To be included in the study, participants must (1) be at least 65 years old, (2) have normal cognition or MCI, (3) live independently in the community (ie, no difficulties with any of the instrumental activities of daily living (IADL), see below), and (4) be able to give written informed consent.

Individuals will be excluded from the study if they have a diagnosis of (1) dementia, (2) any other psychiatric disorder, or (3) any condition (eg, frailty, stroke, or Parkinson disease) that results in limitations to perform basic activities of daily living; individuals who are (4) unwilling to adhere to the use of wearables or to have sensors installed in designated areas of their home, or (5) currently participating in a cognitive or motor training intervention trial will be excluded.

The Lawton IADL scale will be used to assess independent functional skills. These skills are considered more complex than basic daily activities and are used to identify current functional levels. The scale comprises 8 domains, each of which ranges in score from 0 (low functioning and dependent) to 4 (high functioning and independent), with the total score ranging from 0 to 23. The scale has been used in older adults as a measure of functioning level and is an accurate tool for screening dementia [40,41]. The convergent and divergent validity of the IADL was tested against known strengths and group differences in Singapore. The IADL as a whole explained 87.6% of the variance. The internal consistency for the physical part of the IADL was 0.91, while 0.78 for the cognitive IADL [41].

The modified Barthel Index (MBI) of Activities of Daily Living will be used to assess the basic activities one needs to be independent at home. It has been widely used in various large-scale studies, including studies conducted in Singapore [42,43]. The scale comprises 10 areas: 6 items on feeding, dressing, bladder, bowel, toilet use, and stair climbing are scored using a 3-point scale (0, 1, or 2 points), 2 items on transferring and mobility on a 4-point scale (0, 1, 2, or 3 points), and the last 2 on bathing and grooming on a 2-point scale. The maximum score is thus 20, and the higher the score, the greater the independence [44]. The MBI has been validated against assessments from occupational therapists for older residents of long-term care facilities. The Kappa coefficient of intra-rater reliability was 0.70‐1.00 for individual tasks; the Kappa coefficients for inter-rater reliability were 0.72‐1.00. Cronbach coefficient alpha of internal consistency was 0.93 [43].

The Montreal Cognitive Assessment (MoCA) is a brief screening tool for detecting global cognitive changes in patients with MCI and dementia. The effectiveness of the MoCA in detecting MCI and dementia has been validated in Singapore, and it has also been shown to exhibit diagnostic utility in discriminating between mild and major neurocognitive disorders with an area under the curve (AUC) of 0.77 and 0.99, respectively [45].

The Mini-Mental State Examination (MMSE) is the most common screening tool for the early detection of MCI and dementia worldwide. A localized Singapore version can discriminate between older adults with and without dementia, with a high sensitivity of 97.5% [46]. Normative data have also been determined for Chinese older adults and are widely used in Singapore [47]. With a cut-off point of 26, the MMSE has been shown to have a sensitivity and specificity of 61% and 84%, respectively, and a diagnostic accuracy, based on AUC, of 0.78.

The Clinical Dementia Rating Scale is a semi-structured dementia staging instrument that is completed on a 5-point scale used to characterize 6 domains of cognitive and functional performance. The rating for each domain will be used to calculate a global score between 0 (normal) and 3 (severe dementia) and to determine dementia severity. The scale has been shown to provide reliable and valid assessments for community-dwelling older adults even in the absence of an informant, and there was good agreement between the Clinical Dementia Rating Scale and the clinical assessment status of MCI and dementia (internal consistency =0.82‐0.84; kappa coefficient =0.79) [48].

The Rey Auditory-Verbal Learning Test will be used to evaluate immediate verbal memory, retention of delayed information, learning strategies, proactive and retrospective interference, and delayed recognition. This test has been used to measure immediate and delayed memory performance in older Chinese adults in Singapore [49]. The Rey Auditory-Verbal Learning Test immediate and delayed recall subtests were able to distinguish between converters and nonconverters to dementia with sensitivities of 83% and 85%, respectively, AUCs of 0.75 and 0.76, respectively, and optimal cut-off points of 1 and 2, respectively [50].

