Mobile apps for real-world evidence in health care

Nurse-led telehealth and mobile health care models for type 2 diabetes and hypertension in low-income US populations: A scoping review

BACKGROUND

Increasing numbers of underserved people with chronic diseases and decreasing providers in rural areas have contributed to the care shortage in the United States. Nurse-led telehealth/mobile care models have potential benefits for this population. However, there is a substantial gap in the literature regarding this topic.

PURPOSE

To examine the available literature on nurse-led telehealth/mobile health care models with a particular focus on care model settings, nursing roles, care components, achieved outcomes, and the identification of both facilitative factors and encountered challenges. The ultimate goal is to offer recommendations based on these findings, thereby aiding the development or refinement of evidence-based care models that meet to the unique needs of low-income populations.

METHODOLOGY

Literature published from 2010 to 2023 was searched in six electronic databases (Cumulative Index to Nursing and Allied Health Literature, Communication and Mass Media Complete, Medline, APA PsycINFO, Social Sciences Index, and Web of Science databases).

RESULTS

Commonalities identified among included studies with significant improvements were the provision of home monitors and education to participants, multiple engagements, and extensive community and/or family involvement.

CONCLUSIONS

Nurse-led telehealth/mobile health care models for chronic diseases are an emerging approach. Nurse educators must ensure that future nurses are adept in diverse telehealth modes, collaborating across disciplines. Leveraging advanced practice registered nurses and interdisciplinary teams provides holistic care.

IMPLICATIONS

Our review outlined recent research findings that suggest enhanced patient outcomes through technology, communication, and community support. In addition, we offered suggestions for future research and practice, emphasizing the importance of exploring the requirements of diverse and underserved communities.

Consumer Perspectives for a Future Mobile App to Document Real-World Listening Difficulties: Qualitative Study

BACKGROUND

By enabling individuals with hearing loss to collect their own hearing data in their personal real-world settings, there is scope to improve clinical care, empower consumers, and support shared clinical decision-making and problem-solving. Clinician support for this approach has been established in a separate study.

OBJECTIVE

This study aims to explore, for consumers with hearing loss, their (1) experiences of listening difficulties, to identify the data an app could usefully collect; (2) preferences regarding the features of mobile apps in general; and (3) opinions on the potential value and desirable features of a yet-to-be designed app for documenting listening difficulties in real-world settings.

METHODS

A total of 3 focus groups involved 27 adults who self-reported hearing loss. Most were fitted with hearing devices. A facilitator used a topic guide to generate discussion, which was video- and audio-recorded. Verbatim transcriptions were analyzed using inductive content analysis.

RESULTS

Consumers supported the concept of a mobile app that would facilitate the documenting of listening difficulties in real-world settings important to the individual. Consumers shared valuable insights about their listening difficulties, which will help determine the data that should be collected through an app designed to document these challenges. This information included early indicators of hearing loss (eg, mishearing, difficulty communicating in groups and on the phone, and speaking overly loudly) and prompts to seek hearing devices (eg, spousal pressure and the advice or example provided by others, and needing to rely on lipreading or to constantly request others to repeat themselves). It also included the well-known factors that influence listening difficulties (eg, reverberation, background noise, group conversations) and the impacts and consequences of their difficulties (eg, negative impacts on relationships and employment, social isolation and withdrawal, and negative emotions). Consumers desired a visual-based app that provided options for how data could be collected and how the user could enter data into an app, and which enabled data sharing with a clinician.

CONCLUSIONS

These findings provide directions for the future co-design and piloting of a prototype mobile app to provide data that are useful for increasing self-awareness of listening difficulties and can be shared with a clinician.

Just-in-Time Adaptive Intervention for Stabilizing Sleep Hours of Japanese Workers: Microrandomized Trial

BACKGROUND

Sleep disturbance is a major contributor to future health and occupational issues. Mobile health can provide interventions that address adverse health behaviors for individuals in a vulnerable health state in real-world settings (just-in-time adaptive intervention).

OBJECTIVE

This study aims to identify a subpopulation with vulnerable sleep state in daily life (study 1) and, immediately afterward, to test whether providing mobile health intervention improved habitual sleep behaviors and psychological wellness in real-world settings by conducting a microrandomized trial (study 2).

