A Community-Based Intervention to Enhance Subjective Well-Being in Older Adults: Study Design and Baseline Participant Profiles.

Promoting subjective well-being is a crucial challenge in aging societies. In 2022, we launched a community-based intervention trial (the Chofu-Digital-Choju Movement). This initiative centered on fostering in-person and online social connections to enhance the subjective well-being of older adults. This paper describes the study design and baseline survey. This quasi-experimental study involved community-dwelling older adults aged 65-84 years in Chofu City, Tokyo, Japan. A self-administered questionnaire was distributed to 3742 residents (1681 men and 2061 women), and a baseline survey was conducted in January 2022. We assessed subjective well-being (primary outcome); psychosocial, physical, and dietary factors; and the use of information and communication technology variables (secondary outcomes) among the participants. After the intervention involving online classes, community hubs, and community events, a 2-year follow-up survey will be conducted to evaluate the effects of the intervention, comparing the intervention group (participants) with the control group (non-participants). We received 2503 questionnaires (66.9% response rate); of these, the analysis included 2343 questionnaires (62.6% valid response rate; mean age, 74.4 (standard deviation, 5.4) years; 43.7% male). The mean subjective well-being score was 7.2 (standard deviation, 1.9). This study will contribute to the development of a prototype subjective well-being strategy for older adults.

Alzheimer Dementia Detection Based on Time-series Instability of Heart Rate.

Towards early detection of Alzheimer dementia (AD), this paper focuses on time-series instability of heart rate of AD patient, and proposes the AD detection method based on heart rate acquired by an unconstrained mattress sensor for daily life use. Through the human subject experiments on 124 days of heart rate of one AD patient and 39 days of heart rate of 21 healthy subjects, the following implication have been revealed: (1) an accuracy of the proposed AD detection method based on the time-series features of heart rate is approximately 98%; and (2) the time-series instability of heart rate is found in the rapid oscillation of heart rate (i.e., an increase/decrease of heart rate over 2 seconds).

Personalized Non-contact Sleep Stage Estimation with Weighted Probability Estimation by Ultradian Rhythm.

This paper focused on ultradian rhythms (a sleep cycle of approximately 60 to 120 minute) for personalizing sleep stage estimation, and proposed a personalized sleep stage estimation method that weights the results estimated by machine learning with the predicted ultradian rhythms. The ultradian rhythms are predicted by the body movement density which is correlated with ultradian rhythm. To investigate the effectiveness of the proposed method, this paper conducts human subjects experiment for eight subjects.Clinical relevance- The proposed method is compared with the results estimated by conventional ML, and the result of the proposed method is competitive with their conventional counterparts. This indicates that the ultradian rhythm has the potential for developing personalized sleep stage estimation.

Unstable Circadian Rhythm of Heart Rate of Alzheimer Dementia Based on Biological Data of Mattress Sensor.

It is important to detect daily Alzheimer dementia (AD) possibility using unconstrained mattress sensors because dementia takes time before subjective symptoms appear and the main treatment is to slow the rate of progression. Forcusing on circadian rhythm disorder which tend to occur with AD, this paper analyzes the features of unstable circadian rhythms of heart rate associated with its weakening and misalignment in AD. It is found that the external feature of heart rate of AD that of up and down changes seen frequently compared to that of healthy subjects makes the multiple types of effects on the estimation process of the AD detection method based on the circadian instability represented by the trigonometric regression equation estimated from the heart rate. So, we designed two feature values from the effects and analyzed them in an analysis experiment. An analysis experiment was conducted on the heart rate of total 72 days data of one AD patient and total 30 days data of 21 healthy people, and the result confirmed significant differences between the AD and the healthy people at from 0.01% to 0.1 % level for the first feature and at from 0.1% to 5% level for the second feature. And the result shows the possibility that differences in the heart rate features is found between AD and healthy people.

Non-Contact REM Sleep Estimation Correction by Time-Series Confidence of Predictions: From Binary to Continuous Prediction in Machine Learning for Biological Data.

This paper focuses on the REM sleep estimation with bio-vibration data acquired from mattress sensor, and proposes its "correction" method based on Time-Series Confidence (TSC) of the REM sleep prediction calculated by Random Forest (RF) as one of the Machine Learnings (MLs). Unlike the conventional MLs that classify whether the REM sleep or not as its binary prediction, the proposed method determines whether the estimated REM sleep should be corrected or not from its continuous prediction. Concretely, the proposed method computes the REM sleep prediction as the percentage of trees that classify the REM sleep for each epoch (30 seconds), calculates TSC of the REM sleep prediction by windowing the REM sleep prediction of a certain number of epochs to smooth them, and the REM sleep estimated by other MLs is corrected when TSC is lower than a certain threshold. Through the human subject experiments, the following implications have been revealed: (1) the proposed method shows a small TSC in the sudden wrong REM sleep estimation, which contributes to correct it; and (2) because of this feature of the proposed method, the number of False-Positive of the REM sleep estimation is successfully reduced, which improves Precision from 51.4% (w/o TSC) to 59.4% (w/ TSC).

Sleep Apnea Syndrome Detection Based on Degree of Convexity of Logarithmic Spectrum Calculated from Overnight Bio-vibration Data of Mattress Sensor.

This paper proposes the novel Sleep Apnea Syndrome (SAS) detection method based on the frequency analysis of the overnight bio-vibration data acquired from mattress sensor. Concretely, this paper designs the index called Degree of Convexity of the Logarithmic Spectrum (DCLS), which quantifies the degree of convexity by computing the difference between the waveform of the averaged logarithmic spectrum and the waveform of its approximation formula, and employs it to detect SAS. Through the human subject experiment on the SAS detection, the following implications have been revealed: (1) the SAS subjects tend to have the large density around 3Hz, and the average of DCLS in SAS subjects and healthy subjects are 98.6±10.1 and 48.2±6.8 respectively, which succeeds to correctly separate the nine SAS subjects and the nine healthy subjects; and (2) the characteristics of the WAKE stage are different between the SAS and healthy subjects.

Alzheimer Dementia Detection Based on Unstable Circadian Rhythm Waves Extracted from Heartrate.

This paper proposes the novel Alzheimer dementia (AD) detection method based on unstable circadian rhythm of heartrate acquired from mattress sensor. Concretely, the pro-posed method, UCRADD (Unstable Circadian Rhythm based Alzheimer Dementia Detection), estimates the circadian rhythm of heartrate by calculating the regression of the trigonometric functions with the maximum likelihood estimation, and judges instability of the circadian rhythm by the coefficients of the equation estimated trigonometric functions. Through the human subject experiment with one elderly AD subject in two months (i.e., August and December), three elderly (age from 60-70) non-AD subjects, the ten middle-aged non-AD subjects and eight young non-AD subjects, the following implications have been revealed: (1) UCRADD succeeds to detect the AD patients in the high rate and keeps it in two months (78.9% in August and 82.4% in December), while our previous method cannot keep the rate at the same level in two months (57.9% in August and 82.4% in December). (2) the instability of circadian rhythm of heartrate has the potential of being new symptom of AD.