Treffer: Methodological approaches in developing and implementing digital health interventions amongst underserved women.
Title:
Methodological approaches in developing and implementing digital health interventions amongst underserved women.
Authors:
Crawford AD; School of Nursing, The University of Texas Health at San Antonio, San Antonio, Texas, USA., Slavin R; Department of Computer Science, The University of Texas San Antonio, San Antonio, Texas, USA., Tabar M; Department of Computer Science, The University of Texas San Antonio, San Antonio, Texas, USA., Radhakrishnan K; School of Nursing, The University of Texas at Austin, Austin, Texas, USA., Wang M; Department of Management Science and Statistics, The University of Texas San Antonio, San Antonio, Texas, USA., Estrada A; School of Nursing, The University of Texas Health at San Antonio, San Antonio, Texas, USA., McGrath JM; School of Nursing, The University of Texas Health at San Antonio, San Antonio, Texas, USA.
Source:
Public health nursing (Boston, Mass.) [Public Health Nurs] 2024 Nov-Dec; Vol. 41 (6), pp. 1612-1621. Date of Electronic Publication: 2024 Sep 02.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Blackwell Scientific Publications Country of Publication: United States NLM ID: 8501498 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1525-1446 (Electronic) Linking ISSN: 07371209 NLM ISO Abbreviation: Public Health Nurs Subsets: MEDLINE
Imprint Name(s):
Original Publication: [Boston, MA] : Blackwell Scientific Publications, [c1984-
MeSH Terms:
References:
§6501 15 U.S.C. (1998). Children's online Privacy Protection Act of 1998. https://uscode.house.gov/view.xhtml?req=granuleid%3AUSC‐prelim‐title15‐chapter91&edition=prelim.
Alirezaei, S., & Latifnejad Roudsari, R. (2022). The needs of incarcerated pregnant women: A systematic review of literature. International Journal of Community Based Nursing and Midwifery, 10(1), 1. https://doi.org/10.30476/ijcbnm.2021.89508.1613.
Anderson‐Lewis, C., Darville, G., Mercado, R. E., Howell, S., & Di Maggio, S. (2018). mHealth technology use and implications in historically underserved and minority populations in the United States: Systematic literature review. JMIR mHealth and uHealth, 6(6), e128. https://doi.org/10.2196/mhealth.8383.
Angwin, J., Larson, J., Mattu, S., & Kirchner, L. (2016). Machine bias. ProPublica. https://www.propublica.org/article/machine‐bias‐risk‐assessments‐in‐criminal‐sentencing.
Barberi, D., & Taxman, F. S. (2019). Diversion and alternatives to arrest: A qualitative understanding of police and substance users’ perspective. Journal of Drug Issues, 49(4), 703–717. https://doi.org/10.1177/0022042619861273.
Baumgartner, R. (2021). Precision medicine and digital phenotyping: Digital medicine's way from more data to better health. Big Data & Society, 8(2), 205395172110664. https://doi.org/10.1177/20539517211066452.
Borenstein, J. (2020). Stigma, prejudice and discrimination against people with mental illness. American Psychiatric Association.
Cain, A. E., Depp, C. A., & Jeste, D. V. (2009). Ecological momentary assessment in aging research: A critical review. Journal of Psychiatric Research, 43(11), 987–996. https://doi.org/10.1016/j.jpsychires.2009.01.014.
California Consumer Privacy Act. (2020). Assembly Bill No. 375, 2017–2018 Session, California Civil Code 100. chrome‐extension://efaidnbmnnnibpcajpcglclefindmkaj/ https://cppa.ca.gov/regulations/pdf/cppa_act.pdf.
Carson, A. E., & Kluckow, R. (2023). Correctional populations in the United States, 2021 statistical tables. U.S. Department of Justice. https://bjs.ojp.gov/library/publications/correctional‐populations‐united‐states‐2021‐statistical‐tables.
Caton, S., & Haas, C. (2023). Fairness in machine learning: A survey. ACM Computing Surveys, 56, 1–38. https://doi.org/10.1145/3616865.
Collins, L. M., & Collins, L. M. (2018). Introduction to adaptive interventions. In Optimization of behavioral, biobehavioral, and biomedical interventions. Springer.
