Assessing Real-Time Moderation for Developing Adaptive Mobile Health Interventions for Medical Interns: Micro-Randomized Trial.

BACKGROUND: Individuals in stressful work environments often experience mental health issues, such as depression. Reducing depression rates is difficult because of persistently stressful work environments and inadequate time or resources to access traditional mental health care services. Mobile health (mHealth) interventions provide an opportunity to deliver real-time interventions in the real world. In addition, the delivery times of interventions can be based on real-time data collected with a mobile device. To date, data and analyses informing the timing of delivery of mHealth interventions are generally lacking. OBJECTIVE: This study aimed to investigate when to provide mHealth interventions to individuals in stressful work environments to improve their behavior and mental health. The mHealth interventions targeted 3 categories of behavior: mood, activity, and sleep. The interventions aimed to improve 3 different outcomes: weekly mood (assessed through a daily survey), weekly step count, and weekly sleep time. We explored when these interventions were most effective, based on previous mood, step, and sleep scores. METHODS: We conducted a 6-month micro-randomized trial on 1565 medical interns. Medical internship, during the first year of physician residency training, is highly stressful, resulting in depression rates several folds higher than those of the general population. Every week, interns were randomly assigned to receive push notifications related to a particular category (mood, activity, sleep, or no notifications). Every day, we collected interns' daily mood valence, sleep, and step data. We assessed the causal effect moderation by the previous week's mood, steps, and sleep. Specifically, we examined changes in the effect of notifications containing mood, activity, and sleep messages based on the previous week's mood, step, and sleep scores. Moderation was assessed with a weighted and centered least-squares estimator. RESULTS: We found that the previous week's mood negatively moderated the effect of notifications on the current week's mood with an estimated moderation of -0.052 (P=.001). That is, notifications had a better impact on mood when the studied interns had a low mood in the previous week. Similarly, we found that the previous week's step count negatively moderated the effect of activity notifications on the current week's step count, with an estimated moderation of -0.039 (P=.01) and that the previous week's sleep negatively moderated the effect of sleep notifications on the current week's sleep with an estimated moderation of -0.075 (P<.001). For all three of these moderators, we estimated that the treatment effect was positive (beneficial) when the moderator was low, and negative (harmful) when the moderator was high. CONCLUSIONS: These findings suggest that an individual's current state meaningfully influences their receptivity to mHealth interventions for mental health. Timing interventions to match an individual's state may be critical to maximizing the efficacy of interventions. TRIAL REGISTRATION: ClinicalTrials.gov NCT03972293; http://clinicaltrials.gov/ct2/show/NCT03972293.

Effectiveness of gamified team competition as mHealth intervention for medical interns: a cluster micro-randomized trial.

Gamification, the application of gaming elements to increase enjoyment and engagement, has the potential to improve the effectiveness of digital health interventions, while the effectiveness of competition gamification components remains poorly understood on residency. To address this gap, we evaluate the effect of smartphone-based gamified team competition intervention on daily step count and sleep duration via a micro-randomized trial on medical interns. Our aim is to assess potential improvements in the factors (namely step count and sleep) that may help interns cope with stress and improve well-being. In 1779 interns, team competition intervention significantly increases the mean daily step count by 105.8 steps (SE 35.8, p = 0.03) relative to the no competition arm, while does not significantly affect the mean daily sleep minutes (p = 0.76). Moderator analyses indicate that the causal effects of competition on daily step count and sleep minutes decreased by 14.5 steps (SE 10.2, p = 0.16) and 1.9 minutes (SE 0.6, p = 0.003) for each additional week-in-study, respectively. Intra-institutional competition negatively moderates the causal effect of competition upon daily step count by -90.3 steps (SE 86.5, p = 0.30). Our results show that gamified team competition delivered via mobile app significantly increases daily physical activity which suggests that team competition can function as a mobile health intervention tool to increase short-term physical activity levels for medical interns. Future improvements in strategies of forming competition opponents and introducing occasional competition breaks may improve the overall effectiveness.

Results of emergency colectomy in nonagenarians and octogenarians previously labeled as prohibitive surgical risk.

