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1. Opportunities for Oral Health Monitoring Technologies Beyond the Dental Clinic

   https://doi.org/10.1145/3240925.3240973  

   Parsons, Adele; Chung, Chia-Fang; Donohue, Molly; Munson, Sean A.; Seibel, Eric J. (January 2018, PervasiveHealth'18)

   Personal health and wellness technologies can improve people’s care at home, connect everyday activities to clinical settings, and allow more efficient use of clinical resources. Recently, the Human-Computer Interaction community has begun to develop tools to improve oral care. In this research, we investigate dental practices and information needs through surveys and interviews with a range of patients and oral health providers. We find that personal users want to track their progress—or lack thereof—between dental visits for feedback, so they can adjust their home care routines, or so they can seek an escalation in care if they identify a problem. Among providers and clinical health workers, there exists an opportunity for better screening and diagnostic tools to identify dental caries at early stages. Providers in rural areas desire better tools to communicate problem areas to patients and their caregivers to bridge oral health care disparities in areas with limited access to care. Our results can guide the development of dental technologies that can address currently unmet needs of patients and providers. 

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2. A Hybrid Deep Learning–Based Feature Selection Approach for Supporting Early Detection of Long-Term Behavioral Outcomes in Survivors of Cancer: Cross-Sectional Study

   https://doi.org/10.2196/65001  

   Huang, Tracy; Ngan, Chun-Kit; Cheung, Yin Ting; Marcotte, Madelyn; Cabrera, Benjamin (January 2025, JMIR Bioinformatics and Biotechnology)

   Background: The number of survivors of cancer is growing, and they often experience negative long-term behavioral outcomes due to cancer treatments. There is a need for better computational methods to handle and predict these outcomes so that physicians and health care providers can implement preventive treatments. Objective: This study aimed to create a new feature selection algorithm to improve the performance of machine learning classifiers to predict negative long-term behavioral outcomes in survivors of cancer. Methods: We devised a hybrid deep learning–based feature selection approach to support early detection of negative long-term behavioral outcomes in survivors of cancer. Within a data-driven, clinical domain–guided framework to select the best set of features among cancer treatments, chronic health conditions, and socioenvironmental factors, we developed a 2-stage feature selection algorithm, that is, a multimetric, majority-voting filter and a deep dropout neural network, to dynamically and automatically select the best set of features for each behavioral outcome. We also conducted an experimental case study on existing study data with 102 survivors of acute lymphoblastic leukemia (aged 15-39 years at evaluation and >5 years postcancer diagnosis) who were treated in a public hospital in Hong Kong. Finally, we designed and implemented radial charts to illustrate the significance of the selected features on each behavioral outcome to support clinical professionals’ future treatment and diagnoses. Results: In this pilot study, we demonstrated that our approach outperforms the traditional statistical and computation methods, including linear and nonlinear feature selectors, for the addressed top-priority behavioral outcomes. Our approach holistically has higher F1, precision, and recall scores compared to existing feature selection methods. The models in this study select several significant clinical and socioenvironmental variables as risk factors associated with the development of behavioral problems in young survivors of acute lymphoblastic leukemia. Conclusions: Our novel feature selection algorithm has the potential to improve machine learning classifiers’ capability to predict adverse long-term behavioral outcomes in survivors of cancer. 

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3. “She is courageous because she does not care what people think about her…”: attitudes toward adolescent contraception use among Rwandan family planning providers and adult female modern contraceptive users

   https://doi.org/10.1186/s12978-022-01517-4  

   Schwandt, Hilary; Boulware, Angel; Corey, Julia; Herrera, Ana; Hudler, Ethan; Imbabazi, Claudette; King, Ilia; Linus, Jessica; Manzi, Innocent; Merritt, Madelyn; et al (December 2022, Reproductive Health)

   Abstract Introduction In Rwanda, only 20% of sexually active unmarried young women use family planning as compared to 64% of married women. Adolescence is an important time of growth and development that often includes the initiation of sexual activity. Sexually active adolescents need support in accessing contraceptive services to prevent negative health outcomes. In sub-Saharan Africa, the adolescent population represents a large share of the total population and that proportion is predicted to expand over time. Adolescent contraceptive needs have largely been unmet, and with growing numbers, there is increased potential for negative health sequelae. Due to the low use of contraception by adolescents in Rwanda, and the growing population of adolescents, this study aims to explore the perspectives of family planning providers and adult modern contraceptive users on adolescent contraceptive use. Inclusion of adult community members in the study is a unique contribution, as research on adolescent contraceptive use in sub-Saharan Africa relies primarily on perspectives from adolescents and family planning providers. Methods This qualitative study in 2018 utilized 32 in-depth interviews with modern contraceptive users and eight focus group discussions with family planning providers. Respondents were from Musanze and Nyamasheke districts in Rwanda, the districts with the highest and lowest modern contraceptive use among married women, respectively. Coding was conducted in Atlas.ti. Results Stigma regarding premarital sex results in barriers to adolescent access to contraceptive services. Family planning providers do provide services to adolescents; however, they often recommend secondary abstinence, offer a limited method selection, and accentuate risks associated with sexual activity and contraceptive use. Providers support adolescent clients by emphasizing the need for privacy, confidentiality, and expedient services, particularly through youth corners, which are spaces within health facilities designed to meet youth needs specifically. Modern contraceptive-using adult female community members advocate for youth access to contraception, however mothers have mixed comfort discussing sexual health with their own youth. Conclusion To destigmatize premarital sexual activity, government efforts to initiate communication about this topic must occur at national and community levels with the goal of continued conversation within the family. The government should also train family planning providers and all health personnel interacting with youth on adolescent-friendly health services. Dialogue between community members and family planning providers about adolescent access to contraceptive services could also reduce barriers for adolescents due to community members’ generally supportive views on adolescent contraceptive use. Efforts to engage adolescent caregivers in how to talk to youth about sex could also contribute to expanded use. 

