The Perceived Benefits of an Artificial Intelligence–Embedded Mobile App Implementing Evidence-Based Guidelines for the Self-Management of Chronic Neck and Back Pain: Observational Study
Abstract:BackgroundChronic musculoskeletal neck and back pain are disabling conditions among adults. Use of technology has been suggested as an alternative way to increase adherence to exercise therapy, which may improve clinical outcomes.ObjectiveThe aim was to investigate the self-perceived benefits of an artificial intelligence (AI)–embedded mobile app to self-manage chronic neck and back pain.MethodsA total of 161 participants responded to the invitation. The evaluation questionnaire included 14 questions that were… Show more
“…After title and abstract screening, 83 articles were included in the full-text screening; 22 unique studies with a total of 4679 patients met the eligibility criteria (1515 in non-RCT designs, 3164 in RCT designs) ( Figure 1). For the meta-analysis, 12 of these studies were considered as RCTs [36][37][38][39][40][41][42][43][44][45][46][47], while 10 were observational or studies of similar design, which compared baseline measures to post-intervention measures of the same individuals [48][49][50][51][52][53][54][55][56][57]. Studies varied in sample sizes, sex distributions, populations, assessment instruments, and study quality.…”
Section: Resultsmentioning
confidence: 99%
“…Studies varied in sample sizes, sex distributions, populations, assessment instruments, and study quality. Six studies were aimed at individuals with general chronic pain [40,41,51,52,56,57], nine studies at individuals with chronic lower back pain (LBP) [36,37,39,44,46,47,49,53,54], three studies at individuals with arthritis (e.g., osteoarthritis and rheumatoid arthritis) [43,45,55], and one study each at individuals with menstrual pain [48], frozen shoulder pain [38], chronic neck pain [42], and migraine [50]. Fifteen of the studies recruited patients during clinic visits or through their general practitioners (GP) [36][37][38][40][41][42][43][46][47][48]51,52,[55][56][57] or research institutions, and seven recruited participants from the community via the internet or flyers [39,44,45,49,50,53].…”
Section: Resultsmentioning
confidence: 99%
“…In the current analyses, ten observational studies investigated the effect of pain apps [48][49][50][51][52][53][54][55][56][57]. All apps included pain tracking tools; one used Fitbit for the tracking of physical activity [58].…”
Chronic pain is one of the major causes of disability in the general population. Even though there are effective treatment options available for reducing symptoms, these treatments often do not have consistent lasting effects. As the usage of mobile devices has increased enormously during the last few years, mobile application-based treatment options are widespread. Such app-based programs are not yet empirically proven but might enable patients to become more independent in their pain management in order to prevent relapse. The aim of this meta-analysis was to summarize the literature on mobile application-based interventions for chronic pain patients. Therefore, three electronic bibliographic databases, PubMed, PsycINFO, and Web of Science, were searched for studies that investigated the effectiveness of mobile application-based intervention for chronic pain on pain intensity. The final sample comprised twenty-two studies, with a total of 4679 individuals. Twelve of these twenty-two studies used a randomized control trial (RCT) design, while ten studies only used an observational design. For all twenty-two studies, a small but significant effect (d = −0.40) was found when compared to baseline measures or control groups. The results suggest that apps-based treatment can be helpful in reducing pain, especially in the long-term.
“…After title and abstract screening, 83 articles were included in the full-text screening; 22 unique studies with a total of 4679 patients met the eligibility criteria (1515 in non-RCT designs, 3164 in RCT designs) ( Figure 1). For the meta-analysis, 12 of these studies were considered as RCTs [36][37][38][39][40][41][42][43][44][45][46][47], while 10 were observational or studies of similar design, which compared baseline measures to post-intervention measures of the same individuals [48][49][50][51][52][53][54][55][56][57]. Studies varied in sample sizes, sex distributions, populations, assessment instruments, and study quality.…”
Section: Resultsmentioning
confidence: 99%
“…Studies varied in sample sizes, sex distributions, populations, assessment instruments, and study quality. Six studies were aimed at individuals with general chronic pain [40,41,51,52,56,57], nine studies at individuals with chronic lower back pain (LBP) [36,37,39,44,46,47,49,53,54], three studies at individuals with arthritis (e.g., osteoarthritis and rheumatoid arthritis) [43,45,55], and one study each at individuals with menstrual pain [48], frozen shoulder pain [38], chronic neck pain [42], and migraine [50]. Fifteen of the studies recruited patients during clinic visits or through their general practitioners (GP) [36][37][38][40][41][42][43][46][47][48]51,52,[55][56][57] or research institutions, and seven recruited participants from the community via the internet or flyers [39,44,45,49,50,53].…”
Section: Resultsmentioning
confidence: 99%
“…In the current analyses, ten observational studies investigated the effect of pain apps [48][49][50][51][52][53][54][55][56][57]. All apps included pain tracking tools; one used Fitbit for the tracking of physical activity [58].…”
Chronic pain is one of the major causes of disability in the general population. Even though there are effective treatment options available for reducing symptoms, these treatments often do not have consistent lasting effects. As the usage of mobile devices has increased enormously during the last few years, mobile application-based treatment options are widespread. Such app-based programs are not yet empirically proven but might enable patients to become more independent in their pain management in order to prevent relapse. The aim of this meta-analysis was to summarize the literature on mobile application-based interventions for chronic pain patients. Therefore, three electronic bibliographic databases, PubMed, PsycINFO, and Web of Science, were searched for studies that investigated the effectiveness of mobile application-based intervention for chronic pain on pain intensity. The final sample comprised twenty-two studies, with a total of 4679 individuals. Twelve of these twenty-two studies used a randomized control trial (RCT) design, while ten studies only used an observational design. For all twenty-two studies, a small but significant effect (d = −0.40) was found when compared to baseline measures or control groups. The results suggest that apps-based treatment can be helpful in reducing pain, especially in the long-term.
