Short-term trajectories of use of a caloric-monitoring mobile phone app among patients with type 2 diabetes mellitus in a primary care setting

Feasibility of mobile phone application "Epilepsy care" for self-management of children and adolescents with epilepsy in Phramongkutklao hospital: A randomized controlled trial

Epilepsy is a common neurological disorder in children. Mobile applications have shown potential in improving self-management for patients with chronic illnesses. To address language barriers, we developed the first Thai version of the "Epilepsy care" mobile application for children and adolescents with epilepsy in Thailand. A prospective, randomized controlled trial with 220 children and adolescents living with epilepsy who had a smartphone and were treated at the pediatric neurology clinic was conducted, with one group using the mobile application and the other receiving standard epilepsy guidance. The primary outcome assessed epilepsy self-management using the Pediatric Epilepsy Self-Management Questionnaire (PEMSQ) in the Thai version, which comprised 27 questions. These questions aimed to determine knowledge, adherence to medications, beliefs about medication efficacy, and barriers to medication adherence. The secondary outcome evaluated seizure frequency at baseline, 3, and 6 months after initiation of an application. Eighty-five participants who were randomized to a mobile application achieved significantly higher PEMSQ scores in the domain of barriers to medication adherence (p < 0.05) at 6 months follow-up. Other domains of PEMSQ showed no statistically significant difference. Baseline median seizure frequencies per month were 7 in the control group and 5.5 in the intervention group. At 3 and 6 months, these decreased significantly to 1.5 and 1 for the control group and 2.5 and 1 for the intervention group (p < 0.001). In addition, the study revealed that 94.9 % of the participants in a mobile application group were highly satisfied with using application. These findings suggest that the mobile application "Epilepsy care" may serve as an effective adjunctive therapy to enhance self-management and seizure control in children and adolescents with epilepsy.

Applications of artificial intelligence in the management of childhood obesity

Background

Childhood obesity has emerged as a significant public health challenge, with long-term implications that often extend into adulthood, increasing the susceptibility to chronic health conditions.

Objective

The objective of this review is to elucidate the applications of artificial intelligence (AI) in the prevention and treatment of pediatric obesity, emphasizing its potential to complement and enhance traditional management methods.

Methods

We undertook a comprehensive examination of existing literature to understand the integration of machine learning and other AI techniques in childhood obesity management strategies.

Results

The findings from numerous studies suggest a strong endorsement for AI's role in addressing childhood obesity. Particularly, machine learning techniques have shown considerable efficacy in augmenting current therapeutic and preventive approaches.

Conclusion

The intersection of AI with conventional obesity management practices presents a novel and promising approach to fortify interventions targeting pediatric obesity. This review accentuates the transformative capacity of AI, thereby advocating for continued research and innovation in this rapidly evolving domain.

Use of the Smoking Cessation App Ex-Smokers iCoach and Associations With Smoking-Related Outcomes Over Time in a Large Sample of European Smokers: Retrospective Observational Study

BACKGROUND

Digital interventions are increasingly used to support smoking cessation. Ex-smokers iCoach was a widely available app for smoking cessation used by 404,551 European smokers between June 15, 2011, and June 21, 2013. This provides a unique opportunity to investigate the uptake of a freely available digital smoking cessation intervention and its effects on smoking-related outcomes.

OBJECTIVE

We aimed to investigate whether there were distinct trajectories of iCoach use, examine which baseline characteristics were associated with user groups (based on the intensity of use), and assess if and how these groups were associated with smoking-related outcomes.

METHODS

Analyses were performed using data from iCoach users registered between June 15, 2011, and June 21, 2013. Smoking-related data were collected at baseline and every 3 months thereafter, with a maximum of 8 follow-ups. First, group-based modeling was applied to detect distinct trajectories of app use. This was performed in a subset of steady users who had completed at least 1 follow-up measurement. Second, ordinal logistic regression was used to assess the baseline characteristics that were associated with user group membership. Finally, generalized estimating equations were used to examine the association between the user groups and smoking status, quitting stage, and self-efficacy over time.

RESULTS

Of the 311,567 iCoach users, a subset of 26,785 (8.6%) steady iCoach users were identified and categorized into 4 distinct user groups: low (n=17,422, 65.04%), mild (n=4088, 15.26%), moderate (n=4415, 16.48%), and intensive (n=860, 3.21%) users. Older users and users who found it important to quit smoking had higher odds of more intensive app use, whereas men, employed users, heavy smokers, and users with higher self-efficacy scores had lower odds of more intensive app use. User groups were significantly associated with subsequent smoking status, quitting stage, and self-efficacy over time. For all groups, over time, the probability of being a smoker decreased, whereas the probability of being in an improved quitting stage increased, as did the self-efficacy to quit smoking. For all outcomes, the greatest change was observed between baseline and the first follow-up at 3 months. In the intensive user group, the greatest change was seen between baseline and the 9-month follow-up, with the observed change declining gradually in moderate, mild, and low users.

CONCLUSIONS

In the subset of steady iCoach users, more intensive app use was associated with higher smoking cessation rates, increased quitting stage, and higher self-efficacy to quit smoking over time. These users seemed to benefit most from the app in the first 3 months of use. Women and older users were more likely to use the app more intensively. Additionally, users who found quitting difficult used the iCoach app more intensively and grew more confident in their ability to quit over time.

Multimodal Diabetes Empowerment for Older Adults with Diabetes

Systematically improving empowerment is not easy when operating a diabetes program for older adults. This study aimed to develop and test the feasibility of the diabetes empowerment (Dia-Empower) program for older adults with type 2 diabetes. A non-randomized controlled study with a matched sampling design was conducted. Community-dwelling older adults with diabetes were allocated to either the Dia-Empower program group or a control group. Changes in the primary (diabetes self-care and empowerment) and secondary outcomes (body composition and physical function) were compared between the groups. The scores for diabetes self-care and empowerment were significantly higher in the experimental group than in the control group. Changes in skeletal muscle mass and body fat ratio were significantly different between the groups. Handgrip strength and shoulder flexibility positively changed in the experimental group. The Dia-Empower program was feasible for older adults with diabetes in the community. In the future, it is necessary to study the long-term effects of the program and its effects on blood sugar control.

Great diversity in the utilization and reporting of latent growth modeling approaches in type 2 diabetes: A literature review

Introduction

The progression of complications of type 2 diabetes (T2D) is unique to each patient and can be depicted through individual temporal trajectories. Latent growth modeling approaches (latent growth mixture models [LGMM] or latent class growth analysis [LCGA]) can be used to classify similar individual trajectories in a priori non-observed groups (latent groups), sharing common characteristics. Although increasingly used in the field of T2D, many questions remain regarding the utilization of these methods.

Objective

To review the literature of longitudinal studies using latent growth modeling approaches to study T2D.

Methods

MEDLINE (Ovid), EMBASE, CINAHL and Wb of Science were searched through August 25^th, 2021. Data was collected on the type of latent growth modeling approaches (LGMM or LCGA), characteristics of studies and quality of reporting using the GRoLTS-Checklist and presented as frequencies.

Results

From the 4,694 citations screened, a total of 38 studies were included. The studies were published beetween 2011 and 2021 and the length of follow-up ranged from 8 weeks to 14 years. Six studies used LGMM, while 32 studies used LCGA. The fields of research varied from clinical research, psychological science, healthcare utilization research and drug usage/pharmaco-epidemiology. Data sources included primary data (clinical trials, prospective/retrospective cohorts, surveys), or secondary data (health records/registries, medico-administrative). Fifty percent of studies evaluated trajectory groups as exposures for a subsequent clinical outcome, while 24% used predictive models of group membership and 5% used both. Regarding the quality of reporting, trajectory groups were adequately presented, however many studies failed to report important decisions made for the trajectory group identification.

Conclusion

Although LCGA were preferred, the contexts of utilization were diverse and unrelated to the type of methods. We recommend future authors to clearly report the decisions made regarding trajectory groups identification.

•

There is a growing body of literature on trajectory modeling in type 2 diabetes.

•

Latent class growth analysis can be used in many different contexts.

