A model for predicting utilization of mHealth interventions in low-resource settings: case of maternal and newborn care in Kenya

Table 8 Conclusions drawn from RM-ANOVA analysis

Predictor	H₀	Prediction hypotheses	Conclusion drawn (p < 0.05)
Attitude	H₀1	Attitude has no significant change on fit before, and after use of mHealth intervention	Nonsignificant - accept
Self-Efficacy	H₀2	Self-efficacy has no significant change on fit before, and after use of mHealth intervention	Significant - reject
Sensory	H₀3	Sensory requirements have no significant change on fit before, and after use of mHealth intervention.	Nonsignificant - accept
Relationship	H₀4	Relationship requirement has no significant change on fit before, and after use of mHealth intervention	Significant - reject
Identification and Privacy	H₀5	Identification and privacy has no significant change on fit before, and after use of mHealth intervention	Significant - reject
Synchronism	H₀6	Synchronism requirement has no significant change on fit before, and after use of mHealth intervention	Significant - reject
Representation	H₀7	Representation capability of technology has no significant effect on fit before, and after use of mHealth intervention	Nonsignificant - accept
Reach	H₀8	Reach capability of mHealth technology has no significant change on fit before, and after use of mHealth intervention	Nonsignificant - accept
Monitoring	H₀9	Monitoring capability of technology has no significant change on fit before, and after use of mHealth intervention	Significant - reject
Fit for Use	H₀10	Perceived fit has no significant change before, and after use of mHealth intervention	Significant - reject

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