Lack of regular physical activity (PA) in the general population is a top public health concern, and this problem is even more acute among individuals with spinal cord injury (SCI). Research has shown that only a small percentage (<20%) of persons with SCI reported consistent PA. Individuals with SCI also experience secondary conditions such as pain, fatigue, weight gain, and deconditioning, conditions that are considered preventable through PA and exercise interventions.~The objective of this proposed study is to develop algorithms that incorporate each individual's PA level and a clinician's PA level recommendation to provide a mobile phone application that helps a person set PA goals that are safe, but also highly personalized. By adapting the goals in real-time based on the person's actual behavior, the system aims to keep the individual feeling positive and motivated.~Aim 1: Extend and utilize Physical Activity Monitor System (PAMS) to track PA levels, sedentary behavior, and secondary conditions such as pain, fatigue, and deconditioning in community settings.~Aim 2: Extend and utilize PAMS to passively monitor PA and provide continuous, but passive feedback about PA levels to individuals with SCI in community settings.~Aim 3: Extend and utilize PAMS to passively monitor PA and provide just-in-time persuasive and adaptive feedback to motivate individuals with SCI in community settings.~Sample size: A total of 20 individuals with SCI will take part in the study. The sample size for this pilot study is based on budget constraints and other pilot studies. This study will provide the pilot data required to compute the power for future studies.~Statistical Analysis: Univariate analysis will be performed to obtain a range of values and the central tendency for variables such as PA levels and sedentary behaviors. Sedentary behavior will be assessed by the time duration of non-movement of individuals with SCI and not just the total duration of being seated in their wheelchairs.~The investigators hypothesize that the PA level of individuals with SCI in community will be low compared to the PA level recommendations for individuals with disabilities in general. Furthermore, the sedentary behavior of individuals with SCI will be high compared to the general population.~Multiple regression analysis will be performed to assess a relationship between secondary conditions such as pain (scores), fatigue (scores), and deconditioning (reduced capacity scores) and PA levels. The investigators postulate that secondary conditions will be negatively correlated with the PA levels.~Repeated measures general linear model (GLM) analysis will be performed to assess the change in PA levels, sedentary behaviors and secondary conditions. In addition, linear mixed model analysis will be performed to develop a personal intercept (and maybe slope) for each participant compared to the mean intercept for each group. Mixed effects model analysis will provide correct estimates of intervention (passive feedback and just-in-time adaptive feedback) and other fixed effects (within-subjects factor) in the presence of correlated data (each participant at different time points) that arise from a data hierarchy (group). Non-parametric tests will be performed if the assumptions for parametric tests are not met.
The objective of this study is to develop algorithms that incorporate each individual's automatically detected physical activity (PA) level and a clinician's PA level recommendation to provide a smartphone application that helps a person set safe and highly personalized PA goals. By adapting the goals in real-time based on the person's actual behavior, the system aims to keep the individual feeling positive and motivated towards a change in the PA behavior.