ArticleIntegrative cancer therapies
Feasibility and Usability of EnergyPoints: A Mobile Health App to Guide Acupressure Use for Cancer Symptom Management.
Article in Integrative cancer therapies. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06754722 (Decentralized Clinical Trial of Effectiveness of EnergyPoints App to Guide Acupressure Use for Fatigue and Sleep Disturbances in Cancer Survivors), which is not on this map. Cited by 4 papers.
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.
Decentralized Clinical Trial of Effectiveness of EnergyPoints App to Guide Acupressure Use for Fatigue and Sleep Disturbances in Cancer Survivors
Who cites it
4 citing papers in PubMed, 6 citations in OpenAlex.
- Mobile Health App Attitudes and Adoption Among Oncology Providers: Cross-Sectional National Survey.Journal of medical Internet research · 2026Article
- Insights into effective fatigue reducing interventions in kidney transplant candidates: a scoping review.Annals of behavioral medicine : a publication of the Society of Behavioral Medicine · 2025Article
- Research trends of acupressure from 2004 to 2024: A bibliometric and visualization analysis.Heliyon · 2024Review
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 6 institutions in 1 country.
Funding
Abstract
objectiveTo examine the feasibility and usability of EnergyPoints™, an innovative mobile health app that teaches and guides people with cancer to implement daily acupressure to self-manage their fatigue and sleep disturbances. METHODS AND
interventionThe study used an integrated agile, human-centered approach. Adults (age 18 years and over) with cancer experiencing at least moderate fatigue, and living in the Greater New York City community, were recruited from social media, patient advocacy groups, and referrals. Twenty participants (in 3 sprints of 3, 5, and 12) were video-recorded thinking aloud while using the app for the first time. They then used the app at home to self-administer acupressure (twice daily for 1 week) while continuously wearing a fitness tracker. Each participant completed an exit interview and modified Computer System Usability Questionnaire post-participation.
resultsParticipants were ages 40 to 76 years and 65% female; 65% were non-Hispanic white. Mean pass rates per ritual exceeded 80%. Users completed (totally or partially) greater than 90% of stimulating acupressure and 70% of relaxing acupressure rituals. Sprint 3 SPs totally completed at least 1 ritual 87% of the time. The majority agreed or strongly agreed the app was easy to use (90%), easy to learn (85%), easy to understand (75%), and effective in helping perform self-acupressure (85%). In an analysis of ease of completing 5 key tasks, all successfully completed the tasks; 3 users required some assistance. Of 654 usability statements, those coded as personal experience/context (197), content related to acupressure learning (105), and content related to the onboarding/profile (71) were most frequent. The design team integrated recommendations into the app before the next sprint.
conclusionsFindings supported feasibility and usability, as well as acceptability, and led to significant alterations and improvements. EnergyPoints™ offers an opportunity to mainstream acupressure and help cancer survivors self-manage their symptoms.
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Registered trials
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.