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

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

Supervised machine learning to predict smoking lapses from Ecological Momentary Assessments and sensor data: Implications for just-in-time adaptive intervention development

Specific moments of lapse among smokers attempting to quit often lead to full relapse, which highlights a need for interventions that target lapses before they might occur, such as just-in-time adaptive interventions (JITAIs). To inform the decision points and tailoring variables of a lapse prevention JITAI, we trained and tested supervised machine learning algorithms that use Ecological Momentary Assessments (EMAs) and wearable sensor data of potential lapse triggers and lapse incidence. We aimed to identify a best-performing and feasible algorithm to take forwards in a JITAI. For 10 days, adult smokers attempting to quit were asked to complete 16 hourly EMAs/day assessing cravings, mood, activity, social context, physical context, and lapse incidence, and to wear a Fitbit Charge 4 during waking hours to passively collect data on steps and heart rate. A series of group-level supervised machine learning algorithms (e.g., Random Forest, XGBoost) were trained and tested, without and with the sensor data. Their ability to predict lapses for out-of-sample (i) observations and (ii) individuals were evaluated. Next, a series of individual-level and hybrid (i.e., group- and individual-level) algorithms were trained and tested. Participants (N = 38) responded to 6,124 EMAs (with 6.9% of responses reporting a lapse). Without sensor data, the best-performing group-level algorithm had an area under the receiver operating characteristic curve (AUC) of 0.899 (95% CI = 0.871–0.928). Its ability to classify lapses for out-of-sample individuals ranged from poor to excellent (AUCper person = 0.524–0.994; median AUC = 0.639). 15/38 participants had adequate data for individual-level algorithms to be constructed, with a median AUC of 0.855 (range: 0.451–1.000). Hybrid algorithms could be constructed for 25/38 participants, with a median AUC of 0.692 (range: 0.523 to 0.998). With sensor data, the best-performing group-level algorithm had an AUC of 0.952 (95% CI = 0.933–0.970). Its ability to classify lapses for out-of-sample individuals ranged from poor to excellent (AUCper person = 0.494–0.979; median AUC = 0.745). 11/30 participants had adequate data for individual-level algorithms to be constructed, with a median AUC of 0.983 (range: 0.549–1.000). Hybrid algorithms could be constructed for 20/30 participants, with a median AUC of 0.772 (range: 0.444 to 0.968). In conclusion, high-performing group-level lapse prediction algorithms without and with sensor data had variable performance when applied to out-of-sample individuals. Individual-level and hybrid algorithms could be constructed for a limited number of individuals but had improved performance, particularly when incorporating sensor data for participants with sufficient wear time. Feasibility constraints and the need to balance multiple success criteria in the JITAI development and implementation process are discussed

Characterizing and predicting person-specific, day-to-day, fluctuations in walking behavior

Despite the positive health effect of physical activity, one third of the world's population is estimated to be insufficiently active. Prior research has mainly investigated physical activity on an aggregate level over short periods of time, e.g., during 3 to 7 days at baseline and a few months later, post-intervention. To develop effective interventions, we need a better understanding of the temporal dynamics of physical activity. We proposed here an approach to studying walking behavior at "high-resolution" and by capturing the idiographic and day-to-day changes in walking behavior. We analyzed daily step count among 151 young adults with overweight or obesity who had worn an accelerometer for an average of 226 days (similar to 25,000 observations). We then used a recursive partitioning algorithm to characterize patterns of change, here sudden behavioral gains and losses, over the course of the study. These behavioral gains or losses were defined as a 30% increase or reduction in steps relative to each participants' median level of steps lasting at least 7 days. After the identification of gains and losses, fluctuation intensity in steps from each participant's individual time series was computed with a dynamic complexity algorithm to identify potential early warning signals of sudden gains or losses. Results revealed that walking behavior change exhibits discontinuous changes that can be described as sudden gains and losses. On average, participants experienced six sudden gains or losses over the study. We also observed a significant and positive association between critical fluctuations in walking behavior, a form of early warning signals, and the subsequent occurrence of sudden behavioral losses in the next days. Altogether, this study suggests that walking behavior could be well understood under a dynamic paradigm. Results also provide support for the development of "just-in-time adaptive" behavioral interventions based on the detection of early warning signals for sudden behavioral losses.Peer reviewe

