Article

Dynamic and accretive composition of patient engagement instruments for personalized plan generation

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Abstract

Patient engagement is important to help patients become more informed and active in managing their health. Effective patient engagement demands short, yet valid instruments for measuring self-efficacy in various care dimensions. However, the static instruments are often too lengthy to be effective for assessment purposes. Furthermore, these tests could neither account for the dynamicity of measurements over time, nor differentiate care dimensions that are more critical to certain sub-populations. To remedy these disadvantages, we devise a dynamic instrument composition approach that can model the measurement of patient self-efficacy over time and iteratively select critical care dimensions and appropriate assessment questions based on dynamic user categorization. The dynamically composed instruments are expected to guide patients through self-management reinforcement cycles within or across care dimensions, while tightly integrated into clinical workflow and standard care processes.

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... From the accumulated evidence in the past, self-monitoring has been shown to be good for activation, but not enough for sustaining behavior [44]. More frequent feedback has been expected as an effective counter strategy to address barriers such as stress level, lack of social support, and discomfort with recording that can affect adherence to self-monitoring [45,46]. However, as pointed out in [47], in the area of mobile health, only a limited number of mHealth apps integrated health behavior theory and left room for future work. ...
Article
Objectives: Consumer Health Informatics (CHI) and the use of Patient-Generated Health Data (PGHD) are rapidly growing focus areas in healthcare. The objective of this paper is to briefly review the literature that has been published over the past few years and to provide a sense of where the field is going. Methods: We searched PubMed and the ACM Digital Library for articles published between 2014 and 2016 on the topics of CHI and PGHD. The results of the search were screened for relevance and categorized into a set of common themes. We discuss the major topics covered in these articles. Results: We retrieved 65 articles from our PubMed query and 32 articles from our ACM Digital Library query. After a review of titles, we were left with 47 articles to conduct our full article survey of the activities in CHI and PGHD. We have summarized these articles and placed them into major categories of activity. Within the domain of consumer health informatics, articles focused on mobile health and patient-generated health data comprise the majority of the articles published in recent years. Conclusions: Current evidence indicates that technological advancements and the widespread availability of affordable consumer-grade devices are fueling research into using PGHD for better care. As we observe a growing number of (pilot) developments using various mobile health technologies to collect PGHD, major gaps still exist in how to use the data by both patients and providers. Further research is needed to understand the impact of PGHD on clinical outcomes.
... From the accumulated evidence in the past, self-monitoring has been shown to be good for activation, but not enough for sustaining behavior [44]. More frequent feedback has been expected as an effective counter strategy to address barriers such as stress level, lack of social support, and discomfort with recording that can affect adherence to self-monitoring [45,46]. However, as pointed out in [47], in the area of mobile health, only a limited number of mHealth apps integrated health behavior theory and left room for future work. ...
Article
Full-text available
Objectives: Consumer Health Informatics (CHI) and the use of Patient-Generated Health Data (PGHD) are rapidly growing focus areas in healthcare. The objective of this paper is to briefly review the literature that has been published over the past few years and to provide a sense of where the field is going. Methods: We searched PubMed and the ACM Digital Library for articles published between 2014 and 2016 on the topics of CHI and PGHD. The results of the search were screened for relevance and categorized into a set of common themes. We discuss the major topics covered in these articles. Results: We retrieved 65 articles from our PubMed query and 32 articles from our ACM Digital Library query. After a review of titles, we were left with 47 articles to conduct our full article survey of the activities in CHI and PGHD. We have summarized these articles and placed them into major categories of activity. Within the domain of consumer health informatics, articles focused on mobile health and patient-generated health data comprise the majority of the articles published in recent years. Conclusions: Current evidence indicates that technological advancements and the widespread availability of affordable consumer-grade devices are fueling research into using PGHD for better care. As we observe a growing number of (pilot) developments using various mobile health technologies to collect PGHD, major gaps still exist in how to use the data by both patients and providers. Further research is needed to understand the impact of PGHD on clinical outcomes.
... While this challenges data analytics which has begun to deliver algorithms and heuristics to separate good from bad, and noise from meaningful data, we will hereby address the next challenge in "secondary use of data", i.e., how to utilize cleansed and approved data for purposes beyond their primary context and motivation of collection. Examples of such usages beyond the primary purposes of PGHD include: (i) feeding information back into Medical Product development [20], (ii) use of data as trial outcome measures [21], (iii) identifying predictors of disease progression [22], and (iv) returning usefull information to the patient to assess self-efficacy and induce behavior changes [23,24]. To date, this is still an active research area that interdisciplinary researchers are exploring to collect evidence. ...
Article
Introduction: Various health-related data, subsequently called Person Generated Health Data (PGHD), is being collected by patients or presumably healthy individuals as well as about them as much as they become available as measurable properties in their work, home, and other environments. Despite that such data was originally just collected and used for dedicated predefined purposes, more recently it is regarded as untapped resources that call for secondary use. Method: Since the secondary use of PGHD is still at its early evolving stage, we have chosen, in this paper, to produce an outline of best practices, as opposed to a systematic review. To this end, we identified key directions of secondary use and invited protagonists of each of these directions to present their takes on the primary and secondary use of PGHD in their sub-fields. We then put secondary use in a wider perspective of overarching themes such as privacy, interpretability, interoperability, utility, and ethics. Results: We present the primary and secondary use of PGHD in four focus areas: (1) making sense of PGHD in augmented Shared Care Plans for care coordination across multiple