BACKGROUND: As information and communication technology is becoming more widely implemented across health care organizations, patient-provider email or asynchronous electronic secure messaging has the potential to support patient-centered communication. Within the medical home model of the Veterans Health Administration (VA), secure messaging is envisioned as a means to enhance access and strengthen the relationships between veterans and their health care team members. However, despite previous studies that have examined the content of electronic messages exchanged between patients and health care providers, less research has focused on the socioemotional aspects of the communication enacted through those messages. OBJECTIVE: Recognizing the potential of secure messaging to facilitate the goals of patient-centered care, the objectives of this analysis were to not only understand why patients and health care team members exchange secure messages but also to examine the socioemotional tone engendered in these messages. METHODS: We conducted a cross-sectional coding evaluation of a corpus of secure messages exchanged between patients and health care team members over 6 months at 8 VA facilities. We identified patients whose medical records showed secure messaging threads containing at least 2 messages and compiled a random sample of these threads. Drawing on previous literature regarding the analysis of asynchronous, patient-provider electronic communication, we developed a coding scheme comprising a series of a priori patient and health care team member codes. Three team members tested the scheme on a subset of the messages and then independently coded the sample of messaging threads. RESULTS: Of the 711 messages coded from the 384 messaging threads, 52.5% (373/711) were sent by patients and 47.5% (338/711) by health care team members. Patient and health care team member messages included logistical content (82.6%, 308/373 vs 89.1%, 301/338), were neutral in tone (70.2%, 262/373 vs 82.0%, 277/338), and respectful in nature (25.7%, 96/373 vs 33.4%, 113/338). Secure messages from health care team members sometimes appeared hurried (25.4%, 86/338) but also displayed friendliness or warmth (18.9%, 64/338) and reassurance or encouragement (18.6%, 63/338). Most patient messages involved either providing or seeking information; however, the majority of health care team member messages involved information provision in response to patient questions. CONCLUSIONS: This evaluation is an important step toward understanding the content and socioemotional tone that is part of the secure messaging exchanges between patients and health care team members. Our findings were encouraging; however, there are opportunities for improvement. As health care organizations seek to supplement traditional encounters with virtual care, they must reexamine their use of secure messaging, including the patient centeredness of the communication, and the potential for more proactive use by health care team members.

BACKGROUND: Smoking continues to be the number one preventable cause of premature death in the United States. While evidence for the effectiveness of smoking cessation interventions has increased rapidly, questions remain on how to effectively disseminate these findings. Twitter, the second largest online social network, provides a natural way of disseminating information. Health communicators can use Twitter to inform smokers, provide social support, and attract them to other interventions. A key challenge for health researchers is how to frame their communications to maximize the engagement of smokers. OBJECTIVE: Our aim was to examine current Twitter activity for smoking cessation. METHODS: Active smoking cessation related Twitter accounts (N=18) were identified. Their 50 most recent tweets were content coded using a schema adapted from the Roter Interaction Analysis System (RIAS), a theory-based, validated coding method. Using negative binomial regression, the association of number of followers and frequency of individual tweet content at baseline was assessed. The difference in followership at 6 months (compared to baseline) to the frequency of tweet content was compared using linear regression. Both analyses were adjusted by account type (organizational or not organizational). RESULTS: The 18 accounts had 60,609 followers at baseline and 68,167 at 6 months. A total of 24% of tweets were socioemotional support (mean 11.8, SD 9.8), 14% (mean 7, SD 8.4) were encouraging/engagement, and 62% (mean 31.2, SD 15.2) were informational. At baseline, higher frequency of socioemotional support and encouraging/engaging tweets was significantly associated with higher number of followers (socioemotional: incident rate ratio [IRR] 1.09, 95% CI 1.02-1.20; encouraging/engaging: IRR 1.06, 95% CI 1.00-1.12). Conversely, higher frequency of informational tweets was significantly associated with lower number of followers (IRR 0.95, 95% CI 0.92-0.98). At 6 months, for every increase by 1 in socioemotional tweets, the change in followership significantly increased by 43.94 (P = .027); the association was slightly attenuated after adjusting by account type and was not significant (P = .064). CONCLUSIONS: Smoking cessation activity does exist on Twitter. Preliminary findings suggest that certain content strategies can be used to encourage followership, and this needs to be further investigated.