With the continuous enhancement of health awareness, people are more willing to seek health information to manage their health. Health care has begun to shift from the traditional physician-centered paradigm to the patient-centric paradigm. [1] Studies have shown that this new paradigm can bring many benefits, including improved user satisfaction and health outcomes, and reduced health care costs. [2] [3] [4] As a representative of this new paradigm, consumer health informatics applications, such as online health communities and decision support systems, enhance the engagement of patients in their health management. [5, 6] The recent advancement in data science, including information retrieval and storage technology, enables people to obtain and manage a wide range of complex information about multiple health domains. Health-related information could come from diverse sources, including scientific literature, social media, websites, clinical trials, and community resources. The information sources are increasingly digital and dynamic, and are full of new data types. The wealth of information and the advancement of Internet services offer the potential to revolutionize the paradigm of health decisions and management. However, as shown in Brown et al. (2010) , [7] seeking health information online is not as easy as people expect, especially for older adults. Therefore, there is an urgent need for a tool to help people to cope with the challenges in making a valid selection and use information relevant to them.
Digital personal health library (PHL) has increasingly become a popular consumer health tool for the collection, storage, management, and sharing of health information in many forms. Besides integrating information from different sources, PHL also enables consumers to assess multiple functions of the library with a visual and user-friendly interface. With this revolutionary technology, patients expressed increased interest in their health management. Especially in the recent outbreak of COVID-19, the shelter-in-place requirement significantly affects the traditional way of obtaining health information, which takes healthcare providers as the primary information source. Hence, PHLs stand out for their advantage of remote access to information. At the same time, the human factors, including patient engagement and social determinants, are valuable elements to be considered in the successful construction of a PHL. The ability to access information on health care, disease prevention, and health promotion diminishes with age. [8, 9] The global population aging not only creates a vast customer base of older adults but also puts forward new requirements for PHLs. To meet users' different needs for PHL services, researchers need to develop novel approaches to address users' limited health literacy and to improve usability.
When the Internet was just beginning to take off at the opening of this century, Gunther Eysenbach et al [10] summarized in detail the barriers to consumers' access to health information, which set the standard for future research. In recent years, interdisciplinary researcher teams have made great efforts to promote health self-management and to advance in consumer health informatics. [11] By summarizing the current development of PHL, this paper explores whether previous work has sufficiently addressed the initial barriers, what problems still exist after 20 years of development, more importantly, identifies the direction of future development.
This paper is devoted to describe and assess the following aspects of PHL studies: (1) health topic and target population; (2) study purpose; (3) library function; (4) data source and scientific method; and (5) evaluation measure and status. It further discusses the trust-building between users and PHLs, and the emergence of novel technologies for promoting patient engagement. Finally, it proposes recommendations on leveraging state-of-the-art technology to develop PHL that helps individuals gather, manage, and use data and information for their health.
This paper conducted a review based on the 2009 PRISMA statement. [12] Search strategy and data sources This paper searched PubMed and EMBASE to retrieve articles published up to March 2020. Search terms were selected based on the concept of a PHL. The principle of the search strategy is to include as many articles as possible and maintain reasonable retrieval accuracy. Therefore, the search items for database queries are "Personal" AND "Health" AND "Library". As shown in Figure 1 , searches returned 1,114 articles from PubMed and 2,372 articles from EMBASE, of which 636 were duplicates.
The inclusion criteria for articles in this review were as follows: (1) the article contained the information about authors, title, abstract, and publication year; (2) the article was written in English; and (3) the abstract of the article mentioned health library, a medical health setting, data science method and use of the PHL for health information access, the management or other similar purposes. Literature reviews and duplicate articles were excluded from the analysis. Search results containing the above necessary information were exported from each database. Two authors (H. D, C. Z) independently screened the title and abstract of each retrieved article. With the inclusion criteria, the relevance of an article is marked as "yes", "no" or "maybe". Inconsistent annotations and articles labeled with "maybe" were fully discussed to come to an agreement. For complex cases where consensus was difficult to reach, a third author joined the discussion and achieved the final result based on majority voting. For the articles identified as relevant, H. D and C. Z further independently reviewed the full text of them. The disagreements were settled through discussions.
