Journal of technology in behavioral science最新文献

Chatbot use is increasing for mobile health interventions on sensitive and stigmatized topics like mental health because of their anonymity and privacy. This anonymity provides acceptability to sexual and gendered minority youth (ages 16-24) at increased risk of HIV and other STIs with poor mental health due to higher levels of stigma, discrimination, and social isolation. This study evaluates the usability of Tabatha-YYC, a pilot chatbot navigator created to link these youth to mental health resources. Tabatha-YYC was developed using a Youth Advisory Board (n = 7). The final design underwent user testing (n = 20) through a think-aloud protocol, semi-structured interview, and a brief survey post-exposure which included the Health Information Technology Usability Evaluation Scale. The chatbot was found to be an acceptable mental health navigator by participants. This study provides important design methodology considerations and key insights into chatbot design preferences of youth at risk of STIs seeking mental health resources.

Smartphones can be used to gain insight into mental health conditions through the collection of survey and sensor data. However, the external validity of this digital phenotyping data is still being explored, and there is a need to assess if predictive models derived from this data are generalizable. The first dataset (V1) of 632 college students was collected between December 2020 and May 2021. The second dataset (V2) was collected using the same app between November and December 2021 and included 66 students. Students in V1 could enroll in V2. The main difference between the V1 and V2 studies was that we focused on protocol methods in V2 to ensure digital phenotyping data had a lower degree of missing data than in the V1 dataset. We compared survey response counts and sensor data coverage across the two datasets. Additionally, we explored whether models trained to predict symptom survey improvement could generalize across datasets. Design changes in V2, such as a run-in period and data quality checks, resulted in significantly higher engagement and sensor data coverage. The best-performing model was able to predict a 50% change in mood with 28 days of data, and models were able to generalize across datasets. The similarities between the features in V1 and V2 suggest that our features are valid across time. In addition, models must be able to generalize to new populations to be used in practice, so our experiments provide an encouraging result toward the potential of personalized digital mental health care.

Access to psychosocial interventions for people with dementia, such as Cognitive Stimulation Therapy (CST), has been restricted during the COVID-19 pandemic. Some services have shifted to provision via videoconferencing, but the prevalence of this is unknown. This audit aimed to understand provision of virtual CST (vCST) within National Health Service (NHS) memory clinics throughout the UK and Channel Islands and investigate plans for ongoing CST provision. A cross-sectional survey was circulated to NHS memory clinics, which included closed and open-ended questions to generate quantitative and qualitative data. Thirty-three memory clinics responded to the survey. During the pandemic, 55% of respondents offered vCST, whereas 45% offered no CST. Of those offering vCST, 80% plan to continue with a hybrid model of separate face-to-face and vCST groups, whilst 20% intend to deliver face-to-face CST only. Reported positive aspects of vCST were participant and staff enjoyment, perceived improved digital confidence in participants, and improved accessibility for those who cannot attend face-to-face groups. Negative aspects related to digital poverty, limited digital literacy, support needed from carers, the impact of sensory impairment on engagement, and staff time commitment. Virtual CST has been a feasible alternative to face-to-face services during the pandemic but should not completely replace in-person groups. A hybrid approach would increase accessibility for all. Future research should explore efficacy of vCST and seek to understand patterns of exclusion from such digital interventions.

Supplementary information: The online version contains supplementary material available at 10.1007/s41347-023-00306-5.

Automated text messaging interventions can effectively improve self-care and were used to support the U.S. Veterans Health Administration's (VHA) public health outreach during the COVID pandemic. Currently, significant gaps exist in knowledge about VHA patients' texting protocol preferences that may impact user receptivity, engagement, and effectiveness. This study qualitatively evaluated patient suggestions to improve two VHA Covid-related texting interventions and preferences for future text message protocols. We reviewed cross-sectional type-written survey responses from patients receiving either the "Coronavirus Precautions" or the "Coping During COVID" multi-week text protocols. Two team members independently and inductively coded all responses allowing for an upward abstraction of qualitative data. Nine hundred five patients (72.8% male) responded to the open-response item questions targeted by this research. An item that sought feedback to improve protocol acceptability generated thirteen distinct descriptive categories (inter-rater reliability 83.5%). Codable feedback showed, for example, that patients desired to manipulate message frequency and to have a more sophisticated interaction with messages. Patients' suggestions for future automated text messaging protocols yielded nine distinct topic areas. Patients offered suggestions that may impact receptivity and engagement of future automated text message protocols, particularly as they relate to outreach during a public health crisis. In addition, patients offered specific topics they would like to see in future text message protocols. We discuss how the findings can be used to increase engagement in current and post-pandemic public health interventions.