The digit span test is part of the Wechsler Adult Intelligence Scale-Fourth Edition and requires participants to read a string of numbers forward (ie, to assess efficiency and capacity for paying attention) and backward (ie, to assess executive function and working memory); its results can show indications of short-term verbal memory. This test has been widely used as a valid tool to discriminate between MCI and dementia among older adults [51], and provided specificities of 99% and 95%, respectively, for MCI and mild dementia, with a cut-off score <4.

The Color Trails Test (CTT) 1 and 2 were designed as culturally appropriate tests in comparison to the Trail Making Test. The CTT 1 requires participants to join 25 numbers in sequence as quickly as possible, whereas CTT 2 requires participants to alternate between colors while joining 25 numbers in sequence. They are used to measure selective attention, processing speed, and cognitive flexibility. Test-retest analysis was performed over a 4-week period to determine the reliability of the CTT, and CTT 1 and CTT 2 showed a Pearson correlation r of 0.90 and 0.91, respectively [52].

The block design test is also part of the Wechsler Adult Intelligence Scale-Fourth Edition, requiring participants to recreate a design shown to them using blocks with different colored sides. The test assesses perceptual reasoning, spatial visualization, and visuo-construction abilities. The block design test has been used for epidemiologic studies among older adults [53], and test performance declines with age across the normative data [54].

The semantic verbal fluency (animals) test will also be administered as a measure of executive function, access to semantic memory, and lexical retrieval. A decline in semantic fluency has been reported as an early indicator of dementia, and it can be used to differentiate the development of dementia from normal age-related cognitive decline [55]. A normative study conducted for the tool showed its reliability, as the number of animals named decreased with an advance in age older than 60 years, and it correlated (r=0.52) with orthographic verbal fluency scores [56].

The Victoria Stroop Test will be used to measure response inhibition and cognitive flexibility [57]. This test is divided into 3 conditions and is composed of 24 items. In the first condition, participants are required to name the colors of the dots. In the second condition, they are required to identify the color of the ink each word is printed in (eg, the word Blue is printed in the blue color), while in the last condition, colored words irrelevant to their printed color are presented. The number of errors and the time taken to complete each condition are recorded and scored. The Victoria Stroop Test is reported to be sensitive to both MCI and dementia [58,59].

Age-related memory impairments were assessed using the Common Objects Memory Test (COMT) [60]. The COMT uses pictures of 10 common objects that participants should be familiar with. It involves 3 learning trials with immediate recall and 2 delayed recall trials, one after 5 minutes and another after 30 minutes of distracted activities. The number of objects correctly recalled is scored. The COMT test is not affected by educational levels, language, or ethnicity, and it could be culturally modified to suit the participants. It has been validated in both Caucasian and Asian older adults with dementia [60], and has been included within the Asian Cohort for Alzheimer Disease Data Collection Packet [61].

The Zung Self-Rating Depression Scale and the 15-item Geriatric Depression Scale will be used to determine the level of depressive symptoms among participants. The Zung Self-Rating Depression Scale comprises 20 items that measure depressive symptoms, asking respondents how they felt or behaved in the past week using a 4-point scale ranging from 1 (a little of the time) to 4 (most of the time). A high total score indicates worse depressive symptoms. This scale has been validated among community-dwelling older adults for detecting subsyndromal depression [62]. With cut-off scores of 39 and 40, the scale also showed a sensitivity of 79.2% and an AUC of 0.85 among older adults [63].

The 15-item Geriatric Depression Scale is a shortened form of the original Geriatric Depression Scale and contains 15 questions assessing depressive symptoms over the last 7 days. All 15 items are responded to using a yes-or-no scale, and a score of 5 or above indicates possible depression. This scale showed accuracy in screening for depression among older adults in a meta-analysis, with a pooled sensitivity and specificity of 86% and 76%, respectively, and diagnostic accuracy, based on AUC, of 0.95 [64].

