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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.

Background Methylphenidate, a stimulant used to treat attention deficit hyperactivity disorder, has the potential to be used nonmedically, such as for studying and recreation. In an era when many people actively use social networking services, experience with the nonmedical use or side effects of methylphenidate might be shared on Twitter. Objective The purpose of this study was to analyze tweets about the nonmedical use and side effects of methylphenidate using a machine learning approach. Methods A total of 34,293 tweets mentioning methylphenidate from August 2018 to July 2019 were collected using searches for “methylphenidate” and its brand names. Tweets in a randomly selected training dataset (6860/34,293, 20.00%) were annotated as positive or negative for two dependent variables: nonmedical use and side effects. Features such as personal noun, nonmedical use terms, medical use terms, side effect terms, sentiment scores, and the presence of a URL were generated for supervised learning. Using the labeled training dataset and features, support vector machine (SVM) classifiers were built and the performance was evaluated using F1 scores. The classifiers were applied to the test dataset to determine the number of tweets about nonmedical use and side effects. Results Of the 6860 tweets in the training dataset, 5.19% (356/6860) and 5.52% (379/6860) were about nonmedical use and side effects, respectively. Performance of SVM classifiers for nonmedical use and side effects, expressed as F1 scores, were 0.547 (precision: 0.926, recall: 0.388, and accuracy: 0.967) and 0.733 (precision: 0.920, recall: 0.609, and accuracy: 0.976), respectively. In the test dataset, the SVM classifiers identified 361 tweets (1.32%) about nonmedical use and 519 tweets (1.89%) about side effects. The proportion of tweets about nonmedical use was highest in May 2019 (46/2624, 1.75%) and December 2018 (36/2041, 1.76%). Conclusions The SVM classifiers that were built in this study were highly precise and accurate and will help to automatically identify the nonmedical use and side effects of methylphenidate using Twitter.

BACKGROUND Methylphenidate, a stimulant used to treat attention deficit hyperactivity disorder (ADHD), has the potential for nonmedical uses such as study and recreation. In the era of active use of social networking services (SNSs), experience with the nonmedical use or side effects of methylphenidate might be shared on Twitter. OBJECTIVE To analyze monthly tweets about methylphenidate, its nonmedical use and side effects, and user sentiments about methylphenidate. METHODS Tweets mentioning methylphenidate from August 2018 to July 2019 were collected using search terms for methylphenidate and its brand names. Only tweets written in English were included. The monthly number of tweets about methylphenidate and the number of tweets containing keywords related to the nonmedical use and side effects of methylphenidate were analyzed. Precision was calculated as the number of true nonmedical use or side effects divided by the number of tweets containing each keywords. Sentiment analysis was conducted using the text and emoji in tweets, and tweets were categorized as very negative (less than -3), negative (-3 to -1), neutral (0), positive (1 to 3), or very positive (more than 3), depending on the sentiment score. RESULTS A total of 4,169 tweets were ultimately selected for analysis. The number of tweets per month was lowest in August (n=264) and highest in May (n=435). There were 292 (7.0%) tweets about nonmedical uses of methylphenidate. Among those, 200 (4.8%) described use for studying, and 15 (0.4%) described use for recreation. In 91 (2.2%) tweets, snorting methylphenidate was mentioned. Side effects of methylphenidate, mainly poor appetite (n=74, 1.8%) and insomnia (n=54, 1.3%), were reported in 316 (7.6%) tweets. The average sentiment score was 0.027 ± 1.475, and neutral tweets were the most abundant (n=1,593, 38.2%). CONCLUSIONS Tweets about methylphenidate were most abundant in May, mentioned nonmedical use for study or recreation, and contained information about side effects. Analysis of Twitter has the advantage of saving the cost and time needed to conduct a survey, and could help identify nonmedical uses and side effects of drugs.