The selfBACK artificial intelligence-based smartphone app can improve low back pain outcome even in patients with high levels of depression or stress

被引:15
|
作者
Rughani, Guy [1 ]
Nilsen, Tom I. L. [2 ]
Wood, Karen [1 ]
Mair, Frances S. [1 ]
Hartvigsen, Jan [3 ,4 ]
Mork, Paul J. [2 ]
Nicholl, Barbara I. [1 ,5 ]
机构
[1] Univ Glasgow, Sch Hlth & Wellbeing, Glasgow, Scotland
[2] Norwegian Univ Sci & Technol NTNU, Dept Publ Hlth & Nursing, Trondheim, Norway
[3] Univ Southern Denmark, Dept Sports Sci & Clin Biomech, Odense, Denmark
[4] Chiropract Knowledge Hub, Odense, Denmark
[5] Univ Glasgow, Coll Med Vet & Life Sci, Sch Hlth & Wellbeing, 1 Horselethill Rd, Glasgow G12 9LX, Scotland
基金
欧盟地平线“2020”;
关键词
MORRIS DISABILITY QUESTIONNAIRE; HEALTH; MANAGEMENT; EFFICACY; ONSET;
D O I
10.1002/ejp.2080
中图分类号
R614 [麻醉学];
学科分类号
100217 ;
摘要
BackgroundselfBACK provides individually tailored self-management support for low back pain (LBP) via an artificial intelligence-based smartphone app. We explore whether those with depressive/stress symptoms can benefit from this technology. MethodsSecondary analysis of the selfBACK randomized controlled trial (n = 461). Participants with LBP were randomized to usual care (n = 229), or usual care plus selfBACK (n = 232). Primary outcome: LBP-related disability (Roland-Morris Disability Questionnaire, RMDQ) over 9 months. Secondary outcomes: global perceived effect (GPE)/pain self-efficacy (PSEQ)/satisfaction/app engagement. Baseline depressive symptoms were measured using the patient health questionnaire (PHQ-8) and stress with the perceived stress scale (PSS). Outcomes stratified by baseline PHQ-8/PSS scores to assess associations across the whole cohort, and intervention versus control groups. ResultsParticipants with higher levels of depressive/stress symptoms reported more baseline LBP-related disability (RMDQ 3.1; 1.6 points higher in most vs least depressed/stressed groups respectively); lower self-efficacy (PSEQ 8.1; 4.6 points lower in most vs least depressive/stressed groups respectively). LBP-related disability improved over time; relative risk of improvement in those with greatest depressive/stress symptoms versus nil symptom comparators at 9 months: 0.8 (95% CI: 0.6 to 1.0) and 0.8 (95% CI: 0.7 to 1.0) respectively. No evidence that different baseline levels of depressive/perceived stress symptoms are associated with different RMDQ/GPE/PSEQ outcomes. Whilst participants with higher PHQ-8/PSS were less likely to be satisfied or engage with the app, there was no consistent association among PHQ-8/PSS level, the intervention and outcomes. ConclusionsThe selfBACK app can improve outcomes even in those with high levels of depressive/stress symptoms and could be recommended for patients with LBP. SignificanceWe have demonstrated that an app supporting the self-management of LBP is helpful, even in those with higher levels of baseline depression and stress symptoms. selfBACK offers an opportunity to support people with LBP and provides clinicians with an additional tool for their patients, even those with depression or high levels of stress. This highlights the potential for digital health interventions for chronic pain.
