User Retention and Engagement With a Mobile App Intervention to Support Self-Management in Australians With Type 1 or Type 2 Diabetes (My Care Hub): Mixed Methods Study

Exploring mHealth app utilization for diabetes self-management: survey insights from a northern district in Malaysia

BACKGROUND

Mobile health applications (mHealth apps) offer potential benefits for improving diabetes management, such as better glucose monitoring and patient engagement, but their widespread adoption faces challenges, including privacy concerns and user adherence. This research investigates mHealth app usage among patients living with diabetes in Kinta District, Perak, exploring experiences, challenges and patient perceptions regarding diabetes management using mHealth apps.

METHODOLOGY

A cross-sectional community survey was conducted in September till November 2020 across nine government health clinics focusing on diabetes mellitus (Type 1 or Type 2) patients, aged 18 years and older, receiving Diabetes Medication Adherence Counseling (DMTAC) services and able to use smart devices. A self-developed questionnaire with four sections was used to gather demographic information, explore mHealth apps usage and understand both users and non-users' experiences and perceptions. The questionnaire was tested through cognitive debriefing, translated into Malay, pre-tested and finalized by the expert committee. The questionnaire was digitally implemented using Google® Form and QR code. After obtaining informed consent, data collection was performed by the trained DMTAC pharmacists. Statistical analyses involved descriptive and inferential analyses.

RESULTS

The study analyzed the engagement of 295 patients living with diabetes with mHealth apps. Females (54.9%), of Malay ethnicity (58.3%) and with a mean age of 53.8 years (SD: 12.38) constituted the majority. Diabetes duration had a median of 6 years (IQR: 3.0, 10.0) with prevalent comorbidities like hypertension (58.0%) and dyslipidemia (42.7%). Most patients were employed (44.7%) and their primary source of diabetes management information was through healthcare providers (92.5%). Despite the high app use for social interaction, only 13.6% used mHealth apps for disease management. Users were influenced by social media (65.0%) and favored for wellness apps and disease monitoring. Users perceived the mHealth app as useful (97.5%), yet faced challenges over the app initiation, charges and data security. Non-users cited lack of awareness (70.2%), struggled with app startup (22.4%) and preference for conventional healthcare visits (22.0%). In multivariable analysis, longer diabetes duration reduced mHealth app usage (p = 0.046), while multimorbidity increased the likelihood (p = 0.001). Awareness of the availability of health apps significantly influenced the usage of mHealth apps (p < 0.001).

CONCLUSION

The findings highlight the underutilization of mHealth apps for diabetes management despite their perceived usefulness. Challenges faced by users and non-users underscore the need for more awareness, thus encourage widespread acceptance and usage of mHealth apps in diabetes care.

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.

Randomized controlled trial of digital therapeutics for temporomandibular disorder: A pilot study

OBJECTIVES

Temporomandibular disorder (TMD) is a common condition that affects the temporomandibular joint (TMJ) and the muscles of the jaw, resulting in pain and dysfunction. TMD is affected by both behavioral and psychological factors. Digital therapeutics (DTx) can exert therapeutic effects by controlling behavioral factors through the delivery of appropriate interventions. Here, we report an open-label randomized control trial to evaluate the efficacy of DTx for TMD.

METHODS

We recruited 40 participants diagnosed with TMD. Participants were randomly divided into an intervention group (DTx use, n = 20) and a control group (n = 20). The intervention group received the usual treatment process for TMD in addition to the use of the DTx. The control group received the usual treatments only. Patients in both groups were followed up for 3-4 weeks, and outcome data were collected and analyzed.

RESULT

The intervention group showed a significant reduction in pain scores as measured by the numerical rating scale (NRS) (p = 0.016). Additionally, the intervention group showed a statistically significant increase in maximal mouth opening compared to the control group (p = 0.0079). However, there were no significant differences in improvement in the Jaw Functional Limitation Scale, Oral Behavior Checklist, and Patient Health Questionnaire-4 between the two groups (p = 0503, = 0.820, and = 0.943, respectively).

CONCLUSION

This RCT reveals DTx potential in TMD, showing pain and mouth opening improvements with conventional treatment. But no significant changes were noted in other outcomes. The findings advocate for more extensive, long-term research to solidify DTx's role in TMD management.

CLINICAL SIGNIFICANCE

This research underlines DTx potential to improve pain outcomes in TMD therapy, reinforcing its value as a complementary treatment modality.

