Clinical trial NCT05192226: Counterfactual Strategies, Physical Activity, and Wearable Trackers
Official title: Using Counterfactual Strategies to Understand and Promote Physical Activity Behavior in Low-socioeconomic Status Individuals
Study type: INTERVENTIONAL
Overall status: COMPLETED
Phase: NA
Conditions: Financial Stress
Socioeconomic Status
Social Status
Keywords: low-socioeconomic status
Physical activity
Lead sponsor: Texas A&M University
Sponsor class: OTHER
Collaborators: name: U.S. National Science Foundation
First posted: 2022-01-14
Enrollment: 40
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Allocation: RANDOMIZED
Intervention model: PARALLEL
Primary purpose: BASIC_SCIENCE

Brief summary: Physical activity (PA) has been suggested to lower one's risk of developing cancer, type 2 diabetes, and cardiovascular disease. While there are benefits from engaging in PA, many people do not engage in enough daily PA, thus increasing the chance of developing non-communicable diseases (NCD). Some NCDs, such as type 2 diabetes, have been shown to occur at higher rates within under-resourced populations, such as low socioeconomic status (SES) communities. Among low-SES communities, external barriers, such as cost and the surrounding physical environment, have been shown to impact engagement in PA. A multi-level PA intervention could be beneficial to help lower NCD health outcomes within at-risk groups, as well as serve as a means to further understand the barriers impeding a healthy lifestyle.

At the individual level, past behavior is suggested to be a significant predictor of future behavior. When faced with a NCD diagnosis, one might think about the past and how things could have turned out differently (i.e., counterfactual thinking). For instance, what if a different action had been taken (e.g., "If only I had taken the stairs more at work")? Counterfactuals can also serve as a way of identifying causal links (e.g., "If only there were more green spaces in my area..."). Counterfactuals (CF) on behaviors that can be acted on can facilitate future behavior change by increasing intentions, motivation, and self-efficacy. In this way, CFs might help with 1) breaking a habitual sedentary cycle and 2) identify causal pathways of barriers impacting PA engagement. While preliminary data in the investigators lab suggests that CF strategies are relevant for heightening contemplation to change behaviors and intentions to change behaviors its impact on motivation and self-efficacy remains unknown. Additionally, these preliminary studies were conducted using small, undergraduate student sample, thus generalizability to low-SES individuals living in the surrounding community is unknown. For the proposed study, participants will use CFs to target barriers in different domains and levels of influence impeding PA. This identification effort will be used to work towards increasing PA behavior (collected by wearable fitness trackers). CFs will also be used to work towards increasing psychological domains relevant to behavior change over the span of 14 weeks.

Detailed description: A.1 Significance A.1.1 Physical Activity. The CDC suggests that 150-300 minutes of moderate-intensity physical activity (PA) is important to staving off non-communicable diseases (NCD) such as cancer, type 2 diabetes, and comorbidity health outcomes. However, many communities, such as low-socioeconomic (SES) communities, are not engaging in enough regular PA to reap the benefits, such as decreased chance in experiencing NCD health outcomes. Results from qualitative studies often relay common barriers and themes impeding PA requirements, such as environmental factors and resource availability.

Limitations of physical activity interventions. Common PA interventions largely do not incorporate a multi-level framework to acknowledge external barriers. Focusing on individual-level factors, such as self-efficacy, are small parts of why an individual may or may not engage in PA; casting a wider net to capture an array of barriers would provide better understanding of current inactivity trends. Qualitative studies that inquire about multi-level external barriers typically do not juxtapose the current state of barriers (the facts) with the ideal outcome if the barriers were removed (counterfactuals, e.g., "If only..."). Researchers are missing out on a way to align potential solutions with how community members frame their understanding of their environment and their ability to act in that environment.

