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BackgroundLarge-scale initiatives to improve diet quality through increased vegetable consumption have had small to moderate success. Digital technologies have features that are appealing for health-related behavior change interventions. ObjectiveThis study aimed to describe the implementation and evaluation of a mobile phone app called VegEze, which aims to increase vegetable intake among Australian adults. MethodsTo capture the impact of this app in a real-world setting, the Reach, Effectiveness, Adoption, Implementation, and Maintenance framework was utilized. An uncontrolled, quantitative cohort study was conducted, with evaluations after 21 and 90 days. The app was available in the Apple App Store and was accompanied by television, radio, and social media promotion. Evaluation surveys were embedded into the app using ResearchKit. The primary outcomes were vegetable intake (servings per day) and vegetable variety (types per day). Psychological variables (attitudes, intentions, self-efficacy, and action planning) and app usage were also assessed. Descriptive statistics and multiple linear regression were used to describe the impact of the app on vegetable intake and to determine the characteristics associated with the increased intake. ResultsData were available from 5062 participants who completed the baseline survey; 1224 participants completed the 21-day survey, and 273 completed the 90-day survey. The participants resided across Australia and were mostly women (4265/5062, 84.3%) with a mean age of 48.2 years (SD 14.1). The mean increase in intake was 0.48 servings, from 3.06 servings at baseline to 3.54 servings at the end of the 21-day challenge (t1223=8.71; P

Background: Mobile health (mHealth) technologies, such as wearable sensors, smart health devices, and mobile apps, that are capable of supporting pregnancy care are emerging. Although mHealth could be used to facilitate the tracking of health changes during pregnancy, challenges remain in data collection compliance and technology engagement among pregnant women. Understanding the interests, preferences, and requirements of pregnant women and those of clinicians is needed when designing and introducing mHealth solutions for supporting pregnant women's monitoring of health and risk factors throughout their pregnancy journey. Objective: This study aims to understand clinicians' and pregnant women's perceptions on the potential use of mHealth, including factors that may influence their engagement with mHealth technologies and the implications for technology design and implementation. Methods: A qualitative study using semistructured interviews was conducted with 4 pregnant women, 4 postnatal women, and 13 clinicians working in perinatal care. Results: Clinicians perceived the potential benefit of mHealth in supporting different levels of health and well-being monitoring, risk assessment, and care provision in pregnancy care. Most pregnant and postnatal female participants were open to the use of wearables and health monitoring devices and were more likely to use these technologies if they knew that clinicians were monitoring their data. Although it was acknowledged that some pregnancy-related medical conditions are suitable for an mHealth model of remote monitoring, the clinical and technical challenges in the introduction of mHealth for pregnancy care were also identified. Incorporating appropriate health and well-being measures, intelligently detecting any abnormalities, and providing tailored information for pregnant women were the critical aspects, whereas usability and data privacy were among the main concerns of the participants. Moreover, this study highlighted the challenges of engaging pregnant women in longitudinal mHealth monitoring, the additional work required for clinicians to monitor the data, and the need for an evidence-based technical solution. Conclusions: Clinical, technical, and practical factors associated with the use of mHealth to monitor health and well-being in pregnant women need to be considered during the design and feasibility evaluation stages. Technical solutions and appropriate strategies for motivating pregnant women are critical to supporting their long-term data collection compliance and engagement with mHealth technology during pregnancy. [ABSTRACT FROM AUTHOR]

Objective: Established and emerging technologies-such as wearable sensors, smartphones, mobile apps, and artificial intelligence-are shaping positive healthcare models and patient outcomes. These technologies have the potential to become precision health (PH) innovations. However, not all innovations meet regulatory standards or have the required scientific evidence to be used for health applications. In response, an assessment framework was developed to facilitate and standardize the assessment of innovations deemed suitable for PH.Methods: A scoping literature review undertaken through PubMed and Google Scholar identified approximately 100 relevant articles. These were then shortlisted (n = 12) to those that included specific metrics, criteria, or frameworks for assessing technologies that could be applied to the PH context.Results: The proposed framework identified nine core criteria with subcriteria and grouped them into four categories for assessment: technical, clinical, human factors, and implementation. Guiding statements with response options and recommendations were used as metrics against each criterion.Conclusion: The proposed framework supports health services, health technology innovators, and researchers in leveraging current and emerging technologies for PH innovations. It covers a comprehensive set of criteria as part of the assessment process of these technologies. [ABSTRACT FROM AUTHOR]

