Explore the vast range of titles available.

Patient-generated health data (PGHD), or health-related data gathered from patients to help address a health concern, are used increasingly in oncology to make regulatory decisions and evaluate quality of care. PGHD include self-reported health and treatment histories, patient-reported outcomes (PROs), and biometric sensor data. Advances in wireless technology, smartphones, and the Internet of Things have facilitated new ways to collect PGHD during clinic visits and in daily life. The goal of the current review was to provide an overview of the current clinical, regulatory, technological, and analytic landscape as it relates to PGHD in oncology research and care. The review begins with a rationale for PGHD as described by the US Food and Drug Administration, the Institute of Medicine, and other regulatory and scientific organizations. The evidence base for clinic-based and remote symptom monitoring using PGHD is described, with an emphasis on PROs. An overview is presented of current approaches to digital phenotyping or device-based, real-time assessment of biometric, behavioral, self-report, and performance data. Analytic opportunities regarding PGHD are envisioned in the context of big data and artificial intelligence in medicine. Finally, challenges and solutions for the integration of PGHD into clini-cal care are presented. The challenges include electronic medical record integration of PROs and biometric data, analysis of large and complex biometric data sets, and potential clinic workflow redesign. In addition, there is currently more limited evidence for the use of biometric data relative to PROs. Despite these challenges, the poten-tial benefits of PGHD make them increasingly likely to be integrated into oncology research and clinical care.

Digital innovations continue to shape health and health care. As technology socially integrates into daily living, the lives of health care consumers are transformed into a key source of health information, commonly referred to as patient-generated health data (PGHD). With chronic disease prevalence signaling the need for a refocus on primary prevention, electronic PGHD might be essential in strengthening proactive and person-centered health care. This study aimed to review and synthesize the existing literature on the utilization and implications of electronic PGHD for primary disease prevention and health promotion purposes. Guided by a well-accepted methodological framework for scoping studies, we screened MEDLINE, CINAHL, PsycINFO, Scopus, Web of Science, EMBASE, and IEEE Digital Library. We hand-searched 5 electronic journals and 4 gray literature sources, additionally conducted Web searches, reviewed relevant Web pages, manually screened reference lists, and consulted authors. Screening was based on predefined eligibility criteria. Data extraction and synthesis were guided by an adapted PGHD-flow framework. Beyond initial quantitative synthesis, we reported narratively, following an iterative thematic approach. Raw data were coded, thematically clustered, and mapped, allowing for the identification of patterns. Of 183 eligible studies, targeting knowledge and self-awareness, behavior change, healthy environments, and remote monitoring, most literature (125/183, 68.3%) addressed weight reduction, either through physical activity or nutrition, applying a range of electronic tools from socially integrated to full medical devices. Participants generated their data actively (100/183, 54.6%), in combination with passive sensor-based trackers (63/183, 34.4%) or entirely passively (20/183, 10.9%). The proportions of active and passive data generation varied strongly across prevention areas. Most studies (172/183, 93.9%) combined electronic PGHD with reflective, process guiding, motivational and educational elements, highlighting the role of PGHD in multicomponent digital prevention approaches. Most of these interventions (110/183, 60.1%) were fully automatized, underlining broader trends toward low-resource and efficiency-driven care. Only a fraction (47/183, 25.6%) of studies provided indications on the impact of PGHD on prevention-relevant outcomes, suggesting overall positive trends, especially on vitals (eg, blood pressure) and body composition measures (eg, body mass index). In contrast, the impact of PGHD on health equity remained largely unexplored. Finally, our analysis identified a list of barriers and facilitators clustered around data collection and use, technical and design considerations, ethics, user characteristics, and intervention context and content, aiming to guide future PGHD research. The large, heterogeneous volume of the PGHD literature underlines the topic's emerging nature. Utilizing electronic PGHD to prevent diseases and promote health is a complex matter owing to mostly being integrated within automatized and multicomponent interventions. This underlines trends toward stronger digitalization and weaker provider involvement. A PGHD use that is sensitive to identified barriers, facilitators, consumer roles, and equity considerations is needed to ensure effectiveness.

