The Influence of Personal Health Data on the Health Coaching Process.
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ABSTRACT:
SUBMITTER: Rutjes H
PROVIDER: S-EPMC9237329 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:ObjectiveDespite the potential values self-tracking data could offer, we have little understanding of how much access people have to "their" data. Our goal of this article is to unveil the current state of the data accessibility-the degree to which people can access their data-of personal health apps in the market.Materials and methodsWe reviewed 240 personal health apps from the App Store and selected 45 apps that support semi-automated tracking. We characterized the data accessibility of these apps using two dimensions-data access methods and data types.ResultsMore than 90% of our sample apps (n = 41) provide some types of data access support, which include synchronizing data with a health platform (ie, Apple Health), file download, and application program interfaces. However, the two approachable data access methods for laypeople-health platform and file download-typically put a significant limit on data format, granularity, and amount, which constrains people from easily repurposing the data.DiscussionPersonal data should be accessible to the people who collect them, but existing methods lack sufficient support for people in accessing the fine-grained data. Lack of standards in personal health data schema as well as frequent changes in market conditions are additional hurdles to data accessibility.ConclusionsMany stakeholders including patients, healthcare providers, researchers, third-party developers, and the general public rely on data accessibility to utilize personal data for various goals. As such, improving data accessibility should be considered as an important factor in designing personal health apps and health platforms.
| S-EPMC6433179 | biostudies-literature
Project description:Purpose: Screening the general population for ovarian cancer is not recommended by every major medical or public health organization because the harms from screening outweigh the benefit it provides. To improve ovarian cancer detection and survival many are looking at high-risk populations who would benefit from screening. Methods: We train a neural network on readily available personal health data to predict and stratify ovarian cancer risk. We use two different datasets to train our network: The National Health Interview Survey and Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial. Results: Our model has an area under the receiver operating characteristic curve of 0.71. We further demonstrate how the model could be used to stratify patients into different risk categories. A simple 3-tier scheme classifies 23.8% of those with cancer and 1.0% of those without as high-risk similar to genetic testing, and 1.1% of those with cancer and 24.4% of those without as low risk. Conclusion: The developed neural network offers a cost-effective and non-invasive way to identify those who could benefit from targeted screening.
| S-EPMC7931902 | biostudies-literature
Project description:BackgroundA culture of improvement is an important feature of high-quality health care systems. However, health care teams often need support to translate quality improvement (QI) activities into practice. One method of support is consultation from a QI coach. The literature suggests that coaching interventions have a positive impact on clinical outcomes. However, the impact of coaching on specific process outcomes, like adoption of clinical care activities, is unknown. Identifying the process outcomes for which QI coaching is most effective could provide specific guidance on when to employ this strategy.MethodsWe searched multiple databases from inception through July 2021. Studies that addressed the effects of QI coaching on process of care outcomes were included. Two reviewers independently extracted study characteristics and assessed risk of bias. Certainty of evidence was assessed using GRADE.ResultsWe identified 1983 articles, of which 23 cluster-randomized trials met eligibility criteria. All but two took place in a primary care setting. Overall, interventions typically targeted multiple simultaneous processes of care activities. We found that coaching probably has a beneficial effect on composite process of care outcomes (n = 9) and ordering of labs and vital signs (n = 6), and possibly has a beneficial effect on changes in organizational process of care (n = 5), appropriate documentation (n = 5), and delivery of appropriate counseling (n = 3). We did not perform meta-analyses because of conceptual heterogeneity around intervention design and outcomes; rather, we synthesized the data narratively. Due to imprecision, inconsistency, and high risk of bias of the included studies, we judged the certainty of these results as low or very low.ConclusionQI coaching interventions may affect certain processes of care activities such as ordering of labs and vital signs. Future research that advances the identification of when QI coaching is most beneficial for health care teams seeking to implement improvement processes in pursuit of high-quality care will support efficient use of QI resources.Protocol registrationThis study was registered and followed a published protocol (PROSPERO: CRD42020165069).
| S-EPMC8904663 | biostudies-literature
Project description:BackgroundConnected health has created opportunities for leveraging health data to deliver preventive and personalized health care services. The increasing number of personal devices and advances in measurement technologies contribute to an exponential growth in digital health data. The practices for sharing data across the health ecosystem are evolving as there are more opportunities for using such data to deliver responsive health services.ObjectiveThe objective of this study was to explore user attitudes toward sharing personal health data (PHD). The study was executed within the first year after the implementation of the new General Data Protection Regulation (GDPR) legal framework.MethodsThe authors analyzed the results of an online questionnaire survey to explore the willingness of 8004 people using connected health services across four European countries to share their PHD and the conditions under which they would be willing to do so.ResultsOur findings indicate that the majority of users are willing to share their personal PHD for scientific research (1811/8004, 22.63%). Age, education level, and occupation of the participants, in addition to the level of digitalization in their country were found to be associated with data sharing attitudes.ConclusionsPositive attitudes toward data sharing for scientific research can be perceived as an indication of trust established between users and academia. Nevertheless, the interpretation of data sharing attitudes is a complex process, related to and influenced by various factors.