METHODS

Japanese workers (n=182) were instructed to collect data on their habitual sleep behaviors and momentary symptoms (including depressive mood, anxiety, and subjective sleep quality) using digital devices in a real-world setting. In study 1, we calculated intraindividual mean and variability of sleep hours, midpoint of sleep, and sleep efficiency to characterize their habitual sleep behaviors. In study 2, we designed and conducted a sleep just-in-time adaptive intervention, which delivered objective push-type sleep feedback messages to improve their sleep hours for a subset of participants in study 1 (n=81). The feedback messages were generated based on their sleep data measured on previous nights and were randomly sent to participants with a 50% chance for each day (microrandomization).

RESULTS

In study 1, we applied hierarchical clustering to dichotomize the population into 2 clusters (group A and group B) and found that group B was characterized by unstable habitual sleep behaviors (large intraindividual variabilities). In addition, linear mixed-effect models showed that the interindividual variability of sleep hours was significantly associated with depressive mood (β=3.83; P=.004), anxiety (β=5.70; P=.03), and subjective sleep quality (β=-3.37; P=.03). In study 2, we found that providing sleep feedback prolonged subsequent sleep hours (increasing up to 40 min; P=.01), and this effect lasted for up to 7 days. Overall, the stability of sleep hours in study 2 was significantly improved among participants in group B compared with the participants in study 1 (P=.001).

CONCLUSIONS

This is the first study to demonstrate that providing sleep feedback can benefit the modification of habitual sleep behaviors in a microrandomized trial. The findings of this study encourage the use of digitalized health intervention that uses real-time health monitoring and personalized feedback.

Experience of Older Patients with COPD Using Disease Management Apps: A Qualitative Study

(1) Background: Digital medicine is developing in the management of chronic diseases in older people, but there is still a lack of information on the use of disease management apps in older patients with COPD. This study aims to explore the views and experience of older patients with COPD on disease management apps to provide a basis for the development and promotion of apps for geriatric diseases. (2) Methods: A descriptive qualitative research method was used. Older patients with COPD (N = 32) with experience using disease management apps participated in semi-structured interviews. Thematic analysis was used to analyze the data. (3) Results: Seven themes were defined: (a) feeling curious and worried when facing disease management apps for the first time; (b) actively overcoming barriers to use; (c) gradually becoming independent by continuous online learning; (d) feeling safe in the virtual environment; (e) gradually feeling new value in online interactions; (f) relying on disease management apps under long-term use; (g) expecting disease management apps to meet personalized needs. (4) Conclusions: The adoption and use of disease management apps by older people is a gradual process of acceptance, and they can obtain a wide range of benefits in health and life.

Wearable activity tracker study exploring rheumatoid arthritis patients' disease activity using patient-reported outcome measures, clinical measures, and biometric sensor data (the wear study)

Background

Digital health studies using electronic patient reported outcomes (ePROs), wearables, and clinical data to provide a more comprehensive picture of patient health.

Methods

Newly initiated patients on upadacitinib or adalimumab for RA will be recruited from community settings in the Excellence NEtwork in RheumatoloGY (ENRGY) practice-based research network. Over the period of three to six months, three streams of data will be collected (1) linkable physician-derived data; (2) self-reported daily and weekly ePROs through the ArthritisPower registry app; and (3) biometric sensor data passively collected via wearable. These data will be analyzed to evaluate correlations among the three types of data and patient improvement on the newly initiated medication.

Conclusions

Results from this study will provide valuable information regarding the relationships between physician data, wearable data, and ePROs in patients newly initiating an RA treatment, and demonstrate the feasibility of digital data capture for Remote Patient Monitoring of patients with rheumatic disease.

Causal associations between atrial fibrillation and breast cancer: A bidirectional Mendelian randomization analysis

BACKGROUND

Previous observational studies indicated that atrial fibrillation may increase the risk of breast cancer. Following a breast cancer diagnosis, the chance of developing atrial fibrillation may increase as well. However, it is uncertain whether the link is causal or just due to confounding factors.

OBJECTIVE

Using bidirectional Mendelian randomization (MR) analysis, we sought to assess the bidirectional causal relationship between atrial fibrillation and breast cancer from a genetic level.

METHODS

Large genome-wide association studies yielded summary-level data for atrial fibrillation and breast cancer. The preliminary estimate was inverse variance weighted (IVW) under a random model. MR-Egger, weighted median, simple mode, weighted mode, and multivariable MR (adjusting body mass index, smoking, and alcohol drinking) were performed as sensitivity analyses.