A.1.2 Counterfactuals, Causality, and Goal-Pursuit Theory. An individual might reflect on their past to help inform the present. For instance, thinking "If I had taken a walk during my lunch break, then I would have met my step goal." Known as counterfactual thinking (CF), these thoughts can help us understand how elements within one's environment might be causally connected (i.e., If...then...). According to functional CF theory, CFs describing a better outcome via a self-focused action can lead to heightened goal-pursuit behaviors. The efficacy of CF strategies in a longitudinal PA study has yet to be established.

A.1.3 Multilevel Determinants of Health Disparities. There has been a call for researchers to look beyond individual-level factors impacting PA. One way to answer this call is to use multi-level frameworks, such as the NIMHD research framework, for understanding the ways in which health is impacted within and between different internal and external agents. The NIMHD framework outlines levels of influence (individual, interpersonal, community, and societal) and domains of influence (biological, behavioral, physical/built environment, sociocultural environment, and health care system) that can be used to conceptualize and understand minoritized health outcomes. This allows for a nuanced understanding of factors driving health outcome inequity.

B.1 Innovation and Relation to PATHS-UP ERC This proposal seeks to uniquely address a multi-level need by, 1) inviting the individual to provide a detailed look at various PA barriers participants experience in day-to-day life, 2) highlighting how certain pathways are framed as causally connected to themselves and their community, and 3) providing a relevant antecedent steppingstone to how these barriers could be minimized, altered, or removed altogether. Aligning with PATHS-UP ERC's commitment towards addressing health inequity and health-promotion access and participation, the proposed study will focus on addressing health barriers in underserved communities.

C.1 Specific Aims C.1.1 Specific Aims 1: Identify barriers to PA experienced by low-SES individuals. Participants will engage in a semi-structured interview with a trained researcher on the experiment day. During this time, participants will be randomly assigned to either the CF strategy or the control condition. Both conditions will be asked to identify, using the NIMHD Research Framework, any barriers towards engaging in PA. Hypothesis 1: Participants will identify more externally focused, compared to internally focused, barriers that then impact them at the individual and behavioral level.

C.1.2 Specific Aim 2: Determine the effect of a CF strategies on PA behavior. Participants in both conditions will complete demographics and psychological measures of contemplation to change PA engagement, motivation, self-efficacy, and intention to engage in PA at baseline, experiment day, and follow-up visits. Participants will be given a Fitbit to wear for 14 weeks to record step count, heartrate, and additional exercise data. Hypothesis 1: Compared to the control, the CF strategy condition will report higher levels of contemplation to change, motivation, self-efficacy, and intentions to engage in PA. Hypothesis 2: The CF strategy condition will report greater increases in PA correlates over the 14-week period compared to the control condition.

D.1 Approach D.1.1 Baseline Session (week 1). Participants will go over informed consent with a researcher and then fill out a measure of demographics. The researcher will walk the participant through creating a Fitbit profile and input demographic info (age, gender, height, weight). Participants will receive a Fitbit to collect PA data, a charger, and download the Fitbit phone app to sync their data. Participants will be asked to wear the Fitbit during the hours that the participants are awake and throughout the time the participants are asleep. Participants will complete PA psychological measures of contemplation to change, self-efficacy, motivation, and intentions.

D.1.2 Experiment Day (week 2). Participants will undergo a semi-structured interview. The interview audio will be recorded and transcribed via Zoom. Participants will first be asked to describe past events where the participant were unable to participate or engage in PA. After describing the event(s), participants will be randomly assigned to one of two conditions, the CF strategy or control. All participants will walk through the NIMHD framework with a researcher and be guided to identify barriers at various domains and levels of influence, which impacted their events the participant just described. A printout of the NIMHD framework with examples will be provided to the participant.

CF Strategy: Participants will engage in CFs on barriers the participant believes the participant could have reasonably acted on to increase PA in their described event(s) that would have led to a better outcome. The researcher will write these down as the participants speaks so what is listed can be easily referred to for the next step. After identifying the CFs, participants will then select three CFs the participant just identified that the participant could use at some point in the upcoming week. Participants will identify a time in the upcoming week to engage in the CF, any obstacles to using that CF, ways to overcome those obstacles, their intention to use the CF over the next week, and how likely the participant think the CF would have happened and led to the better outcome. Participants will then fill out the PA psychological measures.

