Integrating Information Technology in Healthcare: Recent Developments, Challenges, and Future Prospects for Urban and Regional Health

The use of technology in healthcare has become increasingly popular in recent years, with the potential to improve how healthcare is delivered, patient outcomes, and cost-effectiveness. This review paper provides an overview of how technology has been used in healthcare, particularly in cities and for personalized medicine. The paper discusses different ways technology is being used in healthcare, such as electronic health records, telemedicine, remote monitoring, medical imaging, wearable devices, and artificial intelligence. It also looks at the challenges and problems that come with using technology in healthcare, such as keeping patient data private and secure, making sure different technology systems can work together, and ensuring patients are comfortable using technology. In addition, the paper explores the potential of technology in healthcare, including improving how easily patients can get care, the quality of care they receive, and the cost of care. It also talks about how technology can help personalize care to individual patients. Finally, the paper summarizes the main points, makes recommendations for healthcare providers and policymakers, and suggests directions for future research. Overall, this review shows how technology can be used to improve healthcare, while also acknowledging the challenges that come with using technology in this way

Physical activity after cardiac rehabilitation: Explicit and implicit attitudinal components and ambivalence

Objective: Physical activity is crucial in the treatment of cardiac disease. In addition to sociocognitive theories of behavior change, attitudinal ambivalence and nonconscious factors have also been demonstrated to predict physical activity. We propose an extension to the theory of planned behavior with a dual-systems approach including explicit and implicit attitudes, and different types of attitudinal ambivalence as moderators to predict the physical activity of patients after discharge from inpatient cardiac rehabilitation. Method: The sample comprised N = 111 cardiac patients who provided daily diary reports of intention, cognitive, affective, and implicit attitudes for 21 days after discharge (86% male, Mage = 62, SDage = 11, n = 2,017 days). Daily moderate–to-vigorous (MVPA) and light (LPA) physical activity were measured using accelerometers. Five types of ambivalence were calculated. Analyses included Bayesian multilevel modeling. Results: Patients with more positive affective attitudes and more positive implicit attitudes had a higher intention. Higher ambivalence weakened the affective attitudes-intention relationship. On days with more positive implicit attitudes than usual, intention was lower, but only when ambivalence was low. Patients with higher ambivalence engaged in less MVPA. On days with extremely low ambivalence, implicit attitudes were negatively associated with tomorrow’s MVPA. Patients with more positive affective attitudes engaged in more LPA, but only when their ambivalence was very low. On days with higher ambivalence than usual, the next day’s LPA was shorter. However, another type of ambivalence showed the opposite effect. Conclusions: The results emphasize the importance of affective and implicit attitudes and ambivalence for the physical activity of cardiac patients

Automatic Recognition, Segmentation, and Sex Assignment of Nocturnal Asthmatic Coughs and Cough Epochs in Smartphone Audio Recordings: Observational Field Study

