Expanding Access to Perinatal Depression Treatment in Kenya Through Automated Psychological Support: Development and Usability Study (Preprint)
Open accessAbstract
BACKGROUND: Depression during pregnancy and in the postpartum period is associated with poor outcomes for women and their children. Although effective interventions exist for common mental disorders that occur during pregnancy and the postpartum period, most cases in low- and middle-income countries go untreated because of a lack of trained professionals. Task-sharing models such as the Thinking Healthy Program have shown potential in feasibility and efficacy trials as a strategy for expanding access to treatment in low-resource settings; however, there are significant barriers to scale-up. We address this gap by adapting Thinking Healthy for automated delivery via a mobile phone. This new intervention, Healthy Moms, uses an existing artificial intelligence system called Tess (Zuri in Kenya) to drive conversations with users. OBJECTIVE: This prepilot study aims to gather preliminary data on the Healthy Moms perinatal depression intervention to learn how to build and test a more robust service. METHODS: We conducted a single-case experimental design with pregnant women and new mothers recruited from public hospitals outside of Nairobi, Kenya. We invited these women to complete a brief, automated screening delivered via text messages to determine their eligibility. Enrolled participants were randomized to a 1- or 2-week baseline period and then invited to begin using Zuri. We prompted participants to rate their mood via SMS text messaging every 3 days during the baseline and intervention periods, and we used these preliminary repeated measures data to fit a linear mixed-effects model of response to treatment. We also reviewed system logs and conducted in-depth interviews with participants to study engagement with the intervention, feasibility, and acceptability. RESULTS: We invited 647 women to learn more about Zuri: 86 completed our automated SMS screening and 41 enrolled in the study. Most of the enrolled women submitted at least 3 mood ratings (31/41, 76%) and sent at least 1 message to Zuri (27/41, 66%). A third of the sample engaged beyond registration (14/41, 34%). On average, women who engaged post registration started 3.4 (SD 3.2) Healthy Moms sessions and completed 3.1 (SD 2.9) of the sessions they started. Most interviewees who tried Zuri reported having a positive attitude toward the service and expressed trust in Zuri. They also attributed positive life changes to the intervention. We estimated that using this alpha version of Zuri may have led to a 7% improvement in mood. CONCLUSIONS: Zuri is feasible to deliver via SMS and was acceptable to this sample of pregnant women and new mothers. The results of this prepilot study will serve as a baseline for future studies in terms of recruitment, data collection, and outcomes. INTERNATIONAL REGISTERED REPORT: RR2-10.2196/11800
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The second most common use of artificial intelligence in the mental health apps was natural language conversations to deliver mental health support (6/17 studies).
“and natural language conversations to deliver mental health support (6/17 studies) [29,30,38,40,42,43].”
From Artificial intelligence and machine learning in mobile apps for mental health: A scoping review (2022) · cites this paper as [42]
6 of the 17 studies focused on depression.
“6 of the 17 studies focused on depression [29,31,36,40,42,44]”
From Artificial intelligence and machine learning in mobile apps for mental health: A scoping review (2022) · cites this paper as [42]
5 of the 17 studies did not specify what machine learning algorithm was used.
“5 of the 17 studies did not specify what machine learning algorithm was used and are thus not included in Table 5 [38–40,42,43].”
From Artificial intelligence and machine learning in mobile apps for mental health: A scoping review (2022) · cites this paper as [42]
Many of the studies successfully modelled data collected by the app or associated inputs to identify behaviours or responses associated with mental health conditions, provide natural language support, develop risk profiles, and deploy context-sensitive notifications or interventions.
“Many of the studies successfully modelled data collected by the app or associated inputs to identify behaviours or responses associated with mental health conditions [44], provide natural language support [29,30,38,40,42,43], develop risk profiles [31–37,41], and deploy context-sensitive notifications [28] or interventions [44].”
From Artificial intelligence and machine learning in mobile apps for mental health: A scoping review (2022) · cites this paper as [42]
7 of the 17 studies evaluated mental health as an outcome (rather than a model input).
“7 of the 17 studies evaluated mental health as an outcome (rather than a model input) [29,30,35,38,42–44].”
From Artificial intelligence and machine learning in mobile apps for mental health: A scoping review (2022) · cites this paper as [42]
Two studies only reported user experiences; both found generally positive perceptions of the app in question.
