Improving Usability of Social and Behavioral Sciences’ Evidence: A Call to Action for a National Infrastructure Project for Mining Our Knowledge

Over the last century, the social and behavioral sciences have accumulated a vast storehouse of knowledge with the potential to transform society and all its constituents. Unfortunately, this knowledge has accumulated in a form (e.g., journal papers) and scale that makes it extremely difficult to search, categorize, analyze, and integrate across studies. In this commentary based on a National Science Foundation-funded workshop, we describe the social and behavioral sciences’ knowledge-management problem. We discuss the knowledge-scale problem and how we lack a common language, a common format to represent knowledge, a means to analyze and summarize in an automated way, and approaches to visualize knowledge at a large scale. We then describe that we need a collaborative research program between information systems, information science, and computer science (IICS) researchers and social and behavioral science (SBS) researchers to develop information system artifacts to address the problem that many scientific disciplines share but that the social and behavioral sciences have uniquely not addressed

Validation of Consumer-Based Hip and Wrist Activity Monitors in Older Adults With Varied Ambulatory Abilities

BACKGROUND: The accuracy of step detection in consumer-based wearable activity monitors in older adults with varied ambulatory abilities is not known. METHODS: We assessed the validity of two hip-worn (Fitbit One and Omron HJ-112) and two wrist-worn (Fitbit Flex and Jawbone UP) activity monitors in 99 older adults of varying ambulatory abilities and also included the validity results from the ankle-worn StepWatch as a comparison device. Nonimpaired, impaired (Short Physical Performance Battery Score < 9), cane-using, or walker-using older adults (62 and older) ambulated at a self-selected pace for 100 m wearing all activity monitors simultaneously. The criterion measure was directly observed steps. Intraclass correlation coefficients (ICC), mean percent error and mean absolute percent error, equivalency, and Bland-Altman plots were used to assess accuracy. RESULTS: Nonimpaired adults steps were underestimated by 4.4% for StepWatch (ICC = 0.87), 2.6% for Fitbit One (ICC = 0.80), 4.5% for Omron HJ-112 (ICC = 0.72), 26.9% for Fitbit Flex (ICC = 0.15), and 2.9% for Jawbone UP (ICC = 0.55). Impaired adults steps were underestimated by 3.5% for StepWatch (ICC = 0.91), 1.7% for Fitbit One (ICC = 0.96), 3.2% for Omron HJ-112 (ICC = 0.89), 16.3% for Fitbit Flex (ICC = 0.25), and 8.4% for Jawbone UP (ICC = 0.50). Cane-user and walker-user steps were underestimated by StepWatch by 1.8% (ICC = 0.98) and 1.3% (ICC = 0.99), respectively, where all other monitors underestimated steps by >11.5% (ICCs < 0.05). CONCLUSIONS: StepWatch, Omron HJ-112, Fitbit One, and Jawbone UP appeared accurate at measuring steps in older adults with nonimpaired and impaired ambulation during a self-paced walking test. StepWatch also appeared accurate at measuring steps in cane-users

The history and future of digital health in the field of behavioral medicine.

Since its earliest days, the field of behavioral medicine has leveraged technology to increase the reach and effectiveness of its interventions. Here, we highlight key areas of opportunity and recommend next steps to further advance intervention development, evaluation, and commercialization with a focus on three technologies: mobile applications (apps), social media, and wearable devices. Ultimately, we argue that future of digital health behavioral science research lies in finding ways to advance more robust academic-industry partnerships. These include academics consciously working towards preparing and training the work force of the twenty first century for digital health, actively working towards advancing methods that can balance the needs for efficiency in industry with the desire for rigor and reproducibility in academia, and the need to advance common practices and procedures that support more ethical practices for promoting healthy behavior

Behavior change interventions: the potential of ontologies for advancing science and practice

