Construction of a mobile health technology-based early identification model for postpartum depression and evaluation of its application effects in community postpartum visits

Chen Li, Yue Ding, Wei Cui, Lu Wang

Abstract

This study developed a mobile health-based early identification model for postpartum depression (PPD) and evaluated its effectiveness in community postpartum visits. A randomized controlled trial included 102 postpartum women (intervention: n=54; control: n=48). The intervention group used the "Maternal Love Guardian" app, integrating clinical risk factors and digital phenotyping (behavioral, emotional, cognitive, and voice data) to stratify PPD risk. Primary outcomes included detection rates, model performance, and intervention adherence. The model achieved 90.0% sensitivity and 84.1% specificity (AUC=0.871) at 3 weeks postpartum. The intervention group had significantly shorter PPD identification time (11.8 vs. 26.9 days, P<0.001) and higher early intervention rates (25.9% vs. 8.3%, P=0.019). Digital phenotyping revealed key differences in sleep, step count, social activity, and speech rate in PPD cases. At 12 weeks, symptom improvement was faster (4.3 vs. 6.9 weeks, P=0.009), and mother-infant interaction scores improved (77.8 vs. 72.1, P=0.033). Community providers reported 23% reduced consultation time (P<0.001). Cost-effectiveness was favorable (8,924 RMB per QALY). The mobile health model enhances PPD detection, accelerates intervention, and improves outcomes efficiently.

Full Text:

PDF

References

Wang Z, Liu J, Shuai H, Cai Z, Fu X, Liu Y, Xiao X, Zhang

W, Krabbendam E, Liu S, Liu ZC, Li ZH and Yang

BX. Mapping global prevalence of depression among

postpartum

women. Translational

; 11(1), 543.

psychiatry

World Health Organization.. Report of the eighth meeting of

the WHO Strategic and Technical Advisory Group of

Experts for Maternal, Newborn, Child and Adolescent

Health and Nutrition, 14-16 November 2023. World

Health Organization 2024.

Vilarim M, Rebelo F, Vieira I, Mazzoli F, Carta MG, Nardi

AE and Marano D. Prevalence of postpartum

depression symptoms in high-income and low-and

middle-income countries in the COVID-19 pandemic:

a systematic review with meta-analysis. Brazilian

Journal of Psychiatry 2024; 46, e20233453.

Hahn-Holbrook J, Cornwell-Hinrichs T and Anaya I.

Economic and health predictors of national

postpartum depression prevalence: a systematic

review, meta-analysis, and meta-regression of 291

studies from 56 countries. Frontiers in psychiatry

; 8, 248.

Power J, Watson S, Chen W, Lewis A, Van Ijzendoorn M.

and Galbally M. The trajectory of maternal perinatal

depressive symptoms predicts executive function in

early

childhood. Psychological

; 53(16), 7953-7963.

medicine

Dennis CL and Chung‐Lee L. Postpartum depression help

seeking barriers and maternal treatment preferences:

A qualitative systematic review. Birth2006; 33(4),

-331.

Kendall-Tackett KA. Screening for Perinatal Depression:

Barriers,

Guidelines,

and

Measurement

Scales. Journal of Clinical Medicine 2024; 13(21),

Khosravi M and Azar G. A systematic review of reviews on

the advantages of mHealth utilization in mental health

services: a viable option for large populations in low

resource settings. Cambridge Prisms: Global Mental

Health2024; 1-20.

Saeb S, Zhang M, Karr CJ, Schueller SM, Corden ME,

Kording KP and Mohr DC. Mobile phone sensor

correlates of depressive symptom severity in daily-life

behavior: an exploratory study. Journal of medical

Internet research 2015, 17(7), e4273.

Hurwitz E, Meltzer-Brody S, Butzin-Dozier Z, Patel RC,

Elhadad N and Haendel MA. Unlocking the potential

of wearable device wear time to enhance postpartum

depression screening and detection 2024. medRxiv

10.07.24315026.

Mullick T, Radovic A, Shaaban S and Doryab A. Predicting

depression in adolescents using mobile and wearable

sensors:

multimodal

exploratory

machine learning–based

study. JMIR

, 6(6), e35807.

Formative Research

Killaspy H, Harvey C, Brasier C, Brophy L, Ennals P,

Fletcher J and Hamilton B. Community‐based social

interventions for people with severe mental illness: a

systematic review and narrative synthesis of recent

evidence. World Psychiatry 2022, 21(1), 96-123.

Hurwitz E, Butzin-Dozie, Z, Master H, T O'Neil S, Walden

A, Holko M, Patel RC and Haendel, MA. Harnessing

consumer

wearable

digital

biomarkers

for

individualized recognition of postpartum depression

using the all of us research program data set: Cross

sectional

study. JMIR mHealth and uHealth

, 12(1), e54622.

Choi A, Ooi A and Lottridge D. Digital phenotyping for

stress, anxiety, and mild depression: systematic

literature review. JMIR mHealth and uHealth

, 12(1), e40689.

