for self-management in the first year following breast cancer surgery.

This study investigated the feasibility and usability of a personalized mobile health (mHealth) app for self-management during the year following breast cancer surgery. Twenty-nine participants were instructed to use an app and smart band immediately after discharge. Only 18 completed the study. Their perceived necessity and satisfaction for main domains and app were assessed at 1, 2, 4, 6, 9, and 12 months. A self-reporting questionnaire assessed usability at 12 months. Consequently, retention rate as measures of feasibility showed a mean of 75.8%. Exercise and diet management were the most accessed app domains. Perceived necessity was higher than satisfaction. The mean usability score was 80.2. Most participants found the app useful and effective as a delivery for healthcare. Further, 94% of them were willing to pay for and recommend it. Thus, mHealth app can help breast cancer patients improve their healthy behaviors and healthcare further. This study provides insights for designing long-term randomized controlled trials using mHealth interventions.

A Personalized Mobile Health Program for Type 2 Diabetes During the COVID-19 Pandemic: Single-Group Pre–Post Study

BackgroundWith increasing type 2 diabetes prevalence, there is a need for effective programs that support diabetes management and improve type 2 diabetes outcomes. Mobile health (mHealth) interventions have shown promising results. With advances in wearable sensors and improved integration, mHealth programs could become more accessible and personalized.ObjectiveThe study aimed to evaluate the feasibility, acceptability, and effectiveness of a personalized mHealth-anchored intervention program in improving glycemic control and enhancing care experience in diabetes management. The program was coincidentally implemented during the national-level lockdown for COVID-19 in Singapore, allowing for a timely study of the use of mHealth for chronic disease management.MethodsPatients with type 2 diabetes or prediabetes were enrolled from the Singapore Armed Forces and offered a 3-month intervention program in addition to the usual care they received. The program was standardized to include (1) in-person initial consultation with a clinical dietitian; (2) in-person review with a diabetes specialist doctor; (3) 1 continuous glucose monitoring device; (4) access to the mobile app for dietary intake and physical activity tracking, and communication via messaging with the dietitian and doctor; and (5) context-sensitive digital health coaching over the mobile app. Medical support was rendered to the patients on an as-needed basis when they required advice on adjustment of medications. Measurements of weight, height, and glycated hemoglobin A1c (HbA1c) were conducted at 2 in-person visits at the start and end of the program. At the end of the program, patients were asked to complete a short acceptability feedback survey to understand the motivation for joining the program, their satisfaction, and suggestions for improvement.ResultsOver a 4-week recruitment period, 130 individuals were screened, the enrollment target of 30 patients was met, and 21 patients completed the program and were included in the final analyses; 9 patients were lost to follow-up (full data were not available for the final analyses). There were no differences in the baseline characteristics between patients who were included and excluded from the final analyses (age category: P=.23; gender: P=.21; ethnicity: P>.99; diabetes status category: P=.52, medication adjustment category: P=.65; HbA1c category: P=.69; BMI: P>.99). The 21 patients who completed the study rated a mean of 9.0 out of 10 on the Likert scale for both satisfaction questions. For the Yes-No question on benefit of the program, all of the patients selected “Yes.” Mean HbA1c decreased from 7.6% to 7.0% (P=.004). There were no severe hypoglycemia events (glucose level <3.0 mmol/L) reported. Mean weight decreased from 76.8 kg to 73.9 kg (P<.001), a mean decrease of 3.5% from baseline weight. Mean BMI decreased from 27.8 kg/m2 to 26.7 kg/m2 (P<.001).ConclusionsThe personalized mHealth program was feasible, acceptable, and produced significant reductions in HbA1c (P=.004) and body weight (P<.001) in individuals with type 2 diabetes. Such mHealth programs could overcome challenges posed to chronic disease management by COVID-19, including disruptions to in-person health care access.

Motivating Adherence to Exercise Plans Through a Personalized Mobile Health App: Enhanced Action Design Research Approach.

