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Schmitter-Edgecombe M, Luna C, Dai S, Cook DJ. Predicting daily cognition and lifestyle behaviors for older adults using smart home data and ecological momentary assessment. Clin Neuropsychol 2024:1-25. [PMID: 38503715 DOI: 10.1080/13854046.2024.2330143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Accepted: 03/07/2024] [Indexed: 03/21/2024]
Abstract
OBJECTIVE Extraction of digital markers from passive sensors placed in homes is a promising method for understanding real-world behaviors. In this study, machine learning (ML) and multilevel modeling (MLM) are used to examine types of digital markers and whether smart home sensors can predict cognitive functioning, lifestyle behaviors, and contextual factors measured through ecological momentary assessment (EMA). METHOD Smart home sensors were installed in the homes of 44 community-dwelling midlife and older adults for 3-4 months. Sensor data were categorized into eight digital markers. Participants responded to iPad-delivered EMA prompts 4×/day for 2 wk. Prompts included an n-back task and survey on recent (past 2 h) lifestyle and contextual factors. RESULTS ML marker rankings revealed that sensor counts (indicating increased activity) and time outside the home were among the most influential markers for all survey questions. Additionally, MLM revealed for every 1000 sensor counts, mental sharpness, social, physical, and cognitive EMA responses increased by 0.134-0.155 points on a 5-point scale. For every additional 30-minutes spent outside home, social, physical, and cognitive EMA responses increased by 0.596, 0.472, and 0.157 points. Advanced ML joint classification/regression significantly predicted EMA responses from smart home digital markers with error of 0.370 on a 5-point scale, and n-back performance with a normalized error of 0.040. CONCLUSION Results from ML and MLM were complimentary and comparable, suggesting that machine learning may be used to develop generalized models to predict everyday cognition and track lifestyle behaviors and contextual factors that impact health outcomes using smart home sensor data.
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Affiliation(s)
| | - Catherine Luna
- Department of Psychology, Washington State University, Pullman, WA, USA
| | - Shenghai Dai
- College of Education, Washington State University, Pullman, WA, USA
| | - Diane J Cook
- School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA, USA
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Song X, Niu L, Admon R, Long J, Li Q, Peng L, Lee TM, Zhang R. Aberrant positive affect dynamics in individuals with subthreshold depression: Evidence from laboratory and real-world assessments. Int J Clin Health Psychol 2024; 24:100427. [PMID: 38173985 PMCID: PMC10761788 DOI: 10.1016/j.ijchp.2023.100427] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Accepted: 11/30/2023] [Indexed: 01/05/2024] Open
Abstract
Background/Objective Reduced positive affect (PA) is a core feature of major depressive disorder (MDD). However, the precursor of MDD, subthreshold depression (StD), has received less attention in this regard. Therefore, we examined PA dynamics in StD, integrating laboratory-based and ecological momentary assessment (EMA) approaches. Method Participants were college students recruited from Chinese universities (31 with StD, and 39 healthy controls (HC)). Positive mood was induced in the laboratory by an eight-minute comedy clip used to assess PA reactivity and maintenance. To extend findings to the real world and explore mechanisms of PA maintenance, 53 participants with StD and 64 HC reported their emotional states 14 times daily for one week via EMA. Multilevel models were used to test for predictors of PA inertia. Results In the laboratory, participants with StD achieved the same PA reactivity as HC when facing positive stimuli, yet the curve-fitting revealed difficulties for the StD group in maintaining PA over time. Such reduced capacity was further observed in real-world settings, manifesting in significantly greater PA inertia. Conclusions High PA inertia in daily life may reflect resistance to mood change in StD, explaining anhedonia and difficulties with emotional maintenance, and highlighting the need for early identification.