Self-reported anxiety will be captured using the Generalized Anxiety Disorder-7. Participants will respond to 7 questions on how often, over the past 2 weeks, they worried (eg, “Not being able to stop or control worrying,” “Worrying too much about different things”) and experienced somatic tension (eg, “Trouble relaxing”). A 4-point Likert scale response (“0” denoting not at all, while “3” nearly every day) is used. The maximum score is thus 21, while the minimum score is 0, and scores greater than 10 usually suggest a diagnosis of Generalized Anxiety Disorder. This scale has a sensitivity of 89% and specificity of 82% [65]. It has been validated in both cognitively impaired [66] and general populations [67,68], and has been used in Singapore settings [69,70].

The Pittsburgh Sleep Quality Index is used to measure subjective sleep patterns and quality. It includes 4 open-ended questions and 14 frequency- and semantic-scaled questions that assess sleep quality, latency, duration, disturbances, daytime dysfunction, and sleep efficiency. It can be administered verbally to participants, and a high global score of 5 or more indicates poor sleep. Its suitability for use with older adults in Singapore was demonstrated [71]. It showcased an internal consistency of 0.83 and showed a correlation with greater sleepiness on the Epworth Sleepiness Scale (r=0.13) when applied among men [72].

Social vulnerability, such as a lack of social networks, loneliness, and limited social contact, has been shown to predict cognitive decline among older adults [73]. Apathy symptoms are also associated with the conversion from MCI to dementia and incident dementia among community-dwelling older adults [73]. This highlights the importance of considering social networks and apathy when attempting to understand cognitive decline. As such, the 6-item Friendship Scale, Lubben Social Network Scale, and the Apathy Inventory Clinician Version will be used to measure social isolation and apathy.

The 6-item Friendship Scale measures important dimensions contributing to social isolation and connectedness, requiring participants to describe the frequency with which they experience the situations in each question on a 5-point Likert scale ranging from 1 (almost always) to 5 (not at all). The scale has been shown to exhibit concurrent discriminant validity, suggesting its sensitivity to correlates of social isolation among older adults [74]. It has also been validated for use among older adults with a reliability of 0.83 and an excellent internal structure, as the comparative fit index was 0.99 and the root mean square error of approximation was 0.02 [75].

The Lubben Social Network Scale consists of 2 sets of 3 questions aimed at evaluating kinship and nonkinship ties. Total scores range from 0 to 30, with lower scores indicating lower levels of social support. The scale has been validated in past research, showing a Cronbach α of .74 and a convergent validity of −0.3 [76]. It has been used in Singapore to understand social isolation among community-dwelling older adults [77].

The Apathy Inventory clinician version is a scale operationalized into 3 dimensions, namely emotional blunting, lack of initiative, and lack of interest. Items are responded to on a yes-or-no scale to determine the presence or absence of the behavior in the item; then, a 5-point Likert scale ranging from 0 (no problem) to 4 (major problem) is used to determine the score for each dimension. Total scores range from 0 to 12, with scores of 4 and above indicating the presence of apathy. The scale has been validated in older adults and showed a Cronbach α of 0.90 [78]. It has also been applied to determine chronic behavioral changes in Alzheimer disease [79].

Physical frailty will be assessed using the modified Fried frailty phenotype, assessing 5 components: low hand grip strength, slow walking speed, low physical activity, self-reported exhaustion, and low BMI (18.5kg/m²) [80]. Those who screen positive for at least 3 of these criteria are considered frail, while those who meet 1‐2 criteria are classified as prefrail. Participants with a Fried score of 0 are deemed to be robust. Exhaustion will be assessed using the Center for Epidemiological Studies-Depression Scale, which asks how often participants felt the statements “everything I did was an effort” and “I could not get going.” An affirmative answer to either or both questions is considered positive for exhaustion. Grip strength will be measured using a hand dynamometer (Jamar Plus+ Digital Hand Dynamometer, USA), with 2 trials for each hand, and the maximum value of 4 trials will be recorded. Gait speed will be assessed using a 10-meter walk at usual pace, with the average of 2 trials recorded. We will adopt the Asian Working Group for Sarcopenia 2019 cut-offs for defining weak hand grip strength and slow gait speed [81]. Physical activity is self-reported using the Physical Activity Vital Sign that quantifies time spent on physical exercise and activities, and values below the lowest quartile from a local community cohort will be deemed physically inactive [82]. The Fried phenotype is the most commonly used of measures for frailty [83]. The frailty phenotype was operationalized using data from the Cardiovascular Health Study, a large-scale study of over 5000 older adults in the United States [80]. It has been used in a number of large-scale longitudinal studies, including one in Singapore, to quantify physical frailty in community-dwelling older adults [84-86].