Activity and Profile of the Users of a Novel Mobile Application Supporting Proper Diet Among Maintenance Hemodialysis Patients

OBJECTIVE

Diet plays a key role in the management of chronic kidney disease. The aim of our study was to evaluate the usage of a self-developed mobile application supporting proper dietary choices among maintenance hemodialysis (HD) patients.

METHODS

The primary functions of the application are to provide databases of products and recipes. Data on user activity recorded using Internet solutions were collected for 12 months from April 2021. The application was promoted both via the Internet and directly to patients. Additionally, a questionnaire was employed to evaluate the usage of the software.

RESULTS

The application was downloaded by 841 smartphone users, 44.4% of whom were from 2 regions of Poland with the largest populations of HD patients. Residents of cities with a population above 250,000 accounted for 86.0% of users. Sixty HD patients (32 males, 28 females; age 56.2 ± 14.8 years) filled the questionnaire. All features of the application scored a median of 4.0 points or higher on a 5-point Likert scale; however, 63.3% of respondents indicated the need to improve particular functions of the application. There was a significant difference in dialysis vintage between respondents who used the application for less than 1 month and others (1.0 vs. 3.3 years; P = .02). The positive perception of its influence on diet adherence was significantly higher among younger (<50 years) compared to older users (5.0 vs. 4.0; P = .03) and among women compared to men (5.0 vs. 4.0; P = .01).

CONCLUSION

HD patients showed interest in dietary mobile applications, and Internet channels were effective in promoting the software. Place of residency, age, gender, and dialysis vintage are factors that influence patient satisfaction with and the time of using the mobile application.

A review of the factors influencing adoption of digital health applications for people living with dementia

Objective

Researchers have used various theories and models to understand technology adoption, however, with the growing interest and availability of mobile applications (apps) for people living with dementia, it is desirable to have a broader insight into how technology adoption may be further improved. This paper aims to explore the factors influencing the adoption of digital health applications for people living with dementia and add to the current literature on this topic.

Methods

Searches were conducted in CINHAL, Web of Science, Psych Info, ProQuest Health and Medical, IEEE Xplore and Scopus. Citation searching and handsearching were used in the identification of other studies.

Results

Following an assessment of relevancy, nine studies remained and are included within this review. Methodological quality was assessed using The Mixed Methods Appraisal Tool (MMAT). A thematic analysis was used for the data synthesis of included studies. Each study reported on different types of apps.

Conclusion

From the synthesis of included studies, four analytic themes were identified; Theme 1: Personal and contextual factors; Theme 2: Perceived value and benefit; Theme 3: Design and content of app; and Theme 4: Digital Literacy and Confidence. People are diverse and so are their reasons for the adoption of apps. These findings provide an insight into the range of factors that impact the adoption of apps for people living with dementia. Understanding the factors that impact the adoption of mobile applications is critical to their success. These findings can be beneficial for app developers and for people living with dementia and their carers.

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

BACKGROUND

Despite the potential of mobile health (mHealth) interventions to facilitate the early detection of signs of heart failure (HF) decompensation and provide personalized management of symptoms, the outcomes of such interventions in patients with HF have been inconsistent. As engagement with mHealth is required for interventions to be effective, poor patient engagement with mHealth interventions may be associated with mixed evidence. It is crucial to understand how engagement with mHealth interventions is measured in patients with HF, and the effects of engagement on HF outcomes.

OBJECTIVE

In this review, we aimed to describe measures of patient engagement with mHealth interventions and the effects of engagement on HF outcomes.

METHODS

We conducted a systematic literature search in 7 databases for relevant studies published in the English language from 2009 to September 2021 and reported the descriptive characteristics of the studies. We used content analysis to identify themes that described patient engagement with mHealth interventions in the qualitative studies included in the review.

RESULTS

We synthesized 32 studies that operationalized engagement with mHealth interventions in 4771 patients with HF (3239/4771, 67.88%, male), ranging from a sample of 7 to 1571 (median 53.3) patients, followed for a median duration of 90 (IQR 45-180) days. Patient engagement with mHealth interventions was measured only quantitatively based on system usage data in 72% (23/32) of the studies, only qualitatively based on data from semistructured interviews and focus groups in 6% (2/32) of studies, and by a combination of both quantitative and qualitative data in 22% (7/32) of studies. System usage data were evaluated using 6 metrics of engagement: number of physiological parameters transmitted (19/30, 63% studies), number of HF questionnaires completed (2/30, 7% studies), number of log-ins (4/30, 13% studies), number of SMS text message responses (1/30, 3% studies), time spent (5/30, 17% studies), and the number of features accessed and screen viewed (4/30, 13% studies). There was a lack of consistency in how the system usage metrics were reported across studies. In total, 80% of the studies reported only descriptive characteristics of system usage data. The emotional, cognitive, and behavioral domains of patient engagement were identified through qualitative studies. Patient engagement levels ranged from 45% to 100% and decreased over time. The effects of engagement on HF knowledge, self-care, exercise adherence, and HF hospitalization were inconclusive.