•

The current reporting of methods used should be improved.

Smoking Cessation Smartphone App Use Over Time: Predicting 12-Month Cessation Outcomes in a 2-Arm Randomized Trial

BACKGROUND

Little is known about how individuals engage over time with smartphone app interventions and whether this engagement predicts health outcomes.

OBJECTIVE

In the context of a randomized trial comparing 2 smartphone apps for smoking cessation, this study aimed to determine distinct groups of smartphone app log-in trajectories over a 6-month period, their association with smoking cessation outcomes at 12 months, and baseline user characteristics that predict data-driven trajectory group membership.

METHODS

Functional clustering of 182 consecutive days of smoothed log-in data from both arms of a large (N=2415) randomized trial of 2 smartphone apps for smoking cessation (iCanQuit and QuitGuide) was used to identify distinct trajectory groups. Logistic regression was used to determine the association of group membership with the primary outcome of 30-day point prevalence of smoking abstinence at 12 months. Finally, the baseline characteristics associated with group membership were examined using logistic and multinomial logistic regression. The analyses were conducted separately for each app.

RESULTS

For iCanQuit, participants were clustered into 3 groups: "1-week users" (610/1069, 57.06%), "4-week users" (303/1069, 28.34%), and "26-week users" (156/1069, 14.59%). For smoking cessation rates at the 12-month follow-up, compared with 1-week users, 4-week users had 50% higher odds of cessation (30% vs 23%; odds ratio [OR] 1.50, 95% CI 1.05-2.14; P=.03), whereas 26-week users had 397% higher odds (56% vs 23%; OR 4.97, 95% CI 3.31-7.52; P<.001). For QuitGuide, participants were clustered into 2 groups: "1-week users" (695/1064, 65.32%) and "3-week users" (369/1064, 34.68%). The difference in the odds of being abstinent at 12 months for 3-week users versus 1-week users was minimal (23% vs 21%; OR 1.16, 95% CI 0.84-1.62; P=.37). Different baseline characteristics predicted the trajectory group membership for each app.

CONCLUSIONS

Patterns of 1-, 3-, and 4-week smartphone app use for smoking cessation may be common in how people engage in digital health interventions. There were significantly higher odds of quitting smoking among 4-week users and especially among 26-week users of the iCanQuit app. To improve study outcomes, strategies for detecting users who disengage early from these interventions (1-week users) and proactively offering them a more intensive intervention could be fruitful.

Assessing Engagement With Patient-Generated Health Data Recording and Its Impact on Health Behavior Changes in Multicomponent Interventions: Supplementary Analysis

Background

The use and sharing of patient-generated health data (PGHD) by clinicians or researchers is expected to enhance the remote monitoring of specific behaviors that affect patient health. In addition, PGHD use could support patients’ decision-making on preventive care management, resulting in reduced medical expenses. However, sufficient evidence on the use and sharing of PGHD is lacking, and the impact of PGHD recording on patients’ health behavior changes remains unclear.

Objective

This study aimed to assess patients’ engagement with PGHD recording and to examine the impact of PGHD recording on their health behavior changes.

Methods

This supplementary analysis used the data of 47 postpartum women who had been assigned to the intervention group of our previous study for managing urinary incontinence. To assess the patients’ engagement with PGHD recording during the intervention period (8 weeks), the fluctuation in the number of patients who record their PGHD (ie, PGHD recorders) was evaluated by an approximate curve. In addition, to assess adherence to the pelvic floor muscle training (PFMT), the weekly mean number of pelvic floor muscle contractions performed per day among 17 PGHD recorders was examined by latent class growth modeling (LCGM).

Results

The fluctuation in the number of PGHD recorders was evaluated using the sigmoid curve formula (R²=0.91). During the first week of the intervention, the percentage of PGHD recorders was around 64% (30/47) and then decreased rapidly from the second to the third week. After the fourth week, the percentage of PGHD recorders was 36% (17/47), which remained constant until the end of the intervention. When analyzing the data of these 17 PGHD recorders, PFMT adherence was categorized into 3 classes by LCGM: high (7/17, 41%), moderate (3/17, 18%), and low (7/17, 41%).

Conclusions

The number of PGHD recorders declined over time in a sigmoid curve. A small number of users recorded PGHD continuously; therefore, patients’ engagement with PGHD recording was low. In addition, more than half of the PGHD recorders (moderate- and low-level classes combined: 10/17, 59%) had poor PFMT adherence. These results suggest that PGHD recording does not always promote health behavior changes.

Digitally Supported Lifestyle Intervention to Prevent Type 2 Diabetes Through Healthy Habits: Secondary Analysis of Long-Term User Engagement Trajectories in a Randomized Controlled Trial

BACKGROUND

Digital health interventions may offer a scalable way to prevent type 2 diabetes (T2D) with minimal burden on health care systems by providing early support for healthy behaviors among adults at increased risk for T2D. However, ensuring continued engagement with digital solutions is a challenge impacting the expected effectiveness.

OBJECTIVE

We aimed to investigate the longitudinal usage patterns of a digital healthy habit formation intervention, BitHabit, and the associations with changes in T2D risk factors.

METHODS

This is a secondary analysis of the StopDia (Stop Diabetes) study, an unblinded parallel 1-year randomized controlled trial evaluating the effectiveness of the BitHabit app alone or together with face-to-face group coaching in comparison with routine care in Finland in 2017-2019 among community-dwelling adults (aged 18 to 74 years) at an increased risk of T2D. We used longitudinal data on usage from 1926 participants randomized to the digital intervention arms. Latent class growth models were applied to identify user engagement trajectories with the app during the study. Predictors for trajectory membership were examined with multinomial logistic regression models. Analysis of covariance was used to investigate the association between trajectories and 12-month changes in T2D risk factors.

RESULTS

More than half (1022/1926, 53.1%) of the participants continued to use the app throughout the 12-month intervention. The following 4 user engagement trajectories were identified: terminated usage (904/1926, 46.9%), weekly usage (731/1926, 38.0%), twice weekly usage (208/1926, 10.8%), and daily usage (83/1926, 4.3%). Active app use during the first month, higher net promoter score after the first 1 to 2 months of use, older age, and better quality of diet at baseline increased the odds of belonging to the continued usage trajectories. Compared with other trajectories, daily usage was associated with a higher increase in diet quality and a more pronounced decrease in BMI and waist circumference at 12 months.

CONCLUSIONS

Distinct long-term usage trajectories of the BitHabit app were identified, and individual predictors for belonging to different trajectory groups were found. These findings highlight the need for being able to identify individuals likely to disengage from interventions early on, and could be used to inform the development of future adaptive interventions.

TRIAL REGISTRATION

ClinicalTrials.gov NCT03156478; https://clinicaltrials.gov/ct2/show/NCT03156478.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.1186/s12889-019-6574-y.

An integrative review of chronic illness mHealth self-care interventions: Mapping technology features to patient outcomes

Mobile health (mHealth)-hand-held technologies to address health priorities-has significant potential to answer the growing need for patient chronic illness self-care interventions. Previous reviews examined mHealth effect on patient outcomes. None have a detailed examination and mapping of specific technology features to targeted health outcomes. Examine recent chronic illness mHealth self-care interventions; map the study descriptors, mHealth technology features, and study outcomes. (1) Information extracted from PubMed, CINAHL, and Web of Science databases for clinical outcomes studies published 2010-January 2020; and (2) realist synthesis techniques for within and across case analysis. From 652 records, 32 studies were examined. Median study duration was 19.5 weeks. Median sample size was 62 participants. About 47% of interventions used solely patient input versus digital input; 50% sent tailored messages versus generic messages; 22% augmented the intervention with human interaction. Studies with positive clinical outcomes had higher use of digital input. Software descriptions were lacking. Most studies built interventions: only two incorporated target audience participation in development. We recommend researchers provide sufficient system description detail. Future research includes: data input characteristics; impact of augmentation with human interaction on outcomes; and development decisions.