BackgroundPhysical inactivity is a global issue that affects people’s health and productivity. With the advancement of mobile technologies, many apps have been developed to facilitate health self-management. However, few studies have examined the effectiveness of these mobile health (mHealth) apps in motivating exercise adherence.ObjectiveThis study aims to demonstrate the enhanced action design research (ADR) process and improve the design of mHealth apps for exercise self-management. Specifically, we investigate whether sending motivational messages improves adherence to exercise plans, whether the motivational effect is affected by personality, the impact of message type and repetition, and the process of involving a field experiment in the design process and learning new design principles from the results.MethodsThis formative research was conducted by proposing an enhanced ADR process. We incorporated a field experiment into the process to iteratively refine and evaluate the design until it converges into a final mHealth app. We used the Apple ResearchKit to develop the mHealth app and promoted it via trainers at their gyms. We targeted users who used the app for at least two months. Participants were randomly assigned to 1 of the 12 groups in a 2×3×2 factorial design and remained blinded to the assigned intervention. The groups were defined based on personality type (thinking or feeling), message type (emotional, logical, or none), and repetition (none or once). Participants with different personality types received tailored and repeated messages. Finally, we used the self-reported completion rate to measure participants’ adherence level to exercise plans. By analyzing users’ usage patterns, we could verify, correct, and enhance the mHealth app design principles.ResultsIn total, 160 users downloaded the app, and 89 active participants remained during the 2-month period. The results suggest a significant main effect of personality type and repetition and a significant interaction effect between personality type and repetition. The adherence rate of people with feeling personality types was 18.15% higher than that of people with thinking types. Emotional messages were more effective than logical messages in motivating exercise adherence. Although people received repeated messages, they were more likely to adhere to exercise plans. With repeated reminders, the adherence rates of people with thinking personality types were significantly improved by 27.34% (P<.001).ConclusionsThis study contributes to the literature on mHealth apps. By incorporating a field experiment into the ADR process, we demonstrate the benefit of combining design science and field experiments. This study also contributes to the research on mHealth apps. The principles learned from this study can be applied to improve the effectiveness of mHealth apps. The app design can be considered a foundation for the development of more advanced apps for specific diseases, such as diabetes and asthma, in future research.

The Design and Development of a Personalized Leisure Time Physical Activity Application Based on Behavior Change Theories, End-User Perceptions, and Principles From Empirical Data Mining.

Introduction: Many adults do not reach the recommended physical activity (PA) guidelines, which can lead to serious health problems. A promising method to increase PA is the use of smartphone PA applications. However, despite the development and evaluation of multiple PA apps, it remains unclear how to develop and design engaging and effective PA apps. Furthermore, little is known on ways to harness the potential of artificial intelligence for developing personalized apps. In this paper, we describe the design and development of the Playful data-driven Active Urban Living (PAUL): a personalized PA application.Methods: The two-phased development process of the PAUL apps rests on principles from the behavior change model; the Integrate, Design, Assess, and Share (IDEAS) framework; and the behavioral intervention technology (BIT) model. During the first phase, we explored whether location-specific information on performing PA in the built environment is an enhancement to a PA app. During the second phase, the other modules of the app were developed. To this end, we first build the theoretical foundation for the PAUL intervention by performing a literature study. Next, a focus group study was performed to translate the theoretical foundations and the needs and wishes in a set of user requirements. Since the participants indicated the need for reminders at a for-them-relevant moment, we developed a self-learning module for the timing of the reminders. To initialize this module, a data-mining study was performed with historical running data to determine good situations for running.Results: The results of these studies informed the design of a personalized mobile health (mHealth) application for running, walking, and performing strength exercises. The app is implemented as a set of modules based on the persuasive strategies “monitoring of behavior,” “feedback,” “goal setting,” “reminders,” “rewards,” and “providing instruction.” An architecture was set up consisting of a smartphone app for the user, a back-end server for storage and adaptivity, and a research portal to provide access to the research team.Conclusions: The interdisciplinary research encompassing psychology, human movement sciences, computer science, and artificial intelligence has led to a theoretically and empirically driven leisure time PA application. In the current phase, the feasibility of the PAUL app is being assessed.

Biosensors for Personal Mobile Health: A System Architecture Perspective.

Advances in mobile biosensors, integrating developments in materials science and instrumentation, are fueling an expansion in health data being collected and analyzed in decentralized settings. For example, semiconductor-based sensors are enabling measurement of vital signs, and microfluidic-based sensors are enabling measurement of biochemical markers. As biosensors for mobile health are becoming increasingly paired with smart devices, it will become critical for researchers to design biosensors - with appropriate functionalities and specifications - to work seamlessly with accompanying connected hardware and software. This article describes recent research in biosensors, as well as current mobile health devices in use, as classified into four distinct system architectures that take into account the biosensing and data processing functions required in personal mobile health devices. We also discuss the path forward for integrating biosensors into smartphone-based mobile health devices.

Personalized Mobile Health Intervention for Health and Weight Loss in Postpartum Women Receiving Women, Infants, and Children Benefit: A Randomized Controlled Pilot Study.

