Augmenting K-Means Clustering With Qualitative Data to Discover the Engagement Patterns of Older Adults With Multimorbidity When Using Digital Health Technologies: Proof-of-Concept Trial.

BACKGROUND: Multiple chronic conditions (multimorbidity) are becoming more prevalent among aging populations. Digital health technologies have the potential to assist in the self-management of multimorbidity, improving the awareness and monitoring of health and well-being, supporting a better understanding of the disease, and encouraging behavior change. OBJECTIVE: The aim of this study was to analyze how 60 older adults (mean age 74, SD 6.4; range 65-92 years) with multimorbidity engaged with digital symptom and well-being monitoring when using a digital health platform over a period of approximately 12 months. METHODS: Principal component analysis and clustering analysis were used to group participants based on their levels of engagement, and the data analysis focused on characteristics (eg, age, sex, and chronic health conditions), engagement outcomes, and symptom outcomes of the different clusters that were discovered. RESULTS: Three clusters were identified: the typical user group, the least engaged user group, and the highly engaged user group. Our findings show that age, sex, and the types of chronic health conditions do not influence engagement. The 3 primary factors influencing engagement were whether the same device was used to submit different health and well-being parameters, the number of manual operations required to take a reading, and the daily routine of the participants. The findings also indicate that higher levels of engagement may improve the participants' outcomes (eg, reduce symptom exacerbation and increase physical activity). CONCLUSIONS: The findings indicate potential factors that influence older adult engagement with digital health technologies for home-based multimorbidity self-management. The least engaged user groups showed decreased health and well-being outcomes related to multimorbidity self-management. Addressing the factors highlighted in this study in the design and implementation of home-based digital health technologies may improve symptom management and physical activity outcomes for older adults self-managing multimorbidity.

Home-based digital health technologies for older adults to self-manage multiple chronic conditions: A data-informed analysis of user engagement from a longitudinal trial.

Background: Ageing populations are resulting in higher prevalence of people with multiple chronic conditions (multimorbidity). Digital health platforms have great potential to support self-management of multimorbidity, increasing a person's awareness of their health and well-being, supporting a better understanding of diseases and encouraging behaviour change. However, little research has explored the long-term engagement of older adults with such digital interventions. Methods: The aim of this study is to analyse how 60 older adults with multimorbidity engaged with digital symptom and well-being monitoring through a digital health platform over a period of approximately 12 months. Data analysis focused on user retention, frequency of monitoring, intervals in monitoring and patterns of daily engagement. Results: Our findings show that the overall engagement with the digital health platform was high, with more than 80% of participants using the technology devices for over 200 days. The submission frequency for symptom parameters (e.g. blood glucose (BG), blood pressure (BP), etc.) was between three and four times per week which was higher than that of self-report (2.24) and weight (2.84). Submissions of exercise (6.12) and sleep (5.67) were more frequent. The majority of interactions happened in the morning time. The most common time of submission for symptom parameters was 10 am, whereas 8 am was the most common time for weight measurements. Conclusions: The findings indicate the patterns of engagement of older adults with complex chronic diseases with digital home-based self-management systems.

High-resolution ultrasound biomicroscopy for monitoring ovarian structures in mice.

BACKGROUND: Until recently, the limit of spatial resolution of ultrasound systems has prevented characterization of structures <1 mm. Hence, the study of ovarian follicular development in rodents has been based on one-time histological examination of excised tissues; i.e., longitudinal study of day-to-day ovarian changes has not been possible in mice and rats. The objective was to establish an ultrasonographic approach to study follicular and luteal dynamics in mice and rats. METHODS: Experiment 1 was a pilot study to develop methods of immobilization (physical restraint vs. general anesthesia) and determine technical factors affecting ovarian images using ultrasound bio-microscopy in rats vs. mice. The hair coat was removed over the thoraco-lumber area using depilation cream, and a highly viscous acoustic gel was applied while the animals were maintained in sternal recumbency. In Experiment 2, changes in ovarian structures during the estrous cycle were monitored by twice daily ultrasonography in 10 mice for 2 estrous cycles. RESULTS: Ovarian images were not distinct in rats due to attenuation of ultrasound waves. Physical restraint, without general anesthesia, was insufficient for immobilization in mice. By placing the transducer face over the dorsal flank, the kidney was visualized initially as a point of reference. A routine of moving the transducer a few millimetres caudo-laterally from the kidney was established to quickly and consistently localize the ovaries; the total time to scan both ovaries in a mouse was about 10 minutes. By comparing vaginal cytology with non-anesthetized controls, repeated exposure to anesthesia did not affect the estrous cycle. Temporal changes in the number of follicles in 3 different size categories support the hypothesis that follicles >/= 20 microns develop in a wave-like fashion. CONCLUSION: The mouse is a suitable model for the study of ovarian dynamics using transcutaneous ultrasound bio-microscopy. Repeated general anesthesia for examination had no apparent effect on the estrous cycle, and preliminary results revealed a wave-like pattern of ovarian follicle development in mice.

