Design of an mHealth app to promote day-long physical activity in older adults with chronic pain: Phase 1 of the MORPH study (Preprint)

2020 ◽  
Author(s):  
Jason Fanning ◽  
Amber Brooks ◽  
Edward Ip ◽  
Barbara Nicklas ◽  
W. Jack Rejeski
Keyword(s):  
Physical Activity ◽  
Older Adults ◽  
Design Process ◽  
Underserved Populations ◽  
Activity Monitor ◽  
Perceived Usefulness ◽  
Technology Selection ◽  
Large Trial ◽  
User Centered Design ◽  
User Centered

BACKGROUND Participating in physical activity and minimizing time spent sitting is an effective strategy for managing pain in older adults. Theory-based mHealth tools are integral to effective day-long physical activity interventions, but it is vital that mHealth tools undergo an iterative development process alongside members of the target population to ensure their uptake and use. OBJECTIVE We subjected a preliminary social cognitive smartphone application (Companion App) designed to promote day-long movement to a user centered design process with the assistance of low-active older adults with chronic multisite pain. The Companion App integrates ecological momentary assessments of pain, Fitbit activity monitor data, and smart weight scale data to provide real-time feedback on the relationships between movement, sitting, and pain and to facilitate goal setting and achievement. METHODS We recruited participants (N=5; 71.8 5.54 years old) sequentially to participate in a three-phase iterative design study. First, each participant received a brief orientation to physical activity, was exposed to the application, and engaged in a Think Aloud protocol. Use and usability issues were noted by study staff. The participant then used the app for one week in their daily lives, and then returned to provide feedback. Issues were identified from participant feedback, discussed with the study team, and modified before the next participant began the study. RESULTS Participant interviews yielded feedback in areas related to technology selection and operation, app design/form, and intervention clarity. Regarding technology, the use of the Fitbit activity monitor revealed no issues, but there were barriers to the use of the Fitbit Aria 2 scale, including incompatibility with a widely used home internet router. Switching to a cellular enabled scale alleviated this issue. With regard to form, modifications were made to several key interface elements in response to participant feedback to aid in clarity. Finally, initial participant experiences revealed the need to separate the intervention orientation from the technology orientation to minimize informational load. CONCLUSIONS Our brief user-centered design process produced key changes in our intervention orientation, the form and function of the Companion App, and the technologies that support the app. These are vital elements that are likely to hamper the perceived usefulness and utility of the Companion App in the context of a large trial and eventual public use. We recommend the conduct of such a process any time mHealth is used in research or medicine to account for changing populations and preferences. Moreover, publication of lessons learned can help to establish a foundation of knowledge for designing apps for underserved populations such as older adults. CLINICALTRIAL ClinicalTrials.gov Identifier: NCT03377634

scholarly journals Using Natural Language Processing and Sentiment Analysis to Augment Traditional User-Centered Design: Development and Usability Study

10.2196/16862 ◽  
2020 ◽  
Vol 8 (8) ◽  
pp. e16862
Author(s):  
Curtis Lee Petersen ◽  
Ryan Halter ◽  
David Kotz ◽  
Lorie Loeb ◽  
Summer Cook ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Older Adults ◽  
Natural Language Processing ◽  
Sentiment Analysis ◽  
Design Process ◽  
Language Processing ◽  
User Centered Design ◽  
The Mean ◽  
User Friendly ◽  
User Centered

Background Sarcopenia, defined as the age-associated loss of muscle mass and strength, can be effectively mitigated through resistance-based physical activity. With compliance at approximately 40% for home-based exercise prescriptions, implementing a remote sensing system would help patients and clinicians to better understand treatment progress and increase compliance. The inclusion of end users in the development of mobile apps for remote-sensing systems can ensure that they are both user friendly and facilitate compliance. With advancements in natural language processing (NLP), there is potential for these methods to be used with data collected through the user-centered design process. Objective This study aims to develop a mobile app for a novel device through a user-centered design process with both older adults and clinicians while exploring whether data collected through this process can be used in NLP and sentiment analysis Methods Through a user-centered design process, we conducted semistructured interviews during the development of a geriatric-friendly Bluetooth-connected resistance exercise band app. We interviewed patients and clinicians at weeks 0, 5, and 10 of the app development. Each semistructured interview consisted of heuristic evaluations, cognitive walkthroughs, and observations. We used the Bing sentiment library for a sentiment analysis of interview transcripts and then applied NLP-based latent Dirichlet allocation (LDA) topic modeling to identify differences and similarities in patient and clinician participant interviews. Sentiment was defined as the sum of positive and negative words (each word with a +1 or −1 value). To assess utility, we used quantitative assessment questionnaires—System Usability Scale (SUS) and Usefulness, Satisfaction, and Ease of use (USE). Finally, we used multivariate linear models—adjusting for age, sex, subject group (clinician vs patient), and development—to explore the association between sentiment analysis and SUS and USE outcomes. Results The mean age of the 22 participants was 68 (SD 14) years, and 17 (77%) were female. The overall mean SUS and USE scores were 66.4 (SD 13.6) and 41.3 (SD 15.2), respectively. Both patients and clinicians provided valuable insights into the needs of older adults when designing and building an app. The mean positive-negative sentiment per sentence was 0.19 (SD 0.21) and 0.47 (SD 0.21) for patient and clinician interviews, respectively. We found a positive association with positive sentiment in an interview and SUS score (ß=1.38; 95% CI 0.37 to 2.39; P=.01). There was no significant association between sentiment and the USE score. The LDA analysis found no overlap between patients and clinicians in the 8 identified topics. Conclusions Involving patients and clinicians allowed us to design and build an app that is user friendly for older adults while supporting compliance. This is the first analysis using NLP and usability questionnaires in the quantification of user-centered design of technology for older adults.

