Smartphone and Mobile App Use Among Physicians in Clinical Practice: Scoping Review

Piloting a Mobile Clinical Decision Support Application for Pediatric Clinical Guidelines

Mobile Clinical Decision Support Systems (CDSSs) represent an increasingly utilized technology to promote clinical guideline use. We sought to explore clinician guideline use and access preferences during implementation of a mobile guideline app at a free-standing children's hospital integrating 23 guidelines. Surveys included demographic variables and access preferences among anonymous onboarded clinicians in January 2022. Response rate was 21.8% (57/261) among onboarded users, mostly attending (59.6%) and resident/fellow physicians (21.1%) in inpatient (42.1%) and emergency department (31.6%) settings. Onboarded users accessed guidelines on over half of shifts (68.4%) and quickly (80.7%, <1 minute). Overall, most users reported favorable patterns for adoption of mobile CDSSs as useful adjuncts to existing formats. Users reported more ease of access and frequent guideline usage, particularly for younger clinicians. Guidelines related to antibiotic decision-making or newer disease processes were most useful. Further study is needed on electronic health record incorporation, adherence, and patient outcomes.

The PICOTS-ComTeC Framework for Defining Digital Health Interventions: An ISPOR Special Interest Group Report

OBJECTIVES

Digital health definitions are abundant, but often lack clarity and precision. We aimed to develop a minimum information framework to define patient-facing digital health interventions (DHIs) for outcomes research.

METHODS

Definitions of digital-health-related terms (DHTs) were systematically reviewed, followed by a content analysis using frameworks, including PICOTS (population, intervention, comparator, outcome, timing, and setting), Shannon-Weaver Model of Communication, Agency for Healthcare Research and Quality Measures, and the World Health Organization's Classification of Digital Health Interventions. Subsequently, we conducted an online Delphi study to establish a minimum information framework, which was pilot tested by 5 experts using hypothetical examples.

RESULTS

After screening 2610 records and 545 full-text articles, we identified 101 unique definitions of 67 secondary DHTs in 76 articles, resulting in 95 different patterns of concepts among the definitions. World Health Organization system (84.5%), message (75.7%), intervention (58.3%), and technology (52.4%) were the most frequently covered concepts. For the Delphi survey, we invited 47 members of the ISPOR Digital Health Special Interest Group, 18 of whom became the Delphi panel. The first, second, and third survey rounds were completed by 18, 11, and 10 respondents, respectively. After consolidating results, the PICOTS-ComTeC acronym emerged, involving 9 domains (population, intervention, comparator, outcome, timing, setting, communication, technology, and context) and 32 optional subcategories.

CONCLUSIONS

Patient-facing DHIs can be specified using PICOTS-ComTeC that facilitates identification of appropriate interventions and comparators for a given decision. PICOTS-ComTeC is a flexible and versatile tool, intended to assist authors in designing and reporting primary studies and evidence syntheses, yielding actionable results for clinicians and other decision makers.

[Analysis of the interruptions generated by the use of smartphones among Primary Care Health professionals]

OBJECTIVE

To analyze the particular use of smartphones among Primary Care Health professionals during the care act and its consequences.

DESIGN

Multicenter, cross-sectional study in a primary care setting, carried out in 3phases: survey of professionals, checklist of professionals and survey of patients.

PARTICIPANTS

Primary Care Health professionals from the Territorial Primary Care Management of Barcelona (online survey) and health professionals (checklist) and patients (patient survey) from 2primary care teams in Barcelona city.

MAIN MEASUREMENTS

Bivariate descriptive analysis of the variables from the different surveys. Use of the mobile phone in the consultation, time, reason, type of use and perception of appropriateness. Characteristics of interruptions.

RESULTS

In relation to patients, 31% consider that the health professional should only consult the mobile phone if it is to resolve some aspect of their reason for consultation and 10% consider it a lack of respect. Eighteen percent of patients describe interruptions, the majority lasting between 10 and 30s and considering them mostly avoidable. In relation to professionals, the majority (96%) claim to have their mobile phone in the consultation and on mute (77%), with only 2% recognizing its use in the presence of the patient, which is in line with what the patients describe. Furthermore, 80% of professionals say they ask permission to use it, contrasting with what patients report (50%). Eighty-five percent of professionals consider its use appropriate.

CONCLUSIONS

The use of mobile phones is perceived by patients as an interruption that can affect the care act, generating dissatisfaction, which must be taken into account by health professionals. Healthcare organizations should establish recommendations regarding the use of mobile phones in consultations.