| S-EPMC6906622 | biostudies-literature
Project description:Objective:The purpose of this review was to evaluate the modalities (e.g., face-to-face, telephone or electronic) of pharmacist health coaching providing the greatest improvement in patient outcomes, to enable a more comprehensive evaluation to be done and quality decision-making around health coaching modalities to be undertaken by pharmacists. Methods:This systematic review followed the PRISMA guidelines. CINHAL, EMBASE, PubMed, PsychINFO and SCOPUS were searched (2000-2019). Included articles were reviewed for the modality used to health coach, the training provided, and the outcomes. Results:Twelve papers met the eligibility criteria. A majority of studies included involved a combination of modalities of pharmacist health coaching. Four papers referred to face-to-face sessions, and one study used telephone coaching. In each paper, coaching led to an improvement in clinical and non-clinical health outcomes. Conclusion:The training provided to health coaches varied and in some cases was not reported. Inconsistencies in reports led to difficulties when comparing study outcomes. Therefore, conclusions about the modality providing the greatest improvement in patient outcomes and the most pragmatic health coaching modality are not possible. Studies that document the training, the modality, the outcomes and the cost benefits of coaching by pharmacists are warranted to enable a more comprehensive evaluation to be done and quality decision-making around health coaching modalities to be undertaken by pharmacists.
| S-EPMC7457552 | biostudies-literature
Project description:The sequencing of personal genomes enabled analysis of variation in transcription factor (TF) binding, chromatin structure and gene expression and indicated how they contribute to phenotypic variation. It is hypothesized that using the reference genome for mapping ChIP-seq or RNA-seq reads may introduce errors, especially at polymorphic genomic regions.We developed a Personal Genome Editor (perEditor) that changes the reference human genome (NCBI36/hg18) into an individual genome, taking into account single nucleotide polymorphisms (SNPs), insertions and deletions, copy number variation, and chromosomal rearrangements. perEditor outputs two alleles (maternal, paternal) of the individual genome that is ready for mapping ChIP-seq and RNA-seq reads, and enabling the analyses of allele specific binding, chromatin structure and gene expression.perEditor is available at http://biocomp.bioen.uiuc.edu/perEditor.szhong@illinois.edu.
| S-EPMC3232370 | biostudies-literature
Project description:ObjectiveUser-generated content (UGC) in online environments provides opportunities to learn an individual's health status outside of clinical settings. However, the nature of UGC brings challenges in both data collecting and processing. The purpose of this study is to systematically review the effectiveness of applying machine learning (ML) methodologies to UGC for personal health investigations.Materials and methodsWe searched PubMed, Web of Science, IEEE Library, ACM library, AAAI library, and the ACL anthology. We focused on research articles that were published in English and in peer-reviewed journals or conference proceedings between 2010 and 2018. Publications that applied ML to UGC with a focus on personal health were identified for further systematic review.ResultsWe identified 103 eligible studies which we summarized with respect to 5 research categories, 3 data collection strategies, 3 gold standard dataset creation methods, and 4 types of features applied in ML models. Popular off-the-shelf ML models were logistic regression (n = 22), support vector machines (n = 18), naive Bayes (n = 17), ensemble learning (n = 12), and deep learning (n = 11). The most investigated problems were mental health (n = 39) and cancer (n = 15). Common health-related aspects extracted from UGC were treatment experience, sentiments and emotions, coping strategies, and social support.ConclusionsThe systematic review indicated that ML can be effectively applied to UGC in facilitating the description and inference of personal health. Future research needs to focus on mitigating bias introduced when building study cohorts, creating features from free text, improving clinical creditability of UGC, and model interpretability.