“Two studies only reported user experiences; both found generally positive perceptions of the app in question [38,42].”
From Artificial intelligence and machine learning in mobile apps for mental health: A scoping review (2022) · cites this paper as [42]
All of the studies reviewed used data collected via mobile devices to inform AI models-for natural language analysis and output, risk stratification and prediction or for personalising the user's app experience.
“All of the studies reviewed used data collected via mobile devices to inform AI models—for natural language analysis and output [29,30,38,40,42,43], risk stratification and prediction [31,41] or for personalising the user’s app experience [28,44].”
From Artificial intelligence and machine learning in mobile apps for mental health: A scoping review (2022) · cites this paper as [42]
5 of the 12 studies (42%) had a duration of less than one month.
“5 of the 12 studies (42%) had a duration of less than one month [29,30,32,41,42].”
From Artificial intelligence and machine learning in mobile apps for mental health: A scoping review (2022) · cites this paper as [42]
The study classified as a case series study (pre-pilot single-case experimental design) could be rated as a "good" quality investigation because it obtained 8 points of a maximum of 9.
“The study classified as a case series study (pre-pilot single-case experimental design) could also be rated as a “good” quality investigation because it obtained 8 points of a maximum of 9 [85].”
From Using Artificial Intelligence to Enhance Ongoing Psychological Interventions for Emotional Problems in Real- or Close to Real-Time: A Systematic Review (2022) · cites this paper as [85]
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Cites
The PHQ-9: Validity of a brief depression severity measure
Kroenke, K., Spitzer, R. L., & Williams, J. B. W. (2001) · Journal of General Internal Medicine · doi:10.1046/j.1525-1497.2001.016009606.x · Cited by 44,910 · see citing works
<b>brms</b> : An <i>R</i> Package for Bayesian Multilevel Models Using <i>Stan</i>
Bürkner, P.-C. (2017) · Journal of Statistical Software · doi:10.18637/jss.v080.i01 · Cited by 9,476 · see citing works
The Constant Comparative Method of Qualitative Analysis
Glaser, B. G. (1965) · Social Problems · doi:10.2307/798843 · Cited by 4,937 · see citing works
Perinatal Depression: A Systematic Review of Prevalence and Incidence
Gavin, N. I., Gaynes, B. N., Lohr, K. N., Meltzer-Brody, S., Gartlehner, G., & Swinson, T. (2005) · Obstetrics & Gynecology · doi:10.1097/01.aog.0000183597.31630.db · Cited by 3,282 · see citing works
Fitzpatrick, K. K., Darcy, A., & Vierhile, M. (2017) · JMIR Mental Health · doi:10.2196/mental.7785 · Cited by 2,730 · see citing works
Postpartum depression effects on early interactions, parenting, and safety practices: a review
Field, T. (2010) · Infant Behavior and Development · doi:10.1016/j.infbeh.2009.10.005 · Cited by 1,493 · cited 1× · see citing works
“The condition is associated with a number of poor outcomes in women and their children, including increased maternal morbidity and mortality [3,4], poor infant health [5-9], and poor developmental outcomes [10-12].”
BackgroundConversational agents in healthcare: a systematic review
Laranjo, L., Dunn, A. G., Tong, H. L., Kocaballi, A. B., Chen, J., Bashir, R., Surian, D., Gallego, B., Magrabi, F., Lau, A. Y. S., et al. (2018) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocy072 · Cited by 1,388 · cited 1× · see citing works
“Although there has been a proliferation of conversational agents for health in recent years [40], the evidence base is small [41].”