Crawford, A. D., Hutson, T. S., & Kim, M. (2023). Mobile health applications addressing health disparities for women on community supervision: A scoping review. Substance Use & Misuse, 58(6), 6. https://doi.org/10.1080/10826084.2023.2188414.
Crawford, A. D., Salisbury, E. J., & McGrath, J. M. (2024). An intervention development study of an mHealth app to manage women's health and safety while on probation. Health & Justice, 12(1), 22. https://doi.org/10.1186/s40352-024-00277-6.
Crawford, A. D., Testa, A., Darilek, U., Howe, R., McGrath, J. M., & Shlafer, R. (2024). Perinatal health outcomes among women on community supervision: A scoping review. Journal of Correctional Health Care. https://doi.org/10.1089/jchc.23.09.0073.
Crenshaw, K. W. (2013). Mapping the margins: Intersectionality, identity politics, and violence against women of color. Routledge.
Deng, T., Kanthawala, S., Meng, J., Peng, W., Kononova, A., Hao, Q., Zhang, Q., & David, P. (2019). Measuring smartphone usage and task switching with log tracking and self‐reports. Mobile Media & Communication, 7(1), 3–23. https://doi.org/10.1177/2050157918761491.
Devlin, J., Chang, M.‐W., Lee, K., & Toutanova, K. (2019). Bert: Pre‐training of deep bidirectional transformers for language understanding. 1, 2. Accessed August 27, 2024. https://arxiv.org/abs/1810.04805.
Figueroa, C. A., Luo, T., Aguilera, A., & Lyles, C. R. (2021). The need for feminist intersectionality in digital health. The Lancet Digital Health, 3(8), e526–e533.
Heimer, K., Malone, S. E., & De Coster, S. (2023). Trends in women's incarceration rates in US prisons and jails: A tale of inequalities. Annual Review of Criminology, 6(1), 1. https://doi.org/10.1146/annurev‐criminol‐030421‐041559.
Hendl, T., Jansky, B., & Wild, V. (2019). From design to data handling. Why mHealth needs a feminist perspective. In Feminist philosophy of technology (pp. 77–103). Springer Verlag.
Jäger, S., Allhorn, A., & Bießmann, F. (2021). A Benchmark for data imputation methods. Frontiers in Big Data, 4, 693674. https://doi.org/10.3389/fdata.2021.693674.
Johnson, K. B., Wei, W. Q., Weeraratne, D., Frisse, M. E., Misulis, K., Rhee, K., Zhao, J., & Snowdon, J. L. (2021). Precision medicine, AI, and the future of personalized health care. Clinical and Translational Science, 14(1), 86–93.
Jones, N., & Moffitt, M. (2016). Ethical guidelines for mobile app development within health and mental health fields. Professional Psychology: Research and Practice, 47(2), 155–162. https://doi.org/10.1037/pro0000069.
Kim, M., Reingold, O., & Rothblum, G. (2018). Fairness through computationally‐bounded awareness. In Conference on neural information processing systems (Vol. 31).
Kim, M. T., Heitkemper, E. M., Hébert, E. T., Hecht, J., Crawford, A., Nnaka, T., Hutson, T. S., Rhee, H., & Radhakrishnan, K. (2022). Redesigning culturally tailored intervention in the precision health era: Self‐management science context. Nursing Outlook, 70(5), 5. https://doi.org/10.1016/j.outlook.2022.05.015.
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017). ImageNet classification with deep convolutional neural networks. Communications of the ACM, 60(6), 84–90. https://doi.org/10.1145/3065386.
Leach, R., Carreiro, S., Shaffer, P. M., Gaba, A., & Smelson, D. (2021). Digital health interventions for mental health, substance use, and co‐occurring disorders in the criminal justice population: A scoping review. Frontiers in Psychiatry, 12, 794785. https://doi.org/10.3389/fpsyt.2021.794785.
Liddel, T. M., & Kruschke, J. K. (2018). Analyzing ordinal data with metric models: What could possibly go wrong? Journal of Experimental Social Psychology, 79, 228–348.
Lorvick, J., Hemberg, J. L., Browne, E. N., & Comfort, M. L. (2022). Routine and preventive health care use in the community among women sentenced to probation. Health & Justice, 10(1), 1. https://doi.org/10.1186/s40352‐022‐00167‐9.
