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Record W4361989893 · doi:10.2196/43940

Preliminary Efficacy, Feasibility, and Perceived Usefulness of a Smartphone-Based Self-Management System With Personalized Goal Setting and Feedback to Increase Step Count Among Workers With High Blood Pressure: Before-and-After Study

2023· article· en· W4361989893 on OpenAlexvenueno aff
Tomomi Shibuta, Kayo Waki, Kana Miyake, Ayumi Igarashi, Noriko Yamamoto‐Mitani, Akiko Sankoda, Yoshinori Takeuchi, Masahiko Sumitani, Toshimasa Yamauchi, Masaomi Nangaku, Kazuhiko Ohe

Bibliographic record

VenueJMIR Cardio · 2023
Typearticle
Languageen
FieldHealth Professions
TopicMobile Health and mHealth Applications
Canadian institutionsnot available
FundersUniversity of TokyoMinistry of Education, Culture, Sports, Science and Technology
KeywordsGoal settingSelf-managementBlood pressureExtant taxonSelf-monitoringMedicinePhysical therapySelf-efficacyIntervention (counseling)PsychologyComputer scienceNursingSocial psychology

Abstract

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BACKGROUND: High blood pressure (BP) and physical inactivity are the major risk factors for cardiovascular diseases. Mobile health is expected to support patients' self-management for improving cardiovascular health; the development of fully automated systems is necessary to minimize the workloads of health care providers. OBJECTIVE: The objective of our study was to evaluate the preliminary efficacy, feasibility, and perceived usefulness of an intervention using a novel smartphone-based self-management system (DialBetes Step) in increasing steps per day among workers with high BP. METHODS: On the basis of the Social Cognitive Theory, we developed personalized goal-setting and feedback functions and information delivery functions for increasing step count. Personalized goal setting and feedback consist of 4 components to support users' self-regulation and enhance their self-efficacy: goal setting for daily steps, positive feedback, action planning, and barrier identification and problem-solving. In the goal-setting component, users set their own step goals weekly in gradual increments based on the system's suggestion. We added these fully automated functions to an extant system with the function of self-monitoring daily step count, BP, body weight, blood glucose, exercise, and diet. We conducted a single-arm before-and-after study of workers with high BP who were willing to increase their physical activity. After an educational group session, participants used only the self-monitoring function for 2 weeks (baseline) and all functions of DialBetes Step for 24 weeks. We evaluated changes in steps per day, self-reported frequencies of self-regulation and self-management behavior, self-efficacy, and biomedical characteristics (home BP, BMI, visceral fat area, and glucose and lipid parameters) around week 6 (P1) of using the new functions and at the end of the intervention (P2). Participants rated the usefulness of the system using a paper-based questionnaire. RESULTS: We analyzed 30 participants (n=19, 63% male; mean age 52.9, SD 5.3 years); 1 (3%) participant dropped out of the intervention. The median percentage of step measurement was 97%. Compared with baseline (median 10,084 steps per day), steps per day significantly increased at P1 (median +1493 steps per day; P<.001), but the increase attenuated at P2 (median +1056 steps per day; P=.04). Frequencies of self-regulation and self-management behavior increased at P1 and P2. Goal-related self-efficacy tended to increase at P2 (median +5%; P=.05). Home BP substantially decreased only at P2. Of the other biomedical characteristics, BMI decreased significantly at P1 (P<.001) and P2 (P=.001), and high-density lipoprotein cholesterol increased significantly only at P1 (P<.001). DialBetes Step was rated as useful or moderately useful by 97% (28/29) of the participants. CONCLUSIONS: DialBetes Step intervention might be a feasible and useful way of increasing workers' step count for a short period and, consequently, improving their BP and BMI; self-efficacy-enhancing techniques of the system should be improved.

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.016
GPT teacher head0.310
Teacher spread0.294 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations9
Published2023
Admission routes1
Has abstractyes

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