BACKGROUND: Diet-related diseases, such as type 2 diabetes, require strict dietary management to slow down disease progression and call for innovative management strategies. Conventional diet monitoring places a significant memory burden on patients, who may not accurately remember details of their meals and thus frequently falls short in preventing disease progression. Recent advances in sensor and computational technologies have sparked interest in developing eating detection platforms.
OBJECTIVE: This review investigates central hemodynamic and thermoregulatory responses as potential biomarkers for eating detection.
METHODS: We searched peer-reviewed literature indexed in PubMed, Web of Science, and Scopus on June 20, 2022, with no date limits. We also conducted manual searches in the same databases until April 21, 2024. We included English-language papers demonstrating the impact of eating on central hemodynamics and thermoregulation in healthy individuals. To evaluate the overall study quality and assess the risk of bias, we designed a customized tool inspired by the Cochrane assessment framework. This tool has 4 categories: high, medium, low, and very low. A total of 2 independent reviewers conducted title and abstract screening, full-text review, and study quality and risk of bias analysis. In instances of disagreement between the 2 reviewers, a third reviewer served as an adjudicator.
RESULTS: Our search retrieved 11,450 studies, and 25 met our inclusion criteria. Among the 25 included studies, 32% (8/25) were classified as high quality, 52% (13/25) as medium quality, and 16% (4/25) as low quality. Furthermore, we found no evidence of publication bias in any of the included studies. A consistent postprandial increase in heart rate, cardiac output, and stroke volume was observed in at least 95% (heart rate: 19/19, cardiac output: 18/19, stroke volume: 11/11) of the studies that investigated these variables\' responses to eating. Specifically, cardiac output increased by 9%-100%, stroke volume by 18%-41%, and heart rate by 6%-21% across these studies. These changes were statistically significant (P<.05). In contrast, the 8 studies that investigated postprandial thermoregulatory effects displayed grossly inconsistent results, showing wide variations in response with no clear patterns of change, indicating a high degree of variability among these studies.
CONCLUSIONS: Our findings demonstrate that central hemodynamic responses, particularly heart rate, hold promise for wearable-based eating detection, as cardiac output and stroke volume cannot be measured by any currently available noninvasive medical or consumer-grade wearables.
BACKGROUND: PROSPERO CRD42022360600; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=360600.

BACKGROUND: Postoperative infections remain a crucial challenge in health care, resulting in high morbidity, mortality, and costs. Accurate identification and labeling of patients with postoperative bacterial infections is crucial for developing prediction models, validating biomarkers, and implementing surveillance systems in clinical practice.
OBJECTIVE: This scoping review aimed to explore methods for identifying patients with postoperative infections using electronic health record (EHR) data to go beyond the reference standard of manual chart review.
METHODS: We performed a systematic search strategy across PubMed, Embase, Web of Science (Core Collection), the Cochrane Library, and Emcare (Ovid), targeting studies addressing the prediction and fully automated surveillance (ie, without manual check) of diverse bacterial infections in the postoperative setting. For prediction modeling studies, we assessed the labeling methods used, categorizing them as either manual or automated. We evaluated the different types of EHR data needed for the surveillance and labeling of postoperative infections, as well as the performance of fully automated surveillance systems compared with manual chart review.
RESULTS: We identified 75 different methods and definitions used to identify patients with postoperative infections in studies published between 2003 and 2023. Manual labeling was the predominant method in prediction modeling research, 65% (49/75) of the identified methods use structured data, and 45% (34/75) use free text and clinical notes as one of their data sources. Fully automated surveillance systems should be used with caution because the reported positive predictive values are between 0.31 and 0.76.
CONCLUSIONS: There is currently no evidence to support fully automated labeling and identification of patients with infections based solely on structured EHR data. Future research should focus on defining uniform definitions, as well as prioritizing the development of more scalable, automated methods for infection detection using structured EHR data.