The included studies were analyzed from eight key aspects: health topics, target population, study purpose, library function, data sources, data science methods, status, and evaluation measures. This paper did not conduct a quality assessment, as there are no general evaluation criteria for PHL studies to the best of our knowledge.
To better describe the emerging field in PHLs, it is essential to capture the relationships among a core set of keywords using mathematical and statistical methods in Bibliometrics [13] . Accordingly, this paper used the keyword co-occurrence network [14] with the Kamada-Kawai layout [15] to mine and visualize the high-level knowledge structure in PHL literature.
This paper also performed multiple correspondence analysis (MCA) [16] to draw a conceptual structure of the field. MCA performed a homogeneity analysis of a Document x Word matrix to obtain the representation of the terms in a low-dimensional Euclidean space. [17] The K-means method was further adopted to identify clusters of terms that express common concepts.
The results are interpreted based on the relative positions of the points. The more similar the distribution of the terms, the closer the points they represent in the graph. [18] This paper conducted these knowledge mining analysis based on the R package Bibliometrix [19] .
This review included twelve articles upon screening. The majority of eligible articles were indexed in EMBASE (83.3%, n=10), with the other 2 articles (16.7%) indexed in PubMed. Publication years of these articles ranged from 2004 to 2019, with more than 83% of articles published in the past ten years. Table 1 -4 provided the eight aspects of content extracted from these articles, indicating great diversity in the PHL studies. The following sections will explore them in detail.
As shown in Table 1 , the current PHL studies covered a wide range of health domains, with a focus on specific diseases, including infectious diseases [20] , congestive heart failure [21] , motor neuron disease [22] , diabetes [23, 24] , and schizophrenia spectrum illnesses [25] . Besides, the rest six studies worked on improving other health-related issues, including electronic prescribing [26] , clinical practice guidelines (CPGs) in medical care [27] , unhealthy behaviors [28] , individualized patient care plans [29] , co-operative healthcare work [30] , and personal health records (PHRs) [31] . The target populations for these PHLs included patient [20] [21] [22] [23] [24] [25] [26] [28] [29] [30] [31] , health professionals [20] , clinicians [21, 29] , care providers [22, 27] , prescribers [26] , and healthcare personnel [30] . Few existing studies had targeted for specific age groups except for the study in [22] . However, this study only indicated that the age of the target population was the typical age of onset of the disease, and did not specify a certain age range. A handful of studies in the paper described the age range of participants they hired to conduct pilot studies, including the studies in [23] [24] [25] . 
The study purposes of included articles had strong ties with their health topics and target populations ( Table 2 ). The PHLs' main objectives were to support a better quality of care [21, 27, 29] , facilitate patients' self-management [25] , support patients' decision-making [22] , promote co-operative healthcare work [30] , and collect, classify, index, share and provide valuable health information [20, 28, 31] . In general, the current PHLs served a variety of purposes. 
The library functions met the health needs of a specific target population. In addition to the essential information retrieval and browsing functions, the PHLs were able to integrate and utilize information, engaging various forms of health decision support [20, 32] , information sharing, and communication among patients and health personnel [23, 32] . However, there was a lack of mining and visualizing deep-level knowledge from online information.
Different data science methods were used to construct PHLs with multiple functions (Table 3 ). In addition to web development techniques, the primary approaches included relational database [20] , distributed database technology, mHealth App technology [21] , data security [26] , standards-based information exchange [26] , information retrieval technology, ontology construction technology, human-computer interaction [27] , cross-platform software development technology [25] , information system technology [29] , the attribute-based encryption technology and search encryption technology [31] . Most articles did not provide technical details. 
Except for one study that did not evaluate the performance of its PHL [20] , all the others indicated that their PHLs showed promising results in promoting patient's self-management, although valid assessments were not uniformly performed ( [34] , Problem Areas in Diabetes [36, 37] , Diabetes Management Self-Efficacy Scale [38] , and Brief Diabetes Knowledge Test [39] , to evaluate the following aspects respectively: satisfaction with diabetes treatment, diabetes-specific distress, self-efficacy and diabetes knowledge.
