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Fogarty S, Heazell AEP, Munk N, Hay P. Swedish massage as an adjunct approach to Help suppOrt individuals Pregnant after Experiencing a prior Stillbirth (HOPES): a convergent parallel mixed-methods single-arm feasibility trial protocol. Pilot Feasibility Stud 2024; 10:67. [PMID: 38689324 PMCID: PMC11059749 DOI: 10.1186/s40814-024-01499-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Accepted: 04/18/2024] [Indexed: 05/02/2024] Open
Abstract
BACKGROUND Women experiencing pregnancy after stillbirth experience high levels of anxiety, fear and depression. Standard antenatal care may be emotionally unsuitable for many women at this time, and there is a lack of evidence on what interventions or approaches to care might benefit these women. Therapeutic massage may assist women after stillbirth by decreasing anxiety, worry and stress. OBJECTIVE This paper outlines the objectives, methodology, outcome and assessment measures for the Helping suppOrt individuals Pregnant after Experiencing a Stillbirth (HOPES) feasibility trial which evaluates massage as an adjunct approach to care for pregnant women who have experienced a prior stillbirth. It also outlines data collection timing and considerations for analysing the data. METHODS HOPES will use a convergent parallel mixed-methods, single-arm repeated measures trial design in trained massage therapists' private clinics across Australia. HOPES aims to recruit 75 individuals pregnant after a previous stillbirth. The intervention is massage therapy treatments, and participants will receive up to five massages within a 4-month period at intervals of their choosing. Primary quantitative outcomes are the feasibility and acceptability of the massage intervention. Secondary outcomes include determining the optimal timing of massage therapy delivery and the collection of measures for anxiety, worry, stress and self-management. A thematic analysis of women's experiences undertaking the intervention will also be conducted. A narrative and joint display approach to integrate mixed-methods data is planned. DISCUSSION The HOPES study will determine the feasibility and preliminary evidence for massage therapy as an intervention to support women who are pregnant after a stillbirth. TRIAL REGISTRATION CLINICALTRIALS gov NCT05636553. Registered on December 3, 2022, and the trial is ongoing.
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Affiliation(s)
- Sarah Fogarty
- School of Medicine, Western Sydney University, Locked Bag 1797, Penrith, NSW, 2751, Australia.
- Translational Health Research Institute, Western Sydney University, Locked Bag 1797, Penrith, NSW, 2751, Australia.
| | - Alexander E P Heazell
- School of Medical Sciences, Maternal and Fetal Health Research Centre, University of Manchester, Manchester, UK
- Department of Obstetrics, Saint Mary's Hospital, Manchester University NHS Foundation Trust, Manchester, UK
| | - Niki Munk
- School of Health & Human Sciences, Indiana University, Indianapolis, USA
- Australian Research Centre in Complementary and Integrative Medicine (ARCCIM), Fellow and Visiting Faculty of Health, University of Technology Sydney, Massage & MyotherapyAustralia, Sydney, Australia
| | - Phillipa Hay
- Translational Health Research Institute, Western Sydney University, Locked Bag 1797, Penrith, NSW, 2751, Australia
- Mental Health Services, SWSLHD, Campbelltown Hospital, Campbelltown, NSW, Australia
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Becker M, Dai J, Chang AL, Feyaerts D, Stelzer IA, Zhang M, Berson E, Saarunya G, De Francesco D, Espinosa C, Kim Y, Marić I, Mataraso S, Payrovnaziri SN, Phongpreecha T, Ravindra NG, Shome S, Tan Y, Thuraiappah M, Xue L, Mayo JA, Quaintance CC, Laborde A, King LS, Dhabhar FS, Gotlib IH, Wong RJ, Angst MS, Shaw GM, Stevenson DK, Gaudilliere B, Aghaeepour N. Revealing the impact of lifestyle stressors on the risk of adverse pregnancy outcomes with multitask machine learning. Front Pediatr 2022; 10:933266. [PMID: 36582513 PMCID: PMC9793100 DOI: 10.3389/fped.2022.933266] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Accepted: 11/14/2022] [Indexed: 12/15/2022] Open
Abstract
