Dialysis patients are at risk of attrition from Kidney Transplant (KT) wait lists due to sedentary lifestyle and poor nutritional adherence. This study tested the acceptability of several mobile Health (mHealth) intervention components for the development of a lifestyle program to support kidney wait-list patients. Methods: A home-based, 3-month feasibility trial of a distance coaching mHealth program entitled Lifestyle Improvements for Transplant Success (LIFTS) was developed from interviews with 22 kidney wait-list dialysis patients. Program components of the LIFTS program include: 1) A walking program using Fitbit devices and app (Charge 2/Alta HR or latest model in line) to track steps on non-dialysis days, 2) Muscle strengthening using video education (links to You-Tube TM), 3) Renal disease nutrition and physical education using weekly videos, 4) Nutrition tracking using MyFitness-Pal app and 5) Weight/fluid variability tracking using Nokia (formerly Withings) Wi-Fi scales and inclusive smartphone app. Activity steps and weight were remotely monitored in real-time and goals modified by an online coach every two weeks. Acceptability was primary assessed from exit interview content and quantified apps' measures to assess program components' use along with physical function and psychosocial changes. Results: One White male (age 45) and 2 Black women (ages 24, 35) were recruited. Findings demonstrated: (1) Support of using Fitbits in walking program, but some wear ability/technical issues (i.e., 98%, 82%, and 29% wear adherence), (2) Support using weight scales (mean = 2.54 weigh-ins/week), (3) Nonadherence to suspension training protocols, suggesting need for a different approach, (4) Dietary videos were helpful, (5) The dietary tracking app was too burdensome and (6) Participants interested in more social elements and challenges. Overall, all participants felt the program improved their health. Conclusions: Futher methodology refinement guided by dialysis patients to enhance acceptability is needed prior to a subsequent pilot study. Findings may be applicable in mHealth designs in highly fatigued patients.