Advances in Health Technology Use and Implementation in the Era of Healthy Living: Implications for Precision Medicine

A Social and Ethical Framework for Providing Health Information Obtained from Combining Genetics and Fitness Tracking Data

In this paper we explore a new technological intersection in the "big data revolution": the integration of two forms of data, genetic data and fitness tracking data. For example, a small number of direct-to-consumer (DTC) genetic testing companies have recently begun offering customers the ability to link their fitness tracking data with their genetic profile to get personalized diet and exercise recommendations. In this paper we put forward four ethical considerations that should inform potential uses of this health information. Those considerations are: the heightened risks to privacy resulting from combining sensitive data sets; the poor quality of health information that is possible at present in the realm of precision DTC genomics; the limited usefulness of the recommendations; and finally, the cultural assumptions about health and personal responsibility that are embedded within fitness genetic testing and fitness tracking. To conclude, we offer some guidance on how the benefits and risks of returning this type of health information can be weighed.

Biological and psychological predictors of heart rate patterns during physical activity

Objective

Latent class modeling (LCM) offers a promising approach for examining correlates of heart rate (HR) patterns over multiple exercise sessions. This research examined biological and psychological variables associated with different patterns of HR response to physical activity (PA).

Methods

In a three-arm randomized controlled trial (exercise video games vs. standard exercise vs. non-exercise control), HR was recorded during PA sessions over a 12-week period. LCM identified three patterns of HR during PA across 189 participants in active arms: 1) high HR across sessions with low variability within sessions, 2) linear increase in HR across sessions with low variability within sessions, and 3) high variability in HR across all sessions. Associations with biological (resting heart rate, blood pressure, BMI, age, cholesterol, triglycerides, HbA1c) and psychological (depression, motivations for PA, PA-induced feelings) predictors of latent class membership were iteratively tested.

Results

Psychological variables played as important a role in the final model as biological variables for predicting latent class membership. Few differences were found between LC1 and LC2, but LC3 differed from the other two groups in that participants were likelier to report that feel revitalized after PA (vs. LC1 and LC2), to be less motivated for PA (vs. LC1), reported greater depression (vs. LC1 and LC2), and were younger (vs. LC1).

Conclusions

These findings demonstrate the potential of LCM to identify biological and psychological factors associated with chronotropic responses to PA, and advance understanding of the role of psychological factors in chronotropic PA outcomes.

Smart product-service systems in the healthcare industry: Intelligent connected products and stakeholder communication drive digital health service adoption

Smart product-service systems (PSSs) have emerged as a solution for the ongoing digitalization of products and services, especially during the COVID-19 pandemic and under social distancing. However, the conditions for smart PSS adoption remain unclear, requiring the identification of driving attributes and the interrelationships of the attributes for smart PSS implementation in the healthcare industry. This study contributes by determining the cause-effect interrelationship among smart PSS attributes and by identifying and prioritizing the criteria that drive smart PSS adoption in chronic disease management. The study constructed a five-aspect theoretical model to deepen the understanding of digital health service adoption drivers. Data were collected from 233 healthcare industry practitioners to validate the smart PSS adoption attributes. Exploratory factor analysis (EFA) determined the structure of the attributes, the reliability of the criteria, and the validity of the aspects. The EFA result suggested 24 valid and reliable criteria drivers of smart PSS adoption in the healthcare industry, and they were grouped into five aspects. Following the smart PSS literature and stakeholder theory, the aspects are named digital health service adoption, intelligent connected products, stakeholder communication, environmental benefits, and use schemes. In addition, 17 practitioners treating patients with chronic conditions were interviewed to understand the interrelationships among the aspects and criteria. The fuzzy decision-making trial and evaluation laboratory (FDEMATEL) determined the cause-effect interrelationships based on their dependence and driving power. The FDEMATEL results indicated that intelligent connected products and stakeholder communication are the causal and focal attributes of improving digital health service adoption and providing alternative use schemes. For patients and physicians, the driving criteria include managing data, multifunctionality, data reliability, interoperability, patient communication, and resource efficiency. The theoretical and managerial implications are discussed.