McConnell, S. (2023). Sentenced to prison, not to death: Home confinement during the pandemic and moving beyond COVID‐19. 75. Accessed August 27, 2024. https://scholarworks.uark.edu/alr/vol75/iss4/7.
McGlothen‐Bell, K., McGrath, J. M., Brownell, E. A., Shlafer, R. J., & Crawford, A. D. (2023). Applying a reproductive justice lens to enhance research engagement among systematically underrepresented childbearing women. Nursing Research, 72(2), 2. https://doi.org/10.1097/NNR.0000000000000639.
Nabi, R., Malinsky, D., & Sphpitser, I. (2019). Learning optimal fair policies. 4674–4682. Accessed August 27, 2024. https://proceedings.mlr.press/v97/nabi19a.html.
Oleson, J. J., Jones, M. A., Jorgensen, E. J., & Wu, Y.‐H. (2022). Statistical considerations for analyzing ecological momentary assessment data. Journal of Speech, Language, and Hearing Research: JSLHR, 65(1), 344–360. https://doi.org/10.1044/2021_JSLHR‐21‐00081.
Rice, E., Yoshioka‐Maxwell, A., Petering, R., Onasch‐Vera, L., Craddock, J., Tambe, M., Yadav, A., Wilder, B., Woo, D., Winetrobe, H., & Wilson, N. (2018). Piloting the use of artificial intelligence to enhance HIV prevention interventions for youth experiencing homelessness. Journal of the Society for Social Work and Research, 9(4), 551–573. https://doi.org/10.1086/701439.
Ross, L. (2020). Understanding reproductive justice. In Feminist theory reader. Routledge.
Ruderman, R. S., Dahl, E. C., Williams, B. R., Davis, K., Feinglass, J. M., Grobman, W. A., Kominiarek, M. A., & Yee, L. M. (2021). Provider perspectives on barriers and facilitators to postpartum care for low‐income individuals. Women's Health Reports, 2(1), 254–262. https://doi.org/10.1089/whr.2021.0009.
Sambrook Smith, M., Lawrence, V., Sadler, E., & Easter, A. (2019). Barriers to accessing mental health services for women with perinatal mental illness: Systematic review and meta‐synthesis of qualitative studies in the UK. BMJ Open, 9(1), e024803. https://doi.org/10.1136/bmjopen‐2018‐024803.
Satveit, S. (2018). Addressing the unique healthcare needs of women: Opportunity for change exists at the intersection of precision health and learning health systems. Learning Health Systems, 2(2), e10033. https://doi.org/10.1002/lrh2.10033.
Serre, F., Fatseas, M., Swendsen, J., & Auriacombe, M. (2015). Ecological momentary assessment in the investigation of craving and substance use in daily life: A systematic review. Drug and Alcohol Dependence, 148, 1–20. https://doi.org/10.1016/j.drugalcdep.2014.12.024.
Shiffman, S., Stone, A. A., & Hufford, M. R. (2008). Ecological momentary assessment. Annual Review of Clinical Psychology, 4, 1–32. https://doi.org/10.1146/annurev.clinpsy.3.022806.091415.
Śmieja, M., Struski, Ł., Tabor, J., Zieliński, B., & SpureK, P. (2018). Processing of missing data by neural networks. 31. Accessed August 27, 2024. https://doi.org/10.5555/3327144.3327196.
Smith, S. A., Mays, G. P., Collins, T. C., & Ramaswamy, M. (2019). The role of the community health delivery system in the health and well‐being of justice‐involved women: A narrative review. Health & Justice, 7(1), 12. https://doi.org/10.1186/s40352‐019‐0092‐y.
Song, Y., Wang, T., Cai, P., Mondal, S. K., & Sahoo, J. P. (2023). A comprehensive survey of few‐shot learning: Evolution, applications, challenges, and opportunities. ACM Computing Surveys, 55(13s), 1–40. https://doi.org/10.1145/3582688.
Sturmey, P. (2020). Functional analysis in clinical treatment: Twelve years of progress, current status, and future directions. In Functional analysis in clinical treatment (pp. 533–550). Elsevier. https://doi.org/10.1016/B978‐0‐12‐805469‐7.00022‐X.
