Measures of Engagement With mHealth Interventions in Patients With Heart Failure: Scoping Review

Adherence Trends to Physical Activity Guidelines in Adults With Cardiovascular Diseases and the Impact of Wearables on Adherence: Findings From a National Representative Sample

BACKGROUND

Physical activity (PA) is crucial for primary and secondary prevention of cardiovascular diseases (CVDs); however, adherence to PA guidelines remains challenging.

OBJECTIVES

We examined adherence trends to PA guidelines among adults with CVD and the effects of engagement with wearables on adherence.

METHODS

We used data from 3 cycles of the Health Information National Trends Survey collected in 2019, 2020, and 2022. Adults 18 years or older with a self-reported history of CVD were included in the study. Adherence to PA guidelines was measured as self-reported engagement in at least 150 min/wk of moderate-intensity aerobic PA and a minimum of 2 d/wk of muscle-strengthening activity. Engagement with wearables was assessed as the use of wearables within the past 12 months and the frequency of use in the past month. Weighted multivariate logistic regression was used to examine the effect of engagement on adherence to the PA guidelines.

RESULTS

The sample comprised 1540 respondents. The estimated proportions of adults with CVD who adhered to aerobic PA guidelines were 22.9% (95% confidence interval [CI], 16.8-27.8), 29.6% (95% CI, 21.8-35.4), and 27.2% (95% CI, 21.8-30.0) in 2019, 2020, and 2022, respectively. In addition, 24.0% (95% CI, 18.4-29.7), 25.6% (95% CI, 18.9-32.3), and 26.8% (95% CI, 21.1-32.4) adhered to muscle-strengthening activity guidelines in 2019, 2020, and 2022, respectively. There were no significant changes in adherence trends for either aerobic (odds ratio [OR], 1.12; P = .228) or muscle-strengthening (OR, 1.07; P = .508) activities in the included years. The use of wearables was positively associated with adherence to aerobic PA (OR, 2.72; P = .023) and muscle-strengthening activity (OR, 2.85; P = .020) in the cumulative years.

CONCLUSIONS

Adherence to PA guidelines among adults with CVD remains consistently low. However, the use of wearables may be beneficial for promoting adherence.

Assessing women's and health professionals' views on developing a midwifery-led mobile health app intervention in pregnancy: A descriptive qualitative study

AIMS

To explore women's and health professionals' views on the development of a midwifery-led mHealth app intervention in antenatal care and their demands for app functionality.

DESIGN

Descriptive qualitative research was utilized.

METHODS

In total, 15 pregnant or postpartum women were interviewed via in-depth interviews and 10 health professionals including obstetricians, midwives and obstetric nurses were invited to participate in a focus group discussion (FGD). All interviews and the FGD were analysed using qualitative content analysis.

RESULTS

Four key themes emerged from the data, including (1) limitations of current maternity care services; (2) potential benefits for mHealth app-based midwifery care; (3) possible challenges for providing midwifery care through mHealth apps and (4) suggestions and needs for developing a midwifery-led mHealth app. Participants agreed on the potential need of developing a midwifery-led mHealth app in antenatal care to increase access to midwifery care services and to meet women's diverse needs. Participants preferred to develop professional, reliable, full-featured and interactive mobile applications. The main functions of midwifery-led mHealth apps included personalized assessment and health education, self-monitoring and feedback, data sharing and interactive functions. Women mentioned that online communication and consultation with midwives could help them receive continuous support outside facilities. Health professionals expressed it would be of great convenience and timeliness to send personalized messages to women and to inform them of healthy lifestyles during pregnancy. The challenges included a shortage of human resources, medico-legal risks associated with mHealth and data security risks.

CONCLUSIONS

This study explores the individual views and functional needs of target users and healthcare providers for developing a midwifery-led mHealth app in antenatal care, which will serve as a reference for future application development.

IMPACT

Our study has important and practical implications for guiding the development of future midwifery-led mHealth app interventions.

PATIENT OR PUBLIC CONTRIBUTION

No patient or public contribution.

Efficacy of Save Medical Corporation (SMC)-01, a Smartphone App Designed to Support Type 2 Diabetes Self-Management Based on Established Guidelines: Randomized Controlled Trial

BACKGROUND

Lifestyle modifications are a key part of type 2 diabetes mellitus treatment. Many patients find long-term self-management difficult, and mobile apps could be a solution. In 2010, in the United States, a mobile app was approved as an official medical device. Similar apps have entered the Japanese market but are yet to be classified as medical devices.

