Latent subgroups with distinct patterns of factors associated with self-rated successful aging among 1,510 community-dwelling Americans: potential role of wisdom as an implicit promoter.

OBJECTIVES: To investigate whether latent subgroups with distinct patterns of factors associated with self-rated successful aging can be identified in community-dwelling adults, and how such patterns obtained from analysis of quantitative data are associated with lay perspectives on successful aging obtained from qualitative responses. METHODS: Cross-sectional data were collected from 1,510 community-dwelling Americans aged 21-99 years. Latent class regression was used to identify subgroups that explained the associations of self-rated successful aging with measures of physical, cognitive, and mental health as well as psychological measures related to resilience and wisdom. Natural language processing was used to extract important themes from qualitative responses to open-ended questions, including the participants' definitions of successful aging. RESULTS: Two latent subgroups were identified, and their main difference was that the wisdom scale was positively associated with self-rated successful aging in only one subgroup. This subgroup had significantly lower self-rated successful aging and worse scores for all health and psychological measures. In the subgroup's qualitative responses, the theme of wisdom was only mentioned by 10.6%; this proportion was not statistically different from the other subgroup, for which the wisdom scale was not statistically associated with the self-rated successful aging. CONCLUSION: Our results showed heterogeneous patterns in the factors underpinning successful aging even in community-dwelling adults. We found the existence of a latent subgroup with lower self-rated successful aging as well as worse health and psychological scores, and we suggest a potential role of wisdom in promoting successful aging for this subgroup, even though individuals may not explicitly recognize wisdom as important for successful aging.

Atypical Repetition in Daily Conversation on Different Days for Detecting Alzheimer Disease: Evaluation of Phone-Call Data From Regular Monitoring Service.

BACKGROUND: Identifying signs of Alzheimer disease (AD) through longitudinal and passive monitoring techniques has become increasingly important. Previous studies have succeeded in quantifying language dysfunctions and identifying AD from speech data collected during neuropsychological tests. However, whether and how we can quantify language dysfunction in daily conversation remains unexplored. OBJECTIVE: The objective of this study was to explore the linguistic features that can be used for differentiating AD patients from daily conversations. METHODS: We analyzed daily conversational data of seniors with and without AD obtained from longitudinal follow-up in a regular monitoring service (from n=15 individuals including 2 AD patients at an average follow-up period of 16.1 months; 1032 conversational data items obtained during phone calls and approximately 221 person-hours). In addition to the standard linguistic features used in previous studies on connected speech data during neuropsychological tests, we extracted novel features related to atypical repetition of words and topics reported by previous observational and descriptive studies as one of the prominent characteristics in everyday conversations of AD patients. RESULTS: When we compared the discriminative power for AD, we found that atypical repetition in two conversations on different days outperformed other linguistic features used in previous studies on speech data during neuropsychological tests. It was also a better indicator than atypical repetition in single conversations as well as that in two conversations separated by a specific number of conversations. CONCLUSIONS: Our results show how linguistic features related to atypical repetition across days could be used for detecting AD from daily conversations in a passive manner by taking advantage of longitudinal data.

Association Between Seasonal Influenza and Absolute Humidity: Time-Series Analysis with Daily Surveillance Data in Japan.

Seasonal influenza epidemics are associated with various meteorological factors. Recently absolute humidity (AH) has garnered attention, and some epidemiological studies show an association between AH and human influenza infection. However, they mainly analyzed weekly surveillance data, and daily data remains largely unexplored despite its potential benefits. In this study, we analyze daily influenza surveillance data using a distributed lag non-linear model to examine the association of AH with the number of influenza cases and the magnitude of the association. Additionally, we investigate how adjustment for seasonality and autocorrelation in the model affect results. All models used in the study showed a significant increase in the number of influenza cases as AH decreased, although the magnitude of the association differed substantially by model. Furthermore, we found that relative risk reached a peak at lag 10-14 with extremely low AH. To verify these findings, further analysis should be conducted using data from other locations.

Automatic Quantification of Atypical Topic Repetition in Single Daily-Conversation Resulting from Alzheimer's Disease.

Identifying signs of Alzheimer's disease (AD) in everyday situations has become increasingly important. Previous studies have succeeded in quantifying language dysfunctions and identifying AD from speech data typically during neuropsychological tests. However, no study has yet investigated atypical topic repetition within single daily-conversations, although it was reported as a prominent characteristic by previous observational and descriptive studies. In this study, we analyzed daily conversational data collected from a monitoring service and compared topic patterns in single conversations of seniors with and without AD. We first found that all features extracted from manual transcription to measure topic repetition showed significant increases in the AD group. Moreover, these features fully automatically extracted from voice data by using a speech-to-text algorithm could capture atypical topic repetition with comparable and large effect sizes of those extracted from manual transcriptions. The results indicate that quantifying atypical repetition could help automatically detect AD in everyday situations.