Sufrin, C. (2017). Jailcare: Finding the safety net for women behind bars (1st ed.). University of California Press.
Sufrin, C., Beal, L., Clarke, J., Jones, R., & Mosher, W. D. (2019). Pregnancy outcomes in US prisons, 2016–2017. American Journal of Public Health, 109(5), 5. https://doi.org/10.2105/AJPH.2019.305006.
Tabar, M., Park, H., Winkler, S., Lee, D., Barman‐Adhikari, A., & Yadav, A. (2020). Identifying homeless youth at‐risk of substance use disorder: Data‐driven insights for policymakers. In Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining (pp. 3092–3100). https://doi.org/10.1145/3394486.3403360.
Voigt, P., & Von dem Bussche, A. (2017). “The eu general data protection regulation (gdpr).” A practical guide (1st ed.). Springer International Publishing.
Wang, L., & Miller, L. C. (2020). Just‐in‐the‐moment adaptive interventions (JITAI): A meta‐analytical review. Health Communication, 35(12), 1531–1544. https://doi.org/10.1080/10410236.2019.1652388.
Wilder, B., Onasch‐Vera, L., Diguiseppi, G., Petering, R., Hill, C., Yadav, A., Rice, E., & Tambe, M. (2021). Clinical trial of an AI‐augmented intervention for HIV prevention in youth experiencing homelessness. Proceedings of the AAAI Conference on Artificial Intelligence, 35(17), 14948–14956. https://doi.org/10.1609/aaai.v35i17.17754.
Wolfe, M. K., McDonald, N. C., & Holmes, G. M. (2020). Transportation barriers to health care in the United States: Findings from the national health interview survey, 1997–2017. American Journal of Public Health, 110(6), 815–822. https://doi.org/10.2105/AJPH.2020.305579.
Wu, J., Ping, L., Ge, X., Wang, Y., & Fu, J. (2010). Cloud storage as the infrastructure of cloud computing. In 2010 International conference on intelligent computing and cognitive informatics (pp. 380–383). https://doi.org/10.1109/ICICCI.2010.119.
Yadav, A., Chan, H., Jiang, A. X., Xu, H., Rice, E., & Tambe, M. (2016). Using social networks to aid homeless shelters: Dynamic influence maximization under uncertainty. In Proceedings of the 2016 international conference on autonomous agents & multiagent systems (Vol. 16, pp. 740–748).
Alirezaei, S., & Latifnejad Roudsari, R. (2022). The needs of incarcerated pregnant women: A systematic review of literature. International Journal of Community Based Nursing and Midwifery, 10(1), 1. https://doi.org/10.30476/ijcbnm.2021.89508.1613.
Anderson‐Lewis, C., Darville, G., Mercado, R. E., Howell, S., & Di Maggio, S. (2018). mHealth technology use and implications in historically underserved and minority populations in the United States: Systematic literature review. JMIR mHealth and uHealth, 6(6), e128. https://doi.org/10.2196/mhealth.8383.
Angwin, J., Larson, J., Mattu, S., & Kirchner, L. (2016). Machine bias. ProPublica. https://www.propublica.org/article/machine‐bias‐risk‐assessments‐in‐criminal‐sentencing.
Barberi, D., & Taxman, F. S. (2019). Diversion and alternatives to arrest: A qualitative understanding of police and substance users’ perspective. Journal of Drug Issues, 49(4), 703–717. https://doi.org/10.1177/0022042619861273.
Baumgartner, R. (2021). Precision medicine and digital phenotyping: Digital medicine's way from more data to better health. Big Data & Society, 8(2), 205395172110664. https://doi.org/10.1177/20539517211066452.
Borenstein, J. (2020). Stigma, prejudice and discrimination against people with mental illness. American Psychiatric Association.
Cain, A. E., Depp, C. A., & Jeste, D. V. (2009). Ecological momentary assessment in aging research: A critical review. Journal of Psychiatric Research, 43(11), 987–996. https://doi.org/10.1016/j.jpsychires.2009.01.014.
California Consumer Privacy Act. (2020). Assembly Bill No. 375, 2017–2018 Session, California Civil Code 100. chrome‐extension://efaidnbmnnnibpcajpcglclefindmkaj/ https://cppa.ca.gov/regulations/pdf/cppa_act.pdf.