Future Mobile Device Usage, Requirements, and Expectations of Physicians in German University Hospitals: Web-Based Survey

Background

The use of mobile devices in hospital care constantly increases. However, smartphones and tablets have not yet widely become official working equipment in medical care. Meanwhile, the parallel use of private and official devices in hospitals is common. Medical staff use smartphones and tablets in a growing number of ways. This mixture of devices and how they can be used is a challenge to persons in charge of defining strategies and rules for the usage of mobile devices in hospital care.

Objective

Therefore, we aimed to examine the status quo of physicians’ mobile device usage and concrete requirements and their future expectations of how mobile devices can be used.

Methods

We performed a web-based survey among physicians in 8 German university hospitals from June to October 2019. The online survey was forwarded by hospital management personnel to physicians from all departments involved in patient care at the local sites.

Results

A total of 303 physicians from almost all medical fields and work experience levels completed the web-based survey. The majority regarded a tablet (211/303, 69.6%) and a smartphone (177/303, 58.4%) as the ideal devices for their operational area. In practice, physicians are still predominantly using desktop computers during their worktime (mean percentage of worktime spent on a desktop computer: 56.8%; smartphone: 12.8%; tablet: 3.6%). Today, physicians use mobile devices for basic tasks such as oral (171/303, 56.4%) and written (118/303, 38.9%) communication and to look up dosages, diagnoses, and guidelines (194/303, 64.0%). Respondents are also willing to use mobile devices for more advanced applications such as an early warning system (224/303, 73.9%) and mobile electronic health records (211/303, 69.6%). We found a significant association between the technical affinity and the preference of device in medical care (χs2=53.84, P<.001) showing that with increasing self-reported technical affinity, the preference for smartphones and tablets increases compared to desktop computers.

Conclusions

Physicians in German university hospitals have a high technical affinity and positive attitude toward the widespread implementation of mobile devices in clinical care. They are willing to use official mobile devices in clinical practice for basic and advanced mobile health uses. Thus, the reason for the low usage is not a lack of willingness of the potential users. Challenges that hinder the wider adoption of mobile devices might be regulatory, financial and organizational issues, and missing interoperability standards of clinical information systems, but also a shortage of areas of application in which workflows are adapted for (small) mobile devices.

An integrative review and theoretical examination of chronic illness mHealth studies using the Middle-Range Theory of Self-care of Chronic Illness

Self-management, or self-care, by individuals and/or families is a critical element in chronic illness management as more care shifts to the home setting. Mobile device-enhanced health care, or mHealth, is being touted as a means to support self-care. Previous mHealth reviews examined the effect of mHealth on patient outcomes, however, none used a theoretical lens to examine the interventions themselves. The aims of this integrative review were to examine recent (e.g., last 10 years) chronic illness mHealth empiric studies and (1) categorize self-care behaviors engaged in the intervention according to the Middle-Range Theory of Self-care of Chronic Illness, and (2) conduct an analysis of gaps in self-care theory domains and behaviors utilized. Methods included: (1) Best practice study identification, collection, and data extraction procedures and (2) realist synthesis techniques for within and across case analysis. From a pool of 652 records, 33 primarily North American clinical trials, published between 2010 and 2019 were examined. Most mHealth interventions used apps, clinician contact, and behavioral prompts with some wireless devices. Examination found self-care maintenance behaviors were supported in most (n = 30) trials whereas self-care monitoring (n = 12) and self-care management behaviors (n = 8) were less so. Few trials (n = 2) targeted all three domains. Investigation of specific behaviors uncovered an overexamination of physical activity and diet behaviors and an underexamination of equally important behaviors. By examining chronic illness mHealth interventions using a theoretical lens we have categorized current interventions, conducted a gap analysis uncovering areas for future study, and made recommendations to move the science forward.

Acceptability and practicability of self-management for patients with Parkinson's disease based on smartphone applications in China

BACKGROUND

China has had about 1.2 billion mobile-phone users, and this number continues to grow. However, mobile-health services (mHealth) are currently in the initial stage, and have not yet prevailed in China. Additionally, the prevalence of Parkinson's disease (PD) in China is 1700/100,000 (≥65 years). Indeed, these PD patients would benefit from mHealth to manage their disease. Therefore, we designed a study to determine attitudes toward smartphone applications (apps) for chronic condition self-management, and to discover the practicality of these apps among PD patients in China.

METHODS

We selected 204 participants with PD between 52 and 87 years old and surveyed their attitudes concerning the use of smartphone apps for chronic condition management via questionnaires.

RESULTS

Among the participants, 65.19% had smartphones. Among these smartphone users, 82.84% expressed a preference for using apps for PD management. This group tended to be younger and more frequent web users with higher education and better medication compliance, and they tended to have a longer PD course and worse conditions (P < 0.001, P = 0.001, P < 0.001, P = 0.041, P < 0.001, P = 0.013). Additionally, the willingness to apply apps for PD self-management was positively related to education (P < 0.001) and negatively related to age and PD course (P = 0.017, P < 0.001).

CONCLUSION

In China, patients with PD have a generally positive attitude towards self-management through smartphone apps. Consequently, improving the coverage of smartphones with practical and handy apps is a promising strategy for PD self-management.

Digital Phenotyping Self-Monitoring Behaviors for Individuals With Type 2 Diabetes Mellitus: Observational Study Using Latent Class Growth Analysis

Background

Sustained self-monitoring and self-management behaviors are crucial to maintain optimal health for individuals with type 2 diabetes mellitus (T2DM). As smartphones and mobile health (mHealth) devices become widely available, self-monitoring using mHealth devices is an appealing strategy in support of successful self-management of T2DM. However, research indicates that engagement with mHealth devices decreases over time. Thus, it is important to understand engagement trajectories to provide varying levels of support that can improve self-monitoring and self-management behaviors.

Objective

The aims of this study were to develop (1) digital phenotypes of the self-monitoring behaviors of patients with T2DM based on their engagement trajectory of using multiple mHealth devices, and (2) assess the association of individual digital phenotypes of self-monitoring behaviors with baseline demographic and clinical characteristics.

Methods

This longitudinal observational feasibility study included 60 participants with T2DM who were instructed to monitor their weight, blood glucose, and physical activity using a wireless weight scale, phone-tethered glucometer, and accelerometer, respectively, over 6 months. We used latent class growth analysis (LCGA) with multitrajectory modeling to associate the digital phenotypes of participants’ self-monitoring behaviors based on their engagement trajectories with multiple mHealth devices. Associations between individual characteristics and digital phenotypes on participants’ self-monitoring behavior were assessed by analysis of variance or the Chi square test.

Results

The engagement with accelerometers to monitor daily physical activities was consistently high for all participants over time. Three distinct digital phenotypes were identified based on participants’ engagement with the wireless weight scale and glucometer: (1) low and waning engagement group (24/60, 40%), (2) medium engagement group (20/60, 33%), and (3) consistently high engagement group (16/60, 27%). Participants that were younger, female, nonwhite, had a low income, and with a higher baseline hemoglobin A_1c level were more likely to be in the low and waning engagement group.

Conclusions

We demonstrated how to digitally phenotype individuals’ self-monitoring behavior based on their engagement trajectory with multiple mHealth devices. Distinct self-monitoring behavior groups were identified. Individual demographic and clinical characteristics were associated with different self-monitoring behavior groups. Future research should identify methods to provide tailored support for people with T2DM to help them better monitor and manage their condition.

International Registered Report Identifier (IRRID)

RR2-10.2196/13517

A Mobile Application to Collect Daily Data on Preexposure Prophylaxis Adherence and Sexual Behavior Among Men Who Have Sex With Men: Use Over Time and Comparability With Conventional Data Collection

Supplemental digital content is available in the text.

In a study on preexposure prophylaxis in Amsterdam, a mobile app for reporting adherence and sexual behavior was used frequently, especially by participants on daily preexposure prophylaxis or recently started.

Background

We studied the use of a mobile application (app) to measure human immunodeficiency virus (HIV) preexposure prophylaxis (PrEP) adherence and sexual behavior, assessed determinants of app use, and we compared data in app and questionnaires.