Ovarian imaging in the mouse using ultrasound biomicroscopy (UBM): a validation study.

The mouse is a well accepted model for studies of human reproduction despite little being known about follicle dynamics in this species. Longitudinal studies of mouse folliculogenesis have been hampered by the lack of an appropriate imaging tool. Ultrasound biomicroscopy (UBM) may overcome this obstacle as it confers near-microscopic resolution through the use of high-frequency ultrasound waves. The objective of the present study was to determine whether UBM could be used to count and measure ovarian follicles and corpora lutea (CL) reliably in mice. Ovaries of 25 adult CD-1 mice were imaged using a 55-MHz transducer and then excised and processed for histology. Follicles and CL were counted and measured from digitally stored UBM cine-loops and photographed histological sections. Differences between techniques were assessed by Bland-Altman agreement analyses. Follicle counts yielded by the two techniques varied by only +/-1 follicle when follicles ranged between 300 and 499 microm. Perfect agreement among counts was evident when follicles were >500 microm. The total number of CL was accurately estimated using UBM; however, the number of 350-699 microm CL was underestimated and the number of CL>or=700 microm was overestimated. In conclusion, UBM can be used reliably to count and measure follicles in mice.

A simple ultrasound test to predict the superstimulatory response in cattle.

We tested the hypotheses that: (1) the superstimulatory response is related to the intrinsic number of follicles recruited into a follicular wave; and (2) the number of follicles recruited into a wave is correlated to the number of follicles recruited into the successive wave. A positive correlation will form the basis of a test for predicting the superstimulatory response. Cows (n = 141) were treated with estradiol and progesterone to synchronize follicular wave emergence (first synchronization) and ranked according to the number of follicles > or =2mm at wave emergence to select the upper and lower 10% of the herd. Follicular wave emergence was synchronized again in the high-end (n = 16) and low-end (n = 20) groups (second synchronization), and cows were treated with FSH twice daily for 3 days. High-end cows had a greater number of follicles (P < 0.001) than low-end cows at the time of wave emergence after both the first and second synchronizations in the 2-3 and 4-6mm categories. The numbers of 2-3 and 4-6mm follicles at wave emergence after the first and second synchronizations were positively correlated (P < 0.001; r = 0.77 and 0.71, respectively). Endogenous FSH peak at the time of wave emergence was higher in the low-end group than in the high-end group. Superstimulatory treatment resulted in more than double the number of follicles (P < 0.003) in the 5-7mm and > or =8mm categories in the high-end group than in the low-end group (16.8 +/- 2.2 versus 8.1 +/- 0.9 and 22.7 +/- 4.1 versus 9.7 +/- 1.6, respectively). The number of follicles > or =5 and > or =8mm at the end of superstimulation was positively correlated (P < 0.001) with the total number of follicles > or =2mm at the time of wave emergence after both the first (r = 0.64 and 0.54, respectively) and second ( r = 0.65 and 0.5, respectively) synchronizations. Based on the results of this study, the superstimulatory response can be predicted by the number of follicles > or =2mm at wave emergence. For practical purposes, practitioners can expect the number of follicles > or =5mm after ovarian superstimulation to be approximately 71% of the number of follicles > or =2mm at the time of wave emergence. Results validated the proposed simple ultrasound-based test for predicting the superstimulatory response of individual cows.