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

Conroy, David E.; Hojjatinia, Sarah; Lagoa, Constantino; Yang, Chih‐Hsiang; Lanza, Stephanie T.; Smyth, Joshua M.

doi:10.1016/j.psychsport.2018.06.011

Cited by 61 publications

(77 citation statements)

References 65 publications

(77 reference statements)

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“…As chatbots increasingly become a convenient digital communication channel, they open up many opportunities for delivering personalized behavior change programs for disease prevention and health promotion on a large scale. Beyond connectivity and feasibility, the advantages of AI chatbot programs lie essentially in the computational power to develop and deliver personalized interventions [ 22 - 24 ]. Such interventions have the potential to overcome several limitations in the traditional paradigm of nonpersonalized interventions, as they are designed based on understanding individual characteristics and behavior trajectories and can incrementally adapt intervention strategies based on contextual conditions and personal cognitive and emotional states over time.…”

Section: Introductionmentioning

confidence: 99%

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

Zhang¹,

Oh²,

Lange³

et al. 2020

J Med Internet Res

193

116

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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.

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Section: Introductionmentioning

confidence: 99%

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

Zhang¹,

Oh²,

Lange³

et al. 2020

J Med Internet Res

193

116

View full text Add to dashboard Cite

show abstract

“…Consequently, the current study design was not appropriate to test a key hypothesis of the present study, i.e., low levels of goal achievement has a negative impact on physical activity over time. A logical next step for the present study would be to conduct a "close loop experiment" (see Conroy et al, 2019;Hekler et al, 2018), where the daily allocation of a goal would be determined based on past physical activity and level of achievement. This design would allow us to test more dynamic hypotheses about goal setting and determine, for example, how the optimal goal setting zone changes over time and, perhaps, in different contexts (Hekler et al, 2012).…”

Section: Study Strengths Limitation and Perspectivesmentioning

confidence: 99%

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

Chevance¹,

Baretta²,

Golaszewski³

et al. 2020

Preprint

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Objective: Despite evidences that goal setting is valuable for physical activity promotion, recent research pointed out a risk of oversimplification in the application of this behavior change technique. While more difficult goals might trigger higher physical activity levels, higher goals might concurrently be more difficult to achieve, which could reduce long-term motivation. This study explores the associations between goal difficulty, physical activity, and goal achievement. Methods: 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 pseudo-random 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. Generalized additive models were fit for each participant individually. Results: The results of the present study confirm that, for a majority of the participants involved in the present study, goal difficulty was positively and significantly associated with physical activity (n = 14 participants out of 20), but, concurrently, negatively and significantly associated with goal achievement (n = 19 participants out of 20). Conclusion: These results suggest the existence of an ‘optimal goal setting zone’ triggering challenging behavioral performances while being still doable. Practitioners or researchers involved in goal setting for physical activity should “tune” goal difficulty levels according to not only behavioral performance but also goal achievement.

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“…Genetic algorithm, i.e. one of the most popular evolutionary algorithms are applicable in solving optimization problems with a complex fitness landscape (Kellerer, Pferschy, & Pisinger, 2004;Conroy et al, 2019).…”

Section: Plain Language Summarymentioning

confidence: 99%

Improvement of the Izhikevich model based on the rat basolateral amygdala and hippocampus neurons, and recognition of their possible firing patterns

Hojjatinia

Shoorehdeli

Fatahi

et al. 2019

Basic Clin. Neurosci. J.

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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.

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

Cited by 61 publications

References 65 publications

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

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

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

Improvement of the Izhikevich model based on the rat basolateral amygdala and hippocampus neurons, and recognition of their possible firing patterns

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