Natural language processing (NLP) techniques can be used to make inferences about peoples’ mental states from what they write on Facebook, Twitter and other social media. These inferences can then be used to create online pathways to direct people to health information and assistance and also to generate personalized interventions. Regrettably, the computational methods used to collect, process and utilize online writing data, as well as the evaluations of these techniques, are still dispersed in the literature. This paper provides a taxonomy of data sources and techniques that have been used for mental health support and intervention. Specifically, we review how social media and other data sources have been used to detect emotions and identify people who may be in need of psychological assistance; the computational techniques used in labeling and diagnosis; and finally, we discuss ways to generate and personalize mental health interventions. The overarching aim of this scoping review is to highlight areas of research where NLP has been applied in the mental health literature and to help develop a common language that draws together the fields of mental health, human-computer interaction and NLP. [ABSTRACT FROM PUBLISHER]

Current methods to promote awareness of the sun's ultraviolet (UV) radiation have focussed on delivering population level information and some location-based reporting of UV Index (UVI). However, diseases related to excessive (e.g. sunburn, skin cancer) or insufficient (e.g. vitamin D deficiency) exposure to sunlight still remain a global burden. The emergence of wearable sensors and the application of persuasive technology in health domains raise the possibility for technology to influence awareness of sufficient sun intake for vitamin D production, as well as preventing risk of skin damage. This paper presents a personalised solution to promote healthy, safe sun exposure using wearable devices and persuasive techniques. [ABSTRACT FROM AUTHOR]

As online mental health support groups become increasingly popular, they require more support from volunteers and trained moderators who help their users through "interventions" (i.e., responding to questions and providing support). We present a system that supports such human interventions using Natural Language Generation (NLG) techniques. The system generates draft responses aimed at reducing moderators' workload, and improving their efficacy. NLG and human interventions were compared through the ratings of 35 psychology interns. The NLG-based system was capable of generating messages that are grammatically correct with clear language. The system needs improvement, however, moderators can already use it as draft responses. [ABSTRACT FROM AUTHOR]

Cognitive load (CL) is experienced during critical tasks and also while engaged emotional states are induced either by the task itself or by extraneous experiences. Emotions irrelevant to the working memory representation may interfere with the processing of relevant tasks and can influence task performance and behavior, making the accurate detection of CL from nonverbal information challenging. This paper investigates automatic CL detection from facial features, physiology and task performance under affective interference. Data were collected from participants (n=20) solving mental arithmetic tasks with emotional stimuli in the background, and a combined classifier was used for detecting CL levels. Results indicate that the face modality for CL detection was more accurate under affective interference, whereas physiology and task performance were more accurate without the affective interference. Multimodal fusion improved detection accuracies, but it was less accurate under affective interferences. More specifically, the accuracy decreased with an increasing intensity of emotional arousal. [ABSTRACT FROM AUTHOR]

Background: The growth in patient-centered care delivery combined with the rising costs of health care have perhaps not unsurprisingly been matched by a proliferation of patient-centered technology. This paper takes a multistakeholder approach to explore how digital technology can support the cocreation of value between patients and their care teams in the delivery of total knee replacement (TKR) surgery, an increasingly common procedure to return mobility and relieve pain for people suffering from osteoarthritis.Objective: The aim of this study was to investigate communications and interactions between patients and care teams in the delivery of TKR to identify opportunities for digital technology to add value to TKR health care service by enhancing the cocreation of value.Methods: A multistakeholder qualitative study of user needs was conducted with Australian stakeholders (N=34): surgeons (n=12), physiotherapists (n=3), patients (n=11), and general practitioners (n=8). Data from focus groups and interviews were recorded, transcribed, and analyzed using thematic analysis.Results: Encounters between patients and their care teams are information-rich but time-poor. Results showed seven different stages of the TKR journey that starts with referral to a surgeon and ends with a postoperative review at 12 months. Each stage of the journey has different information and communication challenges that can be enhanced by digital technology. Opportunities for digital technology include improved waiting list management, supporting and reinforcing patient retention and recall of information, motivating and supporting rehabilitation, improving patient preparation for hospital stay, and reducing risks and anxiety associated with postoperative wound care.Conclusions: Digital technology can add value to patients' care team communications by enhancing information flow, assisting patient recall and retention of information, improving accessibility and portability of information, tailoring information to individual needs, and by providing patients with tools to engage in their own health care management. For care teams, digital technology can add value through early detection of postoperative complications, proactive surveillance of health data for postoperative patients and patients on waiting lists, higher compliance with rehabilitation programs, and reduced length of stay. Digital technology has the potential to improve patient satisfaction and outcomes, as well as potentially reduce hospital length of stay and the burden of disease associated with postoperative morbidity. [ABSTRACT FROM AUTHOR]