Patient-generated health data (PGHD) encompass health-related information created, recorded, and gathered by patients in their daily lives, and are distinct from data collected in clinical settings. PGHD can offer insight into patients’ everyday health behaviors and conditions, supporting health management and clinical decision-making. The Veterans Health Administration (VHA) has developed a robust infrastructure to collect PGHD, including automatically collected data from digital sensors and patient-entered data. This effort is guided by comprehensive policy and strategy documents to ensure the secure storage and effective use of PGHD. This paper describes the development and implementation of an infrastructure to support PGHD within the VHA and highlights envisioned clinical and research uses of PGHD to advance health care for US veterans. The PGHD database was built to Fast Healthcare Interoperability Resources standards, facilitating secure data storage and exchange of PGHD. Clinical tools, such as the provider-facing dashboards, make PGHD accessible from the electronic health records. Research and evaluation efforts focus on evaluating PGHD’s impact on patient engagement, clinical outcomes, and health care equity. The VHA’s comprehensive PGHD infrastructure represents a significant advancement in personalized health care and patient engagement. The integration of PGHD into clinical practice can enhance shared decision-making and self-management, while research and evaluation efforts can address how to maximize the benefits of PGHD for veterans. The VHA’s approach sets a benchmark for other US health care systems in leveraging PGHD to achieve the broad aims of enhancing stakeholder health care experiences, improving population health and health equity, and reducing costs.

The integration of patient-generated health data (PGHD) into health care has the potential to significantly transform patient care and clinical practice. PGHD includes health-related data created by patients, enabling the collection of health data beyond traditional health care settings. The Veterans Health Administration (VA) has taken proactive steps to incorporate PGHD into health care through its Share My Health Data (SMHD) mobile app. Launched in 2023, the SMHD app allows veterans to securely share data from their personal digital health devices with the VA for clinical and research use. However, data characterizing patients who use such tools in real-world health care systems are lacking, creating an evidence gap for implementing PGHD-informed care equitably. This study aimed to identify the characteristics of patients using the VA SMHD mobile app, which allows veterans to share PGHD with the VA. We conducted a cross-sectional analysis of veterans who began using SMHD between October 2023 and September 2024 (n=3157, \"SMHD users\"). We collected demographic information, including age, sex, race/ethnicity, and rurality, and clinical information, including physiological and mental health conditions, from VA administrative data. We compared characteristics of SMHD users to a 10% random sample of veterans from the same underlying administrative data cohort that had never used the app (n=632,187, \"nonusers\"). Statistical analyses were performed using chi-square tests, independent t tests, and multivariable regression to assess the relationship between use and key characteristics. Middle-aged veterans were more likely to be SMHD users (40-49 years: odds ratio [OR] 1.55, P<.001; 50-59 years: OR 1.37, P<.001), while veterans aged 60 years and over were less likely (60-69 years: OR 0.72, P<.001; ≥70 years: OR 0.24, P<.001). Female (OR 1.23, P<.001) and married (OR 1.31, P<.001) veterans were more likely to be SMHD users than male and unmarried veterans. In contrast, Black or African American (OR 0.62, P<.001) and rural (OR 0.82, P<.001) veterans were less likely to be SMHD users than White and urban veterans. Veterans in higher-income zip codes (OR 1.36, P<.001) were more likely to have used the app than those in lower-income zip codes. Clinically, SMHD users were more likely to have a service-connected disability (OR 1.81, P<.001), multiple physiological conditions (OR 1.86, P<.001), and multiple mental health diagnoses (OR 1.35, P<.001) versus none. Veterans who used the SMHD app differed significantly from nonusers across several demographic and clinical characteristics. These insights identify specific demographic and clinical subgroups with higher and lower app adoption, providing an evidence base to inform targeted implementation and outreach and support strategies to promote enhanced engagement in PGHD-informed care.

Technologies, including mobile health applications (apps) and wearables, offer new potential for gathering patient-generated health data (PGHD) from patients; however, little is known about patient preferences for and willingness to collect and share PGHD with their providers and healthcare systems. Describe how patients use their PGHD and factors important to patients when deciding whether to share PGHD with a healthcare system. Cross-sectional mailed longitudinal survey supplemented with administrative data within the Veterans Health Administration (VHA). National sample of Veterans who use VHA healthcare. Survey questions asked about demographics, willingness to use different devices to collect and share PGHD, what Veterans do with their PGHD, and factors important to Veterans when deciding whether to share PGHD with VHA. Administrative data provided information on Veteran health conditions. Multiple logistic regression models assessed factors associated with sharing PGHD with VHA. Overall, 47% of our analytic cohort (n = 383/807) indicated that they share PGHD collected through apps or digital health devices with VHA. In adjusted logistic regression models, Veterans who believed the following factors were Very Important (versus Somewhat/Not At All Important) had higher odds of sharing PGHD with VHA: if their doctor (OR = 1.4; 95%CI, 1.0-2.0) or other healthcare team members (OR = 1.4; 95%CI, 1.0-1.9) recommended they do so; and knowing that their healthcare team would look at the data (OR = 1.4; 95%CI, 1.0-2.0) or use the information to inform their healthcare (OR = 1.5; 95%CI, 1.1-2.1). Our data suggest that healthcare team members can influence patient sharing of PGHD, as can a patient's knowledge that PGHD will be used in clinical practice. Efforts to increase the number of patients who share PGHD with a healthcare system may benefit from buy-in among healthcare team members, who appear to play an influential role in patient decisions to share data.