BACKGROUND: Acute exacerbations of chronic obstructive pulmonary disease (AECOPD) are associated with high mortality, morbidity, and poor quality of life and constitute a substantial burden to patients and health care systems. New approaches to prevent or reduce the severity of AECOPD are urgently needed. Internationally, this has prompted increased interest in the potential of remote patient monitoring (RPM) and digital medicine. RPM refers to the direct transmission of patient-reported outcomes, physiological, and functional data, including heart rate, weight, blood pressure, oxygen saturation, physical activity, and lung function (spirometry), directly to health care professionals through automation, web-based data entry, or phone-based data entry. Machine learning has the potential to enhance RPM in chronic obstructive pulmonary disease by increasing the accuracy and precision of AECOPD prediction systems.
OBJECTIVE: This study aimed to conduct a dual systematic review. The first review focuses on randomized controlled trials where RPM was used as an intervention to treat or improve AECOPD. The second review examines studies that combined machine learning with RPM to predict AECOPD. We review the evidence and concepts behind RPM and machine learning and discuss the strengths, limitations, and clinical use of available systems. We have generated a list of recommendations needed to deliver patient and health care system benefits.
METHODS: A comprehensive search strategy, encompassing the Scopus and Web of Science databases, was used to identify relevant studies. A total of 2 independent reviewers (HMGG and CM) conducted study selection, data extraction, and quality assessment, with discrepancies resolved through consensus. Data synthesis involved evidence assessment using a Critical Appraisal Skills Programme checklist and a narrative synthesis. Reporting followed PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines.
RESULTS: These narrative syntheses suggest that 57% (16/28) of the randomized controlled trials for RPM interventions fail to achieve the required level of evidence for better outcomes in AECOPD. However, the integration of machine learning into RPM demonstrates promise for increasing the predictive accuracy of AECOPD and, therefore, early intervention.
CONCLUSIONS: This review suggests a transition toward the integration of machine learning into RPM for predicting AECOPD. We discuss particular RPM indices that have the potential to improve AECOPD prediction and highlight research gaps concerning patient factors and the maintained adoption of RPM. Furthermore, we emphasize the importance of a more comprehensive examination of patient and health care burdens associated with RPM, along with the development of practical solutions.

UNASSIGNED: The COVID-19 pandemic highlighted gaps in the public health workforce\'s capacity to deploy digital technologies while upholding ethical, social justice, and health equity principles. Existing public health competency frameworks have not been updated to reflect the prominent role digital technologies play in contemporary public health, and public health training institutions are seeking to integrate digital technologies in their curricula.
UNASSIGNED: As a first step in a multiphase study exploring recommendations for updates to public health competency frameworks within the Canadian public health context, we conducted a rapid review of literature aiming to identify recommendations for digital competencies, training approaches, and inter- or transdisciplinary partnerships that can enhance public health practitioners\' capacity to support the digital transformation of public health.
UNASSIGNED: Following the World Health Organization\'s (2017) guidelines for rapid reviews, a systematic search was conducted on Ovid MEDLINE, Ovid Embase, ERIC (Education Resources Information Center), and Web of Science for peer-reviewed articles. We also searched Google Scholar and various public health agency and public health association websites for gray literature using search terms related to public health, digital health, practice competencies, and training approaches. We included articles with explicit practice competencies and training recommendations related to digital technologies among public health practitioners published between January 2010 and December 2022. We excluded articles describing these concepts in passing or from a solely clinical perspective.
UNASSIGNED: Our search returned 2023 titles and abstracts, of which only 12 studies met the inclusion criteria. We found recommendations for new competencies to enable public health practitioners to appropriately use digital technologies that cut across all existing categories of the core competencies for public health framework of the Public Health Agency of Canada. We also identified a new competency category related to data, data systems management, and governance. Training approaches identified include adapted degree-awarding programs like combined public health and informatics or data science degree programs and ongoing professional certifications with integration of practice-based learning in multi- and interdisciplinary training. Disciplines suggested as important to facilitate practice competency and training recommendations included public health, public health informatics, data, information and computer sciences, biostatistics, health communication, and business.
UNASSIGNED: Despite the growth of digital technologies in public health, recommendations about practice competencies and training approaches necessary to effectively support the digital transformation of public health remain limited in the literature. Where available, evidence suggests the workforce requires new competencies that cut across and extend existing public health competencies, including new competencies related to the use and protection of new digital data sources, alongside facilitating health communication and promotion functions using digital media. Recommendations also emphasize the need for training approaches that focus on interdisciplinarity through adapted degree-awarding public health training programs and ongoing professional development.

