Personalized models of physical activity responses to text message micro-interventions: A proof-of-concept application of control systems engineering methods

Feasibility of Fit24, a Digital Diabetes Prevention Program for Hispanic Adolescents: Qualitative Evaluation Study

BACKGROUND

Digital health interventions are promising for reaching and engaging high-risk youth in disease prevention opportunities; however, few digital prevention interventions have been developed for Hispanic youth, limiting our knowledge of these strategies among this population.

OBJECTIVE

This study qualitatively assessed the feasibility and acceptability of Fit24, a 12-week goal-setting intervention that uses a Fitbit watch (Fitbit Inc) and theoretically grounded SMS text messages to promote physical activity and sleep among Hispanic adolescents (aged between 14 and 16 years) with obesity.

METHODS

After completing the intervention, a subsample of youth (N=15) participated in an in-depth interview. We categorized the themes into dimensions based on participant perspectives using the Practical, Robust Implementation, and Sustainability Model (PRISM) framework.

RESULTS

Participants shared positive perceptions of wearing the Fitbit and receiving SMS text messages. Youth were highly engaged in monitoring their behaviors and perceived increased activity and sleep. Almost all youth organically received social support from a peer or family member and suggested the use of a group chat or team challenge for integrating peers into future interventions. However, most youth also expressed the need to take personal responsibility for the change in their behavior. Barriers that impacted the feasibility of the study included the skin-irritating material on the Fitbit watch band and environmental barriers (eg, lack of resources and school schedules), that limited participation in activity suggestions. Additionally, sync issues with the Fitbit limited the transmission of data, leading to inaccurate feedback.

CONCLUSIONS

Fit24 is a promising approach for engaging Hispanic youth in a diabetes prevention program. Strategies are needed to address technical issues with the Fitbit and environmental issues such as message timing. While integrating peer social support may be desired by some, peer support strategies should be mindful of youth's desire to foster personal motivation for behavior change. Findings from this study will inform future diabetes prevention trials of Fit24 and other digital health interventions for high-risk pediatric populations.

Boosts for walking: how humorous messages increase brisk walking among cognitively fatigued individuals

BACKGROUND

A well-studied internal barrier to regular physical activity, and more specifically brisk walking, is cognitive fatigue. However, thus far little research examined how cognitively fatigued individuals can be motivated to exercise, more specifically to engage in brisk walking. This study investigates whether humorous intervention messages might be an effective strategy to motivate cognitively fatigued individuals to brisk walk, and through which underlying processes.

METHODS

An online experiment was performed in which variation in cognitive fatigue was induced through mental arithmetic questions. Afterwards, participants (n = 250) recruited through Prolific, randomly received either humorous or non-humorous intervention messages related to brisk walking. Potential mediators of the relations between physical activity, humour and cognitive fatigue were measured, were self-efficacy, self-control, and motivation.

RESULTS

First, regression analyses confirmed that cognitive fatigue negatively influences brisk walking intentions and that the perceived humour of the intervention messages moderated this relationship. Second, results showed that self-control and self-efficacy are mediators explaining the relationship between cognitive fatigue and brisk walking intentions. Lastly, this study found that perceived humour of the intervention messages moderated the relationship between cognitive fatigue and self-control, indicating that perceptions of self-control were positively changed after receiving messages that were perceived as humorous compared to messages that were not perceived as humorous, subsequently increasing brisk walking intentions.

CONCLUSIONS

This study is the first to unravel the underlying relationship between humorous intervention messages and brisk walking intentions through positive changes in perceptions of self-control within a cognitively fatigued sample. Results of this study suggest that existing smartphone applications monitoring and promoting brisk walking should integrate tailored message strategies within their cues to brisk walk by implementing humour as a strategy to motivate users when they are cognitively fatigued.

Motivational Message Framing Effects on Physical Activity Dynamics in a Digital Messaging Intervention: Secondary Analysis

BACKGROUND

Digital smartphone messaging can be used to promote physical activity to large populations with limited cost. It is not clear which psychological constructs should be targeted by digital messages to promote physical activity. This gap presents a challenge for developing optimal content for digital messaging interventions.

OBJECTIVE

The aim of this study is to compare affectively framed and social cognitively framed messages on subsequent changes in physical activity using dynamical modeling techniques.