Social-Ecological Measurement of Daily Life: How Relationally Focused Ambulatory Assessment can Advance Clinical Intervention Science

Individuals’ daily behaviors and social interactions play a central role in the diagnosis and treatment of psychological disorders. Despite this, observational ambulatory assessment methods—research methods that allow for direct and passive assessment of individuals’ momentary activities and interactions—have a remarkably scant history in the clinical science field. Prior discussions of ambulatory assessment methods in clinical science have focused on subjective methods (e.g., ecological momentary assessment) and physiological methods (e.g., wearable heart rate monitoring). Comparatively less attention has been dedicated to ambulatory assessment methods that collect objective, relational data about individuals’ social behaviors and their interactions with their momentary environmental contexts. Drawing on extant social-ecological measurement frameworks, this article first provides a conceptual and psychometric rationale for the integration of daily relational data into clinical science research. Next, the nascent research applying such methods to clinical science is reviewed, and priorities for further research organized by the NIH Stage Model for Clinical Science Research are recommended. These data can provide unique information about the social contexts of diverse patient populations; identify social-ecological targets for transdiagnostic, precision, and culturally responsive interventions; and contribute novel data about the effectiveness of established interventions at creating behavioral and relational change.

Bibliometric Analysis of Health Technology Research: 1990~2020

This paper aims to summarize the publishing trends, current status, research topics, and frontier evolution trends of health technology between 1990 and 2020 through various bibliometric analysis methods. In total, 6663 articles retrieved from the Web of Science core database were analyzed by Vosviewer and CiteSpace software. This paper found that: (1) The number of publications in the field of health technology increased exponentially; (2) there is no stable core group of authors in this research field, and the influence of the publishing institutions and journals in China is insufficient compared with those in Europe and the United States; (3) there are 21 core research topics in the field of health technology research, and these research topics can be divided into four classes: hot spots, potential hot spots, margin topics, and mature topics. C21 (COVID-19 prevention) and C10 (digital health technology) are currently two emerging research topics. (4) The number of research frontiers has increased in the past five years (2016-2020), and the research directions have become more diverse; rehabilitation, pregnancy, e-health, m-health, machine learning, and patient engagement are the six latest research frontiers.

Technology-based and supervised exercise interventions for individuals with type 2 diabetes: Randomized controlled trial

AIMS

The purpose of this study was to estimate, for people with type 2 diabetes (T2D), the extent to which glycemic control was affected by a 12-week program using mobile app and wearable smartwatch in comparison to supervised exercise training.

METHODS

This study was a stratified, randomized, assessor-blind, controlled, pragmatic trial with three parallel groups which were supervised, mobile app and wearable smartwatch. Individually tailored exercise regimens delivered through a supervisor, mobile app and wearable smartwatch. Programs consisted of aerobic, resistance exercises, calisthenic, flexibility, balance, and coordination exercises. Primary outcome was change in glycemic control (HbA1c); secondary outcome was Six Minute Walk Test; and explanatory outcomes were exercise behaviour, muscle function, and physical capacity. The groups were contrasted for change in HbA1c and absolute reduction of ≥0.5% (Minimal Important Change). Linear and logistic regressions were used to compare the groups and generalized estimated equations were used to analyze the explanatory outcomes.

RESULTS

In total, 90 people were randomized, 6 were lost over 12 weeks, leaving 84 with outcome data. The difference in HbA1c did not differ between the supervised and the technology groups combined and between the mobile app and smartwatch group. Proportions of people achieving a clinically meaningful difference on HbA1c between the supervised and technology groups were similar (46% vs 43%) and the associated OR was 0.87 (95%CI:0.34-2.28). Within the two technology groups, proportions of people achieving a clinically meaningful difference in HbA1c were 48% in the mobile app and 38% in the smartwatch groups and the associated OR was 0.65 (95%CI:0.21-2.03). The groups did not differ on secondary and explanatory outcomes.

CONCLUSIONS

The results of our trial provide evidence that all outcomes have improved in all groups regardless of the exercise delivery method. Considering the supervised programs are not available for everybody, technological options are crucial to implement to help individuals self-manage most aspects of their diabetes.

