Causal message-passing for experiments with unknown and general network interference.

Randomized experiments are a powerful methodology for data-driven evaluation of decisions or interventions. Yet, their validity may be undermined by network interference. This occurs when the treatment of one unit impacts not only its outcome but also that of connected units, biasing traditional treatment effect estimations. Our study introduces a framework to accommodate complex and unknown network interference, moving beyond specialized models in the existing literature. Our framework, termed causal message-passing, is grounded in high-dimensional approximate message-passing methodology. It is tailored for multiperiod experiments and is particularly effective in settings with many units and prevalent network interference. The framework models causal effects as a dynamic process where a treated unit's impact propagates through the network via neighboring units until equilibrium is reached. This approach allows us to approximate the dynamics of potential outcomes over time, enabling the extraction of valuable information before treatment effects reach equilibrium. Utilizing causal message-passing, we introduce a practical algorithm to estimate the total treatment effect, defined as the impact observed when all units are treated compared to the scenario where no unit receives treatment. We demonstrate the effectiveness of this approach across five numerical scenarios, each characterized by a distinct interference structure.

GEMF: a novel geometry-enhanced mid-fusion network for PLA prediction.

Accurate prediction of protein-ligand binding affinity (PLA) is important for drug discovery. Recent advances in applying graph neural networks have shown great potential for PLA prediction. However, existing methods usually neglect the geometric information (i.e. bond angles), leading to difficulties in accurately distinguishing different molecular structures. In addition, these methods also pose limitations in representing the binding process of protein-ligand complexes. To address these issues, we propose a novel geometry-enhanced mid-fusion network, named GEMF, to learn comprehensive molecular geometry and interaction patterns. Specifically, the GEMF consists of a graph embedding layer, a message passing phase, and a multi-scale fusion module. GEMF can effectively represent protein-ligand complexes as graphs, with graph embeddings based on physicochemical and geometric properties. Moreover, our dual-stream message passing framework models both covalent and non-covalent interactions. In particular, the edge-update mechanism, which is based on line graphs, can fuse both distance and angle information in the covalent branch. In addition, the communication branch consisting of multiple heterogeneous interaction modules is developed to learn intricate interaction patterns. Finally, we fuse the multi-scale features from the covalent, non-covalent, and heterogeneous interaction branches. The extensive experimental results on several benchmarks demonstrate the superiority of GEMF compared with other state-of-the-art methods.

Fundamental limits in structured principal component analysis and how to reach them.

How do statistical dependencies in measurement noise influence high-dimensional inference? To answer this, we study the paradigmatic spiked matrix model of principal components analysis (PCA), where a rank-one matrix is corrupted by additive noise. We go beyond the usual independence assumption on the noise entries, by drawing the noise from a low-order polynomial orthogonal matrix ensemble. The resulting noise correlations make the setting relevant for applications but analytically challenging. We provide characterization of the Bayes optimal limits of inference in this model. If the spike is rotation invariant, we show that standard spectral PCA is optimal. However, for more general priors, both PCA and the existing approximate message-passing algorithm (AMP) fall short of achieving the information-theoretic limits, which we compute using the replica method from statistical physics. We thus propose an AMP, inspired by the theory of adaptive Thouless-Anderson-Palmer equations, which is empirically observed to saturate the conjectured theoretical limit. This AMP comes with a rigorous state evolution analysis tracking its performance. Although we focus on specific noise distributions, our methodology can be generalized to a wide class of trace matrix ensembles at the cost of more involved expressions. Finally, despite the seemingly strong assumption of rotation-invariant noise, our theory empirically predicts algorithmic performance on real data, pointing at strong universality properties.

Approximate message passing from random initialization with applications to Z2 synchronization.

This paper is concerned with the problem of reconstructing an unknown rank-one matrix with prior structural information from noisy observations. While computing the Bayes optimal estimator is intractable in general due to the requirement of computing high-dimensional integrations/summations, Approximate Message Passing (AMP) emerges as an efficient first-order method to approximate the Bayes optimal estimator. However, the theoretical underpinnings of AMP remain largely unavailable when it starts from random initialization, a scheme of critical practical utility. Focusing on a prototypical model called [Formula: see text] synchronization, we characterize the finite-sample dynamics of AMP from random initialization, uncovering its rapid global convergence. Our theory-which is nonasymptotic in nature-in this model unveils the non-necessity of a careful initialization for the success of AMP.