scholarly journals Using Natural Language Processing and Sentiment Analysis to Augment Traditional User-Centered Design: Development and Usability Study (Preprint)

2019 ◽  
Author(s):  
Curtis Lee Petersen ◽  
Ryan Halter ◽  
David Kotz ◽  
Lorie Loeb ◽  
Summer Cook ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Older Adults ◽  
Natural Language Processing ◽  
Sentiment Analysis ◽  
Design Process ◽  
Language Processing ◽  
User Centered Design ◽  
The Mean ◽  
User Friendly ◽  
User Centered

BACKGROUND Sarcopenia, defined as the age-associated loss of muscle mass and strength, can be effectively mitigated through resistance-based physical activity. With compliance at approximately 40% for home-based exercise prescriptions, implementing a remote sensing system would help patients and clinicians to better understand treatment progress and increase compliance. The inclusion of end users in the development of mobile apps for remote-sensing systems can ensure that they are both user friendly and facilitate compliance. With advancements in natural language processing (NLP), there is potential for these methods to be used with data collected through the user-centered design process. OBJECTIVE This study aims to develop a mobile app for a novel device through a user-centered design process with both older adults and clinicians while exploring whether data collected through this process can be used in NLP and sentiment analysis METHODS Through a user-centered design process, we conducted semistructured interviews during the development of a geriatric-friendly Bluetooth-connected resistance exercise band app. We interviewed patients and clinicians at weeks 0, 5, and 10 of the app development. Each semistructured interview consisted of heuristic evaluations, cognitive walkthroughs, and observations. We used the Bing sentiment library for a sentiment analysis of interview transcripts and then applied NLP-based latent Dirichlet allocation (LDA) topic modeling to identify differences and similarities in patient and clinician participant interviews. Sentiment was defined as the sum of positive and negative words (each word with a +1 or −1 value). To assess utility, we used quantitative assessment questionnaires—System Usability Scale (SUS) and Usefulness, Satisfaction, and Ease of use (USE). Finally, we used multivariate linear models—adjusting for age, sex, subject group (clinician vs patient), and development—to explore the association between sentiment analysis and SUS and USE outcomes. RESULTS The mean age of the 22 participants was 68 (SD 14) years, and 17 (77%) were female. The overall mean SUS and USE scores were 66.4 (SD 13.6) and 41.3 (SD 15.2), respectively. Both patients and clinicians provided valuable insights into the needs of older adults when designing and building an app. The mean positive-negative sentiment per sentence was 0.19 (SD 0.21) and 0.47 (SD 0.21) for patient and clinician interviews, respectively. We found a positive association with positive sentiment in an interview and SUS score (ß=1.38; 95% CI 0.37 to 2.39; <i>P</i>=.01). There was no significant association between sentiment and the USE score. The LDA analysis found no overlap between patients and clinicians in the 8 identified topics. CONCLUSIONS Involving patients and clinicians allowed us to design and build an app that is user friendly for older adults while supporting compliance. This is the first analysis using NLP and usability questionnaires in the quantification of user-centered design of technology for older adults.

scholarly journals Exergaming Platform for Older Adults Residing in Long-Term Care Homes: User-Centered Design, Development, and Usability Study (Preprint)

2020 ◽  
Author(s):  
Charlene H Chu ◽  
Renée K Biss ◽  
Lara Cooper ◽  
Amanda My Linh Quan ◽  
Henrique Matulis
Keyword(s):  
Physical Activity ◽  
Older Adults ◽  
Long Term Care ◽  
Cognitive Impairments ◽  
Usability Testing ◽  
User Centered Design ◽  
Term Care ◽  
Activity Enjoyment ◽  
User Centered