Appraisal of Triglyceride-Related Markers as Early Predictors of Metabolic Outcomes in the PREVIEW Lifestyle Intervention: A Controlled Post-hoc Trial

Background: Individuals with pre-diabetes are commonly overweight and benefit from dietary and physical activity strategies aimed at decreasing body weight and hyperglycemia. Early insulin resistance can be estimated via the triglyceride glucose index {TyG = Ln [TG (mg/dl) × fasting plasma glucose (FPG) (mg/dl)/2]} and the hypertriglyceridemic-high waist phenotype (TyG-waist), based on TyG x waist circumference (WC) measurements. Both indices may be useful for implementing personalized metabolic management. In this secondary analysis of a randomized controlled trial (RCT), we aimed to determine whether the differences in baseline TyG values and TyG-waist phenotype predicted individual responses to type-2 diabetes (T2D) prevention programs. Methods: The present post-hoc analyses were conducted within the Prevention of Diabetes through Lifestyle intervention and population studies in Europe and around the world (PREVIEW) study completers (n = 899), a multi-center RCT conducted in eight countries (NCT01777893). The study aimed to reduce the incidence of T2D in a population with pre-diabetes during a 3-year randomized intervention with two sequential phases. The first phase was a 2-month weight loss intervention to achieve ≥8% weight loss. The second phase was a 34-month weight loss maintenance intervention with two diets providing different amounts of protein and different glycemic indices, and two physical activity programs with different exercise intensities in a 2 x 2 factorial design. On investigation days, we assessed anthropometrics, glucose/lipid metabolism markers, and diet and exercise questionnaires under standardized procedures. Results: Diabetes-related markers improved during all four lifestyle interventions. Higher baseline TyG index (p < 0.001) was associated with greater reductions in body weight, fasting glucose, and triglyceride (TG), while a high TyG-waist phenotype predicted better TG responses, particularly in those randomized to physical activity (PA) of moderate intensity. Conclusions: Two novel indices of insulin resistance (TyG and TyG-waist) may allow for a more personalized approach to avoiding progression to T2D. Clinical Trial Registration: https://clinicaltrials.gov/ct2/show/NCT01777893 reference, identifier: NCT01777893.

Tailored physical activity behavior change interventions: challenges and opportunities

A physically active lifestyle provides innumerable benefits; yet, few individuals are physically active enough to reap those benefits. Tailored physical activity interventions may address low rates of physical activity by offering individualized strategies that consider a person's characteristics, needs, preferences, and/or context, rather than the traditional one-size-fits-all approach. However, the tailoring methodology is in its nascency, and an understanding of how best to develop such interventions is needed. In this commentary, we identify future directions to enhance the impact of tailored interventions designed to increase physical activity participation. A multi-country collaborative was established to review the literature and discuss an agenda for future research. Two overarching research opportunities are suggested for improving the development of tailored, behavioral physical activity interventions: (a) optimize the engagement of diverse knowledge users in intervention co-design and (b) examine ethical considerations that may impact the use of technology to support tailored physical activity delivery. Specifically, there is a need for better reporting and evaluation of knowledge user involvement alongside targeting diversity in the inclusion of knowledge users. Furthermore, while technology boasts many opportunities to increase the scale and precision of interventions, examinations of how it impacts recipients' experiences of and participation in tailored interventions are needed to ensure the benefits of technology use outweigh the risks. A better understanding of these research areas will help ensure that the diverse needs of individuals are met, technology is appropriately used to support tailoring, and ultimately it improves the effectiveness of tailored physical activity interventions.

Expanding the Capabilities of Nutrition Research and Health Promotion Through Mobile-Based Applications

Mobile-based applications are popular and prevalently used in the US population. Applications focusing on nutrition offer platforms for quantifying and changing behaviors to improve dietary intake. Such behavior changes can intervene in the relation of diet to promote health and prevent disease. Mobile applications offer a safe and convenient way to collect user data and share it back to users, researchers, and to health care providers. Other lifestyle factors like activity, sleep, and sedentary behavior, can also be quantified and included in investigations of how lifestyle is related to health. Yet, challenges in the assessment offered through mobile applications and effectiveness to change behavior still remain, including rigorous evaluation, demonstration of successful health improvement, and participant engagement. The data mobile applications generate, however, expands opportunities for discovery of the integrated and time-based nature of various daily activities in relation to health. This article is a summary of a symposium at Nutrition 2020 Live Online on the role of mobile applications as a tool for nutrition research and health promotion. The types and capabilities of mobile applications, challenges in their evaluation and use in research, and opportunities for the data they generate along with a specific example, are reviewed.