Pre-post comparison at intervention and control practice was another critical way to examine whether exposure to such a PHL improved health outcome. Steven H. Woolf et al [28] collected patients' data through an email-based questionnaire when they have used the PHL for 1 and 4 months. Then the study assessed the health behavior changes of patients from the baseline. However, the evaluation required a large quantity of user data to be statistically valid. How to encourage PHL usage is a factor to be considered to solve this problem.
Notably, approximately 25% of the studies did not report the number of distinct patients from which feedback was obtained, and only one-third reported patient demographic characteristics. Studies assessing PHLs' outcomes often did not list the exact contents of the surveys or interviews; instead, many stated that all evaluations were in the form of a questionnaire, limiting the ability to compare and summarize results across studies. 
This section presents other mined knowledge in the following figures. As shown in Figure 2 , JAMIA is the largest source of references beyond JMIR, BMJ BRIT MED J. This shows the long-term contribution that JAMIA has made to the development of PHLs. Figure 4 is a two-dimensional conceptual structure map. It showed that the most common title terms were grouped into three clusters, corresponding to the three aspects of consumer health informatics. Forming the concept of consumer, cluster 1 (blue) bought together terms such as patients, diabetes. Articles used these terms had similar research themes in improving patients' outcomes [43, 44] and behavior [28] . Cluster 2 (green) focused on informatics concepts, such as a web-based decision support system. Cluster 3 (red) mainly referred to general health concepts, which were in all articles. 
This paper screened 2,850 articles retrieved from PubMed and EMBASE databases to get a general picture of the latest progress on PHL researches, identify gaps, and put forward the prospects of further study. It considered diverse topics addressed in each article, and selected eight aspects to help describe the diversity of the research with comprehensive information for comparison. Overall, the review analysis found that existing PHL research tended to (1) use data science method to enhance self-management of diseases, (2) provide platforms and information resources to improve the efficiency of healthcare, and (3) provide convenient services for various health domains. After 20 years of development, information accessibility has been continuously increasing, including the generation of many professional databases available to the public, and enhanced access to network services. This development eases barriers to access quality information. However, consumers' access to information still faces barriers such as low health literacy and information quality control.
The existing few studies are insufficient to meet the soaring demand for PHL, especially in light of the COVID-19
outbreak. Notably, there are few personalized tools for older people to collect and use cognitive health information. Older adults often find it challenging to handle effective information technology or knowledge to meet their needs because of the complexity of these technologies and a lack of training in tool use. [45] To a certain extent, this has dampened the enthusiasm for the development of PHLs for older adults. Although some studies have designed user-friendly interfaces, researchers did not pay enough attention to health literacy. Health literacy represents skills, knowledge, and the expectations that health professionals have of the public's interest in and understanding of health information and services. [46] Research has shown that people's limited health literacy [47] dramatically affects their ability to use health information and constrains the popularization of PHL. More research should study the association between effective use of PHL and health literacy. The development of face-to-face instructions and user-friendly guides will be the next important step in supporting older adults to learn and use PHL in their daily lives effectively.
User trust is critical in the development of consumer health informatics. [48] The lack of trust in online privacy and security affects the popularity of PHLs. Consumers need to provide highly sensitive personal information, including medical conditions, to use the services of PHLs. In particular, with the trend of personalized service, users must provide information, including social determinants, so that the PHLs can deliver information accurately through a recommendation algorithm.
Another part of the distrust stems from the quality of the information provided on the PHLs. Users may wonder whether the information provided on the PHL for disease intervention and medication is correct. PHLs should recognize the importance of consumer trust and take steps to maintain it, for example, publishing online privacy policies. [49] Recently, researchers began to improve the privacy and data security of websites on a technical level. Jin Sun et al. proposed a security scheme that guarantees safe storage and sharing of patients' PHRs on a cloud server while keeping personal information confidential. [31] However, this framework has not yet been deployed in practice.
