Social Determinants of Mobile Health App Adoption - A Qualitative Study of Older Adults' Perceptions in Australia.

Mobile health applications (mHealth apps) can provide effective self-management instruments for patients and offer advanced approaches to treatment. However, little is known about how the older population perceives the opportunity of using mHealth apps as a non-drug intervention. We aimed to identify the opinions and experiences of older people in Australia and gain new insights into their engagement with this modern approach to health treatment. We conducted a qualitative study with 21 Participants to explore users' perspectives on adopting and using mHealth apps and their awareness of the social factors influencing their uptake. The results show that a trusting doctor-patient relationship positively affects older adults' perceptions of mHealth apps. Consequently, the social influence of the General Practitioner (GP) plays a crucial role in the use of mHealth apps, while the social influence through family and friends seems to be less critical.

The use and predictive performance of the Peninsula Health Falls Risk Assessment Tool (PH-FRAT) in 25 residential aged care facilities: a retrospective cohort study using routinely collected data.

BACKGROUND: The Peninsula Health Falls Risk Assessment Tool (PH-FRAT) is a validated and widely applied tool in residential aged care facilities (RACFs) in Australia. However, research regarding its use and predictive performance is limited. This study aimed to determine the use and performance of PH-FRAT in predicting falls in RACF residents. METHODS: A retrospective cohort study using routinely-collected data from 25 RACFs in metropolitan Sydney, Australia from Jul 2014-Dec 2019. A total of 5888 residents aged ≥65 years who were assessed at least once using the PH-FRAT were included in the study. The PH-FRAT risk score ranges from 5 to 20 with a score > 14 indicating fallers and ≤ 14 non-fallers. The predictive performance of PH-FRAT was determined using metrics including area under receiver operating characteristics curve (AUROC), sensitivity, specificity, sensitivityEvent Rate(ER) and specificityER. RESULTS: A total of 27,696 falls were reported over 3,689,561 resident days (a crude incident rate of 7.5 falls /1000 resident days). A total of 38,931 PH-FRAT assessments were conducted with a median of 4 assessments per resident, a median of 43.8 days between assessments, and an overall median fall risk score of 14. Residents with multiple assessments had increased risk scores over time. The baseline PH-FRAT demonstrated a low AUROC of 0.57, sensitivity of 26.0% (sensitivityER 33.6%) and specificity of 88.8% (specificityER 82.0%). The follow-up PH-FRAT assessments increased sensitivityER values although the specificityER decreased. The performance of PH-FRAT improved using a lower risk score cut-off of 10 with AUROC of 0.61, sensitivity of 67.5% (sensitivityER 74.4%) and specificity of 55.2% (specificityER 45.6%). CONCLUSIONS: Although PH-FRAT is frequently used in RACFs, it demonstrated poor predictive performance raising concerns about its value. Introducing a lower PH-FRAT cut-off score of 10 marginally enhanced its predictive performance. Future research should focus on understanding the feasibility and accuracy of dynamic fall risk predictive tools, which may serve to better identify residents at risk of falls.

Out-of-pocket spending among a cohort of Australians living with gout.

OBJECTIVE: To measure the direct and indirect out-of-pocket (OOP) costs borne by Australians with gout. METHODS: A cross-sectional, Australia-wide, web-based survey was conducted over 12 months between May 2017 and April 2018. Participants were recruited via advertisements in doctors' clinics and healthcare organizations' websites, and social media platforms such as Facebook and Twitter. Survey questions collected information about participants' OOP spending on direct medical and non-medical gout-related healthcare costs. Participant demographics, gout status, healthcare sought, workdays lost to due gout and health-related quality of life were also collected. RESULTS: Seventy-nine patients with gout completed the survey; 70 (89%) were male, and on average were 56 (SD 16) years of age and had gout for 14 (SD 12) years. For this cohort, the median total OOP direct medical cost was AU$200 per year (interquartile range [IQR]: AU$60-AU$570). Sixty (76%) people with gout reported being affected by gout during work; however, only 0.25 (IQR: 0-3) days of work (approximately $60) were lost due to gout in a year. Nine percent (n = 7) of participants experienced cost-related treatment attrition and 33% reported economic hardship (n = 26). Participants who experienced economic hardship or cost-related treatment attrition had higher median total gout-related direct costs than those who did not. CONCLUSION: In Australia, gout has an OOP financial cost and reduces work productivity. The presence of cost-related treatment attrition among people with gout indicates that financial costs may be a significant barrier to seeking treatment for a subset of patients with gout.

The co-design of timely and meaningful information needed to enhance social participation in community aged care services: Think tank proceedings.

OBJECTIVE: To determine what information from community aged care social participation and quality of life assessments needs to be captured, and meaningfully utilised as part of an integrated information and communication technology system. METHODS: Two think tank sessions comprised of community aged care staff and researchers (n = 9) were conducted over 5 weeks. The sessions were guided by the Continuous Quality Improvement framework. Thematic analysis was used to categorise the think tank data. RESULTS: To monitor progress over time, participants needed more contextual information captured in the assessment forms, such as client goals and outcomes of assessments. The aged care provider agreed to embed outcome measure score and action following assessment into its information and communication technology system. CONCLUSION: Collaboration between aged care staff and researchers resulted in adjustments to the aged care provider's information and communication technology system to better target the monitoring and planning of its clients' psychosocial needs.