Psychosocial and stress-related factors (PSFs), defined as internal or external stimuli that induce biological changes, are potentially modifiable factors and accessible targets for interventions that are associated with adverse pregnancy outcomes (APOs). Although individual APOs have been shown to be connected to PSFs, they are biologically interconnected, relatively infrequent, and therefore challenging to model. In this context, multi-task machine learning (MML) is an ideal tool for exploring the interconnectedness of APOs on the one hand and building on joint combinatorial outcomes to increase predictive power on the other hand. Additionally, by integrating single cell immunological profiling of underlying biological processes, the effects of stress-based therapeutics may be measurable, facilitating the development of precision medicine approaches. Objectives The primary objectives were to jointly model multiple APOs and their connection to stress early in pregnancy, and to explore the underlying biology to guide development of accessible and measurable interventions. Materials and Methods In a prospective cohort study, PSFs were assessed during the first trimester with an extensive self-filled questionnaire for 200 women. We used MML to simultaneously model, and predict APOs (severe preeclampsia, superimposed preeclampsia, gestational diabetes and early gestational age) as well as several risk factors (BMI, diabetes, hypertension) for these patients based on PSFs. Strongly interrelated stressors were categorized to identify potential therapeutic targets. Furthermore, for a subset of 14 women, we modeled the connection of PSFs to the maternal immune system to APOs by building corresponding ML models based on an extensive single cell immune dataset generated by mass cytometry time of flight (CyTOF). Results Jointly modeling APOs in a MML setting significantly increased modeling capabilities and yielded a highly predictive integrated model of APOs underscoring their interconnectedness. Most APOs were associated with mental health, life stress, and perceived health risks. Biologically, stressors were associated with specific immune characteristics revolving around CD4/CD8 T cells. Immune characteristics predicted based on stress were in turn found to be associated with APOs. Conclusions Elucidating connections among stress, multiple APOs simultaneously, and immune characteristics has the potential to facilitate the implementation of ML-based, individualized, integrative models of pregnancy in clinical decision making. The modifiable nature of stressors may enable the development of accessible interventions, with success tracked through immune characteristics.
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Affiliation(s)
- Martin Becker
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Palo Alto, CA, United States
- Department of Pediatrics, Stanford University, Palo Alto, CA, United States
- Department of Biomedical Data Science, Stanford University, Palo Alto, CA, United States
- Chair for Intelligent Data Analytics, Institute for Visual and Analytic Computing, Department of Computer Science and Electrical Engineering, University of Rostock, Rostock, Germany
| | - Jennifer Dai
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Palo Alto, CA, United States
- Department of Pediatrics, Stanford University, Palo Alto, CA, United States
- Department of Biomedical Data Science, Stanford University, Palo Alto, CA, United States
| | - Alan L. Chang
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Palo Alto, CA, United States
- Department of Pediatrics, Stanford University, Palo Alto, CA, United States
- Department of Biomedical Data Science, Stanford University, Palo Alto, CA, United States
| | - Dorien Feyaerts
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Palo Alto, CA, United States
| | - Ina A. Stelzer
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Palo Alto, CA, United States
| | - Miao Zhang
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Palo Alto, CA, United States
- Department of Pediatrics, Stanford University, Palo Alto, CA, United States
- Department of Biomedical Data Science, Stanford University, Palo Alto, CA, United States
| | - Eloise Berson
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Palo Alto, CA, United States
- Department of Pediatrics, Stanford University, Palo Alto, CA, United States
- Department of Pathology, Stanford University, Palo Alto, CA, United States
| | - Geetha Saarunya
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Palo Alto, CA, United States
- Department of Pediatrics, Stanford University, Palo Alto, CA, United States
- Department of Biomedical Data Science, Stanford University, Palo Alto, CA, United States
| | - Davide De Francesco
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Palo Alto, CA, United States
- Department of Pediatrics, Stanford University, Palo Alto, CA, United States
- Department of Biomedical Data Science, Stanford University, Palo Alto, CA, United States