引用
收藏
页码:568 / 579
页数:12
相关论文
共 12 条
  • [1] Outcome of low back pain in general practice - Evidence based practice can improve outcome
    Deane, M
    Crick, D
    BRITISH MEDICAL JOURNAL, 1998, 317 (7165): : 1083 - 1083
  • [2] Effect of an Artificial Intelligence-Based Self-Management App on Musculoskeletal Health in Patients With Neck and/or Low Back Pain Referred to Specialist Care A Randomized Clinical Trial
    Marcuzzi, Anna
    Nordstoga, Anne Lovise
    Bach, Kerstin
    Aasdahl, Lene
    Nilsen, Tom Ivar Lund
    Bardal, Ellen Marie
    Boldermo, Nora Ostbo
    Bertheussen, Gro Falkener
    Marchand, Gunn Hege
    Gismervik, Sigmund
    Mork, Paul Jarle
    JAMA NETWORK OPEN, 2023, 6 (06) : e2320400
  • [3] An Artificial Intelligence-Based App for Self-Management of Low Back and Neck Pain in Specialist Care: Process Evaluation From a Randomized Clinical Trial
    Marcuzzi, Anna
    Klevanger, Nina Elisabeth
    Aasdahl, Lene
    Gismervik, Sigmund
    Bach, Kerstin
    Mork, Paul Jarle
    Nordstoga, Anne Lovise
    JMIR HUMAN FACTORS, 2024, 11
  • [4] Factors influencing the use of an artificial intelligence-based app (<sc>self</sc>BACK) for tailored self-management support among adults with neck and/or low back pain
    Hurmuz, M. Z. M.
    Jansen-Kosterink, S. M.
    Mork, P. J.
    Bach, K.
    Hermens, H. J.
    DISABILITY AND REHABILITATION, 2025, 47 (04) : 958 - 967
  • [5] PET/CT imaging of spinal inflammation and microcalcification in patients with low back pain: A pilot study on the quantification by artificial intelligence-based segmentation
    Piri, Reza
    Noddeskou-Fink, Amalie H.
    Gerke, Oke
    Larsson, Mans
    Edenbrandt, Lars
    Enqvist, Olof
    Hoilund-Carlsen, Poul-Flemming
    Stochkendahl, Mette J.
    CLINICAL PHYSIOLOGY AND FUNCTIONAL IMAGING, 2022, 42 (04) : 225 - 232
  • [6] An Artificial Intelligence-Based, Personalized Smartphone App to Improve Childhood Immunization Coverage and Timelines Among Children in Pakistan: Protocol for a Randomized Controlled Trial
    Kazi, Abdul Momin
    Qazi, Saad Ahmed
    Khawaja, Sadori
    Ahsan, Nazia
    Ahmed, Rao Moueed
    Sameen, Fareeha
    Mughal, Muhammad Ayub Khan
    Saqib, Muhammad
    Ali, Sikander
    Kaleemuddin, Hussain
    Rauf, Yasir
    Raza, Mehreen
    Jamal, Saima
    Abbasi, Munir
    Stergioulas, Lampros K.
    JMIR RESEARCH PROTOCOLS, 2020, 9 (12):
  • [7] Using Intervention Mapping to Develop a Decision Support System-Based Smartphone App (selfBACK) to Support Self-management of Nonspecific Low Back Pain: Development and Usability Study
    Svendsen, Malene Jagd
    Sandal, Louise Fleng
    Kjaer, Per
    Nicholl, Barbara, I
    Cooper, Kay
    Mair, Frances
    Hartvigsen, Jan
    Stochkendahl, Mette Jensen
    Sogaard, Karen
    Mork, Paul Jarle
    Rasmussen, Charlotte
    JOURNAL OF MEDICAL INTERNET RESEARCH, 2022, 24 (01)
  • [8] Artificial intelligence-based image analysis can predict outcome in high-grade serous carcinoma via histology alone
    Laury, Anna Ray
    Blom, Sami
    Ropponen, Tuomas
    Virtanen, Anni
    Carpen, Olli Mikael
    SCIENTIFIC REPORTS, 2021, 11 (01)
  • [9] Artificial intelligence-based image analysis can predict outcome in high-grade serous carcinoma via histology alone
    Anna Ray Laury
    Sami Blom
    Tuomas Ropponen
    Anni Virtanen
    Olli Mikael Carpén
    Scientific Reports, 11
  • [10] Smartphone Based Artificial Intelligence Platform Demonstrates High Rates of Adherence and Viral Outcome in Patients Receiving Fixed-Dose Ledipasvir and Sofosbuvir: A Pilot Study
    Litwin, Alain H.
    Shafner, Laura
    Agyemang, Linda
    HEPATOLOGY, 2017, 66 : 862A - 862A