OBJECTIVE

The objective of this study was to determine the efficacy of Save Medical Corporation (SMC)-01, a mobile app for the support of lifestyle modifications among Japanese patients with type 2 diabetes mellitus.

METHODS

This was a 24-week multi-institutional, prospective randomized controlled trial. The intervention group received SMC-01, an app with functions allowing patients to record data and receive personalized feedback to encourage a healthier lifestyle. The control group used paper journals for diabetes self-management. The primary outcome was the between-group difference in change in hemoglobin A1c from baseline to week 12.

RESULTS

The change in hemoglobin A1c from baseline to week 12 was -0.05% (95% CI -0.14% to 0.04%) in the intervention group and 0.06% (95% CI -0.04% to 0.15%) in the control group. The between-group difference in change was -0.11% (95% CI -0.24% to 0.03%; P=.11).

CONCLUSIONS

There was no statistically significant change in glycemic control. The lack of change could be due to SMC-01 insufficiently inducing behavior change, absence of screening for patients who have high intention to change their lifestyle, low effective usage of SMC-01 due to design issues, or problems with the SMC-01 intervention. Future efforts should focus on these issues in the early phase of developing interventions.

TRIAL REGISTRATION

Japan Registry of Clinical Trials jRCT2032200033; https://jrct.niph.go.jp/latest-detail/jRCT2032200033.

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

BACKGROUND

Digital health care apps, including digital therapeutics, have the potential to increase accessibility and improve patient engagement by overcoming the limitations of traditional facility-based medical treatments. However, there are no established tools capable of quantitatively measuring long-term engagement at present.

OBJECTIVE

This study aimed to evaluate an existing engagement index (EI) in a commercial health management app for long-term use and compare it with a newly developed EI.

METHODS

Participants were recruited from cancer survivors enrolled in a randomized controlled trial that evaluated the impact of mobile health apps on recovery. Of these patients, 240 were included in the study and randomly assigned to the Noom app (Noom Inc). The newly developed EI was compared with the existing EI, and a long-term use analysis was conducted. Furthermore, the new EI was evaluated based on adapted measurements from the Web Matrix Visitor Index, focusing on click depth, recency, and loyalty indices.

RESULTS

The newly developed EI model outperformed the existing EI model in terms of predicting EI of a 6- to 9-month period based on the EI of a 3- to 6-month period. The existing model had a mean squared error of 0.096, a root mean squared error of 0.310, and an R2 of 0.053. Meanwhile, the newly developed EI models showed improved performance, with the best one achieving a mean squared error of 0.025, root mean squared error of 0.157, and R2 of 0.610. The existing EI exhibited significant associations: the click depth index (hazard ratio [HR] 0.49, 95% CI 0.29-0.84; P<.001) and loyalty index (HR 0.17, 95% CI 0.09-0.31; P<.001) were significantly associated with improved survival, whereas the recency index exhibited no significant association (HR 1.30, 95% CI 1.70-2.42; P=.41). Among the new EI models, the EI with a menu combination of menus available in the app's free version yielded the most promising result. Furthermore, it exhibited significant associations with the loyalty index (HR 0.32, 95% CI 0.16-0.62; P<.001) and the recency index (HR 0.47, 95% CI 0.30-0.75; P<.001).

CONCLUSIONS

The newly developed EI model outperformed the existing model in terms of the prediction of long-term user engagement and compliance in a mobile health app context. We emphasized the importance of log data and suggested avenues for future research to address the subjectivity of the EI and incorporate a broader range of indices for comprehensive evaluation.

Designing mHealth Apps to Incorporate Evidence-Based Techniques for Prolonging User Engagement

Maintaining user engagement with mobile health (mHealth) apps can be a challenge. Previously, we developed a conceptual model to optimize patient engagement in mHealth apps by incorporating multiple evidence-based methods, including increasing health literacy, enhancing technical competence, and improving feelings about participation in clinical trials. This viewpoint aims to report on a series of exploratory mini-experiments demonstrating the feasibility of testing our previously published engagement conceptual model. We collected data from 6 participants using an app that showed a series of educational videos and obtained additional data via questionnaires to illustrate and pilot the approach. The videos addressed 3 elements shown to relate to engagement in health care app use: increasing health literacy, enhancing technical competence, and improving positive feelings about participation in clinical trials. We measured changes in participants' knowledge and feelings, collected feedback on the videos and content, made revisions based on this feedback, and conducted participant reassessments. The findings support the feasibility of an iterative approach to creating and refining engagement enhancements in mHealth apps. Systematically identifying the key evidence-based elements intended to be included in an app's design and then systematically testing the implantation of each element separately until a satisfactory level of positive impact is achieved is feasible and should be incorporated into standard app design. While mHealth apps have shown promise, participants are more likely to drop out than to be retained. This viewpoint highlights the potential for mHealth researchers to test and refine mHealth apps using approaches to better engage users.