Background: Asthma is one of the most prevalent chronic respiratory diseases. Despite increased investment in treatment, little progress has been made in the early recognition and treatment of asthma exacerbations over the last decade. Nocturnal cough monitoring may provide an opportunity to identify patients at risk for imminent exacerbations. Recently developed approaches enable smartphone-based cough monitoring. These approaches, however, have not undergone longitudinal overnight testing nor have they been specifically evaluated in the context of asthma. Also, the problem of distinguishing partner coughs from patient coughs when two or more people are sleeping in the same room using contact-free audio recordings remains unsolved. Objective: The objective of this study was to evaluate the automatic recognition and segmentation of nocturnal asthmatic coughs and cough epochs in smartphone-based audio recordings that were collected in the field. We also aimed to distinguish partner coughs from patient coughs in contact-free audio recordings by classifying coughs based on sex. Methods: We used a convolutional neural network model that we had developed in previous work for automated cough recognition. We further used techniques (such as ensemble learning, minibatch balancing, and thresholding) to address the imbalance in the data set. We evaluated the classifier in a classification task and a segmentation task. The cough-recognition classifier served as the basis for the cough-segmentation classifier from continuous audio recordings. We compared automated cough and cough-epoch counts to human-annotated cough and cough-epoch counts. We employed Gaussian mixture models to build a classifier for cough and cough-epoch signals based on sex. Results: We recorded audio data from 94 adults with asthma (overall: mean 43 years; SD 16 years; female: 54/94, 57%; male 40/94, 43%). Audio data were recorded by each participant in their everyday environment using a smartphone placed next to their bed; recordings were made over a period of 28 nights. Out of 704,697 sounds, we identified 30,304 sounds as coughs. A total of 26,166 coughs occurred without a 2-second pause between coughs, yielding 8238 cough epochs. The ensemble classifier performed well with a Matthews correlation coefficient of 92% in a pure classification task and achieved comparable cough counts to that of human annotators in the segmentation of coughing. The count difference between automated and human-annotated coughs was a mean –0.1 (95% CI –12.11, 11.91) coughs. The count difference between automated and human-annotated cough epochs was a mean 0.24 (95% CI –3.67, 4.15) cough epochs. The Gaussian mixture model cough epoch–based sex classification performed best yielding an accuracy of 83%. Conclusions: Our study showed longitudinal nocturnal cough and cough-epoch recognition from nightly recorded smartphone-based audio from adults with asthma. The model distinguishes partner cough from patient cough in contact-free recordings by identifying cough and cough-epoch signals that correspond to the sex of the patient. This research represents a step towards enabling passive and scalable cough monitoring for adults with asthma

Leveraging The Potential Of Personality Traits For Digital Health Interventions : A Literature Review On Digital Markers For Conscientiousness And Neurotism

Digital health interventions (DHIs) are designed to help individuals manage their disease, such as asthma, diabetes, or major depression. While there is a broad body of literature on how to design evidence- based DHIs with respect to behavioral theories, behavior change techniques or various design features, targeting personality traits has been neglected so far in DHI designs, although there is evidence of their impact on health. In particular, conscientiousness, which is related to therapy adherence, and neuroticism, which impacts long-term health of chronic patients, are two personality traits with an impact on health. Sensing these traits via digital markers from online and smartphone data sources and providing corresponding personality change interventions, i.e. to increase conscientiousness and to reduce neuroticism, may be an important active and generic ingredient for various DHIs. As a first step towards this novel class of personality change DHIs, we conducted a systematic literature review on relevant digital markers related to conscientiousness and neuroticism. Overall, 344 articles were reviewed and 21 were selected for further analysis. We found various digital markers for conscientiousness and neuroticism and discuss them with respect to future work, i.e. the design and evaluation of personality change DHIs

Human cues in eHealth to promote lifestyle change: An experimental field study to examine adherence to self-help interventions

eHealth lifestyle interventions without human support (self-help interventions) are generally less effective, as they suffer from lower adherence levels. To solve this, we investigated whether (1) using a text-based conversational agent (TCA) and applying human cues contribute to a working alliance with the TCA, and whether (2) adding human cues and establishing a positive working alliance increase intervention adherence. Participants (N = 121) followed a TCA-supported app-based physical activity intervention. We manipulated two types of human cues: visual (ie, message appearance) and relational (ie, message content). We employed a 2 (visual cues: yes, no) x 2 (relational cues: yes, no) between-subjects design, resulting in four experimental groups: (1) visual and relational cues, (2) visual cues only, (3) relational cues only, or (4) no human cues. We measured the working alliance with the Working Alliance Inventory Short Revised form and intervention adherence as the number of days participants responded to the TCA's messages. Contrary to expectations, the working alliance was unaffected by using human cues. Working alliance was positively related to adherence (t(78) = 3.606, p = .001). Furthermore, groups who received visual cues showed lower adherence levels compared to those who received relational cues only or no cues (U = 1140.5, z = −3.520, p < .001). We replicated the finding that establishing a working alliance contributes to intervention adherence, independently of the use of human cues in a TCA. However, we were unable to show that adding human cues impacted the working alliance and increased adherence. The results indicate that adding visual cues to a TCA may even negatively affect adherence, possibly because it may create confusion concerning the true nature of the coach, which may prompt unrealistic expectations