Background: Total knee replacement (TKR) surgeries have increased in recent years. Exercise programs and other interventions following surgery can facilitate the recovery process. With limited clinician contact time, patients with TKR have a substantial burden of self-management and limited communication with their care team, thus often fail to implement an effective rehabilitation plan. Objective: We have developed a digital orthopedic rehabilitation platform that comprises a mobile phone app, wearable activity tracker, and clinical Web portal in order to engage patients with self-management tasks for surgical preparation and recovery, thus addressing the challenges of adherence to and completion of TKR rehabilitation. The study will determine the efficacy of the TKR platform in delivering information and assistance to patients in their preparation and recovery from TKR surgery and a Web portal for clinician care teams (ie, surgeons and physiotherapists) to remotely support and monitor patient progress. Methods: The study will evaluate the TKR platform through a randomized controlled trial conducted at multiple sites (N=5) in a number of states in Australia with 320 patients undergoing TKR surgery; the trial will run for 13 months for each patient. Participants will be randomized to either a control group or an intervention group, both receiving usual care as provided by their hospital. The intervention group will receive the app and wearable activity tracker. Participants will be assessed at 4 different time points: 4 weeks before surgery, immediately before surgery, 12 weeks after surgery, and 52 weeks after surgery. The primary outcome measure is the Oxford Knee Score. Secondary outcome measures include quality of life (Short-Form Health Survey); depression, anxiety, and stress (Depression, Anxiety, and Stress Scales); self-motivation; self-determination; self-efficacy; and the level of satisfaction with the knee surgery and care delivery. The study will also collect quantitative usage data related to all components (app, activity tracker, and Web portal) of the TKR platform and qualitative data on the perceptions of the platform as a tool for patients, carers, and clinicians. Finally, an economic evaluation of the impact of the platform will be conducted. Results: Development of the TKR platform has been completed and deployed for trial. The research protocol is approved by 2 human research ethics committees in Australia. A total of 5 hospitals in Australia (2 in New South Wales, 2 in Queensland, and 1 in South Australia) are expected to participate in the trial. Conclusions: The TKR platform is designed to provide flexibility in care delivery and increased engagement with rehabilitation services. This trial will investigate the clinical and behavioral efficacy of the app and impact of the TKR platform in terms of service satisfaction, acceptance, and economic benefits of the provision of digital services. Trial Registration: Australian New Zealand Clinical Trials Registry (ANZCTR) ACTRN12616000504415; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=370536 (Archived by WebCite at http://www.webcitation.org/6oKES0Gp1) [ABSTRACT FROM AUTHOR]

Background: Mobile health (mHealth) technologies, such as wearable sensors, smart health devices, and mobile apps, that are capable of supporting pregnancy care are emerging. Although mHealth could be used to facilitate the tracking of health changes during pregnancy, challenges remain in data collection compliance and technology engagement among pregnant women. Understanding the interests, preferences, and requirements of pregnant women and those of clinicians is needed when designing and introducing mHealth solutions for supporting pregnant women's monitoring of health and risk factors throughout their pregnancy journey., Objective: This study aims to understand clinicians' and pregnant women's perceptions on the potential use of mHealth, including factors that may influence their engagement with mHealth technologies and the implications for technology design and implementation., Methods: A qualitative study using semistructured interviews was conducted with 4 pregnant women, 4 postnatal women, and 13 clinicians working in perinatal care., Results: Clinicians perceived the potential benefit of mHealth in supporting different levels of health and well-being monitoring, risk assessment, and care provision in pregnancy care. Most pregnant and postnatal female participants were open to the use of wearables and health monitoring devices and were more likely to use these technologies if they knew that clinicians were monitoring their data. Although it was acknowledged that some pregnancy-related medical conditions are suitable for an mHealth model of remote monitoring, the clinical and technical challenges in the introduction of mHealth for pregnancy care were also identified. Incorporating appropriate health and well-being measures, intelligently detecting any abnormalities, and providing tailored information for pregnant women were the critical aspects, whereas usability and data privacy were among the main concerns of the participants. Moreover, this study highlighted the challenges of engaging pregnant women in longitudinal mHealth monitoring, the additional work required for clinicians to monitor the data, and the need for an evidence-based technical solution., Conclusions: Clinical, technical, and practical factors associated with the use of mHealth to monitor health and well-being in pregnant women need to be considered during the design and feasibility evaluation stages. Technical solutions and appropriate strategies for motivating pregnant women are critical to supporting their long-term data collection compliance and engagement with mHealth technology during pregnancy., (©Jane Li, David Silvera-Tawil, Marlien Varnfield, M Sazzad Hussain, Vanitha Math. Originally published in JMIR Formative Research (https://formative.jmir.org), 02.12.2021.)