Carson, A. E., & Kluckow, R. (2023). Correctional populations in the United States, 2021 statistical tables. U.S. Department of Justice. https://bjs.ojp.gov/library/publications/correctional‐populations‐united‐states‐2021‐statistical‐tables.
Caton, S., & Haas, C. (2023). Fairness in machine learning: A survey. ACM Computing Surveys, 56, 1–38. https://doi.org/10.1145/3616865.
Collins, L. M., & Collins, L. M. (2018). Introduction to adaptive interventions. In Optimization of behavioral, biobehavioral, and biomedical interventions. Springer.
Crawford, A. D., Hutson, T. S., & Kim, M. (2023). Mobile health applications addressing health disparities for women on community supervision: A scoping review. Substance Use & Misuse, 58(6), 6. https://doi.org/10.1080/10826084.2023.2188414.
Crawford, A. D., Salisbury, E. J., & McGrath, J. M. (2024). An intervention development study of an mHealth app to manage women's health and safety while on probation. Health & Justice, 12(1), 22. https://doi.org/10.1186/s40352-024-00277-6.
Crawford, A. D., Testa, A., Darilek, U., Howe, R., McGrath, J. M., & Shlafer, R. (2024). Perinatal health outcomes among women on community supervision: A scoping review. Journal of Correctional Health Care. https://doi.org/10.1089/jchc.23.09.0073.
Crenshaw, K. W. (2013). Mapping the margins: Intersectionality, identity politics, and violence against women of color. Routledge.
Deng, T., Kanthawala, S., Meng, J., Peng, W., Kononova, A., Hao, Q., Zhang, Q., & David, P. (2019). Measuring smartphone usage and task switching with log tracking and self‐reports. Mobile Media & Communication, 7(1), 3–23. https://doi.org/10.1177/2050157918761491.
Devlin, J., Chang, M.‐W., Lee, K., & Toutanova, K. (2019). Bert: Pre‐training of deep bidirectional transformers for language understanding. 1, 2. Accessed August 27, 2024. https://arxiv.org/abs/1810.04805.
Figueroa, C. A., Luo, T., Aguilera, A., & Lyles, C. R. (2021). The need for feminist intersectionality in digital health. The Lancet Digital Health, 3(8), e526–e533.
Heimer, K., Malone, S. E., & De Coster, S. (2023). Trends in women's incarceration rates in US prisons and jails: A tale of inequalities. Annual Review of Criminology, 6(1), 1. https://doi.org/10.1146/annurev‐criminol‐030421‐041559.
Hendl, T., Jansky, B., & Wild, V. (2019). From design to data handling. Why mHealth needs a feminist perspective. In Feminist philosophy of technology (pp. 77–103). Springer Verlag.
Jäger, S., Allhorn, A., & Bießmann, F. (2021). A Benchmark for data imputation methods. Frontiers in Big Data, 4, 693674. https://doi.org/10.3389/fdata.2021.693674.
Johnson, K. B., Wei, W. Q., Weeraratne, D., Frisse, M. E., Misulis, K., Rhee, K., Zhao, J., & Snowdon, J. L. (2021). Precision medicine, AI, and the future of personalized health care. Clinical and Translational Science, 14(1), 86–93.
Jones, N., & Moffitt, M. (2016). Ethical guidelines for mobile app development within health and mental health fields. Professional Psychology: Research and Practice, 47(2), 155–162. https://doi.org/10.1037/pro0000069.
Kim, M., Reingold, O., & Rothblum, G. (2018). Fairness through computationally‐bounded awareness. In Conference on neural information processing systems (Vol. 31).
Kim, M. T., Heitkemper, E. M., Hébert, E. T., Hecht, J., Crawford, A., Nnaka, T., Hutson, T. S., Rhee, H., & Radhakrishnan, K. (2022). Redesigning culturally tailored intervention in the precision health era: Self‐management science context. Nursing Outlook, 70(5), 5. https://doi.org/10.1016/j.outlook.2022.05.015.
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017). ImageNet classification with deep convolutional neural networks. Communications of the ACM, 60(6), 84–90. https://doi.org/10.1145/3065386.