METHODS

We conducted a secondary analysis of data collected from a digital messaging intervention in insufficiently active young adults (18-29 years) recruited between April 2019 and July 2020 who wore a Fitbit smartwatch for 6 months. Participants received 0 to 6 messages at random per day across the intervention period. Messages were drawn from 3 content libraries: affectively framed, social cognitively framed, or inspirational quotes. Person-specific dynamical models were identified, and model features of impulse response and cumulative step response were extracted for comparison. Two-way repeated-measures ANOVAs evaluated the main effects and interaction of message type and day type on model features. This early-phase work with novel dynamic features may have been underpowered to detect differences between message types so results were interpreted descriptively.

RESULTS

Messages (n=20,689) were paired with valid physical activity monitoring data from 45 participants for analysis. Received messages were distributed as 40% affective (8299/20,689 messages), 39% social-cognitive (8187/20,689 messages), and 20% inspirational quotes (4219/20,689 messages). There were no statistically significant main effects for message type when evaluating the steady state of step responses. Participants demonstrated heterogeneity in intervention response: some had their strongest responses to affectively framed messages, some had their strongest responses to social cognitively framed messages, and some had their strongest responses to the inspirational quote messages.

CONCLUSIONS

No single type of digital message content universally promotes physical activity. Future work should evaluate the effects of multiple message types so that content can be continuously tuned based on person-specific responses to each message type.

Towards more personalized digital health interventions: a clustering method of action and coping plans to promote physical activity

BACKGROUND

Despite effectiveness of action and coping planning in digital health interventions to promote physical activity (PA), attrition rates remain high. Indeed, support to make plans is often abstract and similar for each individual. Nevertheless, people are different, and context varies. Tailored support at the content level, involving suggestions of specific plans that are personalized to the individual, may reduce attrition and improve outcomes in digital health interventions. The aim of this study was to investigate whether user information relates toward specific action and coping plans using a clustering method. In doing so, we demonstrate how knowledge can be acquired in order to develop a knowledge-base, which might provide personalized suggestions in a later phase.

METHODS

To establish proof-of-concept for this approach, data of 65 healthy adults, including 222 action plans and 204 coping plans, were used and were collected as part of the digital health intervention MyPlan 2.0 to promote PA. As a first step, clusters of action plans, clusters of coping plans and clusters of combinations of action plans and barriers of coping plans were identified using hierarchical clustering. As a second step, relations with user information (i.e. gender, motivational stage, ...) were examined using anova's and chi²-tests.

RESULTS

First, three clusters of action plans, eight clusters of coping plans and eight clusters of the combination of action and coping plans were identified. Second, relating these clusters to user information was possible for action plans: 1) Users with a higher BMI related more to outdoor leisure activities (F = 13.40, P < .001), 2) Women, users that didn't perform PA regularly yet, or users with a job related more to household activities (X² = 16.92, P < .001; X² = 20.34, P < .001; X² = 10.79, P = .004; respectively), 3) Younger users related more to active transport and different sports activities (F = 14.40, P < .001). However, relating clusters to user information proved difficult for the coping plans and combination of action and coping plans.

CONCLUSIONS

The approach used in this study might be a feasible approach to acquire input for a knowledge-base, however more data (i.e. contextual and dynamic user information) from possible end users should be acquired in future research. This might result in a first type of context-aware personalized suggestions on the content level.

TRIAL REGISTRATION

The digital health intervention MyPlan 2.0 was preregistered as a clinical trial (ID:NCT03274271). Release date: 6-September-2017.

An Overview of Innovative Approaches to Support Timely and Agile Health Communication Research and Practice

Innovative approaches are needed to make health communication research and practice more timely, responsive, and effective in a rapidly changing information ecosystem. In this paper we provide an overview of strategies that can enhance the delivery and effectiveness of health communication campaigns and interventions, as well as research approaches that can generate useful data and insights for decisionmakers and campaign designers, thereby reducing the research-to-practice gap. The discussion focuses on the following approaches: digital segmentation and microtargeting, social media influencer campaigns, recommender systems, adaptive interventions, A/B testing, efficient message testing protocols, rapid cycle iterative message testing, megastudies, and agent-based modeling. For each method highlighted, we also outline important practical and ethical considerations for utilizing the approach in the context of health communication research and practice, including issues related to transparency, privacy, equity, and potential for harm.