Methods

Men who have sex with men participating in the Amsterdam PrEP project (AMPrEP) on daily or event-driven PrEP at the Public Health Service of Amsterdam completed the data on sexual risk behavior and PrEP adherence through a standard questionnaire every 3 months and on a daily basis using the project's app. Regression analyses were used to assess factors associated with app use. Among those who reported 90% or greater of data in the app, the number of PrEP pills taken and number of unknown casual sex partners were compared between the app and the questionnaires by Wilcoxon signed-rank test.

Results

Of all participants (n = 374), 94% (352 of 374) reported data in the app at least once; 72% (261 of 362) reported data ≥90% of the days in the sixth month and 62% (222 of 359) in the 12th month following PrEP initiation.

Factors associated with reporting data in the app were using daily PrEP and recent initiation of PrEP. The reported numbers of pills taken and unknown sexual partners were comparable between app and questionnaires.

Conclusions

The AMPrEP app was used frequently, especially by those using a daily PrEP regimen. Data collected by app regarding adherence and sexual risk behavior were consistent with questionnaire data among those who used the app consistently. An app is a promising tool to measure PrEP adherence and sexual risk behavior.

Methods and Measures Used to Evaluate Patient-Operated Mobile Health Interventions: Scoping Literature Review

BACKGROUND

Despite the prevalence of mobile health (mHealth) technologies and observations of their impacts on patients' health, there is still no consensus on how best to evaluate these tools for patient self-management of chronic conditions. Researchers currently do not have guidelines on which qualitative or quantitative factors to measure or how to gather these reliable data.

OBJECTIVE

This study aimed to document the methods and both qualitative and quantitative measures used to assess mHealth apps and systems intended for use by patients for the self-management of chronic noncommunicable diseases.

METHODS

A scoping review was performed, and PubMed, MEDLINE, Google Scholar, and ProQuest Research Library were searched for literature published in English between January 1, 2015, and January 18, 2019. Search terms included combinations of the description of the intention of the intervention (eg, self-efficacy and self-management) and description of the intervention platform (eg, mobile app and sensor). Article selection was based on whether the intervention described a patient with a chronic noncommunicable disease as the primary user of a tool or system that would always be available for self-management. The extracted data included study design, health conditions, participants, intervention type (app or system), methods used, and measured qualitative and quantitative data.

RESULTS

A total of 31 studies met the eligibility criteria. Studies were classified as either those that evaluated mHealth apps (ie, single devices; n=15) or mHealth systems (ie, more than one tool; n=17), and one study evaluated both apps and systems. App interventions mainly targeted mental health conditions (including Post-Traumatic Stress Disorder), followed by diabetes and cardiovascular and heart diseases; among the 17 studies that described mHealth systems, most involved patients diagnosed with cardiovascular and heart disease, followed by diabetes, respiratory disease, mental health conditions, cancer, and multiple illnesses. The most common evaluation method was collection of usage logs (n=21), followed by standardized questionnaires (n=18) and ad-hoc questionnaires (n=13). The most common measure was app interaction (n=19), followed by usability/feasibility (n=17) and patient-reported health data via the app (n=15).

CONCLUSIONS

This review demonstrates that health intervention studies are taking advantage of the additional resources that mHealth technologies provide. As mHealth technologies become more prevalent, the call for evidence includes the impacts on patients' self-efficacy and engagement, in addition to traditional measures. However, considering the unstructured data forms, diverse use, and various platforms of mHealth, it can be challenging to select the right methods and measures to evaluate mHealth technologies. The inclusion of app usage logs, patient-involved methods, and other approaches to determine the impact of mHealth is an important step forward in health intervention research. We hope that this overview will become a catalogue of the possible ways in which mHealth has been and can be integrated into research practice.

mHealth Apps Assessment among Postpartum Women with Obesity and Depression

BACKGROUND

Pregnancy has become the main constituent for women to become overweight or obese during the postpartum phase. This could lead women to suffer from postpartum depression as well. Information technology (IT) has become more prevalent in the healthcare industry. It offers patients the opportunity to manage their health conditions via the use of several applications, one being the mHealth applications.

OBJECTIVE

The main purpose of this study is to experiment and understand the effects the mHealth applications (i.e., fitness and nutrition applications) have on the body mass index (BMI) and depression levels amongst postpartum women.

METHODS

Online questionnaires were sent to postpartum women within one year after their pregnancy, of which 819 completed questionnaires were returned. The frequency of the mHealth applications usage was categorized into daily, weekly, rarely and never streams. Therefore, the frequency of use of the mHealth applications for BMI and depression levels was analyzed based on the available statistical data. Descriptive statistics, ANOVA, and Dunnet tests were applied to analyze the experimental data.

RESULTS

Out of 819 respondents, 37.9% and 42.1% of them were overweight and obese, respectively. Almost 32.9% of the respondents were likely depressed, and 45.6% were at an increased risk. This study reports that only 23.4% and 28.6% of respondents never used the fitness and nutrition applications. The impact of the frequency of using the fitness applications on BMI and depression levels was obvious. This means that with the increased use of the fitness applications, there was also a significant effect in maintaining and decreasing the BMI and depression levels amongst Malaysians postpartum women. However, from the data of weekly and daily use of fitness applications, we found that the contribution toward the BMI and depression levels was high (p = 0.000). However, nutrition applications amongst the users were not significant within the main variables (p > 0.05). From the Dunnet test, the significance of using the fitness applications within the depression levels started from daily usage, whereas for BMI, it started from weekly usage.

CONCLUSION

The efficiency of the fitness applications toward the BMI and depression levels has been proven in this research work. While nutrition applications did not affect the BMI and depression levels, some of the respondents were still categorized as weekly and daily users. Thus, the improvements in BMI and depression levels are associated with the types of mHealth app that had been used.

Smartphones and Apps to Control Glycosylated Hemoglobin (HbA1c) Level in Diabetes: A Systematic Review and Meta-Analysis

Introduction: Diabetes mellitus is a chronic endocrine-metabolic disease, the evolution of which is closely related to people’s self-control of glycemic levels through nutrition, exercise, and medicines. Aim: To determine whether smartphone apps can help persons with diabetes to improve their % levels of glycosylated hemoglobin. Method: A systematic review and meta-analysis were done. ProQuest, Pubmed/Medline, and Scopus databases were used. The search equation used was “(Prevention and Control) AND Diabetes Mellitus AND Smartphones”. The inclusion criteria applied were clinical trials, conducted in 2014–2019. Results: n = 18 studies were included in the review. The studies tried different applications to monitor glycemia and support patients to improve glycosylated hemoglobin (HbA1c) levels. More than half of the studies found statistically significant differences in HbA1c in the intervention group compared with the control group. Eleven studies were included in the meta-analysis and the study sample was n = 545 for the experimental group and n = 454 for the control group. The meta-analytic estimation of the HbA1c % level means differences between intervention and control group was statistically significant in favour of the intervention group with a mean difference of –0.37 (–0.58, –0.15. 95% confidence interval). Conclusion: Smartphone apps can help people with diabetes to improve their level of HbA1c, but the clinical impact is low.

Optimising epilepsy management with a smartphone application: a randomised controlled trial

OBJECTIVE

To assess whether a practical intervention based upon a smartphone application (app) would improve self-management and seizure control in adults with epilepsy.

DESIGN, SETTING

Randomised, controlled trial in western China, December 2017 to August 2018.

PARTICIPANTS

380 eligible people with epilepsy were recruited; 327 completed the 6-month follow-up (176 in the app group, 151 in the control group).

MAIN OUTCOME MEASURES

Self-management of epilepsy (measured with the validated Chinese Epilepsy Self-Management Scale, C-ESMS) and self-reported seizure frequency.

RESULTS

In the intention-to-treat analysis, the mean C-ESMS score increased significantly in the app group between baseline and the 6-month evaluation (from 121.7 [SD, 12.1] to 144.4 [SD, 10.0]; P < 0.001); improvements on the information management, medication management, and safety management subscales were also statistically significant. At 6 months, the mean overall C-ESMS score for the app group was significantly higher than that for the control group (125.4 [SD, 1.5];  P < 0.001). The proportion of patients who were seizure-free at the 6-month follow-up was larger for the app than the control group (54 of 190, 28% v 22 of 190, 12%), as was the proportion with reductions in frequency of between 75 and 100% (22 of 190, 12% v 8 of 190, 4%). Changes in C-ESMS score were not statistically associated with seizure frequency.

