Perception of Older Adults Toward Smartwatch Technology for Assessing Pain and Related Patient-Reported Outcomes: Pilot Study

Background Chronic pain, including arthritis, affects about 100 million adults in the United States. Complexity and diversity of the pain experience across time and people and its fluctuations across and within days show the need for valid pain reports that do not rely on patient’s long-term recall capability. Smartwatches can be used as digital ecological momentary assessment (EMA) tools for real-time collection of pain scores. Smartwatches are generally less expensive than smartphones, are highly portable, and have a simpler user interface, providing an excellent medium for continuous data collection and enabling a higher compliance rate. Objective The aim of this study was to explore the attitudes and perceptions of older adults towards design and technological aspects of a smartwatch framework for measuring patient report outcomes (PRO) as an EMA tool. Methods A focus group session was conducted to explore the perception of participants towards smartwatch technology and its utility for PRO assessment. Participants included older adults (age 65+), with unilateral or bilateral symptomatic knee osteoarthritis. A preliminary user interface with server communication capability was developed and deployed on 10 Samsung Gear S3 smartwatches and provided to the users during the focus group. Pain was designated as the main PRO, while fatigue, mood, and sleep quality were included as auxiliary PROs. Pre-planned topics included participants’ attitude towards the smartwatch technology, usability of the custom-designed app interface, and suitability of the smartwatch technology for PRO assessment. Discussions were transcribed, and content analysis with theme characterization was performed to identify and code the major themes. Results We recruited 19 participants (age 65+) who consented to take part in the focus group study. The overall attitude of the participants toward the smartwatch technology was positive. They showed interest in the direct phone-call capability, availability of extra apps such as the weather apps and sensors for tracking health and wellness such as accelerometer and heart rate sensor. Nearly three-quarters of participants showed willingness to participate in a one-year study to wear the watch daily. Concerns were raised regarding usability, including accessibility (larger icons), notification customization, and intuitive interface design (unambiguous icons and assessment scales). Participants expressed interest in using smartwatch technology for PRO assessment and the availability of methods for sharing data with health care providers. Conclusions All participants had overall positive views of the smartwatch technology for measuring PROs to facilitate patient-provider communications and to provide more targeted treatments and interventions in the future. Usability concerns were the major issues that will require special consideration in future smartwatch PRO user interface designs, especially accessibility issues, notification design, and use of intuitive assessment scales.

[1]  Sri Hastuti Kurniawan,et al.  Older people and mobile phones: A multi-method investigation , 2008, Int. J. Hum. Comput. Stud..

[2]  Pennifer Erickson,et al.  Use of existing patient-reported outcome (PRO) instruments and their modification: the ISPOR Good Research Practices for Evaluating and Documenting Content Validity for the Use of Existing Instruments and Their Modification PRO Task Force Report. , 2009, Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research.

[3]  J. Coughlin,et al.  PERSPECTIVE: Older Adults' Adoption of Technology: An Integrated Approach to Identifying Determinants and Barriers , 2015 .

[4]  A. Stone,et al.  Real-time data collection for pain: appraisal and current status. , 2007, Pain medicine.

[5]  Majid Sarrafzadeh,et al.  Gait velocity estimation for a smartwatch platform using Kalman filter peak recovery , 2016, 2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN).

[6]  H. Goh,et al.  Test-retest reliability, internal consistency and concurrent validity of Fatigue Severity Scale in measuring post-stroke fatigue. , 2017, European journal of physical and rehabilitation medicine.

[7]  J. B. Brooke,et al.  SUS: A 'Quick and Dirty' Usability Scale , 1996 .

[8]  A. Alghadir,et al.  Test–retest reliability, validity, and minimum detectable change of visual analog, numerical rating, and verbal rating scales for measurement of osteoarthritic knee pain , 2018, Journal of pain research.

[9]  Janet Mancini Billson,et al.  Focus Groups: A Practical Guide for Applied Research , 1989 .

[10]  P. Bąbel The effect of affect on memory of pain induced by tooth restoration. , 2014, International dental journal.

[11]  Oksana Zelenko,et al.  Mobile App Rating Scale: A New Tool for Assessing the Quality of Health Mobile Apps , 2015, JMIR mHealth and uHealth.

[12]  J. Stinson,et al.  Development and Testing of a Multidimensional iPhone Pain Assessment Application for Adolescents with Cancer , 2012, Journal of medical Internet research.

[13]  Pranee Liamputtong Rice Focus group methodology : principles and practice , 2011 .

[14]  Painful Memories: Reliability of Pain Intensity Recall at 3 Months in Senior Patients , 2017, Pain research & management.

