A new approach to integrating patient-generated data with expert knowledge for personalized goal setting: A pilot study

Burgermaster, Marissa; Son, Jung Hoon; Davidson, Patricia; Smaldone, Arlene; Kuperman, Gilad J.; Feller, Daniel; Burt, Kate Gardner; Levine, Matthew E.; Albers, David J.; Weng, Chunhua; Mamykina, Lena

doi:10.1016/j.ijmedinf.2020.104158

Cited by 10 publications

(6 citation statements)

References 43 publications

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“… Similar patients: five studies recommended items extracted from patient databases (e.g., a database of 132 obese youth who had succeeded in managing obesity [ 42 ]), social networks sites by crowdsourcing [ 67 ], or patients with similar health problems [ 31 , 59 , 79 ]. Government database: four studies used the United States Department of Agriculture (USDA) food composition guidelines [ 25 , 51 , 52 , 76 ]; other studies extracted dietary information from the Taiwan food nutrition database of the Food and Drug Administration [ 38 ], Taiwanese Snacks Nutrition Analysis [ 37 ], Food Composition Database (FCD)”, and Japan Preventive Association of Life-style related Disease (JPALD) [ 78 ]. Other resources: a range of other information resources were utilized for recommended items, including online health community [ 48 , 49 , 50 ], online registered doctors [ 66 , 68 , 70 , 71 ], restaurants [ 40 ], and psychotherapy approaches [ 47 , 74 ] downloaded from Google Play Store.…”

Section: Resultsmentioning

confidence: 99%

“…Government database: four studies used the United States Department of Agriculture (USDA) food composition guidelines [ 25 , 51 , 52 , 76 ]; other studies extracted dietary information from the Taiwan food nutrition database of the Food and Drug Administration [ 38 ], Taiwanese Snacks Nutrition Analysis [ 37 ], Food Composition Database (FCD)”, and Japan Preventive Association of Life-style related Disease (JPALD) [ 78 ].…”

Section: Resultsmentioning

confidence: 99%

“…For example, an HRS was compared with health conditions in general or no recommendation scenarios. (2) System evaluation not involving users: 19 studies either directly invited experts to evaluate the system or compared HRS generated recommendations with expert recommendations for the same scenario [ 23 , 24 , 30 , 34 , 35 , 37 , 38 , 39 , 47 , 51 , 52 , 56 , 58 , 59 , 62 , 71 , 72 , 84 , 85 ].…”

Section: Resultsmentioning

confidence: 99%

“…A variety of metrics were applied to the performance assessment of personalization. (1) forty-six percent of the studies (29/63) used performance criteria (e.g., recall, mean reciprocal rank, normalized discounted cumulative gain, precision, mean absolute error, receiver operating characteristics, and F1) [ 23 , 29 , 34 , 35 , 36 , 37 , 38 , 39 , 41 , 42 , 45 , 47 , 48 , 49 , 50 , 51 , 52 , 58 , 59 , 62 , 63 , 64 , 66 , 68 , 72 , 74 , 75 , 80 , 81 ]. (2) twenty studies used user-centered usability criteria (e.g., usability, satisfaction, acceptance, perceived usefulness) [ 27 , 28 , 30 , 31 , 32 , 33 , 40 , 44 , 55 , 56 , 57 , 60 , 63 , 64 , 65 , 67 , 70 , 79 , 84 , 85 ].…”

Section: Resultsmentioning

confidence: 99%

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Health Recommender Systems Development, Usage, and Evaluation from 2010 to 2022: A Scoping Review