Predicting Drug-drug Interaction with Graph Mutual Interaction Attention Mechanism.

Effective representation of molecules is a crucial step in AI-driven drug design and drug discovery, especially for drug-drug interaction (DDIs) prediction. Previous work usually models the drug information from the drug-related knowledge graph or the single drug molecules, but the interaction information between molecular substructures of drug pair is seldom considered, thus often ignoring the influence of bond information on atom node representation, leading to insufficient drug representation. Moreover, key molecular substructures have significant contribution to the DDIs prediction results. Therefore, in this work, we propose a novel Graph learning framework of Mutual Interaction Attention mechanism (called GMIA) to predict DDIs by effectively representing the drug molecules. Specifically, we build the node-edge message communication encoder to aggregate atom node and the incoming edge information for atom node representation and design the mutual interaction attention decoder to capture the mutual interaction context between molecular graphs of drug pairs. GMIA can bridge the gap between two encoders for the single drug molecules by attention mechanism. We also design a co-attention matrix to analyze the significance of different-size substructures obtained from the encoder-decoder layer and provide interpretability. In comparison with other recent state-of-the-art methods, our GMIA achieves the best results in terms of area under the precision-recall-curve (AUPR), area under the ROC curve (AUC), and F1 score on two different scale datasets. The case study indicates that our GMIA can detect the key substructure for potential DDIs, demonstrating the enhanced performance and interpretation ability of GMIA.

Identification of microbe-disease signed associations via multi-scale variational graph autoencoder based on signed message propagation.

BACKGROUND: Plenty of clinical and biomedical research has unequivocally highlighted the tremendous significance of the human microbiome in relation to human health. Identifying microbes associated with diseases is crucial for early disease diagnosis and advancing precision medicine. RESULTS: Considering that the information about changes in microbial quantities under fine-grained disease states helps to enhance a comprehensive understanding of the overall data distribution, this study introduces MSignVGAE, a framework for predicting microbe-disease sign associations using signed message propagation. MSignVGAE employs a graph variational autoencoder to model noisy signed association data and extends the multi-scale concept to enhance representation capabilities. A novel strategy for propagating signed message in signed networks addresses heterogeneity and consistency among nodes connected by signed edges. Additionally, we utilize the idea of denoising autoencoder to handle the noise in similarity feature information, which helps overcome biases in the fused similarity data. MSignVGAE represents microbe-disease associations as a heterogeneous graph using similarity information as node features. The multi-class classifier XGBoost is utilized to predict sign associations between diseases and microbes. CONCLUSIONS: MSignVGAE achieves AUROC and AUPR values of 0.9742 and 0.9601, respectively. Case studies on three diseases demonstrate that MSignVGAE can effectively capture a comprehensive distribution of associations by leveraging signed information.

Invariant point message passing for protein side chain packing.

Protein side chain packing (PSCP) is a fundamental problem in the field of protein engineering, as high-confidence and low-energy conformations of amino acid side chains are crucial for understanding (and designing) protein folding, protein-protein interactions, and protein-ligand interactions. Traditional PSCP methods (such as the Rosetta Packer) often rely on a library of discrete side chain conformations, or rotamers, and a forcefield to guide the structure to low-energy conformations. Recently, deep learning (DL) based methods (such as DLPacker, AttnPacker, and DiffPack) have demonstrated state-of-the-art predictions and speed in the PSCP task. Building off the success of geometric graph neural networks for protein modeling, we present the Protein Invariant Point Packer (PIPPack) which effectively processes local structural and sequence information to produce realistic, idealized side chain coordinates using χ $$ \chi $$ -angle distribution predictions and geometry-aware invariant point message passing (IPMP). On a test set of ∼1400 high-quality protein chains, PIPPack is highly competitive with other state-of-the-art PSCP methods in rotamer recovery and per-residue RMSD but is significantly faster.