BACKGROUND Older adults (OAs) residing in long-term care (LTC) homes are often unable to engage in adequate amounts of physical activity because of multiple comorbidities, including frailty and severe cognitive impairments. This level of physical inactivity is associated with declines in cognitive and functional abilities and can be further compounded by social isolation. Exergaming, defined as a combination of exercise and gaming, has the potential to engage OAs in exercise and encourage social interaction. However, previously used systems such as the Nintendo Wii are no longer commercially available, and the physical design of other exergames is not suitable for OAs (ie, fall risks, accessibility issues, and games geared toward a younger population) with diverse physical and cognitive impairments. OBJECTIVE This study aims to design and develop a novel, user-centered, evidence-based exergaming system for use among OAs in LTC homes. In addition, we aim to identify facilitators and barriers to the implementation of our exergaming intervention, the MouvMat, into LTC homes according to staff input. METHODS This study used a user-centered design (UCD) process that consisted of 4 rounds of usability testing. The exergame was developed and finalized based on existing evidence, end user and stakeholder input, and user testing. Semistructured interviews and standardized and validated scales were used iteratively to evaluate the acceptability, usability, and physical activity enjoyment of the MouvMat. RESULTS A total of 28 participants, 13 LTC residents, and 15 staff and family members participated in the UCD process for over 18 months to design and develop the novel exergaming intervention, the MouvMat. The iterative use of validated scales (System Usability Scale, 8-item Physical Activity Enjoyment Scale, and modified Treatment Evaluation Inventory) indicated an upward trend in the acceptability, usability, and enjoyment scores of MouvMat over 4 rounds of usability testing, suggesting that identified areas for refinement and improvement were appropriately addressed by the team. A qualitative analysis of semistructured interview data found that residents enjoyed engaging with the prototype and appreciated the opportunity to increase their PA. In addition, staff and stakeholders were drawn to MouvMat’s ability to increase residents’ autonomous PA. The intended and perceived benefits of MouvMat use, that is, improved physical and cognitive health, were the most common facilitators of its use identified by study participants. CONCLUSIONS This study was successful in applying UCD to collaborate with LTC residents, despite the high number of physical and sensory impairments that this population experiences. By following a UCD process, an exergaming intervention that meets diverse requirements (ie, hardware design features and motivation) and considers environmental barriers and residents’ physical and cognitive needs was developed. The effectiveness of MouvMat in improving physical and cognitive abilities should be explored in future multisite randomized controlled trials. CLINICALTRIAL

scholarly journals A Qualitative Study toward Technologies for Active and Healthy Aging: A Thematic Analysis of Perspectives among Primary, Secondary, and Tertiary End Users

2021 ◽  
Vol 18 (14) ◽  
pp. 7489
Author(s):  
Margherita Rampioni ◽  
Adrian Alexandru Moșoi ◽  
Lorena Rossi ◽  
Sorin-Aurel Moraru ◽  
Dan Rosenberg ◽  
...  
Keyword(s):  
Older Adults ◽  
Qualitative Study ◽  
Information And Communication Technologies ◽  
Healthy Aging ◽  
Perceived Usefulness ◽  
Research Network ◽  
Political Agenda ◽  
User Centered Design ◽  
Sensing Technology ◽  
Care Systems

It is expected that, by 2050, people aged over 60 in 65 nations will constitute 30% of the total population. Healthy aging is at the top of the world political agenda as a possible means for hindering the collapse of care systems. How can ICT/sensing technology meet older people’s needs for active and healthy aging? This qualitative study carried out in Italy and Romania in 2020 involved 30 participants: older adults, caregivers, and stakeholders. Based on a user-centered design approach, this study aimed to understand which requirements of ICT/sensing technologies could match people’s needs of active and healthy aging. Findings highlighted that ICT/sensing technology needs to focus on six major themes: (1) learnability, (2) security, (3) independence, empowerment, and coaching values, (4) social isolation, (5) impact of habit, culture, and education variables, and (6) personalized solutions. These themes are consistent with the Active Aging framework and the factors that influence perceived usefulness and potential benefits among older adults. Consequently, this study shows how well-known, but still unresolved, issues affect the field of information and communication technologies (ICTs) to promote active and healthy aging. This suggests that the reinforcement of the public health system, especially considering the pandemic effect, requires a concrete and formidable effort from an interdisciplinary research network.

scholarly journals A User Centered Methodology for the Design of Smart Apparel for Older Users

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2804
Author(s):  
Silvia Imbesi ◽  
Sofia Scataglini
Keyword(s):  
Design Process ◽  
Quality Function Deployment ◽  
Well Being ◽  
Comparison Method ◽  
User Centered Design ◽  
Smart Clothing ◽  
Older Users ◽  
Smart Garments ◽  
General Response ◽  
User Centered