Smartphone-Based Cognitive Telerehabilitation: A Usability and Feasibility Study Focusing on Mild Cognitive Impairment

The implementation of cognitive health apps in patients with mild cognitive impairment (MCI) is challenging because of their cognitive, age, and other clinical characteristics. In this project, we aimed to evaluate the usability and feasibility of the Rehastart app tested in MCI patients. Eighteen subjects affected by MCI due to neurodegenerative disorders (including Parkinson's disease, multiple sclerosis, and amnestic/multidomain MCI) and eighteen healthcare professionals were recruited to this study. Patients were registered on the app by clinicians and they were assigned a protocol of specific cognitive exercises. The recruitment was conducted in the period between March and June 2023. The trial testing of the app consisted of three sessions per week for three weeks, with each session lasting about 30 min. After three weeks, the participants as well as medical personnel were invited to rate the usability and feasibility of the Rehastart mobile application. The instruments employed to evaluate the usability and feasibility of the app were the System Usability Scale (SUS), The Intrinsic Motivation Inventory (IMI) and the Client Satisfaction Questionnaire (CSQ). We did not find statistically significant differences on the SUS (p = 0.07) between healthcare professionals and patients. In addition, we found promising results on subscales of the Intrinsic Motivation Inventory, suggesting high levels of interest and enjoyment when using the Rehastart app. Our study demonstrated that smartphone-based telerehabilitation could be a suitable tool for people with MCI due to neurodegenerative disorders, since the Rehastart app was easy to use and motivating for both patients and healthy people.

Commercially Available Mobile Apps With Family Behavioral Goal Setting and Tracking for Parents: Review and Quality Evaluation

BACKGROUND

Goal setting and tracking are well established behavior change techniques. Little is known about the extent to which commercially available mobile apps are designed to guide parents in using these strategies, their evidence base, and their quality.

OBJECTIVE

This study aims to review commercially available apps that target parents in relation to setting and tracking behavioral goals for their children. The objectives were to classify the apps' general characteristics, features, evidence base, and target behaviors and assess app quality overall and separately for apps that target health-related behaviors (HRBs) and apps without a health-related behavior (WHRB).

METHODS

Apps were identified using keyword searches in the Apple App Store and Google Play in the United States. Apps were included if their primary purpose was to assist with setting goals, tracking goals, tracking behaviors, or giving feedback pertaining to goals for children by parents. App characteristics and common features were documented and summarized. Two reviewers assessed app quality using the Mobile App Rating Scale (MARS). Descriptive statistics summarized the MARS total score, 4 quality subscales, and 6 app-specific items that reflect the perceived impact of the app on goal setting and tracking, overall and with subgroup analysis for HRB and WHRB apps.

RESULTS

Of the 21 apps identified, 16 (76%) met the review criteria. Overall, 9 apps defined and targeted the following HRBs: nutrition and mealtime (6/16, 38%), physical activity and screen time (5/16, 31%), sleep (7/16, 44%), and personal hygiene (6/16, 38%). Three apps targeted specific age groups (2 apps were for children aged 6-13 years and 1 app was for children aged ≥4 years). None of the apps provided tailored assessments or guidance for goal setting. None of the apps indicated that they were intended for the involvement of a health professional or had been tested for efficacy. The MARS total score indicated moderate app quality overall (mean 3.42, SD 0.49) and ranged from 2.5 to 4.2 out of 5 points. The Habitz app ranked highest on the MARS total score among HRB apps (score=4.2), whereas Thumsters ranked highest (score=3.9) among the WHRB apps. Subgroup analysis revealed a pattern of higher quality ratings in the HRB group than the WHRB group, including the mean MARS total score (mean 3.67, SD 0.34 vs mean 3.09, SD 0.46; P=.02); the engagement and information subscales; and the app-specific items about perceived impact on knowledge, attitudes, and behavior change.

CONCLUSIONS

Several high-quality commercially available apps target parents to facilitate goal setting and tracking for child behavior change related to both health and nonhealth behaviors. However, the apps lack evidence of efficacy. Future research should address this gap, particularly targeting parents of young children, and consider individually tailored guided goal setting and involvement of health professionals.

Automatic Refractive Error Estimation Using Deep Learning-Based Analysis of Red Reflex Images

Purpose/Background: We evaluate how a deep learning model can be applied to extract refractive error metrics from pupillary red reflex images taken by a low-cost handheld fundus camera. This could potentially provide a rapid and economical vision-screening method, allowing for early intervention to prevent myopic progression and reduce the socioeconomic burden associated with vision impairment in the later stages of life. Methods: Infrared and color images of pupillary crescents were extracted from eccentric photorefraction images of participants from Choithram Hospital in India and Dargaville Medical Center in New Zealand. The pre-processed images were then used to train different convolutional neural networks to predict refractive error in terms of spherical power and cylindrical power metrics. Results: The best-performing trained model achieved an overall accuracy of 75% for predicting spherical power using infrared images and a multiclass classifier. Conclusions: Even though the model's performance is not superior, the proposed method showed good usability of using red reflex images in estimating refractive error. Such an approach has never been experimented with before and can help guide researchers, especially when the future of eye care is moving towards highly portable and smartphone-based devices.