A central goal of behavioral medicine is the creation of evidence-based interventions for promoting behavior change. Scientific knowledge about behavior change could be more effectively accumulated using "ontologies." In information science, an ontology is a systematic method for articulating a "controlled vocabulary" of agreed-upon terms and their inter-relationships. It involves three core elements: (1) a controlled vocabulary specifying and defining existing classes; (2) specification of the inter-relationships between classes; and (3) codification in a computer-readable format to enable knowledge generation, organization, reuse, integration, and analysis. This paper introduces ontologies, provides a review of current efforts to create ontologies related to behavior change interventions and suggests future work. This paper was written by behavioral medicine and information science experts and was developed in partnership between the Society of Behavioral Medicine's Technology Special Interest Group (SIG) and the Theories and Techniques of Behavior Change Interventions SIG. In recent years significant progress has been made in the foundational work needed to develop ontologies of behavior change. Ontologies of behavior change could facilitate a transformation of behavioral science from a field in which data from different experiments are siloed into one in which data across experiments could be compared and/or integrated. This could facilitate new approaches to hypothesis generation and knowledge discovery in behavioral science

Moderators and Mediators of Exercise-Induced Objective Sleep Improvements in Midlife and Older Adults With Sleep Complaints

Objective: Exercise can improve sleep quality, but for whom and by what means remains unclear. We examined moderators and mediators of objective sleep improvements in a 12-month randomized controlled trial among underactive midlife and older adults reporting mild/moderate sleep complaints. Methods: Participants (N ϭ 66, 67% women, 55-79 years) were randomized to moderate-intensity exercise or health education control. Putative moderators were gender, age, physical function, selfreported global sleep quality, and physical activity levels. Putative mediators were changes in BMI, depressive symptoms, and physical function at 6 months. Initially less active individuals with higher initial physical function and poorer sleep quality improved the most. Affective, functional, and metabolic mediators specific to sleep architecture parameters were suggested. These results indicate strategies to more efficiently treat poor sleep through exercise in older adults

Why we need a small data paradigm

Abstract Background There is great interest in and excitement about the concept of personalized or precision medicine and, in particular, advancing this vision via various ‘big data’ efforts. While these methods are necessary, they are insufficient to achieve the full personalized medicine promise. A rigorous, complementary ‘small data’ paradigm that can function both autonomously from and in collaboration with big data is also needed. By ‘small data’ we build on Estrin’s formulation and refer to the rigorous use of data by and for a specific N-of-1 unit (i.e., a single person, clinic, hospital, healthcare system, community, city, etc.) to facilitate improved individual-level description, prediction and, ultimately, control for that specific unit. Main body The purpose of this piece is to articulate why a small data paradigm is needed and is valuable in itself, and to provide initial directions for future work that can advance study designs and data analytic techniques for a small data approach to precision health. Scientifically, the central value of a small data approach is that it can uniquely manage complex, dynamic, multi-causal, idiosyncratically manifesting phenomena, such as chronic diseases, in comparison to big data. Beyond this, a small data approach better aligns the goals of science and practice, which can result in more rapid agile learning with less data. There is also, feasibly, a unique pathway towards transportable knowledge from a small data approach, which is complementary to a big data approach. Future work should (1) further refine appropriate methods for a small data approach; (2) advance strategies for better integrating a small data approach into real-world practices; and (3) advance ways of actively integrating the strengths and limitations from both small and big data approaches into a unified scientific knowledge base that is linked via a robust science of causality. Conclusion Small data is valuable in its own right. That said, small and big data paradigms can and should be combined via a foundational science of causality. With these approaches combined, the vision of precision health can be achieved.https://deepblue.lib.umich.edu/bitstream/2027.42/152218/1/12916_2019_Article_1366.pd

Technology-mediated just-in-time adaptive interventions (JITAIs) to reduce harmful substance use: A systematic review