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Affiliation(s)
- Xiaoqi Song
- Cognitive control and Brain Healthy Laboratory, Department of Psychology, School of Public Health, Southern Medical University, Guangzhou, China
| | - Lijing Niu
- Cognitive control and Brain Healthy Laboratory, Department of Psychology, School of Public Health, Southern Medical University, Guangzhou, China
| | - Roee Admon
- School of Psychological Sciences, University of Haifa, Haifa, Israel
- The Integrated Brain and Behavior Research Center (IBBRC), University of Haifa, Haifa, Israel
| | - Jixin Long
- Cognitive control and Brain Healthy Laboratory, Department of Psychology, School of Public Health, Southern Medical University, Guangzhou, China
| | - Qian Li
- Cognitive control and Brain Healthy Laboratory, Department of Psychology, School of Public Health, Southern Medical University, Guangzhou, China
| | - Lanxin Peng
- Cognitive control and Brain Healthy Laboratory, Department of Psychology, School of Public Health, Southern Medical University, Guangzhou, China
| | - Tatia M.C. Lee
- State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong, SAR China
- Laboratory of Neuropsychology and Human Neuroscience, The University of Hong Kong, Hong Kong, SAR China
- Center for Brain Science and Brain-Inspired Intelligence, Guangdong-Hong Kong-Macao Greater Bay Area, Guangzhou, China
| | - Ruibin Zhang
- Cognitive control and Brain Healthy Laboratory, Department of Psychology, School of Public Health, Southern Medical University, Guangzhou, China
- Center for Brain Science and Brain-Inspired Intelligence, Guangdong-Hong Kong-Macao Greater Bay Area, Guangzhou, China
- Department of Psychiatry, Zhujiang Hospital, Southern Medical University, Guangzhou, China
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Chen C, Lifset ET, Han Y, Roy A, Hogarth M, Moore AA, Farcas E, Weibel N. Screen or No Screen? Lessons Learnt from a Real-World Deployment Study of Using Voice Assistants With and Without Touchscreen for Older Adults. ASSETS. ANNUAL ACM CONFERENCE ON ASSISTIVE TECHNOLOGIES 2023; 2023:52. [PMID: 39086515 PMCID: PMC11290471 DOI: 10.1145/3597638.3608378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 08/02/2024]
Abstract
While voice user interfaces offer increased accessibility due to hands-free and eyes-free interactions, older adults often have challenges such as constructing structured requests and perceiving how such devices operate. Voice-first user interfaces have the potential to address these challenges by enabling multimodal interactions. Standalone voice + touchscreen Voice Assistants (VAs), such as Echo Show, are specific types of devices that adopt such interfaces and are gaining popularity. However, the affordances of the additional touchscreen for older adults are unknown. Through a 40-day real-world deployment with older adults living independently, we present a within-subjects study (N = 16; age M = 82.5, SD = 7.77, min. = 70, max. = 97) to understand how a built-in touchscreen might benefit older adults during device setup, conducting self-report diary survey, and general uses. We found that while participants appreciated the visual outputs, they still preferred to respond via speech instead of touch. We identified six design implications that can inform future innovations of senior-friendly VAs for managing healthcare and improving quality of life.