CONCLUSIONS

The measures of patient engagement with mHealth interventions in patients with HF are underreported and lack consistency. The application of inferential analytical methods to engagement data is extremely limited. There is a need for a working group on mHealth that may consolidate the previous operational definitions of patient engagement into an optimal and standardized measure.

Measurement of Adherence to mHealth Physical Activity Interventions and Exploration of the Factors That Affect the Adherence: Scoping Review and Proposed Framework

BACKGROUND

Mobile health (mHealth) is widely used as an innovative approach to delivering physical activity (PA) programs. Users' adherence to mHealth programs is important to ensure the effectiveness of mHealth-based programs.

OBJECTIVE

Our primary aim was to review the literature on the methods used to assess adherence, factors that could affect users' adherence, and the investigation of the association between adherence and health outcomes. Our secondary aim was to develop a framework to understand the role of adherence in influencing the effectiveness of mHealth PA programs.

METHODS

MEDLINE, PsycINFO, EMBASE, and CINAHL databases were searched to identify studies that evaluated the use of mHealth to promote PA in adults aged ≥18 years. We used critical interpretive synthesis methods to summarize the data collected.

RESULTS

In total, 54 papers were included in this review. We identified 31 specific adherence measurement methods, which were summarized into 8 indicators; these indicators were mapped to 4 dimensions: length, breadth, depth, and interaction. Users' characteristics (5 factors), technology-related factors (12 factors), and contextual factors (1 factor) were reported to have impacts on adherence. The included studies reveal that adherence is significantly associated with intervention outcomes, including health behaviors, psychological indicators, and clinical indicators. A framework was developed based on these review findings.

CONCLUSIONS

This study developed an adherence framework linking together the adherence predictors, comprehensive adherence assessment, and clinical effectiveness. This framework could provide evidence for measuring adherence comprehensively and guide further studies on adherence to mHealth-based PA interventions. Future research should validate the utility of this proposed framework.

Digital Health Technologies for Long-term Self-management of Osteoporosis: Systematic Review and Meta-analysis

Background

Osteoporosis is the fourth most common chronic disease worldwide. The adoption of preventative measures and effective self-management interventions can help improve bone health. Mobile health (mHealth) technologies can play a key role in the care and self-management of patients with osteoporosis.

Objective

This study presents a systematic review and meta-analysis of the currently available mHealth apps targeting osteoporosis self-management, aiming to determine the current status, gaps, and challenges that future research could address, as well as propose appropriate recommendations.

Methods

A systematic review of all English articles was conducted, in addition to a survey of all apps available in iOS and Android app stores as of May 2021. A comprehensive literature search (2010 to May 2021) of PubMed, Scopus, EBSCO, Web of Science, and IEEE Xplore was conducted. Articles were included if they described apps dedicated to or useful for osteoporosis (targeting self-management, nutrition, physical activity, and risk assessment) delivered on smartphone devices for adults aged ≥18 years. Of the 32 articles, a random effects meta-analysis was performed on 13 (41%) studies of randomized controlled trials, whereas the 19 (59%) remaining studies were only included in the narrative synthesis as they did not provide enough data.

Results

In total, 3906 unique articles were identified. Of these 3906 articles, 32 (0.81%) articles met the inclusion criteria and were reviewed in depth. The 32 studies comprised 14,235 participants, of whom, on average, 69.5% (n=9893) were female, with a mean age of 49.8 (SD 17.8) years. The app search identified 23 relevant apps for osteoporosis self-management. The meta-analysis revealed that mHealth-supported interventions resulted in a significant reduction in pain (Hedges g −1.09, 95% CI −1.68 to −0.45) and disability (Hedges g −0.77, 95% CI −1.59 to 0.05). The posttreatment effect of the digital intervention was significant for physical function (Hedges g 2.54, 95% CI −4.08 to 4.08) but nonsignificant for well-being (Hedges g 0.17, 95% CI −1.84 to 2.17), physical activity (Hedges g 0.09, 95% CI −0.59 to 0.50), anxiety (Hedges g −0.29, 95% CI −6.11 to 5.53), fatigue (Hedges g −0.34, 95% CI −5.84 to 5.16), calcium (Hedges g −0.05, 95% CI −0.59 to 0.50), vitamin D intake (Hedges g 0.10, 95% CI −4.05 to 4.26), and trabecular score (Hedges g 0.06, 95% CI −1.00 to 1.12).