BACKGROUND: Despite more than 2 decades of telehealth use in Australia and the rapid uptake during the COVID-19 pandemic, little is known about its unintended consequences beyond its planned and intended outcomes.
OBJECTIVE: The aim of this review was to synthesize evidence on the unintended consequences of telehealth use in Australia to clarify its impact beyond its planned and intended outcomes.
METHODS: We conducted a search of 4 electronic databases: Ovid MEDLINE, Ovid Embase, EBSCO CINAHL, and Scopus. A critical interpretive synthesis approach was adopted for its flexibility and interpretive nature. We extracted data about study characteristics and the types and models of telehealth services. The extracted unintended consequences were coded and mapped into the domains and dimensions of the Australian Health Performance Framework.
RESULTS: Of the 4241 records identified by the search, 94 (2.22%) studies were eligible for data extraction and analysis. Of these 94 studies, 23 (24%) reported largely positive unintended consequences of telehealth associated with health status, while 6 (6%) noted a potential negative impact of telehealth on socioeconomic status. The findings of 4 (4%) of the 94 studies highlighted societal and financial consequences of telehealth beyond the health system. Almost all studies (93/94, 99%) reported unintended consequences under the 5 dimensions of the Australian Health Performance Framework.
CONCLUSIONS: Our synthesis offers a framework for understanding the unintended consequences of the use of telehealth as an alternative to in-person care in Australia. While we have documented many unintended benefits of telehealth use, our findings also shed light on many challenges of delivering care via telehealth across different domains and dimensions. These findings hold significant practice and policy-making implications for ensuring safe and high-quality care delivery via telehealth.

BACKGROUND: The declaration of the COVID-19 pandemic led to public health restrictions that impacted the lives of people across the globe. Parents were particularly burdened with balancing multiple responsibilities, such as working from home while caring for and educating their children. Alcohol use among parents is an area that warrants further exploration.
OBJECTIVE: This study aimed to investigate patterns of parental alcohol consumption during the COVID-19 pandemic, focusing on relative changes in the frequency and quantity of alcohol use compared to prepandemic use, nonparent adult samples, or both.
METHODS: A scoping review informed by the methodology of Arksey and O\'Malley explored patterns of parental alcohol consumption during the COVID-19 pandemic. Searches were conducted in CINAHL, Ovid MEDLINE, PsycINFO, and Web of Science. Search terms were created using the Joanna Briggs Institute framework of Population, Concept, and Context, with the population being parents and the concept being alcohol consumption during the COVID-19 pandemic.
RESULTS: The database search yielded 3568 articles, which were screened for eligibility. Of the 3568 articles, 40 (1.12%) met the inclusion criteria and were included in the scoping review. Findings indicated the following: (1) having children at home was a factor associated with parental patterns of alcohol use; (2) mixed findings regarding gender-related patterns of alcohol consumption; and (3) linkages between parental patterns of alcohol use and mental health symptoms of stress, depression, and anxiety.
CONCLUSIONS: This scoping review revealed heterogeneous patterns in parental alcohol use across sociocultural contexts during the COVID-19 pandemic. Given the known harms of alcohol use, it is worthwhile for clinicians to assess parental drinking patterns and initiate conversations regarding moderation in alcohol use.