MiRLoc: predicting miRNA subcellular localization by incorporating miRNA-mRNA interactions and mRNA subcellular localization.

Subcellular localization of microRNAs (miRNAs) is an important reflection of their biological functions. Considering the spatio-temporal specificity of miRNA subcellular localization, experimental detection techniques are expensive and time-consuming, which strongly motivates an efficient and economical computational method to predict miRNA subcellular localization. In this paper, we describe a computational framework, MiRLoc, to predict the subcellular localization of miRNAs. In contrast to existing methods, MiRLoc uses the functional similarity between miRNAs instead of sequence features and incorporates information about the subcellular localization of the corresponding target mRNAs. The results show that miRNA functional similarity data can be effectively used to predict miRNA subcellular localization, and that inclusion of subcellular localization information of target mRNAs greatly improves prediction performance.

A new framework for drug-disease association prediction combing light-gated message passing neural network and gated fusion mechanism.

With the development of research on the complex aetiology of many diseases, computational drug repositioning methodology has proven to be a shortcut to costly and inefficient traditional methods. Therefore, developing more promising computational methods is indispensable for finding new candidate diseases to treat with existing drugs. In this paper, a model integrating a new variant of message passing neural network and a novel-gated fusion mechanism called GLGMPNN is proposed for drug-disease association prediction. First, a light-gated message passing neural network (LGMPNN), including message passing, aggregation and updating, is proposed to separately extract multiple pieces of information from the similarity networks and the association network. Then, a gated fusion mechanism consisting of a forget gate and an output gate is applied to integrate the multiple pieces of information to extent. The forget gate calculated by the multiple embeddings is built to integrate the association information into the similarity information. Furthermore, the final node representations are controlled by the output gate, which fuses the topology information of the networks and the initial similarity information. Finally, a bilinear decoder is adopted to reconstruct an adjacency matrix for drug-disease associations. Evaluated by 10-fold cross-validations, GLGMPNN achieves excellent performance compared with the current models. The following studies show that our model can effectively discover novel drug-disease associations.

Drug-drug interaction prediction with learnable size-adaptive molecular substructures.

Drug-drug interactions (DDIs) are interactions with adverse effects on the body, manifested when two or more incompatible drugs are taken together. They can be caused by the chemical compositions of the drugs involved. We introduce gated message passing neural network (GMPNN), a message passing neural network which learns chemical substructures with different sizes and shapes from the molecular graph representations of drugs for DDI prediction between a pair of drugs. In GMPNN, edges are considered as gates which control the flow of message passing, and therefore delimiting the substructures in a learnable way. The final DDI prediction between a drug pair is based on the interactions between pairs of their (learned) substructures, each pair weighted by a relevance score to the final DDI prediction output. Our proposed method GMPNN-CS (i.e. GMPNN + prediction module) is evaluated on two real-world datasets, with competitive results on one, and improved performance on the other compared with previous methods. Source code is freely available at https://github.com/kanz76/GMPNN-CS.

Accelerated model-based T1, T2* and proton density mapping using a Bayesian approach with automatic hyperparameter estimation.

PURPOSE: To achieve automatic hyperparameter estimation for the model-based recovery of quantitative MR maps from undersampled data, we propose a Bayesian formulation that incorporates the signal model and sparse priors among multiple image contrasts. THEORY: We introduce a novel approximate message passing framework "AMP-PE" that enables the automatic and simultaneous recovery of hyperparameters and quantitative maps. METHODS: We employed the variable-flip-angle method to acquire multi-echo measurements using gradient echo sequence. We explored undersampling schemes to incorporate complementary sampling patterns across different flip angles and echo times. We further compared AMP-PE with conventional compressed sensing approaches such as the l 1 $$ {l}_1 $$ -norm minimization, PICS and other model-based approaches such as GraSP, MOBA. RESULTS: Compared to conventional compressed sensing approaches such as the l 1 $$ {l}_1 $$ -norm minimization and PICS, AMP-PE achieved superior reconstruction performance with lower errors in T 2 ∗ $$ {\mathrm{T}}_2^{\ast } $$ mapping and comparable performance in T 1 $$ {\mathrm{T}}_1 $$ and proton density mappings. When compared to other model-based approaches including GraSP and MOBA, AMP-PE exhibited greater robustness and outperformed GraSP in reconstruction error. AMP-PE offers faster speed than MOBA. AMP-PE performed better than MOBA at higher sampling rates and worse than MOBA at a lower sampling rate. Notably, AMP-PE eliminates the need for hyperparameter tuning, which is a requisite for all the other approaches. CONCLUSION: AMP-PE offers the benefits of model-based recovery with the additional key advantage of automatic hyperparameter estimation. It works adeptly in situations where ground-truth is difficult to obtain and in clinical environments where it is desirable to automatically adapt hyperparameters to individual protocol, scanner and patient.