The advancement of information and communication technologies has spurred a growing interest in and increased applications of patient-generated health data (PGHD). In particular, PGHD may be promising for older adults with cancer who have increased survival rates and experience a variety of symptoms. This scoping review aimed to identify the characteristics of research on PGHD as applied to older adults with cancer and to assess the current use of PGHD. Guided by Arksey and O'Malley as well as the JBI (Joanna Briggs Institute) methodology for scoping reviews, 6 electronic databases were searched: PubMed, Embase, CINAHL, Cochrane Library, Scopus, and Web of Science. In addition, the reference lists of the selected studies were screened to identify gray literature. The researchers independently screened the literature according to the predefined eligibility criteria. Data from the selected studies were extracted, capturing study, participant, and PGHD characteristics. Of the 1090 identified studies, 88 were selected. The publication trend gradually increased, with a majority of studies published since 2017 (69/88, 78%). Almost half of the studies were conducted in North America (38/88, 43%), followed by Europe (30/88, 34%). The most common setting in which the studies were conducted was the participant's home (69/88, 78%). The treatment status varied; the median sample size was 50 (IQR 33.8-84.0). The devices that were used to measure the PGHD were classified as research-grade wearable devices (57/113, 50.4%), consumer-grade wearable devices (28/113, 24.8%), or smartphones or tablet PCs for mobile apps (23/113, 20.4%). More than half of the studies measured physical activity (69/123, 56.1%), followed by patient-reported outcomes (23/123, 18.7%), vital signs (13/123, 10.6%), and sleep (12/123, 9.8%). The PGHD were mainly collected passively (63/88, 72%), and active collection methods were used from 2015 onward (20/88, 23%). In this review, the stages of PGHD use were classified as follows: (1) identification, monitoring, review, and analysis (88/88, 100%); (2) feedback and reporting (32/88, 39%); (3) motivation (30/88, 34%); and (4) education and coaching (19/88, 22%). This scoping review provides a comprehensive summary of the overall characteristics and use stages of PGHD in older adults with various types and stages of cancer. Future research should emphasize the use of PGHD, which interacts with patients to provide patient-centered care through patient engagement. By enhancing symptom monitoring, enabling timely interventions, and promoting patient involvement, PGHD have the potential to improve the well-being of older adults with cancer, contributing to better health management and quality of life. Therefore, our findings may provide valuable insights into PGHD that health care providers and researchers can use for geriatric cancer care. Open Science Framework Registry OSF.IO/FZRD5; https://doi.org/10.17605/OSF.IO/FZRD5.

To assess the relative validity of a new, web-based, self-administered 24 h dietary recall, the R24W, for assessment of energy and nutrient intakes among French Canadians. Each participant completed a 3d food record (FR) and the R24W on three occasions over a 4-week period. Intakes of energy and of twenty-four selected nutrients assessed by both methods were compared. Québec City metropolitan area. Fifty-seven women and fifty men (mean (sd) age: 47·2 (13·3) years). Equivalent proportions of under-reporters were found with the R24W (15·0%) and the FR (23·4%). Mean (sd) energy intake from the R24W was 7·2% higher than that from the FR (10 857 (3184) kJ/d (2595 (761) kcal/d) v. 10 075 (2971) kJ/d (2408 (710) kcal/d); P<0·01). Significant differences in mean nutrient intakes between the R24W and the FR ranged from -54·8% (i.e. lower value with R24W) for niacin to +40·0% (i.e. higher value with R24W) for alcohol. Sex- and energy-adjusted deattenuated correlations between the two methods were significant for all nutrients except Zn (range: 0·35-0·72; P<0·01). Cross-classification demonstrated that 40·0% of participants were classified in the same quartile with both methods, while 40·0% were classified in the adjacent quartile and only 3·6% were grossly misclassified (1st v. 4th quartile). Analysis of Bland-Altman plots revealed proportional bias between the two assessment methods for 8/24 nutrients. These data suggest that the R24W presents an acceptable relative validity as compared with the FR for estimating usual dietary intakes in a cohort of French Canadians.

