Background Smartphones and wearable devices can be used to obtain diverse daily log data related to circadian rhythms. For patients with mood disorders, giving feedback via a smartphone app with appropriate behavioral correction guides could play an important therapeutic role in the real world. Objective We aimed to evaluate the effectiveness of a smartphone app named Circadian Rhythm for Mood (CRM), which was developed to prevent mood episodes based on a machine learning algorithm that uses passive digital phenotype data of circadian rhythm behaviors obtained with a wearable activity tracker. The feedback intervention for the CRM app consisted of a trend report of mood prediction, H-score feedback with behavioral guidance, and an alert system triggered when trending toward a high-risk state. Methods In total, 73 patients with a major mood disorder were recruited and allocated in a nonrandomized fashion into 2 groups: the CRM group (14 patients) and the non-CRM group (59 patients). After the data qualification process, 10 subjects in the CRM group and 33 subjects in the non-CRM group were evaluated over 12 months. Both groups were treated in a similar manner. Patients took their usual medications, wore a wrist-worn activity tracker, and checked their eMoodChart daily. Patients in the CRM group were provided with daily feedback on their mood prediction and health scores based on the algorithm. For the CRM group, warning alerts were given when irregular life patterns were observed. However, these alerts were not given to patients in the non-CRM group. Every 3 months, mood episodes that had occurred in the previous 3 months were assessed based on the completed daily eMoodChart for both groups. The clinical course and prognosis, including mood episodes, were evaluated via face-to-face interviews based on the completed daily eMoodChart. For a 1-year prospective period, the number and duration of mood episodes were compared between the CRM and non-CRM groups using a generalized linear model. Results The CRM group had 96.7% fewer total depressive episodes (n/year; exp β=0.033, P=.03), 99.5% shorter depressive episodes (total; exp β=0.005, P<.001), 96.1% shorter manic or hypomanic episodes (exp β=0.039, P<.001), 97.4% fewer total mood episodes (exp β=0.026, P=.008), and 98.9% shorter mood episodes (total; exp β=0.011, P<.001) than the non-CRM group. Positive changes in health behaviors due to the alerts and in wearable device adherence rates were observed in the CRM group. Conclusions The CRM app with a wearable activity tracker was found to be effective in preventing and reducing the recurrence of mood disorders, improving prognosis, and promoting better health behaviors. Patients appeared to develop a regular habit of using the CRM app. Trial Registration ClinicalTrials.gov NCT03088657; https://clinicaltrials.gov/ct2/show/NCT03088657

Gastric inflammation is an indication of gastric ulcers and possible other underlying gastric malignancies. Epidemiological studies have revealed that several Asian countries, including South Korea, suffer from a high incidence of gastric diseases derived from high levels of stress, alcoholic consumption, pyloric infection and usage of non-steroidal anti-inflammatory drugs (NSAIDs). Clinical treatments of gastric ulcers are generally limited to proton pump inhibitors that neutralize the stomach acid, and the application of antibiotics for Helicobacter pylori eradication, both of which are known to have a negative effect on the gut microbiota. The potential of probiotics for alleviating gastrointestinal diseases such as intestinal bowel syndrome and intestinal bowel disease receives increasing scientific interest. Probiotics may support the amelioration of disease-related symptoms through modulation of the gut microbiota without causing dysbiosis. In this study the potential of Lactobacillus plantarum APSulloc 331261 (GTB1 TM ), isolated from green tea, was investigated for alleviating gastric inflammation in an alcohol induced gastric ulcer murine model (positive control). Treatment with the test strain significantly influenced the expression of pro-inflammatory and anti-inflammatory biomarkers, interleukin 6 (IL6) and interleukin 10 (IL10), of which the former was downand the latter up-regulated when the alcohol induced mice were treated with the test strain. This positive effect was also indicated by less severe gastric morphological changes and the histological score of the gastric tissues. A significant increase in the abundance of Akkermansia within the GTB1 TM treated group compared to the positive control group also correlated with a decrease in the ratio of acetate over propionate. The increased levels of propionate in the GTB1 TM group appear to result from the impact of the test strain on the microbial population and the resulting metabolic activities. Moreover, there was a significant increase in beta-diversity in the group that received GTB1 TM over that of the alcohol induced control group.