The effect of digital health interventions on postpartum depression or anxiety: a systematic review and meta-analysis of randomized controlled trials.

OBJECTIVE: This study aimed to examine the effect of digital health interventions compared with treatment as usual on preventing and treating postpartum depression and postpartum anxiety. DATA SOURCES: Searches were conducted in Ovid MEDLINE, Embase, Scopus, Cochrane Database of Systematic Reviews, Cochrane Central Register of Controlled Trials, and ClinicalTrials.gov. STUDY ELIGIBILITY REQUIREMENTS: The systematic review included full-text randomized controlled trials comparing digital health interventions with treatment as usual for preventing or treating postpartum depression and postpartum anxiety. STUDY APPRAISAL AND SYNTHESIS METHODS: Two authors independently screened all abstracts for eligibility and independently reviewed all potentially eligible full-text articles for inclusion. A third author screened abstracts and full-text articles as needed to determine eligibility in cases of discrepancy. The primary outcome was the score on the first ascertainment of postpartum depression or postpartum anxiety symptoms after the intervention. Secondary outcomes included screening positive for postpartum depression or postpartum anxiety --as defined in the primary study --and loss to follow-up, defined as the proportion of participants who completed the final study assessment compared with the number of initially randomized participants. For continuous outcomes, the Hedges method was used to obtain standardized mean differences when the studies used different psychometric scales, and weighted mean differences were calculated when studies used the same psychometric scales. For categorical outcomes, pooled relative risks were estimated. RESULTS: Of 921 studies originally identified, 31 randomized controlled trials-corresponding to 5532 participants randomized to digital health intervention and 5492 participants randomized to treatment as usual-were included. Compared with treatment as usual, digital health interventions significantly reduced mean scores ascertaining postpartum depression symptoms (29 studies: standardized mean difference, -0.64 [95% confidence interval, -0.88 to -0.40]; I2=94.4%) and postpartum anxiety symptoms (17 studies: standardized mean difference, -0.49 [95% confidence interval, -0.72 to -0.25]; I2=84.6%). In the few studies that assessed screen-positive rates for postpartum depression (n=4) or postpartum anxiety (n=1), there were no significant differences between those randomized to digital health intervention and treatment as usual. Overall, those randomized to digital health intervention had 38% increased risk of not completing the final study assessment compared with those randomized to treatment as usual (pooled relative risk, 1.38 [95% confidence interval, 1.18-1.62]), but those randomized to app-based digital health intervention had similar loss-to-follow-up rates as those randomized to treatment as usual (relative risk, 1.04 [95% confidence interval, 0.91-1.19]). CONCLUSION: Digital health interventions modestly, but significantly, reduced scores assessing postpartum depression and postpartum anxiety symptoms. More research is needed to identify digital health interventions that effectively prevent or treat postpartum depression and postpartum anxiety but encourage ongoing engagement throughout the study period.

Associations of educational and marketing messages with beliefs about nicotine and reduced nicotine cigarettes.