BackgroundGelaye, B., Rondon, M. B., Araya, R., & Williams, M. A. (2016) · The Lancet Psychiatry · doi:10.1016/s2215-0366(16)30284-x · Cited by 1,155 · see citing works
Rahman, A., Malik, A., Sikander, S., Roberts, C., & Creed, F. (2008) · The Lancet · doi:10.1016/s0140-6736(08)61400-2 · Cited by 993 · see citing works
It's only a computer: virtual humans increase willingness to disclose
Lucas, G. M., Gratch, J., King, A., & Morency, L.-P. (2014) · Computers in Human Behavior · doi:10.1016/j.chb.2014.04.043 · Cited by 977 · see citing works
Lattie, E. G., Adkins, E. C., Winquist, N., Stiles-Shields, C., Wafford, Q. E., & Graham, A. K. (2019) · Journal of Medical Internet Research · doi:10.2196/12869 · Cited by 843 · see citing works
Grigoriadis, S., VonderPorten, E. H., Mamisashvili, L., Tomlinson, G., Dennis, C.-L., Koren, G., Steiner, M., Mousmanis, P., Cheung, A., Radford, K., et al. (2013) · The Journal of Clinical Psychiatry · doi:10.4088/jcp.12r07968 · Cited by 797 · see citing works
Fulmer, R., Joerin, A., Gentile, B., Lakerink, L., & Rauws, M. (2018) · doi:10.2196/preprints.9782 · Cited by 715 · see citing works
Self-citationLund, C., Tomlinson, M., De Silva, M., Fekadu, A., Shidhaye, R., Jordans, M., Petersen, I., Bhana, A., Kigozi, F., Prince, M., et al. (2012) · PLoS Medicine · doi:10.1371/journal.pmed.1001359 · Cited by 584 · see citing works
The effects of postpartum depression on child development: A meta-analysis
Beck, C. T. (1998) · Archives of Psychiatric Nursing · doi:10.1016/s0883-9417(98)80004-6 · Cited by 558 · see citing works
Watson, P. J., & Workman, E. A. (1981) · Journal of Behavior Therapy and Experimental Psychiatry · doi:10.1016/0005-7916(81)90055-0 · Cited by 458 · see citing works
Gentile, S. (2015) · Neuroscience · doi:10.1016/j.neuroscience.2015.09.001 · Cited by 431 · see citing works
The Use and Effectiveness of Mobile Apps for Depression: Results From a Fully Remote Clinical Trial
Arean, P. A., Hallgren, K. A., Jordan, J. T., Gazzaley, A., Atkins, D. C., Heagerty, P. J., & Anguera, J. A. (2016) · Journal of Medical Internet Research · doi:10.2196/jmir.6482 · Cited by 376 · see citing works
Survey of conversational agents in health
Montenegro, J. L. Z., da Costa, C. A., & da Rosa Righi, R. (2019) · Expert Systems with Applications · doi:10.1016/j.eswa.2019.03.054 · Cited by 322 · see citing works
Perinatal psychiatric disorders: a leading cause of maternal morbidity and mortality
Oates, M. (2003) · British Medical Bulletin · doi:10.1093/bmb/ldg011 · Cited by 317 · see citing works
O’Connor, E., Senger, C. A., Henninger, M. L., Coppola, E., & Gaynes, B. N. (2019) · JAMA · doi:10.1001/jama.2018.20865 · Cited by 316 · see citing works
Padmanathan, P., & De Silva, M. J. (2013) · Social Science & Medicine · doi:10.1016/j.socscimed.2013.08.004 · Cited by 278 · see citing works
Khalifeh, H., Hunt, I. M., Appleby, L., & Howard, L. M. (2016) · The Lancet Psychiatry · doi:10.1016/s2215-0366(16)00003-1 · Cited by 221 · see citing works
Baranov, V., Bhalotra, S. R., Biroli, P., & Maselko, J. (2020) · American Economic Review · doi:10.1257/aer.20180511 · Cited by 202 · see citing works
Rahman, A. (2007) · Archives of Women's Mental Health · doi:10.1007/s00737-007-0193-9 · Cited by 201 · see citing works
Garman, E. C., Hanlon, C., Mall, S., Honikman, S., Breuer, E., Kathree, T., Luitel, N. P., Nakku, J. E. M., Lund, C., Medhin, G., et al. (2016) · BMC Health Services Research · doi:10.1186/s12913-016-1291-z · Cited by 195 · see citing works
