Impact of Shared Decision-Making on Opioid Prescribing Among Patients With Chronic Pain: A Retrospective Cohort Study.

Shared decision-making (SDM) involving patient and physician is a desirable goal that is recommended in chronic pain management guidelines. This study measured whether SDM affects opioid prescribing frequency for chronic low back pain. A retrospective cohort study involving 1,478 participants was conducted within a national pain research registry. The patient participation and patient orientation (PPPO) scale of the Communication Behavior Questionnaire was used to measure SDM, including the classification of greater SDM (PPPO scale score ≥ 80) or lesser SDM (PPPO scale score < 80). Opioid prescribing frequency was measured at quarterly intervals from enrollment through 12 months. Baseline and longitudinal covariates were collected to adjust for potential confounding using generalized estimating equations. The mean age of participants was 53.1 (SD, 13.2) years, and 1,098 (74.3%) were female. A total of 473 (32.0%) participants were prescribed opioids at baseline. Participants completed 5,968 encounters wherein multivariable analyses demonstrated that PPPO scale scores were associated with more frequent opioid prescribing (ß = .013; 95% CI, .005-.021; P < .001). Greater SDM was associated with more frequent opioid prescribing than lesser SDM (ß = .441; 95% CI, .160-.722; P = .002). Opioids were prescribed in 34.3% versus 25.2% of encounters with greater versus lesser SDM (OR, 1.55; 95% CI, 1.17-2.06). SDM remained associated with more frequent opioid prescribing in a series of sensitivity analyses. Although SDM is desirable in chronic pain management, complex issues and challenging patient conversations may arise during serial assessments of the appropriateness of opioid therapy. Physicians need better education and training to address such difficult situations. PERSPECTIVE: The more frequent use of opioid therapy among patients who reported greater SDM with their physicians underscores the need for better medical education and training in dealing with the complex issues and challenges pertaining to serial assessments of the appropriateness of opioid therapy for chronic pain.

Free-water imaging reveals unique brain microstructural deficits in hispanic individuals with Dementia.

Prior evidence suggests that Hispanic and non-Hispanic individuals differ in potential risk factors for the development of dementia. Here we determine whether specific brain regions are associated with cognitive performance for either ethnicity along various stages of Alzheimer's disease. For this cross-sectional study, we examined 108 participants (61 Hispanic vs. 47 Non-Hispanic individuals) from the 1Florida Alzheimer's Disease Research Center (1Florida ADRC), who were evaluated at baseline with diffusion-weighted and T1-weighted imaging, and positron emission tomography (PET) amyloid imaging. We used FreeSurfer to segment 34 cortical regions of interest. Baseline Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) were used as measures of cognitive performance. Group analyses assessed free-water measures (FW) and volume. Statistically significant FW regions based on ethnicity x group interactions were used in a stepwise regression function to predict total MMSE and MoCA scores. Random forest models were used to identify the most predictive brain-based measures of a dementia diagnosis separately for Hispanic and non-Hispanic groups. Results indicated elevated FW values for the left inferior temporal gyrus, left middle temporal gyrus, left banks of the superior temporal sulcus, left supramarginal gyrus, right amygdala, and right entorhinal cortex in Hispanic AD subjects compared to non-Hispanic AD subjects. These alterations occurred in the absence of different volumes of these regions in the two AD groups. FW may be useful in detecting individual differences potentially reflective of varying etiology that can influence cognitive decline and identify MRI predictors of cognitive performance, particularly among Hispanics.

Time of Day Preferences and Daily Temporal Consistency for Predicting the Sustained Use of a Commercial Meditation App: Longitudinal Observational Study.

BACKGROUND: The intensive data typically collected by mobile health (mHealth) apps allows factors associated with persistent use to be investigated, which is an important objective given users' well-known struggles with sustaining healthy behavior. OBJECTIVE: Data from a commercial meditation app (n=14,879; 899,071 total app uses) were analyzed to assess the validity of commonly given habit formation advice to meditate at the same time every day, preferably in the morning. METHODS: First, the change in probability of meditating in 4 nonoverlapping time windows (morning, midday, evening, and late night) on a given day over the first 180 days after creating a meditation app account was calculated via generalized additive mixed models. Second, users' time of day preferences were calculated as the percentage of all meditation sessions that occurred within each of the 4 time windows. Additionally, the temporal consistency of daily meditation behavior was calculated as the entropy of the timing of app usage sessions. Linear regression was used to examine the effect of time of day preference and temporal consistency on two outcomes: (1) short-term engagement, defined as the number of meditation sessions completed within the sixth and seventh month of a user's account, and (2) long-term use, defined as the days until a user's last observed meditation session. RESULTS: Large reductions in the probability of meditation at any time of day were seen over the first 180 days after creating an account, but this effect was smallest for morning meditation sessions (63.4% reduction vs reductions ranging from 67.8% to 74.5% for other times). A greater proportion of meditation in the morning was also significantly associated with better short-term engagement (regression coefficient B=2.76, P<.001) and long-term use (B=50.6, P<.001). The opposite was true for late-night meditation sessions (short-term: B=-2.06, P<.001; long-term: B=-51.7, P=.001). Significant relationships were not found for midday sessions (any outcome) or for evening sessions when examining long-term use. Additionally, temporal consistency in the performance of morning meditation sessions was associated with better short-term engagement (B=-1.64, P<.001) but worse long-term use (B=55.8, P<.001). Similar-sized temporal consistency effects were found for all other time windows. CONCLUSIONS: Meditating in the morning was associated with higher rates of maintaining a meditation practice with the app. This is consistent with findings from other studies that have hypothesized that the strength of existing morning routines and circadian rhythms may make the morning an ideal time to build new habits. In the long term, less temporal consistency in meditation sessions was associated with more persistent app use, suggesting there are benefits from maintaining flexibility in behavior performance. These findings improve our understanding of how to promote enduring healthy lifestyles and can inform the design of mHealth strategies for maintaining behavior changes.