Spanish Society of Pulmonology and Thoracic Surgery positioning on the use of telemedine in sleep-disordered breathing and mechanical ventilation

The rapid introduction of new information and communication technologies into medical practice has prompted Spanish Society of Pulmonology and Thoracic SurgeryR to publish a position paper on sleep-disordered breathing, especially in relation to positive pressure treatment. It should be pointed out that the scientific literature is to some extent controversial due to a paucity of large randomized multicenter studies with long-term follow-up. Moreover, the telematics devices and systems on the market vary widely. As a result, the recommendations are based primarily on a consensus of expert professionals. Another very important aspect addressed extensively in this document is the obvious lack of regulations on legal matters and the operations of commercial companies. The most important recommendations included in this position paper are that telemedicine is primarily advocated in subjects with travel problems or who live far from the hospital, in patients with poor CPAP compliance, and in most cases treated with non-invasive mechanical ventilation. A key element is patient individualization. It is imperative that the relevant technical, legal and ethical requirements (medical device regulations, data protection, and informed consent) are met. Finally, expert professionals from our society must contribute to and become involved in spearheading this technological change.

Technology readiness of medical students and the association of technology readiness with specialty interest

BACKGROUND

Technology readiness (TR) is a construct which characterizes an individual's propensity to utilize new technology. Despite increased technology use in healthcare, limited data exists on medical student TR and the relation of TR with specialty interest. This study assesses the TR of 2nd year medical students and its association with specialty interest.

METHODS

Respondents completed a survey assessing their most preferred specialty, specialty interests, and technology readiness using a 5-point Likert scale. Using Chi-square analysis, we examined the relation between demographics, TR, and specialty interest.

RESULTS

This study obtained a 45.7% (n = 53/116) response rate demonstrating that 79.2% (n = 42/53) of students were "technology ready." Male students were more likely to be technology ready (95.2%, n = 20/21, vs 68.8%, n = 22/32, p = 0.02) when compared to female students. Technology ready students were associated with being more interested in "Technology-Focused" specialties compared to students who were not technology ready (88.5%, n = 23/26 vs 70.4%, n = 19/27, p = 0.104).

CONCLUSIONS

As a cohort, most medical students were technology ready. It is inconclusive if technology ready students are more likely to be interested in technology-focused specialties due to the limited sample size of this study, although with an increased sample size, an improved understanding on technology readiness and its potential impact on student specialty interest may be obtained. Furthermore, knowledge of TR may aid in developing targeted technology-based education programs and in improving remedial approaches for students who are less comfortable with new technology.

Personalized eHealth Program for Life-style Change: Results From the "Do Cardiac Health Advanced New Generated Ecosystem (Do CHANGE 2)" Randomized Controlled Trial

OBJECTIVE

Unhealthy life-style factors have adverse outcomes in cardiac patients. However, only a minority of patients succeed to change unhealthy habits. Personalization of interventions may result in critical improvements. The current randomized controlled trial provides a proof of concept of the personalized Do Cardiac Health Advanced New Generation Ecosystem (Do CHANGE) 2 intervention and evaluates effects on a) life-style and b) quality of life over time.

METHODS

Cardiac patients (n = 150; mean age = 61.97 ± 11.61 years; 28.7% women; heart failure, n = 33; coronary artery disease, n = 50; hypertension, n = 67) recruited from Spain and the Netherlands were randomized to either the "Do CHANGE 2" or "care as usual" group. The Do CHANGE 2 group received ambulatory health-behavior assessment technologies for 6 months combined with a 3-month behavioral intervention program. Linear mixed-model analysis was used to evaluate the intervention effects, and latent class analysis was used for secondary subgroup analysis.

RESULTS

Linear mixed-model analysis showed significant intervention effects for life-style behavior (Finteraction(2,138.5) = 5.97, p = .003), with improvement of life-style behavior in the intervention group. For quality of life, no significant main effect (F(1,138.18) = .58, p = .447) or interaction effect (F(2,133.1) = 0.41, p = .67) was found. Secondary latent class analysis revealed different subgroups of patients per outcome measure. The intervention was experienced as useful and feasible.

CONCLUSIONS

The personalized eHealth intervention resulted in significant improvements in life-style. Cardiac patients and health care providers were also willing to engage in this personalized digital behavioral intervention program. Incorporating eHealth life-style programs as part of secondary prevention would be particularly useful when taking into account which patients are most likely to benefit.

TRIAL REGISTRATION

https://clinicaltrials.gov/ct2/show/NCT03178305.