CONCLUSIONS

Using a smartphone app improved epilepsy self-management scores in people in western China. It should be further tested in larger populations in other areas. Our preliminary investigation of building digital communities for people with epilepsy should encourage similar approaches to managing other chronic diseases.

TRIAL REGISTRATION

Chinese Clinical Trial Registry, ChiCTR1900026864, 24 October 2019.

Mobile-Based Interventions for Dietary Behavior Change and Health Outcomes: Scoping Review

Background

Mobile apps are being widely used for delivering health interventions, with their ubiquitous access and sensing capabilities. One such use is the delivery of interventions for healthy eating behavior.

Objective

The aim of this study was to provide a comprehensive view of the literature on the use of mobile interventions for eating behavior change. We synthesized the studies with such interventions and mapped out their input methods, interventions, and outcomes.

Methods

We conducted a scoping literature search in PubMed/MEDLINE, Association for Computing Machinery Digital Library, and PsycINFO databases to identify relevant papers published between January 2013 and April 2018. We also hand-searched relevant themes of journals in the Journal of Medical Internet Research and registered protocols. Studies were included if they provided and assessed mobile-based interventions for dietary behavior changes and/or health outcomes.

Results

The search resulted in 30 studies that we classified by 3 main aspects: input methods, mobile-based interventions, and dietary behavior changes and health outcomes. First, regarding input methods, 5 studies allowed photo/voice/video inputs of diet information, whereas text input methods were used in the remaining studies. Other than diet information, the content of the input data in the mobile apps included user’s demographics, medication, health behaviors, and goals. Second, we identified 6 categories of intervention contents, that is, self-monitoring, feedback, gamification, goal reviews, social support, and educational information. Although all 30 studies included self-monitoring as a key component of their intervention, personalized feedback was a component in 18 studies, gamification was used in 10 studies, goal reviews in 5 studies, social support in 3 studies, and educational information in 2 studies. Finally, we found that 13 studies directly examined the effects of interventions on health outcomes and 12 studies examined the effects on dietary behavior changes, whereas only 5 studies observed the effects both on dietary behavior changes and health outcomes. Regarding the type of studies, although two-thirds of the included studies conducted diverse forms of randomized control trials, the other 10 studies used field studies, surveys, protocols, qualitative interviews, propensity score matching method, and test and reference method.

Conclusions

This scoping review identified and classified studies on mobile-based interventions for dietary behavior change as per the input methods, nature of intervention, and outcomes examined. Our findings indicated that dietary behavior changes, although playing a mediating role in improving health outcomes, have not been adequately examined in the literature. Dietary behavior change as a mechanism for the relationship between mobile-based intervention and health outcomes needs to be further investigated. Our review provides guidance for future research in this promising mobile health area.

A Library of Analytic Indicators to Evaluate Effective Engagement with Consumer mHealth Apps for Chronic Conditions: Scoping Review

Background

There is mixed evidence to support current ambitions for mobile health (mHealth) apps to improve chronic health and well-being. One proposed explanation for this variable effect is that users do not engage with apps as intended. The application of analytics, defined as the use of data to generate new insights, is an emerging approach to study and interpret engagement with mHealth interventions.

Objective

This study aimed to consolidate how analytic indicators of engagement have previously been applied across clinical and technological contexts, to inform how they might be optimally applied in future evaluations.

Methods

We conducted a scoping review to catalog the range of analytic indicators being used in evaluations of consumer mHealth apps for chronic conditions. We categorized studies according to app structure and application of engagement data and calculated descriptive data for each category. Chi-square and Fisher exact tests of independence were applied to calculate differences between coded variables.

Results

A total of 41 studies met our inclusion criteria. The average mHealth evaluation included for review was a two-group pretest-posttest randomized controlled trial of a hybrid-structured app for mental health self-management, had 103 participants, lasted 5 months, did not provide access to health care provider services, measured 3 analytic indicators of engagement, segmented users based on engagement data, applied engagement data for descriptive analyses, and did not report on attrition. Across the reviewed studies, engagement was measured using the following 7 analytic indicators: the number of measures recorded (76%, 31/41), the frequency of interactions logged (73%, 30/41), the number of features accessed (49%, 20/41), the number of log-ins or sessions logged (46%, 19/41), the number of modules or lessons started or completed (29%, 12/41), time spent engaging with the app (27%, 11/41), and the number or content of pages accessed (17%, 7/41). Engagement with unstructured apps was mostly measured by the number of features accessed (8/10, P=.04), and engagement with hybrid apps was mostly measured by the number of measures recorded (21/24, P=.03). A total of 24 studies presented, described, or summarized the data generated from applying analytic indicators to measure engagement. The remaining 17 studies used or planned to use these data to infer a relationship between engagement patterns and intended outcomes.

Conclusions

Although researchers measured on average 3 indicators in a single study, the majority reported findings descriptively and did not further investigate how engagement with an app contributed to its impact on health and well-being. Researchers are gaining nuanced insights into engagement but are not yet characterizing effective engagement for improved outcomes. Raising the standard of mHealth app efficacy through measuring analytic indicators of engagement may enable greater confidence in the causal impact of apps on improved chronic health and well-being.

Patients' attitudes towards mobile health in Singapore: a cross-sectional study

BACKGROUND

Smartphone-mediated mobile health (mHealth) has the potential to assist patients with medication adherence and disease monitoring. This study aimed to describe the awareness and usage of, and attitudes towards, mHealth among smartphone-owning patients in a tertiary hospital in Singapore.

METHODS

A self-administered cross-sectional survey was systematically offered to patients at the Singapore General Hospital from August to September 2018. Participants were included if they were at least 18 years old, owned a smartphone, and could speak and read simple English. No identifiable data was collected. Responses were summarized using descriptive statistics. Multiple logistic regression analysis was used to identify factors associated with awareness and usage of, and attitudes towards, mHealth.

RESULTS

Four-hundred and two eligible responses were received, with most participants reporting having completed tertiary education (63.7%) and having chronic medical conditions (71.1%), with a mean age of about 43 years. On average, participants were aware of 3.7 out of 7 mHealth functions and used 1.9 functions. Most patients were aware that smartphones could be used for general health/fitness tracking, obtaining health information, and appointment management. Most (76.3%) participants were keen to learn to use mHealth in future, and 63.2% agreed that mHealth could help them better manage their health.

CONCLUSIONS

Although mHealth usage among patients was low, most patients held positive attitudes towards mHealth. For mHealth to fulfill its potential, strategies to improve the awareness and usage among patients need to be explored.

Developing Typologies of User Engagement With the BRANCH Alcohol-Harm Reduction Smartphone App: Qualitative Study

BACKGROUND

Understanding how users engage with electronic screening and brief intervention (eSBI) is a critical research objective to improve effectiveness of app-based interventions to reduce harmful alcohol consumption. Although quantitative measures of engagement provide a strong indicator of how the user engages with an app at the group level, they do not elucidate finer-grained details of how apps function from an individual, experiential perspective and why, or how, users engage with an intervention in a particular manner.

OBJECTIVE

The aim of this study was to (1) understand why and how participants engaged with the BRANCH app, (2) explore facilitators and barriers to engagement with app features, (3) explore how the BRANCH app impacted drinking behavior, (4) use these data to identify typologies of users of the BRANCH app in terms of engagement behaviors, and (5) identify future eSBI app design implications.

METHODS

In total, 20 one-to-one semistructured telephone interviews were conducted with participants recruited from a randomized controlled trial, which evaluated the effectiveness of engagement-promoting strategies in the BRANCH app targeting harmful drinking in young adults (aged 18-30 years). The topic guide explored users' current engagement levels with existing health promotion apps, their views toward the effectiveness of such apps, and what they liked and disliked about BRANCH, specifically focusing on how they engaged with the app. Framework analysis was used to develop typologies of user engagement.