Participants are also scored on the Clinical Frailty Scale (CFS) for a global clinical assessment of frailty status. CFS is a 9-point scale that ranges from 1 (very fit) to 9 (terminally ill) [87,88]. Specifically, CFS has been highly recommended as the instrument of choice for frailty screening in the community and primary care [89]. The CFS has been validated against the Frailty Index in hospitalized older adults in Singapore with an AUC score of 0.91 (95% CI 0.87‐0.95; P<.001) [90]. A classification tree had been introduced to facilitate the reliable scoring of CFS [91], and this was modified for our local context (Figure 2) [92]. This method uses information gathered from other validated toolkits—specifically the IADL, MBI, and SF-12 (Exhaustion)—to classify physical frailty. This classification tree approach to CFS will provide us with a more nuanced approach to quantifying frailty in older residents living in the community.

Health-related quality of life will be captured using the EQ-5D-3L (Singapore version) questionnaire. This questionnaire has 2 parts: the descriptive system and the visual analog scale (VAS). The descriptive system of the EQ-5D-3L has 5 dimensions: mobility, self-care, usual care pain/discomfort, and anxiety/depression. Each dimension has 3 levels of impairment: no problems (level 1), some problems (level 2), and extreme problems (level 3). An EQ-5D summary index (single utility index), which ranges between −1.00 (worst possible health state) and 1.00 (perfect health state), is derived from the 5-digit health state profile using a weighted method. As for the VAS part, it has a visual scale from 0 to 100, with 0 being the “worst health the participant can imagine” while 100 being the “best health the participant can imagine” [93]. The EQ-5D-3L is widely used around the world and has been translated into more than 170 languages. Additionally, it has been validated among patients with chronic noncommunicable diseases [94-96] as well as persons with cognitive impairments. It has been validated against the Health Utilities Index Mark 3 in dementia patients in Singapore [97]. It has been validated against community-dwelling older adults with cognitive impairment [98]. The EQ-5D-3L was validated against corresponding assessment scales: mobility against the Tinetti Index (correlation: 0.371), self-care against the Barthel Index (0.340), usual activities against the Lawton Index (0.628), pain against the VAS pain scale (0.504), and anxiety against GDS (0.703). Reliability (internal consistency) has been reported as 0.69 (Cronbach α).

The Mini Nutritional Assessment Short Form (MNA-SF) is a 6-item questionnaire that has been used to evaluate the nutritional status of older frail adults [99]. It has been used in large cohort studies involving community-dwelling older adults to investigate malnutrition and frailty [100,101]. The MNA-SF comprises 6 questions that cover anthropometric measurements (BMI and unintentional weight loss), diet changes, neuropsychological problems, acute disease, and mobility. Anthropometric measurements are scored between 0 and 3, while all the others range between 0 and 2. The maximum total score for the MNA-SF is thus 14, and a score of less than 7 indicates malnutrition, 8 to 11 indicates a risk of malnutrition, while a score above 12 indicates normal nutritional status [102]. The MNA-SF has been well-validated in a number of large-scale studies involving community-dwelling older adults and has been included in studies that investigated frailty in the Singapore context [99,103-105]. It has been validated against anthropometric measurements and functional status (IADL) in Malaysian older adults—sensitivity: 93.2%, specificity: 79.4% [106].