Conclusions

Osteoporosis apps have the potential to support and improve the management of the disease and its symptoms; they also appear to be valuable tools for patients and health professionals. However, most of the apps that are currently available lack clinically validated evidence of their efficacy and focus on a limited number of symptoms. A more holistic and personalized approach within a cocreation design ecosystem is needed.

Trial Registration

PROSPERO 2021 CRD42021269399; https://tinyurl.com/2sw454a9

Evaluating a Strengths-Based mHealth Tool (MyStrengths): Explorative Feasibility Trial

BACKGROUND

As the number of people living with chronic illnesses increases, providing wide-reaching and easy-to-use support tools is becoming increasingly important. Supporting people in this group to recognize and use more of their personal strengths has the potential to improve their quality of life. With this in mind, we have developed the MyStrengths app prototype, a gamefully designed app aimed at aiding users in both identifying their strengths and using these strengths more actively in their daily life.

OBJECTIVE

The goal of this study was to evaluate the user-reported feasibility and usefulness of the MyStrengths app. The study additionally aimed to explore whether the use of MyStrengths could be associated with selected psychosocial outcomes.

METHODS

A 31-day explorative feasibility trial with a pretest-posttest design and an optional end of study interview was conducted. Data collection included system-use log data, demographic information, pre- and post-psychosocial measures (ie, strengths use, self-efficacy, health-related quality of life, depression), user experience measures (ie, usability, engagement, flow), and interview data.

RESULTS

In total, 34 people with at least 1 chronic condition were enrolled in the study, with 26 participants (mean age 48 years, range 29-62 years; 1 male) completing the trial. Among these individuals, 18 were also interviewed posttrial. Participants used the MyStrengths app an average of 6 days during the trial period, with 54% (14/26) using the app over a period of at least 19 days. In total, 8738 unique app actions were registered. Of the psychosocial outcome measures, only 1 subscale, general health in the RAND 36-Item Health Survey, yielded significant pre- and posttest changes. Posttrial interviews showed that the number of participants who considered the MyStrengths app to be useful, somewhat useful, or not useful was evenly distributed across 3 groups. However, every participant did voice support for the strengths approach. All participants were able to identify a multitude of personal strengths using the MyStrengths app. Most participants that reported it to be useful had little or no previous experience with the personal strengths approach. A multitude of users welcomed the gameful design choices, particularly the rolling die feature, suggesting strengths exercises, activities that use a specific strength, were well received.

CONCLUSIONS

Although the reported usefulness and feedback from use varied, most participants were favorable to the strengths-focused approach to care and support. Consequently, low-threshold and wide-reaching mobile health tools that use a strengths-focused approach, such as MyStrengths, hold the potential to support people living with chronic illness in performing self-management and achieving mastery of their life.

The "Worktivity" mHealth intervention to reduce sedentary behaviour in the workplace: a feasibility cluster randomised controlled pilot study

Background

Office work generally consists of high amounts of sedentary behaviour (SB) which has been associated with negative health consequences. We developed the “WorktivIty” mobile app to help office workers reduce their SB through self-monitoring and feedback on sedentary time, prompts to break sedentary time, and educational facts. The aim of this paper is to report the feasibility of delivering the Worktivity intervention to desk-based office workers in the workplace setting and describe methodological considerations for a future trial.

Methods

We conducted a three-arm feasibility cluster randomised controlled pilot study over an 8-week period with full time-desk based employees. Clustered randomisation was to one of three groups: Worktivity mobile app (MA; n = 20), Worktivity mobile app plus SSWD (MA+SSWD; n = 20), or Control (C; n = 16). Feasibility was assessed using measures of recruitment and retention, intervention engagement, intervention delivery, completion rates and usable data, adverse events, and acceptability.

