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Record W4281479926 · doi:10.2196/36515

Mitigating Feelings of Loneliness and Depression by Means of Web-Based or Print-Based Physical Activity Interventions: Pooled Analysis of 2 Community-Based Intervention Trials

2022· article· en· W4281479926 on OpenAlexvenueno aff
Sonia Lippke, Tiara Ratz, Franziska Maria Keller, Dennis Juljugin, Manuela Peters, Claudia R. Pischke, Claudia Voelcker‐Rehage

Bibliographic record

VenueJMIR Aging · 2022
Typearticle
Languageen
FieldMedicine
TopicPhysical Activity and Health
Canadian institutionsnot available
FundersBundesministerium für Bildung und Forschung
KeywordsPsychological interventionLonelinessMediationPsychologyIntervention (counseling)MedicineClinical psychologyGerontologyPsychiatry

Abstract

fetched live from OpenAlex

Background Physical activity (PA) is associated with benefits, such as fewer depressive symptoms and loneliness. Web- and print-based PA interventions can help older individuals accordingly. Objective We aimed to test the following research questions: Do PA interventions delivered in a web- or print-based mode improve self-reported PA stage of change, social-cognitive determinants of PA, loneliness, and symptoms of depression? Is subjective age a mediator and stage of change a moderator of this effect? Methods Overall, 831 adults aged ≥60 years were recruited and either allocated to a print-based or web-based intervention group or assigned to a wait-list control group (WLCG) in 2 community-based PA intervention trials over 10 weeks. Missing value imputation using an expectation-maximization algorithm was applied. Frequency analyses, multivariate analyses of variance, and moderated mediation analyses were conducted. Results The web-based intervention outperformed (47/59, 80% of initially inactive individuals being adopters, and 396/411, 96.4% of initially active individuals being maintainers of the recommended PA behavior) the print-based intervention (20/25, 80% of adopters, and 63/69, 91% of maintainers) and the WLCG (5/7, 71% of adopters; 141/150, 94% of maintainers). The pattern regarding adopters was statistically significant (web vs print Z=–1.94; P=.02; WLCG vs web Z=3.8367; P=.01). The pattern was replicated with stages (χ24=79.1; P<.001; contingency coefficient 0.314; P<.001); in the WLCG, 40.1% (63/157) of the study participants moved to or remained in action stage. This number was higher in the groups receiving web-based (357/470, 76%) or print-based interventions (64/94, 68.1%). A significant difference was observed favoring the 2 intervention groups over and above the WLCG (F19, 701=4.778; P<.001; η2=0.098) and a significant interaction of time and group (F19, 701=2.778; P<.001; η2=0.070) for predictors of behavior. The effects of the interventions on subjective age, loneliness, and depression revealed that both between-group effects (F3, 717=8.668; P<.001; η2=0.018) and the interaction between group and time were significant (F3, 717=6.101; P<.001; η2=0.025). In a moderated mediation model, both interventions had a significant direct effect on depression in comparison with the WLCG (web-based: c′ path −0.86, 95% CI −1.58 to −0.13, SE 0.38; print-based: c′ path −1.96, 95% CI −2.99 to −0.92, SE 0.53). Furthermore, subjective age was positively related to depression (b path 0.14, 95% CI 0.05-0.23; SE 0.05). An indirect effect of the intervention on depression via subjective age was only present for participants who were in actor stage and received the web-based intervention (ab path −0.14, 95% CI −0.34 to −0.01; SE 0.09). Conclusions Web-based interventions appear to be as effective as print-based interventions. Both modes might help older individuals remain or become active and experience fewer depression symptoms, especially if they feel younger. Trial Registration German Registry of Clinical Trials DRKS00010052 (PROMOTE 1); https://tinyurl.com/nnzarpsu and DRKS00016073 (PROMOTE 2); https://tinyurl.com/4fhcvkwy International Registered Report Identifier (IRRID) RR2-10.2196/15168

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 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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.423
Threshold uncertainty score0.547

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.100
GPT teacher head0.425
Teacher spread0.324 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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
Published2022
Admission routes1
Has abstractyes

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