Using the NANA toolkit at home to predict older adults’ future depression

Andrews, Jacob A; Harrison, R.F.; Brown, Laura J. E.; Maclean, Linda M.; Hwang, Faustina; Smith, Thomas Aneurin; Williams, Elizabeth A.; Timon, Claire; Adlam, Tim; Khadra, Hassan; Astell, Arlene

doi:10.1016/j.jad.2017.02.019

Cited by 21 publications

(13 citation statements)

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“…Measuring mood and appetite [19][20][21][22] was recently used for predicting depression and anxiety in older adults. These, together with other studies highlighted in our literature, have motivated us to develop our autonomous remote monitoring and decision support tool further and to employ various machine learning approaches including deep learning, while this was previously considered impossible due to big data issues [8].…”

Section: Resultsmentioning

confidence: 99%

“…Computer Interactive Reminiscence and Conversation Aid (CIRCA) has been adopted and developed for cognitive stimulation therapy (CST) by various authors [16][17][18]. NANA (Novel Assessment of Nutrition and Ageing) was developed as a mean for computerized self-administered measures of mood and appetite [19][20][21][22] and was recently used for predicting depression and anxiety in older adults [21,22]. Subramaniam and Woods [23] presented original works on ICT reminiscence systems for dementia with a therapeutic view.…”

Section: Ict For MCImentioning

confidence: 99%

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Toward Kinecting cognition by behaviour recognition-based deep learning and big data

Soufian

Nefti-Mezian

Drake³

2020

Univ Access Inf Soc

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The majority of older people wish to live independently at home as long as possible despite having a range of age-related conditions including cognitive impairment. To facilitate this, there has been an extensive focus on exploring the capability of new technologies with limited success. This paper investigates whether MS Kinect (a motion-based sensing 3-D scanner device) within the MiiHome (My Intelligent Home) project in conjunction with other sensory data, machine learning and big data techniques can assist in the diagnosis and prognosis of cognitive impairment and hence prolong independent living. A pool of Kinect devices and various sensors powered by minicomputers providing internet connectivity are being installed in up to 200 homes. This enables continuous remote monitoring of elderly residents living alone. Passive and off-the-shelf sensor technologies were chosen to implement data acquisition specifically from sources that are part of the fabric of the homes, so that no extra effort is required from the participants. Various constraints including environmental, geometrical and big data were identified and appropriately dealt with. A visualization tool (MAGID) was developed for validation and verification of numerous behavioural activities. Then, a subset of data, from twelve pensioners aged over 65 with age-related cognitive decline and frailty, were collected over a period of 6 months. These data were subjected to several machine learning algorithms (multilayer perceptron neural network, neuro-fuzzy and deep learning) for classification and to extract routine behavioural patterns. These patterns were then analysed further to ascertain any health-related information and their attributes. For the first time, important routine behaviour related to Activities of Daily Living (ADL) of elderly people with cognitive and physical decline has been learnt by machine learning techniques from selected sample data obtained by MS Kinect. Medically important behaviour, e.g. eating, walking, sitting, was best learnt by deep learning with accuracy of 99.30% during training stage and average error rate of 1.83% with maximum of 12.98% during the implementation phase. Observations obtained from the application of the above learnt behaviours are presented as trends over a period of time. These trends, supplemented by other sensory signals, have provided a clearer picture of physical (in)activities (including falls) of the pensioners. The calculated behavioural attributes related to key indicators of health events can be used to model the trajectory of health status related to cognitive decline in a home setting. These results, based on a small number of elderly residents over a short period of time, imply that within the results obtained from the MiiHome project, it is possible to find indicators of cognitive decline. However, further studies are needed for full clinical validation of these indications in conjunction with assessment of cognitive decline of the participants.

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Section: Resultsmentioning

confidence: 99%

Section: Ict For MCImentioning

confidence: 99%

Toward Kinecting cognition by behaviour recognition-based deep learning and big data

Soufian

Nefti-Mezian

Drake³

2020

Univ Access Inf Soc

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“…The first category includes six articles, focusing on outcomes relating to post-surgical limitations or improvements, such as quality of life after cancer surgery [21] and (walking) limitations or improvements (minimal clinically important difference (MCID)) after total joint arthroplasty [22][23][24][25][26]. The second category includes four articles, focusing on identifying patients with depression based on self-reports [18,27] and prognosis of outcome of anti-depression treatment [28,29]. The third category includes three articles focusing on predicting pain volatility amongst users of a pain-management mobile application [30,31] and self-referral decision support for patients with low back pain in primary care [32].…”