conditions; (2) making sense of PGHD from patient-held sensors to inform cancer care; (3) fitting situational use of PGHD to evaluate personal informatics tools in adaptive concurrent trials; (4) making sense of environment risk exposure data in an integrated context with clinical and omics-data for biomedical research. Discussion: Fast technological progress in all the four focus areas calls for a societal debate and decision-making process on a multitude of challenges: how emerging or foreseeable results transform privacy; how new data modalities can be interpreted in light of clinical data and vice versa; how the sheer mass and partially abstract mathematical properties of the achieved insights can be interpreted to a broad public and can consequently facilitate the development of patient-centered services; and how the remaining risks and uncertainties can be evaluated against new benefits. This paper is an initial summary of the status quo of the challenges and proposals that address these issues. The opportunities and barriers identified can serve as action items individuals can bring to their organizations when facing challenges to add value from the secondary use of patient-generated health data.
... While this challenges data analytics which has begun to deliver algorithms and heuristics to separate good from bad, and noise from meaningful data, we will hereby address the next challenge in "secondary use of data", i.e., how to utilize cleansed and approved data for purposes beyond their primary context and motivation of collection. Examples of such usages beyond the primary purposes of PGHD include: (i) feeding information back into Medical Product development [20], (ii) use of data as trial outcome measures [21], (iii) identifying predictors of disease progression [22], and (iv) returning usefull information to the patient to assess self-efficacy and induce behavior changes [23,24]. To date, this is still an active research area that interdisciplinary researchers are exploring to collect evidence. ...
Article
Full-text available
Introduction: Various health-related data, subsequently called Person Generated Health Data (PGHD), is being collected by patients or presumably healthy individuals as well as about them as much as they become available as measurable properties in their work, home, and other environments. Despite that such data was originally just collected and used for dedicated predefined purposes, more recently it is regarded as untapped resources that call for secondary use. Method: Since the secondary use of PGHD is still at its early evolving stage, we have chosen, in this paper, to produce an outline of best practices, as opposed to a systematic review. To this end, we identified key directions of secondary use and invited protagonists of each of these directions to present their takes on the primary and secondary use of PGHD in their sub-fields. We then put secondary use in a wider perspective of overarching themes such as privacy, interpretability, interoperability, utility, and ethics. Results: We present the primary and secondary use of PGHD in four focus areas: (1) making sense of PGHD in augmented Shared Care Plans for care coordination across multiple conditions; (2) making sense of PGHD from patient-held sensors to inform cancer care; (3) fitting situational use of PGHD to evaluate personal informatics tools in adaptive concurrent trials; (4) making sense of environment risk exposure data in an integrated context with clinical and omics-data for biomedical research. Discussion: Fast technological progress in all the four focus areas calls for a societal debate and decision-making process on a multitude of challenges: how emerging or foreseeable results transform privacy; how new data modalities can be interpreted in light of clinical data and vice versa; how the sheer mass and partially abstract mathematical properties of the achieved insights can be interpreted to a broad public and can consequently facilitate the development of patient-centered services; and how the remaining risks and uncertainties can be evaluated against new benefits. This paper is an initial summary of the status quo of the challenges and proposals that address these issues. The opportunities and barriers identified can serve as action items individuals can bring to their organizations when facing challenges to add value from the secondary use of patient-generated health data.
Article
Full-text available
Objectives: The understanding of how stress influences health behavior can provide insights into developing healthy lifestyle interventions. This understanding is traditionally attained through observational studies that examine associations at a population level. This nomothetic approach, however, is fundamentally limited by the fact that the environment- person milieu that constitutes stress exposure and experience can vary substantially between individuals, and the modifiable elements of these exposures and experiences are individual-specific. With recent advances in smartphone and sensing technologies, it is now possible to conduct idiographic assessment in users' own environment, leveraging the full-range observations of actions and experiences that result in differential response to naturally occurring events. The aim of this paper is to explore the hypothesis that an ideographic N-of-1 model can better capture an individual's stress- behavior pathway (or the lack thereof) and provide useful person-specific predictors of exercise behavior. Methods: This paper used the data collected in an observational study in 79 participants who were followed for up to a 1-year period, wherein their physical activity was continuously and objectively monitored by actigraphy and their stress experience was recorded via ecological momentary assessment on a mobile app. In addition, our analyses considered exogenous and environmental variables retrieved from public archive such as day in a week, daylight time, temperature and precipitation. Leveraging the multiple data sources, we developed prediction algorithms for exercise behavior using random forest and classification tree techniques using a nomothetic approach and an N-of-1 approach. The two approaches were compared based on classification errors in predicting personalized exercise behavior. Results: Eight factors were selected by random forest for the nomothetic decision model, which was used to predict whether a participant would exercise on a particular day. The predictors included previous exercise behavior, emotional factors (e.g., midday stress), external factors such as weather (e.g., temperature), and self-determination factors (e.g., expectation of exercise). The nomothetic model yielded an average classification error of 36%. The ideographic N-of-1 models used on average about two predictors for each individual, and had an average classification error of 25%, which represented an improvement of 11 percentage points. Conclusions: Compared to the traditional one-size-fits-all, nomothetic model that generalizes population-evidence for individuals, the proposed N-of-1 model can better capture the individual difference in their stressbehavior pathways. In this paper, we demonstrate it is feasible to perform personalized exercise behavior prediction, mainly made possible by mobile health technology and machine learning analytics.
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