AIMS: Korea has the highest suicide rate of developed countries, two times higher than the USA. Suicide trends among Koreans Americans living in the USA during the same period have not yet been described. We report suicide mortality rates and trends for four groups: (1) Korean Americans, (2) non-Hispanic White (NHW) Americans, (3) selected Asian American subgroups and (4) Koreans living in the Republic of Korea. METHODS: We used US national (n = 18 113 585) and World Health Organization (WHO) (n = 232 919 253) mortality records for Korea from 2003 to 2012 to calculate suicide rates, all expressed per 100 000 persons. We assessed temporal trends and differences in age, gender and race/ethnicity using binomial regression. RESULTS: Suicide rates are highest in Koreans living in the Republic of Korea (32.4 for men and 14.8 for women). Suicide rates in Korean Americans (13.9 for men and 6.5 for women) have nearly doubled from 2003 to 2012 and exceed rates for all other Asian American subgroups (5.4-10.7 for men and 1.6-4.2 for women). Suicide rates among NHWs (21.0 for men and 5.6 for women) remain high. Among elders, suicide in Korean Americans (32.9 for men and 15.4 for women) is the highest of all examined racial/ethnic groups in the USA. CONCLUSIONS: Suicide in Korean Americans is higher than for other Asian Americans and follows temporal patterns more similar to Korea than the USA. Interventions to prevent suicide in Korean American populations, particularly among the elderly, are needed.

Background

Health behavior change interventions delivered by social media allow for real-time, dynamic interaction, peer social support, and experimenter-provided content.

Aims

We tested the feasibility, acceptability, and preliminary efficacy of a novel Twitter-based walking break intervention with daily behavior change strategies and prompts for social support, combined with a Fitbit, vs. Fitbit alone.

Methods

In a 2-group pilot, 45 sedentary women from a heart clinic were randomized to Twitter + Fitbit activity tracker (Tweet4Wellness, n = 23) or Fitbit-only (control, n = 22). All received a Fitbit and 13 weeks of tailored weekly step goals. Tweet4Wellness consisted of a private Twitter support group, with daily automated behavior change "tweets" informed by behavior change theory, and encouragement to communicate within the group. Feasibility outcomes included recruitment and enrollment numbers, implementation challenges, and number and type of help requests from participants throughout the study period. Preliminary efficacy outcomes provided by Fitbit data were sedentary minutes, number of hours with >250 steps, maximum sitting bout, weighted sedentary median bout length, total steps, intensity minutes (>3.0 METS), and ratio of time spent sitting-to-moving. Acceptability outcomes included level of Twitter participation within Tweet4Wellness, and Likert scale plus open-ended survey questions on enjoyment and perceived effectiveness of intervention components. Survey data on acceptability of the features of the intervention were collected at 13 weeks (end-of-treatment [EOT]) and 22 weeks (follow-up).

Results

The study was feasible, with addressable implementation challenges. Tweet4Wellness participants changed significantly from baseline to EOT relative to control participants on number of active hours p = .018, total steps p = .028, and ratio of sitting-to-moving, p = .014. Only sitting-to-moving was significant at follow-up (p = .047). Among Tweet4Wellness participants, each tweet sent during treatment was associated with a 0.11 increase in active hours per day (p = .04) and a 292-step increase per day (p < .001). Tweet4Wellness participants averaged 54.8 (SD = 35.4) tweets, totaling 1304 tweets, and reported liking the accountability and peer support provided by the intervention.

Conclusion

A Twitter-delivered intervention for promoting physical activity among inactive women from a heart clinic was feasible, acceptable, and demonstrated preliminary efficacy in increasing daily active hours, daily total steps, and the ratio of sitting-to-moving from pre to post for the intervention compared with the control. Lessons learned from this pilot suggest that the next study should expand the recruitment pool, refine the intervention to increase group engagement, and select active hours, total steps, and ratio of sitting-to-movement as primary sedentary behavior measures.