Physical Activity Dynamics During a Digital Messaging Intervention Changed After the Pandemic Declaration

BACKGROUND

The COVID-19 pandemic adversely impacted physical activity, but little is known about how contextual changes following the pandemic declaration impacted either the dynamics of people's physical activity or their responses to micro-interventions for promoting physical activity.

PURPOSE

This paper explored the effect of the COVID-19 pandemic on the dynamics of physical activity responses to digital message interventions.

METHODS

Insufficiently-active young adults (18-29 years; N = 22) were recruited from November 2019 to January 2020 and wore a Fitbit smartwatch for 6 months. They received 0-6 messages/day via smartphone app notifications, timed and selected at random from three content libraries (Move More, Sit Less, and Inspirational Quotes). System identification techniques from control systems engineering were used to identify person-specific dynamical models of physical activity in response to messages before and after the pandemic declaration on March 13, 2020.

RESULTS

Daily step counts decreased significantly following the pandemic declaration on weekdays (Cohen's d = -1.40) but not on weekends (d = -0.26). The mean overall speed of the response describing physical activity (dominant pole magnitude) did not change significantly on either weekdays (d = -0.18) or weekends (d = -0.21). In contrast, there was limited rank-order consistency in specific features of intervention responses from before to after the pandemic declaration.

CONCLUSIONS

Generalizing models of behavioral dynamics across dramatically different environmental contexts (and participants) may lead to flawed decision rules for just-in-time physical activity interventions. Periodic model-based adaptations to person-specific decision rules (i.e., continuous tuning interventions) for digital messages are recommended when contexts change.

The Physical Activity Messaging Framework (PAMF) and Checklist (PAMC): International consensus statement and user guide

Effective physical activity messaging plays an important role in the pathway towards changing physical activity behaviour at a population level. The Physical Activity Messaging Framework (PAMF) and Checklist (PAMC) are outputs from a recent modified Delphi study. This sought consensus from an international expert panel on how to aid the creation and evaluation of physical activity messages. In this paper, we (1) present an overview of the various concepts within the PAMF and PAMC, (2) discuss in detail how the PAMF and PAMC can be used to create physical activity messages, plan evaluation of messages, and aid understanding and categorisation of existing messages, and (3) highlight areas for future development and research. If adopted, we propose that the PAMF and PAMC could improve physical activity messaging practice by encouraging evidence-based and target population-focused messages with clearly stated aims and consideration of potential working pathways. They could also enhance the physical activity messaging research base by harmonising key messaging terminologies, improving quality of reporting, and aiding collation and synthesis of the evidence.

Dynamic models of stress-smoking responses based on high-frequency sensor data

Self-reports indicate that stress increases the risk for smoking; however, intensive data from sensors can provide a more nuanced understanding of stress in the moments leading up to and following smoking events. Identifying personalized dynamical models of stress-smoking responses can improve characterizations of smoking responses following stress, but techniques used to identify these models require intensive longitudinal data. This study leveraged advances in wearable sensing technology and digital markers of stress and smoking to identify person-specific models of stress and smoking system dynamics by considering stress immediately before, during, and after smoking events. Adult smokers (n = 45) wore the AutoSense chestband (respiration-inductive plethysmograph, electrocardiogram, accelerometer) with MotionSense (accelerometers, gyroscopes) on each wrist for three days prior to a quit attempt. The odds of minute-level smoking events were regressed on minute-level stress probabilities to identify person-specific dynamic models of smoking responses to stress. Simulated pulse responses to a continuous stress episode revealed a consistent pattern of increased odds of smoking either shortly after the beginning of the simulated stress episode or with a delay, for all participants. This pattern is followed by a dramatic reduction in the probability of smoking thereafter, for about half of the participants (49%). Sensor-detected stress probabilities indicate a vulnerability for smoking that may be used as a tailoring variable for just-in-time interventions to support quit attempts.