The 10-meter Walk Test assesses functional mobility and gait speed. Participants will be instructed to walk at their usual (self-selected) walking pace over a distance of 14 meters—2 meters are added at the beginning and at the end of the test to account for the acceleration and deceleration during the test. Participants will walk 3 times—for the first 2, participants will walk without talking, while for the third walk, which is the dual-task gait speed trial, they will be required to count upwards in multiples of 2, starting from the number 16. A handheld stopwatch and a measuring tape will be used to facilitate this test.

Figure 4 shows the process of extracting digital biomarkers from the raw sensor data. The key steps are elaborated below.

Daily biomarkers are considered missing when the corresponding behavior states related to the biomarkers cannot be determined. For example, the absence of readings from all the motion sensors during the entire day causes the positional state of the participant to be undetectable, leaving the activity biomarkers of that day missing. Similarly, when the sleeping and awake states cannot be detected because of a prolonged absence of readings from the bed pressure sensor in a day, all sleep biomarkers are considered missing.

On the other hand, biomarker values can be considered as not applicable when the values are not relevant or meaningful under specific circumstances. For example, the sleep time biomarker is considered not applicable when the sleep duration biomarker is 0; forgetting medication is not applicable as well, when the participant does not have any medication prescription.

At baseline, descriptive statistics for demographic information and psychometric scores will be analyzed and presented for all participants. Baseline characteristics will be compared between healthy participants and participants with MCI or PF/frailty using 2 independent sample t-tests or Wilcoxon rank sum tests (depending on the normality assumption) for continuous variables, and Pearson chi-square tests or Fisher exact tests (where appropriate) for categorical variables.

Group-based trajectory modeling will be used to classify the study population into distinct subgroups based on the patterns of the individual sensor-derived variables and the coefficient of variations recorded over time. The analysis will be conducted via SAS PROC TRAJ (SAS Inc, Cary, NC, USA), which will be used to perform the analyses, a method described by Jones et al [109] in their seminal work. This procedure integrates 2 statistical models estimated simultaneously via maximum likelihood. The first model develops trajectories for the different latent cohorts as a function of time from the baseline. Quadratic or cubic functions of time may be included if they improve model fitness, as determined by the Bayesian information criterion. The second model is a multinomial regression model, which examines the associations of participants’ baseline characteristics with the probability of membership in the homogeneous latent groups. To deal with missing values, complete case analysis is the default method, but SAS PROC TRAJ also allows for the use of multiple imputations to address missingness, where necessary.

To detect individuals at high risk of developing MCI/dementia or PF/frailty, machine learning prediction models will be developed and cross-validated according to the extracted clinical data and spatiotemporal daily activity data (from sensors). Model performance (ie, sensitivity, specificity, and AUC) of these prediction models against gold-standard diagnostic outcomes. The models will undergo rigorous cross-validation (CV) to ensure robustness, using subsets of the data to prevent overfitting and ensure generalizability. CV will involve partitioning the data into training and testing sets, enabling the models to be trained on one portion while performance is assessed on another. This approach ensures that the models can reliably predict high-risk individuals across different subsets of the population.

We will apply artificial intelligence (AI) methods, namely spatial-temporal neural network models for predictive modeling, using the sensor-derived data and clinical assessment data to classify older adults’ behavioral change patterns based on the outcomes assessed at the yearly visits across the 3 participant groups (ie, healthy control, MCI/early dementia, and PF/frailty groups). The goal here will be to build predictive models using the longitudinal data regarding specific daily activities (eg, particularly sleep state, heart rate, medication intake, step count, outings, and room transitions) as learning data for the spatiotemporal neural network models, which will then capture the longitudinal dynamics of the participants’ cognitive performance and status.