INTRODUCTION: Widespread misperceptions about nicotine may have unintended effects on public health. We examined associations between existing messages about nicotine or tobacco and beliefs about nicotine and reduced nicotine cigarettes (RNC). METHODS: 2962 U.S. 18-45-year-olds were randomized in a May 2022 web-based survey to view one of 26 text-based messages about tobacco or nicotine from three sources: ongoing research (n = 8), messages authorized by FDA for VLN cigarettes (n = 6), and FDA's "From Plant to Product to Puff" campaign (n = 12); six messages from FDA's campaign did not reference nicotine and were treated as the reference source. Analyses examined associations between messages, grouped by source and individually, with beliefs about nicotine and RNC addictiveness and harms. RESULTS: Relative to FDA messages that did not reference nicotine, all message sources were associated with greater odds of a correct belief about nicotine (Odds Ratios [ORs] = 1.40-1.87, p's < 0.01); VLN messages were associated with greater correct beliefs about RNC addictiveness (b = 0.23, p < .05). No campaign produced greater correct beliefs about RNC harms. At the individual level, only five messages were associated with a correct belief about nicotine (ORs = 2.12-2.56, p-values < .01), and one with correct beliefs about RNC harms (b = 1.09, p < .05), vs. the reference message. CONCLUSIONS: Few existing messages improved understanding of the risks of nicotine separately from the risks of combustible products. Communication research is needed to promote greater public understanding of nicotine while minimizing unintended effects on nicotine and tobacco use.

Message Framing Strategies to Promote the Uptake of PrEP: Results from Formative Research with Diverse Adult Populations in the United States.

There are no evidence-based recommendations for communicating about pre-exposure prophylaxis (PrEP) as part of a broader HIV-prevention messaging approach. To inform future message development related to PrEP uptake, we interviewed 235 individuals across ten locations in the U.S. to explore their understanding and perceptions of draft HIV prevention messages and assess their overall preferences for a broad or PrEP-focused messaging approach. Participants responded favorably to and related to both draft messages. Participants who were not aware of PrEP were more likely to say the broad HIV-prevention message was personally relevant than those aware of PrEP. There were no significant differences in perceived personal relevance for the PrEP-focused message. Qualitative findings suggest that HIV prevention messages should use specific well-defined terms, include links to additional information, and use choice-enhancing language that emphasizes personal agency and frames the call to action as an informed decision among an array of effective prevention options.

RESUMEN: No existen recommendaciones basadas en evidencia para comunicar sobre la profilaxis prexposición (PrEP) como parte de un efoque más amplio de mensajes de prevención del VIH. Para informar el desarrollo de mansajes relacionados con el consumo de la PrEP, entrevistamos a 235 personas en 10 ubicaciones en los EE.UU. para explorar su comprensión y percepciones de los borradores de mensajes de prevención del VIH y evaluar sus preferencias generales por un enfoque de mensajeria amplio o centrado en la PrEP. Los participantes respondieron favorablemente y relacionadoscon ambos barradores de mensajes. Los participantes que no conocían la PrEP tenían más probabilidades de decir que el mansaje general de prevención amplia de VIH era personalmente relevent que aquellos que conocían la PrEP. No existe differencias significativas en la relevancia personal percibida para el mensaje centrado en la PrEP. Los hallazgos cualitativos sugieren que los mensajes de prevención del VIH deben utilizar términos especificos bien definidos, incluir enlaces a información adicional y utilizar un lenguaje que mejore las opciones, que enfatice la agencia personal y enmarque el llamado a la acción como una decisión informada entre una variedad de opciones de prevención efectivas.

Audience segmentation approach to conservation messaging for transforming the exotic pet trade.

Advancing transformative change for sustainability requires population-wide behavior change. Yet, many behavioral interventions tackling environmental problems only examine average effects on the aggregate, overlooking the heterogeneous effects in a population. We developed and preregistered a novel audience segmentation approach to test the diverse impact of conservation messaging on reducing demand for exotic pets (private action - i.e., desire to own exotic pets or visit wildlife entertainment places) and fostering citizen engagement for system-wide change (civic action - e.g., signing a petition or participating in a protest against the exotic pet trade). Through an online survey with US participants (n = 2953), we identified 4 population segments (early adopters, early majority, late majority, and laggards), representing varying levels of commitment to wildlife conservation and then randomly assigned each segment to one of 3 messaging conditions. Messages highlighting negative consequences of the exotic pet trade and the power of collective action for system change effectively promoted private action among all segments except early adopters (ηp 2 = 0.005). Among civic actions, only the collective action message motivated early adopters and the early majority to sign petitions (φC = 0.193 and φC = 0.097, respectively). Furthermore, the 4 segments showed distinct reasoning for action and inaction on wildlife conservation, with certain relational values, such as care, serving as both motivations and barriers to action. These findings highlight the need for targeted behavioral interventions across diverse populations.