Smart clothing plays a big role to foster innovation and to. boost health and well-being, improving the quality of the life of people, especially when addressed to niche users with particular needs related to their health. Designing smart apparel, in order to monitor physical and physiological functions in older users, is a crucial asset that user centered design is exploring, balancing needs expressed by the users with technological requirements related to the design process. In this paper, the authors describe a user centered methodology for the design of smart garments based on the evaluation of users’ acceptance of smart clothing. This comparison method can be considered as similar to a simplified version of the quality function deployment tool, and is used to evaluate the general response of each garment typology to different categories of requirements, determining the propensity of the older user to the utilization of the developed product. The suggested methodology aims at introducing in the design process a tool to evaluate and compare developed solutions, reducing complexity in design processes by providing a tool for the comparison of significant solutions, correlating quantitative and qualitative factors.

Developing Clinical Decision Support System for Sleep Staging Tasks by Formulating Explanations from Artificial Intelligence: User-Centered Design and Evaluation. (Preprint)

2021 ◽  
Author(s):  
Jeonghwan Hwang ◽  
Taeheon Lee ◽  
Honggu Lee ◽  
Seonjeong Byun
Keyword(s):  
Artificial Intelligence ◽  
Decision Support ◽  
Design Process ◽  
Clinical Decision Support ◽  
User Study ◽  
Clinical Decision ◽  
Sleep Staging ◽  
User Centered Design ◽  
Iterative Design ◽  
User Centered

BACKGROUND Despite the unprecedented performances of deep learning algorithms in clinical domains, full reviews of algorithmic predictions by human experts remain mandatory. Under these circumstances, artificial intelligence (AI) models are primarily designed as clinical decision support systems (CDSSs). However, from the perspective of clinical practitioners, the lack of clinical interpretability and user-centered interfaces block the adoption of these AI systems in practice. OBJECTIVE The aim of this study was to develop an AI-based CDSS for assisting polysomnographic technicians in reviewing AI-predicted sleep staging results. This study proposed and evaluated a CDSS that provides clinically sound explanations for AI predictions in a user-centered fashion. METHODS User needs for the system were identified during interviews with polysomnographic technicians. User observation sessions were conducted to understand the workflow of the practitioners during sleep scoring. Iterative design process was performed to ensure easy integration of the tool into clinical workflows. Then, we evaluated the system with polysomnographic technicians. We measured the improvements in sleep staging accuracies after adopting our tool and assessed qualitatively how the participants perceived and used the tool. RESULTS The user study revealed that technicians desire explanations relevant to key electroencephalogram (EEG) patterns for sleep staging when assessing the correctness of the AI predictions. Here, technicians could evaluate whether AI models properly locate and use those patterns during prediction. Based on this, information in AI models that is closely related to sleep EEG patterns was formulated and visualized during the iterative design process. Furthermore, we developed a different visualization strategy for each pattern based on the way the technicians interpreted the EEG recordings with these patterns during their workflows. Generally, the tool evaluation results from the nine polysomnographic technicians were positive. Quantitatively, technicians achieved better classification performances after reviewing the AI-generated predictions with the proposed system; classification accuracies measured with Macro-F1 scores improved from 60.20 to 62.71. Qualitatively, participants reported that the provided information from the tool effectively supported them, and they were able to develop notable adoption strategies for the tool. CONCLUSIONS Our findings indicate that formulating clinical explanations for automated predictions using the information in the AI with a user-centered design process is an effective strategy for developing a CDSS for sleep staging.

scholarly journals User-centered design of a patient’s work station for haptic robot-based telerehabilitation after stroke

2017 ◽  
Vol 3 (1) ◽  
pp. 39-43 ◽  
Author(s):  
Ekaterina Ivanova ◽  
Michael Minge ◽  
Henning Schmidt ◽  
Manfred Thüring ◽  
Jörg Krüger
Keyword(s):  
Hospital Discharge ◽  
Design Process ◽  
Neurological Rehabilitation ◽  
User Centered Design ◽  
Work Station ◽  
Rehabilitation Training ◽  
Robotic Therapy ◽  
Rehabilitation Hospitals ◽  
Early Phases ◽  
User Centered

Abstract:Robotic therapy devices have been an important part of clinical neurological rehabilitation for several years. Until now such devices are only available for patients receiving therapy inside rehabilitation hospitals. Since patients should continue rehabilitation training after hospital discharge at home, intelligent robotic rehab devices could help to achieve this goal. This paper presents therapeutic requirements and early phases of the user-centered design process of the patient’s work station as part of a novel robot-based system for motor telerehabilitation.

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