| S-EPMC7647332 | biostudies-literature
Project description:BackgroundPersonal electronic health records (PEHRs) allow patients to view, generate, and manage their personal and medical data that are relevant across illness episodes, such as their medications, allergies, immunizations, and their medical, social, and family health history. Thus, patients can actively participate in the management of their health care by ensuring that their health care providers have an updated and accurate overview of the patients' medical records. However, the uptake of PEHRs remains low, especially in terms of patients entering and managing their personal and medical data in their PEHR.ObjectiveThis scoping review aimed to explore the barriers and facilitators that patients face when deciding to review, enter, update, or modify their personal and medical data in their PEHR. This review also explores the extent to which patient-generated and -managed data affect the quality and safety of care, patient engagement, patient satisfaction, and patients' health and health care services.MethodsWe searched the MEDLINE, Embase, CINAHL, PsycINFO, Cochrane Library, Web of Science, and Google Scholar web-based databases, as well as reference lists of all primary and review articles using a predefined search query.ResultsOf the 182 eligible papers, 37 (20%) provided sufficient information about patients' data management activities. The results showed that patients tend to use their PEHRs passively rather than actively. Patients refrain from generating and managing their medical data in a PEHR, especially when these data are complex and sensitive. The reasons for patients' passive data management behavior were related to their concerns about the validity, applicability, and confidentiality of patient-generated data. Our synthesis also showed that patient-generated and -managed health data ensures that the medical record is complete and up to date and is positively associated with patient engagement and patient satisfaction.ConclusionsThe findings of this study suggest recommendations for implementing design features within the PEHR and the construal of a dedicated policy to inform both clinical staff and patients about the added value of patient-generated data. Moreover, clinicians should be involved as important ambassadors in informing, reminding, and encouraging patients to manage the data in their PEHR.
| S-EPMC9832357 | biostudies-literature
Project description:BackgroundIn England, almost all general practices (GPs) have implemented GP online services such as electronic personal health records (ePHRs) that allow people to schedule appointments, request repeat prescriptions, and access parts of their medical records. The overall adoption rate of GP online services has been low, reaching just 28% in October 2019. In a previous study, Abd-Alrazaq et al adopted a model to assess the factors that influence patients' use of GP online services in England. According to the previous literature, the predictive power of the Abd-Alrazaq model could be improved by proposing new associations between the existing variables in the model.ObjectiveThis study aims to improve the predictive power of the Abd-Alrazaq model by proposing new relationships between the existing variables in the model.MethodsThe Abd-Alrazaq model was amended by proposing new direct, mediating, moderating, and moderated mediating effects. The amended model was examined using data from a previous study, which were collected by a cross-sectional survey of a convenience sample of 4 GPs in West Yorkshire, England. Structural equation modeling was used to examine the theoretical model and hypotheses.ResultsThe new model accounted for 53% of the variance in performance expectancy (PE), 76% of the variance in behavioral intention (BI), and 49% of the variance in use behavior (UB). In addition to the significant associations found in the previous study, this study found that social influence (SI) and facilitating conditions (FCs) are associated with PE directly and BI indirectly through PE. The association between BI and UB was stronger for younger women with higher levels of education, income, and internet access. The indirect effects of effort expectancy (EE), perceived privacy and security (PPS), and SI on BI were statistically stronger for women without internet access, patients with internet access, and patients without internet access, respectively. The indirect effect of PPS on BI was stronger for patients with college education or diploma than for those with secondary school education and lower, whereas the indirect effect of EE on BI was stronger for patients with secondary school education or lower than for those with college education or a diploma.ConclusionsThe predictive power of the Abd-Alrazaq model improved by virtue of new significant associations that were not examined before in the context of ePHRs. Further studies are required to validate the new model in different contexts and to improve its predictive power by proposing new variables. The influential factors found in this study should be considered to improve patients' use of ePHRs.
| S-EPMC7578819 | biostudies-literature
Project description:We studied the use, uptake, and effects of e-Vita, a personal health record, with self-management support and personalized asynchronized coaching, for type 2 diabetes patients treated in primary care. Patients were invited by their practice nurse to join the study aimed at testing use and effects of a personal health record. Patients were followed up for 6 months. Uptake and usage were monitored using log data. Outcomes were self-reported diabetes self-care, diabetes-related distress, and emotional wellbeing. Patients' health status was collected from their medical chart. 132 patients agreed to participate in the study of which less than half (46.1%) did not return to the personal health record after 1st login. Only 5 patients used the self-management support program within the personal health record, 3 of whom asked a coach for feedback. Low use of the personal health record was registered. No statistical significant differences on any of the outcome measures were found between baseline and 6 month follow-up. This study showed minimal impact of implementing a personal health record including self-management support in primary diabetes care. Successful adoption of web-based platforms, as ongoing patient centered care, is hard to achieve without additional strategies aimed at enhancing patient motivation and engaging professionals.
| S-EPMC4756133 | biostudies-other