Fuhr, D. C., Weobong, B., Lazarus, A., Vanobberghen, F. M., Weiss, H. A., Singla, D. R., Tabana, H., Afonso, E., Sa, A. D., D’Souza, E., et al. (2018) · The Lancet Psychiatry · doi:10.1016/s2215-0366(18)30466-8 · Cited by 183 · see citing works
The Impact of Perinatal Depression on Children’s Social-Emotional Development: A Longitudinal Study
Junge, C., Garthus‐Niegel, S., Slinning, K., Polte, C., Simonsen, T. B., & Eberhard‐Gran, M. (2016) · Maternal and Child Health Journal · doi:10.1007/s10995-016-2146-2 · Cited by 143 · see citing works
Postpartum Depression Among Rural Women From Developed and Developing Countries: A Systematic Review
Villegas, L., McKay, K., Dennis, C., & Ross, L. E. (2010) · The Journal of Rural Health · doi:10.1111/j.1748-0361.2010.00339.x · Cited by 133 · see citing works
Maselko, J., Sikander, S., Bhalotra, S. R., Bangash, O., Ganga, N. S., Mukherjee, S., EGGER, H. L., Franz, L., Bibi, A., Liaqat, R., et al. (2015) · The Lancet Psychiatry · doi:10.1016/s2215-0366(15)00109-1 · Cited by 115 · see citing works
Who benefits from psychotherapies for adult depression? A meta-analytic update of the evidence
Cuijpers, P., Karyotaki, E., Reijnders, M., & Huibers, M. J. H. (2018) · Cognitive Behaviour Therapy · doi:10.1080/16506073.2017.1420098 · Cited by 115 · see citing works
The impact of perinatal depression on exclusive breastfeeding: a cohort study
Rahman, A., Hafeez, A., Bilal, R., Sikander, S., Malik, A., Minhas, F., Tomenson, B., & Creed, F. H. (2015) · Maternal and Child Nutrition · doi:10.1111/mcn.12170 · Cited by 84 · see citing works
Emotion-focused therapy for social anxiety disorder: results from a multiple-baseline study
Shahar, B., Bar-Kalifa, E., & Alon, E. (2017) · Journal of Consulting and Clinical Psychology · doi:10.1037/ccp0000166 · Cited by 69 · see citing works
Multilevel modeling of single-case data: A comparison of maximum likelihood and Bayesian estimation.
Moeyaert, M., Rindskopf, D., Onghena, P., & Noortgate, W. V. D. (2017) · Psychological Methods · doi:10.1037/met0000136 · Cited by 67 · see citing works
Preventing infant and child morbidity and mortality due to maternal depression
Surkan, P. J., Patel, S. A., & Rahman, A. (2016) · Best Practice & Research Clinical Obstetrics & Gynaecology · doi:10.1016/j.bpobgyn.2016.05.007 · Cited by 57 · see citing works
Green, E. P., Tuli, H., Kwobah, E., Menya, D., Chesire, I., & Schmidt, C. (2018) · Journal of Affective Disorders · doi:10.1016/j.jad.2017.11.027 · Cited by 53 · see citing works
Self-citationGreen, E. P., Pearson, N., Rajasekharan, S., Rauws, M., Joerin, A., Kwobah, E. K., Musyimi, C. W., Bhat, C., Jones, R., & Lai, Y. (2019) · JMIR Research Protocols · doi:10.2196/11800 · Cited by 25 · see citing works
Self-citationBayesian analysis of data from single case designs
Rindskopf, D. (2014) · Neuropsychological Rehabilitation · doi:10.1080/09602011.2013.866903 · Cited by 22 · see citing works
Murphy, G. A. V., Waters, D., Ouma, P., Gathara, D., Shepperd, S., Snow, R. W., & English, M. (2017) · BMJ Global Health · doi:10.1136/bmjgh-2017-000472 · Cited by 21 · see citing works
Filho, W. L., Wall, T., Azul, A. M., Brandli, L., & Özuyar, P. G. (2019) · Encyclopedia of the UN sustainable development goals · doi:10.1007/978-3-319-95681-7_300094 · Cited by 2 · see citing works
Baranov, V. (2019) · AEA Randomized Controlled Trials · doi:10.1257/rct.4687-1.0 · Cited by 1 · see citing works
Healthy MomsPreprint
Eric Green, P. D. (2018) · OSF Preprints (OSF Preprints) · doi:10.17605/osf.io/4kpz2 · Cited by 1 · see citing works
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Sorted by citations