Control: After barrier identification, participants will be asked to select three barriers to talk aloud and list out additional details about the barriers identified. Participants will then fill out the PA psychological measures.

D.1.3 Weekly Follow-Up Sessions (weeks 3-13). Weeks 3-13 are spaced one week apart. Participants will be emailed a weekly survey to fill out PA psychological measures and complete condition specific measures. CF strategy participants will be presented with their selected CFs from the previous week and indicate CF engagement over the past week. Participants will also be given a chance to write about new PA events that occurred over the previous week and barriers; CF strategy participants will generate CFs on the controllable barriers, while the control participants will list details. CF strategy participants can use the same three CFs for the upcoming week or swap them for new or more relevant CFs; the participant will then indicate intentions to use them for the upcoming week and the likelihood that the CFs would have actually happened and led to change. Control participants will only be asked to list details about any new events and barriers.

D.1.4 Conclusion (week 14). All participants will return to the lab to return study materials and sync PA data. Participants will complete a final follow up and engage in a brief semi-structured interview on their reception to their PA condition, perceptions of barriers, and future use of CF strategies. Participants will then be thoroughly debriefed on the goals and purpose of the study.

E.1 Broader Impacts The proposed study will provide further information on using non-invasive wearable trackers (i.e., Fitbits) and a low-cost, self-identification strategy plan for addressing PA outcomes among underserved communities. Specifically, the qualitative nature of the study design can directly speak towards PATHS-UP mission within Industry and Culture of Inclusion. Speaking directly with underserved community members and understanding everyday barriers towards achieving optimal health and wellness via PA will be invaluable. The investigators will be able to ensure PATHS-UP present and future efforts to address barriers to PA health outcomes aligns with the foci of community members. Lastly, the efforts of this proposal will be used as the foundation for future research grant submission to work towards addressing health outcomes among underserved communities.

Eligibility criteria: Inclusion Criteria:

* 18 years old or older
* access to the internet
* has a smartphone capable of syncing with a Fitbit Inspire 2 via Bluetooth
* and must report low SES by indicating a response of 7 or higher on the Subjective SES Ladder

Exclusion Criteria:

* Having any physical or cognitive impairment that would impact one's ability to engage in low to moderate levels of exercise
* Must not currently be participating or plan to participate in similar studies or programs.