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Affiliation(s)
- Chen Chen
- Computer Science and Engineering, University of California San Diego, La Jolla, CA, United States
| | - Ella T Lifset
- Biological Sciences, University of California San Diego, La Jolla, CA, United States
| | - Yichen Han
- Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, United States
| | - Arkajyoti Roy
- Department of Mathematics, University of California San Diego, La Jolla, CA, United States
| | - Michael Hogarth
- School of Medicine, University of California San Diego, La Jolla, CA, United States
| | - Alison A Moore
- School of Medicine, University of California San Diego, La Jolla, CA, United States
| | - Emilia Farcas
- Qualcomm Institute, University of California San Diego, La Jolla, CA, United States
| | - Nadir Weibel
- Computer Science and Engineering, University of California San Diego, La Jolla, CA, United States
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Wu X, Du J, Jiang H, Zhao M. Application of Digital Medicine in Addiction. JOURNAL OF SHANGHAI JIAOTONG UNIVERSITY (SCIENCE) 2022; 27:144-152. [PMID: 34866856 PMCID: PMC8627382 DOI: 10.1007/s12204-021-2391-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Accepted: 07/20/2021] [Indexed: 10/29/2022]
Affiliation(s)
- Xiaojun Wu
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030 China
| | - Jiang Du
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030 China
| | - Haifeng Jiang
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030 China
| | - Min Zhao
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030 China
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai, 201108 China
- CAS Center for Excellence in Brain Science and Intelligence Technology (CEBSIT), Chinese Academy of Sciences, Shanghai, 200031 China
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Schmitter-Edgecombe M, Brown K, Luna C, Chilton R, Sumida CA, Holder L, Cook D. Partnering a Compensatory Application with Activity-Aware Prompting to Improve Use in Individuals with Amnestic Mild Cognitive Impairment: A Randomized Controlled Pilot Clinical Trial. J Alzheimers Dis 2022; 85:73-90. [PMID: 34776442 PMCID: PMC9922794 DOI: 10.3233/jad-215022] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
BACKGROUND Compensatory aids can help mitigate the impact of progressive cognitive impairment on daily living. OBJECTIVE We evaluate whether the learning and sustained use of an Electronic Memory and Management Aid (EMMA) application can be augmented through a partnership with real-time, activity-aware transition-based prompting delivered by a smart home. METHODS Thirty-two adults who met criteria for amnestic mild cognitive impairment (aMCI) were randomized to learn to use the EMMA app on its own (N = 17) or when partnered with smart home prompting (N = 15). The four-week, five-session manualized EMMA training was conducted individually in participant homes by trained clinicians. Monthly questionnaires were completed by phone with trained personnel blind to study hypotheses. EMMA data metrics were collected continuously for four months. For the partnered condition, activity-aware prompting was on during training and post-training months 1 and 3, and off during post-training month 2. RESULTS The analyzed aMCI sample included 15 EMMA-only and 14 partnered. Compared to the EMMA-only condition, by week four of training, participants in the partnered condition were engaging with EMMA more times daily and using more basic and advanced features. These advantages were maintained throughout the post-training phase with less loss of EMMA app use over time. There was little differential impact of the intervention on self-report primary (everyday functioning, quality of life) and secondary (coping, satisfaction with life) outcomes. CONCLUSION Activity-aware prompting technology enhanced acquisition, habit formation and long-term use of a digital device by individuals with aMCI. (ClinicalTrials.gov NCT03453554).
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Affiliation(s)
- Maureen Schmitter-Edgecombe
- Department of Psychology, Washington State University, Pullman, WA, USA,Correspondence to: Maureen Schmitter-Edgecombe, PhD, Psychology Department, Johnson Tower 233, Washington State University, Pullman, WA, 99164-4820, USA. Tel.: +1 509 592 0631; Fax: +1 509 335 5043;
| | - Katelyn Brown
- Department of Psychology, Washington State University, Pullman, WA, USA
| | - Catherine Luna
- Department of Psychology, Washington State University, Pullman, WA, USA
| | - Reanne Chilton
- Department of Psychology, Washington State University, Pullman, WA, USA
| | | | - Lawrence Holder
- School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA, USA
| | - Diane Cook
- School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA, USA
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Dias R, Vaz R, Rodrigues MJ, Serra-Negra JM, Bracci A, Manfredini D. Utility of Smartphone-based real-time report (Ecological Momentary Assessment) in the assessment and monitoring of awake bruxism: A multiple-week interval study in a Portuguese population of university students. J Oral Rehabil 2021; 48:1307-1313. [PMID: 34536309 DOI: 10.1111/joor.13259] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Revised: 08/27/2021] [Accepted: 09/01/2021] [Indexed: 12/12/2022]
Abstract
