Feasibility of a Smartphone-Based Exercise Program for Office Workers With Neck Pain: An Individualized Approach Using a Self-Classification Algorithm.
To explore the feasibility of a newly developed smartphone-based exercise program with an embedded self-classification algorithm for office workers with neck pain, by examining its effect on the pain intensity, functional disability, quality of life, fear avoidance, and cervical range of motion (ROM).



Single-group, repeated-measures design.



The laboratory and participants' home and work environments.



Offices workers with neck pain (N=23; mean age ± SD, 28.13±2.97y; 13 men).



Participants were classified as having 1 of 4 types of neck pain through a self-classification algorithm implemented as a smartphone application, and conducted corresponding exercise programs for 10 to 12min/d, 3d/wk, for 8 weeks.



The visual analog scale (VAS), Neck Disability Index (NDI), Medical Outcomes Study 36-Item Short-Form Health Survey (SF-36), Fear-Avoidance Beliefs Questionnaire (FABQ), and cervical ROM were measured at baseline and postintervention.



The VAS (P<.001) and NDI score (P<.001) indicated significant improvements in pain intensity and functional disability. Quality of life showed significant improvements in the physical functioning (P=.007), bodily pain (P=.018), general health (P=.022), vitality (P=.046), and physical component scores (P=.002) of the SF-36. The FABQ, cervical ROM, and mental component score of the SF-36 showed no significant improvements.



The smartphone-based exercise program with an embedded self-classification algorithm improves the pain intensity and perceived physical health of office workers with neck pain, although not enough to affect their mental and emotional states.
Department of Physical Therapy, College of Health Science, Korea University, Seoul, South Korea.
Minyoung
M
Lee
Department of Physical Medicine and Rehabilitation, Korea University Medical Center, Seoul, South Korea.
Sang Heon
SH
Lee
Department of Physical Therapy, College of Health Science, Korea University, Seoul, South Korea.
TaeYeong
T
Kim
Department of Physical Medicine and Rehabilitation, Korea University Anam Hospital, Korea University College of Medicine, Seoul, South Korea.
Hyun-Joon
HJ
Yoo
Department of Physical Medicine and Rehabilitation, Korea University Anam Hospital, Korea University College of Medicine, Seoul, South Korea.
Sung Hoon
SH
Kim
Barunsesang Hospital, Seongnam, South Korea.
Dong-Won
DW
Suh
Department of Biomedical Engineering, University of Los Andes, Bogotá, Colombia.
Jaebum
J
Son
Department of Physical Therapy, College of Health Science, Korea University, Seoul, South Korea. Electronic address: yoonbc@korea.ac.kr.
BumChul
B
Yoon
D000328:Adult; D000465:Algorithms; D001362:Avoidance Learning; D004185:Disability Evaluation; D005081:Exercise Therapy; D005240:Feasibility Studies; D005260:Female; D006304:Health Status; D006801:Humans; D008297:Male; D063731:Mobile Applications; D019547:Neck Pain / Q000145:classification* / Q000534:rehabilitation*; D010147:Pain Measurement; D011788:Quality of Life; D016059:Range of Motion, Articular; D000068997:Smartphone; D011795:Surveys and Questionnaires; D017132:Workplace
Neck pain; Rehabilitation; Smartphone