Two classes of neural network models will be explored and compared, that is, the hierarchical form of self-organizing networks known as fusion adaptive resonance theory (ART) models [110], and deep recurrent neural networks, in particular, long short-term memory [111]. These two neural network models have been shown to provide promising performance in sequence and temporal modeling. For this project, we will formulate the prediction task as a spatial-temporal classification problem, which can typically be addressed via a supervised learning paradigm, wherein sensor-derived data and clinical assessment data collected over a period of time (window) are used as input pattern features, while older adults’ cognitive states (ie, considering the healthy control, MCI, and early dementia groups) are used as the output classes (labels). A key feature of fusion ART models is the ability to perform online one-shot learning, which is advantageous for learning from a small number of training samples. This will be critical in our study because of the technical challenge when using machine learning methods, named as “small data problem,” which is typically faced when working with user data collected from real-world settings. The performance of the fusion ART and long short-term memory–based models will be benchmarked against the commonly used machine learning models and methods, including k-nearest neighbor, support vector machine, decision tree, random forest, and LightGBM.

The AI-based predictive model will be built using data collected from 200 older adults recruited in this project. Then, considering a period of 3 years and using a sliding prediction window of over 3×365 days, this study can yield up to 1095 daily data points for each participant, together with the cognitive and physical frailty status assessments. In total, we expect to have 2,19,000 daily data points across 200 participants in a 3-year period.

Based on the collected daily data, a CV methodology will be adopted to train and evaluate the performance of the predictive model. For example, using 10-fold CV, 90% of the full dataset will be used to train the model, while the remaining 10% will be used to validate the classification accuracy of the model. The evaluation is repeated 10 times so that all data samples are used for validation once across the 10 experiments. Also, CV evaluations will be further performed in both the cross-sectional (ie, across different participants over the entire time period) and temporal holdout manners (ie, for the entire group of participants over different time periods). Specifically, under cross-sectional CV, data samples from 90% of the participants will be used to train the model, while the data of the remaining 10% participants will be used for validation. This allows us to validate the performance of the trained model on new and unseen participants. For temporal holdout CV, the first 90% data collected over time from all participants are used for training the model, and the last 10% data are used for validation. This enables us to validate the performance of the trained model in predicting the cognitive classes of the existing cohort in the future over time. Standard performance measures for classification will be applied to assess the sensitivity, specificity, and AUC of the predictive models against the gold standard diagnostic outcomes.

CV experiments will be conducted using the data collected in the first 2 to 3 years of the study. Data collected in the subsequent stage of the project can be used as an external validation set for evaluating the predictive models developed.

The home-based sensor system that will be used in this study does not have voice or video-recording capabilities. Specifically, PIR sensors can only detect the presence or absence of motion, while the beacon tags lose their connection to the gateway device once they are out of their homes; thus, they do not have tracking capability.

In an earlier feasibility study, there were concerns that the intrusiveness of the continuous monitoring procedures could be an obstacle to recruitment [26]. However, the older adults who lived alone in this cited study expressed enthusiasm to have the system developed to assist them with cognitive monitoring and did not express concerns about intrusiveness, as long as there were no video cameras. Moreover, this study proposal has measures to safeguard data confidentiality. Regardless of these efforts, we will gather qualitative feedback from participants upon study conclusion to better understand the usability and areas for improvement regarding the monitoring system.

Since it is anticipated that the sensors need to run smoothly and continuously (24-7) over an extended period of time, we expect that there would be a need to mitigate hardware failures (eg, issues with battery lifespan, faulty hardware), an online dashboard system will be used to monitor the status of all deployed sensors and to promptly alert our data engineers for attention and necessary actions.

Maintenance visits will be scheduled periodically (every 3‐4 months) for battery replacement, when the participant calls to inform about faulty devices, or when the study team determines that the sensors require servicing. Maintenance visits will be arranged as soon as the participant’s schedule permits.

The research team will also provide participants with regular reimbursements to mitigate the electricity costs of the gateway device, which needs to be plugged into an electrical outlet.

Our current sensor configuration offers an elaborate setup, consisting of a home gateway, motion sensors, door contact sensors, medication box, a bed pressure mat, BLE beacons, and wearables. As it may be too complex for low-tech literacy homes to handle and create many failure points over the years, a tiered deployment scheme (such as Base: Gateway+ PIR+door; Plus: Base+ beacons+medbox; and Full: Plus+ bed+wearable) could be considered, whereby the seniors may elect to participate with a preferred level of deployment. Augmented with criteria to upgrade/downgrade per household based on early adherence and living context, this tiered deployment will help to reduce dropouts, wastage in hardware, and effort of deployment/maintenance, while preserving data quality.