RESULTS

Genetically predicted atrial fibrillation presented no statistically significant association with overall breast cancer (odds ratio [OR] = 1.00; 95% confidence interval [CI]: 0.97-1.04; p = 0.79), estrogen receptor (ER) + (OR = 1.00; 95% CI: 0.96-1.03; p = 0.89) or ER- subtypes (OR = 1.00; 95% CI: 0.97-1.04; p = 0.89). Similarly, genetically predicted overall breast cancer (OR = 1.01; 95% CI: 0.98-1.04; p = 0.37), ER+ (OR = 1.02; 95% CI: 0.99-1.05; p = 0.16) or ER- (OR = 0.98; 95% CI: 0.93-1.02; p = 0.32) subtypes had no causal effect on atrial fibrillation. Sensitivity analyses yielded similar results. Individual single nucleotide polymorphism had little effect on the total estimate. We did not observe any evidence of horizontal pleiotropy.

CONCLUSIONS

Our bidirectional MR studies revealed that there may be no causal links between atrial fibrillation and breast cancer.

One-year prediction of cognitive decline following cognitive-stimulation from real-world data

Clinical evidence based on real-world data (RWD) is accumulating exponentially providing larger sample sizes available, which demand novel methods to deal with the enhanced heterogeneity of the data. Here, we used RWD to assess the prediction of cognitive decline in a large heterogeneous sample of participants being enrolled with cognitive stimulation, a phenomenon that is of great interest to clinicians but that is riddled with difficulties and limitations. More precisely, from a multitude of neuropsychological Training Materials (TMs), we asked whether was possible to accurately predict an individual's cognitive decline one year after being tested. In particular, we performed longitudinal modelling of the scores obtained from 215 different tests, grouped into 29 cognitive domains, a total of 124,610 instances from 7902 participants (40% male, 46% female, 14% not indicated), each performing an average of 16 tests. Employing a machine learning approach based on ROC analysis and cross-validation techniques to overcome overfitting, we show that different TMs belonging to several cognitive domains can accurately predict cognitive decline, while other domains perform poorly, suggesting that the ability to predict decline one year later is not specific to any particular domain, but is rather widely distributed across domains. Moreover, when addressing the same problem between individuals with a common diagnosed label, we found that some domains had more accurate classification for conditions such as Parkinson's disease and Down syndrome, whereas they are less accurate for Alzheimer's disease or multiple sclerosis. Future research should combine similar approaches to ours with standard neuropsychological measurements to enhance interpretability and the possibility of generalizing across different cohorts.

Posyandu Application in Indonesia: From Health Informatics Data Quality Bridging Bottom-Up and Top-Down Policy Implementation

The community’s mother and child health (MCH) and nutrition problems can be overcome through evidence-based health policy. Posyandu is an implementation of community empowerment in health promotion strategies. The iPosyandu application (app) is one of the health informatics tools, in which data quality should be considered before any Posyandu health interventions are made. This study aims to describe and assess differences in data quality based on the dimensions (completeness, accuracy, and consistency) of the secondary data collected from the app in Purwakarta Regency in 2019–2021. Obstacles and suggestions for improving its implementation were explored. This research applies a mixed-method explanatory approach. Data completeness was identified as the number of reported visits of children under five per year. Data accuracy was analyzed using WHO Z-score anthropometry and implausible Z-score values. Data consistency was measured using Cronbach’s alpha coefficient, followed by qualitative research with focus group discussions, in-depth interviews, and field observation notes. The quantitative study results found that some of the data were of good quality. The qualitative research identified the obstacles experienced using the iPosyandu app, one of them being that there were no regulations governing the use of iPosyandu to bridge the needs of the government, and provided suggestions from the field to improve its implementation.

Emerging Technology: Preparing Tomorrow's MCH Workforce to Innovate for Equity

Purpose

This commentary proposes a new direction to train the MCH workforce by leveraging today’s rapidly changing innovation and technology to address persistent health inequities.

Description

We outline the creation of an MCH technology and innovation training pipeline developed by harnessing creative funding opportunities, diversifying training modalities, and expanding partnerships beyond traditional academic-practice partners, that be replicated and adapted by other academic programs.

Assessment

Technology and innovation will continue to be a growing intersection between health and equity, and we must create a robust pipeline of MCH leaders prepared to collaborate with entrepreneurial and innovation leaders.

Conclusion

Technology offers an important opportunity to improve MCH outcomes and reduce disparities, but only if we train the MCH workforce to seize these opportunities.

How can real-world evidence aid decision making during the life cycle of nonprescription medicines?