Mobile app activity engagement by cancer patients and their caregivers informs remote monitoring

Mobile phone applications ("apps") are potentially an effective, low-burden method to collect patient-reported outcomes outside the clinical setting. Using such apps consistently and in a timely way is critical for complete and accurate data capture, but no studies of concurrent reporting by cancer patient-caregiver dyads have been published in the peer-reviewed literature. This study assessed app engagement, defined as adherence, timing, and attrition with two smartphone applications, one for adult cancer patients and one for their informal caregivers. This was a single-arm, pilot study in which adult cancer patients undergoing IV chemotherapy or immunotherapy used the DigiBioMarC app, and their caregivers used the TOGETHERCare app, for approximately one month to report weekly on the patients' symptoms and wellbeing. Using app timestamp metadata, we assessed user adherence, overall and by participant characteristics. Fifty patient-caregiver dyads completed the study. Within the one-month study period, both adult cancer patients and their informal caregivers were highly adherent, with app activity completion at 86% for cancer patients and 84% for caregivers. Caregivers completed 86% of symptom reports, while cancer patients completed 89% of symptom reports. Cancer patients and their caregivers completed most activities within 48 h of availability on the app. These results suggest that the DigiBioMarC and TOGETHERCare apps can be used to collect patient- and caregiver-reported outcomes data during intensive treatment. From our research, we conclude that metadata from mobile apps can be used to inform clinical teams about study participants' engagement and wellbeing outside the clinical setting.

Participant Engagement in Microrandomized Trials of mHealth Interventions: Scoping Review

BACKGROUND

Microrandomized trials (MRTs) have emerged as the gold standard for the development and evaluation of multicomponent, adaptive mobile health (mHealth) interventions. However, not much is known about the state of participant engagement measurement in MRTs of mHealth interventions.

OBJECTIVE

In this scoping review, we aimed to quantify the proportion of existing or planned MRTs of mHealth interventions to date that have assessed (or have planned to assess) engagement. In addition, for the trials that have explicitly assessed (or have planned to assess) engagement, we aimed to investigate how engagement has been operationalized and to identify the factors that have been studied as determinants of engagement in MRTs of mHealth interventions.

METHODS

We conducted a broad search for MRTs of mHealth interventions in 5 databases and manually searched preprint servers and trial registries. Study characteristics of each included evidence source were extracted. We coded and categorized these data to identify how engagement has been operationalized and which determinants, moderators, and covariates have been assessed in existing MRTs.

RESULTS

Our database and manual search yielded 22 eligible evidence sources. Most of these studies (14/22, 64%) were designed to evaluate the effects of intervention components. The median sample size of the included MRTs was 110.5. At least 1 explicit measure of engagement was included in 91% (20/22) of the included MRTs. We found that objective measures such as system usage data (16/20, 80%) and sensor data (7/20, 35%) are the most common methods of measuring engagement. All studies included at least 1 measure of the physical facet of engagement, but the affective and cognitive facets of engagement have largely been neglected (only measured by 1 study each). Most studies measured engagement with the mHealth intervention (Little e) and not with the health behavior of interest (Big E). Only 6 (30%) of the 20 studies that measured engagement assessed the determinants of engagement in MRTs of mHealth interventions; notification-related variables were the most common determinants of engagement assessed (4/6, 67% studies). Of the 6 studies, 3 (50%) examined the moderators of participant engagement-2 studies investigated time-related moderators exclusively, and 1 study planned to investigate a comprehensive set of physiological and psychosocial moderators in addition to time-related moderators.

CONCLUSIONS

Although the measurement of participant engagement in MRTs of mHealth interventions is prevalent, there is a need for future trials to diversify the measurement of engagement. There is also a need for researchers to address the lack of attention to how engagement is determined and moderated. We hope that by mapping the state of engagement measurement in existing MRTs of mHealth interventions, this review will encourage researchers to pay more attention to these issues when planning for engagement measurement in future trials.