Journal Articles
Permanent URI for this collectionhttps://mro.massey.ac.nz/handle/10179/7915
Browse
4 results
Search Results
Item Nationwide Implementation of Unguided Cognitive Behavioral Therapy for Adolescent Depression: Observational Study of SPARX(JMIR Publications, 2024-09-03) Fleming T; Lucassen M; Frampton C; Parag V; Bullen C; Merry S; Shepherd M; Stasiak KBackground: Internet-based cognitive behavioral therapy (iCBT) interventions are effective in clinical trials; however, iCBT implementation data are seldom reported. Objective: The objective of this study is to evaluate uptake, adherence, and changes in symptoms of depression for 12-to 19-year-olds using an unguided pure self-help iCBT intervention (SPARX; Smart, Positive, Active, Realistic, X-factor thoughts) during the first 7 years of it being publicly available without referral in Aotearoa New Zealand. Methods: SPARX is a 7-module, self-help intervention designed for adolescents with mild to moderate depression. It is freely accessible to anyone with a New Zealand Internet Protocol address, without the need for a referral, and is delivered in an unguided “serious game” format. The New Zealand implementation of SPARX includes 1 symptom measure—the Patient Health Questionnaire adapted for Adolescents (PHQ-A)—which is embedded at the start of modules 1, 4, and 7. We report on uptake, the number of modules completed, and changes in depressive symptoms as measured by the PHQ-A. Results: In total, 21,320 adolescents aged 12 to 19 years (approximately 2% of New Zealand 12‐ to 19-year-olds) registered to use SPARX. Of these, 63.6% (n=13,564; comprising n=8499, 62.7% female, n=4265, 31.4% male, and n=800, 5.9% another gender identity or gender not specified; n=8741, 64.4% New Zealand European, n=1941, 14.3% Māori, n=1202, 8.9% Asian, n=538, 4.0% Pacific, and n=1142, 8.4% another ethnic identity; mean age 14.9, SD 1.9 years) started SPARX. The mean PHQ-A at baseline was 13.6 (SD 7.7) with 16.1% (n=1980) reporting no or minimal symptoms, 37.4% (n=4609) reporting mild to moderate symptoms (ie, the target group) and 46.7% (n=5742) reporting moderately severe or severe symptoms. Among those who started, 51.1% (n=6927) completed module 1, 7.4% (n=997) completed at least 4 modules, and 3.1% (n=416) completed all 7 modules. The severity of symptoms reduced from baseline to modules 4 and 7. Mean PHQ-A scores for baseline, module 4, and module 7 for those who completed 2 or more assessments were 14.0 (SD 7.0), 11.8 (SD 7.9), and 10.5 (SD 8.5), respectively; mean difference for modules 1-4 was 2.2 (SD 5.7; P<.001) and for modules 1-7 was 3.6 (SD 7.0; P<.001). Corresponding effect sizes were 0.38 (modules 1-4) and 0.51 (modules 1-7). Conclusions: SPARX reached a meaningful proportion of the adolescent population. The effect size for those who engaged with it was comparable to trial results. However, completion was low. Key challenges included logistical barriers such as slow download speeds and compatibility with some devices. Ongoing attention to rapidly evolving technologies and engagement with them are required. Real-world implementation analyses offer important insights for understanding and improving the impact of evidence-based digital tools and should be routinely reported.Item What Does Social Support Sound Like? Challenges and Opportunities for Using Passive Episodic Audio Collection to Assess the Social Environment.(Frontiers Media S.A., 2021-03-29) Poudyal A; van Heerden A; Hagaman A; Islam C; Thapa A; Maharjan SM; Byanjankar P; Kohrt BA; Kyriakopoulos MBackground: The social environment, comprised of social support, social burden, and quality of interactions, influences a range of health outcomes, including mental health. Passive audio data collection on mobile phones (e.g., episodic recording of the auditory environment without requiring any active input from the phone user) enables new opportunities to understand the social environment. We evaluated the use of passive audio collection on mobile phones as a window into the social environment while conducting a study of mental health among adolescent and young mothers in Nepal. Methods: We enrolled 23 adolescent and young mothers who first participated in qualitative interviews to describe their social support and identify sounds potentially associated with that support. Then, episodic recordings were collected for 2 weeks from the mothers using an app to record 30 s of audio every 15 min from 4 A.M. to 9 P.M. Audio data were processed and classified using a pretrained model. Each classification category was accompanied by an estimated accuracy score. Manual validation of the machine-predicted speech and non-speech categories was done for accuracy. Results: In qualitative interviews, mothers described a range of positive and negative social interactions and the sounds that accompanied these. Potential positive sounds included adult speech and laughter, infant babbling and laughter, and sounds from baby toys. Sounds characterizing negative stimuli included yelling, crying, screaming by adults and crying by infants. Sounds associated with social isolation included silence and TV or radio noises. Speech comprised 43% of all passively recorded audio clips (n = 7,725). Manual validation showed a 23% false positive rate and 62% false-negative rate for speech, demonstrating