Section: Intervention Domains and Length Of Predictionmentioning

confidence: 99%

“…Currently, PROMs data are widely used in explanatory research, where researchers typically test hypotheses using a preconceived theoretical construct by applying statistical methods (for example, low back pain is associated to lower quality of life and depression [16,17]. In contrast, PROMs in predictive research can be used to predict outcomes in the future by applying statistical or machine learning methods without any preconceived theoretical constructs (for example, predicting the risk of depression [18]), and is therefore an important step towards patient-centred care with a shift in focus towards the patient's perspective [19].…”

Section: Introductionmentioning

confidence: 99%

Application of Machine Learning Methods on Patient Reported Outcome Measurements for Predicting Outcomes: A Literature Review

2021

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The field of patient-centred healthcare has, during recent years, adopted machine learning and data science techniques to support clinical decision making and improve patient outcomes. We conduct a literature review with the aim of summarising the existing methodologies that apply machine learning methods on patient-reported outcome measures datasets for predicting clinical outcomes to support further research and development within the field. We identify 15 articles published within the last decade that employ machine learning methods at various stages of exploiting datasets consisting of patient-reported outcome measures for predicting clinical outcomes, presenting promising research and demonstrating the utility of patient-reported outcome measures data for developmental research, personalised treatment and precision medicine with the help of machine learning-based decision-support systems. Furthermore, we identify and discuss the gaps and challenges, such as inconsistency in reporting the results across different articles, use of different evaluation metrics, legal aspects of using the data, and data unavailability, among others, which can potentially be addressed in future studies.

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“…Additionally, negative affect was found to be predictive of depression onset in youth [62]. There are a very limited number of prognostic studies available, focusing on older adults [63]. Authors have identified the items "sad" and "tired" as sensitive measures that have the potential to predict future depression status in older adults.…”

Section: Comparison With Prior Workmentioning

confidence: 99%

Screening for Depression in Daily Life: Development and External Validation of a Prediction Model Based on Actigraphy and Experience Sampling Method (Preprint)

Minaeva¹,

Riese²,

Lamers³

et al. 2020

Preprint

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BACKGROUND In many countries, depressed individuals often first visit primary care settings for consultation, but a considerable number of clinically depressed patients remains unidentified. Introducing additional screening tools may facilitate the diagnostic process. We aimed to examine whether Experience Sampling Method (ESM)-based measures of depressive affect and behaviors can discriminate depressed from non-depressed individuals. In addition, the added value of actigraphy-based measures was examined. OBJECTIVE The aim of our study was to examine whether ESM-assessed depression-related affect and behavior can discriminate between depressed and non-depressed individuals, whether actigraphy data can discriminate between depressed and non-depressed individuals on its own, and whether it has added value with respect to the use of ESM. METHODS We used data from two samples to develop and validate prediction models. The development dataset included 14 days of ESM and continuous actigraphy of currently depressed (n=43) and non-depressed individuals (n=82). The validation dataset included 30 days of ESM and continuous actigraphy of currently depressed (n=27) and non-depressed individuals (n=27). Backward stepwise logistic regression analyses were applied to build the prediction models. Performance of the models was assessed with goodness of fit indices, calibration curves, and discriminative ability (AUC - the area under the receiver operating characteristic curve). RESULTS In the development dataset, the discriminative ability was good for the actigraphy model (AUC=.790) and excellent for the ESM (AUC=.991) and combined-domains model (AUC=.993). In the validation dataset, the discriminative ability was reasonable for the actigraphy model (AUC=.648) and excellent for the ESM (AUC=.891) and combined-domains model (AUC=.892). CONCLUSIONS ESM was a good diagnostic predictor and is easy to calculate, and it therefore holds promise for implementation in clinical practice. Actigraphy showed no added value to ESM as a diagnostic predictor, but might still be useful when ESM use is restricted.

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Using the NANA toolkit at home to predict older adults’ future depression

Abstract: We have identified self-reported scales of sadness and tiredness as sensitive measures which have the potential to predict future depression status in older adults, partially addressing the problem of underdiagnosis.

Cited by 21 publications

References 10 publications

Toward Kinecting cognition by behaviour recognition-based deep learning and big data

Toward Kinecting cognition by behaviour recognition-based deep learning and big data

Application of Machine Learning Methods on Patient Reported Outcome Measurements for Predicting Outcomes: A Literature Review

Screening for Depression in Daily Life: Development and External Validation of a Prediction Model Based on Actigraphy and Experience Sampling Method (Preprint)

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