Evaluation of a smartwatch-based intervention providing feedback of daily activity within a research-naive stroke ward: a pilot randomised controlled trial

Sophie Lawrie, Yun Dong, Dax Steins, Zhidao Xia, Patrick Esser, Shanbin Sun, Fei Li, James D Amor, Christopher James, Hooshang Izadi, Yi Cao, Derick Wade, Nancy Mayo, Helen Dawes, Smart Watch Activity Feedback Trial Committee (SWAFT), Lingzhi Wu, Peifang Li, Ying Wang, Chong Chen, Peiyang Sun, Jinji Wang, Feifei Wang, Panfu Hao, Weiwei Wu, Yubao Gao, Xiaoli Sun, Haiyang Wu, Yujie Yang, Yuanfeng Peng, Jingjing Xue, Xiaoli Guo, Xuesong Xie, Na Zuo, Xinkui Gao, Sophie Lawrie, Yun Dong, Dax Steins, Zhidao Xia, Patrick Esser, Shanbin Sun, Fei Li, James D Amor, Christopher James, Hooshang Izadi, Yi Cao, Derick Wade, Nancy Mayo, Helen Dawes, Smart Watch Activity Feedback Trial Committee (SWAFT), Lingzhi Wu, Peifang Li, Ying Wang, Chong Chen, Peiyang Sun, Jinji Wang, Feifei Wang, Panfu Hao, Weiwei Wu, Yubao Gao, Xiaoli Sun, Haiyang Wu, Yujie Yang, Yuanfeng Peng, Jingjing Xue, Xiaoli Guo, Xuesong Xie, Na Zuo, Xinkui Gao

Abstract

Background: The majority of stroke patients are inactive outside formal therapy sessions. Tailored activity feedback via a smartwatch has the potential to increase inpatient activity. The aim of the study was to identify the challenges and support needed by ward staff and researchers and to examine the feasibility of conducting a randomised controlled trial (RCT) using smartwatch activity monitors in research-naive rehabilitation wards. Objectives (Phase 1 and 2) were to report any challenges and support needed and determine the recruitment and retention rate, completion of outcome measures, smartwatch adherence rate, (Phase 2 only) readiness to randomise, adherence to protocol (intervention fidelity) and potential for effect.

Methods: First admission, stroke patients (onset < 4 months) aged 40-75, able to walk 10 m prior to stroke and follow a two-stage command with sufficient cognition and vision (clinically judged) were recruited within the Second Affiliated Hospital of Anhui University of Traditional Chinese Medicine. Phase 1: a non-randomised observation phase (to allow practice of protocol)-patients received no activity feedback. Phase 2: a parallel single-blind pilot RCT. Patients were randomised into one of two groups: to receive daily activity feedback over a 9-h period or to receive no activity feedback. EQ-5D-5L, WHODAS and RMI were conducted at baseline, discharge and 3 months post-discharge. Descriptive statistics were performed on recruitment, retention, completion and activity counts as well as adherence to protocol.

Results: Out of 470 ward admissions, 11% were recruited across the two phases, over a 30-week period. Retention rate at 3 months post-discharge was 48%. Twenty-two percent of patients dropped out post-baseline assessment, 78% completed baseline and discharge admissions, from which 62% were assessed 3 months post-discharge. Smartwatch data were received from all patients. Patients were correctly randomised into each RCT group. RCT adherence rate to wearing the smartwatch was 80%. Baseline activity was exceeded for 65% of days in the feedback group compared to 55% of days in the no feedback group.

Conclusions: Delivery of a smartwatch RCT is feasible in a research-naive rehabilitation ward. However, frequent support and guidance of research-naive staff are required to ensure completeness of clinical assessment data and protocol adherence.

Trials registration: ClinicalTrials.gov Identifier, NCT02587585-30th September 2015.

Keywords: Activity feedback; Feasibility; Physical activity; Rehabilitation; Research naive; Stroke.