Health-focused conversational agents in person-centered care: a review of apps
Parmar, P., Ryu, J., Pandya, S., Sedoc, J., & Agarwal, S. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00560-6 · Cited by 113 · see citing works
The use of artificial intelligence in psychotherapy: development of intelligent therapeutic systems
Spytska, L. V. (2025) · BMC Psychology · doi:10.1186/s40359-025-02491-9 · Cited by 126 · see citing works
Gual-Montolio, P., Jaén, I., Martínez-Borba, V., Castilla, D., & Suso-Ribera, C. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph19137737 · Cited by 93 · see citing works
van de Vijver, S. J. M., Tensen, P., Asiki, G., Requena‐Méndez, A., Heidenrijk, M., Stronks, K., Cobelens, F. G. J., Bont, J., & Agyemang, C. O. (2023) · Digital Health · doi:10.1177/20552076231185434 · Cited by 92 · see citing works
Artificial intelligence and machine learning in mobile apps for mental health: A scoping review
Milne-Ives, M., Selby, E., Inkster, B., Lam, C., & Meinert, E. (2022) · PLOS Digital Health · doi:10.1371/journal.pdig.0000079 · Cited by 68 · see citing works
Artificial intelligence in perinatal mental health research: A scoping review
Kwok, W. H., Zhang, Y., & Wang, G. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108685 · Cited by 50 · see citing works
Delanerolle, G., Yang, X., Shetty, S., Raymont, V., Shetty, A., Phiri, P., Hapangama, D. K., Tempest, N., Majumder, K., & Shi, J. (2021) · Women s Health · doi:10.1177/17455065211018111 · Cited by 47 · see citing works
van den Broek, M., Gandhi, Y., Sureshkumar, D. S., Prina, M. A., Bhatia, U., Patel, V., Singla, D. R., Velleman, R. D. B., Weiss, H. A., Garg, A., et al. (2023) · PLOS Global Public Health · doi:10.1371/journal.pgph.0002302 · Cited by 56 · see citing works
Debate and Dilemmas Regarding Generative AI in Mental Health Care: Scoping Review
Xian, X., Chang, A., Xiang, Y., & Liu, M. T. (2024) · Interactive Journal of Medical Research · doi:10.2196/53672 · Cited by 45 · see citing works
Health Chatbots in Africa: Scoping Review
Phiri, M., & Munoriyarwa, A. (2022) · Journal of Medical Internet Research · doi:10.2196/35573 · Cited by 39 · see citing works
Cho, Y., Rai, S., Ungar, L., Sedoc, J., & Guntuku, S. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2023.emnlp-main.698 · Cited by 33 · see citing works
Scaling up community-delivered mental health support and care: A landscape analysis
Siddiqui, S., Morris, A., Ikeda, D. J., Balsari, S., Blanke, L., Pearsall, M., Rodriguez, R., Saxena, S., Miller, B. F., Patel, V., et al. (2022) · Frontiers in Public Health · doi:10.3389/fpubh.2022.992222 · Cited by 38 · see citing works
Chua, J. Y. X., Choolani, M. A., Chee, C. Y. I., Chan, Y. H., Lalor, J., Chong, Y. S., & Shorey, S. (2023) · Journal of Midwifery & Women s Health · doi:10.1111/jmwh.13472 · Cited by 33 · see citing works
A systematic review of substance use and substance use disorder research in Kenya
Jaguga, F., Kiburi, S. K., Temet, E., Barasa, J., Karanja, S., Kinyua, L., & Kwobah, E. K. (2022) · PLoS ONE · doi:10.1371/journal.pone.0269340 · Cited by 26 · see citing works
Self-citationChau, L. W., Lam, R. W. M., Minas, H., Hayashi, K., Nguyen, V. C., & O’Neil, J. (2025) · JMIR Mental Health · doi:10.2196/68296 · Cited by 30 · see citing works
Design recommendations for chatbots to support people with depression
de Souza, P. M., Pires, I. D. C., Motti, V. G., Caseli, H. D. M., Neto, J. B. B., Martini, L. C., & Néris, V. P. D. A. (2022) · Brazilian Symposium on Human Factors in Computing Systems (IHC) · doi:10.1145/3554364.3559119 · Cited by 19 · see citing works