The proliferation of wearable devices and advances in data analytics are accelerating the adoption of personalized digital health care, relying heavily on patient-generated health data (PGHD). However, the sensitive nature of this data creates significant privacy boundaries. While previous research has focused on rational cost-benefit trade-offs, there is a limited understanding of how social and cognitive factors-specifically interpersonal trust and digital literacy (DL)-shape the data-sharing decisions of the general public. This study aims to identify the factors predicting individuals' willingness to share health data (WS) by integrating privacy calculus and communication privacy management theories. It specifically examines the comparative influence of DL, interpersonal trust, and moral motivation on data-sharing decisions. We analyzed data from the 2023 Korea Panel Survey on the Digital Society (n=4518), a nationwide representative sample of internet users. Survey-weighted structural equation modeling with weighted least squares mean and variance adjusted estimation was used to examine the relationships among perceived risk (PR), perceived benefit (PB), DL, interpersonal trust, and WS. PR was the strongest negative predictor of WS (standardized coefficient [std β]=-0.189, P<.001), whereas PB was the strongest positive predictor (std β=0.076, P=.009), followed by moral motivation (std β=0.062, P=.03) and interpersonal trust. DL showed a significant negative direct association with willingness to share (std β = -0.060, P<.001). However, subdimension analysis revealed heterogeneous mechanisms: the \"understanding\" dimension was associated with lower PR and indirectly promoted sharing, whereas use and engagement were associated with higher PR. Age-stratified analyses suggested potential heterogeneity in these relationships, although the overall interaction was not statistically significant. PBs and risks were the strongest determinants of PGHD sharing, with benefits increasing and risks decreasing willingness to share. Beyond this risk-benefit balance, DL (particularly understanding) and interpersonal trust also played important roles, highlighting the need for trust-based and user-centric strategies to promote PGHD sharing and support the expansion of digital health care.

Purpose The purpose of this data visualization study was to identify patterns in patient‐generated health data (PGHD) of women with and without Circulation signs or symptoms. Specific aims were to (a) visualize and interpret relationships among strengths, challenges, and needs of women with and without Circulation signs or symptoms; (b) generate hypotheses based on these patterns; and (c) test hypotheses generated in Aim 2. Design The design of this visualization study was retrospective, observational, case controlled, and exploratory. Methods We used existing de‐identified PGHD from a mobile health application, MyStrengths+MyHealth (N = 383). From the data, women identified with Circulation signs or symptoms (n = 80) were matched to an equal number of women without Circulation signs or symptoms. Data were analyzed using data visualization techniques and descriptive and inferential statistics. Findings Based on the patterns, we generated nine hypotheses, of which four were supported. Visualization and interpretation of relationships revealed that women without Circulation signs or symptoms compared to women with Circulation signs or symptoms had more strengths, challenges, and needs—specifically, strengths in connecting; challenges in emotions, vision, and health care; and needs related to info and guidance. Conclusions This study suggests that visualization of whole‐person health including strengths, challenges, and needs enabled detection and testing of new health patterns. Some findings were unexpected, and perspectives of the patient would not have been detected without PGHD, which should be valued and sought. Such data may support improved clinical interactions as well as policies for standardization of PGHD as sharable and comparable data across clinical and community settings. Clinical Relevance Standardization of patient‐generated whole‐person health data enabled clinically relevant research that included the patients’ perspective.

Patient-generated health data (PGHD) enhance traditional healthcare by enabling continuous monitoring and supporting personalized care, yet concerns over privacy, security, and integration into existing systems hinder broader adoption. This study examined the perceptions, awareness, and concerns of patients and caregivers regarding PGHD and assessed their willingness to share such data for clinical, research, and commercial purposes. A cross-sectional survey was conducted from 6 to 12 November 2023, involving 400 individuals with experience using PGHD. Participants completed structured questionnaires addressing health information management, PGHD usage, and attitudes toward its application. PGHD was most commonly used by patients with chronic conditions and guardians of minors, with tethered personal health record apps frequently utilized. Respondents identified improved self-management and better access to information as key benefits. However, significant concerns about data privacy and security emerged, especially regarding non-clinical use. Younger adults, particularly those in their 20s, showed lower willingness to engage with PGHD due to heightened privacy concerns. These findings suggest that, while support for clinical use of PGHD is strong, barriers related to trust and consent remain. Addressing privacy concerns and simplifying consent processes will be essential to promote equitable and responsible PGHD utilization across diverse patient populations.