Background: Acute lymphoblastic leukemia (ALL) is the most common childhood malignancy. Caring for children with ALL is challenging for parents. A mobile health (mHealth) supportive care intervention was developed to meet parents’ needs. Objective: This study aims to evaluate the potential effectiveness of this mHealth supportive care intervention on emotional distress, social support, care burden, uncertainty in illness, quality of life, and knowledge. Methods: We conducted a quasi-experimental pre- and postdesign study from June 2015 to January 2016. In total, 101 parents were enrolled in the study, with 50 in the observation group and 51 in the intervention group. Parents in the observation group received the standard health education and were observed for 3 months. Parents in the intervention group received the mHealth supportive care intervention, in addition to the standard health education. The intervention consisted of 2 parts—an Android smartphone app “Care Assistant (CA)” and a WeChat Official Account. The CA with 8 modules (Personal Information, Treatment Tracking, Family Care, Financial and Social Assistance, Knowledge Center, Self- Assessment Questionnaires, Interactive Platform, and Reminders) was the main intervention tool, whereas the WeChat Official Account was supplementary to update information and realize interaction between parents and health care providers. Data of parents’ social support, anxiety, depression, care burden, uncertainty in illness, quality of life, their existing knowledge of ALL and care, and knowledge need were collected before and after the 3-month study period in both groups. For the intervention group, parents’ experience of receiving the intervention was also collected through individual interviews. Results: Overall, 43 parents in the observation group and 49 in the intervention group completed the study. Results found that the intervention reduced parents’ anxiety (Dint(Post-Pre)=−7.0 [SD 13.1], Dobs(Post-Pre)=−0.4 [SD 15.8], t90=−2.200, P=.03) and uncertainty in illness (Dint(Post-Pre)=−25.0 [SD 8.2], Dobs(Post-Pre)=−19.8 [SD 10.1], t90=−2.761, P=.01), improved parents’ social function (Dint(Post-Pre)=9.0 [SD 32.8], Dobs(Post-Pre)=−7.5 [SD 30.3], t90=2.494, P=.01), increased parents’ knowledge of ALL and care (Dint(Post-Pre)=28.4 [SD 12.4], Dobs(Post-Pre)=17.2 [SD 11.9], t90=4.407, P<.001), and decreased their need for knowledge (Dint(Post-Pre)=−9.9 [SD 11.6], Dobs(Post-Pre)=−1.9 [SD 6.4], t90=−4.112, P<.001). Qualitative results showed that parents were satisfied with the intervention and their role in the caregiving process. Conclusions: The mHealth intervention in supporting parents of children with ALL is effective. This study is informative for other future studies on providing mHealth supportive care for parents of children with cancer.

Aim: The aim of this study was to identify unobserved subgroups of Chinese parents’ caregiving ability for children with haematological malignancies and examine the associations of the latent class membership with individual characteristics.Design: A multicentre cross‐sectional survey study was conducted.Methods: A total of 392 parents of children with haematological malignancies in China were surveyed with the Hematologic Malignancies’ Family Caregiver Skills Scale and a study‐specific demographic information questionnaire. Latent class analysis (LCA) and multinomial logistic regression model were applied in data analysis.Results: LCA results suggested that there existed three distinct a priori unknown classes of parents of children with haematological malignancies in regard to caregiving ability: Class 1—“high caregiving ability” class (n = 131, 33.4%), Class 2—“medium caregiving ability” class (n = 170, 43.4%) and Class 3—“low caregiving ability” class (n = 91, 23.2%). Socio‐demographics and clinical characteristics had significant associations with the latent class membership.

AimTo revise the Supportive Care Needs Survey for Partners and Caregivers of Cancer Patients (SCNS-P&C) and evaluate the psychometric properties of the Chinese Version of the Supportive Care Needs Survey for Caregivers of Children with Paediatric Cancer (SCNS-C-Ped-C) in caregivers of children with paediatric cancer. DesignA cross sectional design was used. MethodsIn this methodological research, the reliability and validity of the SCNS-C-Ped-C were measured by a questionnaire survey among 336 caregivers of children with paediatric cancer in China. The construct validity was evaluated by exploratory factor analysis and internal consistency was examined by Cronbach's alpha, split-half reliability, and corrected item-to-total correlation coefficients. ResultsThe exploratory factor analysis revealed six factors consist of: Healthcare and Informational Needs, Daily Care and Communication Needs, Psychological and Spiritual Needs, Medical Service Needs, Economic Needs, and Emotional Needs, explaining 65.615% of the variance. The Cronbach's alpha was 0.968 at full scale and 0.603-0.952 on the six domains. The split-half reliability coefficient was 0.883 at full scale and 0.659-0.931 on the six domains. ConclusionsThe SCNS-C-Ped-C demonstrated both reliability and validity. It can be used to evaluate multi-dimensional supportive care needs for caregivers of children with paediatric cancer in China.

[This corrects the article DOI: 10.2196/mhealth.9981.].

To determine parents' supportive care needs trajectories over the first 8 months following their children's diagnosis and identify the predictors of these trajectories. Overall, 100/112 Chinese parents of children newly diagnosed with ALL were recruited. The parents' supportive care needs were tracked at four key time points when the needs were most likely to change: post-diagnosis, induction remission phase, consolidation therapy phase, and post-acute treatment. Latent class growth modeling (LCGM) or growth mixture model (GMM) was used to identify trajectories within each of six needs domains of supportive care needs. Logistic regression was used to identify factors predicting trajectory patterns. Overall, parents' supportive care needs peaked immediately after diagnosis, declined throughout treatment, and stabilized between the consolidation and post-acute stages. Notably, the trajectories of each domain varied across parent subgroups. Three health care and informational needs trajectories, two daily care and communication needs trajectories, two psychological and spiritual needs trajectories, two medical service needs trajectories, three economic needs trajectories, and two emotional needs trajectories were identified. The class membership was significantly associated with parents' education level, employment status, the family's residency area, average monthly household income, type of medical payment, daily caregiving time, age of child, single child, parents' self-efficacy, and caregiving ability (P < 0.05). This study identified the dynamic evolution and distinct trajectories of parents’ supportive care needs, along with their key predictors. The findings provide a solid foundation for developing stage- and population-specific interventions, offering practical guidance for optimizing family-centered care and informing supportive policy formulation in pediatric oncology settings.