Estrategia de segmentación del público en los mensajes de conservación para transformar el mercado de mascotas exóticas Resumen El progreso en el cambio transformativo para la sustentabilidad requiere de cambios conductuales a nivel poblacional. Sin embargo, muchas intervenciones conductuales que abordan los problemas ambientales sólo analizan los efectos promedio sobre el agregado, lo que ignora los efectos heterogéneos sobre la población. Desarrollamos y preinscribimos una estrategia novedosa de segmentación del público para evaluar los diversos impactos de los mensajes de conservación sobre la reducción de la demanda de mascotas exóticas (acción privada [es decir, el deseo de poseer mascotas exóticas o visitar sitios de entretenimiento con fauna] y promover la participación ciudadana para un cambio sistémico [por ejemplo, firmar una petición o participar en una protesta contra el mercado de mascotas exóticas]). Realizamos una encuesta en línea con participantes estadunidenses (n = 2953) para identificar cuatro segmentos de la población (adoptadores tempranos, mayoría temprana, mayoría tardía y rezagados), los cuales representan diferentes niveles de compromiso con la conservación de fauna, y después le asignamos aleatoriamente a cada segmento una de las siguientes condiciones de mensaje: las consecuencias negativas del mercado de mascotas exóticas, el poder de la acción colectiva para el cambio sistémico e información neutral como control. Los mensajes que resaltaban las consecuencias negativas del mercado de mascotas exóticas y el poder de la acción colectiva promovieron de forma eficiente la acción privada en todos los segmentos excepto los adoptadores tempranos (ηp 2 = 0.005). Entre las acciones cívicas, sólo el mensaje de acción colectiva motivó a los adoptadores tempranos y a la mayoría temprana a firmar peticiones (φC = 0.193 y φC = 0.097, respectivamente). Además, los cuatro segmentos mostraron un razonamiento distinto para la acción e inacción para la conservación de fauna, con ciertos valores de relación, como el cuidado, fungiendo como motivación o barreras para la acción. Estos resultados enfatizan la necesidad de tener intervenciones conductuales focalizadas entre las diferentes poblaciones.

Randomized comparison of the effects of tailored text messaging versus pillbox organizers on medication adherence of heart failure patients.

BACKGROUND: Heart failure (HF) is a major public health issue worldwide, affecting approximately 64.3 million people in 2017. Non-adherence to medication is a common and serious issue in the management of HF. However, new reminder systems utilizing mobile technology, such as text messaging, have shown promise in improving medication adherence. The purpose of this study was to compare the impact of tailored text messaging (TTM) and pillbox organizers on medication adherence in individuals with HF. METHODS: A randomized controlled trial was conducted, involving 189 eligible patients with HF who were randomly assigned to either the TTM, pillbox organizer, or control group. Medication adherence was evaluated using pill counting and the Medication Adherence Rating Scale (MARS) over a period of three months and compared across the groups. The data were analyzed using Kruskal-Wallis, Analysis of Variance (ANOVA), and Repeated Measures ANOVA tests. RESULTS: The results indicate that both the TTM and pillbox organizers groups had significantly higher medication adherence compared to the control group, as measured by pill counting (MD = 0.05, 95%CI = 0.03-0.06; p < 0.001 for TTM group, MD = 0.04, 95%CI = 0.03-0.06; p < 0.001 for pillbox organizers group) and the MARS (MD = 1.32, 95%CI = 0.93 to 1.72; p < 0.001 for TTM group, MD = 1.33, 95%CI = 0.95 to 1.72; p < 0.001 for pillbox organizers group). However, there was no statistically significant difference in medication adherence between the two intervention groups using either measurement method. The TTM group exhibited a lower hospitalization rate than the other groups in the first follow up (p = 0.016). CONCLUSIONS: Both the TTM and pillbox organizers were shown to be effective in enhancing medication adherence among patients with HF. Therefore, healthcare providers should take into account the patient's condition and preferences when selecting one of these methods to promote medication adherence. Future research should aim to address the limitations of this study, such as controlling for confounding variables, considering long-term effects, and comparing the effectiveness of different interventions.