The social determinants of health (SDoH) have gradually gained recognition as a way to address health disparities. [50] The researchers and doctors defined SDoH as the "conditions in which people are born, grow, live, work, and age that shape health". [51] It has been shown to have a significant influence on health outcomes. [52] Society and government agencies have begun to take measures to address this problem actively. [53] However, according to the findings of this paper, SDOH has not been well addressed in the existing PHLs when providing information services to users. A particular technology or application of PHL is not expected to be available to all soon. [54] There is still a digital divide [55] between the developed and the developing countries in PHL research, even as technologies such as the Internet seem to become more affordable. Figure 5 indicates that the number of research production of developed countries is higher than that of developing countries. It takes time for the PHL to become widely accessible.
Based on the above findings, this paper summarized several aspects of PHL worthy of further exploration, including the following topics.
Quality evaluation of health information from the Internet has always been critical for PHL and other applications in consumer health informatics. However, the threshold of online information dissemination has been lowered along with the emergence of new online media platforms and the popularity of social media. This change has led to many sites containing unverified information. In particular, people with lower health literacy have weaker discrimination. Therefore, future studies should focus on developing an effective data quality assessment method to verify the quality of information sources and ensure the high quality of the underlying data of knowledge engineering.
Data-driven models can derive insights from health information by interpreting and extracting valuable patterns from online data. These modeling techniques include mathematical models and various machine learning methods, especially knowledge graphs and recommendation algorithms. At present, the PHLs focus on the collection, retrieval, and display of information but lack the further processing of massive information. Research has shown that graphical display has many advantages, including promoting healthy behaviors and reducing errors caused by narratives. [56, 57] Knowledge graph provides a way to mine, analyze, construct, and display knowledge and their interconnections. It has gradually been applied to the field of health informatics, from electronic medical records [58] to health risk prediction [59] . Using this technology to improve the effectiveness of PHL is a promising development direction.
After mining out high-quality health information, the recommendation algorithms can help deliver this information to the most interested user. Data science models combining with user's information (age, gender, browsing behavior, and more) may help the user to obtain accurate, personalized health information.
PHLs varied widely in medical applications and user populations, including the specific functions that they provide, the outcomes evaluated, and the data sources, making it more challenging to conduct a comparable statement on the libraries' practicability for self-management. Besides, it can force people to register multiple PHLs in order to obtain information about different diseases, which complicates the information acquisition process. At the same time, personal data is stored on multiple websites, which increases the possibility of privacy disclosure and puts forward higher requirements for data security. The integration of multiple functions not only solves the above problems but also collects more comprehensive user behavior data, enhancing the learning process of PHL's recommendation algorithm, and delivering more accurate information for users.
Future research could explore how individuals and various combinations of PHL elements support disease management and prevention mechanisms.
While many researchers employed similar outcome measurements, such as effectiveness, efficiency, and satisfaction, the methods for gathering those measurements varied widely. Different evaluation methods prevented a direct comparison of libraries' performance. Therefore, efforts are urgently needed to standardize the evaluation method for identifying the challenges of improving the quality of PHL.
With the rapid expansion of the PHL, differences in user health outcomes will likely continue to persist with differences in social determinants such as technology access. [60] [61] [62] Income level, Internet access, or other living conditions, may play a role in PHL engagement. For example, although older adults have a greater need for online health information, their inadequate health literacy makes them the most difficult demographic group to use the PHL. Therefore, collecting users' social determinants and further exploring their influence on user interaction will improve personalized service and make the PHL more engaging. The PHL should be made more attractive to many older people, women, and minorities. This attraction lies in the design, language, and culture issues contained and addressed in the PHL development. The causes of the digital divide are also an important topic to explore. As researchers focus on improving the technological infrastructure, they should be aware that public attitudes may also be contributing to the gap. Although technological limitations such as Internet access ability may be a factor restricting the development of PHL in developing countries, it is more important to change public attitude regarding technology, taking it as a necessity to achieve personal health. This paper is to improve people's awareness of health information acquisition in regions where technology is not too backward.
Although this article attempted to minimize the possibility of missing articles by generalizing search terms, retrieval of