Smartphone-based digital phenotyping for dry eye toward P4 medicine: a crowdsourced cross-sectional study

Multidimensional integrative data analysis of digital phenotyping is crucial for elucidating the pathologies of multifactorial and heterogeneous diseases, such as the dry eye (DE). This crowdsourced cross-sectional study explored a novel smartphone-based digital phenotyping strategy to stratify and visualize the heterogenous DE symptoms into distinct subgroups. Multidimensional integrative data were collected from 3,593 participants between November 2016 and September 2019. Dimension reduction via Uniform Manifold Approximation and Projection stratified the collected data into seven clusters of symptomatic DE. Symptom profiles and risk factors in each cluster were identified by hierarchical heatmaps and multivariate logistic regressions. Stratified DE subgroups were visualized by chord diagrams, co-occurrence networks, and Circos plot analyses to improve interpretability. Maximum blink interval was reduced in clusters 1, 2, and 5 compared to non-symptomatic DE. Clusters 1 and 5 had severe DE symptoms. A data-driven multidimensional analysis with digital phenotyping may establish predictive, preventive, personalized, and participatory medicine.

Machine Learning Application in Genomic, Exercise, and Vital Datasets

PURPOSE: Machine learning (ML) refers to newly developed computer algorithms that are improved through iterative experiences. ML applications are expected to assist humans in analyzing large amounts of data. This review has outlined the application of ML in analyzing variable vital data such as walking steps, exercise intensity, heart rate, sleeping hours, sleep quality, resting heart rate, blood pressure, and calorie consumption in a day. Vital data consist of different variables that are closely related to genomic or exercise data. The prediction of healthy traits from a vital dataset has become a necessity in personalized medicine.METHODS: Considerations and repeated tasks in supervised, semi-supervised, and unsupervised ML methods are presented. ML methods such as artificial neural networks, Bayesian networks, support vector machines, and decision trees have been widely used in biomedical studies to develop predictive models. Through vital data, these models can help in effective and accurate decision-making for a healthier life.<br/>PURPOSE: Models based on genomic, exercise, and vital datasets provide a healthy lifestyle through regular exercise. We have provided guidelines to help in the selection of these ML methods and their practical application for variable vital data analysis.CONCLUSIONS: Our guidelines could serve as a foundation for implementing both participatory medicine and data-driven exercise science.

Factors Driving Users’ Engagement in Patient Social Network Systems

Participatory medicine and e-health help to promote health literacy among non-medical professionals. Users of e-health systems actively participate in a patient social network system (PSNS) to share health information and experiences with other users with similar health conditions. Users’ activities provide valuable healthcare resources to develop effective participatory medicine between patients, caregivers, and medical professionals. This study aims to investigate the factors of patients’ engagement in a PSNS by integrating and modifying an existing behavioral model and information system model (i.e., affective events theory (AET) and self-determination theory (SDT)). The AET is used to model the structure, the affective aspects of the driven behavior, and actual affective manifestation. The SDT is used to model interest and its relations with behavior. The data analysis and model testing are based on structural equation modeling, using responses from 428 users. The results indicate that interest and empathy promote users’ engagement in a PSNS. The findings from this study suggest recommendations to further promote users’ participation in a PSNS from the sociotechnical perspective, which include sensitizing and constructive engagement features. Furthermore, the data generated from a user’s participation in a PSNS could contribute to the study of clinical manifestations of disease, especially an emerging disease.

Does a Pandemic Preempt Participatory Medicine?

For those of us who believe deeply in a collaborative relationship between patients and doctors, the chaos created by the COVID-19 pandemic has brought an uncomfortable question to the fore: Is participatory medicine still relevant during a pandemic? Drawing liberally upon the Jewish tradition of Talmudic reasoning, I would like to offer 3 considered replies: “Yes,” “no,” and “it depends.” Sometimes, patients may have no choice but to cede control to medical professionals, even though patients are still the experts on their own lives. Other times, the shared control of participatory medicine is both an ethical and clinical imperative. However, as the worldwide toll exacted by COVID-19 has made us grimly aware, no one is really in control. That is why, in these uncertain times, the path forward requires maintaining mutual trust between health care providers and patients, whatever the circumstances. After all, it is our bodies and our selves at stake.

Shifting the Paradigm: The Dress-COV Telegram Bot as a Tool for Participatory Medicine

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic management is limited by great uncertainty, for both health systems and citizens. Facing this information gap requires a paradigm shift from traditional approaches to healthcare to the participatory model of improving health. This work describes the design and function of the Doing Risk sElf-assessment and Social health Support for COVID (Dress-COV) system. It aims to establish a lasting link between the user and the tool; thus, enabling modeling of the data to assess individual risk of infection, or developing complications, to improve the individual’s self-empowerment. The system uses bot technology of the Telegram application. The risk assessment includes the collection of user responses and the modeling of data by machine learning models, with increasing appropriateness based on the number of users who join the system. The main results reflect: (a) the individual’s compliance with the tool; (b) the security and versatility of the architecture; (c) support and promotion of self-management of behavior to accommodate surveillance system delays; (d) the potential to support territorial health providers, e.g., the daily efforts of general practitioners (during this pandemic, as well as in their routine practices). These results are unique to Dress-COV and distinguish our system from classical surveillance applications.

