Introduction: This study aimed at establishing the reliability and validity of the primary health questionnaire (PHQ-15) somatic symptom severity subscale for postpartum women.
Methods: Women (N = 495) completed the PHQ-15 approximately 6 weeks postpartum during the baseline phase of a randomized controlled trial evaluating a writing intervention for postnatal health in England. Reliability was assessed using internal consistency statistics and convergent validity by comparing differences in self-reported physical health, health-related quality of life (QoL) and primary care usage by PHQ-15 symptom severity category.
Results: Cronbach's α for the PHQ-15 was 0.73 and item-total statistics met recommended guidelines. Validity analyzes showed 6% of women reported severe symptoms, 17% medium, 50% low and 27% minimal symptoms. Women with severe symptoms reported poorer overall physical health, poorer physical health-related QoL and greater use of primary care. Women with severe symptoms also rated their baby's health as worse and used primary care more for their baby.
Discussion: This study suggests the PHQ-15 has the potential to be a useful and valid measure of physical symptoms in postpartum women in high-income countries.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1080/0167482X.2017.1289167 | DOI Listing |
JMIR Res Protoc
January 2025
School of Exercise and Nutrition Sciences, Institute for Physical Activity and Nutrition, Deakin University, Burwood, Australia.
Background: Heart failure (HF) is a chronic, progressive condition where the heart cannot pump enough blood to meet the body's needs. In addition to the daily challenges that HF poses, acute exacerbations can lead to costly hospitalizations and increased mortality. High health care costs and the burden of HF have led to the emerging application of new technologies to support people living with HF to stay well while living in the community.
View Article and Find Full Text PDFJMIR Cancer
January 2025
Wolfson Institute of Population Health, Queen Mary University of London, London, United Kingdom.
Background: Skin cancers, including melanoma and keratinocyte cancers, are among the most common cancers worldwide, and their incidence is rising in most populations. Earlier detection of skin cancer leads to better outcomes for patients. Artificial intelligence (AI) technologies have been applied to skin cancer diagnosis, but many technologies lack clinical evidence and/or the appropriate regulatory approvals.
View Article and Find Full Text PDFJMIR Res Protoc
January 2025
Psychiatry Department, Weill Cornell Medicine, New York, NY, United States.
Background: Mental illness is one of the top causes of preventable pregnancy-related deaths in the United States. There are many barriers that interfere with the ability of perinatal individuals to access traditional mental health care. Digital health interventions, including app-based programs, have the potential to increase access to useful tools for these individuals.
View Article and Find Full Text PDFJMIR Ment Health
January 2025
Center for Psychotraumatology, Institute of Psychology, Vilnius University, Vilnius, Lithuania.
Background: Prompts offer a promising strategy to promote client engagement in internet-delivered cognitive behavioral therapy (ICBT). However, if the prompts do not meet the needs of clients, they can potentially be more obtrusive rather than helpful.
Objective: The aim of this study was to test if prompts tailored based on timing and frequency, aligned with preintervention goal setting, can increase usage and the efficacy of a therapist-supported ICBT stress recovery intervention for health care workers.
J Med Internet Res
January 2025
Research Centre Jülich, Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Jülich, Germany.
Background: Traditional in-clinic methods of collecting self-reported information are costly, time-consuming, subjective, and often limited in the quality and quantity of observation. However, smartphone-based ecological momentary assessments (EMAs) provide complementary information to in-clinic visits by collecting real-time, frequent, and longitudinal data that are ecologically valid. While these methods are promising, they are often prone to various technical obstacles.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!