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4. What Makes Digital Support Effective? How Therapeutic Skills Affect Clinical Well-Being

   https://doi.org/10.1145/3641029  

   Yang, Wenjie; Fang, Anna; Shah, Raj Sanjay; Mathur, Yash; Yang, Diyi; Zhu, Haiyi; Kraut, Robert E (April 2024, Proceedings of the ACM on Human-Computer Interaction)

   Online mental health support communities, in which volunteer counselors provide accessible mental and emotional health support, have grown in recent years. Despite millions of people using these platforms, the clinical effectiveness of these communities on mental health symptoms remains unknown. Although volunteers receive some training on the therapeutic skills proven effective in face-to-face environments, such as active listening and motivational interviewing, it is unclear how the usage of these skills in an online context affects people's mental health. In our work, we collaborate with one of the largest online peer support platforms and use both natural language processing and machine learning techniques to examine how one-on-one support chats on the platform affect clients' depression and anxiety symptoms. We measure how characteristics of support-providers, such as their experience on the platform and use of therapeutic skills (e.g. affirmation, showing empathy), affect support-seekers' mental health changes. Based on a propensity-score matching analysis to approximate a random-assignment experiment, results shows that online peer support chats improve both depression and anxiety symptoms with a statistically significant but relatively small effect size. Additionally, support providers' techniques such as emphasizing the autonomy of the client lead to better mental health outcomes. However, we also found that the use of some behaviors, such as persuading and providing information, are associated with worsening of mental health symptoms. Our work provides key understanding for mental health care in the online setting and designing training systems for online support providers. 

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5. Human-to-Computer Interactivity Features Incorporated Into Behavioral Health mHealth Apps: Systematic Search

   https://doi.org/10.2196/44926  

   Collier, Ann Futterman; Hagemann, Shelby; Trinidad, Susan Brown; Vigil-Hayes, Morgan (January 2023, JMIR Formative Research)

   Background While there are thousands of behavioral health apps available to consumers, users often quickly discontinue their use, which limits their therapeutic value. By varying the types and number of ways that users can interact with behavioral health mobile health apps, developers may be able to support greater therapeutic engagement and increase app stickiness. Objective The main objective of this analysis was to systematically characterize the types of user interactions that are available in behavioral health apps and then examine if greater interactivity was associated with greater user satisfaction, as measured by app metrics. Methods Using a modified PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) methodology, we searched several different app clearinghouse websites and identified 76 behavioral health apps that included some type of interactivity. We then filtered the results to ensure we were examining behavioral health apps and further refined our search to include apps that identified one or more of the following terms: peer or therapist forum, discussion, feedback, professional, licensed, buddy, friend, artificial intelligence, chatbot, counselor, therapist, provider, mentor, bot, coach, message, comment, chat room, community, games, care team, connect, share, and support in the app descriptions. In the final group of 34 apps, we examined the presence of 6 types of human-machine interactivities: human-to-human with peers, human-to-human with providers, human-to–artificial intelligence, human-to-algorithms, human-to-data, and novel interactive smartphone modalities. We also downloaded information on app user ratings and visibility, as well as reviewed other key app features. Results We found that on average, the 34 apps reviewed included 2.53 (SD 1.05; range 1-5) features of interactivity. The most common types of interactivities were human-to-data (n=34, 100%), followed by human-to-algorithm (n=15, 44.2%). The least common type of interactivity was human–artificial intelligence (n=7, 20.5%). There were no significant associations between the total number of app interactivity features and user ratings or app visibility. We found that a full range of therapeutic interactivity features were not used in behavioral health apps. Conclusions Ideally, app developers would do well to include more interactivity features in behavioral health apps in order to fully use the capabilities of smartphone technologies and increase app stickiness. Theoretically, increased user engagement would occur by using multiple types of user interactivity, thereby maximizing the benefits that a person would receive when using a mobile health app. 

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