RESULTS

The study identified 3 typologies of engagers. Trackers were defined by their motivations to use health-tracking apps to monitor and understand quantified self-data. They did not have intentions necessarily to cut down and predominantly used only the drinking diary. Cut-downers were motivated to use the app because they wanted to reduce their alcohol consumption Unlike Trackers, they did not use a range of different health apps daily, but saw the BRANCH app as an opportunity to test out a different method of trying to cut down their alcohol use. This typology used more features than Trackers, such as the goal setting function. Noncommitters were characterized as a group of users who were initially enthusiastic about using the app; however, this enthusiasm quickly waned and they gained no benefit from it.

CONCLUSIONS

This was the first study to identify typologies of user engagement with eSBI apps. Although in need of replication, it provides a first step in understanding independent categories of eSBI users, who may benefit from apps tailored to a user's typology or motivation. It also provides new evidence to suggest that apps may be used more effectively as a tool to raise awareness of drinking, instead of reducing alcohol use, and be a step in the care pathway, identifying at-risk individuals and signposting them to more intensive treatment.

TRIAL REGISTRATION

International Standard Randomised Controlled Trial Number ISRCTN70980706; http://www.isrctn.com /ISRCTN70980706 (Archived by WebCite at http://www.webcitation.org/73vfDXYEZ).

Theoretical Advancements in mHealth: A Systematic Review of Mobile Apps

There are now few hundred thousand healthcare apps, yet there is a gap in our understanding of the theoretical mechanisms for which, and how, technological features translate into improved healthcare outcomes. In particular, the technological convergence, within mobile health (mHealth) apps, of the processes of mass and interpersonal communication, and human-computer interaction requires greater parsing in the literature. This paper analyzed 85 empirical studies on mHealth apps using the Input-Mechanism-Output model. We found in the literature that, firstly, there is a greater emphasis on technological inputs (87%) of accessibility, usability, usage, and data quality, than health outputs (52%) such as system process efficiencies and individual level behavioral or health outcomes. Secondly, there is little evidence of explanatory mechanisms (19%) of how the effects of mHealth apps are achieved. While we believe that successful apps would require research that incorporates technological inputs, theoretical mechanisms and health outputs, such studies are a rarity (n = 3). There is a minor increase in rigor with randomized control trials (n = 5), and a preponderance of discussion around social influence (n = 8) and gamification (n = 7), albeit in a scattered manner. We discuss the implications of the trend towards socialization and gamification findings in terms of future research, particularly in terms of study design guided by theoretical mechanisms.

Multi-trajectory modeling of home blood pressure telemonitoring utilization among hypertensive patients in China: A latent class growth analysis

BACKGROUND

Home blood pressure telemonitoring (HBPT) has great potential in improving blood pressure (BP) control among patients with hypertension. However, the longitudinal use trajectories of HBPT have not been identified yet. In addition, there has been a lack of understanding of the relationship between developmental trajectories of HBPT and BP control over time. The primary goal of this study was to identify the longitudinal trajectories of using HBPT among hypertensive patients and to explore the relationship between longitudinal trajectories of HBPT use patterns and BP control.

METHODS

A total of 122 hypertensive patients were enrolled consecutively in Xiling, Huayan, Baisha and Xueyuan communities in Yichang City, Hubei Province, China. Each patient was provided with a portable monitoring device which has unlimited data service at the time of enrollment. Socio-demographics (e.g. name, age, sex, marital status) were collected at baseline. Real-time data including systolic and diastolic blood pressure were automatically uploaded to cloud platform through devices. Latent class growth analysis was conducted to determine the latent trajectory of HBPT use. Joint trajectory method was used to correlate the longitudinal trajectories of HBPT utilization and BP control status.

RESULTS

Five trajectories were identified which are persistently low (47.1%), moderate with decreasing (23.9%), sharply decreasing (11.2%), high with decreasing (11.3%) and persistently high with increasing (6.6%). There was no statistically significant difference among 5 trajectories in the baseline survey in terms of age, marital status, BP (both SBP and DBP) and BP control status. However, there was a strong positive correlation between the HBPT utilization pattern and BP control status over time.

CONCLUSIONS

The latent trajectories of HBPT utilization were identified in our study. However, no predictors of trajectory membership were identified. Nevertheless, we have demonstrated that HBPT was to some extent positively correlated with improved BP control, and this correlation still needs to be further proved.

Factors influencing adherence to an app-based exercise program in adolescents with painful hyperkyphosis

BACKGROUND

Software applications (apps) could potentially promote exercise adherence. However, it is unclear whether adolescents with painful hyperkyphosis will use an app designed for a home exercise program. The purpose of this study is to assess factors regarding adherence to an app-based home exercise program in adolescents with hyperkyphosis and back pain who were provided a one-time exercise treatment.

METHODS

Twenty-one participants were instructed in a one-time exercise treatment and asked to complete a home exercise program 3 times a week for 6 months using an app called PT PAL. At a 6-month follow-up, 14 participants completed a survey assessing factors related to their experiences using the app and their treatment engagement.

RESULTS

Although most participants did not use the app, they reported performing their exercises a few times per week. The adolescent participants considered the app to be more of a barrier than a supportive measure for promoting exercise adherence. Most participants still reported bothersome back pain.

CONCLUSIONS

Although adherence to the 6-month app-based home exercise program was not successful, adolescents still viewed technology support such as text reminders as a potential solution.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT03212664. Registered 11 July 2017. Retrospectively registered.

Trajectories of 12-Month Usage Patterns for Two Smoking Cessation Websites: Exploring How Users Engage Over Time

BACKGROUND

Little is known about how individuals engage with electronic health (eHealth) interventions over time and whether this engagement predicts health outcomes.

OBJECTIVE

The objectives of this study, by using the example of a specific type of eHealth intervention (ie, websites for smoking cessation), were to determine (1) distinct groups of log-in trajectories over a 12-month period, (2) their association with smoking cessation, and (3) baseline user characteristics that predict trajectory group membership.

METHODS

We conducted a functional clustering analysis of 365 consecutive days of log-in data from both arms of a large (N=2637) randomized trial of 2 website interventions for smoking cessation (WebQuit and Smokefree), with a primary outcome of 30-day point prevalence smoking abstinence at 12 months. We conducted analyses for each website separately.

RESULTS

A total of 3 distinct trajectory groups emerged for each website. For WebQuit, participants were clustered into 3 groups: 1-week users (682/1240, 55.00% of the sample), 5-week users (399/1240, 32.18%), and 52-week users (159/1240, 12.82%). Compared with the 1-week users, the 5- and 52-week users had 57% higher odds (odds ratio [OR] 1.57, 95% CI 1.13-2.17; P=.007) and 124% higher odds (OR 2.24, 95% CI 1.45-3.43; P<.001), respectively, of being abstinent at 12 months. Smokefree users were clustered into 3 groups: 1-week users (645/1309, 49.27% of the sample), 4-week users (395/1309, 30.18%), and 5-week users (269/1309, 20.55%). Compared with the 1-week users, 5-week users (but not 4-week users; P=.99) had 48% higher odds (OR 1.48, 95% CI 1.05-2.07; P=.02) of being abstinent at 12 months. In general, the WebQuit intervention had a greater number of weekly log-ins within each of the 3 trajectory groups as compared with those of the Smokefree intervention. Baseline characteristics associated with trajectory group membership varied between websites.

CONCLUSIONS

Patterns of 1-, 4-, and 5-week usage of websites may be common for how people engage in eHealth interventions. The 5-week usage of either website, and 52-week usage only of WebQuit, predicted a higher odds of quitting smoking. Strategies to increase eHealth intervention engagement for 4 more weeks (ie, from 1 week to 5 weeks) could be highly cost effective.

TRIAL REGISTRATION

ClinicalTrials.gov NCT01812278; https://www.clinicaltrials.gov/ct2/show/NCT01812278 (Archived by WebCite at http://www.webcitation.org/6yPO2OIKR).

Evaluating the prevalence and opportunity for technology use in chronic kidney disease patients: a cross-sectional study

BACKGROUND

Chronic kidney disease (CKD) is increasing worldwide and early education to improve adherence to self-management is a key strategy to slow CKD progression. The use of the internet and mobile phone technologies (mHealth) to support patients is considered an effective tool in many other chronic disease populations. While a number of mHealth platforms for CKD exist, few studies have investigated if and how this population use technology to engage in self-management.