Effectiveness of text message reminders on paediatric appointment adherence: a systematic review and meta-analysis.

Attending health appointments is important for the paediatric population, as it allows for earlier detection of health issues and subsequent necessary treatments. It also ensures timely immunisations while also allowing patients or their parents to raise health concerns. Hence, it is crucial to take steps to ensure that such medical appointments are attended. To evaluate the effectiveness of text message reminders (TMRs) in improving paediatric patients' adherence to their appointments. A systematic review and meta-analysis were conducted. The search spanned across eight online databases from their inception dates to January 2024. The random-effects model was utilised to conduct the meta-analysis, where risk ratio was used as the effect measure. Subgroup analyses were conducted for age, number of TMRs sent, and type of appointments attended. In total, 13 studies were included. Compared to standard care (involving non-text message reminders or no reminders), TMRs were significantly more effective in improving appointment attendance among the paediatric population. Significant subgroup differences were found in the type of appointments attended, where TMRs were more effective for medical appointments compared to vaccination appointments. No differences in adherence to appointments were found across age groups or the number of TMRs sent.   Conclusion: Sending TMRs can be a potentially cost-effective way to improve the attendance rate of paediatric medical appointments, given the ease of implementation and the extensive mobile phone usage globally. Future studies should compare TMRs to other modes of automated reminders such as telephone messages or emails, to identify the most optimal method of delivery.   Trial registration: PROSPERO (CRD42023464893).

A randomized controlled trial of the Happy, Healthy, Loved personalized text-message program for new parent couples: impact on breastfeeding self-efficacy and mood.

BACKGROUND: Breastfeeding self-efficacy has been identified as an important influence on breastfeeding outcomes. Among new parent couples, partners are uniquely positioned to be sources of support for developing breastfeeding self-efficacy, yet few breastfeeding programs have attempted to involve partners directly. The purpose of this study was to test the impact of a novel program, Happy, Healthy, Loved, on breastfeeding self-efficacy and maternal mood through emphasizing partner support and actively addressing postpartum-specific stress management in a tailored text message delivery program. METHODS: A randomized trial was conducted in which primiparous mother-partner dyads intending to exclusively breastfeed were recruited at midwestern hospitals 2-3 days after delivery. The clinical trial was pre-registered at clinicaltrials.gov (#NCT04578925, registration date 7/24/2020). Couples were randomized to receive intervention or an attentional control. Couples randomized to the intervention group then completed a brief interactive educational tablet program together (Happy, Healthy, Loved), followed by 6 weeks of tailored text messages providing reminders, coping strategies, and motivational milestones to improve breastfeeding self-efficacy. Participants in the control group received usual care followed by 6 weeks of attentional control text messages about infant development. Surveys were delivered at baseline, 6 weeks, and 6 months postpartum to both mother and partner to assess breastfeeding self-efficacy, mood, and social support (n = 62 couples). RESULTS: Outcomes of ANCOVA with baseline self-efficacy as a covariate showed a significant effect of intervention on 6 months breastfeeding self-efficacy when compared to control group. No other significant differences were found at 6 weeks or 6 months postpartum in breastfeeding self-efficacy, depressive or anxious symptoms. CONCLUSIONS: Results of the present investigation suggest that a text-based dyad intervention improved breastfeeding self-efficacy at 6 months, but not 6 weeks, postpartum, indicating that text-based mother-partner interventions are a promising direction to continue exploring in postpartum health research. TRIAL REGISTRATION: Clinicaltrials.gov #NCT04578925.

Text message questionnaires for Patient-Reported Outcome Measures after Cesarean section-A feasibility study.