Cai

Kumar

et al. 2022

IJERPH

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A health recommender system (HRS) provides a user with personalized medical information based on the user’s health profile. This scoping review aims to identify and summarize the HRS development in the most recent decade by focusing on five key aspects: health domain, user, recommended item, recommendation technology, and system evaluation. We searched PubMed, ACM Digital Library, IEEE Xplore, Web of Science, and Scopus databases for English literature published between 2010 and 2022. Our study selection and data extraction followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews. The following are the primary results: sixty-three studies met the eligibility criteria and were included in the data analysis. These studies involved twenty-four health domains, with both patients and the general public as target users and ten major recommended items. The most adopted algorithm of recommendation technologies was the knowledge-based approach. In addition, fifty-nine studies reported system evaluations, in which two types of evaluation methods and three categories of metrics were applied. However, despite existing research progress on HRSs, the health domains, recommended items, and sample size of system evaluation have been limited. In the future, HRS research shall focus on dynamic user modelling, utilizing open-source knowledge bases, and evaluating the efficacy of HRSs using a large sample size. In conclusion, this study summarized the research activities and evidence pertinent to HRSs in the most recent ten years and identified gaps in the existing research landscape. Further work shall address the gaps and continue improving the performance of HRSs to empower users in terms of healthcare decision making and self-management.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 2 more Smart Citations

Health Recommender Systems Development, Usage, and Evaluation from 2010 to 2022: A Scoping Review

Cai

Kumar

et al. 2022

IJERPH

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“…However, their consistency with clinical guidelines is currently not sufficient to make them feasible for daily use. 106 The patients' role in these approaches is not necessarily limited to providing data. There are also opportunities to empower patients, that is, encouraging a more active role in shared decision-making, and cocreation of therapy plans that are informed by food practice data.…”

Section: The Ultimate Goal: Integrating Food Practice Into Clinical D...mentioning

confidence: 99%

Opportunities and Challenges of Integrating Food Practice into Clinical Decision-Making

2022

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Background Food practice plays an important role in health. Food practice data collected in daily living settings can inform clinical decisions. However, integrating such data into clinical decision-making is burdensome for both clinicians and patients, resulting in poor adherence and limited utilization. Automation offers benefits in this regard, minimizing this burden resulting in a better fit with a patient's daily living routines, and creating opportunities for better integration into clinical workflow. Although the literature on patient-generated health data (PGHD) can serve as a starting point for the automation of food practice data, more diverse characteristics of food practice data provide additional challenges. Objectives We describe a series of steps for integrating food practices into clinical decision-making. These steps include the following: (1) sensing food practice; (2) capturing food practice data; (3) representing food practice; (4) reflecting the information to the patient; (5) incorporating data into the EHR; (6) presenting contextualized food practice information to clinicians; and (7) integrating food practice into clinical decision-making. Methods We elaborate on automation opportunities and challenges in each step, providing a summary visualization of the flow of food practice-related data from daily living settings to clinical settings. Results We propose four implications of automating food practice hereinafter. First, there are multiple ways of automating workflow related to food practice. Second, steps may occur in daily living and others in clinical settings. Food practice data and the necessary contextual information should be integrated into clinical decision-making to enable action. Third, as accuracy becomes important for food practice data, macrolevel data may have advantages over microlevel data in some situations. Fourth, relevant systems should be designed to eliminate disparities in leveraging food practice data. Conclusion Our work confirms previously developed recommendations in the context of PGHD work and provides additional specificity on how these recommendations apply to food practice.

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Connecting Patients and Clinicians: Shedding Light on Functionalities for Mental Health Apps in Depression Care

Reindl-Spanner,

Prommegger,

Ikonomi

et al. 2024

Lecture Notes in Computer Science

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A new approach to integrating patient-generated data with expert knowledge for personalized goal setting: A pilot study

Cited by 10 publications

References 43 publications

Health Recommender Systems Development, Usage, and Evaluation from 2010 to 2022: A Scoping Review

Health Recommender Systems Development, Usage, and Evaluation from 2010 to 2022: A Scoping Review

Opportunities and Challenges of Integrating Food Practice into Clinical Decision-Making

Connecting Patients and Clinicians: Shedding Light on Functionalities for Mental Health Apps in Depression Care

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