METHODS

Using a cross-sectional design across five health districts in Queensland (Australia), a 38-item self-report survey was distributed to adults with CKD attending outpatient clinics or dialysis units to measure current use and type of engagement with mHealth, perceived barriers to use, and opportunities to support CKD self-management. Odds ratio (OR) were calculated to identify associations between demographic characteristic and mHealth use.

RESULTS

Of the 708 participants surveyed, the majority had computer access (89.2%) and owned a mobile phone (83.5%). The most likely users of the internet were those aged ≤ 60 years (OR: 7.35, 95% confidence interval [CI]: 4.25-12.75, p < 0.001), employed (OR: 7.67, 95% CI: 2.58-22.78, p < 0.001), from non-indigenous background (OR: 6.98, 95% CI: 3.50-13.93, p < 0.001), or having completed higher levels of education (OR: 3.69, CI: 2.38-5.73, p < 0.001). Those using a mobile phone for complex communication were also younger (OR: 6.01, 95% CI: 3.55-10.19, p < 0.001), more educated (OR: 1.99, 95% CI: 1.29-3.18, p < 0.01), or from non-indigenous background (OR: 3.22, 95% CI: 1.58-6.55, p < 0.001). Overall, less than 25% were aware of websites to obtain information about renal healthcare. The mHealth technologies most preferred for communication with their renal healthcare teams were by telephone (56.5%), internet (50%), email (48.3%) and text messages (46%).

CONCLUSION

In the CKD cohort, younger patients are more likely than older patients to use mHealth intensively and interactively although all patients' technology literacy ought to be thoroughly assessed by renal teams before implementing in practice. Further research testing mHealth interventions to improve self-management in a range of patient cohorts is warranted.

Contribution of mobile health applications to self-management by consumers: review of published evidence

Objective The aim of the present study was to review the contribution of mobile health applications ('apps') to consumers' self-management of chronic health conditions, and the potential for this practice to inform health policy, procedures and guidelines. Methods A search was performed on the MEDLINE, Cochrane Library, ProQuest and Global Health (Ovid) databases using the search terms 'mobile app*', 'self-care', 'self-monitoring', 'trial', 'intervention*' and various medical conditions. The search was supplemented with manual location of emerging literature and government reports. Mapping review methods identified relevant titles and abstracts, followed by review of content to determine extant research, reports addressing the key questions, and gaps suggesting areas for future research. Available studies were organised by disease state, and presented in a narrative analysis. Results Four studies describing the results of clinical trials were identified from Canada, England, Taiwan and Australia; all but the Australian study used custom-made apps. The available studies examined the effect of apps in health monitoring, reporting positive but not robust findings. Australian public policy and government reports acknowledge and support self-management, but do not address the potential contribution of mobile interventions. Conclusions There are limited controlled trials testing the contribution of health apps to consumers' self-management. Further evidence in this field is required to inform health policy and practice relating to self-management. What is known about the topic? Australian health policy encourages self-care by health consumers to reduce expenditure in health services. A fundamental component of self-care in chronic health conditions is self-monitoring, which can be used to assess progress towards treatment goals, as well as signs and symptoms of disease exacerbation. An abundance of mobile health apps is available for self-monitoring. What does this study add? A limited number of randomised control trials have assessed the clinical impact of health apps for self-monitoring. The body of evidence relating to current and long-term clinical impact is developing. Despite endorsing self-care, Australian health policy does not address the use and potential contribution of mobile health apps to health care. What are the implications? Widespread and sustained use of validated mobile health apps for chronic health conditions should have potential to improve consumer independence, confidence and burden on health services in the longer term. However, a significant body of scientific evidence has not yet been established; this is mirrored in the lack of acknowledgement of health apps in Australian health policy referring to consumers' self-management.

Public attitudes towards mobile health in Singapore: a cross-sectional study

BACKGROUND

Smartphone-mediated mobile health (mHealth) may assist patients with medication adherence, and disease monitoring. This study aimed to describe awareness and usage of, and attitudes towards, mHealth among the public in Singapore who own a smartphone. It also aimed to identify factors that influenced the above in the study population.

METHODS

An online cross-sectional survey was administered via convenience sampling in November 2017. Participants were included if they were at least 18 years old and owned a smartphone. No identifiable data was collected. Responses were summarized using descriptive statistics. Multiple logistic regression analysis was used to identify factors associated with awareness and usage of, and attitudes towards, mHealth.

RESULTS

Participants (n=199) were mostly of Chinese ethnicity (84.4%), female (64.8%), young (mean age 33.7 years), and generally healthy (86.9% reported no chronic medical conditions). On average, participants were aware of 4.4 out of 7 mHealth functions and used 2.2 functions. Managing appointments, and fitness/diet tracking were the most well-known (93.5% and 82.4% respectively), and widely used (80.6% and 59.8% respectively) functions. A simple interface, data security, and being free to use, were rated as the most important factors influencing participants' willingness to use mHealth. Most (64.3%) participants were keen to learn to use mHealth in future, 49.7% believed mHealth could help improve their health, but only 13.1% were willing to pay for it. Being employed (OR 3.71) was associated with higher mHealth usage, adjusted for baseline smartphone usage. Participants living in non-subsidized housing were more keen to try (OR 3.18), and willing to pay (OR 3.36) for mHealth.

CONCLUSIONS

Participants generally held positive attitudes towards mHealth, although usage was low. Lack of willingness to pay, and socioeconomic factors, are potential barriers to the widespread adoption of mHealth. Future research specifically involving patients is needed.

The Impact of Patient Education with a Smartphone Application on the Quality of Bowel Preparation for Screening Colonoscopy

Background/Aims

Few studies have evaluated the use of a smartphone application (app) for educating people undergoing colonoscopy and optimizing bowel preparation. Therefore, this study was designed to develop a smartphone app for people to use as a preparation guide and to evaluate the efficacy of this app when used prior to colonoscopy.

Methods

In total, 142 patients (male:female=84:58, mean age=43.5±9.3 years), who were scheduled to undergo a colonoscopy at Myongji Hospital, were enrolled in this study. Seventy-one patients were asked to use a smartphone app that we had recently developed to prepare for the colonoscopy, while the 71 patients of the sex and age-matched control group were educated via written and verbal instructions.

Results

The quality of bowel cleansing, evaluated using the Boston Bowel Preparation Scale, was significantly higher in the smartphone app group than in the control group (7.70±1.1 vs. 7.24±0.8, respectively, p=0.007 by t-test). No significant differences were found between the two groups regarding work-up time and the number of patients with polyps.

Conclusions

In this study, targeting young adults (≤50 years), the bowel preparation achieved by patients using the smartphone app showed significantly better quality than that of the control group.

How to Become a Smart Patient in the Era of Precision Medicine?

The objective of this paper is to define the definition of smart patients, summarize the existing foundation, and explore the approaches and system participation model of how to become a smart patient. Here a thorough review of the literature was conducted to make theory derivation processes of the smart patient; "data, information, knowledge, and wisdom (DIKW) framework" was performed to construct the model of how smart patients participate in the medical process. The smart patient can take an active role and fully participate in their own health management; DIKW system model provides a theoretical framework and practical model of smart patients; patient education is the key to the realization of smart patients. The conclusion is that the smart patient is attainable and he or she is not merely a patient but more importantly a captain and global manager of one's own health management, a partner of medical practitioner, and also a supervisor of medical behavior. Smart patients can actively participate in their healthcare and assume higher levels of responsibility for their own health and wellness which can facilitate the development of precision medicine and its widespread practice.

Smartphone applications for seizure care and management in children and adolescents with epilepsy: Feasibility and acceptability assessment among caregivers in China

OBJECTIVE

to determine the feasibility as well as the attitudes among caregivers of children and adolescents with epilepsy in China towards the use of smart phone applications (apps) for the management of seizures.