Real-world evidence (RWE) is an emerging scientific discipline which is being increasingly utilized for decision making on prescription-only medicines. However, there has been little focus to date on the application of RWE within the nonprescription sector. This paper reviews the existing and potential applications of RWE for nonprescription medicines, using the nonprescription medicine life cycle as a framework for discussion. Relevant sources of real-world data (RWD) are reviewed and compared with those available for prescribed medicines. Existing life-cycle data gaps are identified where RWE is required or where use of RWE can complement data from randomized controlled trials. Published RWE examples relating to nonprescription medicines are summarized, and potential relevant future sources of RWD discussed. Challenges and limitations to the use of RWE on nonprescription medicines are discussed, and recommendations made to promote optimal and appropriate use of RWE in this sector. Overall, RWE currently plays a key role in specific phases of the nonprescription medicine life cycle, including reclassification and postmarketing safety surveillance. The increasing availability of patient-generated health data is likely to further increase the utilization of RWE to aid decision making on nonprescription medicines.

Exploring perceptions of healthcare technologies enabled by artificial intelligence: an online, scenario-based survey

Background

Healthcare is expected to increasingly integrate technologies enabled by artificial intelligence (AI) into patient care. Understanding perceptions of these tools is essential to successful development and adoption. This exploratory study gauged participants’ level of openness, concern, and perceived benefit associated with AI-driven healthcare technologies. We also explored socio-demographic, health-related, and psychosocial correlates of these perceptions.

Methods

We developed a measure depicting six AI-driven technologies that either diagnose, predict, or suggest treatment. We administered the measure via an online survey to adults (N = 936) in the United States using MTurk, a crowdsourcing platform. Participants indicated their level of openness to using the AI technology in the healthcare scenario. Items reflecting potential concerns and benefits associated with each technology accompanied the scenarios. Participants rated the extent that the statements of concerns and benefits influenced their perception of favorability toward the technology. Participants completed measures of socio-demographics, health variables, and psychosocial variables such as trust in the healthcare system and trust in technology. Exploratory and confirmatory factor analyses of the concern and benefit items identified two factors representing overall level of concern and perceived benefit. Descriptive analyses examined levels of openness, concern, and perceived benefit. Correlational analyses explored associations of socio-demographic, health, and psychosocial variables with openness, concern, and benefit scores while multivariable regression models examined these relationships concurrently.

Results

Participants were moderately open to AI-driven healthcare technologies (M = 3.1/5.0 ± 0.9), but there was variation depending on the type of application, and the statements of concerns and benefits swayed views. Trust in the healthcare system and trust in technology were the strongest, most consistent correlates of openness, concern, and perceived benefit. Most other socio-demographic, health-related, and psychosocial variables were less strongly, or not, associated, but multivariable models indicated some personality characteristics (e.g., conscientiousness and agreeableness) and socio-demographics (e.g., full-time employment, age, sex, and race) were modestly related to perceptions.

Conclusions

Participants’ openness appears tenuous, suggesting early promotion strategies and experiences with novel AI technologies may strongly influence views, especially if implementation of AI technologies increases or undermines trust. The exploratory nature of these findings warrants additional research.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12911-021-01586-8.

Protocol of a Single-Blind Two-Arm (Waitlist Control) Parallel-Group Randomised Controlled Pilot Feasibility Study for mHealth App among Incontinent Pregnant Women

Background: The delivery of pelvic floor muscle training (PFMT) through mHealth apps has been shown to produce promising results in improving pelvic floor muscle strength and urinary incontinence (UI). However, there is limited evidence on mHealth apps designed for pregnant women who are at high risk of developing UI. This pilot study aims to evaluate the feasibility of conducting an effectiveness trial for a newly developed PFMT app among pregnant women in Malaysia. Methods: This is a prospective, single-centre, single-blind, randomised controlled pilot feasibility study: The Kegel Exercise Pregnancy Training app (KEPT-app) Trial. Sixty-four incontinent pregnant women who attended one primary care clinic for the antenatal follow-up will be recruited and randomly assigned to either intervention or waitlist control group. The intervention group will receive the intervention, the KEPT-app developed from the Capability, Opportunity, Motivation-Behaviour (COM-B) theory with Persuasive Technology and Technology Acceptance Model. Discussion: This study will provide a fine-tuning for our future randomised control study on the recruitment feasibility methods, acceptability, feasibility, and usability of the KEPT-app, and the methods to reduce the retention rates among pregnant women with UI. Trial registration: This study was registered on ClinicalTrials.gov on 19 February 2021 (NCT04762433) and is not yet recruiting.