Conflict of interest statement

The study was approved by the Chinese Ethics Committee of Registering Clinical Trials (ChiECRCT-20150034), West China Hospital, Sichuan University, 37 Guoxuexiang, Chengdu, Sichuan, China. All research was in compliance with the Helsinki Declarations and the Research Governance Framework for Health and Social Care. Informed written consent was obtained by a recruiting doctor based on the wards whereby sufficient mental capacity (based on clinical judgement) and understanding of anonymity and the inclusion of their data was ensured.Not applicable.The gait measurement system and the smartwatch software were developed by the research team (HD, PE and DW) and (CJ and JA) respectively, and both systems are in the process of being commercialised by the universities concerned.Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Figures

Fig. 1
Fig. 1
Schematic representation of the research design showing two phases (observation and pilot Randomised controlled trial (pilot RCT)) and three groups- observation, feedback and no feedback
Fig. 2
Fig. 2
Activity feedback as displayed on the smartwatch screen for the feedback group (a), which included both the feedback bars and clock face, and the no feedback (control) group (b), which included the clock face only
Fig. 3
Fig. 3
Visual representation of the feedback provided on the smartwatch
Fig. 4
Fig. 4
Participant recruitment within the observation and the pilot randomised controlled trial (RCT) phase
Fig. 5
Fig. 5
Box plots showing the variance of activity score amongst participants in the observation (O), feedback (F) and no feedback (NF) group across 15 weekdays. + Crosses indicate outliers
Fig. 6
Fig. 6
Graphs showing different analysis metrics of activity score (AS) from participant 1033 (Pilot RCT-No Feedback Group). Graphs (a) and (b) show the difference in AS from baseline AS (BAS). Graphs (c) and (d) show the difference in AS from the previous day’s values. P1 to P4 refer to each 2-h time period between 08:00 and 16:00
Fig. 7
Fig. 7
Graphs showing different analysis metrics of activity score (AS) from participant 1029 (Pilot RCT-Feedback Group). Graphs (a) and (b) show the difference in AS from baseline AS (BAS). Graphs (c) and (d) show the difference in AS from the previous day’s values. P1 to P4 refer to each 2-h time period between 08:00 and 16:00

