Is There a Relationship between County-Level Food Insecurity Rates and Breast Cancer Stage at Diagnosis?

The relationship between county food insecurity (FI) rate and breast cancer stage at diagnosis is not clear. Using 2010-2016 Surveillance Epidemiology and End Results (SEER) and Feeding America data we analyzed the association between county FI rate in quartiles (low, medium, high, very high) and breast cancer stage at diagnosis among adult females (≥18 years). We also analyzed the effect of insurance status and county poverty level on this relationship, and whether this relationship varies among non-elderly (<65 years) and elderly (≥ 65 years) individuals. Bivariate and multivariable multilevel logistic regression were used for analyses. Bivariate analysis showed increased likelihood of late-stage breast cancer with increasing county FI rate. This relationship persisted after adjusting for insurance status but was no longer significant after adjusting for county-level poverty rate. There was a statistically significant association between counties with very high food insecurity rates and late-stage breast cancer diagnosis (OR = 1.07; 95% CI = 1.00, 1.14) among the elderly population. Very high county food insecurity rate was associated with late-stage breast cancer among elderly women. Population-level interventions focused on counties with very high food insecurity rates could reduce disparities in stage at breast cancer diagnosis among elderly women.

Identifying App-Based Meditation Habits and the Associated Mental Health Benefits: Longitudinal Observational Study.

BACKGROUND: Behavioral habits are often initiated by contextual cues that occur at approximately the same time each day; so, it may be possible to identify a reflexive habit based on the temporal similarity of repeated daily behavior. Mobile health tools provide the detailed, longitudinal data necessary for constructing such an indicator of reflexive habits, which can improve our understanding of habit formation and help design more effective mobile health interventions for promoting healthier habits. OBJECTIVE: This study aims to use behavioral data from a commercial mindfulness meditation mobile phone app to construct an indicator of reflexive meditation habits based on temporal similarity and estimate the association between temporal similarity and meditation app users' perceived health benefits. METHODS: App-use data from June 2019 to June 2020 were analyzed for 2771 paying subscribers of a meditation mobile phone app, of whom 86.06% (2359/2771) were female, 72.61% (2012/2771) were college educated, 86.29% (2391/2771) were White, and 60.71% (1664/2771) were employed full-time. Participants volunteered to complete a survey assessing their perceived changes in physical and mental health from using the app. Receiver operating characteristic curve analysis was used to evaluate the ability of the temporal similarity measure to predict future behavior, and variable importance statistics from random forest models were used to corroborate these findings. Logistic regression was used to estimate the association between temporal similarity and self-reported physical and mental health benefits. RESULTS: The temporal similarity of users' daily app use before completing the survey, as measured by the dynamic time warping (DTW) distance between app use on consecutive days, significantly predicted app use at 28 days and at 6 months after the survey, even after controlling for users' demographic and socioeconomic characteristics, total app sessions, duration of app use, and number of days with any app use. In addition, the temporal similarity measure significantly increased in the area under the receiver operating characteristic curve (AUC) for models predicting any future app use in 28 days (AUC=0.868 with DTW and 0.850 without DTW; P<.001) and for models predicting any app use in 6 months (AUC=0.821 with DTW and 0.802 without DTW; P<.001). Finally, a 1% increase in the temporal similarity of users' daily meditation practice with the app over 6 weeks before the survey was associated with increased odds of reporting mental health improvements, with an odds ratio of 2.94 (95% CI 1.832-6.369). CONCLUSIONS: The temporal similarity of the meditation app use was a significant predictor of future behavior, which suggests that this measure can identify reflexive meditation habits. In addition, temporal similarity was associated with greater perceived mental health benefits, which demonstrates that additional mental health benefits may be derived from forming reflexive meditation habits.