PURPOSE The Ecological Momentary Assessment (EMA) concept was the basis for developing a smartphone application for the on-time report of awake bruxism (AB) activities. This study aims to monitor AB report over time in a population of healthy young adults. METHODS A population of young adults recruited among dental students in good general health was recruited. All answered to a questionnaire, including the Oral Behavior Checklist (OBC-1). They were then monitored with a smartphone application for AB report during seven consecutive days and completed three further observation periods (EMA-1, 2 and 3) at one-month intervals. After the third period (EMA-3), participants answered again the OBC questionnaire (OBC-2). Changes over time were described, and Pearson Correlation test was performed to assess the correlation between EMA and OBC items reports. A significance level of p = .01 was set. RESULTS Thirty-one University students completed the study protocol. Answers to the OBC showed an increase in the prevalence of self-reported bracing and teeth clenching from the first to second report (38.7%-54.8% and 77.4%-90.3%, respectively). A slight increase in the 'relaxed' condition (62.5%-69.0%) was observed with EMA-based smartphone application over time. No correlation between OBC items and EMA was detected between OBC-1 and EMA-1. A moderate positive correlation in bracing report (+0.509, p = .01) and weak positive correlation in teeth contact report (+0.380, p = .05) were found between OBC-2 and EMA-3. CONCLUSION Using a smartphone-based approach to AB report may be helpful to monitor AB over time and increase an individual's awareness to recognise actions such as bracing and teeth contact concerning the single-time report.
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Affiliation(s)
- Ricardo Dias
- Institute of Oral Implantology and Prosthodontics, Dentistry Department, Faculty of Medicine, University of Coimbra, Coimbra, Portugal
| | - Rui Vaz
- Dentistry Department, Faculty of Medicine, University of Coimbra, Coimbra, Portugal
| | - Maria João Rodrigues
- Institute of Orofacial Pain, Dentistry Department, Faculty of Medicine, University of Coimbra, Coimbra, Portugal
| | - Júnia Maria Serra-Negra
- Department of Pediatric Dentistry and Orthodontics, Federal University of Minas Gerais, Pampulha, Belo Horizonte, Minas Gerais, Brazil
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Stewart MT, Nezich T, Lee JM, Hasson RE, Colabianchi N. Using a Mobile Phone App to Analyze the Relationship Between Planned and Performed Physical Activity in University Students: Observational Study. JMIR Mhealth Uhealth 2021; 9:e17581. [PMID: 33913812 PMCID: PMC8120422 DOI: 10.2196/17581] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2019] [Revised: 06/14/2020] [Accepted: 03/08/2021] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND The relationship between intention and behavior has been well researched, but most studies fail to capture dynamic, time-varying contextual factors. Ecological momentary assessment through mobile phone technology is an innovative method for collecting data in real time, including time-use data. However, only a limited number of studies have examined day-level plans to be physically active and subsequent physical activity behavior using real-time time-use data to better understand this relationship. OBJECTIVE This study aims to examine whether plans to be physically active (recorded in advance on an electronic calendar) were associated with objectively assessed physical activity (accelerometry), to identify activities that replaced planned periods of physical activity by using the mobile app Life in a Day (LIAD), and to test the feasibility and acceptability of LIAD for collecting real-time time-use data. METHODS The study included 48 university students who were randomly assigned to 1 of 3 protocols, which were defined by 1, 3, or 5 days of data collection. Participants were asked to record their planned activities on a Google Calendar and were provided with mobile phones with LIAD to complete time-use entries in real time for a set of categories (eg, exercise or sports, eating or cooking, school, or personal care). Participants were instructed to wear an accelerometer on their nondominant wrist during the protocol period. A total of 144 days of protocol data were collected from the 48 participants. RESULTS Protocol data for 123 days were eligible for analysis. A Fisher exact test showed a statistically significant association between plans and physical activity behavior (P=.02). The congruence between plans and behavior was fair (Cohen κ=0.220; 95% CI 0.028-0.411). Most participants did not plan to be active, which occurred on 75.6% (93/123) of days. Of these 93 days, no physical activity occurred on 76 (81.7%) days, whereas some physical activity occurred on 17 (18.3%) days. On the remaining 24.4% (30/123) of days, some physical activity was planned. Of these 30 days, no physical activity occurred on 18 (60%) days, whereas some physical activity occurred on 12 (40%) days. LIAD data indicated that activities related to screen time most often replaced planned physical activity, whereas unplanned physical activity was often related to active transport. Feasibility analyses indicated little difficulty in using LIAD, and there were no significant differences in feasibility by protocol length. CONCLUSIONS Consistent with previous literature, physical activity plans and physical activity behaviors were linked, but not strongly linked. LIAD offers insight into the relationship between plans and behavior, highlighting the importance of active transport for physical activity and the influence of screen-related behaviors on insufficient physical activity. LIAD is a feasible and practical method for collecting time-use data in real time.