BACKGROUND: Virtual reality (VR) in different immersive conditions has been increasingly used as a nonpharmacological method for managing chronic musculoskeletal pain.
OBJECTIVE: We aimed to assess the effectiveness of VR-assisted active training versus conventional exercise or physiotherapy in chronic musculoskeletal pain and to analyze the effects of immersive versus nonimmersive VR on pain outcomes.
METHODS: This systematic review of randomized control trials (RCTs) searched PubMed, Scopus, and Web of Science databases from inception to June 9, 2024. RCTs comparing adults with chronic musculoskeletal pain receiving VR-assisted training were included. The primary outcome was pain intensity; secondary outcomes included functional disability and kinesiophobia. Available data were pooled in a meta-analysis. Studies were graded using the Cochrane Risk-of-Bias Tool version 2.
RESULTS: In total, 28 RCTs including 1114 participants with some concerns for a high risk of bias were identified, and 25 RCTs were included in the meta-analysis. In low back pain, short-term outcomes measured post intervention showed that nonimmersive VR is effective in reducing pain (standardized mean difference [SMD] -1.79, 95% CI -2.72 to -0.87; P<.001), improving disability (SMD -0.44, 95% CI -0.72 to -0.16; P=.002), and kinesiophobia (SMD -2.94, 95% CI -5.20 to -0.68; P=.01). Intermediate-term outcomes measured at 6 months also showed that nonimmersive VR is effective in reducing pain (SMD -8.15, 95% CI -15.29 to -1.01; P=.03), and kinesiophobia (SMD -4.28, 95% CI -8.12 to -0.44; P=.03) compared to conventional active training. For neck pain, immersive VR reduced pain intensity (SMD -0.55, 95% CI -1.02 to -0.08; P=.02) but not disability and kinesiophobia in the short term. No statistical significances were detected for knee pain or other pain regions at all time points. In addition, 2 (8%) studies had a high risk of bias.
CONCLUSIONS: Both nonimmersive and immersive VR-assisted active training is effective in reducing back and neck pain symptoms. Our study findings suggest that VR is effective in alleviating chronic musculoskeletal pain.
BACKGROUND: PROSPERO CRD42022302912; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=302912.

BACKGROUND: The integration of smart technologies, including wearables and voice-activated devices, is increasingly recognized for enhancing the independence and well-being of older adults. However, the long-term dynamics of their use and the coadaptation process with older adults remain poorly understood. This scoping review explores how interactions between older adults and smart technologies evolve over time to improve both user experience and technology utility.
OBJECTIVE: This review synthesizes existing research on the coadaptation between older adults and smart technologies, focusing on longitudinal changes in use patterns, the effectiveness of technological adaptations, and the implications for future technology development and deployment to improve user experiences.
METHODS: Following the Joanna Briggs Institute Reviewer\'s Manual and PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines, this scoping review examined peer-reviewed papers from databases including Ovid MEDLINE, Ovid Embase, PEDro, Ovid PsycINFO, and EBSCO CINAHL from the year 2000 to August 28, 2023, and included forward and backward searches. The search was updated on March 1, 2024. Empirical studies were included if they involved (1) individuals aged 55 years or older living independently and (2) focused on interactions and adaptations between older adults and wearables and voice-activated virtual assistants in interventions for a minimum period of 8 weeks. Data extraction was informed by the selection and optimization with compensation framework and the sex- and gender-based analysis plus theoretical framework and used a directed content analysis approach.
RESULTS: The search yielded 16,143 papers. Following title and abstract screening and a full-text review, 5 papers met the inclusion criteria. Study populations were mostly female participants and aged 73-83 years from the United States and engaged with voice-activated virtual assistants accessed through smart speakers and wearables. Users frequently used simple commands related to music and weather, integrating devices into daily routines. However, communication barriers often led to frustration due to devices\' inability to recognize cues or provide personalized responses. The findings suggest that while older adults can integrate smart technologies into their lives, a lack of customization and user-friendly interfaces hinder long-term adoption and satisfaction. The studies highlight the need for technology to be further developed so they can better meet this demographic\'s evolving needs and call for research addressing small sample sizes and limited diversity.
CONCLUSIONS: Our findings highlight a critical need for continued research into the dynamic and reciprocal relationship between smart technologies and older adults over time. Future studies should focus on more diverse populations and extend monitoring periods to provide deeper insights into the coadaptation process. Insights gained from this review are vital for informing the development of more intuitive, user-centric smart technology solutions to better support the aging population in maintaining independence and enhancing their quality of life.
UNASSIGNED: RR2-10.2196/51129.