| | - Camilo Espinosa
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Palo Alto, CA, United States
- Department of Pediatrics, Stanford University, Palo Alto, CA, United States
- Department of Biomedical Data Science, Stanford University, Palo Alto, CA, United States
| | - Yeasul Kim
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Palo Alto, CA, United States
- Department of Pediatrics, Stanford University, Palo Alto, CA, United States
- Department of Biomedical Data Science, Stanford University, Palo Alto, CA, United States
| | - Ivana Marić
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Palo Alto, CA, United States
- Department of Pediatrics, Stanford University, Palo Alto, CA, United States
- Department of Biomedical Data Science, Stanford University, Palo Alto, CA, United States
| | - Samson Mataraso
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Palo Alto, CA, United States
- Department of Pediatrics, Stanford University, Palo Alto, CA, United States
- Department of Biomedical Data Science, Stanford University, Palo Alto, CA, United States
| | - Seyedeh Neelufar Payrovnaziri
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Palo Alto, CA, United States
- Department of Pediatrics, Stanford University, Palo Alto, CA, United States
- Department of Biomedical Data Science, Stanford University, Palo Alto, CA, United States
| | - Thanaphong Phongpreecha
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Palo Alto, CA, United States
- Department of Biomedical Data Science, Stanford University, Palo Alto, CA, United States
- Department of Pathology, Stanford University, Palo Alto, CA, United States
| | - Neal G. Ravindra
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Palo Alto, CA, United States
- Department of Pediatrics, Stanford University, Palo Alto, CA, United States
- Department of Biomedical Data Science, Stanford University, Palo Alto, CA, United States
| | - Sayane Shome
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Palo Alto, CA, United States
- Department of Pediatrics, Stanford University, Palo Alto, CA, United States
- Department of Biomedical Data Science, Stanford University, Palo Alto, CA, United States
| | - Yuqi Tan
- Department of Microbiology & Immunology, Stanford University, Palo Alto, CA, United States
- Baxter Laboratory for Stem Cell Biology, Stanford University, Palo Alto, CA, United States
| | - Melan Thuraiappah
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Palo Alto, CA, United States
- Department of Pediatrics, Stanford University, Palo Alto, CA, United States
- Department of Biomedical Data Science, Stanford University, Palo Alto, CA, United States
| | - Lei Xue
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Palo Alto, CA, United States
- Department of Pediatrics, Stanford University, Palo Alto, CA, United States
- Department of Biomedical Data Science, Stanford University, Palo Alto, CA, United States
| | - Jonathan A. Mayo
- Department of Pediatrics, Stanford University, Palo Alto, CA, United States
| | | | - Ana Laborde
- Department of Pediatrics, Stanford University, Palo Alto, CA, United States
| | - Lucy S. King
- Department of Psychology, Stanford University, Palo Alto, CA, United States
| | - Firdaus S. Dhabhar
- Department of Psychiatry & Behavioral Science, University of Miami, Miami, FL, United States
- Department of Microbiology & Immunology, University of Miami, Miami, FL, United States
- Sylvester Comprehensive Cancer Center, University of Miami, Miami, FL, United States
- Miller School of Medicine, University of Miami, Miami, FL, United States
| | - Ian H. Gotlib
- Department of Psychology, Stanford University, Palo Alto, CA, United States
| | - Ronald J. Wong
- Department of Biomedical Data Science, Stanford University, Palo Alto, CA, United States
| | - Martin S. Angst
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Palo Alto, CA, United States
| | - Gary M. Shaw
- Department of Pediatrics, Stanford University, Palo Alto, CA, United States
| | - David K. Stevenson
- Department of Pediatrics, Stanford University, Palo Alto, CA, United States
| | - Brice Gaudilliere
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Palo Alto, CA, United States
- Department of Pediatrics, Stanford University, Palo Alto, CA, United States
| | - Nima Aghaeepour
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Palo Alto, CA, United States
- Department of Pediatrics, Stanford University, Palo Alto, CA, United States
- Department of Biomedical Data Science, Stanford University, Palo Alto, CA, United States
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