The Electronic Pain Management Life History Calendar: Development and Usability.

BACKGROUND: Changes over time to self-managed chronic pain treatments are not a routine part of pain management discussions and might provide insight into adjustments that improve pain outcomes. AIMS: The purpose of this study was to develop and test an electronic pain management life history calendar (ePMLHC) for use with older adults with chronic pain. DESIGN: An instrument development design was used to develop and test the ePMLHC. METHODS: Twenty-four community-dwelling older adults with osteoarthritis pain completed the ePMLHC describing their pain treatment regimens and treatment response history. Accuracy of the ePMLHC data was examined through post-ePMLHC audiorecorded interviews, with the older adults describing their pain treatment history. Feedback on use of the ePMLHC was also measured. An iterative process was used to refine and retest the ePMLHC. The final ePMLHC version was examined with the remaining 12 older adults. RESULTS: Significant differences between data reported via the ePMLHC and interviews did not support feasibility of independently reported data via the ePMLHC. Older adults reported that completing the ePMLHC helped them more fully self-reflect on their pain self-management. CONCLUSIONS: The ePMLHC has the potential to enhance communication about past pain management treatments and promote more personalized pain treatment regimens, but further development is required.

Can apps and calendar methods predict ovulation with accuracy?

OBJECTIVE: The accuracy of prediction of ovulation by cycle apps and published calendar methods was determined by comparing to true probability of ovulation. METHODS: A total of 949 volunteers collected urine samples for one entire menstrual cycle. Luteinizing hormone was measured to assign surge day, enabling probability of ovulation to be determined across different cycle lengths. Cycle-tracking apps were downloaded. As none provided their methodology, four published calendar-based methods were also examined: standard days, rhythm, alternative rhythm and simple calendar method. The volunteer ovulation data was applied to the app/calendar methods to determine their accuracy. RESULTS: Mean cycle length was 28 days (range: 23-35); 34% of women believed they had a 28-day cycle, but only 15% did. No LH surge was seen for 99 women. Most likely day of ovulation for a 28-day cycle was day 16 (21%). Accuracy of ovulation prediction was no better than 21% by the apps. The standard days and rhythm methods were most likely to predict ovulation (70% and 89%, respectively) but had very low accuracy. CONCLUSIONS: Ovulation day varies considerably for any given menstrual cycle length, thus it is not possible for calendar/app methods that use cycle-length information alone to accurately predict the day of ovulation. National Clinical Trial Code: NCT01577147. Registry website: www.clinicaltrials.gov .

Designing a spoken dialogue interface to an intelligent cognitive assistant for people with dementia.

Intelligent cognitive assistants support people who need help performing everyday tasks by detecting when problems occur and providing tailored and context-sensitive assistance. Spoken dialogue interfaces allow users to interact with intelligent cognitive assistants while focusing on the task at hand. In order to establish requirements for voice interfaces to intelligent cognitive assistants, we conducted three focus groups with people with dementia, carers, and older people without a diagnosis of dementia. Analysis of the focus group data showed that voice and interaction style should be chosen based on the preferences of the user, not those of the carer. For people with dementia, the intelligent cognitive assistant should act like a patient, encouraging guide, while for older people without dementia, assistance should be to the point and not patronising. The intelligent cognitive assistant should be able to adapt to cognitive decline.