Natural Language Processing Technologies for Public Health in Africa: Scoping Review
Hu, S., Oppong, A., Mogo, E., Barford, A., Occhini, G., Collins, C., & Korhonen, A. (2025) · Journal of Medical Internet Research · doi:10.2196/68720 · Cited by 20 · see citing works
Anaduaka, U. S., Oladosu, A. O., Katsande, S., Frempong, C. S., & Awuku-Amador, S. (2025) · BMJ Mental Health · doi:10.1136/bmjment-2024-301445 · Cited by 18 · see citing works
Toto, G. A., Grilli, L., Traetta, L., Villani, R., Petito, A., & Serviddio, G. (2025) · Frontiers in Digital Health · doi:10.3389/fdgth.2025.1400338 · Cited by 11 · see citing works
Goyal, D., Rabemananjara, K. M., Lara-Cinisomo, S., & Le, H. (2023) · Mental Health & Prevention · doi:10.1016/j.mhp.2023.200260 · Cited by 12 · see citing works
Joshi, A. C., Ghogare, A. S., & Madavi, P. B. (2025) · Industrial Psychiatry Journal · doi:10.4103/ipj.ipj_363_24 · Cited by 8 · see citing works
The Rise of the Mental Health Chatbot
Rauws, M. (2022) · Springer eBooks · doi:10.1007/978-3-030-64573-1_218 · Cited by 6 · see citing works
Self-citationRaeber, F., Haldemann, M. I., Ray, S., Huber, J. P., Firima, E., Fernández, L. G., Amstutz, A., Gerber, F., Labhardt, N. D., & Belus, J. M. (2024) · Frontiers in Psychology · doi:10.3389/fpsyg.2024.1241403 · Cited by 5 · see citing works
Yang, H., Chang, F., Muroi, F., Liu, Z., Zhang, W., & Cai, J. (2026) · Psychotherapy and Psychosomatics · doi:10.1159/000552072 · Cited by 2 · see citing works
The use of e-mental health tools in the perinatal context
Fonseca, A., Mancinelli, E., & Branquinho, M. (2024) · BJPsych Advances · doi:10.1192/bja.2024.23 · Cited by 4 · see citing works
Muzik, M., Bhattacharya, S., & Biery, H. L. (2021) · Journal of Women s Health · doi:10.1089/jwh.2021.0164 · Cited by 3 · see citing works
Tembo, C. P., Portsmouth, L., & Burns, S. K. (2023) · Journal of Child and Adolescent Mental Health · doi:10.2989/17280583.2024.2323922 · Cited by 3 · see citing works
Oyeniji, O. A., Wakunuma, K., & Owoseni, A. (2025) · IBRO Neuroscience Reports · doi:10.1016/j.ibneur.2025.06.002 · Cited by 2 · see citing works
Wu, Y., Song, H., Ye, C., Lin, R., Huang, W., Wang, Y., & Yang, X. (2026) · BMC Psychiatry · doi:10.1186/s12888-026-07925-1 · Cited by 2 · see citing works
Non-pharmacological interventions for depression in Kenya: a scoping review
Otanga, H., Nyaga, J., March, A., & Khan, F. F. (2024) · Discover Psychology · doi:10.1007/s44202-024-00186-7 · Cited by 2 · see citing works
Carlo, L., Cruz, E., Arias, A. C., Baca, M., Carlo, M. P. J., Carpio-Arias, T. V., Waters, W. F., Mackintosh, N. J., & Verdezoto, N. X. (2025) · Digital Health · doi:10.1177/20552076251343684 · Cited by 5 · see citing works
A Systematic Review of Substance Use and Substance Use Disorder Research in KenyaPreprint
Jaguga, F., Kiburi, S. K., Temet, E., Barasa, J., Karanja, S. W., Kinyua, L., & Kwobah, E. K. (2021) · Research Square · doi:10.21203/rs.3.rs-631093/v1 · Cited by 2 · see citing works
Self-citationThe Rise of the Mental Health Chatbot
Rauws, M. (2021) · Springer eBooks · doi:10.1007/978-3-030-58080-3_218-1 · Cited by 1 · see citing works
Self-citationExploring the Requirements for an mHealth App to Prevent and Manage Perinatal Anxiety and Depression
Shirdeli, M., Rezaee, R., Bashiri, A., Mani, A., Zakerabasali, S., & Holl, F. (2025) · Health Science Reports · doi:10.1002/hsr2.71160 · Cited by 1 · see citing works