References

    1. Billinger SA, Arena R, Bernhardt J, Eng JJ, Franklin BA, Johnson CM, et al. Physical activity and exercise recommendations for stroke survivors: a statement for healthcare professionals from the American Heart Association/American Stroke Association. Stroke. 2014;45:2532–2553. doi: 10.1161/STR.0000000000000022.
    1. Saunders DH, Greig CA, Mead GE. Physical activity and exercise after stroke: review of multiple meaningful benefits. Stroke. 2014;45:3742–3747. doi: 10.1161/STROKEAHA.114.004311.
    1. Janssen H, Ada L, Bernhardt J, McElduff P, Pollack M, Nilsson M, et al. Physical, cognitive and social activity levels of stroke patients undergoing rehabilitation within a mixed rehabilitation unit. Clin Rehabil. 2014;28:91–101. doi: 10.1177/0269215512466252.
    1. West Tanya, Bernhardt Julie. Physical Activity in Hospitalised Stroke Patients. Stroke Research and Treatment. 2012;2012:1–13. doi: 10.1155/2012/813765.
    1. Lyons Elizabeth J, Lewis Zakkoyya H, Mayrsohn Brian G, Rowland Jennifer L. Behavior Change Techniques Implemented in Electronic Lifestyle Activity Monitors: A Systematic Content Analysis. Journal of Medical Internet Research. 2014;16(8):e192. doi: 10.2196/jmir.3469.
    1. Prestwich A, Conner M, Hurling R, Ayres K, Morris B. An experimental test of control theory-based interventions for physical activity. Br J Health Psychol. 2016;21:812–826. doi: 10.1111/bjhp.12198.
    1. Strath SJ, Swartz AM, Parker SJ, Miller NE, Grimm EK, Cashin SE. A pilot randomized controlled trial evaluating motivationally matched pedometer feedback to increase physical activity behavior in older adults. J Phys Act Health. 2011;8:S267–SS74. doi: 10.1123/jpah.8.s2.s267.
    1. Jauho Anna-Maiju, Pyky Riitta, Ahola Riikka, Kangas Maarit, Virtanen Paula, Korpelainen Raija, Jämsä Timo. Effect of wrist-worn activity monitor feedback on physical activity behavior: A randomized controlled trial in Finnish young men. Preventive Medicine Reports. 2015;2:628–634. doi: 10.1016/j.pmedr.2015.07.005.
    1. Peel NM, Paul SK, Cameron ID, Crotty M, Kurrle SE, Gray LC. Promoting activity in geriatric rehabilitation: a randomized controlled trial of accelerometry. PLoS One. 2016;11:e0160906. doi: 10.1371/journal.pone.0160906.
    1. Room Jonathan, Hannink Erin, Dawes Helen, Barker Karen. What interventions are used to improve exercise adherence in older people and what behavioural techniques are they based on? A systematic review. BMJ Open. 2017;7(12):e019221. doi: 10.1136/bmjopen-2017-019221.
    1. Godino JG, Watkinson C, Corder K, Marteau TM, Sutton S, Sharp SJ, et al. Impact of personalised feedback about physical activity on change in objectively measured physical activity (the FAB study): a randomised controlled trial. PLoS One. 2013;8:e75398. doi: 10.1371/journal.pone.0075398.
    1. Block VA, Pitsch E, Tahir P, Cree BA, Allen DD, Gelfand JM. Remote physical activity monitoring in neurological disease: a systematic review. PLoS One. 2016;11:e0154335. doi: 10.1371/journal.pone.0154335.
    1. Molier BI, Van Asseldonk EH, Hermens HJ, Jannink MJ. Nature, timing, frequency and type of augmented feedback; does it influence motor relearning of the hemiparetic arm after stroke? A systematic review. Disabil Rehabil. 2010;32:1799–1809. doi: 10.3109/09638281003734359.
    1. Dorsch AK, Thomas S, Xu X, Kaiser W, Dobkin BH. SIRRACT: an international randomized clinical trial of activity feedback during inpatient stroke rehabilitation enabled by wireless sensing. Neurorehabil Neural Repair. 2015;29:407–415. doi: 10.1177/1545968314550369.
    1. Mbuagbaw L, Thabane L, Ongolo-Zogo P, Lang T. The challenges and opportunities of conducting a clinical trial in a low resource setting: the case of the Cameroon mobile phone SMS (CAMPS) trial, an investigator initiated trial. Trials. 2011;12:145. doi: 10.1186/1745-6215-12-145.
    1. Dong Y, Steins D, Sun S, Li F, Amor JD, James CJ, et al. Does feedback on daily activity level from a smart watch during inpatient stroke rehabilitation increase physical activity levels? Study protocol for a randomized controlled trial. Trials. 2018;19:177. doi: 10.1186/s13063-018-2476-z.
    1. Koski L. Validity and applications of the Montreal cognitive assessment for the assessment of vascular cognitive impairment. Cerebrovasc Dis. 2013;36:6–18. doi: 10.1159/000352051.
    1. Hu J-B, Zhou W-H, Hu S-H, Huang M-L, Wei N, Qi H-L, et al. Cross-cultural difference and validation of the Chinese version of Montreal Cognitive Assessment in older adults residing in Eastern China: preliminary findings. Arch Gerontol Geriatr. 2013;56:38–43. doi: 10.1016/j.archger.2012.05.008.
    1. Nie K, Zhang Y, Wang L, Zhao J, Huang Z, Gan R, et al. A pilot study of psychometric properties of the Beijing version of Montreal Cognitive Assessment in patients with idiopathic Parkinson’s disease in China. J Clin Neurosci. 2012;19:1497–1500. doi: 10.1016/j.jocn.2011.11.039.