“… Bansal, Zahedi [ 23 ] Perceived benefits The extent to which an individual believes that AI-based tools can improve diagnostics and care planning for patients. Lo, Lei [ 24 ] Intention to use AI-based tools The extent to which an individual is willing to use AI-based services for diagnostics and treatments. Turja, Aaltonen [ 11 ] Fig.…”
Background
Several studies highlight the effects of artificial intelligence (AI) systems on healthcare delivery. AI-based tools may improve prognosis, diagnostics, and care planning. It is believed that AI will be an integral part of healthcare services in the near future and will be incorporated into several aspects of clinical care. Thus, many technology companies and governmental projects have invested in producing AI-based clinical tools and medical applications. Patients can be one of the most important beneficiaries and users of AI-based applications whose perceptions may affect the widespread use of AI-based tools. Patients should be ensured that they will not be harmed by AI-based devices, and instead, they will be benefited by using AI technology for healthcare purposes. Although AI can enhance healthcare outcomes, possible dimensions of concerns and risks should be addressed before its integration with routine clinical care.
Methods
We develop a model mainly based on value perceptions due to the specificity of the healthcare field. This study aims at examining the perceived benefits and risks of AI medical devices with clinical decision support (CDS) features from consumers’ perspectives. We use an online survey to collect data from 307 individuals in the United States.
Results
The proposed model identifies the sources of motivation and pressure for patients in the development of AI-based devices. The results show that technological, ethical (trust factors), and regulatory concerns significantly contribute to the perceived risks of using AI applications in healthcare. Of the three categories, technological concerns (i.e., performance and communication feature) are found to be the most significant predictors of risk beliefs.
Conclusions
This study sheds more light on factors affecting perceived risks and proposes some recommendations on how to practically reduce these concerns. The findings of this study provide implications for research and practice in the area of AI-based CDS. Regulatory agencies, in cooperation with healthcare institutions, should establish normative standard and evaluation guidelines for the implementation and use of AI in healthcare. Regular audits and ongoing monitoring and reporting systems can be used to continuously evaluate the safety, quality, transparency, and ethical factors of AI-based services.
“…Several published studies mentioned that the effects of perceived usefulness and use willingness on technology adoption are higher than that of perceived ease of use [36,37]. When perceived ease of use and attitude toward information quality are positive, perceived usefulness is often high [38][39][40]. These findings might help in app design, and developers of an emergency app will need to provide a service that is easy to use, clear and understandable, easy to learn to operate, and easy to navigate and that has clear help messages [41][42][43][44][45].…”
Background
Improving the quality of patient care through the use of mobile devices is one of the hot topics in the health care field. In unwanted situations like an accident, ambulances and rescuers often require a certain amount of time to arrive at the scene. Providing immediate cardiopulmonary resuscitation (CPR) to patients might improve survival.
Objective
The primary objective of this study was to evaluate the feasibility of an emergency and mutual-aid app model in Taiwan and to provide a reference for government policy.
Methods
A structured questionnaire was developed as a research tool. All questionnaires were designed according to the technology acceptance model, and a Likert scale was used to measure the degree of agreement or disagreement. Moreover, in-depth interviews were conducted with six experts from medical, legal, and mobile app departments. Each expert was interviewed once to discuss feasible countermeasures and suggestions. Statistical Package for the Social Sciences (SPSS version 19; IBM Corp, Armonk, New York) was used to perform all statistical analyses, including descriptive statistics, independent sample t-tests, variance analysis, and Pearson correlation analysis.
Results
We conducted this study between October 20, 2017, and November 10, 2017, at the Taipei Medical University Hospital. Questionnaires were distributed to medical personnel, visiting guests, family members, and volunteers. A total of 113 valid questionnaires were finally obtained after the exclusion of incomplete questionnaires. Cronbach α values for self-efficacy (perceived ease of use), use attitude (perceived usefulness), and use willingness and frequency were above .85, meeting the criterion of greater than .70. We observed that the reliability of each subquestion was acceptable and the values for use attitude (perceive usefulness) and use willingness and frequency were more than .90.
Conclusions
The findings suggest that perceived ease of use and perceived usefulness of the app model affect use willingness. However, perceived usefulness had an intermediary influence on use willingness. Experts in law, medical, and technology fields consider that an emergency and mutual-aid model can be implemented in Taiwan. Along with the development of an emergency and mutual-aid app model, we recommend an increase in the number of automated external defibrillators per region and promotion of correct knowledge about CPR in order to decrease morbidity and mortality.
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