Results

Recruitment of companies to participate in this study was challenging (8% of those contacted), but retention of individual participants within the recruited groups was high (81% C, 90% MA + SSWD, 95% MA). Office workers’ engagement with the app was moderate (on average 59%). Intervention delivery was partially compromised due to diminishing user engagement and technical issues related to educational fact delivery. Sufficient amounts of useable data were collected, however either missing or unusable data were observed with activPAL™, with data loss increasing at each follow up time point. No serious adverse events were identified during the study. The majority of participants agreed that the intervention could be implemented within the workplace setting (65% MA; 72% MA + SSWD) but overall satisfaction with the intervention was modest (58% MA; 39% MA + SSWD).

Conclusions

The findings suggest that, in principle, it is feasible to implement a mobile app-based intervention in the workplace setting however the Worktivity intervention requires further technical refinements before moving to effectiveness trials. Challenges relating to the initial recruitment of workplaces and maintaining user engagement with the mHealth intervention over time need to be addressed prior to future large-scale implementation. Further research is needed to identify how best to overcome these challenges.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-021-11473-6.

Evaluating Asthma Mobile Apps to Improve Asthma Self-Management: User Ratings and Sentiment Analysis of Publicly Available Apps

BACKGROUND

The development and use of mobile health (mHealth) apps for asthma management have risen dramatically over the past two decades. Asthma apps vary widely in their content and features; however, prior research has rarely examined preferences of users of publicly available apps.

OBJECTIVE

The goals of this study were to provide a descriptive overview of asthma mobile apps that are publicly available and to assess the usability of asthma apps currently available on the market to identify content and features of apps associated with positive and negative user ratings.

METHODS

Reviews were collected on June 23, 2020, and included publicly posted reviews until June 21, 2020. To characterize features associated with high or low app ratings, we first dichotomized the average user rating of the asthma app into 2 categories: a high average rating and a low average rating. Asthma apps with average ratings of 4 and above were categorized as having a high average rating. Asthma apps with average ratings of less than 4 were categorized as having a low average rating. For the sentiment analysis, we modeled both 2-word (bi-gram) and 3-word (tri-gram) phrases which commonly appeared across highly rated and lowly rated apps.

RESULTS

Of the 10 apps that met the inclusion criteria, a total of 373 reviews were examined across all apps. Among apps reviewed, 53.4% (199/373) received high ratings (average ratings of 4 or 5) and 47.2% (176/373) received low ratings (average ratings of 3 or less). The number of ratings across all apps ranged from 188 (AsthmaMD) to 10 (My Asthma App); 30% (3/10) of apps were available on both Android and iOS. From the sentiment analysis, key features of asthma management that were common among highly rated apps included the tracking of peak flow readings (n=48), asthma symptom monitoring (n=11), and action plans (n=10). Key features related to functionality that were common among highly rated apps included ease of use (n=5). Users most commonly reported loss of data (n=14) and crashing of app (n=12) as functionality issues among poorly rated asthma apps.

CONCLUSIONS

Our study results demonstrate that asthma app quality, maintenance, and updates vary widely across apps and platforms. These findings may call into question the long-term engagement with asthma apps, a crucial factor for determining their potential to improve asthma self-management and asthma clinical outcomes.

Efficacy and Acceptability of My Care Hub Mobile App to Support Self-Management in Australians with Type 1 or Type 2 Diabetes

The aim of this study was to evaluate the preliminary efficacy and user acceptance of My Care Hub (MCH) mobile app-developed to provide evidenced-based support and education on diabetes self-management (DSM). Using a mixed-methods design, the efficacy and acceptability of MCH were measured among people with type 1 or type 2 diabetes after three weeks of intervention. The primary outcome measure was level of involvement with DSM, while the mediating factors were skills and self-efficacy for DSM. Telephone interviews were conducted to elucidate information on perceptions of the app's impact on participants' DSM and interest in future use. Statistically significant improvements were observed between pre- and post-intervention measures: DSM activities (4.55 ± 1.14 vs. 5.35 ± 0.84; p = 0.001); skills (7.10 ± 1.99 vs. 7.90 ± 1.67; p = 0.04); and self-efficacy (7.33 ±1.83 vs. 8.07 ± 1.54; p = 0.03). Multivariate analysis showed that self-efficacy had the strongest, though not significant influence on DSM. Interview findings revealed that the app reinforced knowledge and provided motivation to participate in DSM activities. The study suggested a positive impact of MCH on DSM and acceptability by patients. To confirm these promising results, further large scale and long-term studies are required.