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Affiliation(s)
- Matthew T Stewart
- School of Kinesiology, University of Michigan, Ann Arbor, MI, United States
| | - Taylor Nezich
- School of Kinesiology, University of Michigan, Ann Arbor, MI, United States
| | - Joyce M Lee
- Susan B. Meister Child Health Evaluation and Research Center, Department of Pediatrics, University of Michigan Medical School, Ann Arbor, MI, United States
- Department of Nutritional Sciences, School of Public Health, University of Michigan, Ann Arbor, MI, United States
| | - Rebecca E Hasson
- School of Kinesiology, University of Michigan, Ann Arbor, MI, United States
- Department of Nutritional Sciences, School of Public Health, University of Michigan, Ann Arbor, MI, United States
| | - Natalie Colabianchi
- School of Kinesiology, University of Michigan, Ann Arbor, MI, United States
- Institute for Social Research, University of Michigan, Ann Arbor, MI, United States
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McKinney TL, Euler MJ, Butner JE. It’s about time: The role of temporal variability in improving assessment of executive functioning. Clin Neuropsychol 2019; 34:619-642. [DOI: 10.1080/13854046.2019.1704434] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Affiliation(s)
- Ty L. McKinney
- Department of Psychology, University of Utah, Salt Lake City, UT, USA
| | - Matthew J. Euler
- Department of Psychology, University of Utah, Salt Lake City, UT, USA
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Kleiman EM, Glenn CR, Liu RT. Real-Time Monitoring of Suicide Risk among Adolescents: Potential Barriers, Possible Solutions, and Future Directions. JOURNAL OF CLINICAL CHILD AND ADOLESCENT PSYCHOLOGY : THE OFFICIAL JOURNAL FOR THE SOCIETY OF CLINICAL CHILD AND ADOLESCENT PSYCHOLOGY, AMERICAN PSYCHOLOGICAL ASSOCIATION, DIVISION 53 2019; 48:934-946. [PMID: 31560584 PMCID: PMC6864279 DOI: 10.1080/15374416.2019.1666400] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Recent advances in real-time monitoring technology make this an exciting time to study risk for suicidal thoughts and behaviors among youth. Although there is good reason to be excited about these methods, there is also reason for caution in adopting them without first understanding their limitations. In this article, we present several broad future directions for using real-time monitoring among youth at risk for suicide focused around three broad themes: novel research questions, novel analytic methods, and novel methodological approaches. We also highlight potential technical, logistical, and ethical challenges with these methodologies, as well as possible solutions to these challenges.
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Affiliation(s)
- Evan M Kleiman
- Department of Psychology, Rutgers, The State University of New Jersey
| | - Catherine R Glenn
- Department of Clinical & Social Sciences in Psychology, University of Rochester
| | - Richard T Liu
- Department of Psychiatry and Human Behavior, Alpert Medical School of Brown University, Bradley Hospital
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