Person-specific dose-finding for a digital messaging intervention to promote physical activity

OBJECTIVE

Digital messaging is an established method for promoting physical activity. Systematic approaches for dose-finding have not been widely used in behavioral intervention development. We apply system identification tools from control systems engineering to estimate dynamical models and inform decision rules for digital messaging intervention to promote physical activity.

METHOD

Insufficiently active emerging and young adults (n = 45) wore an activity monitor that recorded minute-level step counts and heart rate and received 0-6 digital messages daily on their smartphone for 6 months. Messages were drawn from 3 content libraries (move more, sit less, inspirational quotes). Location recordings via location services in the user's smartphone were used to lookup weather indices at the time and place of message delivery. Following system identification, responses to each message type were simulated under different conditions. Response features were extracted to summarize dynamic processes.

RESULTS

A generic model based on composite data was conservative and did not capture the heterogeneous responses evident in person-specific models. No messages were uniformly ineffective but responses to specific message content in different contexts varied between people. Exterior temperature at the time of message receipt moderated the size of some message effects.

CONCLUSIONS

A generic model of message effects on physical activity can provide the initial evidence for context-sensitive decision rules in a just-in-time adaptive intervention, but it is likely to be error-prone and inefficient. As individual data accumulates, person-specific models should be estimated to optimize treatment and evolve as people are exposed to new environments and accumulate new experiences. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Smartphone-Based Interventions to Reduce Sedentary Behavior and Promote Physical Activity Using Integrated Dynamic Models: Systematic Review

BACKGROUND

Traditional psychological theories are inadequate to fully leverage the potential of smartphones and improve the effectiveness of physical activity (PA) and sedentary behavior (SB) change interventions. Future interventions need to consider dynamic models taken from other disciplines, such as engineering (eg, control systems). The extent to which such dynamic models have been incorporated in the development of interventions for PA and SB remains unclear.

OBJECTIVE

This review aims to quantify the number of studies that have used dynamic models to develop smartphone-based interventions to promote PA and reduce SB, describe their features, and evaluate their effectiveness where possible.

METHODS

Databases including PubMed, PsycINFO, IEEE Xplore, Cochrane, and Scopus were searched from inception to May 15, 2019, using terms related to mobile health, dynamic models, SB, and PA. The included studies involved the following: PA or SB interventions involving human adults; either developed or evaluated integrated psychological theory with dynamic theories; used smartphones for the intervention delivery; the interventions were adaptive or just-in-time adaptive; included randomized controlled trials (RCTs), pilot RCTs, quasi-experimental, and pre-post study designs; and were published from 2000 onward. Outcomes included general characteristics, dynamic models, theory or construct integration, and measured SB and PA behaviors. Data were synthesized narratively. There was limited scope for meta-analysis because of the variability in the study results.

RESULTS

A total of 1087 publications were screened, with 11 publications describing 8 studies included in the review. All studies targeted PA; 4 also included SB. Social cognitive theory was the major psychological theory upon which the studies were based. Behavioral intervention technology, control systems, computational agent model, exploit-explore strategy, behavioral analytic algorithm, and dynamic decision network were the dynamic models used in the included studies. The effectiveness of quasi-experimental studies involved reduced SB (1 study; P=.08), increased light PA (1 study; P=.002), walking steps (2 studies; P=.06 and P<.001), walking time (1 study; P=.02), moderate-to-vigorous PA (2 studies; P=.08 and P=.81), and nonwalking exercise time (1 study; P=.31). RCT studies showed increased walking steps (1 study; P=.003) and walking time (1 study; P=.06). To measure activity, 5 studies used built-in smartphone sensors (ie, accelerometers), 3 of which used the phone's GPS, and 3 studies used wearable activity trackers.

CONCLUSIONS

To our knowledge, this is the first systematic review to report on smartphone-based studies to reduce SB and promote PA with a focus on integrated dynamic models. These findings highlight the scarcity of dynamic model-based smartphone studies to reduce SB or promote PA. The limited number of studies that incorporate these models shows promising findings. Future research is required to assess the effectiveness of dynamic models in promoting PA and reducing SB.

TRIAL REGISTRATION

International Prospective Register of Systematic Reviews (PROSPERO) CRD42020139350; https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=139350.