Factors associated with adherence to a public mobile nutritional health intervention: retrospective cohort study

BACKGROUND: Obesity is a global health issue affecting over 2 billion people. Mobile health apps, specifically nutrition apps, have been identified as promising solutions to combat obesity. However, research on adherence to nutrition apps is scarce, especially for publicly available apps without monetary incentives and personal onboarding. Understanding factors associated with adherence is essential to improve the efficacy of these apps. This study aims to identify such factors by analyzing a large dataset of a free and publicly available app (“MySwissFoodPyramid”) that promotes healthy eating through dietary self-monitoring and nutrition literacy delivered via a conversational agent. METHODS: A retrospective analysis was conducted on 19,805 users who used the app for at least two days between November 2018 and May 2022. Adherence was defined as completing a food diary by tracking dietary intake over a suggested period of three days. Users who finished multiple diaries were considered long-term adherent. The associations between the day and time of installation, tutorial use, reminder use, and conversational agent choice were examined regarding adherence, long-term adherence, and the number of completed diaries. RESULTS: Overall, 66.8% of included users were adherent, and 8.5% were long-term adherent. Users who started the intervention during the day (5 am – 7 pm) were more likely to be adherent and completed more diaries. Starting to use the intervention between Sunday and Wednesday was associated with better adherence and a higher number of completed diaries. Users who chose the female conversational agent were more likely to be adherent, long-term adherent, and completed more diaries. Users who skipped the tutorial were less adherent and completed fewer diaries. Users who set a follow-up reminder were more likely to be long-term adherent and completed more diaries. CONCLUSIONS: This study demonstrates the potential of digital health interventions to achieve comparably high adherence rates, even without monetary incentives or human-delivered support. It also reveals factors associated with adherence highlighting the importance of app tutorials, customizable reminders, tailored content, and the date and time of user onboarding for improving adherence to mHealth apps. Ultimately, these findings may help improve the effectiveness of digital health interventions in promoting healthy behaviors

Detecting Receptivity for mHealth Interventions in the Natural Environment

JITAI is an emerging technique with great potential to support health behavior by providing the right type and amount of support at the right time. A crucial aspect of JITAIs is properly timing the delivery of interventions, to ensure that a user is receptive and ready to process and use the support provided. Some prior works have explored the association of context and some user-specific traits on receptivity, and have built post-study machine-learning models to detect receptivity. For effective intervention delivery, however, a JITAI system needs to make in-the-moment decisions about a user's receptivity. To this end, we conducted a study in which we deployed machine-learning models to detect receptivity in the natural environment, i.e., in free-living conditions. We leveraged prior work regarding receptivity to JITAIs and deployed a chatbot-based digital coach -- Walkie -- that provided physical-activity interventions and motivated participants to achieve their step goals. The Walkie app included two types of machine-learning model that used contextual information about a person to predict when a person is receptive: a static model that was built before the study started and remained constant for all participants and an adaptive model that continuously learned the receptivity of individual participants and updated itself as the study progressed. For comparison, we included a control model that sent intervention messages at random times. The app randomly selected a delivery model for each intervention message. We observed that the machine-learning models led up to a 40% improvement in receptivity as compared to the control model. Further, we evaluated the temporal dynamics of the different models and observed that receptivity to messages from the adaptiveComment: This paper is currently under submission. Please contact the authors for more detai

Effective Behavior Change Techniques in Digital Health Interventions for the Prevention or Management of Noncommunicable Diseases: An Umbrella Review