potential underestimation of speech exposure. Other common sounds were music and vehicular noises. Conclusions: Passively capturing audio has the potential to improve understanding of the social environment. However, a pre-trained model had the limited accuracy for identifying speech and lacked categories allowing distinction between positive and negative social interactions. To improve the contribution of passive audio collection to understanding the social environment, future work should improve the accuracy of audio categorization, code for constellations of sounds, and combine audio with other smartphone data collection such as location and activity.Item Exploring digital interventions to facilitate coping and discomfort for nurses experiencing the menopause in the workplace: An international qualitative study.(John Wiley and Sons Ltd, 2023-05-09) Cronin C; Bidwell G; Carey J; Donevant S; Hughes K-A; Kaunonen M; Marcussen J; Wilson RINTRODUCTION: The global nursing workforce is predominantly female, with a large proportion working in the 45-55 age group. Menopause is a transition for all women, and; therefore needs recognition as it can impact work performance and consequently staff turnover. BACKGROUND: Women will go through the menopause, but not all women are affected. The menopause transition presents a range of signs and symptoms both physical and psychological which can impact the quality of life and individuals' work/life balance. The nursing workforce is predominantly women that will work through the menopause transition. OBJECTIVES: The study explored perspectives on digital health interventions as strategies to support menopausal women and to understand the requirements for designing health interventions for support in the workplace. DESIGN: A qualitative explorative design. SETTINGS: Nurses working in a range of clinical settings in England, Finland, Denmark, New Zealand, Australia and USA. METHODS: Nurses (n = 48) participated in focus groups from six different countries from February 2020-June 2022 during the pandemic from a range of acute, primary care and education settings. Nurses were invited to participate to share their experiences. Thematic analysis was used. RESULTS: All participants were able to describe the physical symptoms of menopause, with some cultural and possible hemisphere differences; more noticeable was the psychological burden of menopause and fatigue that is not always recognized. Four themes were identified: Managing symptoms in the workplace; Recognition in the workplace; Menopause interventions; and Expectation versus the invisible reality. These themes revealed information that can be translated for implementation into digital health interventions. CONCLUSIONS: Managers of nursing female staff in the menopausal age range need greater awareness, and menopause education should involve everyone. Finally, our results demonstrate design attributes suitable for inclusion in digital health strategies that are aligned with likely alleviation of some of the discomforts of menopause. PATIENT OR PUBLIC CONTRIBUTION: No patient or public contribution.Item Artificial intelligence: An eye cast towards the mental health nursing horizon(John Wiley & Sons Australia Ltd, 2023-06) Wilson RL; Higgins O; Atem J; Donaldson AE; Gildberg FA; Hooper M; Hopwood M; Rosado S; Solomon B; Ward K; Welsh BThere has been an international surge towards online, digital, and telehealth mental health services, further amplified during COVID-19. Implementation and integration of technological innovations, including artificial intelligence (AI), have increased with the intention to improve clinical, governance, and administrative decision-making. Mental health nurses (MHN) should consider the ramifications of these changes and reflect on their engagement with AI. It is time for mental health nurses to demonstrate leadership in the AI mental health discourse and to meaningfully advocate that safety and inclusion of end users' of mental health service interests are prioritized. To date, very little literature exists about this topic, revealing limited engagement by MHNs overall. The aim of this article is to provide an overview of AI in the mental health context and to stimulate discussion about the rapidity and trustworthiness of AI related to the MHN profession. Despite the pace of progress, and personal life experiences with AI, a lack of MHN leadership about AI exists. MHNs have a professional obligation to advocate for access and equity in health service distribution and provision, and this applies to digital and physical domains. Trustworthiness of AI supports access and equity, and for this reason, it is of concern to MHNs. MHN advocacy and leadership are required to ensure that misogynist, racist, discriminatory biases are not favoured in the development of decisional support systems and training sets that strengthens AI algorithms. The absence of MHNs in designing technological innovation is a risk related to the adequacy of the generation of services that are beneficial for vulnerable people such as tailored, precise, and streamlined mental healthcare provision. AI developers are interested to focus on person-like solutions; however, collaborations with MHNs are required to ensure a person-centred approach for future mental healthcare is not overlooked.