Prepregnancy maternal obesity and excessive weight gain during pregnancy lead to significant morbidities in mothers and their children. Mothers who never return to their prepregnancy weight begin subsequent pregnancies at a greater weight and have a larger propensity for excess gestational weight gain and postpartum weight retention. In this pilot study, 40 postpartum women credentialed to receive postpartum women, infants, and children (WIC) service were randomized to usual care ("WIC Moms") or a personalized health intervention delivered via a SmartPhone ("E-Moms"). Assessments, including body weight, vital signs, circumferences, and body composition, were completed at week 0 (6-8 weeks postpartum), week 8, and week 16. Results are presented as change from week 0 at 16. As per the completers analysis, body weight change was not different between the groups (WIC Moms vs. E-Moms; 1.8 ± 0.9 vs. -0.1 ± 0.9 kg; p = 0.10), neither was the change in percent body fat (1.7 ± 0.6 vs. 0.1% ± 0.6%; p = 0.90) or waist/hip ratio (-0.01 ± 0.01 vs. -0.02 ± 0.01 cm; p = 0.60). However, due to notable variability in intervention adherence as the study progressed, participants were classified post hoc as having low (<40% adherence), medium (40%-70% adherence), or high adherence (>70% adherence). Participants with high intervention adherence (n = 5) had a significant reduction in body weight (-3.6 ± 1.6 vs. 1.8 ± 0.9 kg; p = 0.005) and percent body fat (-2.5 ± 1.0 vs. 1.7% ± 0.6%; p = 0.001) when compared to WIC Moms. Overall, the E-Moms intervention was not able to decrease postpartum weight retention in women receiving WIC benefits compared to usual care received through the current WIC program. However, there is some evidence to suggest improved adherence to the intervention would improve weight management.

482 - User-Centered Design and Development of a Personalized Mobile Health Application for Adult HCT Patients in the Inpatient Setting

482 - User-Centered Design and Development of a Personalized Mobile Health Application for Adult HCT Patients in the Inpatient Setting

Smartloss: A Personalized Mobile Health Intervention for Weight Management and Health Promotion.

BackgroundSynonymous with increased use of mobile phones has been the development of mobile health (mHealth) technology for improving health, including weight management. Behavior change theory (eg, the theory of planned behavior) can be effectively encapsulated into mobile phone-based health improvement programs, which is fostered by the ability of mobile phones and related devices to collect and transmit objective data in near real time and for health care or research professionals and clients to communicate easily.ObjectiveTo describe SmartLoss, a semiautomated mHealth platform for weight loss.MethodsWe developed and validated a dynamic energy balance model that determines the amount of weight an individual will lose over time if they are adherent to an energy intake prescription. This model was incorporated into computer code that enables adherence to a prescribed caloric prescription determined from the change in body weight of the individual. Data from the individual are then used to guide personalized recommendations regarding weight loss and behavior change via a semiautomated mHealth platform called SmartLoss, which consists of 2 elements: (1) a clinician dashboard and (2) a mobile phone app. SmartLoss includes and interfaces with a network-connected bathroom scale and a Bluetooth-connected accelerometer, which enables automated collection of client information (eg, body weight change and physical activity patterns), as well as the systematic delivery of preplanned health materials and automated feedback that is based on client data and is designed to foster prolonged adherence with body weight, diet, and exercise goals. The clinician dashboard allows for efficient remote monitoring of all clients simultaneously, which may further increase adherence, personalization of treatment, treatment fidelity, and efficacy.ResultsEvidence of the efficacy of the SmartLoss approach has been reported previously. The present report provides a thorough description of the SmartLoss Virtual Weight Management Suite, a professionally programmed platform that facilitates treatment fidelity and the ability to customize interventions and disseminate them widely.ConclusionsSmartLoss functions as a virtual weight management clinic that relies upon empirical weight loss research and behavioral theory to promote behavior change and weight loss.

A Personal Handheld Device to Support People with Life-Threatening Anaphylactic Allergies (PervaLaxis)

This paper presents PervaLaxis, a personal mobile health device designed to help anaphylactic people manage their life-threatening allergies. PervaLaxis was designed to support allergic patients both in everyday life and in emergency scenarios where an injection of adrenaline may be vital. PervaLaxis is implemented on a Smartphone platform and communicates wirelessly with adrenaline injectors. In emergency scenarios, PervaLaxis can detect an injection of adrenaline and send a message automatically to emergency services; in normal life PervaLaxis can support adrenaline injector training, for example with video demonstrations and can support medication management, for instance, managing adrenaline expiration dates. In this paper we present user requirements and evaluation results for PervaLaxis, furthermore we explore the issues associated with the patient-oriented focus of the device (as opposed to health devices designed for expert use) and how this could benefit personal health management. We evaluate usability performance and propose directions for future work based on user feedback.