Sleep patterns often change during pregnancy and postpartum. However, if severe and persistent, these changes can depict a risk factor for significant health complications. It is thus essential to identify and understand changes in women's sleeping pattern over the course of pregnancy and postpartum, to offer an appropriate and timely intervention if necessary. In this paper, we discuss sleep disturbances during pregnancy and their association with pregnancy complications. We also review the state-of-the-art digital devices for real-time sleep assessment, and highlight their strengths and limitations.Clinical Relevance-This review highlights an importance of an individualized holistic pregnancy care program which engages both the healthcare professionals and the obstetric population, together with an educational module to increase the user awareness on the importance of sleep disturbances and their consequences during and after pregnancy.

Background: Total knee replacement (TKR) surgeries have increased in recent years. Exercise programs and other interventions following surgery can facilitate the recovery process. With limited clinician contact time, patients with TKR have a substantial burden of self-management and limited communication with their care team, thus often fail to implement an effective rehabilitation plan., Objective: We have developed a digital orthopedic rehabilitation platform that comprises a mobile phone app, wearable activity tracker, and clinical Web portal in order to engage patients with self-management tasks for surgical preparation and recovery, thus addressing the challenges of adherence to and completion of TKR rehabilitation. The study will determine the efficacy of the TKR platform in delivering information and assistance to patients in their preparation and recovery from TKR surgery and a Web portal for clinician care teams (ie, surgeons and physiotherapists) to remotely support and monitor patient progress., Methods: The study will evaluate the TKR platform through a randomized controlled trial conducted at multiple sites (N=5) in a number of states in Australia with 320 patients undergoing TKR surgery; the trial will run for 13 months for each patient. Participants will be randomized to either a control group or an intervention group, both receiving usual care as provided by their hospital. The intervention group will receive the app and wearable activity tracker. Participants will be assessed at 4 different time points: 4 weeks before surgery, immediately before surgery, 12 weeks after surgery, and 52 weeks after surgery. The primary outcome measure is the Oxford Knee Score. Secondary outcome measures include quality of life (Short-Form Health Survey); depression, anxiety, and stress (Depression, Anxiety, and Stress Scales); self-motivation; self-determination; self-efficacy; and the level of satisfaction with the knee surgery and care delivery. The study will also collect quantitative usage data related to all components (app, activity tracker, and Web portal) of the TKR platform and qualitative data on the perceptions of the platform as a tool for patients, carers, and clinicians. Finally, an economic evaluation of the impact of the platform will be conducted., Results: Development of the TKR platform has been completed and deployed for trial. The research protocol is approved by 2 human research ethics committees in Australia. A total of 5 hospitals in Australia (2 in New South Wales, 2 in Queensland, and 1 in South Australia) are expected to participate in the trial., Conclusions: The TKR platform is designed to provide flexibility in care delivery and increased engagement with rehabilitation services. This trial will investigate the clinical and behavioral efficacy of the app and impact of the TKR platform in terms of service satisfaction, acceptance, and economic benefits of the provision of digital services., Trial Registration: Australian New Zealand Clinical Trials Registry (ANZCTR) ACTRN12616000504415; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=370536 (Archived by WebCite at http://www.webcitation.org/6oKES0Gp1)., (©M Sazzad Hussain, Jane Li, Emily Brindal, Yasmin van Kasteren, Marlien Varnfield, Andrew Reeson, Shlomo Berkovsky, Jill Freyne. Originally published in JMIR Research Protocols (http://www.researchprotocols.org), 01.03.2017.)