A modified Delphi study to enhance and gain international consensus on the Physical Activity Messaging Framework (PAMF) and Checklist (PAMC)

INTRODUCTION

Physical activity messaging is an important step in the pathway towards improving population physical activity levels, but best practice is not yet understood. A gap in the literature exists for a physical activity messaging framework to help guide creation and evaluation of messages. This study aimed to further develop and improve, and gain international expert consensus on, a standardised Physical Activity Messaging Framework and Checklist.

METHODS

A modified Delphi study consisting of three online survey rounds was conducted. Each survey gathered feedback from an international expert panel using quantitative and qualitative methods. The framework and checklist were amended between each round based on survey results until consensus (defined a priori as 80% agreement) was reached.

RESULTS

The final expert panel (n = 40, 55% female) came from nine countries and comprised academics (55%), healthcare and other professionals (22.5%) and government officials or policymakers (22.5%). Consensus was reached in survey 3 with 85 and 87.5% agreement on the framework and checklist, respectively.

CONCLUSION

This study presents an expert- and evidence-informed framework and checklist for physical activity messaging. If used consistently, the Physical Activity Messaging Framework and Checklist may improve practice by encouraging evidence-based and target audience-focused messages, as well as enhance the research base on physical activity messaging by harmonising key terminologies and improving quality of reporting. Key next steps include further refining the Physical Activity Messaging Framework and Checklist based on their use in real-world settings.

The Impact of the COVID-19 Pandemic on US College Students' Physical Activity and Mental Health

BACKGROUND

Investigating the impact of the COVID-19 pandemic on both physical activity (PA) and mental health is important to demonstrate the need for interventions. This study examined the apparent impact of the pandemic on college students' PA, perceived stress, and depressive symptoms.

METHODS

From 2015 through 2020, data were collected at the beginning and end of the spring semester at a large Northeastern US university via an online survey assessing student demographics, PA, perceived stress, and depressive symptoms. Mixed ANOVA examined differences in PA and mental health changes over the spring semester between "normal" and COVID-19 circumstances. Two-way ANOVA examined the interaction between circumstance and changes in PA in relation to changes in mental health.

RESULTS

Participants (n = 1019) were predominately women and non-Hispanic white. There was a significant decline in PA and an increase in perceived stress under COVID-19, but not normal, circumstances and a significant increase in depressive symptoms under COVID-19, but not normal, circumstances among women.

CONCLUSIONS

A significant decline in PA and mental health among college students occurred under COVID-19 circumstances, and PA did not appear to protect against deterioration in mental health. Proactive and innovative policies, programs, and practices to promote student health and well-being must be explored immediately.

Role of AI physical education based on application of functional sports training

The physical health test of college students is an important part of the school physical education work and an important part of the school education evaluation system. It is an educational method that promotes the healthy development of students’ physical fitness and encourages students to actively take physical exercises. It is an individual evaluation standard for students’ physical fitness. It is also one of the necessary conditions for students to graduate. In order to improve the physique and health of college students, this article first introduces functional exercise tests to comprehensively measure the exercise capacity of the main muscle groups and joints of the human body, and integrate flexibility and strength qualities. Secondly, this article quantitatively studies the interaction law between the natural light environment comfort of sports training facilities and architectural design elements, and adopts appropriate dynamic optimization methods to improve the light environment quality of the sports space, thereby enhancing the visual comfort of the sports crowd in the stadium. Finally, the artificial intelligence technology is introduced, through the design of artificial intelligence system, intelligent data collection, and analysis. From the perspective of physical education, the functional exercise test based on artificial intelligence conforms to the essential meaning of the physical fitness test and helps to enhance the awareness of college students’ physical exercise. And the intelligent remote multimedia physical education system based on artificial intelligence makes the physical education process flexible, free from time and place restrictions, and can adopt different teaching strategies according to the different situations of students to implement personalized teaching.

Just-in-time adaptive intervention to promote fluid consumption in patients with kidney stones

OBJECTIVE

Kidney stones are painful and costly. Prevention guidelines emphasize a simple behavior change: increasing fluid intake and urine output. Unfortunately, adherence to those prevention guidelines is limited, and patients report forgetting or not being thirsty enough. This study evaluated the acceptability of using semiautomated tracking of fluid consumption to trigger just-in-time reminders to drink and increase the experienced automaticity of fluid intake.