Primary outcomes:
measure: Change in Fitbit Active Zone Minutes
timeFrame: Weekly (14 weeks); collected every day via the Fitbit device for the 14-week duration of the study. Will assess weekly change over the course of the 14 week period.
Secondary outcomes:
measure: Contemplation Ladder
timeFrame: Weekly (14 weeks); occurring once a week for study weeks 1 through 14.
measure: Physical Activity Self-Efficacy
timeFrame: Weekly (14 weeks); occurring once a week for study weeks 1 through 14.
measure: Physical Activity Motivation
timeFrame: Weekly (14 weeks); occurring once a week for study weeks 1 through 14.
measure: Physical Activity Intentions
timeFrame: Weekly (14 weeks); occurring once a week for study weeks 1 through 14.
Other outcomes:
Locations:
city: College Station
country: United States
facility: Texas A&M University
state: Texas
Study officials:
affiliation: Texas A&M University
name: Rachel E Smallman, PhD
role: PRINCIPAL_INVESTIGATOR
References:
citation: Adler NE, Epel ES, Castellazzo G, Ickovics JR. Relationship of subjective and objective social status with psychological and physiological functioning: preliminary data in healthy white women. Health Psychol. 2000 Nov;19(6):586-92. doi: 10.1037//0278-6133.19.6.586.
citation: Biener L, Abrams DB. The Contemplation Ladder: validation of a measure of readiness to consider smoking cessation. Health Psychol. 1991;10(5):360-5. doi: 10.1037//0278-6133.10.5.360.
citation: Rodgers, W. M., & Sullivan, M. J. L. (2001). Task, coping, and scheduling self-efficacy in relation to frequency of physical activity. Journal of Applied Social Psychology, 31(4), 741-753. https://doi.org/10.1111/j.1559-1816.2001.tb01411.x
citation: Fishbein, M., & Ajzen, I. (2010). Predicting and changing behavior: The reasoned action approach. Psychology Press.
citation: Attig, C., & Franke, T. (2019). I track, therefore I walk - Exploring the motivational costs of wearing activity trackers in actual users. International Journal of Human-Computer Studies, 127, 211-224.
citation: Alvidrez J, Castille D, Laude-Sharp M, Rosario A, Tabor D. The National Institute on Minority Health and Health Disparities Research Framework. Am J Public Health. 2019 Jan;109(S1):S16-S20. doi: 10.2105/AJPH.2018.304883.
citation: Ozemek C, Lavie CJ, Rognmo O. Global physical activity levels - Need for intervention. Prog Cardiovasc Dis. 2019 Mar-Apr;62(2):102-107. doi: 10.1016/j.pcad.2019.02.004. Epub 2019 Feb 22.
citation: Zhang H, Rodriguez-Monguio R. Racial disparities in the risk of developing obesity-related diseases: a cross-sectional study. Ethn Dis. 2012 Summer;22(3):308-16.
citation: Humbert ML, Chad KE, Spink KS, Muhajarine N, Anderson KD, Bruner MW, Girolami TM, Odnokon P, Gryba CR. Factors that influence physical activity participation among high- and low-SES youth. Qual Health Res. 2006 Apr;16(4):467-83. doi: 10.1177/1049732305286051.
citation: Bantham A, Taverno Ross SE, Sebastiao E, Hall G. Overcoming barriers to physical activity in underserved populations. Prog Cardiovasc Dis. 2021 Jan-Feb;64:64-71. doi: 10.1016/j.pcad.2020.11.002. Epub 2020 Nov 5.
citation: Wilson DK, Kirtland KA, Ainsworth BE, Addy CL. Socioeconomic status and perceptions of access and safety for physical activity. Ann Behav Med. 2004 Aug;28(1):20-8. doi: 10.1207/s15324796abm2801_4.
citation: Roese, N. J., & Epstude, K. (2017). The functional theory of counterfactual thinking: New evidence, new challenges, new insights. Advances in Experimental Social Psychology, 56, 1-79.
citation: Kriska AM, Saremi A, Hanson RL, Bennett PH, Kobes S, Williams DE, Knowler WC. Physical activity, obesity, and the incidence of type 2 diabetes in a high-risk population. Am J Epidemiol. 2003 Oct 1;158(7):669-75. doi: 10.1093/aje/kwg191.
citation: Friedenreich CM, Ryder-Burbidge C, McNeil J. Physical activity, obesity and sedentary behavior in cancer etiology: epidemiologic evidence and biologic mechanisms. Mol Oncol. 2021 Mar;15(3):790-800. doi: 10.1002/1878-0261.12772. Epub 2020 Aug 18.
citation: Byrne RM. Counterfactual Thought. Annu Rev Psychol. 2016;67:135-57. doi: 10.1146/annurev-psych-122414-033249. Epub 2015 Sep 14.
citation: Spellman, B. A. & Mandel, D. R. (1999). When possibility informs reality: Counterfactual thinking as a cue to causality. Current Directions in Psychological Science, 8, 120-123.
citation: Dyczewski, E. A., & Markman, K. D. (2012).General attainability beliefs moderate the motivational effects of counterfactual thinking. Journal of Experimental Social Psychology, 48, 1217-1220.
citation: Epstude K, Roese NJ. The functional theory of counterfactual thinking. Pers Soc Psychol Rev. 2008 May;12(2):168-92. doi: 10.1177/1088868308316091.
citation: Roese, N. J. (1994). The functional basis of counterfactual thinking. Journal of Personality and Social Psychology, 66, 805-818.