Leach, R., Carreiro, S., Shaffer, P. M., Gaba, A., & Smelson, D. (2021). Digital health interventions for mental health, substance use, and co‐occurring disorders in the criminal justice population: A scoping review. Frontiers in Psychiatry, 12, 794785. https://doi.org/10.3389/fpsyt.2021.794785.
Liddel, T. M., & Kruschke, J. K. (2018). Analyzing ordinal data with metric models: What could possibly go wrong? Journal of Experimental Social Psychology, 79, 228–348.
Lorvick, J., Hemberg, J. L., Browne, E. N., & Comfort, M. L. (2022). Routine and preventive health care use in the community among women sentenced to probation. Health & Justice, 10(1), 1. https://doi.org/10.1186/s40352‐022‐00167‐9.
McConnell, S. (2023). Sentenced to prison, not to death: Home confinement during the pandemic and moving beyond COVID‐19. 75. Accessed August 27, 2024. https://scholarworks.uark.edu/alr/vol75/iss4/7.
McGlothen‐Bell, K., McGrath, J. M., Brownell, E. A., Shlafer, R. J., & Crawford, A. D. (2023). Applying a reproductive justice lens to enhance research engagement among systematically underrepresented childbearing women. Nursing Research, 72(2), 2. https://doi.org/10.1097/NNR.0000000000000639.
Nabi, R., Malinsky, D., & Sphpitser, I. (2019). Learning optimal fair policies. 4674–4682. Accessed August 27, 2024. https://proceedings.mlr.press/v97/nabi19a.html.
Oleson, J. J., Jones, M. A., Jorgensen, E. J., & Wu, Y.‐H. (2022). Statistical considerations for analyzing ecological momentary assessment data. Journal of Speech, Language, and Hearing Research: JSLHR, 65(1), 344–360. https://doi.org/10.1044/2021_JSLHR‐21‐00081.
Rice, E., Yoshioka‐Maxwell, A., Petering, R., Onasch‐Vera, L., Craddock, J., Tambe, M., Yadav, A., Wilder, B., Woo, D., Winetrobe, H., & Wilson, N. (2018). Piloting the use of artificial intelligence to enhance HIV prevention interventions for youth experiencing homelessness. Journal of the Society for Social Work and Research, 9(4), 551–573. https://doi.org/10.1086/701439.
Ross, L. (2020). Understanding reproductive justice. In Feminist theory reader. Routledge.
Ruderman, R. S., Dahl, E. C., Williams, B. R., Davis, K., Feinglass, J. M., Grobman, W. A., Kominiarek, M. A., & Yee, L. M. (2021). Provider perspectives on barriers and facilitators to postpartum care for low‐income individuals. Women's Health Reports, 2(1), 254–262. https://doi.org/10.1089/whr.2021.0009.
Sambrook Smith, M., Lawrence, V., Sadler, E., & Easter, A. (2019). Barriers to accessing mental health services for women with perinatal mental illness: Systematic review and meta‐synthesis of qualitative studies in the UK. BMJ Open, 9(1), e024803. https://doi.org/10.1136/bmjopen‐2018‐024803.
Satveit, S. (2018). Addressing the unique healthcare needs of women: Opportunity for change exists at the intersection of precision health and learning health systems. Learning Health Systems, 2(2), e10033. https://doi.org/10.1002/lrh2.10033.
Serre, F., Fatseas, M., Swendsen, J., & Auriacombe, M. (2015). Ecological momentary assessment in the investigation of craving and substance use in daily life: A systematic review. Drug and Alcohol Dependence, 148, 1–20. https://doi.org/10.1016/j.drugalcdep.2014.12.024.
Shiffman, S., Stone, A. A., & Hufford, M. R. (2008). Ecological momentary assessment. Annual Review of Clinical Psychology, 4, 1–32. https://doi.org/10.1146/annurev.clinpsy.3.022806.091415.
Śmieja, M., Struski, Ł., Tabor, J., Zieliński, B., & SpureK, P. (2018). Processing of missing data by neural networks. 31. Accessed August 27, 2024. https://doi.org/10.5555/3327144.3327196.
Smith, S. A., Mays, G. P., Collins, T. C., & Ramaswamy, M. (2019). The role of the community health delivery system in the health and well‐being of justice‐involved women: A narrative review. Health & Justice, 7(1), 12. https://doi.org/10.1186/s40352‐019‐0092‐y.