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

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

A decision framework for an adaptive behavioral intervention for physical activity using hybrid model predictive control: illustration with Just Walk

[EN] Physical inactivity is a major contributor to morbidity and mortality worldwide. Many current physical activity behavioral interventions have shown limited success addressing the problem from a long-term perspective that includes maintenance. This paper proposes the design of a decision algorithm for a mobile and wireless health (mHealth) adaptive intervention that is based on control engineering concepts. The design process relies on a behavioral dynamical model based on Social Cognitive Theory (SCT), with a controller formulation based on hybrid model predictive control (HMPC) being used to implement the decision scheme. The discrete and logical features of HMPC coincide naturally with the categorical nature of the intervention components and the logical decisions that are particular to an intervention for physical activity. The intervention incorporates an online controller reconfiguration mode that applies changes in the penalty weights to accomplish the transition between the behavioral initiation and maintenance training stages. Controller performance is illustrated using an ARX model estimated from system identification data of a representative participant for Just Walk, a physical activity intervention designed on the basis of control systems  principles.[ES] La inactividad física es uno de los principales factores que contribuyen a la morbilidad y la mortalidad en todo el mundo. Muchas intervenciones comportamentales de actividad física en la actualidad han mostrado un éxito limitado al abordar el problema desde una perspectiva a largo plazo que incluye el mantenimiento. Este artículo propone el diseño de un algoritmo de decisión para una intervención adaptativa de salud móvil e inalámbrica (mHealth) que se basa en conceptos de ingeniería de control. El proceso de diseño se basa en un modelo dinámico que representa el comportamiento basada en la Teoría Cognitiva Social (TCS), con una formulación de controlador fundamentada en el control predictivo por modelo híbrido (HMPC por sus siglas en inglés) la cual se utiliza para implementar el esquema de decisión. Las características discretas y lógicas del HMPC coinciden naturalmente con la naturaleza categórica de los componentes de la intervención y las decisiones lógicas que son propias de una intervención para actividad física. La intervención incorpora un modo de reconfiguración del controlador en línea que aplica cambios en los pesos de penalización para lograr la transición entre las etapas de entrenamiento de iniciación comportamental y mantenimiento. Resultados de simulación se presentan para ilustrar el desempeño del controlador utilizando un modelo ARX estimado de datos de un participante representativo de Just Walk, una intervención de actividad física diseñada usando principios de sistemas de control.El apoyo para este trabajo ha sido proporcionado por la Fundación Nacional de Ciencias (NSF por sus siglas en inglés) a través de la subvención IIS-449751, y el Instituto Nacional de la Salud (NIH por sus siglas en inglés) a través de la subvención R01CA244777.Cevallos, D.; Martín, CA.; El Mistiri, M.; Rivera, DE.; Hekler, E. (2022). Un esquema de decisiones para intervenciones adaptativas comportamentales de actividad física basado en control predictivo por modelo híbrido: ilustración con Just Walk. Revista Iberoamericana de Automática e Informática industrial. 19(3):297-308. https://doi.org/10.4995/riai.2022.16798OJS29730819

Thinking Health-related Behaviors in a Climate Change Context:A Narrative Review

Background: Human activities have changed the environment so profoundly over the past two centuries that human-induced climate change is now posing serious health-related threats to current and future generations. Rapid action from all scientific fields, including behavioral medicine, is needed to contribute to both mitigation of, and adaption to, climate change.Purpose: This article aims to identify potential bi-directional associations between climate change impacts and health-related behaviors, as well as a set of key actions for the behavioral medicine community.Methods: We synthesized the existing literature about (i) the impacts of rising temperatures, extreme weather events, air pollution, and rising sea level on individual behaviors (e.g., eating behaviors, physical activity, sleep, substance use, and preventive care) as well as the structural factors related to these behaviors (e.g., the food system); and (ii) the concurrent positive and negative roles that health-related behaviors can play in mitigation and adaptation to climate change.Results: Based on this literature review, we propose a first conceptual model of climate change and health-related behavior feedback loops. Key actions are proposed, with particular consideration for health equity implications of future behavioral interventions. Actions to bridge the fields of behavioral medicine and climate sciences are also discussed.Conclusions: We contend that climate change is among the most urgent issues facing all scientists and should become a central priority for the behavioral medicine community.</p