METHODS

The caregivers of children and adolescents with epilepsy, ages ranging from 0 to 15 years, were enrolled in the study from the Epilepsy Prevention and Cure Center of West China Hospital within the time period from June 2015 to December 2015. A 10-item questionnaire gauging the attitudes towards using apps for seizure management was administered to the 390 caregivers. Additionally, data on the demographic and clinic characteristics of the children and adolescents with epilepsy for each caregiver were also collected.

RESULTS

The results indicated that approximately 99.2% of caregivers own a mobile phone, of which, 97.9% of these mobile phones were smart phones. Despite only 3.1% (12/390) of caregivers currently having an app regarding the management of a chronic illness, 70.2%(274/390) reported that they would use a free seizure management app. The results of the current study indicated that the likelihood of using such a free app increased if the participant was a male as opposed to a female (P=0.03) and among caregivers with a higher education level, a higher annual household income as well as stable job (P<0.001, P<0.001, P=0.02). No statistically significant difference was found in the likelihood of using such a free app among caregivers living in rural as opposed to urban areas (P=0.3).

CONCLUSIONS

The results of this study imply a favorable attitude towards the use of apps for epilepsy and seizure management among caregivers. The use of such apps in China thus represents a promising strategy among caregivers for seizure management.

Characteristics of Chinese m-Health Applications for Diabetes Self-Management

PURPOSE

To examine the features and types of health information provided in Chinese diabetes mobile applications (apps) for patients' self-management.

MATERIALS AND METHODS

Through multiple rounds of screening, we identified a total of 95 relevant iOS (Apple, Cupertino, CA) and Android™ (Google, Mountain View, CA) apps and examined each app's features and health information types based on each app's description in the app stores. We used a 15-feature algorithm to evaluate the apps' abilities for supporting diabetic patients' self-management, based on U.S. national standards for diabetes self-management. We also adapted the health information wants framework to analyze the types of information that the apps provided for diabetic patients.

RESULTS

Diabetes education was the most common feature, provided by 75% of the apps. Blood glucose checking was enabled by 65% of the apps. Diet management, insulin checking, and physical activity monitoring were enabled by 53%, 49%, and 44% of the apps, respectively. Only a small percentage of the apps enabled psychosocial support (29%) or tracking of blood pressure (14%), cholesterol (14%), or body mass index (11%). None of the apps provided all seven types of information posited by the health information wants framework. Only a small percentage of the apps provided information about psychosocial support (29%), healthcare providers (24%), or healthcare facilities (24%). Information about complementary and alternative medicine was the least likely type of information provided in the apps, with only 7% of the apps providing this type of information.

CONCLUSIONS

Our findings have important implications for improving the quality of Chinese diabetes mobile apps to facilitate patients' self-management.

Mobile health solutions for the aging population: A systematic narrative analysis

Introduction The ubiquitous nature of mobile technology coupled with the acceptance of mobile health (mHealth) among the elderly offers an opportunity to augment the existing medical workforce in long-term care. The objective of this review and narrative analysis is to identify and analyse facilitators and barriers to adoption of mHealth for the elderly. Methods Studies over the last year were identified in multiple database indices, and three reviewers examined abstracts ( k = 0.82) and analysed articles for themes which were tallied in affinity diagrams to identify frequency of occurrence in the literature (n = 36). Results The three facilitators mentioned most often were independence (18%), understanding (13%), and visibility (13%). The three barriers mentioned most often were complexity (21%), limited by users (12%) and ineffective (12%). Discussion and conclusions The reviewers concluded that the work done so far illustrates that mHealth enables a perception of independence. Future research should focus on the barriers of complexity of technology and improving existing medical literacy in order to facilitate further adoption.

Feasibility and acceptability of smartphone applications for seizure self-management in China: Questionnaire study among people with epilepsy

OBJECTIVE

The aim of this report was to assess the feasibility and acceptability of using smartphone apps for seizure self-management in China.

METHODS

All patients with epilepsy were consecutively recruited from the Neurology Epilepsy Prevention and Cure Center of West China Hospital from January 2015 to June 2015. Data on patients' clinical characteristics, mobile phone utilization habits, preferences for contents of apps for seizure self-management, medication adherence, and attitudes toward the use of smartphone apps were collected from 502 patients with epilepsy by questionnaire.

RESULTS

Among 502 participants, 96.8% had their own mobile phones, and 94.4% owned a smartphone. Although only 9.5% (48/502) of participants had prior knowledge of apps for managing chronic illness, 66.7% (335/502) of participants reported that managing their seizure through an app would be useful. Sixty-five point five percent of participants reported that they would use a smartphone app for seizure self-management if it were free. Patients who were more likely to use an app were those with a low Morisky Scale score (patients with poor medicine adherence), young patients, patients who lived in cities, and patients with frequent seizures (P<0.001, P=0.002, P<0.001, P=0.01). Patients with higher education and with stable employment were also more likely to use an app (P=0.001, P<0.001).

CONCLUSIONS

This is the first study on the feasibility and acceptability of smartphone apps for seizure self-management in China. The findings of this study indicate that there is a positive attitude toward using epilepsy apps among patients with epilepsy. Based on patients' positive attitudes toward using epilepsy apps and the current development of mobile health in China, the use of smartphone apps could be a promising strategy for seizure self-management.

Apps for IMproving FITness and Increasing Physical Activity Among Young People: The AIMFIT Pragmatic Randomized Controlled Trial

Background

Given the global prevalence of insufficient physical activity (PA), effective interventions that attenuate age-related decline in PA levels are needed. Mobile phone interventions that positively affect health (mHealth) show promise; however, their impact on PA levels and fitness in young people is unclear and little is known about what makes a good mHealth app.

Objective

The aim was to determine the effects of two commercially available smartphone apps (Zombies, Run and Get Running) on cardiorespiratory fitness and PA levels in insufficiently active healthy young people. A second aim was to identify the features of the app design that may contribute to improved fitness and PA levels.

Methods

Apps for IMproving FITness (AIMFIT) was a 3-arm, parallel, randomized controlled trial conducted in Auckland, New Zealand. Participants were recruited through advertisements in electronic mailing lists, local newspapers, flyers posted in community locations, and presentations at schools. Eligible young people aged 14-17 years were allocated at random to 1 of 3 conditions: (1) use of an immersive app (Zombies, Run), (2) use of a nonimmersive app (Get Running), or (3) usual behavior (control). Both smartphone apps consisted of a fully automated 8-week training program designed to improve fitness and ability to run 5 km; however, the immersive app featured a game-themed design and narrative. Intention-to-treat analysis was performed using data collected face-to-face at baseline and 8 weeks, and all regression models were adjusted for baseline outcome value and gender. The primary outcome was cardiorespiratory fitness, objectively assessed as time to complete the 1-mile run/walk test at 8 weeks. Secondary outcomes were PA levels (accelerometry and self-reported), enjoyment, psychological need satisfaction, self-efficacy, and acceptability and usability of the apps.

Results

A total of 51 participants were randomized to the immersive app intervention (n=17), nonimmersive app intervention (n=16), or the control group (n=18). The mean age of participants was 15.7 (SD 1.2) years; participants were mostly NZ Europeans (61%, 31/51) and 57% (29/51) were female. Overall retention rate was 96% (49/51). There was no significant intervention effect on the primary outcome using either of the apps. Compared to the control, time to complete the fitness test was –28.4 seconds shorter (95% CI –66.5 to 9.82, P=.20) for the immersive app group and –24.7 seconds (95% CI –63.5 to 14.2, P=.32) for the nonimmersive app group. No significant intervention effects were found for secondary outcomes.

Conclusions

Although apps have the ability to increase reach at a low cost, our pragmatic approach using readily available commercial apps as a stand-alone instrument did not have a significant effect on fitness. However, interest in future use of PA apps is promising and highlights a potentially important role of these tools in a multifaceted approach to increase fitness, promote PA, and consequently reduce the adverse health outcomes associated with insufficient activity.

Trial Registration

Australian New Zealand Clinical Trials Registry: ACTRN12613001030763; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?ACTRN=12613001030763 (Archived by WebCite at http://www.webcitation.org/6aasfJVTJ).