Song, Y., Wang, T., Cai, P., Mondal, S. K., & Sahoo, J. P. (2023). A comprehensive survey of few‐shot learning: Evolution, applications, challenges, and opportunities. ACM Computing Surveys, 55(13s), 1–40. https://doi.org/10.1145/3582688.
Sturmey, P. (2020). Functional analysis in clinical treatment: Twelve years of progress, current status, and future directions. In Functional analysis in clinical treatment (pp. 533–550). Elsevier. https://doi.org/10.1016/B978‐0‐12‐805469‐7.00022‐X.
Sufrin, C. (2017). Jailcare: Finding the safety net for women behind bars (1st ed.). University of California Press.
Sufrin, C., Beal, L., Clarke, J., Jones, R., & Mosher, W. D. (2019). Pregnancy outcomes in US prisons, 2016–2017. American Journal of Public Health, 109(5), 5. https://doi.org/10.2105/AJPH.2019.305006.
Tabar, M., Park, H., Winkler, S., Lee, D., Barman‐Adhikari, A., & Yadav, A. (2020). Identifying homeless youth at‐risk of substance use disorder: Data‐driven insights for policymakers. In Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining (pp. 3092–3100). https://doi.org/10.1145/3394486.3403360.
Voigt, P., & Von dem Bussche, A. (2017). “The eu general data protection regulation (gdpr).” A practical guide (1st ed.). Springer International Publishing.
Wang, L., & Miller, L. C. (2020). Just‐in‐the‐moment adaptive interventions (JITAI): A meta‐analytical review. Health Communication, 35(12), 1531–1544. https://doi.org/10.1080/10410236.2019.1652388.
Wilder, B., Onasch‐Vera, L., Diguiseppi, G., Petering, R., Hill, C., Yadav, A., Rice, E., & Tambe, M. (2021). Clinical trial of an AI‐augmented intervention for HIV prevention in youth experiencing homelessness. Proceedings of the AAAI Conference on Artificial Intelligence, 35(17), 14948–14956. https://doi.org/10.1609/aaai.v35i17.17754.
Wolfe, M. K., McDonald, N. C., & Holmes, G. M. (2020). Transportation barriers to health care in the United States: Findings from the national health interview survey, 1997–2017. American Journal of Public Health, 110(6), 815–822. https://doi.org/10.2105/AJPH.2020.305579.
Wu, J., Ping, L., Ge, X., Wang, Y., & Fu, J. (2010). Cloud storage as the infrastructure of cloud computing. In 2010 International conference on intelligent computing and cognitive informatics (pp. 380–383). https://doi.org/10.1109/ICICCI.2010.119.
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Contributed Indexing:
Keywords: artificial intelligence; criminal justice system; digital health; gendered differences; health disparities; mHealth; methodology; social justice; women's health
Entry Date(s):
Date Created: 20240902 Date Completed: 20241104 Latest Revision: 20241104
Update Code:
20250114
DOI:
10.1111/phn.13410
PMID:
39221663
Database:
MEDLINE
Weitere Informationen
Background: Minority populations are utilizing mobile health applications more frequently to access health information. One group that may benefit from using mHealth technology is underserved women, specifically those on community supervision.
Objective: Discuss methodological approaches for navigating digital health strategies to address underserved women's health disparities.
Description of the Innovative Method: Using an intersectional lens, we identified strategies for conducting research using digital health technology and artificial intelligence amongst the underserved, particularly those with community supervision.
Description of Its Effectiveness: We explore (1) methodological approaches that combine traditional research methods with precision medicine, digital phenotyping, and ecological momentary assessment; (2) implications for artificial intelligence; and (3) ethical considerations with data collection, storage, and engagement.
Discussion: Researchers must address gendered differences related to health, social, and economic disparities concurrently with an unwavering focus on the protection of human subjects when addressing the unique needs of underserved women while utilizing digital health methodologies.
Public Contribution: Women on community supervision in South Central Texas helped inform the design of JUN, the mHealth app we reported in the case exemplar. JUN is named after the Junonia shell, a native shell to South Texas, which means strength, power, and self-sufficiency, like the participants in our preliminary studies.
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