Saqib K, Khan A F and Butt ZA.. Machine learning methods

for

predicting postpartum depression: scoping

review. JMIR mental health2021; 8(11), e29838.

Zhang W, Liu H, Silenzio VMB, Qiu P and Gong W.

Machine learning models for the prediction of

postpartum depression: application and comparison

based on a cohort study. JMIR medical informatics

, 8(4), e15516.

Matsuo S, Ushida T, Emoto R, Moriyama Y, Iitani Y,

Nakamura N, Imai K, Nakano-Kobayashi T, Yoshida

S, Yamashita M, Matsui S, Kajiyama H and Kotani T.

Machine learning prediction models for postpartum

depression: A multicenter study in Japan. Journal of

Obstetrics and Gynaecology Research 2022, 48(7),

-1785.

Hahn L, Eickhoff SB, Habel U, Stickeler E, Schnakenberg

P, Goecke T W, Stickel S, Franz M, Dukart J and Chechko N. Early identification of postpartum

depression using demographic, clinical, and digital

phenotyping. Translational Psychiatry 2021, 11(1),

Carneiro M B, Moreira MW, Pereira SS, Gallindo EL.and

Rodrigues JJ (2021, March). Recommender system

for postpartum depression monitoring based on

sentiment

analysis.2020

IEEE

International

Conference on E-health Networking, Application &

Services (HEALTHCOM) 2021； pp. 1-6.

Lilhore UK, Dalal S, Varshney N, Sharma Y.K, RaoK B,

Rao VM,Alroobaea R, Simaiya S, Margala M and

Chakrabarti P.. Prevalence and risk factors analysis of

postpartum depression at early stage using hybrid

deep learning model. Scientific reports 2024, 14(1),

Feldman N and Perret, S. Digital mental health for

postpartum women: perils, pitfalls, and promise. NPJ

Digital Medicine 2023, 6(1), 11.

Scroggins JK, Harkin, S E, Brown S, Clair V.S, LeBron GK

and BarcelonaV. (2024, July). A systematic review of

community-based interventions to address perinatal

mental health.Semin Perinatol 2024 ;48(6):151945.

Tachibana Y, Koizumi N, Akanuma C, Tarui H, Ishi, E,

Hoshina T, Suzuki A, Asano A, Sekino S, and Ito H.

Integrated mental health care in a multidisciplinary

maternal and child health service in the community:

the findings from the Suzaka trial. BMC pregnancy

and childbirth 2019, 19, 1-11.

Place J MS, RenbargerK, Van De Griend K, Guinn M,

Wheatley C and Holmes O. Barriers to help-seeking

for postpartum depression mapped onto the socio

ecological model and recommendations to address

barriers. Frontiers in Global Women's Health 2024; 5,

Martín-Gómez C, Moreno-Peral P, Bellón JA, Conejo-Cerón

S, Campos-Paino H, Gómez-Gómez, I, Rigabert A,

Benítez I, and Motrico E. Effectiveness of

psychological interventions in preventing postpartum

depression in non-depressed women: a systematic

review and meta-analysis of randomized controlled

trials. Psychological Medicine 2022; 52(6), 1001

Power J, Watson S, Chen W, Lewis A, Van IM, and Galbally

M.The trajectory of maternal perinatal depressive

symptoms predicts executive function in early

childhood. Psychological medicine 2023; 53(16),

-7963.

Hiraoka D, Kawanami A, Sakurai K and Mori C. Within

individual relationships between mother-to-infant

bonding and postpartum depressive symptoms: a

longitudinal

study. Psychological

; 54(8), 1749-1757.

Medicine

Lee Kand Hunter T.. The associations between maternal

depressive symptoms and parenting practices among

low income head start eligible families. Social Work

in Mental Health 2022; 20(2), 203-225.

Tandon SD, Mercer, CD, Saylor EL.and Duggan A K.

Paraprofessional home visitors’ perspectives on

addressing poor mental health, substance abuse, and

domestic violence: A qualitative study. Early

Childhood Research Quarterly 2008; 23(3), 419-428.

Eriksson A, Kimmel MC, Furmark T, Wikman A,

Grueschow M, Skalkidou A, Frick A and Fransson E.

Investigating heart rate variability measures during

pregnancy as predictors of postpartum depression and

anxiety: an exploratory study. Translational

psychiatry 2024; 14(1), 203.

Chen JJ, Chen XJ, She QM, Li, JX and Luo QH.. Clinical

risk factors for preterm birth and evaluating maternal

psychology in the postpartum period. World Journal

of Psychiatry 2024; 14(5), 661.

Gomora D, Kene C, Embiale A, Tekalegn Y, Geta G,

Seyoum K,Beressa G, Atlaw D, Sahiledengle B, Desta

F, Ejigu N, Hussein U and Mwanri, L. . Health related

quality of life and its predictors among postpartum

mother in Southeast Ethiopia: A cross-sectional

study. Heliyon 2024, 10(7):e27843.

Refbacks

  • There are currently no refbacks.