PURPOSE: There are no standardized criteria for what constitutes prohibitive risk for emergency abdominal surgery. METHODS: A retrospective review was performed comparing two groups of patients having emergent colectomy. One group had previously been labeled as being prohibitive surgical risk and the other was a contemporary, non-prohibitive risk group also requiring emergency colectomy. All operations were performed by a single surgeon. RESULTS: There were 27 prohibitive risk patients and 81 non-prohibitive risk (control group) patients. The average age of the prohibitive risk group was 85 years (range 78-99) compared to the control group mean age of 52 years (18-79, p < 0.00001). Prohibitive risk was due to extremes of age combined with congestive heart failure in 44%, followed by chronic obstructive pulmonary disease combined with heart failure in 19%. The groups were closely matched by the type of colectomy performed. The total complication rate was much higher in the prohibitive risk group compared to the non-prohibitive risk patients (81% versus 48%, p 0.005). But the 30-day mortality rate was similar between groups (7% versus 4%, p 0.6). CONCLUSION: Patients who are labeled as prohibitive surgical risk may be inaccurately assessed in the majority of cases. Additional research will need to be performed to evaluate the presence of quantifiable high-risk physiological conditions, and not just comorbidities, that place a patient at high risk of death after abdominal surgery. Until then, elderly patients should not be denied colectomy based upon comorbidities alone.

A Comparison of Endoscopic Versus Surgical Creation of a Cystogastrostomy to Drain Pancreatic Pseudocysts and Walled-Off Pancreatic Necrosis in 5500 Patients.

Objective: To determine the success, morbidity, and mortality rates of endoscopic and surgical creation of pancreatic cystenterostomies for the drainage of peripancreatic fluid collections, pseudocysts with necrotic debris, and walled-off pancreatic necrosis. Summary Background Data: Endoscopic methods of cystenterostomy creation to drain pancreatic pseudocysts (with and without necrotic debris) and infected peripancreatic fluid collections are perceived to be less morbid than surgery. Contemporary reports document a very high complication rate with endoscopic methods. Methods: A meta-analysis of 5500 patients. Results: Open and laparoscopic surgical techniques to drain chronic pancreatic pseudocysts, infected pancreatic fluid collections, and walled-off pancreatic necrosis are more successful with less morbidity and mortality than endoscopic methods. Conclusions: In regards to a surgical step-up approach to treat chronic infected pancreatic fluid collections or walled-off pancreatic necrosis, surgical creation of a cystenterostomy is more successful with fewer complications than endoscopic methods and should be given priority if less invasive or conservative methods fail.

Comparing cluster-level dynamic treatment regimens using sequential, multiple assignment, randomized trials: Regression estimation and sample size considerations.

Cluster-level dynamic treatment regimens can be used to guide sequential treatment decision-making at the cluster level in order to improve outcomes at the individual or patient-level. In a cluster-level dynamic treatment regimen, the treatment is potentially adapted and re-adapted over time based on changes in the cluster that could be impacted by prior intervention, including aggregate measures of the individuals or patients that compose it. Cluster-randomized sequential multiple assignment randomized trials can be used to answer multiple open questions preventing scientists from developing high-quality cluster-level dynamic treatment regimens. In a cluster-randomized sequential multiple assignment randomized trial, sequential randomizations occur at the cluster level and outcomes are observed at the individual level. This manuscript makes two contributions to the design and analysis of cluster-randomized sequential multiple assignment randomized trials. First, a weighted least squares regression approach is proposed for comparing the mean of a patient-level outcome between the cluster-level dynamic treatment regimens embedded in a sequential multiple assignment randomized trial. The regression approach facilitates the use of baseline covariates which is often critical in the analysis of cluster-level trials. Second, sample size calculators are derived for two common cluster-randomized sequential multiple assignment randomized trial designs for use when the primary aim is a between-dynamic treatment regimen comparison of the mean of a continuous patient-level outcome. The methods are motivated by the Adaptive Implementation of Effective Programs Trial which is, to our knowledge, the first-ever cluster-randomized sequential multiple assignment randomized trial in psychiatry.