Using mobile meditation app data to predict future app engagement: an observational study.

OBJECTIVE: Meditation with mobile apps has been shown to improve mental and physical health. However, regular, long-term meditation app use is needed to maintain these health benefits, and many people have a difficult time maintaining engagement with meditation apps over time. Our goal was to determine the length of the timeframe over which usage data must be collected before future app abandonment can be predicted accurately in order to better target additional behavioral support to those who are most likely to stop using the app. METHODS: Data were collected from a randomly drawn sample of 2600 new subscribers to a 1-year membership of the mobile app Calm, who started using the app between July and November of 2018. App usage data contained the duration and start time of all meditation sessions with the app over 365 days. We used these data to construct the following predictive model features: total daily sessions, total daily duration, and a measure of temporal similarity between consecutive days based on the dynamic time warping (DTW) distance measure. We then fit random forest models using increasingly longer periods of data after users subscribed to Calm to predict whether they performed any meditation sessions over 2-week intervals in the future. Model fit was assessed using the area under the receiver operator characteristic curve (AUC), and an exponential growth model was used to determine the minimal amount of data needed to reach an accurate prediction (95% of max AUC) of future engagement. RESULTS: After first subscribing to Calm, 83.1% of the sample used the Calm app on at least 1 more day. However, by day 350 after subscribing, 58.0% of users abandoned their use of the app. For the persistent users, the average number of daily sessions was 0.33 (SD = 0.02), the average daily duration of meditating was 3.93 minutes (SD = 0.25), and the average DTW distance to the previous day was 1.50 (SD = 0.17). The exponential growth models revealed that an average of 64 days of observations after subscribing to Calm are needed to reach an accurate prediction of future app engagement. DISCUSSION: Our results are consistent with existing estimates of the time required to develop a new habit. Additionally, this research demonstrates how to use app usage data to quickly and accurately predict the likelihood of users' future app abandonment. This research allows future researchers to better target just-in-time interventions towards users at risk of abandonment.

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.

A Role for RAGE in DNA Double Strand Breaks (DSBs) Detected in Pathological Placentas and Trophoblast Cells.

Impaired DNA damage responses are associated with several diseases, including pregnancy complications. Recent research identified an ATM-kinase dependent function for the nuclear isoform of the receptor for advanced glycation end-products (RAGE) during double strand break (DSB)-repair. RAGE contributes to end-resectioning of broken DNA sites by binding with the MRE11-Rad50-Nbs1 (MRN) complex. Placental research is limited regarding the impact of genomic instability and the mechanism for potential repair. We tested the hypothesis regarding the involvement of RAGE during the repair of placental DNA-DSBs. We first identified that the pregnancy complications of PE and preterm labor (PTL) experience loss of genomic integrity and an in vitro trophoblast cell model was used to characterize trophoblast DSBs. Colocalized immunofluorescence of γ-H2AX and RAGE support the potential involvement of RAGE in cellular responses to DNA-DSBs. Immunoblotting for both molecules in PE and PTL placenta samples and in trophoblast cells validated a connection. Co-immunoprecipitation studies revealed interactions between RAGE and pATM and MRE11 during DNA-DSBs. Reduced cellular invasion confirmed the role of genomic instability in trophoblastic function. Collectively, these experiments identified genomic instability in pregnancy complications, the impact of defective DNA on trophoblast function, and a possible RAGE-mediated mechanism during DNA-DSB repair.

To what extent does sex, age and BMI impact medical and pharmacy costs? A retrospective cohort study involving employees in a large school district in the USA.

OBJECTIVE: To identify the extent that sex, age and body mass index (BMI) is associated with medical and pharmacy costs. DESIGN: Retrospective cohort. SETTING: A school district in the Western USA involving 2531 workers continuously employed during 2011-2014. MAIN OUTCOME MEASURES: Medical and pharmacy costs and BMI. RESULTS: Approximately 84% of employees participated in wellness screening. Participants were 1.03 (95% CI 1.01 to 1.06) times more likely to be women and younger (M=47.8 vs 49.8, p<0.001). Median medical and pharmacy costs were higher for women than men, increased with age, and were greater in morbidly obese individuals (p<0.001). Annual pharmacy claims were 18% more likely to be filed by women than men, 23% more likely filed by those aged ≥60 versus <40 years, and 6% more likely filed by morbidly obese individuals than of normal weight (p<0.001) individuals. Greater medical and pharmacy costs in older age were most pronounced in underweight and morbidly obese groups. Higher use of medication among women than men was primarily because of drugs involving birth control, osteoporosis, thyroid disease and urinary tract infection. Higher medication use in older age was primarily related to medications used to treat gastrointestinal problems. Medication use was positively associated with BMI weight classifications for most of the 33 drug types considered, with exceptions involving birth control, herpes and osteoporosis. A J-shape relationship was observed between BMI and medication use for acne, antibiotic, cold/influenza/allergy, eye infection, oedema, muscle spasms, pain and ulcers. CONCLUSIONS: Medications associated with higher medical and pharmacy costs among women, older age and underweight or obese individuals are identified. Lowering medical and pharmacy costs requires weight management in older ages, particularly for underweight and obese. Higher pharmacy costs for certain drugs among underweight individuals may be associated with poorer nutrition.