METHOD

In a single-group trial, participants with a history of kidney stones (n = 31) used the sip^IT digital tools (H2OPal connected water bottle, H2OPal mobile app for self-tracking, Fitbit smartwatch app for gesture detection) for 3 months.

RESULTS

The semiautomated monitoring system detected 46,654 drinking events. From baseline to 1-month follow-up, the experienced automaticity of fluid intake increased significantly (d = 0.50) and remained elevated at 3-month follow-up (d = 0.64). A major barrier to adherence (lack of thirst) decreased from baseline to follow-ups. Retention rates and participant feedback indicated that this digital tool was acceptable to patients.

CONCLUSION

Semiautomated tracking of fluid consumption can be used to trigger just-in-time reminders. Based on this demonstration, the sip^IT tools are ready for testing in a rigorous Phase II trial to evaluate efficacy for increasing fluid consumption and urine output as recommended for preventing the recurrence of kidney stones. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Goal setting and achievement for walking: A series of N-of-1 digital interventions

OBJECTIVE

Despite evidence that goal setting is valuable for physical activity promotion, recent studies highlighted a potential oversimplification in the application of this behavior change technique. While more difficult performance goals might trigger higher physical activity levels, higher performance goals might concurrently be more difficult to achieve, which could reduce long-term motivation. This study examined (a) the association between performance goal difficulty and physical activity and (b) the association between performance goal difficulty and goal achievement.

METHOD

This study used data from an e-Health intervention among inactive overweight adults (n = 20). The study duration included a 2-week baseline period and an intervention phase of 80 days. During the intervention, participants received a daily step goal experimentally manipulated by taking participants' baseline physical activity median (i.e., number of steps) multiplied by a pseudorandom factor ranging from 1 to 2.6. A continuous measure of goal achievement was inferred for each day by dividing the daily number of steps by the goal prescribed that day. Linear and generalized additive models were fit for each participant.

RESULTS

The results confirm that, for a majority of the participants involved in the study, performance goal difficulty was positively and significantly associated with physical activity (n = 14), but, concurrently, negatively and significantly associated with goal achievement (n = 19). These associations were mainly linear.

CONCLUSION

At the daily level, setting a higher physical activity goal leads to engaging in higher physical activity levels, but concurrently lower goal achievement. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Precision Health: The Role of the Social and Behavioral Sciences in Advancing the Vision

BACKGROUND

In 2015, Collins and Varmus articulated a vision for precision medicine emphasizing molecular characterization of illness to identify actionable biomarkers to support individualized treatment. Researchers have argued for a broader conceptualization, precision health. Precision health is an ambitious conceptualization of health, which includes dynamic linkages between research and practice as well as medicine, population health, and public health. The goal is a unified approach to match a full range of promotion, prevention, diagnostic, and treatment interventions to fundamental and actionable determinants of health; to not just address symptoms, but to directly target genetic, biological, environmental, and social and behavioral determinants of health.

PURPOSE

The purpose of this paper is to elucidate the role of social and behavioral sciences within precision health.

MAIN BODY

Recent technologies, research frameworks, and methods are enabling new approaches to measure, intervene, and conduct social and behavioral science research. These approaches support three opportunities in precision health that the social and behavioral sciences could colead including: (a) developing interventions that continuously "tune" to each person's evolving needs; (b) enhancing and accelerating links between research and practice; and (c) studying mechanisms of change in real-world contexts. There are three challenges for precision health: (a) methods of knowledge organization and curation; (b) ethical conduct of research; and (c) equitable implementation of precision health.

CONCLUSIONS

Precision health requires active coleadership from social and behavioral scientists. Prior work and evidence firmly demonstrate why the social and behavioral sciences should colead with regard to three opportunity and three challenge areas.

Engineering Person-Specific Behavioral Interventions to Promote Physical Activity

Physical activity is dynamic, complex, and often regulated idiosyncratically. In this article, we review how techniques used in control systems engineering are being applied to refine physical activity theory and interventions. We hypothesize that person-specific adaptive behavioral interventions grounded in system identification and model predictive control will lead to greater physical activity than more generic, conventional intervention approaches.

