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Record W4376849802 · doi:10.2196/46286

The Effectiveness of a Mobile Phone–Based Physical Activity Program for Treating Depression, Stress, Psychological Well-Being, and Quality of Life Among Adults: Quantitative Study

2023· article· en· W4376849802 on OpenAlexvenueno aff
Hyungsook Kim, Ki-Kwang Lee, Ye Hoon Lee, Yoonjung Park, Yong-Hyun Park, Yeonwoo Yu, Jaeyoung Park, Sihyeon Noh

Bibliographic record

VenueJMIR mhealth and uhealth · 2023
Typearticle
Languageen
FieldMedicine
TopicPhysical Activity and Health
Canadian institutionsnot available
Fundersnot available
KeywordsDepression (economics)Repeated measures designQuality of life (healthcare)PopulationExpectancy theoryMedicineAnalysis of varianceGerontologyIntervention (counseling)PsychologyLife expectancyStress managementmHealthPsychological interventionPhysical therapyClinical psychologyPsychiatryEnvironmental health

Abstract

fetched live from OpenAlex

BACKGROUND: Depression is a substantial global health problem, affecting >300 million people and resulting in 12.7% of all deaths. Depression causes various physical and cognitive problems, leading to a 5-year to 10-year decrease in life expectancy compared with the general population. Physical activity is known to be an effective, evidence-based treatment for depression. However, people generally have difficulties with participating in physical activity owing to limitations in time and accessibility. OBJECTIVE: To address this issue, this study aimed to contribute to the development of alternative and innovative intervention methods for depression and stress management in adults. More specifically, we attempted to investigate the effectiveness of a mobile phone-based physical activity program on depression, perceived stress, psychological well-being, and quality of life among adults in South Korea. METHODS: Participants were recruited and randomly assigned to the mobile phone intervention or waitlist group. Self-report questionnaires were used to assess variables before and after treatment. The treatment group used the program around 3 times per week at home for 4 weeks, with each session lasting about 30 minutes. To evaluate the program's impact, a 2 (condition) × 2 (time) repeated-measures ANOVA was conducted, considering pretreatment and posttreatment measures along with group as independent variables. For a more detailed analysis, paired-samples 2-tailed t tests were used to compare pretreatment and posttreatment measurements within each group. Independent-samples 2-tailed t tests were conducted to assess intergroup differences in pretreatment measurements. RESULTS: =0.21) in participants' depression scores, indicating that there were changes in depression level across time. No significant changes were observed in perceived stress (P=.25), psychological well-being (P=.35), or quality of life (P=.07). Furthermore, depression scores significantly decreased in the treatment group (from 7.08 to 4.64; P=.03; Cohen d=0.50) but not in the waitlist group (from 6.72 to 5.08; P=.20; Cohen d=0.36). Perceived stress score of the treatment group also significantly decreased (from 2.95 to 2.72; P=.04; Cohen d=0.46) but not in the waitlist group (from 2.82 to 2.74; P=.55; Cohen d=0.15). CONCLUSIONS: This study provided experimental evidence that mobile phone-based physical activity program affects depression significantly. By exploring the potential of mobile phone-based physical activity programs as a treatment option, this study sought to improve accessibility and encourage participation in physical activity, ultimately promoting better mental health outcomes for individuals with depression and stress.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.082
GPT teacher head0.488
Teacher spread0.406 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations17
Published2023
Admission routes1
Has abstractyes

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