Long-term wristband use is typically the weakest link of in-home sensing due to various issues such as skin irritation, forgetting, and charging. Guidelines will be provided to the participants regarding predefined minimum wear time KPIs (eg,≥10 h/d on ≥70% of day). We will also provide accessory options, such as spare straps and hypoallergenic bands, and institute regular reminder routines to improve the data quality. Critically, our data processing and AI predictive models will also be designed to tolerate missing wearable-related biomarkers and are still capable of learning a model from readings of other sensors.

Adherence to consistently taking medications from the designated box is another challenge, as many elders may decant pills into weekly organizers or multiple storage sites. Possible measures to mitigate such risk include briefings to participants and obtaining user acknowledgment during deployment regarding proper use of medication boxes, running calibration against prescriptions, and recording of “not applicable” days operationally.

The use of key/wallet beacons assumes those items are always attached to the items and carried by the older adults. To mitigate the risk, such as adhesive failures and missing beacons, spare beacons and robust attachments will be provided. In addition, a monthly self-check can be instituted to remind elders of the importance of bringing beacons with them outdoors. To reduce false alarms, the “forgetting” features will be triangulated with the door/PIR patterns and medication timing variability.

Our target expanded cohort will include people both living alone and with others. As the PIR and door sensors are unable to attribute movement to individuals, additional BLE receivers will be installed in place of the PIR sensors to monitor the whereabouts of the specific participants.

For measuring data completeness and quality, various operational feasibility outcomes can be tracked and reported quarterly, such as sensor uptime, proportion of analyzable days per participant, mean devices online/day, time-to-alert-triggered maintenance visit, and device-related complaint rates. These metrics will help us assess real-world scalability beyond a research setting.

With ≈200 participants but up to 219,000 daily rows over 3 years, cross-validation must avoid leakage. We will replace the generic 10-fold CV with participant-level grouped CV and temporal holdouts (train on early windows, test on later windows). In addition, we will also consider quarterly intermediate labels derived from cognitive trajectories to address annual label sparsity for temporal models.

To ensure the safety of the older adult participants who live alone, we have incorporated algorithms within our dashboard system to alert the research team if the sensors system does not detect any expected movement (when it was expected, ie, the algorithm “identifies that participant is at home”) for a total of 24 h. Such cases are first checked against the participants’ travel schedule and indoor/outdoor status based on the prior sensor readings. The verified alert cases will be elevated to the research team who will then contact the participant, followed by caregivers/next of kin and community partners to ensure that the participant is well and safe.

Each participant will be assigned a unique participant identifier number upon successful enrollment. Once assigned, the participant will be followed up anonymously using this unique number. Only the principal investigator and the assigned study team member will have access to the participant data link to the identifiers. All consent forms and hardcopy data collection forms will be stored under lock and key, and in areas of the hospital with restricted access. Data will only be accessed by the principal investigator and the assigned study team member.

All research data will be managed in accordance with Good Clinical Practice standards. Study data will be entered into a secure institutional electronic system that incorporates controlled access and routine backups. Only authorized study personnel will have access according to predefined roles. Administrative files (eg, enrolment logs or scheduling sheets) will be maintained in password-protected spreadsheets stored on an encrypted, access-restricted institutional server. These documents are operational in nature and do not constitute the study’s primary research database.

Three levels of data monitoring will be used to secure data accuracy. At the first level, research assistants will ensure that the data entry and scoring on the hardcopy forms are accurate, and they will also ensure accuracy in the transfer of the hardcopy data to the electronic datasets. At the second level, another research staff member will go through the hardcopy forms and electronic datasets to ensure that the data comply with each other.

Serious adverse events and unanticipated problems will be reported within 24 hours to the SingHealth Centralized Institutional Review Board from the day the principal investigator becomes aware of the issue.

All findings from this study will be communicated to the scientific community via peer-reviewed publications and conferences. Key findings will be shared with the general public via community workshops and media interviews.