Background and Aims: Lapse risk when trying to stop or reduce harmful substance use is idiosyncratic, dynamic and multi-factorial. Just-in-time adaptive interventions (JITAIs) aim to deliver tailored support at moments of need or opportunity. We aimed to synthesize evidence on decision points, tailoring variables, intervention options, decision rules, study designs, user engagement and effectiveness of technology-mediated JITAIs for reducing harmful substance use.  Methods: Systematic review of empirical studies of any design with a narrative synthesis. We searched Ovid MEDLINE, Embase, PsycINFO, Web of Science, the ACM Digital Library, the IEEE Digital Library, ClinicalTrials.gov, the ISRCTN register and dblp using terms related to substance use/mHealth/JITAIs. Outcomes were user engagement and intervention effectiveness. Study quality was assessed with the mHealth Evidence Reporting and Assessment checklist.  Findings: We included 17 reports of 14 unique studies, including two randomized controlled trials. JITAIs targeted alcohol (S = 7, n = 120 520), tobacco (S = 4, n = 187), cannabis (S = 2, n = 97) and a combination of alcohol and illicit substance use (S = 1, n = 63), and primarily relied on active measurement and static (i.e. time-invariant) decision rules to deliver support tailored to micro-scale changes in mood or urges. Two studies used data from prior participants and four drew upon theory to devise decision rules. Engagement with available JITAIs was moderate-to-high and evidence of effectiveness was mixed. Due to substantial heterogeneity in study designs and outcome variables assessed, no meta-analysis was performed. Many studies reported insufficient detail on JITAI infrastructure, content, development costs and data security.  Conclusions: Current implementations of just-in-time adaptive interventions (JITAIs) for reducing harmful substance use rely on active measurement and static decision rules to deliver support tailored to micro-scale changes in mood or urges. Studies on JITAI effectiveness are lacking

Determining who responds better to a computer vs. human-delivered physical activity intervention: Results from the community health advice by telephone (CHAT) trial

Background Little research has explored who responds better to an automated vs. human advisor for health behaviors in general, and for physical activity (PA) promotion in particular. The purpose of this study was to explore baseline factors (i.e., demographics, motivation, interpersonal style, and external resources) that moderate intervention efficacy delivered by either a human or automated advisor. Methods Data were from the CHAT Trial, a 12-month randomized controlled trial to increase PA among underactive older adults (full trial N = 218) via a human advisor or automated interactive voice response advisor. Trial results indicated significant increases in PA in both interventions by 12 months that were maintained at 18-months. Regression was used to explore moderation of the two interventions. Results Results indicated amotivation (i.e., lack of intent in PA) moderated 12-month PA (d = 0.55, p \u3c 0.01) and private self-consciousness (i.e., tendency to attune to one’s own inner thoughts and emotions) moderated 18-month PA (d = 0.34, p \u3c 0.05) but a variety of other factors (e.g., demographics) did not (p \u3e 0.12). Conclusions Results provide preliminary evidence for generating hypotheses about pathways for supporting later clinical decision-making with regard to the use of either human- vs. computer-delivered interventions for PA promotion

Symptoms as a moderator of the relationship between beliefs and behaviors among patients undergoing coronary artery bypass surgery

There is growing evidence suggesting health behaviors (e.g., physical activity, medications) significantly improve health outcomes and quality of life following coronary artery bypass graft (CABG) surgery. Despite the clear benefits of these behaviors, adherence is poor and interventions designed to promote them have yielded mixed results. This dissertation, guided by Leventhal's Commonsense Model of Self-Regulation (CSM) and Bandura's Social Cognitive Theory (SCT), was a descriptive study designed to identify beliefs that might predict adherence and serve as intervention targets. Participants were 89 CABG (M age = 65.4, 73% male, 79.8% white) surgery patients who spoke English and were free of any neurological, cognitive, or medical condition that might influence their ability to complete the study. They were interviewed prior to surgery about their CSM and SCT beliefs and their health behaviors (i.e., physical activity, medication adherence) using structured interviews. All measures exhibited factor structures that fit with a priori expectations and had acceptable reliability (αs between .67 and .91). Demographic information was gathered during the structured interviews. Medical information was gathered from medical records and aggregated to create a single cardiac risk factor index. Results suggested that personal control and emotional cause beliefs were positively associated with physical activity, whereas medical cause beliefs were inversely associated with physical activity. In addition, the relationship between symptoms and physical activity appeared to be statistically mediated by emotional cause beliefs. With regard to SCT beliefs, negative medication outcome expectancies (NMOE) was inversely associated with medication adherence, and the relationship between medication adherence self-efficacy and medication adherence was statistically mediated by NMOE. Examination of the possible moderating influence of symptoms on beliefs suggested that both self-efficacy and bed rest outcome expectancies were associated with physical activity if an individual was symptomatic but they were not associated with physical activity if an individual was asymptomatic. Overall, results suggest that integrating the CSM with SCT provides a useful conceptual framework for understanding medication adherence and physical activity. Future research is required to evaluate the prospective, predictive utility of this framework. In addition, interventions that are tailored to patients' symptom status seem worth pursuing.Ph.D.Includes bibliographical references (p. 58-62)