[15]  M. Kliger,et al.  Measuring the Intensity of Chronic Pain: Are the Visual Analogue Scale and the Verbal Rating Scale Interchangeable? , 2015, Pain practice : the official journal of World Institute of Pain.

[16]  B. Thomas,et al.  Usability Evaluation In Industry , 1996 .

[17]  Sebastian Möller,et al.  Duration Neglect in Multi-episodic Perceived Quality , 2016 .

[18]  Benjamin H. Natelson,et al.  A real-time assessment of the effect of exercise in chronic fatigue syndrome , 2007, Physiology & Behavior.

[19]  W. Adamczyk,et al.  Pain begets pain. When marathon runners are not in pain anymore, they underestimate their memory of marathon pain––A mediation analysis , 2018, European journal of pain.

[20]  Jon-Kar Zubieta,et al.  Real-Time Sharing and Expression of Migraine Headache Suffering on Twitter: A Cross-Sectional Infodemiology Study , 2014, Journal of medical Internet research.

[21]  Alexander Seifert,et al.  The use of mobile devices for physical activity tracking in older adults’ everyday life , 2017, Digital health.

[22]  K. Craig,et al.  Social communication model of pain. , 2015, Pain.

[23]  A. DeLongis,et al.  Gender Differences in Pain-Physical Activity Linkages among Older Adults: Lessons Learned from Daily Life Approaches , 2016, Pain research & management.

[24]  A. Strauss,et al.  The discovery of grounded theory: strategies for qualitative research aldine de gruyter , 1968 .

[25]  Gary S Collins,et al.  Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): Explanation and Elaboration , 2015, Annals of Internal Medicine.

[26]  Tom Page,et al.  Touchscreen mobile devices and older adults: a usability study , 2014 .

[27]  J. Dartigues,et al.  Daily life functioning of community‐dwelling elderly couples: an investigation of the feasibility and validity of Ecological Momentary Assessment , 2014, International journal of methods in psychiatric research.

[28]  Parisa Rashidi,et al.  ROAMM: A software infrastructure for real-time monitoring of personal health , 2016, 2016 IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom).

[29]  Harald Burgsteiner,et al.  A Smartwatch-Based Assistance System for the Elderly Performing Fall Detection, Unusual Inactivity Recognition and Medication Reminding , 2016, eHealth.

[30]  Mark P. Jensen,et al.  Validity of four pain intensity rating scales , 2011, PAIN®.

[31]  M. Berking,et al.  Can a One-Item Mood Scale Do the Trick? Predicting Relapse over 5.5-Years in Recurrent Depression , 2012, PloS one.

[32]  C. Brodsky The Discovery of Grounded Theory: Strategies for Qualitative Research , 1968 .

[33]  D. Ariely,et al.  In Pain Thou Shalt Bring Forth Children , 2014, Psychological science.

[34]  Anja Bachmann,et al.  Leveraging smartwatches for unobtrusive mobile ambulatory mood assessment , 2015, UbiComp/ISWC Adjunct.

[35]  Patrick Richard,et al.  The economic costs of pain in the United States. , 2012, The journal of pain : official journal of the American Pain Society.

[36]  Eleni Stroulia,et al.  International Journal of Medical Informatics , 2016 .

[37]  Sil Aarts,et al.  Can smart home technology deliver on the promise of independent living?: A critical reflection based on the perspectives of older adults , 2009 .

[38]  R. Fillingim Individual differences in pain: understanding the mosaic that makes pain personal , 2017, Pain.

[39]  A. Anastasi Individual differences. , 2020, Annual review of psychology.

[40]  D. Wong,et al.  Smiling face as anchor for pain intensity scales , 2001, Pain.

[41]  Lucy R. Betts,et al.  Older adults' experiences and perceptions of digital technology: (Dis)empowerment, wellbeing, and inclusion , 2015, Comput. Hum. Behav..

[42]  R. Lipton,et al.  Ecological momentary assessment of the relationship between headache pain intensity and pain interference in women with migraine and obesity , 2016, Cephalalgia : an international journal of headache.

[43]  P. Parmelee,et al.  Pain Variability and Its Predictors in Older Adults , 2013, Journal of aging and health.

[44]  A. Stone,et al.  Expanding Options for Developing Outcome Measures From Momentary Assessment Data , 2012, Psychosomatic medicine.

[45]  M. Mccaffery,et al.  Pain : clinical manual , 1999 .

[46]  Hua Wang,et al.  Age differences in perceptions of online community participation among non-users: An extension of the Technology Acceptance Model , 2010, Comput. Hum. Behav..

[47]  N. Charness,et al.  Factors Predicting the Use of Technology: Findings From the Center for Research and Education on Aging and Technology Enhancement (CREATE) , 2006 .