Background Mood disorders require consistent management of symptoms to prevent recurrences of mood episodes. Circadian rhythm (CR) disruption is a key symptom of mood disorders to be proactively managed to prevent mood episode recurrences. This study aims to predict impending mood episodes recurrences using digital phenotypes related to CR obtained from wearable devices and smartphones. Methods The study is a multicenter, nationwide, prospective, observational study with major depressive disorder, bipolar disorder I, and bipolar II disorder. A total of 495 patients were recruited from eight hospitals in South Korea. Patients were followed up for an average of 279.7 days (a total sample of 75 506 days) with wearable devices and smartphones and with clinical interviews conducted every 3 months. Algorithms predicting impending mood episodes were developed with machine learning. Algorithm-predicted mood episodes were then compared to those identified through face-to-face clinical interviews incorporating ecological momentary assessments of daily mood and energy. Results Two hundred seventy mood episodes recurred in 135 subjects during the follow-up period. The prediction accuracies for impending major depressive episodes, manic episodes, and hypomanic episodes for the next 3 days were 90.1, 92.6, and 93.0%, with the area under the curve values of 0.937, 0.957, and 0.963, respectively. Conclusions We predicted the onset of mood episode recurrences exclusively using digital phenotypes. Specifically, phenotypes indicating CR misalignment contributed the most to the prediction of episodes recurrences. Our findings suggest that monitoring of CR using digital devices can be useful in preventing and treating mood disorders.

The objective of this study was to determine whether the circadian rhythm of heart rate or step count using wearable devices was related to that of the salivary cortisol levels and to test the possibility that the data from wearable devices could be used as an indicator of circadian rhythm misalignment, which is emerging as a cause of insomnia and mood disorders. Methods: The heart rate and step count were continuously measured in 12 healthy young adults using wearable wrist devices for 5 days, and saliva was sampled every 4 hours, excluding sleeping time, for a total of 48 hours to measure the circadian rhythm of salivary cortisol concentration. Cortisol concentrations were assessed using the enzyme-linked immunosorbent assay. The cosinor analysis for the three measurements, salivary cortisol concentrations, heart rate, and step count, was used to estimate the circadian rhythm. Results: The mean values of the acrophase of the cosine-fitted curve of cortisol, heart rate, and step count were 9.06, 15.84, and 19.09, respectively, while those of the amplitude were 7.70, 12.60, and 10.68, respectively. In addition, the mean values of the mesor of the cosine-fitted curve for cortisol, heart rate, and step count were 17.19, 73.55, and 45.45, respectively, and those of robustness were 0.82, 0.56, and 0.18, respectively. There was a possible positive correlation between the acrophase of the cosine-fitted curve of salivary cortisol and that of heart rate (r=0.55, p=0.064). However, there was no correlation between the acrophase of the cosine-fitted curve of salivary cortisol and that of step count (r=-0.2, p=0.533). Conclusion: The findings suggest that the heart rate measured using the wearable activity tracker was a relatively reliable biomarker of circadian rhythm.

Background: Many mood disorder patients experience seasonal changes in varying degrees. Studies on seasonality have shown that bipolar disorder has a higher prevalence rate in such patients; however, there is limited research on seasonality in early-onset mood disorder patients. This study estimated the prevalence of seasonality in early-onset mood disorder patients, and examined the association between seasonality and mood disorders. Methods: Early-onset mood disorder patients (n = 378; 138 major depressive disorder; 101 bipolar I disorder; 139 bipolar II disorder) of the Mood Disorder Cohort Research Consortium and healthy control subjects (n = 235) were assessed for seasonality with Seasonality Pattern Assessment Questionnaire (SPAQ).Results: A higher global seasonality score, an overall seasonal impairment score, and the prevalence of seasonal affective disorder (SAD) and subsyndromal SAD showed that mood disorder subjects had higher seasonality than the healthy subjects. The former subject group had a significantly higher mean overall seasonal impairment score than the healthy subjects (p < .001); in particular, bipolar II disorder subjects had the highest prevalence of SAD, and the diagnosis of bipolar II disorder had significantly higher odds ratios for SAD when compared to major depression and bipolar I disorder (p < .05).Conclusions: Early-onset mood disorders, especially bipolar II disorder, were associated with high seasonality. A thorough assessment of seasonality in early-onset mood disorders may be warranted for more personalized treatment and proactive prevention of mood episodes.