    1. Wade D, Collin C. The Barthel ADL Index: a standard measure of physical disability? Int Disabil Stud. 1988;10:64–67. doi: 10.3109/09638288809164105.
    1. Üstün TB, editor. Measuring health and disability: manual for WHO disability assessment schedule WHODAS 2.0. Geneva: World Health Organization; 2010.
    1. Krupp LB, LaRocca NG, Muir-Nash J, Steinberg AD. The fatigue severity scale: application to patients with multiple sclerosis and systemic lupus erythematosus. Arch Neurol. 1989;46:1121–1123. doi: 10.1001/archneur.1989.00520460115022.
    1. Tang WK, Lu JY, Chen YK, Mok VC, Ungvari GS, Wong KS. Is fatigue associated with short-term health-related quality of life in stroke? Arch Phys Med Rehabil. 2010;91:1511–1515. doi: 10.1016/j.apmr.2010.06.026.
    1. Herdman M, Gudex C, Lloyd A, Janssen M, Kind P, Parkin D, et al. Development and preliminary testing of the new five-level version of EQ-5D (EQ-5D-5L) Qual Life Res. 2011;20:1727–1736. doi: 10.1007/s11136-011-9903-x.
    1. Lee CF, Ng R, Luo N, Wong NS, Yap YS, Lo SK, et al. The English and Chinese versions of the five-level EuroQoL group's five-dimension questionnaire (EQ-5D) were valid and reliable and provided comparable scores in Asian breast cancer patients. Support Care Cancer. 2013;21:201–209. doi: 10.1007/s00520-012-1512-x.
    1. Collen FM, Wade DT, Robb G, Bradshaw C. The Rivermead mobility index: a further development of the Rivermead motor assessment. Int Disabil Stud. 1991;13:50–54. doi: 10.3109/03790799109166684.
    1. Wade D, Wood V, Heller A, Maggs J, Langton HR. Walking after stroke. Measurement and recovery over the first 3 months. Scand J Rehabil Med. 1987;19(1):25–30.
    1. Bertrand AM, Fournier K, Brasey M-GW, Kaiser M-L, Frischknecht R, Diserens K. Reliability of maximal grip strength measurements and grip strength recovery following a stroke. Hand Ther. 2015;28:356–363. doi: 10.1016/j.jht.2015.04.004.
    1. Esser P, Dawes H, Collett J, Feltham MG, Howells K. Assessment of spatio-temporal gait parameters using inertial measurement units in neurological populations. Gait Posture. 2011;34:558–560. doi: 10.1016/j.gaitpost.2011.06.018.
    1. ZGPax Website; S8 Techincal Specification. Available at . Assessed 1 Jun 2018.
    1. Ahanathapillai V, Amor J, James C. Assistive technology to monitor activity, health and wellbeing in old age: the wrist wearable unit in the USEFIL project. Technol Disabil. 2015;27:17–29. doi: 10.3233/TAD-150425.
    1. Amor JD, Ahanathapillai V, James CJ. XIII Mediterranean Conference on Medical and Biological Engineering and Computing 2013 IFMBE Proceedings. 2014. Detecting and analyzing activity levels for the wrist wearable unit in the USEFIL project; pp. 1201–1204.
    1. Ahanathapillai V, Amor JD, Goodwin Z, James CJ. Preliminary study on activity monitoring using an android smart-watch. Healthc Technol Lett. 2015;2:34–39. doi: 10.1049/htl.2014.0091.
    1. Amor JD, Hattersley JG, Barber TM, James CJ. Engineering in Medicine and Biology Society (EMBC), 2015 37th Annual International Conference of the IEEE. 2015. Characterization of wrist-wearable activity measurement using whole body calorimetry in semi-free living conditions; pp. 3735–3738.
    1. Serra MC, Balraj E, DiSanzo BL, Ivey FM, Hafer-Macko CE, Treuth MS, et al. Validating accelerometry as a measure of physical activity and energy expenditure in chronic stroke. Top Stroke Rehabil. 2017;24:18–23. doi: 10.1080/10749357.2016.1183866.
    1. Wallace S, Myles P. Solving the challenges of large multicenter trials in anesthesia. HSR Proc Intensive Care Cardiovasc Anesth. 2009;1(3):46.
    1. McCambridge J, Witton J, Elbourne DR. Systematic review of the Hawthorne effect: new concepts are needed to study research participation effects. J Clin Epidemiol. 2014;67:267–277. doi: 10.1016/j.jclinepi.2013.08.015.
    1. Hayward Kathryn S., Eng Janice J., Boyd Lara A., Lakhani Bimal, Bernhardt Julie, Lang Catherine E. Exploring the Role of Accelerometers in the Measurement of Real World Upper-Limb Use After Stroke. Brain Impairment. 2015;17(01):16–33. doi: 10.1017/BrImp.2015.21.
    1. Noorkõiv M, Rodgers H, Price CI. Accelerometer measurement of upper extremity movement after stroke: a systematic review of clinical studies. J Neuroengin Rehabil. 2014;11(1):144. doi: 10.1186/1743-0003-11-144.
    1. Gebruers N, Vanroy C, Truijen S, Engelborghs S, De Deyn PP. Monitoring of physical activity after stroke: a systematic review of accelerometry-based measures. Arch Phys Med Rehabil. 2010;91:288–297. doi: 10.1016/j.apmr.2009.10.025.
    1. Devasenapathy Niveditha, Singh Kavita, Prabhakaran Dorairaj. Conduct of clinical trials in developing countries: a perspective. Current Opinion in Cardiology. 2009;24(4):295–300. doi: 10.1097/HCO.0b013e32832af21b.

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