Background Interest in and funding for digital health interventions have rapidly grown in recent years. Despite the increasing familiarity with mobile health from regulatory bodies, providers, and patients, overarching research on digital health adoption has been primarily limited to morbidity-specific and non-US samples. Consequently, there is a limited understanding of what personal factors hold statistically significant relationships with digital health uptake. Moreover, this limits digital health communities’ knowledge of equity along digital health use patterns. Objective This study aims to identify the social determinants of digital health tool adoption in Georgia. Methods Web-based survey respondents in Georgia 18 years or older were recruited from mTurk to answer primarily closed-ended questions within the following domains: participant demographics and health consumption background, telehealth, digital health education, prescription management tools, digital mental health services, and doctor finder tools. Participants spent around 15 to 20 minutes on a survey to provide demographic and personal health care consumption data. This data was analyzed with multivariate linear and logistic regressions to identify which of these determinants, if any, held statistically significant relationships with the total number of digital health tool categories adopted and which of these determinants had absolute relationships with specific categories. Results A total of 362 respondents completed the survey. Private insurance, residence in an urban area, having a primary care provider, fewer urgent emergency room (ER) visits, more ER visits leading to inpatient stays, and chronic condition presence were significantly associated with the number of digital health tool categories adopted. The separate logistic regressions exhibited substantial variability, with 3.5 statistically significant predictors per model, on average. Age, federal poverty level, number of primary care provider visits in the past 12 months, number of nonurgent ER visits in the past 12 months, number of urgent ER visits in the past 12 months, number of ER visits leading to inpatient stays in the past 12 months, race, gender, ethnicity, insurance, education, residential area, access to the internet, difficulty accessing health care, usual source of care, status of primary care provider, and status of chronic condition all had at least one statistically significant relationship with the use of a specific digital health category. Conclusions The results demonstrate that persons who are socioeconomically disadvantaged may not adopt digital health tools at disproportionately higher rates. Instead, digital health tools may be adopted along social determinants of health, providing strong evidence for the digital health divide. The variability of digital health adoption necessitates investing in and building a common framework to increase mobile health access. With a common framework and a paradigm shift in the design, evaluation, and implementation strategies around digital health, disparities can be further mitigated and addressed. This likely will begin with a coordinated effort to determine barriers to adopting digital health solutions.
3618 Background: The rise in young-onset rectal cancer (YORC) calls for better understanding of the long-term impact of radiation therapy(RT) on gastrointestinal(GI) toxicity and pelvic organ function. We aimed to prospectively capture the longitudinal trajectories of patient-reported outcomes(PROs) in YORC patients who received RT vs. no RT and to identify factors associated with unfavorable PROs. Methods: We prospectively enrolled 120 YORC patients undergoing curative-intent treatment. The validated EORTC QLQ-CR29 was self-administered at time intervals grouped as: 0-11 months, 12-23 months, and 24+ months post-resection. Responses were stratified by receipt of neoadjuvant RT (yes vs. no). The longitudinal change in PROs was described by a linear mixed effects model. Multivariate linear regression was used to determine the impact of treatment factors on long-term PROs. Results: The median age at diagnosis was 44. The majority (N = 92, 77%) presented with cT3,4/N+ disease. Preoperative therapy included: no RT (N = 38, 32%; 8 [7%] who received chemotherapy alone, and 30 [25%] who received no neoadjuvant therapy), vs. RT (N = 82, 68%; where 59 [72%] also received concurrent capecitabine). More patients in the RT group had advanced T stage (3 or 4; 94% vs. 56%, P< 0.001), distal tumor (median 7 vs. 12.5 cm from the anal verge, P< 0.01), and underwent abdominal perineal resection (19 vs. 0%, P< 0.001). After a median follow-up of 70 months, all were alive: 103 (86%) were disease-free, 9 (8%) had recurrence with successful salvage, and 8 (7%) had disease progression. In the RT group, sore skin improved at 12-23 and 24+ months (Estimate [ B]: -16.5, P= 0.03 and B: -14.4, P= 0.03), dyspareunia improved at 12-23 months ( B: -31.8, P< 0.01), and blood/mucus in stool improved at 24+ months ( B: -8.01, P< 0.01) vs. 0-11 months. At 24+ months, RT receipt was associated with worse stool frequency ( B: 26.4, P< 0.01), urinary frequency ( B: 18.4, P= 0.04), and flatulence ( B: 23.0, P= 0.02). Conclusions: YORC often require multimodality therapy including RT. Sore skin, dyspareunia, and blood/mucus in stool improved, but flatulence and frequency can persist beyond 2 years post RT. Proactive counseling and supportive measures are needed to inform treatment choices and mitigate long-term impact. [Table: see text]
BACKGROUND Interest in and funding for digital health interventions have rapidly grown in recent years. Despite the increasing familiarity with mHealth from regulatory bodies, providers, and patients, overarching research on digital health adoption has been primarily limited to morbidity-specific and non-American samples. OBJECTIVE To describe uptake and utilization patterns of common digital health tools in Georgia and describe predictors of uptake. METHODS Online survey respondents in Georgia over 18 were recruited from mTurk to answer primarily closed-ended questions within the following domains: (1) participant demographics and health consumption background, (2) telehealth, (3) digital health education, (4) prescription management tools, (5) digital mental health services, and (6) doctor finder tools. Multivariate linear and logistic regressions were used to identify predictors of digital health tool usage. RESULTS A total of 362 respondents completed the survey. Private insurance, residence in an urban area, having a primary care provider, fewer urgent ER visits, more ER visits leading to inpatient stays, and chronic condition presence were significantly associated with the number of digital health tools used. The separate logistic regressions exhibited substantial variability with 3.5 statistically significant predictors per model, on average. Age, federal poverty level, number of PCP visits in the past 12 months, number of non-urgent ER visits in the past 12 months, number of urgent ER visits in the past 12 months, number of ER visits leading to inpatient stays in the past 12 months, race, gender, ethnicity, insurance, education, residential area, access to internet, difficulty accessing healthcare, usual source of care, status of PCP, and status of chronic condition all had at least one statistically significant relationship with use of a specific digital health category. CONCLUSIONS The results demonstrate that socioeconomically disadvantaged persons who would benefit the most from using certain tools do not adopt them at disproportionately higher rates than more advantaged populations. The variability of digital health adoption necessitates investing in and building a common framework to increase mHealth access. This likely will begin with a coordinated effort to determine barriers to adopting digital health solutions.
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