BACKGROUND: Inadequate pain relief with moderate to severe pain remains a challenge after cesarean section and may significantly impair postoperative recovery. However, detailed assessment on the timing of severe pain, opioid consumption, influence on activities such as mobilization, breastfeeding, and caring for the infant are difficult to conduct, especially after discharge. Short message services (SMS)-based questionnaires may offer a low-cost way of providing such data but with the risk of insufficient response rates. We assessed the feasibility of collecting detailed, prospective data on postoperative pain and recovery during the initial hours and days following cesarean section using SMS-based questionnaires. METHODS: Prospective Danish single-center cohort study involving elective cesarean sections under spinal anesthesia with fentanyl and bupivacaine. The postoperative pain regimen consisted of paracetamol, NSAID and oral morphine by request. Patients received an SMS-based questionnaire at 6, 12, 18, 24, and 48 h postoperatively, as well as on days 7 and 30. PRIMARY OUTCOME: Response rate and time from receiving the SMS to completion of the questionnaires. SECONDARY OUTCOMES: Opioid consumption and Patient Reported Outcomes Measures on pain and recovery. RESULTS: From December 2022 to June 2023; 100 patients were included. The response rate was 78% at 6 h postoperatively, decreasing to 63% at 24 h. The median response time from receiving to answering the SMS-based questionnaire at 6 h after cesarean section was 23 min (IQR 2-72), decreasing to 20 min (IQR 2-78) after 24 h. Severe pain, corresponding to a Numeric Rating Scale (NRS) score >6, was reported by 57% (95% CI 65-84) at 6 h, decreasing to 28% (95% CI 34-58) at 24 h. Median opioid consumption within the first 24 h was 30 mg (IQR 20-50). CONCLUSION: SMS-based questionnaires on Patient Reported Outcome Measures are a feasible and cost-effective way of prospectively collecting frequent data with acceptable response rates, even shortly after cesarean section. Secondarily 66% of patients reported severe pain during the first 24 h following cesarean section, with the highest pain scores within the initial 12 h. Future studies should focus on optimizing pain-management within this timeframe.

Effectiveness of a culturally tailored text messaging program for promoting cervical cancer screening in accra, Ghana: a quasi-experimental trial.

INTRODUCTION: Despite breakthroughs in cervical cancer detection, resource-constrained countries continue to have a disproportionately high incidence and death rate. Mhealth has been identified as an important tool for increasing cervical cancer screening rates in Sub-Saharan Africa. We determined whether sending Ghanaian women culturally tailored one-way mobile phone SMS text messages about cervical cancer would encourage the uptake of the human papillomavirus (HPV) test. METHODS: From August to November 2016, 88 women aged 18 to 39 living or working in an urban community (Accra, Ghana) participated in a quasi-experimental study. For 8 weeks, 32 SMS messages regarding cervical cancer were developed and sent to the personal phones of intervention arm participants (n = 42). Women in the control group (n = 46) received SMS texts with general health and lifestyle advice. Fischer's exact tests were performed to assess cervical cancer screening uptake and associated reasons for non-uptake between the intervention and control groups (p < 0.05). RESULTS: At the baseline, women differed in terms of ethnicity and wealth. After the intervention, participants' self-reported risk factors for cervical cancer, such as early menarche, usual source of medical treatment, family history of cancer, smoking, and alcohol history, changed. None of the women in the intervention group sought cervical cancer screening after the intervention, but only one (2.2%) of the control arm participants did. Almost all the women (> 95%) agreed that an HPV test was essential and that regular healthcare check-ups could help prevent cervical cancer. Some women believed that avoiding particular foods could help prevent cervical cancer (23.8% intervention vs. 58.7% control, p < 0.001). Time constraints and out-of-pocket expenses were significant barriers to cervical cancer screening. CONCLUSION: A one-way SMS delivered to urban women did not increase cervical cancer screening attendance. The time spent in screening facilities and the lack of coverage by the National Health Insurance Scheme limited screening uptake. We urge for the establishment of screening centers in all healthcare facilities, as well as the inclusion of cervical cancer screening in healthcare programs through cost-sharing.