Does a Pandemic Preempt Participatory Medicine? (Preprint)

UNSTRUCTURED For those of us who believe deeply in a collaborative relationship between patients and doctors, the chaos created by the COVID-19 pandemic has brought an uncomfortable question to the fore: Is participatory medicine still relevant during a pandemic? Drawing liberally upon the Jewish tradition of Talmudic reasoning, I would like to offer 3 considered replies: “Yes,” “no,” and “it depends.” Sometimes, patients may have no choice but to cede control to medical professionals, even though patients are still the experts on their own lives. Other times, the shared control of participatory medicine is both an ethical and clinical imperative. However, as the worldwide toll exacted by COVID-19 has made us grimly aware, no one is really in control. That is why, in these uncertain times, the path forward requires maintaining mutual trust between health care providers and patients, whatever the circumstances. After all, it is our bodies and our selves at stake.

Experiencing Positive Health, as a Family, While Living With a Rare Complex Disease: Bringing Participatory Medicine Through Collaborative Decision Making Into the Real World

Physician–patient collaboration was recognized as a critical core of participatory medicine more than a century ago. However, the subsequent focus on scientific research to enable cures and increased dominance of physicians in health care subordinated patients to a passive role. This paternalistic model weakened in the past 50 years—as women, minorities, and the disabled achieved greater rights, and as incurable chronic diseases and unrelieved pain disorders became more prevalent—promoting a more equitable role for physicians and patients. By 2000, a shared decision-making model became the pinnacle for clinical decisions, despite a dearth of data on health outcomes, or the model’s reliance on single patient or solo practitioner studies, or evidence that no single model could fit all clinical situations. We report about a young woman with intractable epilepsy due to a congenital brain malformation whose family and medical specialists used a collaborative decision-making approach. This model positioned the health professionals as supporters of the proactive family, and enabled them all to explore and co-create knowledge beyond the clinical realm. Together, they involved other members of the community in the decisions, while harnessing diverse relationships to allow all family members to achieve positive levels of health, despite the resistance of the seizures to medical treatment and the incurable nature of the underlying disease.

From Passive Patient to Engaged Partner: My Journey With Parkinson Disease

This patient narrative by Richard Higgins with Maureen Hennessey describes Richard's journey of learning to live with a chronic and progressive illness. It begins with Richard's diagnosis and shares many of the lessons learned along the way. Richard copes daily with this condition, relying on the support and expertise of his wife and the treatment team he has assembled while also encouragingly drawing on the skills and knowledge gained as a longtime running coach. A clinical commentary is provided at the article's conclusion, drafted by Richard's friend, Maureen Hennessey, PhD, CPCC, CPHQ, offering observations about the relevance of Richard's story to participatory medicine and suggesting pertinent resources for patients and health care professionals.

Multidimensional Group Recommendations in the Health Domain

Providing useful resources to patients is essential in achieving the vision of participatory medicine. However, the problem of identifying pertinent content for a group of patients is even more difficult than identifying information for just one. Nevertheless, studies suggest that the group dynamics-based principles of behavior change have a positive effect on the patients’ welfare. Along these lines, in this paper, we present a multidimensional recommendation model in the health domain using collaborative filtering. We propose a novel semantic similarity function between users, going beyond patient medical problems, considering additional dimensions such as the education level, the health literacy, and the psycho-emotional status of the patients. Exploiting those dimensions, we are interested in providing recommendations that are both high relevant and fair to groups of patients. Consequently, we introduce the notion of fairness and we present a new aggregation method, accumulating preference scores. We experimentally show that our approach can perform better recommendations to small group of patients for useful information documents.

Experiencing Positive Health, as a Family, While Living With a Rare Complex Disease: Bringing Participatory Medicine Through Collaborative Decision Making Into the Real World (Preprint)

UNSTRUCTURED Physician–patient collaboration was recognized as a critical core of participatory medicine more than a century ago. However, the subsequent focus on scientific research to enable cures and increased dominance of physicians in health care subordinated patients to a passive role. This paternalistic model weakened in the past 50 years—as women, minorities, and the disabled achieved greater rights, and as incurable chronic diseases and unrelieved pain disorders became more prevalent—promoting a more equitable role for physicians and patients. By 2000, a <i>shared decision-making model</i> became the pinnacle for clinical decisions, despite a dearth of data on health outcomes, or the model’s reliance on single patient or solo practitioner studies, or evidence that no single model could fit all clinical situations. We report about a young woman with intractable epilepsy due to a congenital brain malformation whose family and medical specialists used a <i>collaborative decision-making approach</i>. This model positioned the health professionals as supporters of the proactive family, and enabled them all to explore and co-create knowledge beyond the clinical realm. Together, they involved other members of the community in the decisions, while harnessing diverse relationships to allow all family members to achieve positive levels of health, despite the resistance of the seizures to medical treatment and the incurable nature of the underlying disease.