Development of an addiction index and delineation 15-year trends of illicit drugs from the Taiwan national drug enhancement database.

Illicit drug use contributes to substantial morbidity and mortality. Drug scheduling, a legal measure in drug enforcement, is often structured as a hierarchy based on addiction tendency, abuse trends, and harm, but may lack data-driven evidence when classifying substances. Our study aims to measure addiction tendency and use trends based on real-world data. We used the open access database of National Police Agency, Ministry of the Interior in Taiwan and analyzed all daily criminal cases of illicit drugs from 2013 to 2017 and monthly illicit drug enforcement data from the same database from 2002 to 2017. We hypothesized that repeat and frequent use despite legal consequence may be a reflection of addictive behavior, and empirical mode decomposition was applied in analysis to calculate addiction tendency indices and intrinsic 15-year use trends. Our analysis showed heroin has the highest addiction index, followed by methamphetamine. 3,4-Methyl enedioxy methamphetamine, marijuana, and ketamine had lower addictive propensities. This result is consistent with most drug scheduling hierarchies. 15-year use trends of substances were consistent with previous epidemiological studies.

Validation of the Mobile App-Recorded Circadian Rhythm by a Digital Footprint.

BACKGROUND: Modern smartphone use is pervasive and could be an accessible method of evaluating the circadian rhythm and social jet lag via a mobile app. OBJECTIVE: This study aimed to validate the app-recorded sleep time with daily self-reports by examining the consistency of total sleep time (TST), as well as the timing of sleep onset and wake time, and to validate the app-recorded circadian rhythm with the corresponding 30-day self-reported midpoint of sleep and the consistency of social jetlag. METHODS: The mobile app, Rhythm, recorded parameters and these parameters were hypothesized to be used to infer a relative long-term pattern of the circadian rhythm. In total, 28 volunteers downloaded the app, and 30 days of automatically recorded data along with self-reported sleep measures were collected. RESULTS: No significant difference was noted between app-recorded and self-reported midpoint of sleep time and between app-recorded and self-reported social jetlag. The overall correlation coefficient of app-recorded and self-reported midpoint of sleep time was .87. CONCLUSIONS: The circadian rhythm for 1 month, daily TST, and timing of sleep onset could be automatically calculated by the app and algorithm.

Development of a mobile application (App) to delineate "digital chronotype" and the effects of delayed chronotype by bedtime smartphone use.

The widespread use and deep reach of smartphones motivate the use of mobile applications to continuously monitor the relationship between circadian system, individual sleep patterns, and environmental effects. We selected 61 adults with 14-day data from the "Know Addiction" database. We developed an algorithm to identify the "sleep time" based on the smartphone behaviors. The total daily smartphone use duration and smartphone use duration prior to sleep onset were identified respectively. We applied mediation analysis to investigate the effects of total daily smartphone use on sleep through pre-sleep use (PS). The results showed participants' averaged pre-sleep episodes within 1 h prior to sleep are 2.58. The duration of three pre-sleep uses (PS1∼3) maybe a more representative index for smartphone use before sleep. Both total daily duration and the duration of the last three uses prior to sleep of smartphone use significantly delayed sleep onset, midpoint of sleep and reduced total sleep time. One hour of increased smartphone use daily, delays the circadian rhythm by 3.5 min, and reduced 5.5 min of total sleep time (TST). One hour of increased pre-sleep smartphone use delayed circadian rhythm by 1.7 min, and reduced 39 s of TST. The mediation effects of PS1∼3 significantly impacted on these three sleep indicators. PS1∼3 accounted for 14.3% of total daily duration, but the proportion mediated of delayed circadian rhythm was 44.0%. We presented "digital chronotype" with an automatic system that can collect high temporal resolution data from naturalistic settings with high ecological validity. Smartphone screen time, mainly mediated by pre-sleep use, delayed the circadian rhythm and reduced the total sleep time.