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This proposed longitudinal study will examine the influences of neighborhood factors and momentary contextual and psychosocial factors, which the investigators call acute precipitants, on physical activity (PA) adoption and maintenance among Pacific Islanders (PcIs). Specifically, the investigators will enroll 150 sedentary PcI adults and examine their PA adoption and maintenance based on measurements at five separate weeks during a 20-month period. The investigators will use MotionSense, an innovative and proven mHealth kit developed by team members, Global Positioning System (GPS), and ecological momentary assessment to measure PA and its key determinants. Our central hypotheses are that individual and neighborhood factors are associated with PA adoption and maintenance among previously sedentary PcIs, and that psychosocial precipitants mediate these associations.
Full description
Pacific Islanders (PcIs), one of the fastest growing racial/ethnic groups in the United States, bear the highest obesity rate among all racial/ethnic groups in the United States and suffer from a disproportionate burden of health disparities in cardiovascular diseases, diabetes, and cancers. PA is important for addressing these disparities because regular PA decreases obesity and reduces the risks of these diseases. In general, PcIs are less physically active than non-Hispanic Whites, with the majority not meeting nationally-recommended PA levels. However, few studies have investigated the underlying mechanism(s) of PA behavior change for PcIs. Recent research highlighted the importance of contextual social and built environments on PA participation among racial/ethnic minorities and the necessity of understanding psychosocial pathways to PA behaviors. However, research in this area has been significantly limited by the lack of data measured at micro timescales (e.g., across minutes, hours, or days) that can capture the dynamic interactions between contextual factors and PA behavior. The paucity of research on PA behaviors among PcIs and the lack of micro timescale data for understanding PA mechanisms have hampered the search for effective policies and interventions to reduce PA-related health disparities for this high-risk population.
Our long-term goal is to use mobile Health (mHealth) technologies to help PcIs increase their PA levels and thereby to reduce their health disparities. This proposed longitudinal study will examine the influences of neighborhood factors and momentary contextual and psychosocial factors, which the investigators call acute precipitants, on PA adoption and maintenance among PcIs. Specifically, the investigators will enroll 150 sedentary PcI adults and examine their PA adoption and maintenance based on measurements at five separate weeks during a 20-month period. The investigators will use MotionSense, an innovative and proven mHealth kit developed by team members, GPS, and ecological momentary assessment to measure PA and its key determinants. An innovation of this mHealth system is that it can continuously measure PA and contextual and psychosocial acute precipitants in natural environments, therefore providing the much-needed micro timescale data for studying the dynamic nature of PA behavioral change. Our preliminary studies have confirmed the feasibility of using this system to measure PA and PA correlates. Our multidisciplinary team has expertise in PA behavior analysis, PA intervention, mHealth technologies, geographical analysis, and health psychology.
Our central hypotheses are that individual and neighborhood factors are associated with PA adoption and maintenance among previously sedentary PcIs, and that psychosocial precipitants mediate these associations. The investigators have designed the following two aims to test these central hypotheses.
Aim 1: To examine the influences of neighborhood factors on PA adoption and maintenance among sedentary PcI adults. The investigators hypothesize that lower levels of PA adoption and maintenance are associated with disadvantageous neighborhood factors (e.g., lack of access to PA facilities, higher levels of poverty, lower social cohesion) after controlling for individual-level factors. The investigators will first define PA adoption and maintenance based on the change of PA level across the five measurement weeks for each participant, and then link them to neighborhood factors using linear mixed effects models.
Aim 2: To determine the dynamic relationships between contextual and psychosocial acute precipitants and PA.
2.a. The investigators hypothesize that lower frequency and duration of PA are associated with disadvantageous acute precipitants (e.g., decreased access to PA facilities, increased negative affect, and decreased self-efficacy); and the associations vary between stages of PA adoption and maintenance. The investigators will use the micro timescale data measured by our mHealth system to construct PA indicators and acute precipitants. Then, an innovative dynamic prediction model will be used to investigate their temporal, dynamic associations.
2.b. The investigators hypothesize that psychosocial acute precipitants (e.g., self-efficacy, negative affect) mediate the influence of contextual acute precipitants (e.g., access to PA resources) on PA, should the investigators find any significant associations between the latter two.
This is an observational study (rather than an interventional study) that contains a physical activity intervention. The primary goal is not to evaluate the effectiveness of the PA intervention in increasing PA levels, but how neighborhood and contextual factors influence PA adoption and maintenance. The investigators use the PA intervention to stimulate PA engagement for all participants so that the investigators can observe their PA behavior change in a real-world setting.
Since the purpose of the study is not to evaluate the effectiveness of the intervention, it does not fulfill the third criterion of the NIH definition of clinical trials and should be considered an observational study.
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150 participants in 1 patient group
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Neng Wan, Ph.D.
Data sourced from clinicaltrials.gov
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