Background Despite an abundance of digital health interventions (DHIs) targeting the prevention and management of noncommunicable diseases (NCDs), it is unclear what specific components make a DHI effective. Purpose This narrative umbrella review aimed to identify the most effective behavior change techniques (BCTs) in DHIs that address the prevention or management of NCDs. Methods Five electronic databases were searched for articles published in English between January 2007 and December 2022. Studies were included if they were systematic reviews or meta-analyses of DHIs targeting the modification of one or more NCD-related risk factors in adults. BCTs were coded using the Behavior Change Technique Taxonomy v1. Study quality was assessed using AMSTAR 2. Results Eighty-five articles, spanning 12 health domains and comprising over 865,000 individual participants, were included in the review. We found evidence that DHIs are effective in improving health outcomes for patients with cardiovascular disease, cancer, type 2 diabetes, and asthma, and health-related behaviors including physical activity, sedentary behavior, diet, weight management, medication adherence, and abstinence from substance use. There was strong evidence to suggest that credible source, social support, prompts and cues, graded tasks, goals and planning, feedback and monitoring, human coaching and personalization components increase the effectiveness of DHIs targeting the prevention and management of NCDs. Conclusions This review identifies the most common and effective BCTs used in DHIs, which warrant prioritization for integration into future interventions. These findings are critical for the future development and upscaling of DHIs and should inform best practice guidelines

Detecting Receptivity for mHealth Interventions in the Natural Environment

Just-In-Time Adaptive Intervention (JITAI) is an emerging technique with great potential to support health behavior by providing the right type and amount of support at the right time. A crucial aspect of JITAIs is properly timing the delivery of interventions, to ensure that a user is receptive and ready to process and use the support provided. Some prior works have explored the association of context and some user-specific traits on receptivity, and have built post-study machine-learning models to detect receptivity. For effective intervention delivery, however, a JITAI system needs to make in-the-moment decisions about a user\u27s receptivity. To this end, we conducted a study in which we deployed machine-learning models to detect receptivity in the natural environment, i.e., in free-living conditions. We leveraged prior work regarding receptivity to JITAIs and deployed a chatbot-based digital coach - Ally - that provided physical-activity interventions and motivated participants to achieve their step goals. We extended the original Ally app to include two types of machine-learning model that used contextual information about a person to predict when a person is receptive: a static model that was built before the study started and remained constant for all participants and an adaptive model that continuously learned the receptivity of individual participants and updated itself as the study progressed. For comparison, we included a control model that sent intervention messages at random times. The app randomly selected a delivery model for each intervention message. We observed that the machine-learning models led up to a 40% improvement in receptivity as compared to the control model. Further, we evaluated the temporal dynamics of the different models and observed that receptivity to messages from the adaptive model increased over the course of the study

Exploring the State-of-Receptivity for mHealth Interventions

Recent advancements in sensing techniques for mHealth applications have led to successful development and deployments of several mHealth intervention designs, including Just-In-Time Adaptive Interventions (JITAI). JITAIs show great potential because they aim to provide the right type and amount of support, at the right time. Timing the delivery of a JITAI such as the user is receptive and available to engage with the intervention is crucial for a JITAI to succeed. Although previous research has extensively explored the role of context in users’ responsiveness towards generic phone notiications, it has not been thoroughly explored for actual mHealth interventions. In this work, we explore the factors afecting users’ receptivity towards JITAIs. To this end, we conducted a study with 189 participants, over a period of 6 weeks, where participants received interventions to improve their physical activity levels. The interventions were delivered by a chatbot-based digital coach ś Ally ś which was available on Android and iOS platforms. We deine several metrics to gauge receptivity towards the interventions, and found that (1) several participant-speciic characteristics (age, personality, and device type) show signiicant associations with the overall participant receptivity over the course of the study, and that (2) several contextual factors (day/time, phone battery, phone interaction, physical activity, and location), show signiicant associations with the participant receptivity, in-the-moment. Further, we explore the relationship between the efectiveness of the intervention and receptivity towards those interventions; based on our analyses, we speculate that being receptive to interventions helped participants achieve physical activity goals, which in turn motivated participants to be more receptive to future interventions. Finally, we build machine-learning models to detect receptivity, with up to a 77% increase in F1 score over a biased random classiier