BACKGROUND: Telecare and telehealth are important care-at-home services used to support individuals to live more independently at home. Historically, these technologies have reactively responded to issues. However, there has been a recent drive to make better use of the data from these services to facilitate more proactive and predictive care.
OBJECTIVE: This review seeks to explore the ways in which predictive data analytics techniques have been applied in telecare and telehealth in at-home settings.
METHODS: The PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist was adhered to alongside Arksey and O\'Malley\'s methodological framework. English language papers published in MEDLINE, Embase, and Social Science Premium Collection between 2012 and 2022 were considered and results were screened against inclusion or exclusion criteria.
RESULTS: In total, 86 papers were included in this review. The types of analytics featuring in this review can be categorized as anomaly detection (n=21), diagnosis (n=32), prediction (n=22), and activity recognition (n=11). The most common health conditions represented were Parkinson disease (n=12) and cardiovascular conditions (n=11). The main findings include: a lack of use of routinely collected data; a dominance of diagnostic tools; and barriers and opportunities that exist, such as including patient-reported outcomes, for future predictive analytics in telecare and telehealth.
CONCLUSIONS: All papers in this review were small-scale pilots and, as such, future research should seek to apply these predictive techniques into larger trials. Additionally, further integration of routinely collected care data and patient-reported outcomes into predictive models in telecare and telehealth offer significant opportunities to improve the analytics being performed and should be explored further. Data sets used must be of suitable size and diversity, ensuring that models are generalizable to a wider population and can be appropriately trained, validated, and tested.

BACKGROUND: Pathogenesis of seborrheic dermatitis involves lipid secretion by sebaceous glands, Malassezia colonization, and an inflammatory response with skin barrier disruption. Each of these pathways could be modulated by diet, obesity, and nutritional supplements. Current treatment options provide only temporary control of the condition; thus, it is essential to recognize modifiable lifestyle factors that may play a role in determining disease severity.
OBJECTIVE: This study aimed to summarize published evidence on diet, nutritional supplements, alcohol, obesity, and micronutrients in patients with seborrheic dermatitis and to provide useful insights into areas of further research.
METHODS: A literature search of Scopus, PubMed, and MEDLINE (Ovid interface) for English language papers published between 1993 and 2023 was conducted on April 16, 2023. Case-control studies, cohort studies, and randomized controlled trials with 5 or more subjects conducted on adult participants (>14 years) were included, case reports, case series, and review papers were excluded due to insufficient level of evidence.
RESULTS: A total of 13 studies, 8 case-control, 3 cross-sectional, and 2 randomized controlled trials, involving 13,906 patients were included. Seborrheic dermatitis was correlated with significantly increased copper, manganese, iron, calcium, and magnesium concentrations and significantly lower serum zinc and vitamin D and E concentrations. Adherence to the Western diet was associated with a higher risk for seborrheic dermatitis in female patients and an increased consumption of fruit was associated with a lower risk of seborrheic dermatitis in all patients. The prebiotic Triphala improved patient satisfaction and decreased scalp sebum levels over 8 weeks. Most studies find associations between regular alcohol use and seborrheic dermatitis, but the association between BMI and obesity on seborrheic dermatitis severity and prevalence is mixed.
CONCLUSIONS: This review sheds light on specific promising areas of research that require further study, including the need for interventional studies evaluating serum zinc, vitamin D, and vitamin E supplementation for seborrheic dermatitis. The negative consequences of a Western diet, alcohol use, obesity, and the benefits of fruit consumption are well known; however, to fully understand their specific relationships to seborrheic dermatitis, further cohort or interventional studies are needed.
BACKGROUND: PROSPERO CRD42023417768; https://tinyurl.com/bdcta893.