Identification of switched autoregressive exogenous systems from large noisy datasets

The article introduces novel methodologies for the identification of coefficients of switching autoregressive moving average with exogenous input systems and switched autoregressive exogenous linear models. We consider cases where system's outputs are contaminated by possibly large values of noise for both cases of measurement noise and process noise. It is assumed that only partial information on the probability distribution of the noise is available. Given input-output data, we aim at identifying switched system coefficients and parameters of the distribution of the noise, which are compatible with the collected data. We demonstrate the efficiency of the proposed approach with several academic examples. The method is shown to be effective in the situations where a large number of measurements is available; cases in which previous approaches based on polynomial or mixed-integer optimization cannot he applied due to very large computational burden.

Artificial Intelligence Chatbot Behavior Change Model for Designing Artificial Intelligence Chatbots to Promote Physical Activity and a Healthy Diet: Viewpoint

BACKGROUND

Chatbots empowered by artificial intelligence (AI) can increasingly engage in natural conversations and build relationships with users. Applying AI chatbots to lifestyle modification programs is one of the promising areas to develop cost-effective and feasible behavior interventions to promote physical activity and a healthy diet.

OBJECTIVE

The purposes of this perspective paper are to present a brief literature review of chatbot use in promoting physical activity and a healthy diet, describe the AI chatbot behavior change model our research team developed based on extensive interdisciplinary research, and discuss ethical principles and considerations.

METHODS

We conducted a preliminary search of studies reporting chatbots for improving physical activity and/or diet in four databases in July 2020. We summarized the characteristics of the chatbot studies and reviewed recent developments in human-AI communication research and innovations in natural language processing. Based on the identified gaps and opportunities, as well as our own clinical and research experience and findings, we propose an AI chatbot behavior change model.

RESULTS

Our review found a lack of understanding around theoretical guidance and practical recommendations on designing AI chatbots for lifestyle modification programs. The proposed AI chatbot behavior change model consists of the following four components to provide such guidance: (1) designing chatbot characteristics and understanding user background; (2) building relational capacity; (3) building persuasive conversational capacity; and (4) evaluating mechanisms and outcomes. The rationale and evidence supporting the design and evaluation choices for this model are presented in this paper.

CONCLUSIONS

As AI chatbots become increasingly integrated into various digital communications, our proposed theoretical framework is the first step to conceptualize the scope of utilization in health behavior change domains and to synthesize all possible dimensions of chatbot features to inform intervention design and evaluation. There is a need for more interdisciplinary work to continue developing AI techniques to improve a chatbot's relational and persuasive capacities to change physical activity and diet behaviors with strong ethical principles.

Improving the Izhikevich Model Based on Rat Basolateral Amygdala and Hippocampus Neurons, and Recognizing Their Possible Firing Patterns

Introduction

Identifying the potential firing patterns following different brain regions under normal and abnormal conditions increases our understanding of events at the level of neural interactions in the brain. Furthermore, it is important to be capable of modeling the potential neural activities to build precise artificial neural networks. The Izhikevich model is one of the simplest biologically-plausible models, i.e. capable of capturing most recognized firing patterns of neurons. This property makes the model efficient in simulating the large-scale networks of neurons. Improving the Izhikevich model for adapting with the neuronal activity of rat brain with great accuracy would make the model effective for future neural network implementations.

Methods

Data sampling from two brain regions, the HIP and BLA, was performed by the extracellular recordings of male Wistar rats, and spike sorting was conducted by Plexon offline sorter. Further analyses were performed through NeuroExplorer and MATLAB. To optimize the Izhikevich model parameters, a genetic algorithm was used. In this algorithm, optimization tools, like crossover and mutation, provide the basis for generating model parameters populations. The process of comparison in each iteration leads to the survival of better populations until achieving the optimum solution.

Results

In the present study, the possible firing patterns of the real single neurons of the HIP and BLA were identified. Additionally, an improved Izhikevich model was achieved. Accordingly, the real neuronal spiking pattern of these regions' neurons and the corresponding cases of the Izhikevich neuron spiking pattern were adjusted with great accuracy.

Conclusion

This study was conducted to elevate our knowledge of neural interactions in different structures of the brain and accelerate the quality of future large-scale neural networks simulations, as well as reducing the modeling complexity. This aim was achievable by performing the improved Izhikevich model, and inserting only the plausible firing patterns and eliminating unrealistic ones.

