Trial reportJMIR mHealth and uHealth2020
A Smartphone-Based Health Care Chatbot to Promote Self-Management of Chronic Pain (SELMA): Pilot Randomized Controlled Trial.
Trial report in JMIR mHealth and uHealth, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05003518 (ParentText User Engagement and Effectiveness Study), which is not on this map. Cited by 104 papers, 12 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
ParentText User Engagement and Effectiveness Study
Who cites it
104 citing papers in PubMed, 12 syntheses or guidelines pooled it, 225 citations in OpenAlex.
- [Cognitive-Behavioral therapy for patients with disability associated with chronic pain: systematic review of clinical trials]Revista de la Facultad de Ciencias Medicas (Cordoba, Argentina) · 2025Pooled it
- Effectiveness of Communication Competence in AI Conversational Agents for Health: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Conversational Agents Supporting Self-Management in People With a Chronic Disease: Systematic Review.Journal of medical Internet research · 2025Pooled it
- Moving towards the use of artificial intelligence in pain management.European journal of pain (London, England) · 2025Pooled it
- A systematic review of artificial intelligence-powered (AI-powered) chatbot intervention for managing chronic illness.Annals of medicine · 2024Pooled it
- Perioperative application of chatbots: a systematic review and meta-analysis.BMJ health & care informatics · 2024Pooled it
- Patient Engagement with Conversational Agents in Health Applications 2016-2022: A Systematic Review and Meta-Analysis.Journal of medical systems · 2024Pooled it
- Conversational Agent Interventions for Mental Health Problems: Systematic Review and Meta-analysis of Randomized Controlled Trials.Journal of medical Internet research · 2023Pooled it
- Digital Health Interventions for Musculoskeletal Pain Conditions: Systematic Review and Meta-analysis of Randomized Controlled Trials.Journal of medical Internet research · 2022Pooled it
- Factors Influencing Adherence to mHealth Apps for Prevention or Management of Noncommunicable Diseases: Systematic Review.Journal of medical Internet research · 2022Pooled it
- Digital Health Technologies for Long-term Self-management of Osteoporosis: Systematic Review and Meta-analysis.JMIR mHealth and uHealth · 2022Pooled it
- A Systematic Review on Healthcare Artificial Intelligent Conversational Agents for Chronic Conditions.Sensors (Basel, Switzerland) · 2022Pooled it
- A feasibility randomised controlled trial of patient education videos assessing multimedia design effects on engagement and learning in adolescents with scoliosis.BMC medical education · 2026Trial
- A Chatbot-Delivered Stress Management Coaching for Students (MISHA App): Pilot Randomized Controlled Trial.JMIR mHealth and uHealth · 2024Trial
- Development and Evaluation of a Smartphone-Based Chatbot Coach to Facilitate a Balanced Lifestyle in Individuals With Headaches (BalanceUP App): Randomized Controlled Trial.Journal of medical Internet research · 2024Trial
- Mental Health Chatbot for Young Adults With Depressive Symptoms During the COVID-19 Pandemic: Single-Blind, Three-Arm Randomized Controlled Trial.Journal of medical Internet research · 2022Trial
- Increasing the Effectiveness of a Physical Activity Smartphone Intervention With Positive Suggestions: Randomized Controlled Trial.Journal of medical Internet research · 2022Trial
- Recruitment in a research study via chatbot versus telephone outreach: a randomized trial at a minority-serving institution.Journal of the American Medical Informatics Association : JAMIA · 2021Trial
- Efficacy of Zemedy, a Mobile Digital Therapeutic for the Self-management of Irritable Bowel Syndrome: Crossover Randomized Controlled Trial.JMIR mHealth and uHealth · 2021Trial
- TensioBot: a Chatbot Assistant for Self-Managed in-House Blood Pressure Checking.Journal of medical systems · 2021Trial
44 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundOngoing pain is one of the most common diseases and has major physical, psychological, social, and economic impacts. A mobile health intervention utilizing a fully automated text-based health care chatbot (TBHC) may offer an innovative way not only to deliver coping strategies and psychoeducation for pain management but also to build a working alliance between a participant and the TBHC.
objectiveThe objectives of this study are twofold: (1) to describe the design and implementation to promote the chatbot painSELfMAnagement (SELMA), a 2-month smartphone-based cognitive behavior therapy (CBT) TBHC intervention for pain self-management in patients with ongoing or cyclic pain, and (2) to present findings from a pilot randomized controlled trial, in which effectiveness, influence of intention to change behavior, pain duration, working alliance, acceptance, and adherence were evaluated.
methodsParticipants were recruited online and in collaboration with pain experts, and were randomized to interact with SELMA for 8 weeks either every day or every other day concerning CBT-based pain management (n=59), or weekly concerning content not related to pain management (n=43). Pain-related impairment (primary outcome), general well-being, pain intensity, and the bond scale of working alliance were measured at baseline and postintervention. Intention to change behavior and pain duration were measured at baseline only, and acceptance postintervention was assessed via self-reporting instruments. Adherence was assessed via usage data.
resultsFrom May 2018 to August 2018, 311 adults downloaded the SELMA app, 102 of whom consented to participate and met the inclusion criteria. The average age of the women (88/102, 86.4%) and men (14/102, 13.6%) participating was 43.7 (SD 12.7) years. Baseline group comparison did not differ with respect to any demographic or clinical variable. The intervention group reported no significant change in pain-related impairment (P=.68) compared to the control group postintervention. The intention to change behavior was positively related to pain-related impairment (P=.01) and pain intensity (P=.01). Working alliance with the TBHC SELMA was comparable to that obtained in guided internet therapies with human coaches. Participants enjoyed using the app, perceiving it as useful and easy to use. Participants of the intervention group replied with an average answer ratio of 0.71 (SD 0.20) to 200 (SD 58.45) conversations initiated by SELMA. Participants' comments revealed an appreciation of the empathic and responsible interaction with the TBHC SELMA. A main criticism was that there was no option to enter free text for the patients' own comments.
conclusionsSELMA is feasible, as revealed mainly by positive feedback and valuable suggestions for future revisions. For example, the participants' intention to change behavior or a more homogenous sample (eg, with a specific type of chronic pain) should be considered in further tailoring of SELMA.
trial registrationGerman Clinical Trials Register DRKS00017147; https://tinyurl.com/vx6n6sx, Swiss National Clinical Trial Portal: SNCTP000002712; https://www.kofam.ch/de/studienportal/suche/70582/studie/46326.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.