Williams, L., Ankrah, E. A., Draper, C. E., Cook, C. J., Cibrian, F. L., Beltran, J. A., Lakes, K. D., & Hayes, G. R. (2025) · African Human Computer Interaction Conference (AfriCHI) · doi:10.1145/3757232.3757235 · Cited by 1 · see citing works
Zhao, Q., Wen, S., & Yu, W. (2026) · Australian Journal of Psychology · doi:10.1080/00049530.2026.2710168 · Cited by 1 · see citing works
Cognitive-behavioral interventions for mental health conditions among women in sub-Saharan Africa
Le, H., Rabemananjara, K. M., & Goyal, D. (2023) · Elsevier eBooks · doi:10.1016/b978-0-323-85726-0.00015-6 · Cited by 1 · see citing works
Charting the African Neuroethics Discourse: Ethical Issues of Neuroscience in AfricaPreprint
Oyeniji, O. A. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4824538
Tozawa, A., & Tsuchiya, M. (2025) · Environmental and Occupational Health Practice · doi:10.1539/eohp.2024-0015-fs
Wu, Y., Song, H., Chen, Y., Lin, R., Huang, W., Wang, Y., & Yang, X. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5334606
Navigating Therapy Practice in the Digital World
Bhola, P., Duggal, C., & Isaac, R. (2025) · Springer eBooks · doi:10.1007/978-981-96-9732-8_8
The Quality of Life in Terminal Care
Rhanbouri, M. E., Amane, M., Aimad, H., Madihi, F. E., Fahli, A. E., Khalyfa, M., & Boussaa, S. (2025) · IGI Global eBooks · doi:10.4018/979-8-3373-4099-9.ch002
Ghirotti, M. E. L. P., de Oliveira, W. D., Camargo, E. L. S., de Sousa, A. F. L., Gonçalves, V. P., de Oliveira, L. B., Lovadini, V. D. L., de Carvalho, H. E. F., Carneiro, L. M., Júnior, A. G. D. S., et al. (2026) · doi:10.2196/preprints.100402
Tsuchiya, M., & Tozawa, A. (2024) · OSF Preprints (OSF Preprints)
Artificial Intelligence in Mental Health Monitoring
Pabbi, V., Sandhu, M. K., & Angurala, M. (2026) · International Conference on Computing for Sustainable Global Development · doi:10.23919/indiacom70271.2026.11525482
Nungo, M. A., Abubakar, R., & Makokha, C. J. (2026) · doi:10.1002/9781394282470.ch20
Aboul-Enein, B. H., Ballout, S., & Kelly, P. J. (2026) · PLOS mental health. · doi:10.1371/journal.pmen.0000739
Ghirotti, M. E. L. P., Oliveira, W. D. D. D., Camargo, E. L. S., Gonçalves, V. P., de Oliveira, L. B., Lovadini, V. D. L., de Carvalho, H. E. F., Carneiro, L. M., dos Santos, A. G., Fronteira, I., et al. (2026) · JMIR Nursing · doi:10.2196/100402
- Authors
- Green, E. P., Lai, Y., Pearson, N., Rajasekharan, S., Rauws, M., Joerin, A., Kwobah, E., Musyimi, C., Jones, R. M., Bhat, C., Mulinge, A., & Puffer, E. S.
- Affiliations
- Duke University (US)
- Jacaranda Health (US)
- xAI (United States) (US)
- Africa Mental Health Foundation (KE)
- Corresponding author
- Green, Eric P
- ORCID
- Venue
- JMIR Formative Research
- Publisher
- JMIR Publications Inc.
- Published
- 2020-01-20
- Volume
- 4
- Issue
- 10
- Pages
- e17895-e17895
- ISSN
- 2561-326X (ISSN-L)
- PMC
- PMC7573703
- OpenAlex
- W3091632721
- Cited by
- 49
- References
- 42
- Citations by year
- 2026: 9 · 2025: 14 · 2024: 7 · 2023: 7 · 2022: 8 · 2021: 4
- FWCI
- 3.56
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- JMIR Formative Research (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Maternal Mental Health During Pregnancy and Postpartum
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Keywords
- perinatal depression, chatbots, task sharing, artificial intelligence, pregnant women, postpartum women, mood tracking, feasibility and acceptability, resource-limited settings, Kenya, maternal mental health, treatment access, intervention development, user engagement, trust in chatbots, scale-up barriers
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