Ambiguity in Communicating Intensity of Physical Activity: Survey Study

Background

Communicating physical activity information with sufficient details, such as activity type, frequency, duration, and intensity, is vital to accurately delineate the attributes of physical activity that bring positive health impact. Unlike frequency and duration, intensity is a subjective concept that can be interpreted differently by people depending on demographics, health status, physical fitness, and exercise habits. However, activity intensity is often communicated using general degree modifiers, degree of physical exertion, and physical activity examples, which are the expressions that people may interpret differently. Lack of clarity in communicating the intensity level of physical activity is a potential barrier to an accurate assessment of exercise effect and effective imparting of exercise recommendations.

Objective

This study aimed to assess the variations in people’s perceptions and interpretations of commonly used intensity descriptions of physical activities and to identify factors that may contribute to these variations.

Methods

A Web-based survey with a 25-item questionnaire was conducted using Amazon Mechanical Turk, targeting adults residing in the United States. The questionnaire included questions on participants’ demographics, exercise habits, overall perceived health status, and perceived intensity of 10 physical activity examples. The survey responses were analyzed using the R statistical package.

Results

The analyses included 498 responses. The majority of respondents were females (276/498, 55.4%) and whites (399/498, 79.9%). Numeric ratings of physical exertion after exercise were relatively well associated with the 3 general degree descriptors of exercise intensity: light, moderate, and vigorous. However, there was no clear association between the intensity expressed with those degree descriptors and the degree of physical exertion the participants reported to have experienced after exercise. Intensity ratings of various examples of physical activity differed significantly according to respondents’ characteristics. Regression analyses showed that those who reported good health or considered regular exercise was important for their health tended to rate the intensity levels of the activity examples significantly higher than their counterparts. The respondents’ age and race (white vs nonwhite) were not significant predictors of the intensity rating.

Conclusions

This survey showed significant variations in how people perceive and interpret the intensity levels of physical activities described with general severity modifiers, degrees of physical exertion, and physical activity examples. Considering that these are among the most widely used methods of communicating physical activity intensity in current practice, a possible miscommunication in assessing and promoting physical activity seems to be a real concern. We need to adopt a method that represents activity intensity in a quantifiable manner to avoid unintended miscommunication.

Get the message? A scoping review of physical activity messaging

BACKGROUND

Understanding how to create and deliver effective physical activity (PA) messages for and to various population subgroups may play a role in increasing population PA levels. This scoping review aimed to provide an overview of what is known about PA messaging and highlight key research gaps.

METHODS

We followed a 5-stage protocol proposed by Arksey & O'Malley and the Preferred Reporting Items For Systematic Reviews and Meta-Analyses (PRISMA) extension for scoping reviews checklist. Stage 1: research questions were identified. Stage 2: we identified relevant studies by searching electronic databases, contacting existing networks and hand searching reference lists. Stage 3: studies were screened in Covidence™ software. Stage 4: study data were extracted and charted. Stage 5: findings from included studies were collated, summarised and reported in two ways: (1) a descriptive numerical analysis providing insight into extent, nature and distribution of the included studies, and (2) a narrative summary summarizing the evidence reviewed organised by messaging concepts and by population subgroup.

RESULTS

A total of 9525 references were imported into Covidence™ for screening. Of these, 123 studies were included in final analysis. We found that PA messaging evidence is complex and multidimensional in nature, with numerous concepts to consider when creating or evaluating messages. The extent to which these different PA messaging concepts have been researched is variable. Where research has accumulated and evidence is consistent, it supports the following: (1) PA messages should be framed positively and highlight short-term outcomes specifically relating to social and mental health, (2) message content should be tailored or targeted to intended recipient(s), and (3) when developing messages, formative research, psychological theory and/or social marketing principles should be used.

CONCLUSION

While it is unlikely to address global inactivity on its own, PA messaging may play a valuable role improving population PA levels. However, it is a complex and multidimensional concept and greater understanding is still needed. We present a synthesis of the existing evidence, highlighting key areas where evidence has accumulated and where gaps lie, as well as recommendations for PA messaging to different population subgroups.

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.